Environment Variables

In this episode we take a look back at another talk from our Decarb 2023 Event while we’re all on the New Year break at the GSF. Titled Driving Climate Change Solutions with AI, Charlotte Degot of CO2 AI and Diana Dimitrova from Boston Consulting Group talk about the Third Annual Carbon Emissions Survey titled Why Some Companies are Ahead in the Race to Net Zero. 

Learn more about our people:

• Charlotte Deogot: LinkedIn 
• Diana Dimitrova: LinkedIn

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

Topics:

• CO2 AI and BCG’s Third Annual Carbon Emissions Survey | Boston Consulting Group [2:55] 

Resources:

• CO2.AI | CO2AI [1:19]
• Boston Consulting Group | BCG [1:19]
• Klöckner & Co. [6:00]
• Nexigen® Data Services (PCF) | Klöckner & Co SE  [6:45]
• The Economist Group [8:55]
• CodeCarbon.io | Code Carbon [14:53]

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
• Follow and rate on Spotify
• Watch our videos on The Green Software Foundation YouTube Channel!
• Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:
Asim Hussain: Hello and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

Chris Skipper: Welcome to another episode of Environment Variables. Happy New Year, and I hope you are all enjoying this holiday period and looking forward to 2024. Seeing as we're all on a bit of a break here at the Green Software Foundation, we've got another highlight from the recent Decarbonize Software 2023 event.

We'll be showcasing a talk on driving climate change solutions with AI. From Charlotte Degot, CEO and co founder of CO2 AI, and Diana Dimitrova, managing director and partner at BCG. They are introduced by the wonderful Namrata Narayan, director of communications and member relations at the GSF. So it will be her voice that you hear first.

So, without further ado, here's driving climate change solutions with AI.

Namrata Narayan: Now we're going to hear from another steering member, BCG, and their partner, CO2 AI. I'd like to welcome Charlotte Degot from CO2 AI and Diana Dimitrova from BCG. The GSF is focused on addressing the environmental impact of software. With the rise of AI, we wanted to take a moment at Decarb to discuss how this technology can assist our efforts to decarbonize software.

Charlotte and Diana are here to cover this topic and highlight the results from the Carbon Emission Survey Report that was just released. Charlotte and Diana, over to you.

Diana Dimitrova: Great. Thanks so much. And thanks so much for hosting us today. We're super pleased to be here. And as you mentioned, today was the launch date of our third annual survey that actually found some fascinating insights around green software and how that propels climate action globally. So Charlotte and I are thrilled to take you through it.

So maybe as a first step, we can just introduce ourselves. Charlotte.

Charlotte Degot: Yes, very nice to have us, I'm Charlotte Degot, I'm the founder and CEO of CO2 AI. Just in a few words, CO2 AI is a sustainability platform that helps large organizations on their end to end net zero journeys from measuring and reporting their emissions down to reducing and proving their impact.

Diana Dimitrova: Excellent. And, um, I'm Diana Dimitrova. I'm a managing director and partner, uh, in BCG, and I focus on building digital solutions for clients, um, that advance their climate goals, um, that's specifically in our unit called BCGx. I'm going to kick us off with the main findings, um, of the report, and, and let me just contextualize that a little bit.

So, we publish a third annual report around carbon emissions, uh, and measurement. It includes. 1,850 organizations that reported back to us. It does represent over 20 countries in 18 major industries. And based on the reported emissions, it's about 40 percent of global emissions. And the report is called Why Some Companies Are Ahead in the Race to Net Zero.

Now what we found, the unfortunate bit, which is the first column, is that comprehensive measurement of scope 1, 2, and 3 actually hasn't improved. It was 10 percent this year, it was 10 percent the year before, and 9 percent the year before that, so real stagnation there. Now, we did see some elements that gave us quite a bit of hope, and that was really in scope 3.

So in scope 3 measurement has landed at 53 percent or partial scope 3 measurement has landed at 53 percent which is up 19 percentage points in two years. So we are seeing organizations actually be selective of which categories they measure but really starting to get at scope 3. And more interestingly is they've actually set targets and that's the 12 percentage points that you see on the bottom there.

Organizations at this stage are setting targets for scope three categories at 35 percent rate, which is a notable improvement. And regionally, we are seeing changes and we are seeing certain regions pull ahead in the race. And again, this is a scope one, two, and the comprehensive measurement that they have.

South America, APAC are really pulling ahead when it comes to their measurement. And then finally, the most important bit is why are companies doing this? And 40 percent of the folks who responded are telling us, well, they do this because they're seeing a hundred million or more dollars in annual financial benefit when they get on the reduction journey.

So there's a real financial incentive for them to be tackling these massive challenges. And then let me tell you a little of how they did it on the next slide. So these companies are doing four things twice as well as the average organization. The first thing that they're doing well is they're collaborating with their suppliers, meaning they have workshops together, they have joint targets, they have joint programs.

So they're engineering products together for a lower carbon solution. The second bit is Digital solutions that allow you to measure at a product level, so it's not good enough to just know what Apple's total emissions are, it's actually much more interesting to know what the iPhone's emissions are, and getting at that product level is fundamental to enable consumers to make a greener choice.

And the highest number on this slide, as you see, is the use of digital technologies, and Charlotte will give us a few examples of that, but those that adopt digital solutions or green software really make a dent versus those that don't. And then lastly, I heard our host kick it off, regulation, and those that view regulation in a positive way, catalyze around it, really are seeing those benefits when it comes to meeting their reduction targets.

Now, to give you an example, I'm going to take you through a client where we've done this. The client is called Klöckner & Co. What does Klöckner do? Klöckner actually distributes steel. So steel is a heavy emitting sector. It represents 7 percent of the world's emissions. We all have a stake in the ground to really decarbonize steel.

Unfortunately, there is no net zero steel, but there is a lot greener steel than what we use on average. What Klöckner did is recognize that they needed to get to that product carbon footprint element, which is the second pillar I just talked about. So they actually built a software that was able to calculate their product carbon footprint for over 200,000 products, and you see that's called Nexigen PCF algorithm.

It's cradle to gate emissions. And it covers, as I said, 200,000 products, and it is certified, which makes it really important that whoever gets a number actually gets a certification. And then they decided that wasn't good enough, they needed to push a little bit further, and then they created Nexigen Data Services, which is an online purchasing platform where anybody who's buying steel can log on and look at what the carbon footprint of their basket was in the last purchase, and how, what greener alternative is available to them.

And so this platform allows procurement individuals to actually really arbitrage where they're going to spend their carbon because they're given the transparency and the choice to make the lower carbon solutions. So it's a great example of leveraging technology and getting at product carbon footprints, which is still a bit of a panacea that organizations are gunning towards.

Having said that, I'm going to hand it over to Charlotte and she'll take us through some of the other details on digital.

Charlotte Degot: Yes, thank you. So, as Diana was saying, one of the four key things that companies will succeed better on their reduction journey do is adopting digital solutions. As you can see on the left part of the slide, what we see is that companies who use automated digital solutions are actually two times 2.5 times more likely to comprehensively measure their emissions.

And this is very important because what gets measured gets done. This may look like a basic, but it's a basic that just sets the foundations right for you to then decarbonize. And what's also encouraging is that when we ask the 1,800 companies who answered our survey, what is the number one enabler that they see and that they think they need to adopt to accelerate on emissions reduction, they quote, technology, uh, as the first enabler.

Before leadership buy in, before sustainability focused, uh, culture. So it really means that they have, uh, perceived the importance of digital and that, uh, we can hope for a wave of adoption in the years to come.

I just want to give you one example from real life of what it means to be using tech and how it can bring value to a very large and complex organization. We are talking here about The Economist Group, so the press media group as they are very large and they, um, they issue a lot of newspapers. They are very committed to sustainability and what they do with technology is using CO2 AI to help them steer their end to end net zero journey. So what they use it for is first to measure and automate their carbon footprint calculations. They have a very complex carbon footprint. They need to capture a lot of data points, and CO2 AI helps them collect those data points, structure those data points, and get to a level of granularity in their footprint, which is really good and really helpful to make decisions.

The second thing that they do with CO2 AI is to really set up a road map. And define the hotspots and the reduction levers that go with the roadmap so that they really have a plan and they can cascade the plan across the organization. This is a big pain point that sustainability face team, um, sustainability teams face today.

They tend to have targets, but high level plans and no resources to actually make that happen. Technology can really help broadcasting the information, make sure that operational teams get involved into the decarbonization journey. And last but not least, as we were saying, scope three. So the emissions that come from outside the direct operations of the company, typically the emissions from the supply chain, the suppliers, et cetera, are, um, extremely important.

They are on average 90 percent of total emissions. And those are the hardest to tackle. What The Economist is doing with CO2 AI is mapping suppliers and being able to really prioritize with who, with what suppliers to engage and what to discuss with them to go at a level of granularity. We were talking about product level at a level of granularity, which is good enough to have proper discussions about reductions and, and impact measurements.

Finally, I want to say a few words about AI before taking questions. We talked about tech and digital in a broad sense, which is extremely helpful and will drastically help us accelerate on our sustainability journey. In this field, artificial intelligence is Extremely important. You see here on the page, 30% of respondents who plan to adopt AI, uh, to, uh, steer their net zero sustainability and journey moving forward.

Uh, they see artificial intelligence and, uh, as an enabler and a helpful way on many that different dimension. Uh, a couple of ones are making intelligent decisions. Typically on energy usage or decarbonization initiative. Artificial intelligence is a key lever to automate and increase the quality of carbon footprints.

This is what we do at CO2 AI, and I could talk for hours about that, but there is a huge accuracy issue and granularity issue with carbon footprints. On carbon footprint when made manually, and artificial intelligence can really help solve this topic. And finally, making predictions and making sure that roadmaps ahead, both from a business and from a sustainability standpoint, are optimized.

So we're reaching the end of our presentation. You have two QR codes here to go deeper into either the report or what we do more generally. And I think it's time to take questions.

Namrata Narayan: Thank you so much for sharing. I know we've got a ton of questions to follow up with, but first, I do see we've got a few audience questions, so I'm going to ask you both. The first one is from Jean Luc Bonnet, and he's asking about the baseline. He said two times of 10 companies is not so great, but two in 100,000 companies is a great achievement, so could you comment on that?

Charlotte Degot: I think the question is related to the four levers that accelerate and that multiply by two the likelihood of managing your reduction journey. So what I can say is that what we ask for in the survey is what percentage of companies are measuring the emissions comprehensively. And as Diana was saying, this has not moved and this is stagnating at 10%.

And the other key metric that we asked for in the survey is how, what percentage of companies have actually managed to reduce their emissions in line with their ambition. And here this number is 14%. So I will I will see the glass half empty on this question, and I will say that the baseline is not high enough, and we need to really accelerate the adoption of those four levers, and that will increase the baseline.

Namrata Narayan: Thank you so much, and then we have a question with regards to the carbon emissions survey, a couple which I'm going to you know, group together. So, one is, how many companies were part of the survey, and did the survey find any advice regarding how to decarbonize AI itself, since it's also a cause of emissions?

Diana Dimitrova: Great, so let me take the first part and Charlotte, I think you can comment on AI itself. So it was 1,850 organizations up from 1,600 last year and up from 1,450 the year before, so we have been seeing a steady interest in folks giving us this information back and the sample is hoovering for some of the kind of large organizations that are between 10 and 25,000 employees, and it said based on their self declaration it was 40,000 percent of global emissions, so we're quite comfortable with the sample that came through this year.

Charlotte Degot: And on, uh, uh, how to reduce the footprint of AI, this was not part of the questions we've been asking, uh, on the survey, but what I can say is that, um, there are, uh, many ways to optimize the carbon emissions of any model. Also, tools that exist like CodeCarbon, et cetera, I'm not going to teach anything to this group about it, but clearly, the impact of AI needs to be monitored as any other type of impact, and it needs to be used wise.

Namrata Narayan: Well, Charlotte and Diana, thank you so much. There are several more questions, so we might have to come back to you at a later time to get them answered. Thank you so much for joining us at DeCarb.

Chris Skipper: So, that's all for this episode of Environment Variables. If you liked what you heard, you can actually check out the video version of this on our YouTube channel. Links to that, as well as everything that we mentioned, can be found in the show notes below. While you're down there, feel free to click follow so you don't miss out on the very latest in the world of sustainable software here on Environment Variables.

Bye for now!

Asim Hussain: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show and of course, we want more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. Thanks again and see you in the next episode.

TWiGS host Chris Adams is joined by guest Savannah Goodman from Google, to bring you the latest news and updates from the world of sustainable software development. They discuss insights from Google’s Sustainability report, the need for a fossil fuel free internet by 2030 and the importance of microgrids and nanogrids. They also highlight Google's sustainability tools including Cloud Region Picker, ActiveAssist and  Carbon Footprint.

Learn more about our people:

• Chris Adams: LinkedIn | GitHub | Website
• Savannah Goodman: LinkedIn

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

Topics:

• We need a fossil fuel free internet by 2030  | Branch Magazine [6:38]
• Tesla Megapack | Microgrids & Tesla [12:27]
• Google Sustainability Goals | Google [21:19] 
• Google & Fervo Geothermal Partnership | Google  [22:58]
• Google Cloud Region Picker | Google [26:44]
• Google Active Assist | Google [29:20]
• Google Carbon Footprint | Google [30:13]
• The Realtime Cloud Project | Green Software Foundation [32:15]

Resources:

• Octopus Energy  [15:04]
• The Week in Green Software: Modelling Carbon Aware Software | Environment Variables episode with Iegor Riepen at TU Berlin [20:07]
• UN Global 24/7 Compact | UN [24:46]
• Vincent Poncet LinkedIn | Linux Foundation [32:08]

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
• Follow and rate on Spotify
• Watch our videos on The Green Software Foundation YouTube Channel!
• Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Savannah Goodman: [00:00:00] While it's important for us to reduce our own carbon footprint, we think the opportunity to provide these flexibility services to the grid will actually help drive broader system decarbonization and allow for broader carbon emission reduction.

Chris Adams: Hello, and welcome to another episode of This Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. your host, Chris Adams. when we talk about green software, whether you're diving into papers about carbon aware computing. or hearing someone talk about GreenOps, a new [00:01:00] variation of the term DevOps, or even as a consumer, looking at the work that the company's been doing to make it easier to understand how you can reduce the carbon footprint of things like flying. It's hard to escape the work of Google when you look at climate and tech. be honest, it's not that surprising. They operate at a scale that's hard for many of us to really comprehend. Alphabet, the holding company for Google, has a market capitalization of 1. 75 trillion dollars these days. And they use enough digital infrastructure to really care. Their own sustainability report in 2023 shows that Google used something in the region of 22 terawatt hours of electricity last year. This is not a small company. Since 2007, they've generally been one of the leaders when it comes to decarbonizing infrastructure. And recently, they set a public goal to match every hour of power they use with an hour of generation from carbon free sources by 2030. This is something we've seen a [00:02:00] number of organizations get behind, and even the UN with their recent 24 7 compact. So we know there's a lot of smart people working at Google, working on the greening of digital. And today, I'm joined by Savannah Goodman from Google to talk about the finer points of doing green software at scale. Hi Savannah, the floor is yours to introduce yourself.

Savannah Goodman: Thanks, Chris. Hi, everyone. I'm Savannah Goodman. I lead the data and software climate solutions team at Google, and I'm super excited to be here. I'm an energy nerd at heart, and I've been studying and working in the climate space for the last decade.

Chris Adams: Thank you, Savannah. Okay, so if you are new to this podcast, here's a quick reminder of how it tends to work. Uh, When we mention projects or papers or any links like that, we'll add them into the show notes. And if there's a thing that you heard us talk about that you'd really like to learn more about, please do leave a comment.

It really helps other people who are trying to actually learn things about this as well. The other thing I should probably do is introduce myself. My name is Chris [00:03:00] Adams. I work at the Green Web Foundation, a nonprofit focused on reaching an entirely fossil free internet by 2030. And I'm also one of the chairs of the policy work group inside the Green Software Foundation, which Google is also a member of. All right. I think that's pretty much it. Savannah, if you're happy for us to go and sitting comfortably, shall we start?

Savannah Goodman: Yeah, let's go for it.

Chris Adams: All right. Okay. So we're going to talk about some of the cool carbon intelligent computing stuff that Google does shortly, but before we dive into there, I figured it might help to talk a little bit about how you got here first, because as I understand it, you've worked with technology that interacts with grids at various scales in your career.

And if we start at the small end, I realize that if you're going to be talking about data centers, it helps developers to understand some of the principles that are behind the kind of power grids in the first place. And this is something you worked on before. And I think what I learned about was there was a project that you worked [00:04:00] on previously with the idea of an off grid internet cafe in a box.

This seemed too cool to not ask you about basically. So I should ask, how did this come about before you worked at Google?

Savannah Goodman: Yeah. Yeah. Thank you for asking. It was a super interesting project and happy to get the chance to start small and then go all the way to data centers. But yeah, I was in grad school and I was interning at a company that was focused on improving energy access in East Africa. And they were working on a couple of different products for pretty specific applications and they were really tailored to the needs of the local communities that we were working with.

So just a few examples, they had very small microgrids, you'd maybe almost call it a nanogrid to provide lighting at night or to charge a few cell phones. So really small kind of mobile solar and storage. And during their research they identified one of the other kind of key pain points in these [00:05:00] communities was the need for better and more reliable internet access.

And while some internet cafes existed in the cities, they could be cost prohibitive, they may be very far from some of the local communities, and oftentimes they were actually unreliable because of grid reliability issues in the region. And so my job was essentially to do the research and propose what an off grid internet cafe in a box might look like as a product, and how it could actually solve the pain points in these

from these local users. And so I did a lot of really interesting landscape research, developed some product specs, and ultimately also came up with the business case. And I think what I really liked most about this project was the fact that it had a ton of potential impact. I was really excited about the idea of enabling information access for everyone, not just folks in developed countries and big cities, and especially through more [00:06:00] reliable clean energy access.

Chris Adams: It's actually timely that we're having this conversation now, just after COP28, because we've seen a few specific announcements that tie into this, actually, things like this goal of tripling renewables globally, for example, by 2030. And there's actually, it's interesting that you talk about some of those things there, because T that was maybe a few years back, and I want to ask you, if you were to come back to this, were there any trends that you saw back then that you might have seen playing out that you'd probably look for immediately, or you'd probably want to dive into if you were coming back to this?

Because you mentioned some things like nanogrids, and the idea of moving past basically fossil based generation, because we know in lots of places, like for example Nigeria, You still have two thirds of the power is coming from generation rather than the grid. So it's extremely dirty power compared to what you have now. Maybe I'll just give it a bit of space for you to say what you would be looking for, what you're excited about seeing over the next few years in this particular field.

Savannah Goodman: Yeah, thank you. It's definitely some really interesting trends and a [00:07:00] lot has changed even since I was in grad school. And I think one of the first things I remember thinking about is how big, right? What is the optimal size of a microgrid? I mentioned this term nanogrid, which was, like I said, a few solar panels, a small battery that was very mobile.

I think that that sort of solution can target a few very specific and acute pain points, but didn't necessarily, I think, scale to the level that these companies were looking to have, especially given the impact that they wanted to drive. So I think one of the interesting trends we've seen is maybe moving from these nano grids towards more true micro grids that can enable multiple applications and are not necessarily mobile, but actually require more robust grid infrastructure.

And so that's something very interesting and especially in the context of the developed region, especially in the U. S. where I'm from, grid infrastructure is a very big focus right now. And I think it'll be [00:08:00] really interesting to see how we can take some of the learnings of how we've grown our own grid infrastructure and hopefully leapfrog in some of these areas that don't quite have the existing distribution or transmission build out.

Certainly a challenge as I don't think we've fully figured it out for ourselves, but there's definitely a lot of lessons learned along the way. So I think that's . Something really interesting that will play out. Another area that always really stood out to me was the energy and water nexus. And this is something that is really important in a lot of developed countries where there's also a lack of clean water access.

And there's been a lot of kind of technology development over the last decade, and I'm sure there will continue to be. And I think the challenge with this is more, again, the kind of go to market of those products. How do we actually ensure that these products fulfill really specific needs and have an appropriate business model that actually meshes well with the type of [00:09:00] communities that need it most.

So that's an interesting one that I'm really curious to see how this will continue, especially as there's more and more focus on the global south and adaptation for climate change, which is closely tied both to water and energy as well. And then the last thing.

 

Chris Adams: Oh if I can just stop you there, you said you introduced this term, which some other listeners might not be so familiar with. You mentioned the energy water nexus. Could you maybe expand on that a little bit? This is the idea that basically generating water uses energy, but there's also a flip side to that.

Could you maybe expand on that? Because I think that's quite an interesting topic

Savannah Goodman: Yeah.

Chris Adams: up some people's ears actually.

Savannah Goodman: Yeah, definitely. And, um, I think there's, yeah, this sort of exciting concept of the fact that energy and water are two absolutely necessary resources for thriving communities. Like you said, you can, you need usually energy to make water accessible, especially to make it clean and to make it drinkable and portable, right?

There's usually some sort, usually using [00:10:00] electricity to do that can be really effective. There's other ways to do it without electricity, but it's a lot more challenging to have infrastructure that scales and is robust enough. The flip side too is water can be a really great energy source. We think of pump storage and hydro, and that's really important for providing clean, dispatchable resources.

Of course, hydro is very dependent on the weather and the climate, and as climate changes, it can become actually a less reliable resource. And so there's just a lot of interesting intersections there. We can also think about the climate impacts and droughts and how that might impact both water access for drinking, but also for energy.

So, it's a generic term, but it's intended to capture the fact that there's actually a lot of intersection and dependencies between these two fields both ways, and I think it's a really important focus area, right, as we think about you know, impacts on the climate and how communities are going to need to adapt.

Chris Adams: Thank you. That's really useful. I [00:11:00] didn't, I've been looking for a term to describe some of this and this immediately makes me think of, say in France or in Germany, where we saw in the heat wave last year, for example, we saw some of this, we saw the reliance on water from things like say nuclear power stations in France, for example, which were essentially. They end up having to come offline in some cases simply because they didn't have enough access to water to actually keep them cool, for example. And this is something that a lot of us don't really think about necessarily in the first place. Okay. It's power, but where does the water fit into this? But flipping, that's actually a really good example of some of this. Okay, cool. So we spoke a little bit about grids. You mentioned that there's like nano grids and say slightly larger grids, like a micro grid. It's worth moving to there, actually, because I understand that when you were working at that company, then you've also worked at Tesla working with microgrids. And when most of us think about, say Tesla, they might think about electric cars more than microgrids. And in fact, I suspect that most developers might not have heard what a microgrid actually is. So for people who are not familiar [00:12:00] with the term, you maybe just explain what a microgrid might actually be? And just to give people some context, because we'll talk about how this relates to data centers a little bit later, but in the short term, maybe you could just briefly provide a kind of microgrid 101 and why you might care about it, why an electric car company might care about some of this, because that will probably be useful context when we talk about data centers and how they integrate with grids and stuff like that.

Savannah Goodman: Yeah, absolutely. So a microgrid is a localized group of energy sources and loads that can typically operate independently of a larger grid system. And so this sort of independent network of resources can be useful for a few reasons. First, microgrids can improve energy security and reliability. They tend to be less vulnerable to large scale outages because they can usually kind of disconnect or what we might call island and continue to [00:13:00] operate even if the main grid system goes down.

So this is especially beneficial for critical loads. So oftentimes hospitals or emergency shelters may have their own kind of generators and may be able to create their own sort of micro grids. Further, microgrids can actually help reduce, you know, the cost in carbon, uh, depending on the situation. So, for example, a lot of island nations typically have some form of microgrids.

Because they're not physically connected to large land areas that have the privilege of having these large scale grids and a lot of infrastructure and robustness. And so while a lot of these island microgrids have historically relied on diesel generators, they can instead integrate wind, solar, and batteries, which can then actually reduce the cost compared to diesel, importing diesel, and will actually reduce the carbon emissions.

So that's another big advantage of, or opportunity for microgrids. And to answer your other question on [00:14:00] why companies like Tesla might have incentives to build batteries for the grid, it's really a way for them to scale their battery production for EVs. So Tesla Energy was created actually a while back and it really helped drive down the costs 

of manufacturing batteries at scale, and it also has the, you know, added benefit of actually creating new business value, new business lines for the company. And the other thing I would say is there's also some really interesting synergies between electric vehicle charging and using grid connected batteries to manage consumption spikes and, and some of the charging costs.

So when Tesla deployed the supercharger network, they also looked at deploying grid scale batteries at those charging networks to help manage the costs that they were paying to the electric utilities. So it not only can be a business line, but batteries for the grid can be used to help manage their overall kind of infrastructure costs for the [00:15:00] EVs.

Chris Adams: Ah okay, so that makes it a bit clearer for me to understand, and also this makes me think of some of the stuff we've recently seen in the UK, for example, there's a company called Octopus Energy, they're an energy company, and they basically They have this new deal working with people who are building homes and they have this kind of this deal basically if you have batteries or renewable energy fitted into the house when it's being built they're offering zero energy bills for the next for the first five years as long as you're able to make the house integrate into the grid and it seems to be like you said because sometimes energy is expensive when there's, it might be cheap when there's lots of renewables on the grid.

For example, it's taking advantage of some of that and storing energy when it's cheap and then using it locally rather than having to buy expensive energy from the grid and vice versa. Ah, okay. That helps me understand that now. Thank you. you said something else, actually, about the fact that you've got grids which are interconnected.

So they're independent, but they're connected in some ways because there's a larger thing. This made me think a lot about the internet, actually, how the internet is [00:16:00] basically made of a series of smaller networks which are interacting with larger networks. So there's some parallels there. And I can imagine how some of these ideas might scale all the way up to something like a large hyperscale data center. I suspect there's probably a bit more to it than that. And I guess this is probably where the work that you've been doing with Google might come in, actually. So for folks who are following along for the ride, when you're talking about, say, hyperscale data centers versus micro grids or small grids here, what are the differences when you're working at that different kind of scale, for example?

And is this idea that, okay, you've got a series of small grids connected. That's a little bit like the internet. Is that a comparison that you could really make? Well, maybe you could just expand on that a bit more. Cause that's what I immediately think about when you talk to me about series of grids connected to each other, for example.

Savannah Goodman: Yeah, I think that's a great analogy. Like you said, the internet is made up all of these different kind of nodes and is really a network of nodes and connection points. And that's [00:17:00] exactly what the grid is. And some areas are better connected, right? They have more interconnectivity and nodes, and that can make them more reliable than others.

Also similar to the internet, because there's this network and different demand and supply pockets. There can be congestion of just like there may be network congestion for internet or data transfer. There can be grid congestion for the flow of electrons. And so keeping this in mind, as I alluded to earlier, there are a lot of opportunities to really better optimize the grid when you have 

flexible resources, whether it's EV charging or storage for the grid. This flexibility makes it a lot easier to manage the grid during peak times. The example you, you mentioned with Octopus, I think is a great one, where they're fully recognizing the value of having flexible grid to the point where they won't even charge you for the energy because they know that's really important for the way they manage their portfolio.

And so for data centers, the way that we [00:18:00] contribute to this load flexibility is we can actually shift our compute load. And we can do that in response to signals from the grid in specific locations, where there may be congestion or during kind of the peak times of the grid. And this is what we would typically call demand response.

And just,

Chris Adams: Ah, okay.

Savannah Goodman: yeah, and it's- Demand response has actually been around for a long time, but what's really, I think, new and innovative about what we're doing at Google is leveraging compute demand to be able to participate in these demand response programs. Historically, demand response has typically been from industrial factories who are turning down their manufacturing or from thermostats, right, who are turning down the heating.

But what's really great about compute load is that it's a virtual load. And so the fact that we can shift it not only in time. But also in space is really, um, the unique part of, of, um, compute. And we've [00:19:00] actually been shifting compute at, at Google for a few years now. Historically, we were shifting in response to a carbon intensity signal from the grid.

In order to minimize our own carbon emissions. And for demand response, we do it a little bit differently. We will typically receive a notification from the grid operator, or we'll agree on the local peak time. And then we dispatch our kind of global compute planning system to overwrite that existing schedule that was carbon optimized.

And that basically limits non urgent compute tasks for the duration of that event at the data center. And I think we're really excited about these capabilities because while it's important for us to reduce our own carbon footprint, we think the opportunity to provide these flexibility services to the grid.

Will actually help drive broader system decarbonization and allow for broader carbon emission reduction through enabling the increase in clean [00:20:00] energy and just being able to turn down load when the grid is being supported by gas peakers, for example.

Chris Adams: I see. Okay. So for people who may have listened to this before, we, we did a podcast with, I think, I believe his name is Igor Repin at the Technical University of Berlin. He was talking and going into quite a lot of really nerdy detail about how some of this stuff was modeled on the European grid to explain this and saying, if you're able to smooth out these kinds of spikes, then you don't need to actually have quite so much infrastructure in the first place.

Or it may be that you don't need to have things like, say, as many peaker plants, often tend to be very carbon intensive and tend to burn a lot of fossil fuels. I see now. All right then. So maybe we can actually just talk a little bit about this being part of a of broader strategy. So we spoke a little bit about there being a kind of target to be entirely fossil free by 2030, for example. Is it a chance we could maybe just dive into some of that a bit more? Because for most of us U understanding [00:21:00] why you'd have a target to be running entirely a fossil free energy by 2030 might not be obvious to everyone. And sorry, I think the term that Google uses is carbon free, but basically this idea of you want to have things running 24 7 rather than just saying, having an annual kind of claim, for example. Could you talk a little bit about some of that and what some of the thinking behind that might have been?

Savannah Goodman: Yeah, definitely. So As you mentioned, Google has two main climate goals. One of them is to be net zero by 2030. The other is to be running on 24 7 carbon free energy by 2030. And just to clarify too, 24 7 carbon free energy is much more complex. Than the annual matching schemes that have been most common to date, because we're essentially moving from global annual matching to local hourly matching.

And so you can imagine how, especially over a global, uh, system, how complex that gets and there's no playbook. But we see these goals as a way to actually help scale new [00:22:00] global solutions that drive broader system wide decarbonization because we're actually aligning our own goals with what the grid needs.

Through these hourly local matching, that's how the grid operates, right? It operates, you have to have local constraints. You have to match supply and demand every hour. And so we've seen research from folks like TU Berlin and Princeton and the IEA that show 24 seven procurement is one of the best ways.

For corporates to help accelerate the energy transition towards grids operating on clean energy every hour, every day. And for 24 7, load flexibility is really complementary because it provides this nhe gew sort of flexible resource that can help us better match the clean energy that's on the grid or the clean energy that we procure on a local hourly basis.

So we're also looking, besides load flexibility, at other new next gen technologies like geothermal. We've [00:23:00] announced the starting operations of our geothermal plant with Fervo in Nevada. We're also really excited about battery storage. And there's a lot of other technologies that we're looking at, and research shows that having this diverse portfolio, both of Load flexibility and next gen technologies is what can make 24 7 more cost effective and more accessible and actually meet the grid needs, especially when we consider rising demand in electricity from things like electrification and data center growth.

So, so yeah, all of this demand response effort is really a key part of our kind of sustainability strategy, and we've recently announced a couple of pilots to prove out that this is real, this can work, it has benefits for us, it has benefits for the grid, and we're going to continue to evolve our capabilities and work with our utility and grid operator partners to make sure that we're maximizing the shared impact [00:24:00] of this system.

Chris Adams: ah, okay. So it sounds like rather than just we're saying I'm going to buy a green energy tariff and that, it's all more I want to shift the entire paradigm that the grid actually operates on. And I know that Microsoft have also come on board, but they're not going to call it 24 7, of course, because that's what's called by the competitor.

So they're using the term 100, 100, 0. I think it's 100 percent of the time. Coming from a hundred percent renewable energy or zero carbon. It's something like that. It's not as easy to remember as 24 seven, but that's one thing we've seen. And we've also seen the federal government. I believe they've made us, they made it last year, actually saying they're aiming to have by 2030, 50 percent of all of their power. And that's the entire federal government, not just a single company, for example. So that was like another example of this. Okay, cool. And I think we might have alluded to this. This is actually a kind of wider scheme. The UN has this global 24 7 compact that any organization can sign on to and get on board with as well, I think, right?

Savannah Goodman: Yeah, exactly. And we're really excited to see during COP that the US government officially also [00:25:00] signed on to the compact. There's over 100 signatories at this point. And I think what's most exciting to me is that it is really a community of different kinds of companies and organizations from all across The energy sector that need to come together.

So there's some energy buyers. There's energy suppliers. There's governments. There's cities. There's software and data providers. There's hardware providers. And I think we're not going to do this alone. But what we're really excited about is creating this ecosystem And developing technologies to advance that ecosystem so that we can all collectively work together to meet the ambitious decarbonization goals for the grid that are really needed to enable the broader climate targets for 1.5 degrees. 

 

Chris Adams: I see. Okay. All right. Thank you for that. So we've spoken a little bit about data centers and how flexibility there can actually have some kind of impact inside this. And we spoke also a little bit about, [00:26:00] uh, the kind of wider context of why you might think about this in terms of decarbonizing a grid. This gives a bit of visibility there, and you spoke about some internal things. Maybe we could talk a little bit about some of the things that end users of services that, say, Google might offer, might use, because we've got a lot of developers who use, say, Google Cloud Platform or even other such platforms as well, and I know that I've used some of Google's tools previously, and I know that there's some projects inside the GSF, the Green Software Foundation, that talk about this and are contributing to that, but maybe you could just tell touch on some of the tooling that you've seen in use or made available for end users, because there's a couple of cool things, which I think are worth develop, worth some of our listeners not knowing about.

Savannah Goodman: Yeah, absolutely. And just to set the context right now, as part of our Carbon Intelligent Compute or our demand response programs, we're not actually shifting customer workloads. We're focused first on shifting some of the internal workloads that operate Google [00:27:00] products like YouTube videos and things like that.

However, we're very keen on exploring with customers who are interested in reducing their footprint to shift their customer workloads. In the meantime, we've developed some tools that can help these cloud customers reduce their carbon footprint themselves. And so, just to talk through a couple of the different tools, one of the first ones we launched was called the Region Picker.

The Region Picker allows customers to look at all of the different kind of characteristics that they may want to optimize for, in particular, latency, cost, and then carbon footprint. And based on,

Chris Adams: Hmm.

Savannah Goodman: you can essentially adjust the weighting of those different Aspects, depending on what is most important for you and your business.

And based on that, the tool will actually provide you a optimal region or a set of optimal regions to site your new workloads in. And this tool, we've also embedded [00:28:00] the essentially green leafs into the cloud platform. So when customers are choosing regions, they can actually see which ones are cleaner.

by based on meeting a certain threshold of carbon free energy. The reason we started with the region picker tool is through our own analysis and data we've seen that one of the most, pretty much the most impactful factor when siting workloads is location. So, eventually we'd love to be able to shift workloads in time as well for customers, but this spatial sort of shifting or site selection is really impactful because customers can move their workloads from a dirty to a clean grid, and that makes a really big difference.

Chris Adams: So the region picker, for people who haven't seen this before, as I understand it, when you're using this, it basically gives you an idea of saying if your audience is in Germany, rather than running it in say, one part of there, you might want to consider looking at Switzerland. Who are still in the same place, but have much, much cleaner power, for example, [00:29:00] and still would be staying inside your latency requirements is tools like that.

And that's the kind of stuff that I, uh, that I saw. And I think that's the first time I've ever seen any large organization sharing some of that stuff. So that is included in active assist now, or some of the tools inside Google. Is that what you were saying?

Savannah Goodman: Yeah, that's exactly right. That's part of our region picker tool. The active assist tool can help customers reduce their footprint in a slightly different way. So the focus of the active assist tool is, let's say customers have some projects that have just been running in the background. The ActiveAssist tool can recommend automatically certain optimizations, could turn down a project, or minimize the runtime of a project, which will not only help reduce cost, but also reduce carbon.

So the ActiveAssist tool is using machine learning and AI to serve predictions and recommendations for cloud customers.

Chris Adams: So the active assisting, so it's a bit like, so yes, there's AI and things, but it also just tells you, by [00:30:00] the way, mate, you've left your computer on, or you've left this project running. Maybe you want to turn it off if it's not serving any traffic, because this is one thing that comes up. It'll basically do some stuff like that, as well as providing some specific, much more tailored recommendations as well.

Yeah? 

Savannah Goodman: Yeah, exactly. That's right. And then the last tool that we've developed is called Carbon Footprint. And this is really a reporting tool. There's also some kind of insights that customers can glean to optimize how they're setting up their infrastructure. But this is the rounding out the suite of tools we developed.

It helps customers understand what their actual carbon footprint is from the use of cloud services. So as they make changes, They can see how that impacts the trends of their carbon footprint over time, and they can also use the data for their own corporate reporting. Usually the use of cloud falls into a customer's scope three, indirect emissions, and so this is an important, an important [00:31:00] reporting tool for our customers to be able to meet their reporting needs.

Chris Adams: I see. Okay. And this is something that is just built into the system from day one, or is it part of something you need to purchase separately, for example?

Savannah Goodman: So this tool is available and free for everyone in the cloud console, can access it directly and it's organized per billing account. You can see pretty granular data too, per month, per project, per region. So there's a lot of different ways you can slice and dice the data. There's also a data export, right?

If you want to integrate that data directly into some of your own dashboarding and marry it with other kind of cloud operations data. So yep, it's free and available for everyone on Google Cloud.

Chris Adams: Cool. Thank you for that, Savannah. All right, then. So I know that you work for a specific vendor, and I am mindful of us spending too much time talking about this, because I know that, I can see why it's useful for one company to share this visibility. And from the perspective of a non profit, I'm glad that there's one company pushing this stuff and has been. forthcoming sharing this information compared to some of the other providers in this [00:32:00] kind of space. And I know that from speaking to one of your colleagues, I think Vincent, I never pronounce, I'm going to pronounce, mispronounce his name, Ponset or Ponset. He's also, he's been involved in some of this as well.

And I met him at the Linux Foundation at LF Energy Summit in, in, in Paris, basically. I understand that there's a project called the Green Software Foundation Real Time Cloud Project, which is, being led by both Adrian Cockcroft and Pindy. I, oh, Pindy, I totally forgot your surname, but there's, we have a couple of people inside the GSF who are members who are leading on this, maybe we could talk a little bit about why that's important and why as a member of GSF, you've been involved in some of these projects to create some kind of consistency and conventions around this data that gets exported or exposed to customers.

Savannah Goodman: Yeah, definitely. So we've heard from our customers that the data we're providing is super helpful, but they also need more, more transparent data that's consistent and comparable across their cloud providers. [00:33:00] Often customers will have multi tenant use cases and will have multiple cloud providers. And so it's really important that we can provide more accurate data, more transparent data and comparable data to help them, like I mentioned, with their reporting needs.

And to also make better informed decision for taking action within the cloud environment on how to reduce their carbon emissions. And so we're really excited to be working on the cloud project with Green Software Foundation. Um, that's looking at two main use cases for the project. The first one is this emissions reporting, and the second one is really carbon optimization.

Ideally, these two use cases would be tied closely together, but in reality, because of the way the current accounting and auditing systems work, the reporting Use case typically has a significant lag in the data. And so we're hoping that this project will enable better standardization across metrics for better comparability for reporting.

But at the same [00:34:00] time, we think there's a lot of opportunity to provide more real time, more accurate data for the optimization aspects. And so we're hoping to develop methodologies and tools that can enable cloud providers to offer this more granular and realtime Data. That will support the carbon optimization use case in the near term and then ultimately with the goal of reducing the lag of carbon reporting in the long term . So we're super eager actually to explore working with our cloud customers and seeing what tools are most useful for them, what metrics, how can we help them optimize their carbon footprint, whether it's in an automated way.

Through our own carbon intelligent compute system or through the tools that we're providing through the console.

Chris Adams: gotcha. Okay. Thanks for clearing some of that up. And I know that because I'm also in that working group right now, I can actually speak to you and say, quite honestly, that some two years ago, Google started publishing some of the information at a kind of region by region basis, showing [00:35:00] like the carbon intensity or like the amount of power that is considered carbon free for every single region, um, sharing it as like a CSV on GitHub. This has actually been really helpful to provide a kind of starting point for sorting out some kind of consistent metric, consistent way of reporting this or requesting this from different providers. Cause we know that Amazon has a cloud calculator and Google has a cloud one and Microsoft have one, but having a consistent thing to refer to has been really helpful for this and it's made it quite a bit easier to then say. For example, I'm glad that Google has been one of the earlier companies to talk about information, emissions across all three scopes, scope one, two, and three. And this is something that we don't have from all the providers right now. So being able to point to an example and a data set has been really helpful in this scenario here. I wanted to ask you, coming to the end of the time for this chat, and we've spoken a little bit about like changing some of the kind of paradigms around, rather than basically just buying green energy, like essentially changing how [00:36:00] people think about using power to actually incentivize more carbon aware or approaches for reducing the emissions associated technology, for example. I want to ask you, is there anything that you'd like me to draw attention to? Or you, you reckon we should talk about as well, because we've covered quite a ssions ssions lot of ground from like nanogrids all the way up to ginormous data centers that use as much power as small cities, basically. So what else are you looking at or what else would you like to draw people's attention to right now in this field?

Savannah Goodman: Yeah, I think overall we're super excited about the potential for green software to reduce emissions. I think there's a lot of different ways that we can go about making software more green, whether it's through our hyper scale carbon intelligent compute type platforms or even individual developers choosing cleaner regions for their workloads.

We're really optimistic about how these solutions can come together to enable broader capabilities across the GSF community and beyond. [00:37:00] We think that load flexibility is such an important part for the future of the grid and we think software has some really unique Flexible capabilities that other kind of loads don't necessarily have.

And so, that's why we're really thrilled to be part of this community to see how we can really maximize the, you know, level of flexibility that green software can offer to really help drive the energy transition at the broader grid scale and reduce carbon emissions.

Chris Adams: Brilliant. All right, then I think we're coming up to the end of our time. So I just want to ask if people have, if their interest has been peaked by any of this, where would you suggest people look, or if people were interested about what you're doing specifically, where would you suggest people look to follow on for updates about what's taking place in this field, beyond the projects we just mentioned, like the real time cloud thing in GitHub, or like Google's account on GitHub, for example, or some of the other things. So maybe, let's say I'm a developer. I'm [00:38:00] curious about some of this. I don't know where to look. Where should I be looking to learn more about this or continue my research?

Savannah Goodman: Yeah, definitely. So we publish quite a few updates through the sustainability topics in the Google Cloud blog where you can see the updates on the latest work and not just for green software, but a lot of the different sustainability areas that we're developing new technologies for. If you're interested in learning more about kind of Google's overall strategy, not just cloud specific, Sustainability work, then google.com/sustainability

 . It's a good place to start. That's where we publish all of our papers and annual reports. But yeah, otherwise, we definitely recommend the Google Cloud blog and following the sustainability topic. Lots of exciting updates to come.

Chris Adams: Brilliant. Okay. And what we'll do now is we'll just try. And if, if you're a listener and you've listened to some of this and you caught your eye, we're going to take a moment now to just get as many links as possible to all this stuff that we've spoke about, because we've covered a lot of ground. All right. Savannah, I've enjoyed this. I've learned [00:39:00] a lot and I think our listeners probably have as well. I want to wish you have a lovely week and yeah. Have a lovely winter break. If you're, if you celebrate your time away over winter.

Savannah Goodman: Thank you so much. Have a good one.

Chris Adams: Okay. Cheers.

​[00:40:00] 

From the recent Decarbonize Software 2023 event, this episode showcases a fireside chat on Responsible AI with Tammy McClellan from Microsoft and Jesse McCrosky from ThoughtWorks. Jesse shares his thoughts and experiences from years of working in the field of Sustainable Tech on the topics of risks, sustainability, and more regarding AI, before answering some questions from the audience. 
Learn more about our people:

• Sophie Trinder: LinkedIn
• Jesse McCrosky: LinkedIn | Website
• Tammy McClellan: LinkedIn

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

Events:

• Decarbonize Software | GSF 

Resources:

• AI Transparency in Practice | Mozilla Foundation [04:19] 
• Reducing bias and improving safety in DALL·E 2 | OpenAI [09:31] 
• Responsible AI: Fireside Chat | Decarb 2023 [18:41]

If you enjoyed this episode then please either:

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TRANSCRIPT BELOW:

Asim Hussain: Hello, and welcome to Environment Variables brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

Chris Skipper: Welcome to Environment Variables. Today, we've got another highlight from the recent Decarbonize Software 2023 event. We'll be showcasing the fireside chat on Responsible AI from Jesse McCrosky, Head of Sustainability and Social Change and Principal Data Scientist at ThoughtWorks, and Tammy McClellan, Senior Cloud Solution Architect at Microsoft and Co-Chair of the Community Working Group and Oversight Committee at the Green Software Foundation. They are introduced by our very own Senior Technical Project Manager for open source projects, Sophie Trinder, so it will be her voice that you hear first. So, without further ado, here's the Fireside Chat on Responsible AI.

Sophie Trinder: Hi everyone, I'm Sophie, the technical project manager for our open source projects at the Green Software Foundation. Today I'm going to introduce a special fireside chat to our Decarbonize Software event. We're continuing the conversation that began on the 5th of October at our panel on responsible AI. The conversation surrounding responsible AI is dynamic, oscillating between optimism and skepticism. one side, practitioners believe that AI has the potential to drive sustainable development goals, from responsible consumption to waste management and energy conservation. The promise lies in our improvements in measuring software's environmental impacts, and innovation across energy-efficient algorithms, hardware optimizations, and the growing use of renewable energy sources. On the other side, the rapid expansion of AI, particularly large learning language models, and the insatiable demand for this technology, are raising concerns. If left unchecked, the energy consumption and resource utilization associated with AI make many feel like we're endangering a future where software causes zero harmful environmental impacts. To help us explore the path forward, I'm thrilled to introduce Tammy McClellan, Senior Cloud Solution Architect at Microsoft, and Jesse McCrosky, Head of Responsible Tech and Principal Data Scientist at ThoughtWorks. Thanks, both. Take it away.

Tammy McClellan: Thanks, Sophie. And a hello to all you sustainability addicts. Jesse, hello. Let's start the question with, how do you see the relationship between responsible AI and sustainability?

Jesse McCrosky: Hey Tammy, great question and nice to see you all. So at ThoughtWorks we use a framework that I like which we refer to as the greening of tech and greening by tech and I think this is the best lens through which to view that question. Greening of tech refers to the fact that these systems and especially generative AI as we're talking about now have serious energy consumption, they have serious sustainability issues that need to be tackled.

The other side is greening by tech and recognizes the potential that this technology has to actually improve sustainability of other processes, either within or outside of the tech world. And I think what ties these, these two questions together is issues of transparency and information and ensuring that people have the information they need to make the right decisions for our environment.

Tammy McClellan: I like that, greening of tech and greening for tech. It's my new mantra now. So how can, uh, we use this to make more sustainable solutions?

Jesse McCrosky: So it's a big question. To begin with, I think that I refer to transparency, and when we talk about transparency, a lot of people think that means you share your source code, or you share your model weights, and then you're transparent. Or it means you have to explain the decisions the AI is making, and that's transparency. Transparency is more than that. There's a report I did with the Mozilla Foundation on AI transparency, and we talk about meaningful AI transparency that needs to be legible, auditable, and actionable. And this means that we have to consider the specific stakeholders that the information is being provided to, what are their needs, what are they going to do with this information. So it comes down to the old adage that you can't manage what you can't measure. So for example, in order to support meaningful policy, meaningful regulation, we need to have information about the sustainability characteristics of these systems.

Tammy McClellan: So talk to us a little bit about some possible solutions in this area.

Jesse McCrosky: Yeah, absolutely. So when we're looking at solutions, especially using the kind of transparency lens, we can think about who is the transparency being provided to. So, for example, we can talk about consumers. And right now, consumers are very excited about ChatGPT or whatever else, Stable Diffusion, DALL-E, and everything like that. It's a lot of fun to play with. And they do not have meaningful information about the carbon implications of that play. So someone was suggesting to me that ChatGPT should have a real-time counter across the top somewhere that's telling you how much carbon have you emitted so far in your session, how many, you know, gallons of water have been consumed, whatever else. And it is not a matter of just shaming people, but it's helping people make the right choices, because there might be applications for which ChatGPT is really worthwhile to use, but there's other times that somebody's just idly playing or something like that, and if they realize the implications of doing, they might make other choices. This becomes more interesting when we talk about communication between, for example, model developers and model deployers. So, for example, if somebody is using the OpenAI APIs in their product, they need to be able to have information about what the implications are of those API calls so they can make good choices in how they build their software.

Tammy McClellan: So awareness is key, absolutely. So Jesse, what is the potential for Gen AI to support greater sustainability?

Jesse McCrosky: Yeah, it's an exciting question, and I think there is some potential here. There's a case that ThoughtWorks took, it's a couple years back now, I think, in which we worked with a international manufacturing and services company. They were interested in finding solutions to meet their sustainability goals, and they just weren't sure which way to go.

They weren't sure, "should we start sourcing our energy from a different place, or using different sorts of transportation, or using different industrial processes or offering different products?" And so what we did for them was built a mathematical model of their operations and their supply chains. once we had that mathematical model, we were able to build a sort of scenario modeling dashboard where we could show them like, "hey, if you switch to delivery trucks that are using electricity instead of gas, this is what happens to your emissions, this is what happens to your bottom line, this is what happens to your customers."

And likewise, depending on / considering different product mixes, considering different sourcing, whatever else. So the mathematical model here was not rocket science, to be honest, it was fairly simple stuff. The hard part of this engagement was really understanding the business at the level that we needed to in order to build that model. There were many hours of interviews and poring over notes and internal documents and everything else, as well as actually some basic desk research to determine the necessary carbon emissions factors, that sort of thing. I'm excited at the potential of generative AI to make this sort of process more accessible and more scalable. And I think that we've seen evidence so far that these models do a very good job of looking at these sorts of documents, looking at recordings and interviews, and it may be possible that you could create this model semi automatically with far, far less of the kind of very heavyweight and expensive all sorts of interventions. As well, it was challenging to understand the exchangeability. And so, for example, if the company is buying cotton in one particular country, it might be obvious to us that they can instead buy the same cotton from some other country, and that's the only possible change that could be made. But it's not so simple for the model to figure that sort of thing out automatically.

Whereas GenAI, I think when we connect to these sorts of emissions factors databases, has the potential to make this process much easier. 

Tammy McClellan: Yeah, awesome. Let's move a little bit and talk about risks. How do you think businesses can manage the risks of AI? 

Jesse McCrosky: Yeah, it's it's a big question. I think everybody's talking about this. And I think what I would say is it's critical to understand that risks must be mitigated, not removed. I think a lot of people are talking, for example, about bias and discrimination, and they say, okay, we're going to produce a model that's perfectly fair and perfectly unbiased, or we're going to eliminate this bias from our model or whatever else. And this is just not the way things work. We live in the real world, and these systems are based on data from the real world. And the real world is unjust, and so we need to be able to be ready to tackle that. So, one example that I like is OpenAI with their DALL-E interface generation system. For a while, maybe some months ago, I think, if you asked it for pictures of lawyers, it was going to give you eight pictures of white men, basically.

And OpenAI recognized that there was a problem there, as did the community, of course. So eventually, OpenAI had a short blog post where they talked about how they were going to fix this. And it was apparently fixed, so when people tried to get pictures, they would see pictures of lawyers, and some of them would be women, and some of them would be of different ethnicities, and everything else. So People were curious how this had been fixed and it turned out that all that OpenAI was doing was just randomly appending words like women or black or Asian or whatever else to these prompts and people were not super impressed with this solution but I think it's an important illustrated example, because it's a mitigation, there was a problem with a model, there was a problem with the data, this is not a problem that can be solved fundamentally, it needed to be mitigated, and they found a way, they said, "here's the harm that's going to come from the system. It's going to not be producing an adequate representation, and we found a way that we can show more representation." So this is the sort of mitigation that companies need to take. So when there's issues, and this is where transparency comes in around the carbon impacts as well, so that they can be mitigated, so that if I'm an engineer sitting in front of my laptop writing some software, I need to have awareness that if I call this Gen AI call or whatever else, I have to understand this is going to spike the carbon emissions of my product, and I need to find another solution.

Tammy McClellan: Gotcha. Yeah, that makes sense. Tell me. So are you optimistic or pessimistic about Gen AI at this point?

Jesse McCrosky: I think I'm mixed. I think that ultimately solving the climate crisis means simultaneously solving a social crisis. And I think it's very hard to solve climate change without also solving issues of social justice globally. And I think that Gen AI is a tool that might enable some of these conversations to be tackled in a more interesting way.

So I think as long as we're mindful and honest and clear eyed about how we apply this technology, there can be some optimism there. We need to ensure that we have adequate transparency so that people understand the carbon implications of the choices they're making when they're using these systems, but given that, there is potential to do better.

Tammy McClellan: Gotcha. So I know when you and I chatted before, you said that you had a fun story of AI. Did you want to tell us what that is?

Jesse McCrosky: Ah, so actually, I think there's a misunderstanding. The fun story was an expanded version of what I was talking about before, but

Tammy McClellan: Gotcha. 

Jesse McCrosky: if we have a moment, I think one thing I want to add when we come back to the idea of how, how transparency can help Gen AI be used more responsibly. So, a lot of people are familiar with the concepts of DevOps or MLOps or CD4ML, these sorts of processes. And I think this is a really critical place for transparency around carbon emissions to be integrated. I think the point I would make is that right now, a software developer that's working in kind of a modern setup has the ability, as they're writing code, to see immediately if the code that they're changing is causing some test to fail, or is causing some performance degradation, or is introducing some bug or whatever else. And I think we need to have the same process for carbon so that it if an engineer is making a choice and for any devs out there, maybe you have a case where you need to use a regular expression, but it seems like too much work to figure it out. "Hey, I can just call a Gen AI model and it'll do it for me as well."

It'll work just fine. And you might make that choice because it saves you a couple minutes or whatever. But if you then see that all of a sudden your dashboard turns red and says, okay, your carbon has just increased like 100 percent or whatever, you're going to come back and you're going to revisit that decision. And also your team is going to see that, the trail of what's happening because of what you've done. And so it creates this sort of accountability in the development process.

Tammy McClellan: All right. So I'm curious. What are the top three recommendations you would give to people who are interested in reducing carbon emissions of AI?

Jesse McCrosky: Good questions. And yeah, I think that's something I didn't really touch on so far, but there are a lot of choices that can be made when applying AI. So we don't need to use the biggest general purpose models for everything. I think that there are cases where a general purpose model is really needed. But um, I think that in most cases, no. And so we can talk about using much simpler application-specific models. We can talk about using a smaller model and fine tuning it for the particular task. There's processes like quantization and distillation that can make models much more carbon efficient and nearly as effective. So investigating these options, and again, I think this kind of hinges on the MLOps setup where you need to be ready to evaluate performance. You need to be able to say "how small can I make this model and still actually meet the requirements in my product." Beyond that, I think it's a matter of providing transparency to the end user. So if you're producing something, if users understand the choices that they're making when they're using that product, there's a lot of different ways this can play out, and this can mean some Gen AI chatbot or something like that, but this also can be, maybe you have an e-commerce product. platform and you're using AI to make recommendations to your users and the recommendations that you make can influence their behavior and it can encourage them to buy more products that are disposable or made in very carbon-intensive ways, and so considering these sorts of externalities as well is really critical.

Tammy McClellan: Gotcha. I'm curious, do we have any questions from the audience at this point?

Sophie Trinder: Yes, we do. And thank you so much, Tammy and Jesse. It's been a really great session on AI here at Decarb, and it really shows the passion in the industry for these technologies, plus the responsibility that we all must take when it comes to AI. I know we'll be hearing a lot more in the coming months. But yes, we've got a few questions from the audience. I just want to shout out first, Jesse, thanks for the fun story on OpenAI, how they were mitigating the problem with data to show more representation through mitigation. It was a really interesting insight, thanks. So one question from the audience, how important is prompt engineering for improvement of AI efficiency?

Jesse McCrosky: Great question, yes, and it's it's really extremely important, because the energy being consumed by the model is going to depend in some complex ways, depending on how many tokens are coming into it, and in quite a direct sense, how many tokens are coming out of it. So if we can reduce the number of tokens going through the system, we reduce the carbon emissions. And this again, I know I'm sounding like a stuck record, but it really depends on the MLOp setup, where we should be able to test and see how short can we make our prompts and still accomplish what we need to do. And this is both the length of the prompt itself and the length of the output. So for example, go back to that example I was talking about where maybe ChatGPT has a little indicator at the top telling you how much carbon has been emitted in your session so far. Maybe if you see that number growing as you're chatting with it, you're going to say, "hey, ChatGPT, please be a little bit more brief with your answers. I don't need the whole kind of colorful language and going on and on about everything." So yes, it's very important.

Sophie Trinder: Super interesting. Thank you. We've got another one on training the AI ML model, which obviously takes a huge amount of data and processing, which in turn causes a lot of emissions. How do you think that we could best counterpart the same?

Jesse McCrosky: Yeah, good question. And I think that I have an article out where I actually talk about how the comparisons are a little bit overwrought, talking about how training a model is equivalent to driving a car some distance or whatever. I think that, um, the comparison, at least so far, thankfully, is not quite accurate because we have many cars on the vehicle and a relatively small number of models being trained. I think the important thing is to keep it that way. I think the important thing is that we need to encourage use of open models and shared models rather than every single organization in the world trying to train their own LLM. And this is why I would be a strong supporter of open-source models. I think it's nice to see that movement.

I think it's potential. It means that organizations, first of all, save their money, but also save their carbon when they want to be able to explore elements in their business. And there's always the potential for fine tuning, for whatever other tools need to be applied to open models to make them suit people's applications.

Sophie Trinder: Amazing. Thank you. And jumping back to sort of problems on data and representation, we've got another question centered around that. So do you think we should promote digital humanism and ethical AI to raise awareness about the need for sustainable AI?

Jesse McCrosky: Yeah, absolutely. I think we're existing at a moment where responsible AI and such is being discussed everywhere. There's very active regulatory work in many different regions of the world. There's many people in academia, in civil society, and in industry doing this sort of work. And I think that green AI should come along for the ride, so to speak, and it should be an important part of how we think about the risks and the potentials of these models.

So, yes.

Sophie Trinder: Amazing. Thanks very much.

Chris Skipper: So that's all for this episode of Environment Variables. If you liked what you heard, you can actually check out the video version of this on our YouTube channel. Links to that as well as everything that we mentioned can be found in the show notes below. While you're down there, feel free to click follow so you don't miss out on the very latest in the world of sustainable software here on Environment Variables. Bye for now!

Asim Hussain: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show and of course, we want more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation Thanks again and see you in the next episode.

Chris Skipper hosts this episode of Environment Variables with guests Sophie Trinder and Adam Jackson to discuss the unveilings, questions, and highlights from the recent Decarbonize Software 2023 event. The GSF has announced its Impact Framework during the event that had taken place in November, and together with Chris, our guests discuss the realities of (and dreams for) the state of green software in this recap of Decarb 2023.


Learn more about our people:

• Chris Skipper: LinkedIn
• Adam Jackson: LinkedIn
• Sophie Trinder: LinkedIn

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

News:

• Driving Climate Change Solutions with AI | Decarbonize Software 2023 [07:13]
• High-quality energy data for emission optimizations | Decarbonize Software 2023 [11:54] 
• Impact Framework Demo | Decarbonize Software 2023 [14:57]
• Engineering Excellence with GSF Principles & Patterns | Decarbonize Software 2023 [18:27]
• Emissions Impossible: From home insights to a greener workplace | Decarbonize Software 2023 [30:55]

Events:

• UN World Environment Day: The Green Software Revolution (Virtual Event) - Monday June 5th [31:27]

Resources:

• Software Carbon Intensity (SCI) Specification | GSF [11:02]
• Impact Framework | GSF [12:48]
• Introducing the Impact Framework | Decarbonize Software 2023 [13:04]
• Green Software Practitioner [17:37]
• State of Green Software | GSF [21:02]
• Green Software Patterns | GSF [29:38]
• Opensource / Carbon Aware SDK | GSF[30:43]
• Dan Benitah's Projects and Posts [35:52]
• Decarbonize Software [38:19]

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
• Follow and rate on Spotify
• Watch our videos on The Green Software Foundation YouTube Channel!
• Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Sophie Trinder: We've also had some big tech companies adopting the Carbon Aware SDK, which has been great, including Microsoft and NTT Data to shift some of those high workloads, like training machine learning models to a different time in the day, potentially even a different location. So yeah, it was really great to see the Carbon Aware SDK being talked about at Decarb. 

Asim Hussain: Hello and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode we discuss the latest news and events surrounding green software. On our show you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host Asim Hussain.

Chris Skipper: Welcome to this episode of Environment Variables. I'm your host, Chris Skipper. And in this episode, we will be doing a recap of the recent Decarbonize Software 2023 event with the Green Software Foundation's Community Project Manager, Adam Jackson, and Sophie Trinder, Senior Technical Project Manager and Relative New Face at the GESF. Hello, Adam and Sophie, and welcome to Environment Variables.

Adam Jackson: Hi Chris, it's great to be back.

Sophie Trinder: Hi, Chris.

Chris Skipper: Great. So before we dive into the meat of this podcast, for our listeners who don't know you, could you please introduce yourself? And let's start with Adam, seeing as you are a well-seasoned veteran now of environment variables.

Adam Jackson: So I'm Adam Jackson, and I'm the Community and Tech Culture Lead at the Green Software Foundation. And once again, it's, I'm very excited to be here. I look after our community working group, and also many of our projects and programs, including our meetup program, Green Software Champions, and our recent Decarbonize Software event. Plus in 2024, I'll be leading the efforts on our Carbon Hack and our GSF Summit.

Chris Skipper: Great. Thank you. And Sophie?

Sophie Trinder: Yes. Hi, I'm Sophie Trinder. I'm the Senior Technical Project Manager at the Green Software Foundation. Excited to be here. Recently, I've been focusing on the tooling pillar of the Green Software Foundation's three pillar strategy, known as the Theory of Change. Alongside tooling, we also have knowledge and tech culture.

Like I say, I've been focusing on the tooling and driving forward the open source projects that sit under the umbrella of the open source working group. This includes tools for measurement, like our Impact Framework, and also tools for reduction, like our Carbon Aware SDK.

Chris Skipper: Awesome. Thank you, Sophie. And how long have you been in the GSF now? When did you join?

Sophie Trinder: Just over a month ago, 

Chris Skipper: Just over a month ago. Great. Great. Cool. So exciting things to come. And for those of you who don't know me, my name is Chris Skipper. You've probably heard my voice before on this podcast. I am the producer of Environment Variables. And recently I found out that Environment Variables is the top podcast for 55 amazing listeners on Spotify, thanks to Spotify Wrapped. Just a note that spotify is not the only platform that environment variables can be consumed on. And if you want to listen to it directly from the source, please go to podcast.greensoftware.foundation to listen there. Um, anyway, so, uh, I feel like I've, I've spoken enough about that before we dive into the actual topic of today's podcast, which is the debrief, uh, which will be the title of this episode. Just to remind everyone, everything that we talk about in the show will be linked in the show notes. So, to kick us off, I have a few questions about Decarb 2023, seeing as I wasn't there. We'll be going in sort of a, uh, tit for tat. That's a bit, that's a wrong phrase. I'll be asking one question to each of you at a time, which we, we've kind, you've kindly delegated out to each other. So my first question is, Decarbonize Software 2023 was an overwhelming success in the eyes of the Green Software Foundation. Let's start with some statistics, seeing as we've been talking about statistics, just in my little intro there. How many people attended and how many talks were there? Did it meet the goals of the GSF's intentions with this event? Adam?

Adam Jackson: Yeah. So, uh, well first of all, Chris, no, no pressure that you weren't there live because it was the middle of the night for you. And I wanna start by telling everyone that it is available on demand if you haven't watched it already, at decarb.greensoftware.foundation. But yeah, let's jump into the numbers.

So we actually had five community talks and we had three talks from the GSF itself, announcing new programs. And yeah, we had, I think we had a really brilliant event. We had over 2, 500 viewers and almost 400 live questions and comments. And it wasn't all just "hi from London" and stuff like that. It was actually, there were some really great and deep questions.

Also some challenging questions as well, pushing the boundaries. So it's not just the quantity, it's also the quality of those questions that really stood through. But what's really important to me is that we didn't just connect with folks who are already members of the GSF, but actually brought in lots of new folks from the community. And they're the folks who'll tell their friends and colleagues about green software, as well as getting involved in our open source projects in the future. So, yeah, excited about how we did number-wise, but also excited about the type of people that we met along the way.

Chris Skipper: Wow, amazing. So yeah, I didn't expect to hear that statistic of that many questions. That's really great. And hopefully I know that a few of those questions were answered live on stream. Obviously there wasn't time to answer all of them, but hopefully there will be a source for people to go to. I know there's talk of there being a document or an article to do with the questions. 

Adam Jackson: Yeah.

I think it's, I think it's actually published.

And we'll put that in, we'll put that in the notes. But there's an article where we, we pick up on some of the main question themes. Yeah, it's tricky, isn't it? You want to get in all of these different community stories, and we were really careful to get a good balance of different stories from across Green Software. And then we had five minutes for Q& A, and really you can only ever get into two or three questions. And Sophie and Namrata and I were there in the background trying to answer as many questions as we could in the, in the live chat and Asim was there as well. But yeah, we thought it was best to actually look at what, what the main themes were and try and answer as many of those on demand. And, and it's actually also given us some ideas as to the sort of video content we want to create in the future and probably might even spawn a couple of Environment Variables episodes as well.

Chris Skipper: Yeah, well, I was just going to say, if there are any unanswered or burning questions that haven't been answered, perhaps we can rope Asim into answering some on this podcast. So that would be cool. Anyway, let's move on. Sophie, so can you share a standout moment or a particularly surprising insight from decarbonized software 2023 that really stuck with you?

Sophie Trinder: It's so hard to pick just one. But yes, if I have to, one interesting insight that stuck with me and actually that gives me optimism is from a talk given by Diana Ditrimova, managing director and partner at BCGX and Charlotte Degot, CEO, and founder at CO2 AI. So to give a little context for those who may not have joined us at our Decarb event.

Diana and Charlotte shared some of the key findings from their joint carbon emissions report, which is called 'Why Some Companies Are Ahead in the Race to Net Zero.' So, this report is a survey incorporating feedback from 1, 850 organizations across 23 countries. And they estimate that all of the sort of organizations represents about 40 percent of global emissions.

So, we're looking at a good representation of global emissions here. And one interesting insight for me that stood out was that 40 percent of those surveyed estimated an annual financial benefit of at least $100 million for meeting emissions reduction targets. And so often at the GSF we hear the question, "what's the business advantage associated with making greener choices when building software?"

And I think that this stat, it gives a really interesting insight that helps form that answer.

Chris Skipper: That's, yeah, it's interesting that you mentioned that talk in particular. I'm hoping to get them together for an episode of Environment Variables to talk about that in particular, that study, that that's incredible, that, that reach that they have of 40%, yeah, of, yeah, I don't think, I think that's the one thing that that's been covered a lot on, at least on Environment Variables and my engagement with the Green Software Foundation is just having the sheer amount of data is sometimes it was probably the biggest issue out there with any sort of measurement to do with carbon emissions. So, and software in particular, if you haven't seen the Decarb 2023 video, you should go back and check that talk. Cause I think it's only about 10, 15 minutes long. So it won't take a lot of time out of your day if you want to have a look at it.

So.

Adam Jackson: Yeah, we're really hoping to actually make all of that content available in bite sized chunks as well. So, so that people can find that on demand. And one thing I like as well, just to add on what Sophie said, is that Um, that talk was beyond just green software itself, but it was also "how can you use software to further sustainable outcomes?" So that's, that's another thing that we don't directly cover every day at the Green Software Foundation, but a lot of our members and our community are really interested in.

Chris Skipper: Yeah. And it goes to show also the diversity of just how big the community has become in the last sort of two years, just because of the existence of the Green Software Foundation, it's attracted people outside of the software industry, which is fantastic, and I'm sure this probably echoes what Adam is fighting for is we want people from all walks of life to join the cause and come and talk and just increase the dialogue around it.

So that's fantastic. Anyway, so let's move on. I'll pitch this question to you, Adam, given the emphasis on measurement at Decarb 2023, what's one common misconception about carbon emissions measurement in software that was debunked or clarified during the event?

Adam Jackson: It's a really good question, Chris. It's actually quite a challenging question as well. So one of the biggest misconceptions that I see is that it's from people that are coming new to the GSF, is that it's been easy to do measurement at all. It really isn't. Measurement isn't easy today. It's about to get easier.

We're definitely on a journey, but it's important to look at the very short history of measurement. What we have today is some amazing standards, such as the Software Carbon Intensity or SCI standard, which, which actually has been submitted to ISO. Hopefully we'll get an approval and see that in the catalog very soon. But we're still super early days. And we're also going to be talking about, um, some of the, some of the key elements in producing tools that can actually do the measurement for us. It's been very manual working out. We'll see spreadsheets or a few people we've seen have implemented some dashboards. I saw a really great talk from Amadeus, which is one of our GSF members, over the summer at a conference in Berlin, and they'd done some really, really great dashboards, but it had all been very much custom work for them to get to that stage. And yeah, what is great to see is, particularly this year, we're actually seeing a lot of effort put in to measurement and visualization. And if I bring it back to the Decarb event, we did see a wonderful talk from Yvette and Johann. They're from CODE University in Berlin, and they've been building a measurement project. It's really focused on measuring consumption of energy. So if we think about the SCI equation, for anyone that's familiar with that, is the letter E in the equation. If you don't know what the equation is, take the Green Software Practitioner training and it shows you all of that. Anyway, these folks from Berlin, they've actually implemented some visualization. To that energy measurement and it was really great to see because visualization is hard and not everyone agrees on it. And there was a lot of, there's a lot of chats during the event on visualization as well. So, but the important thing is we'll need to see folks like Yvette and Johann. And they'll have to show us what we can do with that visualization. And it's also going to help us build out the capabilities of things like the new Impact Framework, extending it, show us what's going on as well.

Chris Skipper: Yeah, yeah. I just mentioned that data was hard to come by, but yeah, measurement, itself, obviously, is a big challenge. And if you are interested more in the measurement and the struggles of measurement, we actually have a previous episode of Environment Variables with Srini Rakhunathan and Navveen Balani with Asim talking about all the other attempts that have been made at measuring carbon emissions of software prior to the SCI.

So that's a really fascinating episode if you are interested in that. And yeah, it's great that you mentioned visualization as well. I know you love a dashboard, Adam. So that's something you mentioned last time you were on the pod. So I'm just going to bring that up again.

Adam Jackson: Yeah. Actually, one interesting thing is you measure, you mentioned data. Obviously, you need data in order to be able to measure. But also the act of measuring puts pressure on others to produce data. And we do hear, and I'm not going to single out any GSF member, but we do hear, "Ooh, why doesn't company X provide this data?"

And actually a lot of the time it's because people haven't asked for it yet, or people haven't shown that they have a need for that data. So, so I really do truly believe that the more we measure, the more data sources will become available.

Chris Skipper: Yeah, absolutely. Good point. Cool. Okay. Switching back over to Sophie in this game of question tennis. The next question I have is, we just mentioned the Impact Framework and that was obviously the big surprise that was the big mention that was going to happen at this year's Decarb 2023. So for our listeners who don't know what it is, Sophie, do you mind elaborating, please?

Sophie Trinder: Yeah, of course. So, yes, you're right, it was the big unveiling of the Impact Framework in our introduction at Decarb this year. So, the Impact Framework is one of the Green Software Foundation's open source projects. We have been working on it for almost a year now, but it's still in incubation, which is one of the project life-cycle stages.

And that basically means it's still evolving, it's still in its explorative stage. But we did manage to do an alpha release in November ahead of Decarb, which is where we introduced it, and showcased it through a live demo of the tool. So the framework itself is an extension of that software carbon intensity standard, the SCI, that Adam just mentioned.

So just to dig a little bit more into what that covers, the SCI is this equation, which is the energy consumption times the emissions factors plus embodied carbon over a functional rate. So this is the what we should be measuring when considering negative environmental impacts of software. And then the Impact Framework takes that equation and provides a tool into how we can measure that impact.

So if we think about some modern applications, they're composed of many smaller pieces of software, which we call components, running on many different environments. For example, private cloud, public cloud, bare metal, we've got mobile, laptops, desktops. Every environment requires a different model for measurement.

So the Impact Framework provides a framework for running all these different models using model plugins to calculate the output of impact through that SEI equation. So right now at the, the GSF, we've got a standard library of models and a repository of community models, and we envision a future where the number of models just keeps expanding out into the thousands to cover all those different environments.

So it is an open-source project, like I just mentioned we're welcoming contributions in fact, our hackathon, which is happening next February, is Impact Framework focused. So that's where we're hoping to see lots more of these models get made. The opportunities is endless that we see in the ways that we'll be able to start improving the measurement of the negative environmental impacts of software.

Chris Skipper: Great. Yeah, that's amazing. And yeah, I love the community focus of it. I think that we, we talked about the importance of open source in general, and I think everything that the GSF has done has been open source. So the possibilities are endless when we all join in and, and I think the hackathon, which we'll talk about a bit later in this podcast as well, next year is going to be phenomenal, especially when there's a bit of incentive behind it. So yeah, it'll be really interesting to see what comes from that. Moving on, let's talk a bit about the Practitioners course, the Green Software Foundation's Practitioners course, which you mentioned, Adam. This, obviously that's where you can learn more about the SCI, but it's trained over 50, 000 participants in less than a year, which was something huge that was announced also at Decarb 2023. What do you think is driving the surge in interest and how can more professionals be encouraged to participate?

Adam Jackson: Yeah, I've just literally opened the stats. 55, 000 now, so yeah, doing really well. We need another 5, 000 people to do that in the month of December, so we end on a nice round number. And actually, we know that some more people have enrolled as well, so another good thing. One of the cool things we've certainly got some people that have started the training and hopefully they'll, they'll come back to it as well.

But, uh, yeah, it's interesting. If we think about, uh, Chris's talk, that's, uh, another Chris, Chris Howard from EPAM. EPAM is a GSF member. So his talk at Decarb really showed us how important the knowledge, this knowledge is. And why it's so important for both individuals and also organizations. So I've no doubt that a lot of people do take the practitioner course because they're concerned about what has happened, happening to the planet. And they're worried that we're experiencing our hottest year ever, which was just announced at COP, and they're concerned that software is between 4 and 5 percent of global carbon emissions, and that's twice that of air travel. But I think there's something more than just an individual concern. I think that concern is becoming more than something that people worry about, or campaign about and actually, it's starting to turn into something that's also becoming a business driver in a way that, a way of individuals distinguishing themselves in the job market, so to speak. So for companies like EPAM, which is a consultancy, they don't just want to develop their staff. They also see the difference it makes to the organization, um, when winning a customer.

Because their customers are demanding green solutions. Um, and it's also, of course, it's a massive motivator for their employees. You know, they've a fantastic internal community who want to learn these things. But yeah, I'm not surprised we've seen this growth. My, my colleague, Russ, who leads our knowledge efforts at the GSF, he says it's just the beginning, and I think we will really continue to see us moving from just being worried about the environment to actually having people that really want to do something, they really want to take action, learn more about it, and then really measure and reduce.

So, yeah, I'm looking forward to seeing six figures on number of people taking that course, hopefully at some point in the early months of 2024, and I think we'll continue to see both that individual and that organizational pressure to learn and then be able to take action.

Chris Skipper: Yeah, amazing. Yeah, it's gone up to four to five percent already, the emissions of software. 

Adam Jackson: Yeah, yeah, That's right. That's actually a stat that we, that we announced during our State of Green Software report back in May 2023. Yeah, it's, I think it's quite startling to a lot of folks because we see, I think most people think it's in the sort of 2%, 2 - 3 percent band, but we actually did a lot of research and found that it was actually quite a bit higher. And I would certainly recommend people go to stateof.greensoftware.foundation to read the report for themselves. But just a reason that it's higher than people think is because a lot of people just think about the data center or the device, but actually you've got data center, you've got all of that network infrastructure in between, and you've got the device. And I can't remember the exact numbers, but actually I think you can even sometimes think of them, depends on the application, but sometimes it can even be a third each. So yeah, reducing, for example, reducing network bandwidth is a good way of decarbonizing software as well. And people never think of that. So check out that report if you want to hear a bit more. Yeah,

Chris Skipper: Yeah. Cause I think it was just probably this time last year that it was 2%, which is quite, yeah, I think we announced that on the podcast, so that's quite, yeah, really scary, but yeah, and also, yeah, sorry to focus on more positive things and the growth of the green software practitioner courses intake or the amount of people that have gone through it, I think. That number is really impressive, firstly, and then secondly, just that more people are going to be using it as a business decision, that's something you pointed out, I think is something that I think might be a great way of people to think about their approach to green software as a whole, because whether you like it or not, businesses are going to have to adapt.

And yeah, so really great that the GSF is leading the way in providing qualification for people, consultants in particular, that's a huge industry consultancy, so yeah, and obviously we have consultancies that are part of the GSF. I think Accenture, am I right in saying that?

Adam Jackson: We've got a few, and yes, yes, so, other consultancies do exist as well for folks, but yeah, if you want to see who, see the, the big list, our membership's grown massively this year, by the way, so just go onto our website if you want to find out who, who is contributing as a member, and that those members really help us set our standards and they're the ones that are very heavily involved in the day to day running of the foundation. And yeah, consultancy is a big part of that. We also have a lot of representation from the, the cloud and tech companies themselves, but also industry, a lot of, banking in there right now, a lot of regulated industries in particular.

Chris Skipper: Yeah. Yeah. Great. So obviously we just talked about the frightening statistics of the amount of emissions that are being created by the use of software. I think part of that is down to the, the rise of AI and the use of LLMs. ChatGPT, et cetera. And so with that in mind, Sophie, I'm going to ask you a question a bit about that.

The relationship between responsible AI and environmental sustainability was a hot topic at Decarb 2023. What were some enlightening points brought up by Tammy McClellan and Jesse Mccrosky in their fireside chat as part of the event?

Sophie Trinder: Yeah, thank you. It definitely was one of the hot topics of the Decarb event. And so many enlightening points were brought up by Tammy and Jesse, but one particular point that stood out, which actually we've touched on already, is that transparency of data and information is key. We touched on this when we were talking about measurement, weren't we?

We were saying the more we measure and we request measurement, the more likely people are going to be to giving us the data we need in order to measure. So Jesse was touching on that transparency of that data. So we need the full picture on emissions of AI systems. And that's not just looking at open source code, but full transparency of what the emissions of AI systems are.

And he was talking about how In order for software developers to make sustainable choices on whether to integrate AI into their products or how to develop the type of AI that they're developing, they need the full picture. Similarly, he touched on how policy makers need that in order to make good regulation and policy on AI.

But also for us as consumers, how we can make sustainable choices when using AI. I like the term, be a conscious consumer. But for us to be conscious consumers, we need the knowledge in order to be conscious. So we need that transparency of data. And Jesse painted this hypothetical picture. What if there was a carbon counter or there was a water gallons clock at the top of ChatGPT?

Would you use it differently? Would you only use it when it was essential? Maybe you would write a shorter prompt. Would you ask it to write its answers briefer? Similarly, he painted another picture where, what if a software developer's screen went red? If the developer made a decision that, yeah, might make them save some time, might make them slightly more efficient, but what if it came with a huge carbon cost?

So their screen went bright red when there was a carbon cost associated with the code that they were writing. I think it was just a really interesting way to start thinking about being more transparent with that data so that we can be more conscious with our decision-making.

Chris Skipper: Yeah, absolutely. And there's also a gap in the market there for perhaps a browser plugin or something that would really inform day-to-day users, because I know people have, well, I've spoken to people that have been using ChatGPT as if it were Google and that's quite worrying, you know, so, um, you know, we've, there's an episode of environment variables where we speak about the environmental impact of AI and, um, just the sheer amount of water that it uses is quite incredible.

So those ideas. That was spoken about during Decarb 2023 were really great. And I think maybe we'll see some of that as a springboard for what will happen in Carbon Hack 2024. 

Sophie Trinder: Yeah, definitely. We've seen it in certain other industries when you're buying a flight and you see the little green label now on certain flights where they say if you buy this flight ticket it's going to be better for the planet in terms of emissions. Maybe it's It's going to be a fuller aeroplane, those sorts of decisions, it's, it would be great to see some of those, like you say, user interface platforms where we'll be able to start helping people make those more environmentally conscious decisions when it comes to software too.

Chris Skipper: Yeah. And I think that's one of the biggest challenges facing organizations right now, um, at Decarb 2023, there were a lot of challenges that were raised. Um, speaking of which, what's your take on the biggest challenge organizations face when embracing green software right now, Adam, and how do you think it can be overcome?

Adam Jackson: Yeah, so, well, first of all, we've talked a lot about measurement already, and I do think that measurement is probably the biggest challenge for us all right now, but I want to look at a different challenge that some, that organizations specifically have embracing green software. And I touched on this a little with the EPAM example, but in one of the other organizations we had on the Decarb event was Siemens, and Siemens, they're a brand new steering, steering member of the GSF, they joined in the last few months, but their journey with a sustainable software goes way further back than that. They've spent a long time building sustainability into their entire software lifecycle. And that's what their talk was about. And I think actually that, beyond the knowledge, is the hardest part. Often, I often tell folks when they ask me what green software actually is, that there's a lot of parallels with well-crafted software. So well-crafted software, like green software, focuses on built-in quality throughout the complete software lifecycle. So architecture, development, operations, getting all of the stakeholders together to take responsibility. And we need the same thing for green software. So at the GSF, we often talk about software practitioners, and this is a broad term that goes beyond developers alone.

It includes product managers, program managers, designers, UX, testers, IT operations. And what takes the time, and GSF does have some materials that can help here, is developing the best practice, the processes, and the learnings that bring all of these people together into a really holistic software lifecycle, and I think that's why we're really keen to get organizations to hear and listen to others, even if they're competitors, what's the best practice that we can all share that's gonna deliver green outcomes. And then we also have some materials inside the GSF, such as our Patterns Catalog, which is like a set of best practices and people can use that to, to go beyond just the learning and start thinking about how to integrate it into their organization. So yeah, a bit of a long answer. It's, it's a bit of an abstract challenge as well, and it's different for every organization, but really think, yeah, thinking about how, if we think of the Learn, Measure, Reduce, that reduce bit, it can be, sometimes it can be simple for a single product or a single workload, but when you're trying to think about how to get your whole organization on that journey, that takes a bit longer.

Chris Skipper: Yeah, amazing. And also, I think it highlights, there's been an immense amount of growth in the last year, just in terms of the amount of news and the amount of events that are happening around green software, which is fantastic. So Sophie, with that in mind, what are some of the exciting innovations or projects that are making a significant impact that were highlighted at Decarbonize Software 2023?

Sophie Trinder: Thank you. Yeah. So another project that was highlighted at Decarb that's been having a significant impact is another one of our open source projects I mentioned earlier called the Carbon Aware SDK. So we had two of our principal contributors come and talk at Decarb about the SDK. That was Dan Benitah and Vaughan Knight.

They gave a fascinating talk into some of the use cases of adopters of the SDK. So for a bit of context for anyone who is new to the Carbon Aware SDK, but also to Carbon Awareness, that basically means being aware of the carbon intensity of the electricity on the grid and doing more when and where the electricity is cleaner.

So less carbon intense, i. e. generated by renewable resources, and doing less when and where the electricity is dirtier. So the Carbon Aware SDK is a web API and command line interface tool that helps assist in building carbon-aware software by providing the intelligence of knowing when and where the electricity is cleanest.

So you don't need to do all of that work for yourself. There's a tool out there already that can help, help you work out when and where would be best or cleanest for your consumption. And what was really interesting at Decarb, Dan showed us how he's been adopting the SDK himself at home to successfully reduce his impact by 30%.

So, he used the Carbon Aware SDK to simulate different hypothetical scenarios. So, for example, he was looking at his big consumption uses, his dishwasher, tumble dryer, those sorts of things, washing machine. And he used the SDK to, to simulate Different scenarios of if he was to shift his consumption, so that's the kind of the when part of the when and where of carbon awareness, when you consume, he was, yeah, looking at different scenarios and he learned that if he time shifted by seven hours to time where his electricity was cleaner, then a 7 hour shift would be best.

So that's what he did in order to reduce his impact by 30%. So it just shows that with the right data, like we were talking before, with the transparency of that data, we've all got the potential to make some significant impacts in our reduction of consumption. And so whilst Dan shared this example of carbon awareness in the home, and the small things do add up, we've also had some big tech companies adopting the Carbon Aware SDK, which has been great, including Microsoft and NTT Data, for example, like you mentioned earlier, to shift some of those high workloads like training machine learning models to a different time in the day, potentially even a different location. That was the where part of Carbon Awareness, when and where, time and location. So yeah, it was really great to see the Carbon Aware SDK being talked about at Decarb.

It is still growing. We're just about to do a version 1. 2 release in a couple of weeks. But again, it's all open source, open to contribution. And definitely would recommend people taking a look and seeing if you could simulate different hypothetical scenarios for your own workloads. Be that at home or at work.

Chris Skipper: Awesome. Yeah. I haven't heard that story about Dan using it in his home. That's fantastic. I know we spoke a little bit about how people can make small changes in their own life to do with green, with green software and just being more environmentally friendly as a whole the last time Adam was on the podcast.

But with that in mind, Adam, can you tell us a little bit more about how people can use the SDK in their personal life to become more carbon aware?

Adam Jackson: Uh, yeah, actually, Sophie's already given a really good overview and, and Sophie's quite an expert on, on energy. So I've had some really interesting chats with her already, but, but yeah, the, the added dimension for me, if you, if you actually watch that final session from Decarb, you probably can't help but see how excited I was personally to hear from, from Dan and Vaughan talk about carbon awareness to hear in their own homes. So I have a home assistant set up at home. So it's a Raspberry Pi 4 with the Home Assistant software on and that's open source and that piece of kit talks to all of the, all the smart home, all the, all the devices in my house. And we're getting better and better. I feel like every time I open Home Assistant, I can connect to more data sources in my own home. And that makes me really excited as to how I can not just think about the energy, but also everything that's going on in my house. So, yeah, we touched on, upon how the SDK could be used, and Dan is actually writing a blog post right now about how to integrate Carbon Aware SDK into home assistant setup without doing lots of custom code. And I was going to put a bit of pressure on him because he hasn't finished his blog post yet. So hopefully by the time this podcast comes out, it'll be done or soon after and we'll try and get that linked. So, yeah, I'm excited about this because not only do I want to, I want to understand more about my own carbon impact, but I want to combine that with the data that all of the devices in my house provide. And I want to combine that with my, my battery and my solar setup and think about the dimensions, such as having an electric vehicle. So looking at how I can really balance and optimize my use of electricity. And so, sadly, I live in a, in quite a high-carbon area. So if you touched upon the, the location being a dimension, I can't change where my house is sadly, but the intensity does, the carbon intensity does vary dramatically at different times of the day. So I'm hoping I can take something from my work life and make that work for me at home as well. So you definitely watch that, that short video as well from the end of Decarb about, about Carbon Aware SDK. And yeah, you'll see some of the discussions I had were along these lines as well.

Chris Skipper: Yeah, that's incredible. I only learned about a Raspberry Pis through this podcast and yeah, just the use of it to do that is, is a great use of a Raspberry Pi firstly, but then secondly, also just the application from your, in your personal life like that. Yeah, it's great. It's amazing. I think more people should try and do that sort of thing.

I wish I had more smart home devices. I don't have any, I don't think so.

Adam Jackson: Don't get it. Don't fall down the rabbit hole, Chris. This is never ending. My, my toothbrush can talk to it. It's, it's crazy, but it's testament to what the open source community does when you let them get excited about something, you see something like Home Assistant. And this is why I'm excited about Impact Framework as well, because I'm hoping we'll see the same passion. Being able, being able to talk to maybe not my toothbrush, but certainly things in the cloud, network infrastructure, and the devices that are really creating that carbon impact in our life. And see, let's see what the, what that open source community will come up with over the next few months.

Chris Skipper: Yeah, exciting stuff. We're going to keep, at least on the podcast, we're going to keep track of the work that's being done with the Impact Framework. So that's it for our episode regarding the decarbonized software at 2023 event. Just before we go, I've got two quick questions for you guys. I'm sure Adam, you could probably answer these. If people want to find out more about the rundown of what really happened at decarbonized software, where should they go?

Adam Jackson: Yep. That's a, an easy one. It is decarb.greensoftware.foundation. And we'll make sure we put that link in the show notes.

Chris Skipper: Yeah, absolutely. Link will be down in the show notes. And the next thing is what's the next big event for the GSF?

Sophie Trinder: So up next for the GSF, we've got a hackathon in February 2024. So definitely watch this space and watch our newsletter. That hackathon is all going to be centred on Impact Framework. We're really hoping to see not just carbon emissions being recorded, but also all negative environmental impacts like water, waste.

We've said before, the opportunities are endless with measurement.

Chris Skipper: Amazing. And yeah, looking forward to that and there'll be a pretty big announcement about that hopefully sometime soon. We've come to the end of our podcast episode and all that's left for me to say is thank you so much, Adam and Sophie. That was really great. I personally have learned so much. And, um, if you do want to watch, this is another thing about, um, Decarb 2023.

If you do want to watch it, please head over to our YouTube channel, where you can check out the talks that we mentioned in this podcast. So yet again, thanks for your contribution guys, and we really appreciate you coming on to Environment Variables.

Adam Jackson: Thanks, Chris. Yeah, it was great to come on and talk about the Decarb event and hopefully you'll have me on again very soon.

Sophie Trinder: Yes, thanks Chris. Thanks for having me.

Chris Skipper: Thanks very much, Sophie. Yes. Great. So that's all for this episode of Environment Variables. All the resources for this episode are in the show description below, and you can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. See you all in the next episode. Bye for now.

Adam Jackson: Bye.

Sophie Trinder: Bye!

Asim Hussain: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we want more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. Thanks again, and see you in the next episode.

Chris Adams is joined by Ines Akrap from Cognizant to talk all-things sustainable web design. Together, they delve into the nuances of designing energy-efficient websites and the challenges of green coding in frontend development. Ines shares valuable insights from her experiences at the Linux Energy Foundation Summit and the SDIA Green Coding Summit. The episode also explores common mistakes in optimizing sites for carbon efficiency and discusses exciting projects in the field of green software that are generating buzz. Resources like Website Carbon, Ecograder, and Lighthouse are highlighted, alongside discussions on the Software Carbon Intensity Specification and the CarbonAware SDK. This episode is a must-listen for anyone interested in the intersection of web development and sustainability, offering practical tips and exploring new research horizons in the quest to decarbonize the digital world.

Learn more about our people:

• Chris Adams: LinkedIn | GitHub | Website
• Ines Akrap: LinkedIn | Website

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

Topics:

• Designing Energy Efficient Websites | Linux Energy Foundation Summit Paris ‘23 [4:08]
• Green Coding in the Frontend: The Good, The Bad, The Ugly | SDIA Green Coding Summit ‘23 [16:15]
• SDIA 2023 Green Coding Workshop Slides | Chris Adams Green Web Foundation [23:41] 
• Mistakes when optimising sites for carbon [29:53]
• Which projects in this growing field have you excited right now? [41:53]

Resources:

• Website Carbon [10:01]
• Ecograder [10:01]
• Lighthouse [10:11]
• W3C Sustainable Web Design Group [12:19]
• Decarbonize Commerce 2023 | Green Software Foundation [18:56]
• Careful Trouble [19:57]
• How Thoughtworks uses Cloud Carbon Footprint for sustainability | Cameron Casher ThoughtWorks [20:32]
• Power profiling with the Firefox Profiler | Florian Quèze [21:34]
• Sitespeed [26:20]
• Software Carbon Intensity Specification | Green Software Foundation [26:46]
• DIMPACT [27:06]
• The Week in Green Software: New Research Horizons [27:10]
• Netcentric/progux: Progressive User Experience Library  [31:42]
• COP28 [36:16]
• Firefox Profiler [40:30] 
• Linux Perf | [40:56]
• CO2.js [42:12]
• The CarbonAware SDK | Green Software Foundation [43:31]
• Showcase — lowwwcarbon [45:22]
• Sustainablwebdeisgn.org [47:07]
• Green Software Practitioner Course | Green Software Foundation [48:13] 

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
• Follow and rate on Spotify
• Watch our videos on The Green Software Foundation YouTube Channel!
• Connect with us on Twitter, Github and LinkedIn!
  
  

TRANSCRIPT BELOW:
Ines Akrap: You cannot fix something that you cannot measure because you just don't know that you have an issue. So being able to detect this issue and to show these numbers to the decision makers, I think this is something very important.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to another episode of This Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. In this episode, we're joined by Ines Akrap, a developer who I first met in Paris at the Linux Foundation Energy Summit earlier on this summer, and most recently with whom I shared a stage at the SDIA. The Sustainable Digital Infrastructure Alliance Green Coding Summit in Berlin earlier on this month in November. We ended up talking about digital sustainability for front end developers, and we had such a good time that we figured it might be worth continuing this conversation on the podcast for other people who are curious too.

Ines Akrap: Hi Chris. Thank you so much for having me here. I cannot stress enough how excited I am to be here since I'm listening to you so much that it just came out on my Spotify wrapped at the end of the year.

Chris Adams: Wow, I, thank you, I did not know about that. Okay, so, and for people who are new, or who weren't at either of these events and didn't see you speaking, I figured it might be worth just briefly giving you the floor to introduce yourself and talk about some of your background for this before we dive into some of the specifics about what we'll be talking about for the front end, so optimising for carbon at the front end.

So yeah, Ines, the floor is yours.

Ines Akrap: Sure. So hi everyone. My name is Ines. I'm a web performance engineer and sustainable web consultant and cognizant and centric. And yeah, I've been doing this for a few years now with many different clients and hope that I can share some of these things with you today.

Chris Adams: Cool, thank you for that, Ines. And Ines, now, I know that we met in Paris, which for me felt really quite exotic and exciting. And then we met recently in Berlin, but as I understand it, you're based in Munich, in South Germany. Maybe we could talk a little bit about that before we dive into the inner nerdery of what we're going to be covering.

Ines Akrap: Sure. So actually I'm originally from Croatia, from Zagreb. I lived there and finished my master's degree until I was 20, 23, so more or less. Then I went for actually exchange year to Portugal, so I lived a year in Portugal. I came back and thought, Oh yeah, no, I'm not staying here. I need something more, something different. And I actually ended up in Barcelona. So I lived in Barcelona for five years and then this move happened, which people still think I'm crazy about, but I moved from Barcelona to Munich where I am now. And. I'm still very fond of this move. I am very happy to be here because my mind is way more German than people think.

I really love how things function here, but I'm definitely super fond of my memories in Barcelona as well. So yeah, currently I'm living and working in Munich.

Chris Adams: Okay, cool. Thank you for that. All right, then. So if you're new to this podcast, folks, as I mentioned before, my name is Chris Adams, I work as the chair of the policy working group at the Green Software Foundation, and I'm the executive director at the Green Web Foundation. It's a Dutch nonprofit working towards an entirely fossil free internet by 2030.

And as a reminder for people who are curious about what we discuss. Whenever there is a project or a story or a paper or a project online, we try our best to link to it in the show notes below this episode. So if there's something you hear about and want to learn more about, please do get in touch or please check the show notes because we spend a bit of time to make sure they're useful for other people who come along to this.

Okay, Ines, are you sitting comfortably?

Ines Akrap: Absolutely.

Chris Adams: Well, then let's begin. Okay, so as I first mentioned, I first came across your work when you were presenting with, I believe, your colleague Claire Thornwell at the Linux Energy Foundation Summit in Paris, where you were talking about your experiences building calculators and websites.

Tools to help clients optimize for carbon. Uh, and that was mainly for an audience of energy nerds and techies who don't really work at the web, or the web, website, and were actually really captivated by this. I figured it might be worth just talking about how that even came about in the first place.

Because yours was the only presentation specifically about web. And I enjoyed it and appreciated it. But I'm sure there's a backstory, so yeah, take it away.

Ines Akrap: Sure. Okay. So how long of an origin story would you like? Yeah, actually. So I, I was working a lot years as a front end engineer and with my background, so my master degree is actually in telecommunication and information technology. So I actually do have a background in networks and all these like data transfer. So this was always a very interesting part of a front end for me. So bit by bit, I ended up in web performance also as a person who doesn't really like to wait and is a huge rage clicker. This was a logical way. You know, I want to optimize it for myself as much as for everyone else. And I think it was end of 2020 where I was looking at this, of course it was end of 2020, so it was an online conference that I was looking at from the conf comfort of my sofa about JavaScript and just about this sheer amount of unused JavaScript. And the presenter at that point showed calculated just this rough number of unused JavaScript and started comparing it, of course, to carbon emissions and to trees.

And this was actually-

Chris Adams: Just a quick one, this is JavaScript, when you say unused, like when someone visits a webpage, you're talking about a bunch of stuff that is sent as like dead code that isn't actually used by the browser, is that correct?

Ines Akrap: Exactly that. So there is, these days, there is so much JavaScript that is just not being, well, that's being processed, but it just doesn't serve for anything useful on the website. So he was talking about this because there is this tendency in web development where you just put everything on a website and with the big systems, and of course, people coming and going, a lot of people are afraid to remove these things, you know, so this is how this thing came about and it was a moment of the mind blow turnout for me and for my career, when I realized this is even a thing, no? And then I went a bit like to search into the topic to figure out what, what this sustainable web even is and realize that in my work as a web performance, I'm already covering quite some strategies and optimizations that are leading towards, towards cleaner and greener web. And at this point in time, we already had a small web performance group inside of the Cognizant and Centric and Claire was part of this group as well. So we always function really well together. We are quite a dangerous combination because I'm an engineer and she's a business design person. So especially when we go together, we have the same kind of goal. We can cover quite a broad spectrum of generally topics and like points of view, like storytelling and all that. So we'd started working towards this topic a lot together, of course, with our team as well, who was the web performance team, quite embracing this whole topic. And this is how this part slowly came about. I guess the extra just to add on top is that we were building a tool that was a real user monitoring tool. So we can go a bit inside of that, that later. But basically, this tool was originally supposed to measure, you know, core web vitals, web performance metrics, and all that. However, then we came across CO2.js and we realized that we are already collecting the data that we can just plug in and actually give users, well, give our clients in a way, almost real time assessment of the carbon of their website.

Chris Adams: Okay, if I may just check with you one thing, you use this term real user monitoring, and this is, I think it's referred to as RUM sometimes, and this is the idea that rather than using an entirely modelled approach when you might, say, have a virtual browser visit a web page, you're actually, it's much more tied to having a well, as it says, a real user tracking this, so you've got a much better idea of, okay, you're collecting information about, say, it's this particular browser that's doing this particular work, rather than just a generic browser in that sense.

And that ends up being slightly more accurate than an entirely modelled approach. Maybe we could talk a little bit about some of those things, because you mentioned being able to translate from energy to, say, carbon and stuff with CO2.js, and I'm very happy to refer to that a little bit later, but as I understand it, when you, these two approaches of using, say, a RUM based approach or a kind of synthetic based approach, this brings out this this ends up making people aware that there are assumptions that you might use in a typical kind of website carbon calculator tool that might not be, might not hold true when the real user is accessing a website, like whether they're signed in, or they've got cookies set, or stuff like this.

And this can also have an impact on visiting a site, for example. Maybe we could talk a little bit about some of that stuff there, because I realize that this is something that you end up having to deal with when you're building some kind of, uh, actual tools that provide the kind of more robust numbers for this.

Ines Akrap: Exactly. So this was, to be honest, a bit of a game changer, no? Because when you think about, you explain as well, RUM tool very well. So what happens there is just, of course, you add a very small script to the website, to every single page of a website. And this is the huge deal as well. So when you use any synthetic, uh, monitoring, which is no, things like Website Carbon, EcoGrader, or any of these tools, especially in web performance, we usually, if you're in front end development, it's usually going to be web page test, it's going to be Lighthouse, something from that you're emulating. And what's happening actually recently is that you're emulating such a bad scenario that web performance may be quite good. In the case of carbon, actually, what happens is that there is no interaction with the page. And actually, 90 percent of session is happening in these interactions, no? After this first load. So with carbon and at this point in time, kilobytes transfer being a proxy for carbon, what's happening is that there can be a huge difference in between just this first load and actual number that we get if the user is interacting with page, if they're scrolling. So if the page is implemented well, and please do that, implement lazy loading and strategies like this, if this is implemented. We don't know if users ever loaded these assets or not. And this is where the RUM tools comes very handy. So as you already mentioned, accepting cookies and then starting to scroll, starting to click, starting to open stuff. This is where the number, let's say, of the transferred, transfer size can lead up let's say from, I actually, once I examined the website, It was four megabytes on initial load, five megabytes after the cookies were transferred, and 24 megabytes if you would scroll all the way down because of the amount of images. So measuring carbon by just this first view, by just this four megabytes,

Chris Adams: be five times smaller, yeah.

Ines Akrap: inaccurate, inaccurate, number.

So even though this number is still not super accurate, we are all aware of this, it comes much closer to the real scenario than I would say the synthetic one does.

Chris Adams: Oh wow, thank you. Okay, so that's um, a useful thing to take into account for people who are starting out with this, because yes, there are tools which you can begin, but there's often, there's basically quite, there's quite a lot of depth in this rabbit hole if you were to start doing this on a kind of monthly basis or kind of really use this to manage, um, uh, the performance of a website over an extended period of time.

Okay, cool. I also mentioned, I also saw that you have had some involvement with the W3C Sustainable Web Design group as well. And I figured, and I think I've actually heard you talking about that on one other podcast or seen you speak about some of that before. Maybe we could talk a little bit about that as well. Maybe you could talk maybe about the motivations for that and just show, just show maybe how that's going or what some of this might be, where this might be going in the future. Because we did do an interview, I think a couple of weeks ago, with some of the people who have been very involved, but maybe It's interesting to hear your perspective as well, because I think you're speaking and you're working at a larger organization, and the two people we were speaking to were from a smaller organization there, so I was, it was, I'm, I'd be curious about your perspective and how that's been going.

Ines Akrap: Absolutely. So yeah, it actually, this whole thing came to be because one of my colleagues, he was at the web performance conference and he met Zoe from Google. So she told him about this group even existing. So the moment he came back to Munich, he was like, yes, you have to join this. So this was actually very cool.

This was how I found out about this group even. And I actually came right in time because we were still, it was actually about a year ago. It was right. It Now, I think I'm probably hitting one year mark now in W3C, Sustainable Web Community Group. And yeah, it's been very interesting because there is such a nice diversity of people talking about this topic from different perspectives. So we started first talking a lot of, there was, of course, a lot of. so much for joining me today and I look forward to having a lot of interesting discussions about this approach because of course, as you and everyone listening to this knows, it's a very complex topic, there is like so much to it, so many nuances, and I was so happy to actually be a part of this conversation, especially because I was already working with some of the organizations trying to push this topic, so thank you And yeah, a lot of times it happens. I've seen this in web performance as well. A lot of times happens that it's a, quite a different scenario when you have a control, complete control over the things you're trying to improve. And when you don't, and a lot of times in these big companies, for example, the client I'm currently working with, they don't really have control over whole, like all the code because they're inheriting some of the parts. So this is a very tricky part to be in that you have to navigate around. So yeah.

Chris Adams: So just two things there. So you mentioned, so Zoe, this is Zoe Maria Lopez, I think, at Google. Okay, yeah, she's been on the, I've seen her on the mailing list talking about this and asking some questions already. Okay, and if I understand it, there is one flip side, which is, yes, when you've got, say, a very small organization and freelancers, there is possibly a kind of, people might feel like, okay, yeah, this is very much in line with my values, but you might not have the scale.

And then at the large companies, there's this idea that, well, we have the scale, but we're also publicly traded companies, and they might have different incentives. So the idea of having different people in that group should hopefully mean that you don't end up going more towards something which is really lovely but doesn't achieve any kind of scale, or the other side which is totally scalable but not really, maybe slightly more in the interest of shareholders than the planet, or everyone else, or the entire group, basically. Okay.

Ines Akrap: I think it's a very, again, like same as web performance, like it's very tricky topic because it's such a fine balance. No, it's such a fine balance. And actually one thing that I was just discussing recently with the client is just this. Of course, installing this culture of realizing the cost of something because you see the benefits, you see the business value. However, I think a lot of times. Especially the business perspective does not see the cost when it comes down to performance of the website, UX of the website, and of course, the energy efficiency and carbon of the website.

Chris Adams: I guess it's where the costs are being shifted to, for example. You might have something which means you've got nice developer experience, but if everyone's having to download three or four megabytes of JavaScript before they can use a site, that ends up being pretty ropey experience, for example.

That's like an example of a cost in this scenario. Okay, cool. Thank you for that. And we will probably come back to the working group stuff there, because I think that's something that's of interest. But maybe we could talk a little bit about the kind of fireside chat we ended up doing in Berlin together at the SDIA Green Coding Summit because I had a lot of fun. And we ended up talking about quite a wide range of topics given the time available. And I figured before we dive into some of the specifics about common anti patterns or easy mistakes you might make that you could learn from, or good examples of, say, galleries and things like, or things to learn from other people doing this well, are there any kind of takeaways from the event that you had?

Because I really enjoyed it and it was the first time I've seen where you've got, this was largely funded by the German government actually. They were investing in this particular field in a way that I haven't seen so many other countries doing so far. So maybe I'll ask you, are there any kind of takeaways that you had that, that you'd like to share here? Because you were there for the two days with me and there was a lot to take in, wasn't there?

Ines Akrap: Yeah, I was just going this morning, I was going through my notes and I actually quite like that. A lot of talks just confirm some of the things that I also stand behind that are my opinions as well, which is I would probably emphasize the part of the three important things to to move forward that was know that the fireside chat about how to make people care the way forward is awareness responsibility and regulation, so those three I think are very the three pillars in general that we need know so awareness responsibility and regulations I think those three are definitely a way forward to have it in mind And then, of course, a bit about the fact that companies move very slowly. This is something I felt on my skin so, so many times. So it took actually many months to even put this measuring tool that we offer on the client website, just because of the privacy concerns and all that. And it's actually very, it's made in a way that collects only. So even collector script is open source.

So it's making a way that it's very transparent. It collects only, uh, performance data. However, it took quite some time to get all the approvals to even start measuring this data, of course, calculating carbon. So I guess that one, and what else? Oh, yeah. And a great point from Anita as well, that we need sufficiency.

We need to learn to need and to use less. I think that's like a general, even when it comes to development, even when it comes to general life, we just, I have to have a feeling that we're already sufficient. We have enough of, again, I'm a bit generalizing here. Sorry. A lot of times we kind of especially. In this kind of middle, upper middle class, we have a lot, so just to start living with less and start delivering less.

Chris Adams: There were two things you said that I think are interesting that really caught my attention as well because at various, when you see other conferences take place, and we had Decarb from the Green Software Foundation just run last week, or maybe at the time of recording, when this lands, it'll be a few weeks, so please check the website for more details to check about the dates on that.

The thing that really struck me was A, there was this idea that there were organizations basically saying, yes, we're expecting to be regulated, and we're seeing regulation coming here. And that's something that I've generally seen lots of other organizations been pushing back against. But there was an argument being made that, essentially, you need to have this, to provide a kind of level playing field, for everyone to take this first step because, in the short term agreeing to some of this stuff either will be expensive or will incur some costs that will have to happen. And that was one thing that you tend never to see inside technology companies. So seeing organizations say that for the first time was quite a thing. And also, this idea of the notion of, okay, there is a, I think there's a really lovely quote or there's actually, I've ordered, I've ordered a hoodie from an organization called Careful Trouble, and I think it's Rachel Coldicutt, she mentions this lovely quote, just enough internet.

The idea being that there's a certain amount that you do need, and you, and there, and that's not the same as having to just like, just mash the accelerator pedal and just build as much as you can, basically. This is a interesting, in my view, a contrast to how I've seen other people talk about sustainability where there's this idea that, oh, we're going to have exactly the same kind of lifestyle, and, but everything's going to be electric, for example, and we're just going to keep using more and more.

So this was something that really caught my eye, actually. I think maybe there's actually one thing we might talk about when we were there, actually, was there was a number of open projects that were, that we saw, I think we saw Cameron Casher from ThoughtWorks talking about Cloud Carbon Footprint. And, uh, one thing that he mentioned, and we'll share a link to that, which is an open project that people in the GSF may be familiar with.

That kind of caught my eye. I can't remember where, there were multiple tracks and we'll share a link to this. Were there any other kind of talks by particular people that really caught your eye that you'd want to mention? Because the cloud carbon footprint thing was interesting to me because when we had one of the financial institutions listed there, they basically came out and said about half of our emissions are associated with ICT, and we're using tools like this to track this because we are getting numbers from different providers, but we also have things on premise, and this is one of the only ways we have right now to end up with some kind of numbers that are comparable across all the different providers and organizations we do have.

Ines Akrap: Yeah, I would say that my favorite deep dive tech, tech nerd talk, uh, was the one from Florian from Florian Quèze,

Chris Adams: Ah, yeah.

Ines Akrap: pronouncing your surname wrong Florian, but yeah, it was meeting him and talking to him. So he is working on Mozilla Power Profiler. And that was for me, it was just so many insights.

I love that tool and

Chris Adams: That was really deep.

Ines Akrap: out. Yeah, that was super deep and, but it's, it was super amazing for me personally, it was like, again, I'm familiar with this tool, so learning what's behind and then also getting an opportunity to pick his brain, I think that was one of my highlights of the event.

Chris Adams: His story about reverse engineering every single operating system to come up with some numbers was like, pretty like, wow. I didn't realize there was, it was that involved to do this. We'll share a link to both his talk to the slides, and I'm not sure when the recording will be online, but we definitely can share a link to the slides straight away, because that was actually a real, that was a real faith for me as well.

And later on, we'll talk a bit about that, because well, we basically did a workshop as well, and maybe that's one thing we'll spend a bit of time touching on. So, I actually ended up leading a workshop that Fershad, one of my colleagues, Fershad Irani, had designed with me. Basically giving people a way into looking at tools around optimising for carbon on the front end.

So we spoke a little bit about coming to some of the forcing factors that you might have in terms of basically there's regulation driving some of this, like in Europe you have the CSRD, the Corporate Social Responsibility Directive, which ends up manifesting in the laws in all various countries. But even in California, there is the, there's a recent law passed with a similar alphabet soup acronym, which I'm afraid I can't remember off the top of my head as I speak, but I'll add it as soon as I can to the show notes.

But we also speak a little bit about introducing people to tools like CO2.js. And other ones, which basically track this. So we started with that. Then we showed website carbon. Then we actually did a bit of a deep dive into the profiler as well. Actually, maybe I'll ask you actually, like you, you were in that workshop with, although all the way through with me and, uh, I, I realized that you also, you helpfully shared some specifics about the differences between cookie websites and stuff like that for people.

Are there any things that you might share for other people because we've made this an open source workshop and we'll link to the all 100 slides that we did for this. But for people who were about this and coming into this front end developers, are there any things you might share for other people who are coming to this for the first time and trying to talk about, like, maybe the, their first steps of tracking this or what kind of the common things you might need to take into account, for example?

Ines Akrap: Like first and foremost, I love the workshop. So for me as well, like there was a, at the same time on the, so on the green coding summit, they were on the second day, there were different workshops at the same time. So that was the, the worst part because I wanted to see them all. I gave that as a feedback. I was like, please give me, give me time to see them all. Of course I chose as well, I chose to be at yours to see as well how you approach this. So hopefully I can as well serve as an advocate for something like this because this was something I was doing this whole year was trying to raise awareness of this topic and I think what you did as well, did an amazing job. So for me, I knew most of these things. However, I think it was very well done. It was very like comprehensive and it was very, I was going through these basics of how to actually understand it and calculate it. And I think this is the biggest issue that we generally have is that people just not being aware and just not knowing how to do it and then where to start.

Chris Adams: Yeah, I think, I, thank you, first of all. The thing I, one thing that we were trying to do was give people an idea that, yes, there are models and there are tools, but there's also a lot of uncertainty in a lot of this, and there's a really nice quote that I think was shared, that Fershad added in, but that Simon Wardley has used in multiple places and goes back to the 80s, which basically says, all models are wrong, but some models are useful, with the idea being that, yes, we spoke about data transfer as a proxy right now because we currently do not have that much access to the information that you might need to come up with some more to draw more rigorous conclusions when you're trying to track the power used on a website.

And this is partly why we started with high level models, then moving into some tools like Website Carbon that you might be familiar with, and then diving into the profiling tools, because, to my knowledge, they're one of the only ways that you can get actual resource consumption figures from end user devices.

And before that workshop, we hadn't actually realized that these are usable for mobile devices as well. So that was actually a really nice thing to take away. One thing we got was that we had a couple of people from Mozilla, people who work on the Firefox profiler themselves. They joined the workshop to basically help figure stuff out.

And I think actually one thing that happened is over the weekend, the manual profiling steps we were demonstrating during the workshop, by the following weekend, some pull requests had been opened to fix some of the tools to automate this, and now there's a piece of software called Sitespeed, which now can run these on an automated basis.

So, that was actually quite cool, but it means that me and Fershad will now have to redesign the workshop for this, actually. There was also a really nice, it was a good chance to actually talk about some of the other models that we do have that are in the community right now, because we spoke a little bit about the Software Carbon Intensity, which is used by the Green Software Foundation, but it was also useful to be able to compare that to some of the other models that you might see, like the Sustainable Web Design 1 and the 1 byte model, or even Dimpact, which is like the digital impact one, where we had Daniel Schien come on a few weeks back to talk about that in a bit more detail. We'll link to that in the show notes as well for other people. But while I'm here, I figured I could ask you, are there some, there are ways we can do it right, but there's also, there's also some kind of common mistakes that you might see people making when they, when they take their first steps in this field, and I figured it might be asking you while you're here, and when you've seen people do some of this, are there like mistakes you might want to be looking out for if you are first taking a step into this field that you would give people a kind of heads up on if they were maybe a special, they know how to build websites, they might know a little bit about web performance, and they're trying to figure out how to transfer some of this knowledge into this kind of slightly new or updated domain.

Ines Akrap: Yeah, sure. I think the most important thing to have about this topic is that this topic is very vertical. So it's not one role in the development process that is responsible, which usually comes towards the developer. It's, oh yeah, they didn't implement it well. Yes and no. No, like it depends. It could be implemented, potentially not in the most optimized way.

However, a lot of times what I've seen is that no matter how good the implementation is, the moment the content comes in, it's when the things start shifting. No? Because we're talking again, there is just this sheer amount of assets, a lot of times of images, of fonts. I have a feeling that this overconsumption that we were already talking is happening without thinking what is actually beneficial to the user because now working in, in, in, in field of, well, I'm just going to call it now sustainability because the web are sometimes used interchangeably, however, green. Of course, just goes towards just reducing the energy and carbon footprint and sustainability goes more towards people as well, designing for the people, for the planet. So includes stuff like accessibility and in the end of the day, good user experience. And I think this kind of benefits the business a lot as well, because if you design something in a way that's fast, that's responsive, that's efficient, it's probably going to be better user experience, which will potentially convert into better conversions rate into better, which it's something that business is probably trying to do. So I guess my first advice here would be involve everyone. Make everyone in the process aware and make it a joint goal and not just the fight between developers or designers trying to optimize something and then let's say business kind of stopping them because it doesn't make sense on the business. It does, however, everyone has to understand how it actually does.

Chris Adams: Okay, and there was also one thing we spoke about, um, during the fireside chat was that let's say you're going to be doing some things with, say, carbon aware, this idea that websites might change or respond or functionality might be different based on the amount of fossil fuels on the grid, because this might be one way to materially or measurably reduce the carbon emissions in aggregate across a number of users, for example.

Uh, we spoke a little bit about there's some pitfalls and pratfalls around that. Yes, it's a cool idea and you can start using this, but there's, it feels like it's quite important to provide maybe a way to override some of this in many ways. You might be setting a default, but you're not setting a concrete limit in some cases because you might not understand the full context of a user.

Maybe that's something that you, we could talk a little bit about because I've, this is something we've seen done right, and then done poorly in some places, or maybe it's not done poorly, done in ways which, where there's a lot of room for improvement perhaps,

Ines Akrap: Yeah, for sure, because so this is something that I definitely advocate for is this progressive enhancement, which also goes towards the part of respecting your user's decisions, like what your user wants, what their current status is, because of course there is such a differentiation between the user, like just put yourself in the place of a user, which is something I usually try to do, since I'm quite a user of internet myself. It's just when you see this point of just trying to get this information. No, I just need an address of the place I want to go to on my phone. That's let's say in a Metro and my battery is dying. And then it's definitely not the same if I'm planning this trip in advance and I can see this website, whatever it is on my M1 MacBook Pro with my wifi in Germany.

So these are definitely different conditions that users have, and I have a feeling that this is something that's not being accounted for at all in general and going towards this road where we decide to design in a way that progressively enhance, that kind of respect these usual decisions, because not only this, so I worked on a small open source project that's called ProgUX, so of course it's out there, it's on the NPM, we can also link it.

So. Its point is that it adds to your website, adds a few of these parameters. So it adds the user network conditions. It adds data saving mode, if it's on or off. It adds the prefer, reduce motion. It's very important because some of the animations can trigger medical conditions at people. And not respecting this

Chris Adams: Wow,

Ines Akrap: only be mindless, but can be dangerous for some people.

So people can access this so they can enable this on their devices that they don't want the motion to happen. And I'll, well, most of the website now may, there may be some, some edge cases, but most of the website don't respect this. So what we created is just, it's very just few lines of code that just add this to your, to your local storage as well to CSS, however, the useful part is the developer part, which offers you a bit of a simulation of changing between these modes to help you develop faster, switch into these modes. It's been a while that we changed anything because it's, again, it's quite simple. It's quite straightforward. So we didn't improve it a lot, but again, feel free as well.

It's open source. Feel free to build on top of that. So having something like this, where we take these choices into consideration, know the network, the phone, the memory, the current data saving. And of course, these reduced motion stuff could make a huge difference in user experience and of course, with that, no, if we respect data saving and reduce, no, amount of processing, we're also again, reducing amount of energy consumed and working towards something greater.

See, okay.

Chris Adams: I can see a scenario where you might use some tooling like this to essentially simulate, okay, here's what someone's going to experience in a kind of low carbon version or a high carbon version, because one thing that we've seen is that, uh, either people might look at, say, a low carbon version of a website as an afterthought, rather than being something you progressively start with and then progressively enhance, like you mentioned.

But there's also this idea of, like, the ability to override is actually extremely important, because we, as designers or developers, we don't or we don't always know the context something's being consumed in and uh leaving the leaving that at the end user is actually very important and there's a really nice example that i think from Apple was that's worth looking at so Apple rolled out carbon aware charging leeron this year for all their phones which sounds like a cool idea with the idea being that on average, if you were to use this, what happens is that Apple devices, if the energy, if the grid has a particularly high amount of fossil fuels being burned on it, Apple's devices won't try to charge up right now.

They'll wait for the grid to become a little bit cleaner. So, over time, the average carbon intensity for the power you're using in a phone becomes somewhat lower. The thing is, when this was first introduced, because people were by default opted into this, the idea was that if you maybe were to go home, you plug your phone in, or which like you said, is on a really low battery, you leave it overnight, and then there's a like high amount of carbon, high carbon hours when you're asleep.

When you wake up, your phone isn't charged in anymore, and then you've basically had a really, really terrible experience of anything related to grid aware or carbon aware usage of tools. This is why this override seems like quite important. And being able to, like, make this more accessible to users, so they can see what it might be, think through some of the edge cases, actually sounds very helpful, actually. I didn't know that existed. Thank you for sharing that.

Ines Akrap: Yeah, yeah, I completely agree with you. And I think again, it's super important to stress without naming names as well, is that this progressive enhancement is more important than graceful degradation, where we do want to serve the lightest version first and then give an option to enhance it. You can, let's say.

Not serve images and then give users options. Okay, you have data, you have whatever you want. Do you want these images? Yes or no? Yes. Okay. No worries. We'll deliver them to you and not other way around because once the images are downloaded, the fact that you hide them, you didn't do much. No, in a way of this kind of processing power and transferring and all that, because they're already there. So they might as well be there.

Chris Adams: Okay, I work in a non profit and so there's, so I can say this, the, we're just about, I think we've just started COP28 and COP28 is a really good example of this right now. COP28 is the, for the first time we've ever seen, is a very high profile website that has a kind of green mode for this. But unfortunately, the way it was initially implemented was basically to serve the full fat version of a website so that any device that has actually used it has basically done all the hard work and used all the energy for the first time.

And then once you've done it, you've got the option to switch off the images, which does feel like it defeats somewhat some of the point of some of this. And I feel like this feels like a kind of concrete example of the opposite of this progressive enhancement. The idea would be that rather than do that, you would have a nicer experience where you start, and then you would gradually layer on the kind of more consumptive and resource intensive elements.

Is that what you're referring to here?

Ines Akrap: Exactly. That's the exact one, because let's go again in the, for example, the off motion, there's so many, for example, animations on the page that can trigger some people conditions, like you already triggered a condition. So the option that you can turn it off, it doesn't really make sense. The point would be like, okay, you don't serve them at first, but then if someone wants this kind of enhanced performance, no, and again, there is definitely a cool ways to automate that.

No, it's one of my favorite example is that like for shots Carbon Aware Edge Delivery, which is also a very cool thing. Link in the notes. So yeah, which is basically on the edge, it decides how clean or dirty your energy is. And then depending on that, it delivers something different. So you are already taking into consideration user's journey, user's conditions.

And of course, you will get a notification that says you're seeing the downgraded version. Would you like to be, so it leaves this decision to a user, which I think it's a very crucial part in this designing for people part. It's give users a choice. Yeah.

Chris Adams: We'll link to that, that thing in particular, the one that Fershad Irani has worked on, because this was something he did in his spare time, which I think is quite cool. It's essentially some of this carbon aware stuff, but rather than happening at the, um, client side inside the browser, it happens at the server side, so it decides to make a decision to possibly change some of the functionality at that point, rather than having to do something at the client side, where All, in some cases, you might have already downloaded all the information, so it's a little bit late at that point.

Okay, I'm really glad you mentioned that, because I was playing with that last night, and I thought, wow, that's cool. You also said something interesting about motion, and this actually made, it reminds me of one of the things that Florian Quèze, Florian, I'm so sorry if I've mispronounced your surname, Florian's talk, when he was talking about the profiler, yeah, about, he was showing that there is a, he was talking about how basically, depending on how you see these CSS gradients sometimes, like you might have a page which gradually changes colour over time.

He was saying, how basically, if that's done incorrectly, you can essentially cook a laptop, because you're just changing the colour of so many pixels and, oh, so many times, uh, in a given, uh, uh, If you're changing something maybe 120 times a second, and it's a 4K display, and you don't have any way of knowing what you had changed before, something just as innocent seeming as like a CSS transition can end up generating like a lot of power usage.

And like, that was cool, but the thing that was really exciting I saw was the fact that he was able to share a profile of this. So you could visit the page, and we'll share the link to the page so you can cook your own laptop or trigger your own fans if you want to, but he also shared a profile showing exactly where the energy, where this was happening.

So if you don't want to cook your own laptop, you can basically see a jump in power usage as reported using his profile run. That was so, so cool. I hadn't seen, I didn't know that was actually possible.

Ines Akrap: I love that, especially also because it shows as well a bit of this limitations at the moment with the models now, because what we were saying that also you still don't know this processing energy of the laptop and like basically one kilobyte. I don't think the CSS code was big, so I think it was around, no, like the CSS transition could have been like a kilobyte or two and it cooks your laptop.

So this is again, I think this is a huge topic to move forward with now, like to actually figure out how do we see that? But it was quite a cool thing to see with all of also with all of these, his gadgets, he has such a cool gadgets.

Chris Adams: Yeah, we didn't, I, the thing that really blew my mind was him explaining how, now that he's built these profiling tools, he's using it for all kinds of unexpected uses, so the profilers, so we'll show a link to the Mozilla Profiler for this because it's an open tool that you can use on any website, but the thing that was actually interesting is that it It doesn't just work for websites.

So he was showing how he can use it for basically Linux. Linux has a performance tool called Perf. So you can use it to track any kind of piece of software. And he was talking about how, yeah, you can use that. But after this talk, he said, well, Chris, I've also used this to profile my own house. And, uh, it turns out that if, because he knows where the data is from, say, a fridge, or solar panels on his house, or stuff like that, he's been able to just have a track, so like you see a track for, maybe, a web renderer, or something like that, he's got fridge, solar panel, front room, or something, and he's been using this to figure out, okay, well, where are the power hogs in my house, for example?

And the idea that you've actually been able to use, essentially, industrial grade profiling tools to figure out how to optimize your house was I think that was the nerdiest point of the entire conference, but that was pretty mind blowing when I saw that. There's a blog post about it that has to come out, and I think he might be presenting about FOSDM about some of this, because I didn't know that was possible, but it was very fun to see that.

Ines Akrap: Yeah, absolutely.

Chris Adams: Okay, we're coming up to time, and we've been, we've mentioned in a few, we've been nerding out over a couple of projects that we've that's been, that we're both excited about, but I wanted to give a bit of space. Which projects, open source projects, are in this kind of growing field have you excited right now that you would like to give a bit of time to talk about before we wrap up for this?

Ines Akrap: Yeah, I don't know. It's a on the spot. Well, I guess like my still all time favorite and again, like huge kudos to you is co2.js. No, I'm, I'm actually super excited as well about the prospect of having some more time towards the end of the year and to help out possibly with some. And what I've seen now in the pipeline of doing this no actually making it even better and better in a way of providing better carbon intensity data, so I think that's a huge thing no, specially with someone who actually uses this in real life . Also having this huge conversation with the client yesterday, about this measurement reporting and like let's say correct air quotes, correct these number from the ROM tool that we built are actually are having this underlying technology, which in our case is CO2.js getting more and more precise and giving us more and more power and flexibility. This is something I'm super excited about, because again, you cannot fix. Something what you just said with Florian, like you cannot fix something that you cannot measure because you just don't know that you have an issue. So being able to detect this issue and to show these numbers to the decision makers, I think this is something very important. And then improving these tools, especially again, I'm working the front end. So this is for me, big deal. I'm also quite, quite sure that the Green Software Foundation is, is doing quite an amazing job.

Chris Adams: The CarbonAware SDK, which is, which is on the server side primarily. Uh, so that's, uh, there's a somewhat slightly different area that they tend to do. But, and there's a couple of other projects. There's one called the Impact Engine Framework, which was actually introduced at Decarb conference, which does actually, which is extendable in this way.

So it can take data from a number of different providers or models or things like that. But yeah, thank you. That's very nice of you to actually say. This ProgUX thing, I I didn't realize there was actually a tool to make it that easy to simulate these different scenarios in such a fast way. Uh, we definitely should share a link for that one, actually.

Ines Akrap: It's not a simulation of actually how it's, how this reflects, but it's just a simulation of an implementation. So basically the small simulator just changes these CSS classes so you can see, of course, how your implementation changes in different scenarios.

Chris Adams: I see. Okay. Thanks, Ines. So, we spoke a little bit about ProgUX, and you mentioned CO2.js, and we spoke a little bit about the CarbonAware SDK, which is looking to graduate, to be a kind of graduated project inside the GSF, and we also mentioned the Impact Engine. I wanted to ask, if you're coming to this field for the first time, one thing that, maybe I'm showing my age when I say this now, is that when I was first messing around with CSS and realizing I didn't need to use tables for everything, was a website called the CSS Zen Garden that let me show and learn from other people.

And there was tools like CodePen and stuff where I could view source and understand this. I haven't been able to find something for sustainable web design, or kind of low carbon design, or anything like that. And I figured I might ask you, have you seen anything that you might direct people's attention to so they could learn from copying, or at least, or try and figure out where they start, because like we all know, this is open source. We learn from imitation before we create something ourselves for the first time, for example.

Ines Akrap: Absolutely. I'm actually very glad you asked because I just found out recently about this site called A lowwwcarbon. Link in the show notes. And yeah, it showcases all of these low carbon web designs and developments. So it's very cool. I haven't had time to see how I get featured there because I tried to do that with my website, but yeah.

Chris Adams: Okay, so this is low carbon, so it's low with three w's, like the www, so low www carbon dot com. That seems what it is. Okay, wow, this is cool. This is actually really nice, because it's These are real world websites, and some of these are run by actual large companies, as well as just personal pages. So this might be a thing for people who are curious, and want to look at this and see for the first time, see how it's being done by other people, if you're looking for inspiration.

Okay, well, thank you for that, Ines! All right, Ines, we're coming up to time now, and I just want to check with you. For people who are interested in some of the things we've discussed, we'll be talking, we'll be sharing these things in the show notes, but if people want to follow your work, where would you direct people's attention to if they wanted to do this?

Ines Akrap: Sure. So if you're interesting, of course, I'm always happy to answer any questions about web performance, about sustainable web. So I guess, of course, LinkedIn, Twitter, because I just still refuse to call it X. And as well, so also you have my, my, uh, my personal website. So it's InesAkrap.com. However, there is a shortcut to it, which is Ines.codes. So it will also redirect you to there.

And.

Chris Adams: Ines.codes?

Ines Akrap: Exactly. It will give you the bigger URL, but yeah, I have that domain for a while now. So yeah, but also I would definitely, if you want to learn more about this topic in general, I would direct you to W3C Sustainability Guidelines. So again, I think this is a great place to start.

So we put quite some, like a lot of effort in this. Also sustainablewebdesign.org Implemented recently these guidelines. So in the last podcast I was giving, I was, I said, bear with us. It's going to be implemented. So a kudos to sustainablewebdesign.org team. They implemented these guidelines. So it's a bit more user friendly because as you may imagine, W3C documents are not really a beach read.

So it takes some time to go through everything. So it's actually 93 guidelines. So there is quite something, but it's also, it's segmented by roles. So every single role can find their own starting point and to see they're also quite, they don't have a way, like it's not one, two, three, four, five, it's just. Pick one that you like, I guess our personal goal on this would be like, choose the one that says high impact, low effort. This is a pro tip, but yeah, basically I think this is the first place that you can of course learn something. There is also a huge pool of resources added to the guidelines that we used in shaping them.

So I think this is a good place to start familiarizing yourself with the topic.

Chris Adams: Brilliant, thank you for that Ines. I will also mention that for people who are coming to this, the Green Software Foundation actually has a free course available which will get you a kind of nice certified badge with a series of videos to provide a bit of helpful context for this. That's something that's of use, but Ines.codes, I N E S dot C O D E S. Okay, excellent. Thank you so much for this. I really enjoyed this, Ines, and um, thank you again for taking the time to, well, share a stage with me and share a podcast with me. Thank you.

Ines Akrap: Thank you so much for having me.

Chris Adams: All right, have a lovely weekend, Ines. Bye!

Ines Akrap: You too. Ciao!

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts.

And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware. foundation. That's greensoftware.foundation in any browser.

Thanks again, and see you in the next episode!

In this episode of Environment Variables, we dive into a key highlight from Decarbonize Software 2023, featuring Asim Hussain's talk on the Green Software Foundation's newly introduced Impact Framework. This innovative framework aims to model, measure, simulate, and monitor the environmental impacts of software. Asim begins with an insightful overview of the framework's capabilities and objectives. Later, project leads Srinivasan Rakhunathan and Navveen Balani join the discussion to delve deeper into its applications and potential. This episode offers an essential understanding of how the Impact Framework is set to revolutionize the way we assess and mitigate the ecological footprint of software development and use.


Learn more about our people:

• Asim Hussain: LinkedIn / Twitter
• Navveen Balani: LinkedIn
• Srini Rakhunathan: LinkedIn 

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

The Impact Framework:

• https://if.greensoftware.foundation/ 

Events:

• Keep an eye out for the announcement about CARBON HACK ‘23 - coming soon!

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
• Follow and rate on Spotify
• Watch our videos on The Green Software Foundation YouTube Channel!
• Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Asim Hussain: Send us a manifest file so we can see not only how much is this thing going to cost us and how are you going to architect it, but what are the carbon emissions of your proposed approach? And let me compare versus the other people proposing approaches and not just compare on cost, but compare on carbon as well.

And I thought that was really, every time we talk to people about Impact Framework, people always come up with Another use case.

Hello and welcome to Environment Variables brought to you by the Green Software Foundation. In each episode we discuss the latest news and events surrounding green software. On our show you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host Asim Hussain.

Chris Skipper: Hello and welcome to Environment Variables. Today we've got a highlight from the recent decarbonized software 2023 event. We'll be showcasing the talk from Asim Hussain about the recent Impact Framework, which is a framework to model, measure, simulate, and monitor the environmental impacts of software.

That has been introduced by the Green Software Foundation. To begin, Asim will do a brief overview of the framework and later on he will be joined by the project leads Srinivasan Rakhunathan and Navveen Balani. He is introduced by our fabulous director of communications and member relations, Namrata Narayan. So it'll be her voice that you hear first.

So without further ado, here's the introduction to the Impact Framework.

Namrata Narayan: It's the moment we've all been waiting for. Well, one of many. I'm delighted to introduce our chairperson and exec director, Asim Hassan, back to the stage. The GSF believes the most important step in greening software is measurement. An Impact Framework will enable us to measure software across environments and software types. But don't take my word for it. Asim, over to you for a full, brief, and exclusive look.

Asim Hussain: Thank you so much, Namrata. And I was really excited today to see several talks now focused on measurement from Siemens, we just heard from, and from Code University. We're all aligned, and we're all converging to the same point. 2024, I believe, will be the year of measurement. That doesn't surprise me, because when we first launched the foundation, We asked all of our members what their pain points were, and the overwhelming answer was measuring.

And we've been trying to explore all the different avenues of measuring over the last three years of the foundation's existence, and we've made lots of progress. And I mentioned earlier on, we, the stuff, the standards working group launched the software carbon intensity specification, which has reached ISO.

We're very excited about that. It should be in the catalog shortly. We've incubated, incubated various data projects because we knew data for measurement was a problem. And we've drove the creation of, of a number of case studies, software measurement case studies. But now we're in the next evolution to have software measurement be and mainstream activity.

For this to be an industry with thousands of professionals working to decarbonize software, for businesses to grow and thrive in a commercial software measurement ecosystem, we need to formalize software measurement into a discipline with standards and tooling. And we believe Impact Framework is the tooling and SCI is one of the standards.

Today I'm going to explain a little bit about Impact Framework now, then I'll give a demo. I spoke to some of our other co leads on Impact Framework about potential future use cases for the tool last week when I still had some hair. And we'll play that afterwards. So Impact Framework aims to make the environmental impacts of software easier to calculate and share.

Impact Framework allows you to calculate your application's environmental impacts, such as carbon, without writing any code. All you have to do is write a simple manifest file. Sometimes we call it an IMPL. And Impact Framework handles the rest. I want to be very clear. It's called Impact Framework, not Carbon Framework.

Some of the things I'm going to show you today is about measuring carbon, but our goal for the framework is to go beyond carbon, to start measuring all sorts of different environmental impacts. There are three concepts in Impact Framework. There's the manifest file, which contains either raw data or information about where to get the data from to do your measurements.

Then we have models. How do you get from that raw data to, let's say, carbon or water or some other impact? To do that, we need to use models, and we can chain models together to calculate your impacts. Just in a pipeline, just like a Unix pipeline. Then we have Impact Engine itself, which is the CLI tool, the command line interface.

You pass the manifest file to it, and it computes your impacts of your software application. I would say it's saying transparency builds trust. You don't just calculate and share the number, the final number that the Impact Framework gives you. You share the manifest file. You share your workings out. You share.

You share all of your data, all of your assumptions, all of your coefficients so other people can verify it, can challenge it, can compute it themselves, can fork it, can adjust it, can change some of your values and run it again. It's all about being transparent. Only when you share your working out can anyone trust your calculation.

The project is entirely open source and composability is a core design principle. We want you. To be able to create your own models and plug them into the framework or pick from a broad universe of open source models created by others. We've got some to show you right now, which I can do in the demo, but our goal is for there to be thousands of models all working together.

What I'm going to show you today is a result of almost a year's worth of effort. We first started a talk at this project was proposed in December last year as an incubation project, and as it evolved into something incredible. And today we're ready to release the alpha version. Alpha. Means use with extreme caution.

There are bugs. I was trying it for the demo today. I found some more bugs. This is a buggy product. We are looking for early adopters who are comfortable with buggy software, non backward compatible changes. It's moving very fast, but if you are, if this is something that you're interested in, please try it out and please give us some feedback.

But you understand you're on the bleeding edge here. The sense of excitement from everybody involved is something I've rarely seen in this space. It's a bit nerdy. I'm about to show you a bunch of YAML files and get really excited about it. But trust me, these YAML files, I think are going to change the world.

One of the things I say about... My long term goals, and I hope the Foundation's long term goals, to do with sustainability is that we want to do to sustainability what open source did to software. I'm not talking about small, iterative changes. I want to change the game, and I think the Impact Framework will do just that.

You can find more information about Impact Framework at if. greensoftware. foundation. That's if.greensoftware. foundation. IF will also be the main focus of our yearly hackathon, which will be coming in February 24. We'll have all sorts of prizes this time around, including an under 18s prize and prizes for non technical contributions, so watch this space.

So now let's start the demo. So I've got an empty folder here. Well, it's not empty, but first you would create an empty folder where we can do our work. Impact Framework is built in TypeScript, so you can either use NPM or Yarn to initialize a space to work in. So I'm not going to run these commands in the space of interest, but you would probably run Yarn in it to initialize the space.

And then we want to start adding the CLI and the framework itself. So we do Yarn, Add. Run soft slash Impact Framework would bring in the framework itself. And we're also going to be using some models, some of those models I spoke about. So we've built some of our own models and we would install them in using IF models.

All of this, by the way, this is a tutorial at if. github. com. greensoftware. foundation. So you don't have to be furiously writing this out. And we've also got some other models in an unofficial models package as well. So after you've run all of that, what you need to do is you need to create a manifest file.

I mentioned that manifest file earlier on. Now I've already got a manifest file written here, so I'm just going to open it up. Here we go. So, this manifest file is a very simple one. It's going to compute the carbon emissions of a server we have running on Azure. Manifest files can be much more complicated than this.

Compute different software components, compute network, front end servers, user stories, campaigns, whatever you want. But for this demo purpose, we're keeping it very simple. Just one server. Let's walk through the YAML file. I'm just going to show you, let me just make some space here for a second. I'm just going to go through a couple of the aspects, the areas in the YAML file so you can understand it, the manifest file.

I'm going to first go through the initialize. So this is where we load up and globally define all those models that you want to use in your application. I'm going to go through some examples of this. You're going to see what I'm talking about. You would define where you're loading this from because you can, you could create a model.

Which you install. This is not, we expect a universe where there are thousands of models and you're just cherry picking which models you need to use to calculate your emissions. Some are loaded from unofficial models. We're not Microsoft, so it's unofficial for us to maintain the Azure model. But some of these are from our official models.

If it's coming from IF models, we're going to maintain this. We're committing to maintaining this. So this is the path of the module where it's coming from. This is the name of the class you're loading. And this is just how we're going to refer. to this model in the rest of the YAML file. Let me scroll down to another area, which is called a graph.

So this is where we describe all the software components we want to measure. In our case, we just have one server. Uh, cause again, we're keeping it very simple, but you can have dozens, hundreds of software components in it. And it, but it's up to you how you want to group them. Do you want to group all your servers by region?

Do you want to have a front end, a back end networking? Database, however you want to split it up and group it is up to you. We leave that very much up to you. It's just different components. Um, let me dig into my server then. So, this little, we call it a component node. This component node contains all the information required to measure this.

Server. I'm going to start from bottom to top. So I'm first going to start at this node called inputs. These are the inputs. These are the things you've gathered and measured about the server. What we call a key concept in Impact Framework is something called an observation. This is one observation. An observation is just something you've measured about your running system at a given time and for a given duration.

And these are the parameters of your observation. You can actually have multiples of them. We would expect you to have, maybe you grab observations every five minutes throughout the day. So we'd have a large array of observations. I've just got one here. The next thing to really explain is this thing at the pipeline at the top.

I mentioned before that we were all about models and a pipeline of models. The data here is passed in to the first model at the top, and it's just a piece of code. It's just a piece of code. And you pass in that observation and whatever outputs it passes it to this model. And whatever that outputs, it passes it to this model, to this.

And the game we're playing, the game we're playing is we've got this data here, this observation, some data about a virtual machine, and we want to convert this in this use case into carbon. And in order to do that, we chain these models together. They all work and play nicely together to convert all of that data into carbon.

Now to explain this all, the best thing to do is for me just to comment out. Some of the other models, I'm going to go through it one at a time, and I'm just going to run. The command, which is impact engine, you pass in the input file and you pass in an output file and you just let it run. The final thing I'll just measure, talk about while, while that's running is this config section here.

There's a bunch of different places you can configure models. This is one of the places you can configure models. And here. We just have some configuration values, which we're passing into some of those models. And this configuration here is specific to this component. So different components can have different configurations.

There's also a global configuration as well. So you can see here that file has completed, that execution has completed. I'm going to open up the results on this slide here. And excitingly, what we've got at the bottom is it's the same file, but as well as inputs, we now have outputs. And what the Azure importer model does is we've passed it some data.

To, to query and it's going into the Azure Monitor system and it's extracting out information about CP utilization, the memory available, it's 'cause this has all been captured on Azure and we're extracting it as a series of time stamps. What I've said here is, for a one hour period of time, give me 12 five minute buckets of the average CP utilization and, and other data.

And it captures other data as well. And a very important piece of data is what is the instance type of this Azure, the Azure machine that I'm running on. What is the instance type? We know it's Azure because the Azure instance, the Azure importer gave us that data. We know the instance type, but in order to calculate emissions, we need to know what processor is this instance type.

What processor is it? And for this, what we've built is another model called Cloud Instance Metadata. I am going to just run this whilst I talk. So cloud instance metadata is a module we're actually maintaining. There's actually very challenging, there's no real globally maintained database of telling you this instance type is this processor.

Every cloud provider is different, every cloud provider has lots of different rules. So we tried lots of different solutions, but what we ended up with is we're actually maintaining this model ourselves. We're maintaining a database of, we've only got Azure for now, but for Azure. This instance type is this processor and other information about that, and we will, we commit to keep maintaining this for the long term, and hopefully it's all open source, so if you see something missing, please write a pull request and add it yourself.

So you can see now it's complete. I'm going to go into the results. And in the outputs, again, you can see a couple more bits of information have been added. For instance, the physical underlying processor and another very important value, which is the thermal design power. So you can see here, very interestingly, that one instance type, if you actually look at the Azure documentation, it doesn't tell you exactly which underlying processor you're going to use.

Because when you use the instance type, you may get one of these three, one of these underlying processors. So it's quite challenging then to figure out what to do. So we have certain rules and logic which we've Worked with the community to describe and we effectively picked what's called the most energy hungry processor of that list because it's always better to overestimate energy than underestimate energy.

But now we have physical processor, we have thermal design, we have a bit more information from that cloud instance metadata. We still need, we need to convert this into energy to get to carbon. We need to know energy. What are some models that we can use to convert this data into energy? Now a really, uh, popular model that people use all the time is tes.

It was generated by a gentleman called Benjamin Davies, who worked at tes. It was a couple of years ago. It was published, and he figured out a, a, a generalized power curve by analyzing hundreds of servers, different types of servers, and it's quite well used. It's using a lot of our case studies. It's used in a lot of places is, is this tes curve and what Tes Curve needs in order to estimate energy is it needs a C utilization and it needs to know the thermal.

Design power of the processor. Now we have that data, we can run the script and it will effectively do a lookup of both of those bits of data to generate some energy. And, uh, EagleEye, if you would probably see that I also, uh, uncommented sci e. Sci e is just a little helper function that we have to, to copy some data into other, into an energy field, which is useful for the following models.

And you can see now we've calculated energy. So we've basically now, we've got to the point of... We know how much energy for this five minute period we are consuming. And this is in kilowatt hours, 0. 00284 kilowatt hours. This is one of the bugs. I don't, I want to make sure there's one of the bugs we discovered today.

That's slightly off. We will be fixing it tonight, but this is the, now we have energy. Now we have energy. How do we get from energy to carbon? Now, what you need to get from energy to carbon, operational carbon, I should say, is you need to know how clean or dirty was the energy. That is something called grid carbon intensity, and you would need to know every grid has a different measure of grid carbon intensity at different times, and we have several models that we can use in order to calculate grid carbon intensity.

There's a supplier, one, uh, Two electricity maps on Watttime are two of the biggest suppliers of this data. They're both members of the Green Software Foundation. We could be using the Watttime model because we've created one, but just to demonstrate another piece of functionality, what we've done in this case is we've actually just hard coded it.

There are some cases where you want to actually know what the grid carbon intensity every five minutes. There are other cases where you just want to use an annual average. And here's something that's very important. We've put in here 951 grams per kilowatt hour. This isn't hidden away in Appendix 96C of PDF 43.

It's in the YAML file. It's transparent. You don't, when you're being shared this YAML file, if you don't like this, you can go, Well, you know what? I actually want to use the Watttime model to actually get the real time energy or real time carbon intensity. Or actually, I don't agree with your value. I think it's 951.

This is what's so important. Transparency builds trust. We want to share everything, all of your workings out, all of your coefficients, all of your data. That's what you share, not the final number. So now we've run CIO. If we go back to the results, you can see at the bottom, we now have operational carbon.

So we finally got some carbon value. So for this five minute period, it's 2. 7 grams of carbon. And let's scroll down, that's 2. 708. And I think there's, hopefully there's one that's a bit lower to prove that it's working. Hmm. Well, it is working. It just says 2. 7. That's because it's hard to find the peaks right now.

But it's there. 2. 7 grams of carbon per five minute period. But we also want to include embodied carbon. We want to include embodied carbon. We want to include other types of carbon emissions. So, other... Components of the SCI calculation. So obviously being a GSF, we focused on SCI. So now we've also got SCIM, which is, it gives you the embodied carbon.

It's very basic right now. You have to provide a lot of data manually, but we're looking in the future, some of the things we're looking to expand is to, is to make this a bit more automated. So again, it tries to figure out what the embodied carbon is from the instance type and just calculates it more manually.

And SCI then actually calculates a SCI, a SCI score. Thank you. So let me run that again, and then now I have a slight, uh, awkward pause, I'll wait for that to run. And again, it's just that this, the really important, I was trying to, I cannot reiterate it strongly enough, you do not share the final number, you share the manifest file.

Manifest files are executable, there's no need to share the final number, you just share the manifest file and the next person just runs it, and they, and it gets the same data. You can actually hard code the actual data, you don't need to put the Azure, So you can actually put the results of Azure as inputs, and then that would actually be a static file that you could share.

So now that it came through and we can see, here we go, we've got the size score, we've got the embodied carbon per 5 minute period, and we have a size score of 32 grams per hour for that 5 minute period, and a 32. 4 per hour for that 5 minute period, and a 34. 2 per hour for that 5 minute period. So now we've got it.

We converted some raw imports into carbon emissions. Okay. So that is the very final thing I will just mention really quickly is, let me talk about another model that's been built by Intel called IEE, Intel Energy Estimator. Intel Energy Estimator is a drop in replacement for other energy models like TEEDS.

It just has much more accurate energy estimates for Intel policy. It actually has a... Data for each and every Intel chip type. Um, but the, and this is how you would configure it. And the power of IEE is that I can just go into here. And just drop in IEE. Each of these models has standardized interfaces and standardized parameters.

Standards is what creates the ecosystem. By having standardized models, we can plug and play, we can cherry pick, we can tune it and get exactly your calculation pipeline that works for you. So let me run that. Really quickly, and as it's running, I'll just talk a little bit more about how it actually works, because you can see here, it's a little bit different.

I mentioned that Impact Framework is TypeScript, and that means that, um, every model that we've seen so far has been written in TypeScript. They might call out some API, but it's written in TypeScript. You can see here that the IEE was built in Python, and what you can do with Impact Framework is you can actually call out to a CLI tool and do a shell command instead.

So you don't have to write your models in TypeScript. You can actually write your models in any language that you want, as long as you can create a CLI tool, which adheres to a specific open standard that we've created. It will plug and play into the Impact Framework, which is really exciting. That's all for the demo today.

You can find more information again, as I said, at If. greensoftware. foundation. And as a reminder, also in Feb next year, we'll be hosting rCarbonHack again, and Impact Framework will be the focus, so please watch this space. Next up, I spoke to Navveen and Srini, two other co leads of the project, about potential future use cases.

That was last week when I still had hair. Roll the tape, please. So with me today are two of two of the co leads of Impact Framework, Srini and Navveen. Navveen, why don't you give a quick introduction to yourself?

Navveen Balani: Yeah, thank you Asim. Hi everyone, I'm Navveen Balani. I'm a Chief Technologist with Technology Sustainability Innovation Group, working at the intersection of technology and sustainability. I've been involved in the foundation right from its inception and leading various projects in the foundation. Over to you Shimi.

Srinivasan Rakhunathan: Hey, thankNavveenen. Hi everyone, this is Srini. I'm part of Microsoft. I've also been with the foundation since the beginning and really have got an opportunity to do a lot of learning, multiple projects, all open source, so many people, so just love the energy here.

Asim Hussain: I remember the journey of all the projects that both of you have been part of since the very early days, and we've all started working on kind of measurement with the SCI, and you both started looking at... To calculate carbon emissions of software, we needed, we knew we had a data problem and you both went away and started taking the lead on the SCI data project and really started becoming experts in just the data aspects of software.

I won't go into all of the iterations, but we had a couple of iterations there until we landed on Impact Framework. I think everybody's just seen, we've just given a demo of Impact Framework to a lot of people out there in the world. And they've probably seen like how it works, like in a very practical sense.

But I wanted to just have you two here so we can talk a little bit about future thinking. This is what the Impact Framework is right now. What do we think it can be used for in the future? It's a very generic, it's very low level. So I'm going to throw the floor to Navveen probably to start off with.

Maybe just give us some ideas. Like what, what are your, what, what, what inspires you about this? And what do you think about applications for the future?

Navveen Balani: Yeah, so I think the future roadmap, uh, I think would be to extend the back engine framework for measuring the SCI scores for an AI application, uh, both during training as well as inference. And, uh, from a carbon emission measurement, uh, like during the training phase, uh, so we need to capture the server types like, uh, GPU and TPUs, have data for embodied emissions and capture various utilizations like, uh, memory and CPU. Uh, I think next we need to look at how we could integrate with available models or build models that provide the carbon emission calculation based on available data and obtain the SCI score. And also include various other factors like data processing and storage as part of the SCI functional boundary for overall AI training. And for the inference part, I would say, uh, this might be a bit, uh, tricky, uh, based on your deployment model. Uh, for instance, uh, let's say if you're hosting and have a control of the servers, uh, you can follow a similar measurement approach as in training, uh, and using function units, uh, such as number of, uh, inference calls. But if you have a deployment model, And let's say, which involves maybe a generative AI system, like sending prompts to a charge GPT or any LLM models. You basically need to rely on some proxy data such as API latency, maybe a number of iterations to get the right prompt response. Or overall prompt, uh, context and maybe the length and so on. So this is an area of exploration, uh, where we need, uh, open source, uh, communities to collaborate and contribute models, uh, based on various, uh, research and findings. Probably in the end, our goal is to basically look at impact engine framework to realize the SCS specification and make it easier to report SCS code for any software workloads.

Asim Hussain: Would you imagine a future where, as people develop papers and as they develop more advanced algorithms for AI, alongside this research, you deliver a manifest file, an impact manifest file where you're, you're explaining, this is this new type of AI algorithm, and this is the impact that the algorithm might have.

Copy it, fork this file. Play around with the numbers, see how it works for you. Do you see something like that as well in the future?

Navveen Balani: Yeah, definitely. That's a good point. Having all the research. Papers, right? Have this manifest file, right? And which can make the research also more compatible. And this can be a framework, right? Which can be used by any practitioners, right? To, uh, deliver their CS code.

Asim Hussain: Yeah, great. Yeah, we definitely need to apply this to AI. It's a big burgeoning field out there and it's a lot of computes going into that space. Shrini, what about you? What thoughts have you been given to this? What have you given to this area?

Srinivasan Rakhunathan: I think AI is a fantastic concept because both the Impact Framework and AI can learn and build and evolve and emerge together. That's a great synergy opportunity, but also on the, the enterprise side, or even the consumer side where you have multiple devices, multiple environments, multiple cloud providers to hyperscale providers. There is a big opportunity. With Impact Framework, because it truly is an open architecture of bring your own model or bring your own calculation algorithm. And at the very, I think when we spoke about it at the inception, the very concept we wanted, we wanted to do is to make it as granular as possible. So you have building blocks on top and any technology, whether you have Node. js, you have React. js or on the server side, anything that you can. You just have to make sure that the granular component is straightforward so that you can apply it for any kind of architecture, whether it is mobile or desktop or cloud provider. And that's a beauty of the model. And that's the beauty of the architecture I feel so that it can scale to any use cases, whether it is a consumer use case or an enterprise use case.

Asim Hussain: Yeah, I love that term, bring your own model. I think let's put that in all the material. I think that's a really good way of explaining it, isn't it? Bring your own model. Yeah, I remember when we were, like, one of the things, like, both of you have been also to the people who've been really at the cutting edge of actually calculating software.

There's not many people in this world who've actually. Put the time and effort to really dig deep and figure out the emissions of, of, of a piece of software. And I think one of the things that we discovered throughout our entire process is, as you're saying, Srini, everything's a little bit different. One model does not work for everybody, you need to tweak it a little bit for every single use case and every single, yeah, there's a lot of tweaking and can bring your own model to the situation.

Navveen Balani: Yeah, absolutely. I think the other area is how do you make sure that you get more people to do case studies, which we had been doing through manual calculations. And that's where the open source community can bring in user models for all their use cases. And we can learn.

Asim Hussain: Yeah. Oh yeah. There's been so, so many conversations to, to get us to this point where this Impact Framework, it's, yeah. So many things we can talk about. One of the things I've, I, I found quite interesting. I, I was chatting to a large enterprise organ, an airline, a large enterprise organization that's an airline.

And they were describing, and I was explaining Impact Framework to them. And they were quite interested 'cause they were, because they obviously put a lot of RF qs out, a lot of requests for quotes, requests for business. And what, and they also have stringent rules regarding the carbon emissions. They won't have carbon limits on the software that they build.

And what they were proposing is when an organized, when they put an RFQ out and they get a bid back from an organization, they were suggesting as well as a bid, send us a manifest file so we can see not only how much is this thing going to cost us and how are you going to architect it? But what are the carbon emissions of your proposed approach?

And let me compare versus the other people proposing approaches and not just compare on cost. But compare on carbon as well. And I thought that was really, every time we talk to people about Impact Framework, people always come up with another use case, which is what gives me so much excitement about this project, because people are seeing solutions that we didn't think about when we've been talking about this for as long as we've been talking about it.

Navveen Balani: I think this is definitely a good use case, uh, think of this as a calculator, right, which you can first create, right, how much emissions, uh, your infrastructure will take, and maybe that can be integrated with your CI, CD, Terraform, just to know upfront, right, what would be the impact. And if you can make it simplify this whole integration via plugins or calls, right, and make it native to each, uh, programmers, right, the tools that they use, I think we'll achieve what we want to achieve.

Asim Hussain: Awesome. I think we're coming up, coming up to time now. So one of the things I wanted to make sure. That we cover is actually we're going to be running a hackathon. We ran a hackathon last year. It was very successful. It was the Carbon Hack with a Carboner SDK, but the CarbonHack will be coming again.

First quarter of next year. And the main theme of the carbon hat will be measurement and measurement with the Impact Framework. And we're going to be looking for people to build models, that'd be cash prizes. People build models, build impulse, and we're going to have. prizes for best documentation, non technical contributions, all sorts of different prizes.

And I'm going to have an under 18s prize as well, which I'm really excited for. So we've got the Carbon Hack also coming up next year, which is, and the main theme is going to be measurement and Impact Framework. And I'm just so excited to have. Have that on our agenda. I just want to thank again, Navveen and Srini, you both have been just key players in the Green Software Foundation since day one, this project evolved on all the work that you've done from the start.

And I want to thank you and I'm really excited to, really proud and excited to have you here working all of us together on this project.

Srinivasan Rakhunathan: Thanks, Asim.

Navveen Balani: Thank you, Asim, and thanks to the community.

Namrata Narayan: Thank you. Okay, so really excited to finally see Impact Framework. I hope everyone sees why we're so excited and hopeful about the impact this open source tool will have on the industry. We've got a lot of questions, so we're going to try and get through, um, two of them, um, as best as possible. So, as an Annie panel asked, Would Impact Framework be recommended for use on a simple website, or is it better applied to a more robust application? And Neil Clark added to that question and asked if a website is hosted via a CMS hosting partner, like Acquia, abstracted quite far away from the actual server, how can we capture observation data?

Asim Hussain: Yeah, and to the first question, absolutely. Impact Framework is very general purpose. Even right now, I've been talking to you about how to measure software emissions of a server. One of the samples we have in the repository, I believe we still have it, is measuring our website's emissions, which website is built using GitHub Pages, it's a static website.

And obviously it's like a normal website. We haven't actually built the models that are required to measure that. Unfortunately, that's a limiting factor is you need models. You need models that convert network bandwidth to, uh, CO2. We were going to use CO2 JS. We need models that perhaps take data from, uh, Google analytics to help then use other data to figure out, well, what are the, what is the energy consumption on the client side?

So it's all limited by models. That's what we're limited by. But yeah, you can absolutely, uh, measure, measure websites. In fact, we've had the spoiler alert. You can measure anything with it. You can measure physical products. And I think the last question was around, yes. How can you measure when you don't even have access to the server?

Absolutely. That comes up all the time. We're building this thing. You build what's called an adapter model. So for instance, there's a lot of things, even in the service base, you don't have access to, like when you're running like, uh, serverless functions, you don't know the server. You just have some other.

bit of information like gigabyte seconds. Again, it's limited by models. We need models that convert gigabyte seconds to carbon, but we've started to think about how to create those models. And again, as we come on to carbon hack next year, we're really hoping the community comes together and starts filling in those gaps.

This is what I'm talking about. We need thousands of these little models. We want to fill those gaps and build those models. And you can cherry pick them together in order to calculate all sorts of carbon impacts. And other impacts, my dream is to like add water, add other impacts. I want to, we, it's called Impact Framework, not Carbon Framework.

Namrata Narayan: Thanks, Asim. Another question from Cristiano Elias. I hope I did not butcher your name. Is certification support of the results output in the roadmap of Impact Framework?

Asim Hussain: It's not currently in the roadmap of Impact Framework, but it is something that we've discussed. For instance, one of the things that's been, we imagine is the models that you use, perhaps in the future, we will generate some official models. which may be used more in a certification, but essentially if there is any certification, it's a certification of the models.

So you might get a whole bunch of models which are useful for a variety of use cases, only this, this certification agency will only approve this small set of models or for this type of. Computation. We only accept this model pipeline. We imagine it's going to evolve something around that, but we're a little bit early.

We're just alpha today. So we're a little bit early on to have any kind of specific roadmap items on certification. But it's something that we've thought about.

Namrata Narayan: Thank you, Asim. I hope everyone was happy with those answers. We did get a lot of, many more questions, but we will answer them at a later time. So stay tuned, subscribe to our newsletter.

Chris Skipper: So that's all for this episode of Environment Variables. If you liked what you heard, you can actually check out the video version of this on our YouTube channel. Links to that, as well as the Impact Framework, can be found in the show notes below. While you're down there, feel free to click follow so you don't miss out on the very latest in the world of sustainable software here on Environment Variables.

Bye for now!

Asim Hussain: Hey, everyone. Thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we want more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. Thanks again, and see you in the next episode.

This Week, host Chris Adams is joined by TU Berlin researcher Iegor Riepin to talk about the benefits - and trade-offs - associated with load shifting over both space and time. Together, they nerd out over the specifics, discuss numbers, and weigh alternative methods of computing with green energy all around the globe. Iegor and his team did a study alongside Google, where they modeled the entire European electricity grid in order to study the effects of different types of load shifting, and how it can be most efficiently applied to the world of Green Software.

Learn more about our people:

• Chris Adams: LinkedIn | GitHub | Website
• Iegor Riepin: LinkedIn | GitHub |  Website

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

News:

• https://pypsa.org/ [03:21]
• RE100 [09:47]
• What? The sun isn't always shining?! - by David Roberts [10:16]
• XFaaS: Hyperscale and Low Cost Serverless Functions at Meta [47:30] 

Resources:

• https://github.com/PyPSA [03:21]
• An introduction to energy's hottest new trend: 24/7 carbon-free electricity 
• System-level impacts of 24/7 carbon-free electricity procurement in Europe [11:22]
• The value of space-time load-shifting flexibility for 24/7 carbon-free electricity procurement [13:24]
• PyPSA · GitHub [22:20]
• GitHub - PyPSA/atlite: Atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series 
• Electricity Maps [50:40]
• GitHub - PyPSA/247-cfe: Explore the impacts of 24/7 Carbon-Free Energy PPAs [55:55]
• 24/7 Carbon-Free Energy Compact [56:25]
• Open Energy Modelling Initiative
• PyPSA meets Earth 
• PyPSA-Earth: A flexible Python-based open optimisation model to study energy system futures around the world.  

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
• Follow and rate on Spotify
• Watch our videos on The Green Software Foundation YouTube Channel!
• Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:
Iegor Riepen: Capacities of wind, solar, and storage required to achieve hourly matching of demand with carbon or clean electricity are reduced when we increase the share of computing jobs, and that means the share of power loads that are flexible. So, in short, demand flexibility makes carbon-free computing more resource efficient.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to another episode of This Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. When we talk about green software, we often talk about code efficiency, because it's something that we're often already familiar with. But as you learn more about integrating sustainability into software engineering, you end up learning more about the underlying power systems that all our servers and end-user devices like laptops and phones rely on too. And because the power systems we rely on are in the middle of a generational shift from fossil fuels to cleaner forms of energy, there are changes taking place there that can inform how we design systems higher up the stack. This summer, a team of researchers from Technical University Berlin published a study in collaboration with Google to help shed light on this emerging field. They modeled the entire European energy grid with open-source grid modeling software written in Python, along with a set of internet-scale data centers, to better understand how scaling their use of compute to match the availability of clean energy can affect the associated environmental impact with running this kind of infrastructure.

If this was interesting enough, But the modeling also revealed some interesting findings about the cost of transitioning to digital services that run on fossil-free power every hour of every day. This sounded like absolute catnip for sustainable software engineers, but because this is also one of the first open studies published around carbon-aware software, it's possible to understand the assumptions behind these results and figure out how they might change in future. So if you want to learn a bit more about this, to help in this quest, joining me today, we have one researcher from the Department of Digital Transformation in Energy Systems at the Technical University of Berlin, Iegor Riepen. Hey, Iegor.

Iegor Riepen: Hi Chris, thanks for having me today.

Chris Adams: Iegor, it's lovely to hear from you again. Before we dive into the world of grids and carbon-aware software and the like, can I give you just a few moments to introduce yourself?

Iegor Riepen: Yes, of course. Hi, everyone. I'm Iegor Riepen. I'm a postdoc researcher at the Technical University of Berlin, where I'm part of the Energy Systems Department. In our department, we use methods from operations research and mathematical optimization, researching the cost-effective opportunities for climate neutrality.

This means I spend most of my time writing, solving, and debugging mathematical models of energy systems. Our research group also maintains an open-source Python environment for state-of-the-art energy system modeling, which is available at pypsa.org and GitHub/PyPSA.com. 

Chris Adams: Okay, cool. Thank you for that. So, I know that I actually met some other folks in the same department as you from actually an event called Clean Coffee that I used to run at ClimateAction.tech. Tom Brown came along to one of those Zoom calls back in 2019 and from there we just kicked it off. So, And that's how I found out about anything going on in Berlin. And I understand that you've been in Berlin for a while, but you weren't always studying and working in Berlin. You've been in other parts of the world as well, correct?

Iegor Riepen: Yes, that's right. I'm originally Ukrainian. I lived up until I was 20 or so, and then I moved to Germany to make my masters' studies, then academic career. Before Berlin, I was doing my Ph.D. in the Brandenburg Technical University, where I worked with Professor Felix Müsgens, and I worked on my Ph.D. in various questions of modeling energy systems.

When finishing Ph.D., I joined, as a postdoc, Tom Brown's group in TU Berlin.

Chris Adams: Cool. All right then. And that's how we ended up here then. Okay, so we're just about to dive into the meat of the show. And here's a reminder that everything we talk about will be linked in the show notes below in this episode. So if there's a project mentioned, or a site, or a paper that we refer to, please do write in and tell us so we can update the show notes for other curious souls and help you in your quest for knowledge. All right, Iegor, are you sitting comfortably?

Iegor Riepen: Yes, I am standing comfortably.

Chris Adams: Okay, all right then, let's begin. So it's really tempting to jump right into the nerdy specifics about this research and so on. But before we do, can you share a little bit about the background from your current research for this to provide the context for the work that we're about to discuss?

Iegor Riepen: Yes, of course. So over the past year and a half, my colleague, Tom Brown, who is the head and the heart of our research group, and I have been working on the open-source, model-based research dedicated to various aspects of 24/7 carbon-free electricity matching. This concept usually goes under the name hourly CFE matching.

The 24/7 CFE is a new approach for voluntary clean energy procurement, where companies aim to match their electricity consumption with carbon-free or clean energy supply on an hourly basis and around the clock.

Chris Adams: Okay, so let me just check if I understand that. So normally when people say they run on green energy, they might be talking about things on an annual basis. And this is so hourly, so it's what's 8, 760 hours in a year. So it's 8, 760 times higher resolution. So you don't have this whole thing where if you say you're running on green power, you're not making a claim of running on clean energy at night, for example, something like that, right?

Iegor Riepen: Yes, exactly. That's pretty much about that. So, it's interesting because the 24/7 carbon-free energy hourly margin aims to eliminate all in-house gas emissions associated with electricity use of an energy buyer. So, the strategy also aims at addressing the main problems that exist in much in demand with clean energy supply.

When using so-called Established Certification Schemes, the audience of the Environmental Variables might know all this pretty well, but I'll just name a few to outline the context. So, companies who would like to demonstrate their sustainability credentials might opt for buying the guarantees of origin, this has a story related to Europe or the renewable energy certificates or renewable energy credits.

This is a story related to the United States. A common feature of these schemes is that renewable energy credits are, so to say, unbundled from megawatt-hours of energy. When using the schemes, there could be several problems that would arise. So one of the problems is that assets procured with these credits, or the credits you procured from certain assets, this might not be additional to the system.

This means if you buy renewable credits or guarantees of origin from an asset located in a particular system, Norway, and the asset was located in Norway, is located in Norway, and will be located in Norway, no matter what your procurement, it's hard to claim the change of the system associated with your matching strategy.

The second problem is typically arising with using guarantees of origin is so-called mismatching location. So if demand is located in Germany, and the energy asset generating the Renewable Credit is located in Spain, there will be hours when the grid between Spain and Germany is congested. So it creates an accounting problem.

It's likely not those electrons were consumed, that were produced. So, these two are the so called additionality problem and location matching problems that arise.

Chris Adams: Okay, so if I just check if I understand that. So one of these issues was about additionality you mentioned. So if I understand, what you're saying is, let's say that you got some form of generation. Yes, you get paid for the power you generate. But there's another thing that people are paid for, which is like the kind of greenness of it. And this, as you say, ends up being unbundled and traded separately and there, you, basically a bunch of problems tend to happen once they're unbundled in that scenario there.

So you spoke about something in Spain like, yes there might be a grid where you could theoretically have a solar power, solar farm in Spain generating electricity, but whether it's actually really deliverable to somewhere in Germany is another matter, because it might not be physically possible to deliver that power.

So there's, this is some of the kind of complexity that it's trying to address here, if I understand that right.

Iegor Riepen: That's correct. And some energy buyers do think about this and recognize these problems, and they opt for power purchase agreements. These are bilateral contracts between buyer and supplier. And when signing power purchase agreement, these companies would pledge to buy both the energy, so megawatt-hours and the environmental credits bundle to it. The problem here is that under power purchase agreements, renewable energy supply is typically matched over a long period of time with buyers energy demand. So, for example, there is plenty of companies who are joining the known Renewable 100 group who claim to procure right enough renewable electricity to match their consumption on an annual basis.

The problem here is, guess what, renewables are not generating at all hours throughout the day. And this concept is sometimes called in the literature as renewables are non-dispatchable. Sometimes it's called that they're intermittent. Sometimes people say that renewable energy is variable. There was a recent awesome podcast from Volts, where host, David Roberts, had Jesse Jenkins, who suggested the term weather-dependent fuel-saving technologies, which I think is an excellent way to put it forward,

Chris Adams: yeah,

that's 

Iegor Riepen: the way we will likely use it today. So when energy buyers sign PPAs with weather-dependent fuel-saving technologies, there definitely will be times when generation is low, so energy buyers have to depend on procurement from the local markets that likely have some carbon content in it in a given point of time.

Chris Adams: Okay, so, just a quick translation. You said weather-dependent fuel-saving technology. That's opposed to, essentially, if I'm going to be burning gas or coal, I don't have to care so much about the weather, but I'm having to burn a bunch of stuff, and the whole combustion of fossil fuels is one of the problems we have here.

So, that's how you might frame it, right? So, because once you've built something, you don't, presumably, once you've got, say, wind or solar or something like that, it's renewable, so you're not having to purchase the fuel. That's the distinction that we have there, right?

Iegor Riepen: So the term which I referred, which was coined by Jesse Jenkins in that podcast, referred to as dependent fuel-saving technologies, refers to the content that when we use renewables, we do not have to buy fuel, which comes at cost. We do not burn fuel, which comes with carbon emissions associated with burning it, and we don't have to think about the fuel.

Chris Adams: Okay. All right. Thank you. Now we've got a kind of, we've got some of these terms sorted out now. Now we can talk about how some of this might work in the context of data centers and things like that. So maybe I should ask you a little bit about this study. So we've, we've done this work now. Now maybe you could just talk a little bit about what the study that you were doing with Google and with TU Berlin was about for this.

Iegor Riepen: All right, so we have an ongoing research projects where we collaborate with Google. As audience might know, the company has claimed a commitment to achieve 24/7 carbon-free electricity matching in all of their data centers worldwide by 2030. And from our side, we bring an academic look to a broad range of questions relevant for making data centers, and more broadly, any consumers from commercial and industry sectors carbon-free. These questions are like, how can one achieve hourly matching of demand with carbon-free electricity? At what cost this comes? How can advanced tech help? Or what would be the impact on the background grids? We released a series of open studies aiming to address some selected aspects of this complex question. So, where demand flexibility comes to the story? Demand flexibility is the degree of freedom that companies pursuing their goal of reducing carbon emissions can use and benefit from. This refers to a broad range of companies with various degrees of flexibility, which takes many forms and colors, mostly of temporal demand management, and perhaps even more interesting, this refers to the demand flexibility of computing infrastructure or data centers that can be geographically scattered and managed collectively by one entity or one company.

So, data centers as electricity consumers have some special perk. They have an ability to shift computing jobs and associated power loads in space and time. And in July, this year, we released a new study where we focused specifically on space-time load-shifting problem. So we look at the role the space-time load-shifting can play in reducing the costs and resources needed to achieve perfect 24/7 matching.

We thought about the signals that companies can use for shaping their load, and we also looked at the trade-offs and synergies that arise from co-optimization of spatial and temporal load-shifting.

Chris Adams: Okay. So I'll just try to make sure I follow that from a kind of layperson point of view. So there's basically maybe two things that came out of that. So one of the, one of these things is this idea that in addition to, if we know that the amount of power we might have be generated from clean sources might change over time, it sounds like there is a chance to, rather than have to generate more, just actually make supply and demand match just by scaling back some of your own energy usage.

And this is something that isn't just done inside the technology sector. For example, I know that buildings might do this to cool a building down where the energy is cheap, for example, at night, so that when you walk in, it's nice and cool, for example. Or if you're in Texas or somewhere, where it's a, it's a nice, comfortable environment.

So it's taking, that's the kind of moving things through time. And that's one temporal thing, but there's something special about computers and data centers in that. Rather than just moving the energy, you can move the work to somewhere else where they have an abundance of power. And that's the kind of special thing that you ended up doing a bit of study, studies into, right?

Iegor Riepen: Yes, exactly. We focused both on space and time flexibility. We write in the study that space-time nowadays mostly is a story of computing infrastructure, data centers. Temporal flexibility applies to a broad range of companies who have various forms of temporal demand management.

Chris Adams: Okay, cool. All right, then. So we've spoken about that. And as I understand it, maybe I should just ask a little bit about, so what's the benefit of doing it this way? Like, why would you even think about trying to match some of this stuff up rather than just buying a bunch more green energy for example or a bunch new solar farms. 

Iegor Riepen: Well, thinking about this, imagine a company with an inflexible demand wants to match its own consumption with carbon-free electricity. It could source carbon-free electricity from various sources. This would be a combination of re-imports if the grid is clean or clean enough for your purpose. Uh, it could be generation of renewable generators procured with power purchase agreements and dispatch of storage assets. This is a pretty challenging task, since battery storage is helpful for bridging some hours of no wind, no solar, but it's not the right technology or better frame, not the right economic technology for times when you need to firm weather dependent wind and solar over an extended period of time.

So, pre research done by Princeton University, NetZeroLab, folks from Peninsula Clean Energy, and also from us last year, did show that 24/7 CFE hourly matching is possible with commercially available technologies like wind, solar, and lithium ion batteries. But it comes at large price or cost premium and with some curtailment of renewable electricity.

If consumer following our imagination have an access to advanced tech like hydrogen storage or clean firm technologies, their price premium could be reduced. Data centers could use this special perk, so the ability to shift loads over space and also time, to relax this problem of matching demand with carbon-free electricity supply.

Chris Adams: Okay, so what I think you might be referring to here is the fact that because there's this flexibility that reduces the amount that you need to buy to have ready, uh, to match the entire time. So it may be that you don't need to buy so many batteries or have so many wind turbines or something like that.

Yeah.

Iegor Riepen: Yes, so put it simply, you can move loads from the places where you have access to the loads where you don't have enough carbon-free electricity, and by moving the slots you could possibly save potential storage needs, and you can also save or reduce the amount of excess electricity you would have that you might have curtailed otherwise.

Chris Adams: Okay, and by when we just briefly touch on curtailing, that means that rather than just wasting, just not being able to use this energy, you're, you're able to put it to use to some kind of productive use, which basically improve the economics means like you might reduce the cost of running something, for example, so it might pay for itself doing something like this.

Iegor Riepen: Exactly. If you have signed PPAs with an asset or you built an asset on site, you likely have some access of energy in some uh, hours. You could sell this access to the background grid, if the background grid takes it at a price, you could potentially store it, but you would store it up until point. The storage is economical and some part which you would not store or you would not sell.

You would typically curtail.

Chris Adams: Okay, cool. And curtailing for the purposes here is basically having to throw it away because you can't use it productively. All right then. Okay, thank you for explaining that. So we spoke a little bit about some of the details on this, and it might be worth just briefly touching on some of the open approaches for this, because you've mentioned a tool called PyPSA, which I think is Python Power Systems Analysis?

It's something like that, right? And this was, there was a the open part of the study is quite a key thing for, for this project. Is that correct?

Iegor Riepen: Yes. So we use um, um, PyPSA, which is an open-source Python environment for state-of-the-art system modeling. This is a tool which our group develops for and maintains for our own research, but also it's been used by wide range of companies, institutions, NGOs, TSOs, by some others who might find a use in open-source tools.

So PyPSA itself stands for Python for Power System Analysis, as it was originally scoped for the power system analysis. However, nowadays the tool is used for many other applications, but for Power, which includes transport and heating and biomass, industry and industry feedstocks, some carbon management, sequestration, hydrogen networks, and what else.

So the open-source Python environment which we ship or which we maintain includes PyPSA itself, which is a modeling framework, but it also has several individual packages that make it possible to go all of the way through the data processing, such as calculating renewable energy potentials in different countries that we model or collecting energy assets data to creating and solving complex energy optimization problems.

So why would open-source modeling be interesting? There are a couple of things which usually comes in place answering the question. This could be transparency and credibility. So by doing open-source modeling, we show that we have no cherry-picked assumptions. In our, um, studies that would drive certain results. Open-source is also pretty useful for reducing wasteful multiplication of work. We can think about it so there is plenty of energy modeling groups in academia worldwide, but also energy modelers in consultancies or in industry. All of us are doing basically the same job, but if all of us have to create an 

Chris Adams: your own model. Yeah.

Iegor Riepen: Before we fly the airplane, we are not progressing much, and doing open-source is quite helpful too.

Sometimes you can just copy from somebody who did a good job and put it in this open license and go ahead.

Chris Adams: Okay, cool. That's actually, so that sounds quite exciting. So essentially, if someone's going to say, "oh, I think we can decarbonize this industry by this date." You can essentially model it and say, "these are the assumptions I'm making. This is why I think it's possible. This is why I think we can afford it. And, uh, this is how much I think it would cost," for example. And, uh, the idea that because it's open, it becomes easier for, say, policy makers or civil society to basically say," Hey, you've made a really weird assumption here. I challenge that and vice versa." And with it being modern, open, there were, although we've mentioned Google a few times, this could be used by any company or any organization that might also want to see if this would be applicable to them for in running any kind of infrastructure themselves.

Is that about right? 

Iegor Riepen: Yes. When we do, for example, our study, which we today talk about, which is released in GitHub is on a special kit release. And everybody who can run a Python script could reproduce our results, could see how our assumptions are formed, what we put into our optimization problem as parametrization, and if you wish, you could basically get the same result on your local machine.

If you have this way, you can be pretty sure that we did not do some cherry pick stuff to, to shape or drive results in one direction.

Chris Adams: cool. That's really interesting. I didn't know that I could model the entire European grid on my laptop to actually try and settle a bet in the pub. That's quite cool. All right then. So maybe we just, let's go back to this study then. So we've spoken a bit about this and we haven't actually discovered or discussed the findings.

So maybe I could actually ask you, um, are there any particular key findings you'd like to share so far? Or is there any nuance we should be, be aware of before we dive into some of the kind of juicy results here, for example?

Iegor Riepen: Um, yes, sure. I think we could address this nuances topic first. We could just briefly go through the study design and key assumptions driving our results, so the audience would understand where from are. So, for this study, we used the computer model of the entire European electricity system. With this model, we simulated the early operation of the electricity system and the so-called system development, so we looked at the cost optimal investments the system would have in generation and storage assets for the model tier.

We placed five data centers in and we chose the regions where we placed our data center source to capture grids with different sets of features, unique renewable resources, and national characteristics. We assume the data centers have a nominal load of 100 MW for simplicity. This assumption of "What is the exact nominal load of data centers doesn't play any big role in the results?"

If we have smaller or higher capacity assumption, we would observe the same trends. 

Chris Adams: Ah, okay. 

Iegor Riepen: we And we configure our mathematical problem that all datacenters follow 24/7 CFE goal. So, to achieve this goal, datacenters can co-optimize electricity procurement from the local grid and procurement of additional resources such as storage, wind, PV generators that are additional to the system and located in the same meeting zone.

And we assume that datacenters have some degree of load flexibility, which we vary with scenarios. Stepping from 0%, which would mean that there is no flexible workloads, so all loads are inflexible and must be served at places of data center location, up to 40%, meaning that 40 percent of data center loads are flexible and can be shifted to other places or delayed to other times.

This is what we do. What we do not. First, we do not quantify the actual costs and technical potentials of achieving certain share of flexible workloads, we just say, "hey, this data center fleet have some share of flexible workloads. How would you optimize the flexibility utilization and what benefits it might bring?"

We also treat data centers simply as large consumers that can shift a certain share of loads, which is not too far from reality. But what I mean here is that we abstract from the technical aspects and properties of flexible workloads and some physical constraints of quick ramping. There is, uh, tech folks who could focus on these topics with their, uh, uh, with their, uh, knowledge.

And we base our model inputs only on freely available raw input data. So for electricity systems, we parametrize the system mostly from Danish Energy Outlook. And for data centers, we assume pretty generic transparent assumptions. Where, so we by, by this, we try to keep our study design broadly applicable for other companies who might have their specific flexibility shares or forms or shapes.

And we try to keep our workflow available at GitHub for everybody to access and reproduce.

Chris Adams: Okay, so if I understand that correctly, essentially, you're doing this with as much open stuff as possible so that someone can reproduce this, and the assumptions you're making about these data centers, it doesn't, although the size doesn't matter that much, 100 megawatts is about, that's like a medium sized to large hyperscale data center. So this is, again, it's, it's somewhat reflective of the reality. And you also mentioned that they're in different parts of Europe. So I, from memory, I think this was like Ireland, which is like windy and in the West, Denmark, which has loads and loads of wind, there was like a few other places in Europe as well with different kind of generation and different geographics.

So they're in different places. So it was somewhat representative of the regions we might use in cloud, right?

Iegor Riepen: Yeah, so data centers, eventually we scatter it in Ireland, Denmark, uh, west one zone, uh, Finland, Germany, and Portugal. Uh, the idea was that we would take regions with different renewable resources. First, regions that would be pretty far from each other, and also we would take regions where there is data center consumption in national energy mix.

And by that we take different enough regions and we would capture all the system dynamics that we would want to.

Chris Adams: Okay, cool. That sounds somewhat, I can recognize that with my kind of cloud hat on, thinking about running something in Ireland versus running it in Germany in this scenario. Okay, well, that sounds like we've given enough background for this. Should we dive into some of the findings? Is there anything you'd like to, so yeah, maybe I should ask, what was maybe one of the first findings that really caught your attention that you'd like to share from this?

Iegor Riepen: So I think we could go through several steps. That's the first finding that usually has been caught by people looking at our study is this topic of resource efficiency and cost reduction. Just for audience to understand, from our model, as a result of optimization, we get procurement strategies for each data center, which optimize to match demand with carbon-free electricity around the clock with some desired quality score.

So the cost optimal technology mix that we get as an output depends on various factors, for example, renewable resource, this would be wind to solar average energy yield, or cost assumptions and many more. So the clear trend that we observe across all scenarios is that capacities of wind, solar, and storage required to achieve hourly matching of demand with carbon or clean electricity are reduced when we increase the share of computing jobs, and that means the share of power loads that are flexible.

So, in short, demand flexibility makes carbon-free computing more resource efficient. What we also could do, we could retrieve the cost of any procurement strategy from our model, and thus we can map the resource efficiency to the cost effectiveness, meaning you pay less to achieve exactly the same. So the degree of this cost effectiveness scales with the level of flexibility that we assume.

So for the corner scenario where we assume that 40 percent of flexible workloads perfect hourlCFE matchaging and co-optimized space for achieving the overall energy costs of the model data center fleet are reduced by up to 34%. So this refers to the cost saving of an individual data center, but it refers to the cost saving of a group of data centers scattered geographically and managed together by one company.

So these data centers consume basically the same amount of megawatt hours, but do shift their consumption in space and in time to optimize the resources.

Chris Adams: Okay, so if I just, maybe I'm just trying to run that by you so I understand. So essentially, you model different amounts of flexibility in a system if you're controlling multiple data centers here. And essentially, the more flexible you make it, the more you can actually reduce the amount, the cost of actually having to buy all these solar farms and wind turbines and batteries and all the way that, all the way up to the point where, if you're doing 40, if you're got 40% of your loads being flexible, then it reduces the costs by about a third, essentially. That's what I think you're saying there.

Iegor Riepen: Yes, that's right, but we should see it in perspective that this 30 percent cost saving is basically our corner scenarios. We scale the cost very up, so scaling very up means we look at the perfect matching between uh, demand and carbon-free electricity. 100 percent that you are not allowed to have any gram of CO2 peak allowed hour of consumption.

And also we assume in this scenario that 20 percent buyers only have access to wind, solar, and lithium ion storage. So this is a palette of commercially available technologies which are just hard to use out of these technologies to make 24/7 strategy. If these two are right, then your costs are reduced by up to a third, and the costs are reduced less than that for all palette of asset technologies and scenarios that we considered in this study.

Chris Adams: Okay, cool, well thank you, that's, that was bigger than I was expecting it to be. Uh, that's, that's, that's, that's, that's pretty impressive if you're going to be spending, um, literally billions on power, like some large, uh, data center providers or data center users will be using. Okay, then, um, Iegor, so, if I understand correctly, the amount of flexibility you might introduce, this kind of carbon-aware computing, that, if you say that you need to be running everything on 100 percent carbon-free or fossil-free or clean energy, based on this, then this will reduce the amount that you need to purchase, which essentially makes it more affordable or more accessible to a wider number of operators, I suppose. Are there any other findings that you would draw attention to in this study?

Iegor Riepen: Um, yes, of course, what we do in the study, we take a look on the signals that companies might use to shape their load following strategies. So to discuss the signals, we could firstly go for special shifting story and then for temporal shifting story. So for the special shifting story, the one signal which comes up front is the fact that hourly profiles of wind power generation have a low correlation over long distances due to different weather conditions. So as a rule of thumb, you can think of the following. If two generators are located as far as 200 kilometers from each other, the hourly feed in from these assets have very low correlation, and data centers could arbitrage on this effect, or put simply, they could move load to locations when and where there is a high wind generation.

That's saving the cost of energy storage and thus reducing the amount of solar curtailment. So, the hourly profiles of wind generation is not the only signal. Another signal that we discuss in the study is the difference in quality of renewable resources in regions where data centers are located. So, the quality of local resources, or in other words, the average capacity factors of Solar PV in a given region, they translate to the cost of electricity.

The higher the quality of renewable resource, basically the lower the average cost per megawatt hour. Special load-shifting is possible. A rational buyer could just adjust own procurement strategy to contract generators in better locations. So those locations where renewable assets have lower costs and co-optimize special shifts accordingly.

In the study we illustrate this mechanism with a data center located in Ireland, so it's not the most sunny region in Europe, that would tend to shift loads away during daytime through mid spring to mid autumn. So data centers located in Germany and Portugal, the regions with much better solar resources than in Ireland, they would tend to receive loads during this period.

This feature would just work about reciprocally for wind-related load shifts. Data center in Germany would benefit from having partners in Denmark or Ireland that have much better quality of wind resources. And, so there are two signals for spatial shifting. There is one more, which we did not put much of focus in our study because of our geographical focus scope, but it could play a role for spatial load-shifting.

So if you look above Earth from the North Pole, and the Earth would rotate counterclockwise from West to East. And we are pretty sure that it rotates with a constant predictable speed, roughly once per 24 hours. So if data centers are scattered across the globe in distant locations and operated by one company, one could imagine a load-shifting strategy where loads would follow the sun.

Chris Adams: Yeah.

Iegor Riepen: So these are basically three signals for spatial load-shifting for temporal load-shifting story, we illustrate cases when the variability of the regional grid emission intensity could drive the carbon-aware temporal load-shifting. So the grid signal can play a role in load-shifting strategy if data centers have electricity imports from the local grid in their energy mix.

And the temporal flexibility could also be helpful in aligning the demand in time with the generation of procured renewable resources.

Chris Adams: Okay, so it sounds like there's almost two kind of scales you're working at here. So the first thing you spoke about was like, say, Ireland and Germany and Portugal. Essentially, so basically Germany and Portugal are sunnier, and Ireland is windier, and during the summer, they're going to be, Germany and Portugal are way sunnier, so if you were running say computing in these three places, you might choose to run more of it in those two during the summer, and then as it gets a little bit darker, you basically choose to run everything in Ireland instead, and that's going to be a much more efficient way to actually, essentially run, maybe, if you're going to run computing jobs at 100 percent carbon-free energy, that's a way that you can do that at one of the lowest costs.

So that's, there's one thing happening at the annual level, but you also said there's a kind of another thing which is much more tied to the kind of, you know, day and night cycle that you're referring to as well there. So there's different speeds that you might be thinking about, different trends that you might take into 

Iegor Riepen: consideration.

Yes, we talk about pretty complex optimization problems that spans across space, spans across time, and the signals that would drive optimal utilization of flexibility through this space-time graph have various shapes. Some signals have stochastic pattern, like wind and feet, um, which is uncorrelated over long distances. Some signals have predictable pattern, like solar profiles that follows the Earth's rotation. And some, um, some signals have something in between predictable and unpredictable.

Chris Adams: Oh, cool. Wow. That's, I wasn't expecting that. Yeah. So we spoke before about, okay, one thing that you could do is essentially during summer, you're running your computing jobs in Germany and Portugal where there's loads of sun and loads of clean energy and that's relatively cheap. And then in winter, you'll choose to run it maybe Ireland, or somewhere where it's a bit darker, a bit gloomier, but way windier. And, uh, where there's loads, oodles of green energy there. But you certainly have some trade-offs that you have to make here, if you were to choose this. Maybe you could just expand on some of that a bit more, so that people understand, so that it doesn't sound too good to be true, for example.

Or people understand some of the specific nuances here. 

here 

Iegor Riepen: Yes, very right. What we do in the study, we take a look on scenarios where we co-optimize um, and Isolate Utilization of Spatial and Temporal load-shifting. So these scenarios with isolated flexibility can be seen by just academic exercise, but it's pretty useful for us to take a look on the system mechanics and get a feeling of the numbers.

So as a result of utilization, we can retrieve the value, which would represent something like reduction of the overall annual energy cost of a carbon-free electricity supply. If a data center utilizes either spacial load shifts or temporal load shifts or both, if we compare the value of spacial and temporal load management, uh, when, um, spacial and temporal stories are isolated, we come to the numbers of something from 6 to 1, depending on the scenario, in favor of spacial load-shifting.

So this means, spacial shifting workloads across locations brings you 6 to 1 high amount, high value. And this takes place because datacenters can arbitrage on differences in weather conditions and take advantage of. So this is a mechanism which we have just discussed. Shifting workloads across time to bring a higher value, uh, requires a few things.

So for that to, to have a high value, datacenters would need to buy electricity from the background grid, which is high variability of the original grid carbon emission density. So if a local energy mix is flat, dirty, or flat, clean, there is no, basically, value to shifting workloads from one time to another.

And the tr-de offs would appear uh, between spatial and temporal load-shifting when both are implemented together. So one can think about this in this way, if you have a certain share of flexible loads and you would like to shift some in space and some in time, but whatever you shift from other places and other times cannot exceed the upper cap, which would be the computing capacity constraint.

And whatever you shift away, that means to other places or to later times, cannot seek the lower cap, which would be the flexible workloads cap. And, uh, one thing on synergies, what we do show is that co-optimization of space and time load-shifting can yield benefits that go beyond the value of each of the two individual mechanisms could bring alone.

It's sort of an expected outcome for any operation's research problem. If you have two degrees of freedom, and you co-optimize them, uh, you could co-optimize in a way to get the benefit from synergies of them both. So in the study, we come to this point from various angles, but here's just a good example.

Imagine if you have, say, three data centers scattered far from each other and operated by a single entity. Then imagine we have each data center, which has a mix of wind and solar capacity built on site. Let's assume that this data center can shift workloads with any fixed volume of flexible workloads.

Now, somebody comes and says that, "hey, we have one long duration energy storage asset that we could place in either of the three data center locations."

Chris Adams: A big-ass battery, basically. Yeah?

Iegor Riepen: Yeah, the question is here, where would you place it to reduce the energy cost of the entire system? If you would write an optimization problem for this, solve it uh, we would tend to see that the optimization problem suggests us to harvest renewable electricity in the best locations. So those locations where the lowest cost per megawatt hour is achievable. For example, Denmark, Ireland, with good wind conditions. And integrally opening access to this cheap, clean electricity for all locations through the spatial load-shifting.

Chris Adams: So that thing you mentioned there was, you've got this notion of moving things spatially or moving things temporally through time. So it's, essentially, you get more of a gain from moving things geographically, spatially, if you're going to do nothing, if you just only do one. But that can be a little bit harder for organizations. So there is some gain from doing things temporarily, but on the temporal scale, you do need the grid to be a little bit more volatile, moving back and forth between very suddenly and then not very suddenly, for example, so they start using back, go back to dirty energy essentially, but the thing that you can do is these do work together, so you can move through time and space, and you do end up with a, basically the benefits do compound in this case here.

Iegor Riepen: Yeah, and it's worth saying that the benefits actually do not compound simply together, because the spatial and temporal stories are subject to the shared set of computing capacity constraints. So when you co-optimize both, you inevitably have to trade off among them.

Chris Adams: Okay, compound is the wrong word then. All right. But basically, the, by doing the two things together, you can get, you can get a better saving than just doing one of them by themselves, for example.

Okay. All right then. So we've spoken about this, how it's been applied to one company. And we've said that this could be used for multiple organizations. Um, presumably, if you... If someone was to do this, you could do this for an entire sector to figure out what the power might be needed for an entire sector to see how much you might need to deploy to, to displace all the kind of fossil-based energy generation that data centers use, for example. Is that plausible that you could do something like this with this kind of modeling?

Iegor Riepen: Um, well, in our study, we try to keep our assumptions on carbon-aware computing, in the way how we treat data centers, how we treat flexible workloads, as general as possible. So the study results should be applicable to the broad range of companies operating data centers, with their specific features and their specific workloads.

And as well as the study should be applicable to a broad ranges of companies from commerce or service or industry sectors for which, say, only a temporal story is relevant. One cool thing here is that data centers can pave the way for space-time load-shifting applied for other industries. Other applications we are not yet even aware about.

So just to mention, I recently visited, um, the group in the University of Wisconsin, Madison. It's Victor Zavala, uh, Scalable Systems Lab. So this is a bunch of awesome people. I spent with them three weeks. I'm not really sure what they work about, because they work about computational chemistry, on energy systems, on graph theory, optimizations, programming.

I think they crush every problem that people are throwing at them, and when I was there, they were launching a new project which was focused on exploiting space-time interdependencies between electrochemical manufacturing and power grid. So the idea here is that the electrochemical industry would shift loads so as to co-optimize the economics and to reduce the carbon emission intensity of the electrochemical manufacturing, which is pretty dumb.

Uh, because before I thought that, that space-time shifting is more about data centers only. But now, well, there are applications beyond only this sector. And I think this is a future where we are going to.

Chris Adams: Okay, so basically, as more and more, as more and more clean and, what's it, variable fuel saving technologies come onto the electricity grid, it's going to get more and more kind of upy downy variable. And it's not just data centers this would be applicable to. So electrochemical stuff would be like synthesizing fuels or making plastics or things from carbon captured into, by the, from the air, for example, or things like that. I think Tom Brown mentioned a little bit about making methanol in this kind of way, or some of the green hydrogen stuff around splitting water into hydrogen and oxygen for creating chemicals that way. So is that what you're referring to in this scenario?

Iegor Riepen: well, I don't know what they will do in the project. It will be very interesting to take a look. I'm not sure that it's very simply mapping one to another. So for data center special shifting means that you move. Workloads and associated 

Chris Adams: space, that 

Iegor Riepen: one place to another. But this moving means that just, um, computer jobs are being executed in one data center and not in another data center.

While the end consumer is somebody waiting for the YouTube video to be rendered, consuming the goods at the place it would be consuming no matter the shift. How exactly it works for the electrochemical industry, not sure, but we will see once the project is developed.

Chris Adams: I see. Okay, cool. All right. You mentioned one thing about this being something which is more generalizable to a wider set of technologies, and one thing that actually this makes me think of is a new paper that was published by Facebook. They have a serverless platform which they call XFaaS, and one of the key things that they were doing is actually having this kind of geographically movable computing that they refer to, and one thing that, what really strikes me is that Facebook basically said by allowing the actual computation to be flexible in terms of where it's actually run, they were able to massively increase the use of the data centers they were using.

So if you think about it, for most data centers, there might be a single digit percentage utilization. So most of the time, not doing very much. Cloud might be estimates of around 10, 10 times more efficient. So maybe 20 to 30 percent efficiency for most very well-run hyperscale data centers. Facebook themselves say that we've, by introducing some of these ideas, we've been able to get up to 66 percent utilization, which basically means there's a bunch of extra hardware that they don't have to buy and build, which is good because they are spending lots and lots of money building with data centers in lots of places and anything that you can use to reduce the number of data centers you need, in my view, is a good thing because that's a lot of buildings that don't get built, for example. But the key things they mention in the paper, and we'll share it in the paper, we'll share the link to the paper, is that if you have maybe a computing job or you've got a function or anything like that, you basically have like, they make this stuff possible by adding kind of deadlines or saying how tolerant of being moved through time or moved through space a particular job might be. And this feels like, this kind of hints that this might, might be a way that might become a norm for working with computing, where if you, if you don't need to have something happen right away in the same place, then you can basically get all these extra benefits by being a bit more flexible about this and saying up front, saying this stuff up front when you submit a job to a computing cluster or something like that.

Iegor Riepen: And it's pretty relevant to ask the question, it's not only how, what are the benefits for the operator of the data center, but what are also the benefits more broadly to the background system in terms of costs and emissions.

Chris Adams: All right. Okay. So we've, I know that you've just, you released a study in the summer and there's some stuff which people can refer to here, but I also am aware that we are in a fast moving field and you just mentioned some work in the University of Wisconsin, I think. Are there any other things you would like to have included into this kind of research, or you think people should be looking at over the next 12 to 18 months inside this, that might influence how people might think about carbon aware computing or set of changes? So, flexible computing like this with a view to reduce the emissions associated with running infrastructure that we all rely on right now.

Iegor Riepen: Maybe one study or research paper which I would love to see is that if somebody would take the courage and to illustrate the system-level benefits of carbon-aware computing across different contexts and different states of the system. So by system-level benefits, I mean from a society perspective, so we look at the costs or total carbon emissions or total curtailment of renewable energy and so on.

And by different contexts and states of the system, I mean the following. Let's think, nowadays, there are mostly companies who buy electricity from the local grid, they have some flexibility, they would go to the data providers, such as electricity maps, providing the short term forecasts, or carbon emission intensity, and they would factor it in, into their load following strategies.

This can work for the temporal shifting, also soon will work, I believe, broadly for the spatial load-shifting. So in this case, space-time shifting can help if you just buy from the grid. So there is a follow-up to this, so some companies might go beyond that and buy additional resources to eliminate all of their carbon footprint completely.

So for that, space-time shifting could also help, and this is basically what our study is about. It would help you to be more resource efficient, it would help to be more cost effective, it would open 24/7 cFE for a broad palette of companies who would not maybe jump there otherwise because of high cost premium.

And in the future we will hopefully be reaching the net zero electricity systems or more broadly net zero energy systems. And space-time shifting can be of help there too. So we would need some set of solutions where we would firm the variable wind and solar. We could think about a palette of solutions on the supply side.

It could be grid connected battery storage. There could be hydrogen storage if in the region where there are salt caverns. Or there could be even energy storage in the liquid hydrocarbons like methanol storage. My colleagues Tom Brown and Johannes Hump recently published a paper on this. But these are all the solutions from the supply side.

There could be solutions on the demand side, where there are large data centers that can move large loads across space and time. They could help the system to firm the variable wind and solar and provide the service for the system and get some sort of remuneration for that. By the way, Victor Zavala's group has also published a research paper where they make a mathematical modeling sketching out what type of remuneration they can get for providing the service for the system.

Chris Adams: Okay, if I could just quickly stop you there for a second because I want to check I understood it correctly. You're essentially saying that rather than it just being about looking at the cost only to say, in this case, it was like one tech firm looking at how much it would cost them, you're essentially saying it's possible to model this to say how much this kind of flexibility can save everyone else. If you actually had these providers, like a data center as a kind of active participant inside the grid, because that might reduce the amount of generation that the grid might need or that society might need. So essentially, it's like flipping it around saying, well, actually, is there some kind of value that can, or are there benefits that could be shared just outside of just the corporate, just outside of that company? Can it benefit other people as well?

Iegor Riepen: Yes, exactly. And more, more than that, so whenever we look at this context, either your company buys electricity from the grid and tries to move load across space and time, either to reduce costs or to reduce emissions, depending on what signals the company takes. Or if the company goes 24/7 and wants to eliminate all of the emissions and have a high impact on the background grid.

Or even if we even reach net zero systems, in all of these contexts, space-time load-shifting might be of help and might bring benefits, both for the companies operating it and also for the background systems. So if there is a study who would take courage to highlight all of this transition phase and illustrate the benefits for systems, this possibly would be a really good read.

Chris Adams: Cool. All right. Well, that sounds like something for people who are curious about playing around with this on GitHub or want to mess around with some of this modeling themselves to see. And I know there are a number of organizations and people like in software development houses who are actually trying to extend various tools like Kubernetes to incorporate some of this stuff so that you can actually see, so you can essentially design from a very, from the get go, just like I mentioned with Facebook.

So like Facebook's XFaaS paper talks about how they encode a degree of tolerance into this. But I believe that Intel is doing something similar to this for their versions of Kubernetes. There's, I'll share links to that for people who are listening. This has been pro, possibly one of the nerdiest episodes we've ever done, but I've enjoyed myself for this, Iegor.

Thank you, Iegor. But before we wrap up, are there any things that if someone. So, if someone did, if someone has followed this and was able to keep up and was really curious and would like to learn more, where would you direct people to look if they wanted to dive into this some more themselves?

Iegor Riepen: Well, if people would like to know more about our research on spacetime shifting or more generally on 24/7, they could possibly visit our GitHub page. So it's github.com/pypsa/247cfe. There in readme we explain what other research we're doing, how to clone our work on how even to reproduce it.

If people are interested in PyPSA ecosystem for open-source energy modeling, they could visit PyPSA.org or more generally if people are interested in the open energy research in general, it is living an open mode initiative do to which collects various research groups and open models all about energy.

And finally, if com, if there is somebody interested in voluntary energy procurement, one could visit 27 Compact, which is gocarbonfree247.com, which is place collecting people and companies working on this.

Chris Adams: Great. And Iegor, if people want to find you or follow some of your work directly, is it TU Berlin the best place or is there an Iegor Repin on LinkedIn or something that you would direct people to for future questions?

Iegor Riepen: I have my LinkedIn, which I could attach and there is TU Berlin email.

Chris Adams: Brilliant. Okay. Well, Iegor, I've really enjoyed this. Thank you so much for diving down into the depths of CarbonAware and time-space shifting computing like we did today. Oh yeah, and happy birthday, by the way. I forgot. Yeah.

Iegor Riepen: Thank you, Chris. Thanks for having me today.

Chris Adams: All right. Take care of yourself and yeah, have a lovely week. Cheers, Iegor. Hey, everyone. Thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware. foundation. That's greensoftware. foundation in any browser. Thanks again, and see you in the next episode!

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TRANSCRIPT BELOW:

Anne Faubry: This is what is called externalities. It's a debated word. It's a bit controversial because it's, it comes from an economic background, but it's important to have this in mind when you design a product because sometimes you might have the best intentions, but when it comes to environment, you can sometimes make things worse than what you wanted to do.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to another episode of The Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. When you're building digital services, a lot of the time you're relying on standards up and down the tech stack to ensure that each component works the way it does. And if you work with the web standards, as specified by the World Wide Web Consortium, the W3C, play a huge role in shaping expectations about how the web is supposed to work and for whom. Earlier this year, the W3C Sustainable Web Design Community Group published their first, and now second, draft of the Web Sustainability Guidelines, a wide range of recommendations for making websites and products more sustainable. So this now means there's guidance from the W3C, not just about how JavaScript and HTML should work, but also how they should work sustainably. With us, today, we have two key members of the community group behind its publication. Alex Dawson and Anne Faubry, I hope I pronounced it correctly, to learn how it came about, a little bit more about the guidelines and what they recommend, and what's coming next. So, going in alphabetical order, Alex and Anne, I'll give you folks a chance to introduce yourself. So, Alex, um, the floor is yours first, if you'd be so kind.

Alexander Dawson: Yep, okay. I'm Alexander Dawson, and I'm a freelance web developer from the UK. I also am a part-time sustainability researcher and writer, and lately I have had the pleasure of being the co-chair of the Sustainable Web Design Community Group, and I'm a co-editor and contributor to the Web Sustainability Guidelines, which of course we're all here to talk about today.

Chris Adams: Thank you, Alex. And Anne, the floor is yours.

Anne Faubry: Hello everyone, I'm Anne Faubry, so your pronunciation was great, from France, I'm a UX/UI designer. I work as a freelancer for environmental projects like NGOs or public services, and I try to implement accessibility and eco-design in all I do. And I also work part time for an association called Designer Éthiques, Ethical Designers in English, which tries to research the field of responsible designing and also more specifically eco-design. So we published the guidelines on this topic and, and that's how I ended up joining the W3C group and becoming the community co-chair of the UX part.

Chris Adams: Okay, cool. Thank you, Anne. All right. For folks who are new to this podcast, I mentioned my name, Chris Adams. I am the executive director of the Green Web Foundation, a Dutch nonprofit focused around an entirely fossil free internet. And I am the policy chair for the Green Software Foundation, the large industry body that publishes this podcast. Before we dive in, there's also a quick reminder that everything we talk about to the best of our ability will be linked in the show notes. below in this episode. So if there's a project that's mentioned or a site we discuss that isn't in these show notes, please do write in and tell us so we can update these for other curious souls and to help you in your quest for knowledge around all things sustainable and digital. All right, Alex, Anne, are you folks sitting comfortably?

Anne Faubry: Yes, we are.

Chris Adams: Okay. All right. So the first question is that sustainability on the web. This has been a long time coming, and I know the W3C Sustainable Web Design Group, or Sustyweb, if you look at the URL and it's to its friends, it's been around for a while with varying levels of activity. And I've got to ask, why did you look to the W3C group in the first place? And maybe one of you might help explain what the W3C is in this kind of context, because we've done a bit of an introduction, but it might be useful to give your understanding, and for one of you to show your kind of story about why you got involved.

Alexander Dawson: I guess the best way to start things off is to, well, essentially mention that we are effectively a community group, so we are not governed by the W3C. That's an important point to underline from the start. Community groups are a great incubator for groups that are effectively volunteers to gather together their collective experience and to talk about how they want to improve parts of the web and to look at topics that interest them and to progress those subjects further. And obviously our group, the Sustainable Web Design Community Group, it was started in 2013, and it really started as a link sharing group where anything to do with sustainability, people were passing useful bits and pieces to do with research and interesting URLs and such.

But it was really around the time of the pandemic hit that people started to sit up, take notice and think, "actually, something could really be done in terms of sustainability on the web and real improvements could be made." And we had a real influx of new members. And the community really blossomed in terms of life.

And it was around that time that myself and Tim Frick, who's the other

been an 

Chris Adams: institution over this,

Yeah, 

Alexander Dawson: we got talking with all the other members in the group and it came to consensus that maybe looking to other W3C bodies like the Web Accessibility Initiative, they've done such great work with things like the Web Content Accessibility Guidelines in terms of improving accessibility on the web, that actually being able to produce some good solid guidelines that are evidence-backed and collaborate in terms of producing evidence and research, and hook into policy and all the best practices that are out there, all within one collective source, that having something out there that people can quickly and easily reference would be a good way for people to make their websites more sustainable.

So over the course of coming up for two years, we worked very hard and, using various different groups, so we had groups for user experience, which of course Anne was involved in and I was involved in web development and we had groups for business and we had groups for metrics and we had another group which was for hosting and infrastructure and people effectively took their choices as to where their expertise lay, and we built up a huge big collection of these Google documents, these drafts, filled with all of these different guidelines and they've effectively been formatted into the guidelines that we now see today, and that's pretty much a good jumping-off point where I can pass over to Anne to give her experience as to how she found it and what her experience has been throughout the creation process.

Chris Adams: Yeah. Go for it, Anne.

Anne Faubry: Thank you. Indeed, I jumped in the project much later than Alexander. So for me, it was a whole new experience. It was my first contribution to the W3C, but it was not my first contribution to this kind of guidelines and standards because I had done this work previously in France on governmental standards and also by writing the Eco-design guidelines for designers. So, my point was to take this experience and help the group move forward by contributing with all the work we had done in France already on this topic so we didn't have to start from a blank slate and that was really exciting because it was also taking things to a next level, to a worldwide level, and, and so it's not only France in its little part of the world working by itself, and it was contributing and also challenging sometimes the guidelines with people from all, all different countries. So it was very exciting. And how it worked was a lot of collaboration with online tools, frequent video conferences, sharing tools, and contributing on the guidelines all together, and commenting on each other's work, and it was a lot of live exchange, and overall very efficient. Also thanks a lot to Tim and Alexander's work, who overviewed all of it.

Chris Adams: Okay. And this is Tim Frick of Designing for Sustainability, uh, that he wrote in 2016. So it's, uh, nice to actually see this kind of path all the way through. And if I may, I just want to touch on a couple of things that you said there that sounded interesting. You said you did some work earlier in France already, and I understand, would that be a I know there's one organization there, and I think you mentioned Eco-design as in Eco-design FR.

Could you maybe touch on some of those two points, please?

Anne Faubry: Uh huh, so there's actually a lot of different guidelines existing in France, and they are now converging. So there are two governmental ones at the moment, one by the AFNOR, which is the French Standardization Organization, and it's now being taken to the European level. And there's another one, which is the RGESN, which is more, like, only guidelines at the moment, not necessarily a standard so far. But it's, I was actually this afternoon working on this project again. They are now consulting a lot of different professionals and experts on this question to take it to the second version and to make it eventually, if also the legal context and political context allows it, to a standard level. At the moment, it's just guidelines, but we can see in the digital sector industry, it's still, it's already much used by many organizations to promote what they're doing in terms of a sustainable responsibility. And then there's different guidelines, most specific for developers or designers who are more focused on the web, easier to implement. So that's what we've done also with the, within the association for designers. Green IT is a great association doing this for developers and other, also other profiles. And so a lot of associations have popped up within the last years to help understand those guidelines, because they're not necessarily easy to handle.

Chris Adams: Or in English, yeah, as well. Okay, so, it's, I didn't know there was such a strong francophone influence, and it's, this is one thing that we found with the Green Web Foundation. We end up working with Gautier, whose name I never pronounce properly, for the Fog of Enactment, specifically because there's so much good work being done in France, that in many ways is quite a bit further ahead than some of the things that might have been outside of, say, the francophone, like, in, in particularly in primarily English-speaking communities, for example. And maybe this actually speaks to one of the things we might talk about later because there is something in the region, I think it's 93 separate recommendations in the guidelines, or is it 93, Alex? It's more than 90 at least, isn't it?

Alexander Dawson: Yeah.

Chris Adams: it's a lot here. And the thing I was going to ask you is, these guidelines cover a wide range of topics, as you mentioned, so like UX, business, web development, just, and they also don't just talk about carbon, because at the Green Software Foundation, there's a focus primarily with the, on software carbon intensity, and that's the actual specs specifically speaks to this. And while there is an interest in addressing some of the other kind of dimensions of environmental impact that you might care about, that's been the focus for the time being. I wanted to ask, Alex, if I could put to this, this to you first and then come to Anne. Could you maybe talk a little bit about the reasoning behind this decision to cover a number of different areas and then some of the trade-offs you'd have to, you had to make while deciding this kind of scope?

Because you can go wide or you can go narrow and they both have pluses and minuses.

Alexander Dawson: When we came to the decision of producing the specification, we really decided to go with an ESG approach. And obviously with ESG, it's not just about the environmental factors, it's also about social and governance and also economical. And... One of the things that we came to realize when also incorporating and looking towards things like the web content accessibility guidelines, especially for myself, I come from a inclusive design background, and it's often in the cases of things like inclusive design and climate change, the people who are most risk of being affected by subject matters such as climate change, those in at-risk scenarios, so older generations, younger generations, people with accessibility issues, people from low-income backgrounds, they're the individuals who are most often affected by climate related issues, such as people in developing nations and people, as I said, who may have accessibility needs often can be the ones who are at the blunt end of the climate related instrument. So it's one of the areas that we wanted to implicitly tackle with the guidelines to be aware of the human factor. So incorporating people and the planet within the scope of the guidelines. So things like accessibility, privacy by design, security measures, and well, um, there has been debate as to whether these things should be covered within the scope. There's a very good argument that obviously taking into account things like accessibility, for example, if a website is accessible, then it's going to actually reduce carbon emissions by default because someone with accessibility needs is going to be able to access that information much easier and they're going to spend less time on their screens or scrolling through information or having difficulty encountering barriers to access.

And that's going to waste less, not only time actually on the screen, but their device actually having to work around the barriers trying to get through those particular issues so there's a multitude of complexities and variables involved but implicitly working around all these issues such as security issues and privacy issues and accessibility issues, actually, as a secondary factor, can help reduce the carbon factor.

So it's something that we wanted to include within the scope. And it's something that we've run through, not only with the benefits, as can be seen throughout the guidelines, but it's something that we've integrated within the, the ESG measurements.

Chris Adams: Okay, so you're speaking not just about carbon and there's almost, it sounds like in sustainability lingo people talk about co-benefits, as in something that might help the climate but it also has a, kind of a societal good as well. That's one of the angles that you have there. And, Anne, I want to give a bit of space for you to talk about some of this as well because I I do know that France has had a typical, has a history of speaking about more than just carbon. Possibly because of the fact that the energy is already very clean, but it also means that there's other things we need to take into account like water and the material depletion and I forget that, I can never remember the other one that we actually refer to for this, but I know this is some stuff that you might be talking about.

Maybe you could talk a little bit about the range of different communities that you're coming from because you're a UX and UI designer and that's not the same as development and these cover quite a broad range of functions I suppose is the way you might talk about them.

Anne Faubry: That's a very good point. As a designer, you have to think, all the jobs to think about maybe even more as a designer, think about the externalities of what you're designing. And of course, I'm not going to come back on what Alexander said. It was, uh, 

Chris Adams: a second, sorry Sam, and externality in this case, that's like shifting the cost outside onto outside of the organisational brandery? Maybe you could just touch on that because I think it's a word that people might not be so familiar with.

Anne Faubry: So, when you design a product, sometimes you're going to have some rebound effects or other effects which can be good or bad, but which were not anticipated. And this is what is called externalities. It's a debated word, it's a bit controversial because it's, it comes from an economic background. But it's important to have this in mind when you design a product because sometimes you might have the best intentions, but when it comes to environment, you can sometimes make things worse than what you wanted to do and what was the previous case. And that's what happens sometimes, if you only look at carbon, you might have some pollution transfers, especially indeed if you have a clean energy. I don't want to get into the nuclear debate, it's not that simple, but indeed regarding the carbon, it's pretty clean. And what we can totally see in the digital industry is people tend to replace devices for new ones because they're more energy efficient. And they tend also as designers or developers or architects to make their choices regarding a lot of the energy consumed. And uh, if you look at the other planetary boundaries, there's nine of them, carbon is only one of them. But there's also the water use and the pollution of the water, the soil, uh, the air. So if you only look at, uh, energy consumption and carbon related to it, you might forget the mineral extraction and all the biodiversity, uh, destroyed with that. And, and this relates also to the human aspects Alex was mentioning. So it's very important not only to look at carbon, but also to look at different environmental indicators, like water or resources consumption, to make sure you don't have these pollution transfers or bad externalities, eventually even the best, which might be IT for good and everything, but if it's not useful, or if it's actually consuming more resources to run, like with the AI or 5G, then it might be worse than the previous solutions.

Chris Adams: Okay, thank you for explaining that. So it sounds like there's a number of different criteria or dimensions that you're looking at rather than just looking at carbon. And there may be different degrees to which they may be easy to act on or even measure the impact on for some of this stuff. But one thing that I was aware of and one thing that I think was high tied in the second part of this was actually trying to make these standards something a bit more easy, a bit more usable for people. And like we can talk about some of the specific things, the nuances around, say, carbon and water and stuff like that. But, Alex, I thought I might just ask you a little bit about this because this is the first time I've seen some of these standards where they're designed in such a way that they can be consumed by other digital tools so they can, some of this can be surfaced. Maybe you could talk about some of the process for trying to find ways to make this stuff easy for people to see because I know at the Green Web Foundation where we work, us having a really simple check has been a really good way to start a conversation over something that is incredibly nuanced when just talking about energy, for example, just like Anne mentioned. But, yeah, there are some really cool things about these draft guidelines that I haven't seen in a spec before, so maybe I'll just give you the floor and let you nerd out on some of this bit.

Alexander Dawson: Yeah, absolutely. One of the things obviously that we wanted to do after getting the draft specification out was to focus on trying to make this as easy for people to consume as possible because obviously we want people to start looking at the work and trying to start implementing these practices.

Obviously, as it stands, the specification is a really long document. So I think if you include all the stuff that can be expanded on, it's close to 300 pages in length. So quite a lengthy piece of work for people to scroll through. But...

Chris Adams: including the references, right?

Alexander Dawson: we've got supplementary documents which we are consistently working on to help people digest that material and to help other toolmakers and people who want to utilize it to actually take advantage of that work and to more easily consume it. For example, one of the first things that we produced alongside the very first draft was an at-a-glance document. And essentially it works as a functioning summary of the guidelines themselves.

So just giving a brief overview of exactly what is contained within the guidelines. And it's something that the Web Content Accessibility Guidelines also has. And within the second draft, we also released a quick reference guide. And the quick reference essentially boils down all of the guidelines into a easy-to-reference sheet which

Chris Adams: A TL:DR version, right?

Alexander Dawson: Yeah exactly, which people can just scroll through and get the gist of exactly what they need to do in order to complete and work through each of the individual guidelines.

Obviously there's links through to the full in-depth guidelines, if people want to get more detail onto them, but we're obviously trying to help people get through these guidelines as easily as possible. And there's a PDF checklist to go along with that, because obviously we know people like checklists and we know that it's helpful for taking to meetings, being able to highlight and work through it with clients and be able to mark off your progress and such. And we do have a JSON API as well, which is something that got launched with the second draft, which is really useful because it's a JSON file that contains the complete unabridged form of the guidelines in full, which can be queried against via our GitHub repo and people can remix it, utilize it, do what they want with it to be able to use or implement our guidelines, as they wish.

A few people have played about with it, showcasing some of their favorite guidelines within their website. We've got a copy of it, which is going to be used on the Sustainable Web Design website, which we're going to be relaunching soon. So there'll be the ability to filter the guidelines by tags, by specific disciplines, being able to search through individual guidelines, so that will actually make looking through the guidelines much easier, and for helping people to be able to work through the content as well.

Chris Adams: Okay, cool. 

Alexander Dawson: And also we've got some additional stuff which is on earmarked in the GitHub repo, which we're going to be working on in the future as well. Things like we're going to be producing an introductory document, which will give a generic sort of overview to web sustainability and why it's important and guiding people into the guidelines and effectively working as a good solid standardized overview, and also a test suite which will provide implementability.

So it will provide some actually measurable impact reporting for how effective individual guidelines are and hopefully some guidance regarding tooling and user agents. Because hopefully we would like to see our work implemented in things like Google Lighthouse in the future as well. So a lot of 

Chris Adams: linters things 

Alexander Dawson: come.

Chris Adams: Okay, cool. Thank you for that, Alex. All right, we'll link to a few of those things for any people who are interested in this and might want to have a go at building some of that stuff. I'm just going to touch on the next question. So, we've heard the Web Content Accessibility Guidelines mentioned a few times, or, I don't know, WCAG, is that how you pronounce it? I can never remember. Yeah. So, these have been useful for helping people, basically, make space for accessibility, so making sure people allocate time and money to this stuff in a project, and they've been useful, uh, for courts, uh, to actually recognize the rights of people with disabilities to access services, for example.

Like, we've seen cases of government, uh, websites mandating this has to happen, and we've also seen pizza websites being sued for not having accessible websites, like the examples of Domino's and stuff like that. Maybe we could talk, Anne, if I start with you, then maybe we could hand over to Alex. Could you maybe talk a little bit about how much this might have influenced the creation of these guidelines?

Because this is, it's been cited quite a few times now, and this is one thing that I really, that really leapt at me when I was looking over the guidelines.

Anne Faubry: The WCAG is, is indeed a, a reference, um, it's crazy to see how in France it became a compulsory as a law. And currently there's also other companies being sued for not respecting it. So it's really moving forward finally, because for a long time it has been in place, but it was not acted upon. And there was just threats, but nothing was happening.

So nobody really cared, uh, regarding the companies. And, and so you can see how it can shape the online environment and if we can do the same with the sustainability, which is actually a broader, broader side of accessibility because as Alex said, everything is intertwined, if we can do the same, then it can be extremely powerful. It can go up as high as governments, but it can also give very practical guidelines for the designers, the developers, everybody working on digital services. So it was definitely an inspiration. The other thing, which is great with the WCAG is the fact that you can check box and that it's very powerful to see if you're making improvements and how you can compare to each other.

And the comparison is also a very strong drive for many organizations and people. And, and again, to see the W3C publishing these guidelines, it's really accelerated also what happened in France, in the government because they, they saw that they were being caught up worldwide.

Chris Adams: Okay, and I, Alex, I might hand over to you for some of this, because when you work with web accessibility, there's this phrase, POUR, perceivable, operable, understandable and robust. And we've done some work, we being Green Web Foundation, we did some work with Wagtail, a content management system, to work on something we were referring to as GOLD, which was Green, Open, Lean, and Distributed as a way of talking about some of these things you might want to design for in a kind of somewhat memorable fashion. Maybe you could talk a little bit about how, how you might make some of this memorable for people or easy to actually talk about because as Anne just mentioned, when you can make it really explicit when something is or is not, isn't being done, it becomes very easy to actually then specify as a product manager or someone along the way to say, "here's what I'm asking for. Can you please deliver this or deliver that?" And I think that might have influenced some of the reference to, actually we'll talk about a little bit later, about the global reporting, the GRI, because that's something that's referenced. So maybe we talk about POUR and some of those things there, and if there's anything like that you've seen in sustainable, in the world of sustainability, then we could talk a little bit about this other reference because this is something I haven't seen in the, in guidelines before, a reference to other global standards, for example.

Alexander Dawson: Yeah, certainly there are quite a few of guidance out there, as Anne mentioned, things like AFNOR and RGESN, which we've certainly tied ourselves and linked up to whenever possible. And we've hooked ourselves, as we're going to be mentioning later, to GRI. But as far as sustainability goes there aren't a lot of catchy acronyms or things which we can say to people that "you need to do this or do that."

So what we've tried to do is really focus primarily on providing people with things that can be implemented. Much of our work is obviously based on things which people are hopefully already doing, good accessibility practices, good performance practices in terms of web performance, things which are usually, in a good agency setting, things that web developers were already trying to be integrating with their practices, all we're trying to do is showcasing it from a sustainability angle and trying to reinforce it and showcasing how it can actually reduce emissions and where those additional performances can be measured and where the best practices could be additionally tweaked, as it were, so hopefully the work that we're doing can actually be utilized alongside the standards and best practices and actually be used to, um, the benefits with any additional legislation that's coming out, because obviously the EU is seeing legislation as we speak, things like, um, CSRD, which is going,

Chris Adams: what that means, because that's, so the Corporate Sustainability Reporting Directive is what the CSRD stands sorry, we're trying to avoid alphabet soup.

Alexander Dawson: yeah, sorry. Yeah. And obviously with this upcoming legislation, one of the things that they have mentioned is that they are actually going to be aligning this directive with the GRI standard as a measurement of implementation, effectively for people who are listening, if you want to conform with this piece of legislation, what the EU is saying is, "if you are abiding by the GRI standard, that is a great way of showing that you are complying with this legislation when it effectively comes into force."

And because our standard, as we are producing, is aligning 

Chris Adams: referring an existing

Alexander Dawson: Yeah, exactly. Because what we have produced is aligning itself with GRI and we've got GRI measurement impacts with every single guideline, the great thing is that if people can make those measurable improvements, they will have effectively our weighted evidence that they can use in their reporting to show that they are complying with this upcoming legislation, which hopefully will be a sign to show that they are conforming with sustainability regulation as it is coming into force, which is another hopeful, beneficial move to show other governments that potentially they should be moving in this direction as well.

Chris Adams: Okay, and, Anne, I might just ask for... GRI, we haven't defined what GRI means, but we've said it lots and lots of times. GRI, it's the Global Reporting Index. Is that the,

is? Yeah, Initiative, Global Reporting Index, which is a, maybe you could just briefly touch on some of that before we move to another questions about, and we need to be quite short about this one on the W3C because there are recommendations, there's standards, and there's different things, and we might be useful to talk about that. But in the meantime, yeah, maybe you could just briefly, for the uninitiated, explain the role of the GRI and what some of this actually is, because it comes up a lot again and again and again, just as Alex says.

Anne Faubry: Well, actually, if you allow me, I'd rather have Alex answer this because don't use the GRI much.

I didn't know actually of the GRI before joining the W3C, so I don't think we use it that much in France.

Chris Adams: Alright, quickly then, we'll stay on the GRI, because there's another, I didn't mean this to be such a kind of TLA, sorry, three letter acronym soup of an episode, but we are going to talk a little bit about, okay, you've got these guidelines, what happens next? So Alex, if I ask you briefly what the GRI is

then we'll just touch on what happens from here to turning this into like standards or what that process looks like.

Alexander Dawson: Yeah, absolutely. The best way that I can describe it for listeners in a really simple, easy way, the really simple crash course is that effectively you have best practices, you have standards, and then you have regulation. Essentially what we are producing with the Web Sustainability Guidelines is what's known as a best practice.

So it's something which helps you conform with any standards or any regulation because it's effectively out there but it's effectively not endorsed by a regulatory body, but it's something which is bound by evidence and we try to link it in with what standards already exist. You also have standards.

Now these are things like the GRI and you also have the International Standards Organization, otherwise known as ISO. And the GRI have produced a standard and GRI, the Global Reporting Initiative, it's been around for about 20 odd years and it's used by businesses and corporations and later bodies everywhere.

And it's free for anybody to use and it essentially lays down a whole set of different guidelines for things like mineral use and for water usage and for all the things that you would measure under ESG. So that's essentially set down in stone because they're a standards body like the World Wide Web Consortium.

And then above that you obviously have regulation because they're regulated by governments, and they are the people who effectively lay down the laws. So, essentially, that's the crash course method. You've got the governments, the standard bodies, and then you've got people like us who are producing materials to try and comply with those different things.

Chris Adams: Okay. All right. Thanks for spelling that out, I suppose. All right. So we spoke a little bit about the fact that, okay, you have these different kind of tracks and there is a way that you might go from here. Maybe we could just briefly touch on the fact that you've, there's now a second draft of these guidelines that are, that are out for people. Um, what's next on the roadmap? Or maybe I should ask, like, is there a roadmap? What happens over the next six to 12 months now that these are out here, for example? Anne, if I put that question to you, maybe you could talk a little bit about that, and then we'll touch on some of the other questions.

Anne Faubry: So people are actually asking lots of questions, uh, suggesting improvements, debating some of the facts, um, and sometimes contributing with new points of view, uh, and submitting issues on GitHub. And Alexander and Tim are doing an amazing job trying to integrate all of this feedback into the second and then now the currently the third draft of these guidelines.

Chris Adams: Okay, so we got that. So basically, if you read through these, and we'll be linking to these, you're able to run through... Basically, the idea is, go to GitHub, ask some of the questions there, possibly make a pull request or something like that, but that's the main way for collecting people's feedback right now.

So that's the case. Yeah?

Alexander Dawson: A pull request, add an issue to the issues system, and obviously it will be reviewed and integrated into the next draft and yeah, everyone's welcome also to join the community group and come to our meetings because we hold them every month and we discuss current progress.

Chris Adams: Okay, so if you can't use GitHub and you're or maybe you don't want to use GitHub, there are other channels you can either use and I think that there's maybe even something as old as old as low tech as a mailing list. So there's like a mailing list and video and there's also calls people can join to go. So there's a more than one channel by which you might share some of your feedback into the formation of some of these guidelines. Okay, alright, that's really helpful for people to understand. We're just coming to the end of the time, so I just wanted to give a bit of time for you two to talk about, now that we've just been speaking all about these guidelines and them being out there, and how they can be consumed and used, and how they are made, are there any other open projects or communities that, that outside of this, you think are really exciting and interesting?

So Anne, what kind of projects are inspiring you right now that might have helped you shape your thinking for this work here, but also that you spend your time thinking about or wanting to contribute to outside of this as well?

Anne Faubry: There's a great tool I keep using, but it's not even new, actually. It was done by Artefact, which is, I think, a company from England. And they created the Tarot Cards of Tech, a list of questions you can use. I use it in my projects, I use it with my students, I use it with my colleagues to ponder your choices, think long term, think strategy, and think externalities you might have, good or bad. I love the questions. There's actually two of my favorites, which are, "what would you do differently if nature was your client?" So you can think really differently about your service, thinking "maybe I could reduce this footprint or add this feature to raise awareness on this issue with the users." And another one I love is "what happens if 100 million people are using your product?" And then you can also think what externalities you might have on jobs, on social habits, on housing, and on many different topics to rethink more systematically and, and to think about more/other sustainable issues besides environmental ones.

Chris Adams: Okay, cool. Thank you for that, Anne. And Alex, I'll give a bit of space for you before we wrap up on this one. So, what projects are catching your eye or that you're really interested in right now that might have helped inform some of the work that you've been doing recently?

Alexander Dawson: I think the main one that really informed me most lately is CO2.js.

Chris Adams: Fershad will be happy, yeah.

Alexander Dawson: Yeah, I'm working on measurement because that really inspired me on when I was doing some of my own research to investigate more on how carbon affects various different aspects of the web ecosystem.

Chris Adams: I assure you that wasn't planned ahead, alright? Um, for reference folks, CO2.js is one of the projects that, uh, the Green Web Foundation works on and maintains that's used in a number of tools, uh, for this. I'm a little bit embarrassed, so I'm gonna move on quite quickly, uh, to the final question. Basically, we, if people have found this interesting or finding out about the fact that there are, there is guidance from the W3C trying to do this kind of work, and they want to find out about what to do and what's happening with this, could you just tell people where they should look or where they go?

If there is a particular website you would, you could possibly pronounce and spell out for people so they, if they're listening, they know what to Google.

Alexander Dawson: Yeah, if they just Google the Sustainable Web Design Community group, they will find our homepage and there's a great link to the wiki there which has all the information about all of our past meetings, frequently asked questions and links to all the social channels and how they can get involved. So that's a great place where people can start getting involved in the project.

Chris Adams: Cool, thank you, Alex. I've just checked while you're describing that. I've also, if you type in the word Sustyweb, it's a pretty good Google hack. So, there is nothing related to Sustyweb so far, other than this stuff. Okay, Anne, I'm going to hand over to you and then we'll get the final word with Alex. If people are curious about some of your work and if you have any kind of professional presence and you're comfortable sharing that, is there a particular place you would send people to or if they've found what you were talking about as interesting, where should they look?

Anne Faubry: Our main English resource, uh, is the guidelines that we produced. It's called Eco-design. I will share the exact link so people will know where to look. And otherwise I'd be happy to talk with anyone on, on LinkedIn. That's the best place to find me.

Chris Adams: Okay, so Anne Faubry on LinkedIn or, or Eco-design. Okay, and, Alex, yourself? If people want to find you online, what's the best way to find you in a professional context?

Alexander Dawson: With me it's relatively easy, they just go to alexanderdawson.com

Chris Adams: All right, that was easy. Okay, this has been fun. I've learned a lot. And this has been a nice conversation. Thank you so much for taking the time to actually run through this with me and tell me all about how this works and hopefully this should be interesting for other people who are curious about how standards are formed and how the W3C might be having some kind of impact in how people think about sustainable web. All right, thank you folks! Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.

TWiGS host Chris Adams is joined by special guest Tony van Swet from Electricity Maps, to talk about the mapping of the carbon intensity of electricity grid. Tony shares some of the work that Electricity Maps has been doing to make it easier to understand how clean or dirty electricity is around the world, as well as how they’re making this data more accessible and usable to consumers. Join in on this candid conversation discussing the uses of such data and how to access it, as well as Tony talking about carbon intensity, open data, and open source.

Learn more about our people:

• Chris Adams: LinkedIn | GitHub | Website
• Tony van Swet: LinkedIn | Website

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

News:

• How to trace back the origin of electricity (Smoothie Blog Post) | Electricity Maps [06:16]
• The value of space-time load-shifting flexibility for 24/7 carbon-free electricity procurement | Zenodo (TU Berlin’s Study with Google, using PYPSA) [12:11]
• Electricity Maps | Client Story: Monta (EV Smart Charging use case) [15:41]
• GitHub - electricitymaps/electricitymaps-contrib: A real-time visualisation of the CO2 emissions of electricity consumption [21:01]
• Electricity Maps | Reports - Hourly Residual Mix Methodology [27:13]

Resources:

• Electricity Maps | Data Portal [18:29] 
• Electricity Maps Methodology 

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
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• Watch our videos on The Green Software Foundation YouTube Channel!
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TRANSCRIPT BELOW:

Tony van Swet: Looking at Google's use case at their data centers, they have the huge potential to shift their computation based on time or location, so this enables them to manipulate their energy consumption through using our API to increase their consumption when the sun shines and the wind blows. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to another episode of This Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. When we talk about green software, it's often common to talk about energy efficiency, and one of the reasons we care about it at all, is that right now we burn a lot of fossil fuels to generate electricity used in data centers, networks, and end-user devices. But how much of that comes from fossil fuels? And is that changing? This data exists all around the world, and sometimes the data is open, but it's often very messy. In 2017, the Electricity Map project was launched to make it easier to understand how clean or dirty electricity was all around the world. And as the name suggests, it took the form of a map showing the carbon intensity of electricity in as many places around the world as possible. Over the subsequent years, an open source project has grown with hundreds of developers around the world, contributing open web scrapers for data in their parts of the world to make the data more accessible. And earlier this year, the company behind the project released a new open data portal for historical data about how clean electricity was for anyone to use how they wish. So, what does this have to do with green software? Having access to this kind of data makes it much easier to understand the carbon footprint of your software. And this week, we're joined by Tony van Swet from Electricity Maps to talk about carbon intensity, open data, and open source. Hey there, Tony.

Tony van Swet: Hi, great to be here.

Chris Adams: Okay, Tony, before I get ahead of myself, I think we should give you a bit of space to introduce yourself properly. So can you tell us a little bit about what you do at Electricity Maps? And for folks new to the field, what Electricity Maps does these days, please?

Tony van Swet: Yeah, of course. I'm a senior software engineer in the advocacy team at Electricity Maps, and I'll give you a bit of background on what we do at Electricity Maps. So our mission is to organize the world's electricity data to drive the transition to a truly decarbonized electricity system. And as part of the advocacy team, my focus is enabling climate action with transparent insights.

We do this with the help of the open source community, building products such as our map visualization and the data portal that we're here to talk about today.

Chris Adams: Cool. Thanks for that, Tony. Okay. So if you're new to this podcast, um, my name is Chris Adams, as I mentioned before. Um, I work as the executive director at the Green Web Foundation, a Dutch nonprofit focused on an entirely fossil-free internet. And I also work as the chair of the policy working group inside the Green Software Foundation. And before we dive in, here's just a quick reminder, everything we talk about, we'll link to in the show notes below. So if there's a paper that caught your interest, or there's a story you've heard about, we'll do everything we can to make sure there's a helpful set of links that you can follow up for your own research a little bit later. But back to Tony. Tony, I've got to have to ask you, I know you're working in Denmark, but... I suspect you might not be coming from Denmark in the first place. What does a Kiwi end up doing on the opposite side of the world in Denmark, working for a company like Electricity Maps? I'm sure there's a story behind that.

Tony van Swet: Yeah, absolutely. It's definitely a bit of a career shift for me. So I started out about 10 years ago as a truck driver in New Zealand. I was full of self doubt, a bit depressed, struggling to find my place in the world. And to lift myself up out of this, I made it my mission to create technology to combat climate change, and I identified that software was the most powerful way to effect change at scale.

And this led me to enroll in a computer science degree. From there, I worked at a few cool startups in New Zealand, eventually looking to integrate electricity maps data when I saw their job postings and applied, and within a few months, I had the job and was waving goodbye to my friends and family to fly across the world to Denmark.

It's definitely been full of challenges, but it's been amazing to find a company that really shares my values and aligns so perfectly with my mission.

Chris Adams: Wow. So you, when you say you're a truck driver, you're talking about the massive, like 18 wheelers crossing from city to city, right? Something like that.

Tony van Swet: Yeah, I actually worked with the HIAB trucks, which have a crane on the back. So I was delivering building supplies around Auckland. It definitely gave me a lot of time to think about the world and take in the kind of sights and sounds of the city.

Chris Adams: Wow. Okay. So I think you may be the first former truck driver we've ever had onto this podcast. So yeah. Wow. Thank you for, thank you for coming along. That's also a fun story. I, it's, it's quite nice to hear something like that because, uh, I myself, there's a lot of us who are self taught technologists and to hear a nice story about switching careers you're going, "that's cool, actually." All right, before we digress, let's go back to what we were supposed to be here talking about, which is open data and carbon Intensity. So one thing you mentioned is that we're here to talk about open data and there's some recent work at your end that's made that visible. But before we do that, could we briefly just cover what carbon intensity means at your end, because this is something that isn't obvious to most people.

And I remember there being a kind of nice introduction on your website using metaphors like blenders and so on to explain that there's more to electricity to it being just gray versus green, for example. So maybe you could just. provide a bit of a background or how you explain this to people, then we can dive into some of the details about open data.

Tony van Swet: Yeah, the blender analogy is really great. We even did a smoothie maps version of our app for April fools, renaming all of the power sources to different fruits and vegetables to illustrate that. So yeah, carbon intensity to us seems like it's relatively straightforward, but if you're not familiar with this idea, it's quite hard to understand.

And in this case, we refer to carbon intensity as the CO2 equivalent for a given zone where energy is being consumed. We calculate this by determining the carbon intensity for each generation type and then weigh it according to its proportion of the grid mix. We also then calculate the neighboring zones and account for all the imports and exports of the connected zones to figure out a final number for the carbon intensity where you plug into the wall and consume it.

Chris Adams: So basically, if I understand that correctly, you're, what you're saying is you look at all the various parts of the world, and when you say zone, you're referring to maybe a country or a part of a country, depending on how a grid is designed. And then when you're talking about the kinds of generation, you're talking about, say a coal fired power plant or a gas fired power plant or a solar farm or something like this. So these have different levels of CO2 that get emitted for each unit of electricity and you're mixing those together, something like that. Is that correct?

Tony van Swet: Yeah, definitely. When we take a look at a coal plant, it's going to emit a lot more carbon than the equivalent solar or wind farm.

Chris Adams: Okay, cool. So that talks about the consumption, the how, where the electricity comes from. So maybe we can talk a little bit about, okay, how we experienced that and how, like, when I plug something into the wall, for example, what happens next?

Tony van Swet: Yeah, so when we, um, plug into the wall, the energy we consume is, um, considered a mix of all the generation types of the grid you're connected to, um, and it's almost impossible to determine whether an electron comes from a wind farm or a coal plant, even though this will have a significant change in the carbon intensity of the energy you consume.

So this is where it's really useful to consider the grid as a giant blender, mixing together all those generation types. And then we can evaluate the true carbon intensity of the energy that you consume.

Chris Adams: Okay. If we're going to continue this blender analogy, if you put lots and lots of, say, strawberries in a blender, it's going to look one color. And if you had lots of Kiwi fruit in a blender, it's going to look another color. So that's a little bit like what you expose to people and how that might change over the

day.

Right. Okay, cool. I believe what we'll do is we'll share a link to the blog post, because

I found it one of the clearest ways to actually help people get their head around this kind of concept, because it is a bit of a leap when you're first starting to get into this field. So with that, we've got a kind of grounding there. Maybe it's worth talking about this from the point of view of a software engineer. So. Let's say you do know this and you have access to this information. Why is this helpful if you're a software engineer? Like where does this fit into what you might do, for example, or affect your job?

Tony van Swet: I think it's super useful as a software engineer to, to have this information and I see a few main categories where you can apply this data, particularly around raising awareness of when to consume energy. We want people to use power when the sun shines and the wind blows. So I think that there are ways to present this information so people can make decisions in their everyday lives.

But particularly for me, I find it interesting of automating solutions where we can get carbon-aware products that will shift their consumption or the load of the power consumed based on how sustainable the power is available to them.

Chris Adams: Okay. So in this case, this scenario here, you're basically saying, if you have an abundance of power, which is very green, you might kind of tune or change your usage to use more of that, and when the power is particularly dirty, for example, you would try to use less of it so that you're shifting your power through time or possibly through space so that the average carbon intensity might be lower than it otherwise would be.

That's what I think you're saying, right?

Tony van Swet: Yeah, exactly. So the two main ways to optimize your consumption here is over time or via location. Um, so we know that different grids are much cleaner and, um, some people have the luxury to be able to shift their consumption via location as well.

Chris Adams: Okay, cool. So. We've got the kind of general concept for this. Are there any kind of favorite examples that you might point people to of people using this to actually demonstrate their behavior, either at a personal level or an organizational level? Because yeah, having a concrete example would be really helpful for people who are listening to this for the first time.

Tony van Swet: Yeah, I think, um, my favorite example is, um, looking at Google's use case at their data centers. They have the huge potential to shift their computation based on time or location. So this enables them to manipulate their energy consumption through using our API to increase their consumption when the sun shines and the wind blows.

Chris Adams: Okay, so if I understand it correctly, they're like a client of yours or a customer of yours, they pay for this, and then they use it then to essentially either scale things up or down, depending on the amount of power they might be using, depending on where the data centers are. That's, that's what it sounds like, what you're suggesting there, correct?

Tony van Swet: Yeah, definitely. So there is the location aspect and we see a huge variation of the carbon intensity throughout the day. So they also do time-based or scheduled computation based on the carbon intensity available to them.

Chris Adams: Cool. Okay. I'm glad you mentioned this because this is something we've had people come on the show before to talk about some of this, but since we have spoken about this, there's actually, uh, some interesting data. Uh, there was a study published with TU Berlin where we're, I'm based in Berlin so we've, I found out about this study and, uh, there's. I found this actually quite a nice example of this to talk about, because a lot of the time, when you see companies talking about this, it's quite hard to actually find meaningful numbers to say, does this actually translate to a saving in carbon? Or does it translate to a saving in even money, for example? And this is the first time I've seen with really detailed information, which has been modeled through this. Um, we'll share a link to this paper, but there's a few kind of headlines that I saw from this. And as I understand it, one thing that Google is doing, for example, they've basically set a commitment to say, "we want to have the average carbon intensity of our power to be this much." So we want to have a certain percentage coming from what they call is like carbon-free or fossil-free sources of generation. And, uh, the study that I saw basically showed that by moving the load around, it reduces the amount of renewable energy, renewable kind of generation that needs to be deployed in the first place for this.

So there's an embodied carbon saving in the, in not needing to have a bunch of wind turbines or solar all around the globe. And this study that I see, it was modeling five data centers. So five out of say 14 data centers that are around there. And there were. The savings are pretty good, or actually like measurable.

I think with the combination of moving things through time and moving things through space, so moving a compute load to where it was going to be greener, the figures that I saw, some of the headlines were that they're able to reduce the cost of doing this by something in the region of a third of the amount of investment that would need to be possible. And, uh, they also, this is one of the first examples I've seen, which even explains like what the costs on a yearly basis might be for this. And, uh, I think the. There was a couple of scenarios inside this. So there's maybe with zero load shifting or moving, say, about 40 percent of the compute loads that to to different parts of the data centers, where maybe one part of the world might be particularly windy or sunny. When I look at the figures here, I see something in the region of, if you, the savings that are here and we need to, and I will share a link to this, to the actual study for this, so that people can look into this a bit more detail, but with the five data centers modeled in, I think, Germany, in Denmark, in Portugal, in Ireland, and in Finland they were basically able to model savings of around at least 200 million US dollars each year by, in terms of the amount of power that you would need to be, the amount of like generation you would need to match this, to actually hit those targets. Now this is, I think this is actually useful to understand because this actually speaks to the fact that there's economic drivers as well as actually just environmental drivers for this. And this kind of speaks to the wider kind of trend, but. I think it's useful to, for this to be, people to be aware that there's actually something in the public domain to interrogate and look at some of these numbers and see how some of these are modeled and what some of the assumptions are. So we spoke about that. Are there any other use cases that you might point to that may be a little bit more closer to home, for example, or something that you might, that people might experience on a more kind of daily basis or close to themselves, for example?

Tony van Swet: Yeah, absolutely. Yeah. We have a few customers in the EV smart charging space, and we have also done some research with the Frederiksburg commune here in Denmark about the benefits of smart charging. And we... We were quite impressed to see a 10 to 15 percent reduction in carbon emissions if we have grid-aware smart charging products.

So this is plugging your car in the evening and letting it decide when the best time is to charge the car overnight. And even with a small shift in that load, we see a significant reduction in the carbon emissions of the energy consumed. So we were really positive with the results of that. And particularly find it a very nice use case that you put the decision-making power in the hands of the consumers here.

So people can choose whether they want to use these products or not.

Chris Adams: Okay, cool. All right. If you're in the UK, I believe there's a number of companies that do things like this. Octopus is one of the better known examples of this. And I think under some of the tariffs, there are scenarios where you can actually be paid to charge up a car rather than pay to charge a car or to use a car.

So the cost can go negative. Because there's maybe an abundance of power in the grid or like we have here. So that's actually, okay. That's quite useful. So we've covered a couple of use cases now. Maybe it's worth talking a little bit about, little bit about what kind of software supports the use of this data. So I know that at the Green Software Foundation, there's a carbon aware SDK, which is designed to allow people to embed this in some of their software. And where I work at my nonprofit, the Green Web Foundation, we have a library, a Golang library, which is used in a project called Carmado, which is a kind of federated Kubernetes operator. Could you talk a little bit about some of your experiences of what you've seen people use for some of this stuff? For example, maybe you could talk a little bit about some of the pieces of software that you've seen in the wild using some of these tools or using some of this data, for example.

Tony van Swet: Yeah, definitely. Firstly, yeah, we're hugely appreciative of the Green Software Foundation and their work to make it easier for developers to use data like this. We do our best to enable developers and hobbyists with our free data through our API. Previously, it was known as CO2 Signal and we've now incorporated that into the Electricity Maps API.

And we see lots of amazing tools being built. We see people building dashboards so they can make decisions around which data centers they use. And we do see a big community from Home Assistant also integrating our data. So people can connect their smart homes to become carbon aware and give information on the carbon intensity of their homes.

Chris Adams: All right. So we've got some, some stuff like that. And I think we've done a decent job of now establishing what carbon intensity is and how some people might be using it so far. And, uh, we spoke about this idea. There's a, like a free tier, which basically implies that people pay for a data service. But one of the things that we're here to talk about today is open data and this open data portal.

And as I understand it, this is your baby, so to speak, right? So maybe you could talk a little bit about, okay. What is this that we, that that's actually gone live because I've got a history with open data, but I suspect it'd be useful for people who are coming to this to understand what this data portal is and why it's useful and what it lets people do, for example.

Tony van Swet: Yeah, I was super excited to take the lead on the Data Portal project and really happy to come on the show today to talk about it. Providing free and open data really motivates me. And the Data Portal is a product on our website where anyone can download free carbon intensity data for over 50 countries in hourly, daily, monthly, and yearly for both 2021 and 2022.

Chris Adams: Okay. So let me just check if I understand that. So, uh, if people want to start using or experimenting with this data, there's a free tier which you, which folks like yourselves provide. Uh, there's another provider called Watttime that does a, of a free, a free tier. And there's commercial kind of real time feeds from both yourself.

And, uh, this part here is this high resolution historical data that has typically been quite hard for people to give access to. And this is openly licensed in the sense that people are free to use this how, however they wish, is that the case or is there any, or maybe we could talk a little bit about the licensing part so people understand how they could use some of this.

Tony van Swet: Yes, so we have provided the data free for anyone to use. We particularly look at Carbon Accountants and researchers to use the data. People are welcome to use it under our license, as long as they, if they're building a new product with our data, then they'll be required to open source that new product, but if you're using this data for Carbon Accountant, then you're fine to use it and charge for that accordingly.

Chris Adams: Okay, cool. All right. Uh, what we'll do is we'll share a link to the message, to, to the licensing. So people have an understanding for this. So I think when I looked at it was the open database license. So you're able to use it for free in any, in any form, as long as, uh, you're prepared to share under similar terms yourself.

That's basically the kind of general approach that I understand for that. And you, you spoke a little bit about there's an intended audience of people who might be carbon accountants or researchers or energy geeks. Can you talk a little bit about how this data gets published in the first place, where it comes from? Because as I understand it, the data can be quite messy to actually put into a kind of API for someone to consume.

Tony van Swet: Yeah, yeah. It's, it's a huge challenge to collect all the data. So we have an open source repository full of parsers that collect this data from TSOs and data providers around the world. We have an incredible open source community that helps us to maintain those parsers. We then process this raw data with the kind of smoothie idea that we talked about earlier, run data quality checks on top of the data, and then present it in a way that's easy to navigate and consume.

Chris Adams: Okay. So you've used a bit of jargon that I'll need to unpack on there. So you said that you're getting data from a few places and you mentioned a TSO. I'm assuming a TSO is a transmission service operator, like someone who operates part of the grid and they publish information. So that's where some of the data might be coming from.

Is that correct?

Tony van Swet: Yeah. Yeah. Spot on.

Chris Adams: Okay. And one of the challenges is that not every, so maybe I, as I understand it, when I've looked at this data, the data comes out in like grams per kilowatt-hour, what I would typically be paying for, but different places might have different ways of reporting it or different units. Is that the kind of stuff that you, that ends up having to be munged so that there's a kind of clean interface for people to consume?

Tony van Swet: Yeah. So the data providers, the TSOs tend to give the data in the format of a energy breakdown. So the various production types, whether it's wind, solar, coal, gas, and we then process this data and apply emission factors. So we add a direct and life cycle emission factors to each of the generation types, and then compute that to give a final carbon intensity number for each zone.

Chris Adams: Okay. All right. So if I understand that correctly, you're basically saying we know what this kind of coal power station is likely to be doing for each unit of coal. And because we might have some information about it being an old machine, old one or a younger power station. So you'll have some figures like that, and you essentially run through every single form of generation so that you've got a kind of up-to-date, accurate number for that based on what, what people are doing rather than have to look that up because yeah, it's quite hard to find.

So. You've, you've created this data portal. People are able to download it for a set of countries or different parts of the world, and you said that there's data for 2021 and 2022, and this kind of begs the question, what happens next? Is this, is the idea, is the intention to keep having this available on an, on a, on a annual basis so that next year there'll be data for 2023, for example?

Tony van Swet: Yeah, absolutely. I listened to your podcast a few weeks ago and I heard you mention that we were looking to raise the bar of energy data available out there. And I really like that term. It's exactly what we want to do. We plan to release new data early 2023. We want to enable carbon accountants to do granular carbon accounting based on our data.

And we really hope that providing this data for free gives the industry a push to be more open and transparent around what energy data is available.

Chris Adams: Okay, cool. All right. So for the energy nerds here, I, it might be worth just briefly talking about the fact that this currently provides average carbon intensity data. Is that correct? So that's basically the kind of location-based figure. So there, this isn't trying to take into account water or anything to do with market-based figures at present.

That's something that might be on the horizon in future. Could you maybe talk a little bit about what things are on the wishlist or what people are asking about What would they like to use in future from here? Because you alluded to some things about, uh, the life cycle intensity of, of, of energy, for example, and there's a whole other set of footprint impacts that people often ask about when they talk about carbon intensity, or even just the environmental impact of the use of electricity in any kind of service.

Tony van Swet: Yeah, absolutely. So carbon accountants are most interested in the direct emissions that we provide in this data because they're doing their accounting based on the Scope 2 emissions of a company. Um, we do also provide the life cycle analysis emissions for each zone as well. And this is taking a cradle to grave approach of the emissions.

We use the numbers from the IPCC and the,

Chris Adams: So IPCC in this case is the Intergovernmental Panel on Climate Change.

So that's one thing. And then the UNEC, so I'm guessing it's United Nations.

Tony van Swet: United Nations Economic Commission for Europe. 

Chris Adams: Okay, great. Okay. So, so that's basically the kind of bona fide place where you're taking some of these numbers from. And when you talk about the life cycle emissions there, that means that let's say you're talking about solar or wind, for example. That includes the fact that someone has to make the panels in the first place, and there's going to be some pollution that may be caused there, carbon pollution from making the kind of silicon panels or the turbines.

Is that correct? And then the dispose disposal.

Tony van Swet: Yeah, exactly. Yeah. And even in the case of nuclear, the lifecycle analysis takes into account the storage and disposal of nuclear waste over hundreds of years and applies the costs of that to a carbon equivalent.

Chris Adams: Okay, cool. All right. So we've spoken about carbon and we will talk about carbon a bit more, but. It's very, one thing that has come up when people talk about the environmental impact of digital services, there's this term called carbon tunnel vision, where people only look at the one figure, or the one kind of dimension. Is this actually something that, is this on your wishlist, for example, because we know there's a, say, there's a water impact. People talk a lot about machine learning and AI and tools like that, having a water impact, and there's also an impact from the actual generation, for example. Could you maybe talk about a little bit like that?

Is that something that you'd like to be heading towards, or is that on the roadmap, for example, in the, in the long run?

Tony van Swet: I think we'd love to take a step back and, and have a broader look at the impacts. We're relatively limited with our capacity, so, so we do focus on what we know and what we're experts in. But I would love to see us work with partners to be able to provide our data alongside other sources to take a bigger picture approach to this.

Chris Adams: Okay, cool, Tony. So back to carbon then. You spoke a little bit about working with other providers and I realized just as I was doing some research for this recording, this podcast, there was a new paper that came out from Electricity Maps specifically about, this is a, this is a really nerdy, I'm afraid, residual carbon emissions when you look at the environmental impact of electricity and If I understand it, I'm just going to try and run my understanding by you if I can, and then if you can give me an idea about if it's in the right direction, that'd be really helpful.

So, as it stands, electricity maps gives you figures for location-based data. So you look at the carbon intensity of the generation all around the world through, and like dams or wind or solar, uh, you'll look at that part, green energy, they often talk about, say, using green energy in some parts of the world where they've purchased essentially certificates to count electricity as green.

And this is a kind of like a market-based approach that people use. And this is the basis that various companies use to say, "we're using a hundred percent green energy," for example. Now, if I understand it, this paper that goes into this and basically says, if you're going to look at the carbon intensity of electricity, you need to account where these certificates that people use, where they're actually being used in various parts, because that's going to have an impact if, because if someone is claiming green energy in Ireland, for example, and they're claiming that on the basis of certificates that were traded from Norway, that's going to have an impact on how green the power might look in Norway compared to Ireland, for example. This is what I think some of the research is that was in this paper. Is that directionally correct? Is that moving in the general direction of correctness for this stuff?

Tony van Swet: Yeah, absolutely. Yes. I think a lot of companies are buying renewable energy certificates and it has to be a zero-sum game. So the residual mixed paper that our policy team has just released goes into a huge amount of detail into how you calculate the carbon intensity after you have sold those renewable energy certificates for each zone.

Chris Adams: And as I understand it, this is something that's on the roadmap that will be looked at is A, a thing that is unsolved right now, but people are looking to figure out how to incorporate into how they work. I know that in the Green Software Foundation, there's a group called Realtime Cloud, who are working to come up with hourly figures to make it possible to provide this kind of reporting. This seems to be one thing that comes up because when I was looking through this paper just last night, actually, there was a few things which are really eye opening for me. So Ireland and Germany are two large markets in Europe, for example. And as I understand it, something like eight times the certificates were consumed as were issued in Ireland, for example.

So this basically means as I understand it, that eight times as much green energy is being claimed as is generated in Ireland. So therefore you've got a bunch of generation in somewhere else in the world that needs to be accounted for when you look at the carbon intensity of say, a place like Iceland or Norway, for example, but the same things seem to be in Germany as well. Germany has something like seven times the certificates consumed as were issued in Germany. So that suggests to me that seven times as much green energy has been claimed as is being generated. So if Germany had to have an entirely green grid, you would need something like a sevenfold increase in order for them to be saying, "yes, we're running entirely on green energy." That seems, this is pretty eyeopening. I'm really glad this is actually out there because I haven't seen this data provided in this resolution, particularly in hourly level before.

Tony van Swet: Yeah, I think it's really fascinating and definitely highlights why we need the transparency around this market based approach. And it's very early days, so we are hoping to inform the methodology of how we approach this in the future.

Chris Adams: Great. Okay. So what we'll do, we'll share a link to that. The things I've just shared are in the first 10 pages of this paper. It's about 80 to 90 pages long, and it's a really impressive tour de force. So Cyril, I'm really impressed with this work. Really mad props for you to actually get this together. Cyril is the policy lead, Electricity Maps, and he's also on some of these working groups, which is why it really caught my eye. So Tony, we've just spoken a little bit about Open data, different ways of measuring the carbon intensity of electricity here for informing your decisions as a software developer. Is there anything that you would like to draw people's attention to? Any projects or things that you are particularly interested or that you're excited about right now?

Tony van Swet: Yeah, absolutely. Yeah. First off, I'd welcome people to jump on and take a look at the data portal and I would appreciate any feedback around that. And. If anyone would like to contribute to our open-source work, we're also always looking for contributors there. To find out more, jump on our website at electricitymaps.com.

Chris Adams: And if I understand it correctly, you folks are still, it's still mostly Python scrapers and a kind of React app that you had before. Is that still the case for people?

Tony van Swet: Yeah, definitely. Yeah. Python and JavaScript.

Chris Adams: Okay. So common languages that people know their way around. Okay. Brilliant. I think that's pretty much it. I've, I've really enjoyed this, actually. Thank you so much for giving us your time and diving into some of the finer points of carbon intensity of electricity and, uh, some of the nerdery there. And, uh, Tony, thank you so much.

I've really, I've really enjoyed this. Cheers, mate.

Tony van Swet: Yeah. Thank you, Chris. It was really great to be here. I also wanted to say I went digging through our open source repo and found your name on there. So I want to give you a personal thank you for contributing in the past.

Chris Adams: Thank you very much. Um, there, I think there'll be more PRs coming in future when I find the time. Okay. Cheers, Tony.

Tony van Swet: Amazing. Thank you.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts.

And please, do leave a rating and review if you like what we're doing, it helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

Thanks again, and see you in the next episode! 

This Week in Green Software has Dr. Daniel Schien from the University of Bristol, UK, joining host Chris Adams to talk about old research, recent news, and future prospects all revolving around digital sustainability. This conversation touches on some of the work Daniel has done in the past (and plans to do in  the future) as well as their thoughts and reckons on this and how it can be used to steer our efforts towards a sustainable future. Together, they cover topics such as streaming being the new flying, and some ways in which new research has changed their perspectives on some problems in green tech.

Learn more about our people:

• Chris Adams: LinkedIn | GitHub | Website
• Daniel Schien: LinkedIn

Find out more about the GSF:

• The Green Software Foundation Website 
• Sign up to the Green Software Foundation Newsletter

Resources:

• The True Climate Impact of Streaming - About Netflix [04:06] 
• The carbon impact of streaming: an update on BBC TV's energy footprint [05:07]
• Does not compute: Avoiding pitfalls assessing the Internet's energy and carbon impacts - ScienceDirect [21:10]
• Evaluating Sustainable Interaction Design of Digital Services | Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems [40:50]
• A low carbon kubernetes scheduler — University of Bristol [41:47]    

If you enjoyed this episode then please either:

• Follow, rate, and review on Apple Podcasts
• Follow and rate on Spotify
• Watch our videos on The Green Software Foundation YouTube Channel!
• Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Daniel Schien: If we are saying streaming is bad and instead consumers jump into their car and go to the cinema, that would be the worst thing that people could do. And same if we are saying streaming is bad and consumers swap to terrestrial broadcast, makes hardly a difference in the short term to the energy consumption that is associated to the delivery of the service.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Welcome to another episode of The Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. In this episode, we're joined by Dr. Daniel Schien, a member of the Green Software Foundation. Daniel teaches and researches sustainable software at the University of Bristol, England.

Hey, Dan.

Daniel Schien: Hi Chris.

Chris Adams: Dan, if any of our listeners lurk on the Green Software Foundation forums, or the kind of IETF E-Impact mailing list, they might be familiar with you. But for folks who are new, would you mind taking a second to introduce yourself to the pod for our listeners, please?

Daniel Schien: Sure. Thanks for having me. My name is Daniel Schien, and I, as you said, teach and research at the University of Bristol in the United Kingdom. I'm a computer scientist and work here in the Faculty of Engineering, researching questions of sustainability of ICT. And here in Bristol, I'm part of a small but dedicated group of researchers looking at the environmental impact of ICT.

Chris Adams: Okay. And I did a bit of research before this call and I realized the first paper I saw from you was back in 2011, researching the dynamic carbon footprint of digital services back in 2011. So it sounds like you've been doing this a while and you've worked with a number of organizations in the industry as well.

Is that correct?

Daniel Schien: Yes, that's true. Indeed, I joined the University of Bristol at the end of 2010, working on a project for the Guardian newspaper, actually. And at that time, they were interested in understanding the transformation of their business from a paper-based delivery to a digital delivery, reading the news online. And at that point there were no methods, no existing similar analyses of environmental impact from the distribution starting at data centers over networks to user devices. And in this void, we helped to come up with an assessment that can compare the quite well understood print side of delivering news with the digital version.

Chris Adams: Wow. Okay. And you've worked with a few other organizations as well. And I think we're going to touch on that a little bit later on. So wow. Thank you. The little story about the Guardian. I had no idea that the Grawn was doing that stuff so long ago already, actually. Okay. Um, cheers for that, Dan. If you're new to this podcast folks, as mentioned before, my name is Chris Adams.

I work as the chair of the policy working group at the Green Software Foundation. And I'm also the executive director of the Green Web Foundation, a Dutch nonprofit working towards an entirely fossil-free internet by 2030. I'm also going to share that whenever we mention a story or a project or a paper in this podcast, we'll do everything we can to find a link so you can actually research some of this yourselves and learn somewhat more if anything caught your interest.

And if there's something that you heard us talk about that you didn't see in the show notes, please do send us an email or get in touch and we'll do everything we can to capture that information and make sure it's available for everyone else. Okay, Dan, are you sitting comfortably?

Daniel Schien: Yes, I am. Thank you very much.

Chris Adams: All right, I think we should begin then.

So a few years back, one of the tropes that just would not die when people started talking about sustainability in the digital realm was this idea of streaming being the new flying. And, um, I saw your name linked to some work that was published in 2021, uh, working with Netflix that actually, in my view, helped shed some light into this and, uh, hopefully might put away some of the kind of poorly written part articles that you often see in the newspapers.

Could you talk, talk a little bit about some of this work that you did? Cause this was one of the first places I really came across your more mainstream work and it, I found it really interesting, actually.

Daniel Schien: Sure. So I mentioned earlier that my, my work here in Bristol on the sustainability of digital media started working with the Guardian and they wanted to understand the transition from print to digital. A couple of years later in 2015, we then worked with the BBC who also wanted to understand a similar transition. For them, it was the transition from terrestrial broadcast of BBC television to IP-based delivery. There is a broader context within which this transition happens. Terrestrial television, terrestrial broadcast uses up spectrum, and that spectrum is a scarce resource. And so there's a, there's an interest in potentially freeing the spectrum up for other purposes. So that's one of the drivers and the other is of course the affordance that IP-based delivery provides. You're moving from a broadcast mode to a unicast mode and, and enabling a lot of new ways of consuming. First of all, you can, you have video on demand as we know, and the other is more interactive forms of media. However, there was a concern about the environmental implications of it. And this concern was, was born just out of a lack of knowledge. At that time, the colleagues at the BBC, they, of course, had very good understanding of the energy consumption of the servers they were operating themselves, but what they did not have a firm grasp on was the energy consumption in networks that transport the data from data centers to the users and then the energy consumption of the devices in the homes. And this product system is, in its structure, relatively similar to the one that delivers the Guardian News Media. So we could leverage some of the methods that we developed in the work before to, to then come up with an estimate, an assessment model that then helped develop an estimate of the energy consumption for delivering BBC television, uh, over IP, but at the same time we delivered this, uh, we used this model to assess the impact from cable and from satellite as well, including terrestrial. And then we were able to also complement this with scenarios of how this might change in the future as the technology becomes more efficient, as the energy transforms, becomes more green, and as user behaviors change. So, so this was, this was a really, I think, yeah, a very successful collaboration. And there's a couple of things that we can dig into later on that are interesting here when it comes to the interpretation of those assessments. But one of the, one of the great things that came out of this was that at that time, that was one of the first studies that worked with an organization that provided the content and so had primary data of the impact in the infrastructure to some degree. And that was an important advantage compared to other work that is based purely on secondary data, on publicly available data, that it provides maybe a more robust.

Chris Adams: Yeah, you get to speak the people, surely. You're asked, you're asked like, "Hey, have I got this data right? Am I understanding it correctly?" And presumably, assuming they're being transparent and honest with you, you've got much greater access about what assumptions are going into that, rather than what you see elsewhere.

Yeah, that makes total sense, actually, Dan.

Daniel Schien: Yeah. So, and then a couple of years later, we, we had, um, a conversation with the uh, Responsible Media Forum, a small stakeholder representation run by Carnstone and, and they said, "can we somehow create a self-service assessment tool that lifts the, the methods that were developed in the work with the BBC, but makes them available for other organizations to also assess the environmental impact from the delivery of their 

Chris Adams: Oh, wow. 

Daniel Schien: services?" That was then the start of the DIMPACT project. 

Chris Adams: Oh, I see. I hadn't really realized that there was a link going all the way back to public service broadcasting, essentially, in that model, actually. Wow. Okay. Huh.

Daniel Schien: That was the idea. And now many global media organizations are using this tool to report on the environmental impact of the distribution side. So I think this is a great success story. I always say that our role as academics is to create knowledge that we want to make the actual impact more transparent. And in this way, we have enabled the organizations providing those services to do exactly that.

Chris Adams: Wow. Cool. Thank you for that, Dan. All right. So there's a couple of things that I'd like to jump into there. And it sounds like... I thought I was just going to be asking you about Netflix, but it sounds like there's a whole set of different media that we might be thinking about and where there's changes and surprises along the way, basically.

I've got to ask you, were there any kind of counterintuitive findings that you had there? Because I immediately, for example, when you spoke about the idea of switching from analog to digital, I assume there's an energy impact in being able to switch off all the analog TV broadcast, for example, right? Is that something that people talk about?

Cause I hadn't really put those two together until you spoke about that, but surely that's actually going to be an impact, right?

Daniel Schien: Yes, absolutely. So, so this is maybe one of the nuances that, that we can spend a bit of time on. In the publication, in the academic publication, that, and I'm sure you will link to it in the show notes, there's a graph that compares the energy intensity of those four different modes of delivery of television from the BBC, so IP, cable, satellite, and terrestrial. And even though they, they differ, so between, if I remember correctly, between 60 watt-hours to 180 watt-hours, that's in 2016. There's a potential step in the interpretation of those results that consumers might take that needs more support. If you see this graph, you might think as a consumer, "if I change from streaming to watching something via terrestrial broadcast, I am going to save 100 watt-hours per viewer hour." However, that would not be a correct interpretation because all of those delivery modes, they are provided by an infrastructure that is inherently inelastic in its energy

Chris Adams: So you're talking about always being on. So if I've got like an analog television, even though I'm turning off the analog television, it's still broadcasting. So the same, so you've got a lot of the same analogs, sort of discussions we have now about "should I, is me using Netflix less, does that have an impact at the network level?" We have similar comparisons. It sounds 

Daniel Schien: Exactly, exactly. So it's not that... your television, if you turn that off, you're saving energy right now. However, the modem in your home, that's going to stay on even if you're not streaming. The routers in the network, they hardly change. So in your ISP network, they're hardly changing the energy consumption. The terrestrial antenna network outside of your home in the country is not going to change its energy consumption. So on a very short term, your behavior as a consumer between swapping between those different modes of, of consuming television has hardly any difference outside of the home. If you are moving from a television to an iPad or to a phone, that will make a difference, but it won't make a difference outside of your home.

Chris Adams: Okay. I'm glad you said this because I ended up being, uh, on a podcast with, of all places, the Daily Mail, who really wanted to basically, wanted me to say that you're an absolute terrible person for watching Netflix. And if you watch twice as much Netflix, you are twice as bad a person. And, uh, I, I ended up basically not saying what they wanted me to say, which was that in context, not going, it's not going to be that much of a difference.

And yes, you probably going to have the impact at the device level rather than the other parts. I'm really glad that there's an actual doctor saying that as well now, because I feel slightly more validated in actually making the argument because yeah, it does seem somewhat counterintuitive, but it does seem like a kind of somewhat actionable thing for someone to be able to do.

Daniel Schien: Yeah, it's, it is unfortunate that the debate is now being tainted by a lot of, a lot of bias that comes in. You've got, it's been elevated on a very prominent level. There's a discourse that everyone now has, has an opinion on, and everyone has a right to have an opinion on this, but what you have, for example, in the media, you've got a bias to tell sensationalist stories, right?

Headlines that are emotional, that evoke emotions, they sell better. And there's sadly such an enormous amount of attention-grabbing exaggerations out there. It is, it's very sad, actually. And I've been subject to this. There was a Guardian interview that I spent several days in preparation, trying to provide enough data to have a really nuanced perspective on what is known about the carbon impact of streaming, but it's been completely contorted in the final article. That was very upsetting, actually. And I've been in touch frequently with newspapers when the reporting of work of Environmental Impact of not just streaming, but other forms of digital media and ICT services was just represented in a very wrong way, a headline grabbing sensationalist. So there's no, no silver bullet to preventing misinformation.

It is really a Sisyphus kind of work. These tropes, they come back again and again, and there's some, there's some work where the scientists that carry out the work apply methods that aren't robust, that are still under development, and that that happens. That is the nature of, of research and of knowledge evolving over time, our understanding improving, but then there's, once the work has been written down in academic articles, then there is the interpretation and the presentation of that work in the public sphere, or in a political sphere or elsewhere, that, that is, that potentially provides another layer where there's risk for misrepresentation.

Chris Adams: This is particularly relevant given the fact that we're seeing an explosion of laws being passed specifically around this stuff right now, both for and against a bunch of this stuff. We've seen the, actually just this week, I think even yesterday in Germany, where I live, we've seen what translates into English as the Energy Efficiency Law, specifically referring to data centers.

And you see the same thing at the European level as well. So you do see a real role for people's interpretation of this. And I actually wanted to ask you, because we spoke about some things which are like counterintuitive. I wonder how much of this you think might be related to the kind of mental models that people have, because a lot of the time, the mental model that you might have when thinking about digital sustainability, or like build it using a digital service, I can see why you would bias it towards the use that I see being the thing that infor that has the most impact, for example.

And I wonder, are there any papers or any reports or any guides that you've seen that help form a different men- mental model? Does it make sense of this, for example?

Daniel Schien: Yeah. So I want to maybe prefix this by saying that I don't want to be interpreted by saying that there's no environmental impact from our consumption of digital, of digital media or of ICT goods. There is uncertainty in the existing assessments. Um, but I think those that position the, the impact of the sector, the present impact of the sector in the order of 2 to 4%, um, they're probably reasonably robust to work with. And, and those 2 to 4%, they are high enough that the professionals working within the sector need to do something about it. 

Chris Adams: Now this is two before, 2 to 4% of global if, of carbon emissions? 

Daniel Schien: Exactly, exactly. Yeah, so of the 50 or so gigatons of anthropogenic greenhouse gas emissions per year, about 2 to 4% are allocated to the use of ICT services and the making of devices, and that is, and that's too much. We need to do something about it, especially as the rest of the economy hopefully decarbonizes and as our use of ICT increases.

So, so the dynamics that are at play here going forward, they're really, we need to pay attention to so that we can keep this in check. And at the same time, it is relatively small so that when we are trying to do what we can to reduce carbon emissions, then we need to prioritize our actions such that we have a firm grasp on, on the decarbonization of, of the sector. And so one, coming back to video streaming, if we demonize video streaming and the consumer who has to rely on guidance for action, that is straightforward to put into action if we are saying "streaming is bad" and instead consumers jump into their car and go to the cinema, that would be the worst thing that people could do. And same if we're saying streaming is bad and consumers swap to terrestrial broadcast, it makes hardly a difference in the short term to the energy consumption that is associated to the delivery of the service. I

Chris Adams: I'm glad you mentioned this one about terrestrial because I realized until you mentioned the BBC thing and the fact that people switch off a bunch of spectrum, I hadn't realized that there's actually a kind of net impact on that because you do see the same thing with, uh, cellular right now. We have 4G and 5G, and we are now seeing people switch off like old 3G transmitters, and there's like an energy saving from that. But in many ways, that's something that because it's seen as such a kind of industrial level, it's a kind of implementation detail. We're not really aware of a bunch of that stuff. And I feel like that's actually an interesting kind of angle that you presented on that.

So if I can move on a little bit further from this, so you did a bit of work with DIMPACT, I am pronouncing it correct. Is DIMPACT the way I should be pronouncing it? Is that okay?

Daniel Schien: DIMPACT is the pronunciation I use, yeah.

Chris Adams: Okay. Now, and DIMPACT, as I understand it, you mentioned there was a firm called Carnstone, they provide like services around this.

But the methodology is entirely open for other people to actually implement and use as well. And as I understand it, you are the advisory council with some of that, along with a number of other academics. So there was some guidance you were sharing for them as they were developing that. Is that correct?

Daniel Schien: Yeah, so indeed I chair the academic advisory board, and so the advisory board is, there's the methodology document and, and the exact constitution of the panel is there. There's sometimes the personnel changes, so it's best to see there who's there, but I think that the people that we work with are, uh, the absolute top academics, um, in this space, for example, Eric, Eric Masanet from the University of California in Santa Barbara is, is on the advisory board, um, or has been on the advisory board and he, together with Jon Koomey, have released an article in Jewel magazine a few years back, where they identified some of the principles that can lead to a misinterpretation of the academic impact of digital media, actually, that's really worth looking at.

Chris Adams: I think you're referring to, the paper is called Does Not Compute or something along those lines. Yeah?

All right. We'll share a link to that. Cause as, I found that really helpful as well for me to essentially make sense of this as someone who's not an academic. So actually on this subject of changing your mind and, uh, in face of the data, I should probably ask you, you've been researching this for a while, and, uh, we've already come up with a few kind of counterintuitive things.

Are there any areas where you've really changed your own view on something, where you thought something was the case and now there's more data available or the research has improved? You're actually thinking, "actually, that wasn't the lever for change I thought it would be. And there's somewhere else I should be looking instead."

Maybe you could talk a little bit about that, because this is one thing that. I think we're probably going to have to do more and more over the coming few years as the picture becomes clearer, both at the research and the industry level, as more things become more open. 

Daniel Schien: So, less of a surprise moment is the gradual transformation of where I believe the emphasis of the attention should go, I started working on, on trying to, to, to understand the environmental impact of, of digital media now, 12 years ago, and with a keen focus on the use-phase energy consumption, because from a methodological perspective, it was one that, that was set in a, in sort of a gap, it's a distributed system where you've got data center networks and user devices, but the availability of data is poor.

No single party in this system has access to primary data. No single party can know what is the energy consumption in the data center because you can't get a power meter there or in the network or in the user devices. And so there's, methodologically, there was really quite a rich set of questions to explore. Um, now, the understanding of the environmental impact from streaming video has solidified. There's some variability around it and some uncertainty. However, the great new frontier is the impact from the making of the devices and, and in particular now with the electricity production in many countries becoming greener, there's a great success in the UK with decarbonizing electricity and hopefully we can continue along that trajectory. But as the electricity becomes greener, it becomes more and more important to understand and mitigate the carbon impact from the making of devices and, and we said, we spoke before about the interpretation for consumers. So when someone opens the newspaper and then they see something about streaming or about digital media, it is the metabolism of devices.

It is the frequent purchase of new devices that now is one of the key areas of environmental impact that consumers can actually do something about.

Chris Adams: When you say metabolism of devices in that way, you're talking about essentially built in refresh cycles or obsolescence, like a new iPhone each year or something along those lines, yeah? 

Daniel Schien: Exactly so. I think that's, that is something where all consumers can have a meaningful impact by protecting the screen of their phones so they can last longer, and, and by, by not being swayed by the latest marketing stories to buy a new phone that is marginally faster, I think that that's something where, where, where the consumer can do something about it.

But the sector, the professionals within the organizations building the services, designing the services, of course it's their responsibility to do this as efficiently as they can. And, when I'm sitting here on my laptop and I hear the fan kicking in, then I'm often looking what are the threads that are currently ongoing.

What tasks are running there in the 

Chris Adams: What things can I switch off on my computer to make it stop blowing away, right?

Daniel Schien: Yes, exactly. And sometimes you've got, you've got processes that are consuming CPU cycles where I'm pretty convinced there's waste going on. So what are some good examples? So I've got a mouse from a well-known manufacturer and it comes with a device driver, but the device driver consumes sometimes up to six gigabytes of memory. Because there must be a memory, there must be some sort of memory leak.

Chris Adams: This is for the mouse, right? 

Daniel Schien: It's...

It's this one here. It's a good mouse,

but it is just carelessness. There are too many examples that I come across where the software running on my machine is made in a careless way. And I think the work that the Green Software Foundation does there can really help in increasing and improving the efficiency of software that runs on a single device.

I think we need to think more holistically and consider the entire system, not just on, on one device, but the whole balance between servers, networks, and use devices, but there is, there's still a rich space to make things more efficient.

Chris Adams: Okay. So we've got one about, so there's steps you can take at the use phase there. There are things you, as you describe, there are steps you might take in the design of something to make it run more efficient in use. And then there's another set of interventions you describe, which are design interventions you might make to reduce the need to cycle hardware quite so often, for example.

And it sounds like the former, where you might just make your code run more efficient, that's really common and relatively well understood, and we have some incentives aligned there. But this next part, that's something of a kind of newer horizon because the approaches you might take to essentially design something so it doesn't need to be replaced so much, you've got different drivers from an economic point of view, and we haven't figured out all those yet.

Are there any examples that you would point to that give an idea of where some of this might go? Because we talked about things like the Fairphone before as one example, and I know there are some cases in other sectors now. The first thing I think of is maybe like the framework computer, for example, which is designed to be taken apart or designed to be modularly put together.

Do you reckon there's some legs and stuff like that? Or yeah, maybe, maybe you could like elaborate on some of that stuff there.

Daniel Schien: I have the feeling that a cloud-based distribution model or, or a model to run software has got an enormous potential to reduce the impact for, from the manufacturing of devices and also for also for the runtime energy consumption. Just the maintenance of applications that are distributed over a traditional model where you've got a sort of a fat client living somewhere on a customer device for an organization constructing software has enormous implications for the, for the, for the maintenance, yeah, for the, for the ongoing support.

Whereas if you have a, um, if the, if the software runs in the cloud, um, then just by the, by the interface um, that you have over the web, um, you, the, the, the, the cloud software provider has, has, has much higher control, um, over the anatomy, the architecture, and the implementation and the evolution of the software to get, to make it more efficient.

Chris Adams: So to make some of this more concrete, let's say that you wanted to do something at a hardware level. You're like, yeah, you could buy a really fast machine, for example, but there are tools where we work. We use Gitpod because one of the reasons is that basically shifts all the kind of development work into a machine in the cloud.

And there's things like GitHub Spaces, which do this idea of shifting the work to a more centralized place. That's what you're, it sounds like you're talking about, and that might be easier to green, for example, as well?

Daniel Schien: Yeah, uh, so. I think there's probably the devil in the detail. Not all applications, um, will, will suit this model of a, of a cloud-based, um, distributed system equally well, but if you can have a thin client to, to just access that cloud service for many applications, there, I believe this is the opportunity to then hold on to this thin client for longer.

Chris Adams: Okay. So there you have it then. All right. So that's one of the ideas that we might be looking at. And there's a bunch of other questions about, okay, which, whose cloud are you talking about here? And there's a sovereignty issue there. And if you're going to concentrate with it, how can you be sure that you're going to get the data out of it?

Or there's transparency issues and things related to that, which you've, I think you've alluded to elsewhere. Are there any areas in the research community right now where you see some, I don't know, opinions or ideas that are probably a few further along than where the rest of industry might actually be right now, that you think is probably something that people could spend more time looking at? Because I know that you read papers and you publish papers on a regular basis, but we also see that there is a gap between what being discussed at, say, HotCarbon or the E-Impact events, for example, and then what's more mainstream and like the industry at present. Thank you. 

Daniel Schien: So, so my own concern, has moved from trying to understand what the current impact is and in the past, relying on accounting style methods to a concern of how can we robustly enable all the stakeholders to take action that can result in the reduction of that environmental impact. And that includes the consumers. What can I, what concrete decision support, what guidance can I give to consumers when they decide between different options to consume digital media? What can I say to the designers, to the engineers building those services to do this in the least environmentally impactful manner? And infrastructure operators, is there something that they can do so that we are moving towards decarbonizing the system? And what I said before, the concern that there is a high base power consumption of devices and the lack of energy elasticity. There is the overhead from the manufacturing of devices that, that lock you in, um, to amortize this impact over the lifetime, um, of the, of the device that has in, in my mind eroded the confidence in the set of existing accounting-style methods to really provide that decision support in a robust way. I think one of the key questions that we still have to solve is that of electricity consumption and carbon intensity of electricity. There have been some examples how, again, a lack of awareness about the nuances and complexities in, of the electricity system have resulted in guidance and advice that is potentially not correct.

So there's a, for example, there's a difference between the carbon intensity of electricity, uh, that is, the average carbon intensity over the course of the day as it changes with the change of the mix of electricity and the marginal electricity intensity in the sense of, if I bring additional load onto the network or if I take load off, then the effect of the generation of electricity

Chris Adams: Is it going to be particularly dirty? Okay, I think you're referring to the fact that you have different companies who have different ways of talking about this. Watttime is one example, they have a marginal intensity and there's maybe other ways which use average intensity. And as far as I understand it, these end up with different incentives.

If you're going to follow the marginal approach, and it's a lunchtime, where there's loads of solar coming in, for example, you might have a very high marginal intensity, thinking that there's lots of power on the grid, so therefore me doing that, the marginal unit of generation might be gas, which is very bad, but that gives you the idea that you shouldn't charge your car when the sun is in the sky, you should wait till it's in the evening instead, when there isn't so much demand on the grid.

It gives you different conflicting signals. This is what you're talking about here, 

Daniel Schien: That's exactly what I'm, that's the knowledge of when is it better for me to consume? Or rather, how can I use a carbon intensity for electricity to actually make decisions that are really resulting in fewer carbon emissions being released into the atmosphere? That is the question. And, and the, and, and ultimately we use the term marginal to refer to the, the, the, the relative, uh, change of the intensity as a result of my action.

That's how we want to use the term marginal, but the metrics that are behind some of the released marginal factors, they don't actually represent exactly this goal. Yeah. And so, so this is, that needs to be better understood. We can't use the average variable and interpret this as the marginal, but I'm, this is, for example, one of the, one of the questions on my, in my mind that that needs to be better understood.

And there's a lot of, there's a lot of people from a variety of disciplines that are now looking into this. And it is related to, to this question of causally, how can we, what's going on in the wider system? How do my actions now translate into a change of the system in the future?

Chris Adams: I see. So you're talking, this is one about the time scale that you might have. So on a very short term, this might make sense, but in a longer term, it may be that, let's say we had this example of don't use being in center with a number telling you not to do something when there's loads of sun in the sky.

It may be that in the long run, it makes more sense to actually have a signal for demand so that people just build loads, loads more solar there. But that's not going to be something that's going to be captured inside your kind of short term marginal thing. That's a longer term signal that you'd need to be thinking about, that you'd need to somehow capture in, in a number or an index or something like that.

Right?

Daniel Schien: Exactly. And we've got similar effects for other parts of this networked system that we're working with that provides those services. So the electricity, the electric grid is a network, but our.

Chris Adams: network is a network! Yeah.

Daniel Schien: is a network. And, and so you've got, you've got similar, um, similar problems. Um, maybe the answers will, will look slightly different, um, in each case, but they all need to be looked at, um, collectively, uh, in order to, to, to make decisions.

Chris Adams: Okay, so this idea of the timescale where you might see an impact, as I understand it, this is some of your future research that I think you mentioned to me that you might be looking to do going forward. Is there a chance maybe you might touch on a little bit about that? Because as we just round off the hour, I feel like it might be worth asking you, like, where you feel some of the more kind of promising and really interesting areas of research are. Based on what you've said so far, it sounds like there's probably, I've got a good idea, but maybe you might just talk a little bit about where you expect some of this research to go, because, oh yeah, congratulations.

I saw a chunky grant landed for this kind of research, and I'm very excited to see where it goes now.

Daniel Schien: Yeah, thank you very much. Indeed, I've been, um, very pleased to have been awarded a research grant, um, to investigate the, um, a change-based perspective to the environmental impact assessment for digital services. So everything that we said before around the, the complexities in the system that prevent us from, from making easy conclusions, drawing easy conclusions with the existing methods. We want to now work together. We've got a, uh, a really fantastic set of partners that have that primary data to allow us to to understand robustly what is the current impact, and then also evaluate what is the future impact. And together with the engineers working in those organizations, we want to have conversations that, that help us understand what is, what's going on when digital services are being designed and their interaction with the network.

It's the, it's the relationship between the service and the infrastructure and the consumption that we want to untangle. It's this dynamic system and how it changes over time. And that's uncertain to some degree. What is in the future will always be subject to some uncertainty. But unless we, we dare to ask those questions and understand the basic principles, we are leaving this entirely to chance. And that really is a risk to our ambitions to decarbonize um, all parts of this society. Uh, yeah. So very happy, um, that, that, that I've been trusted with, with this piece of work. And here's a call out, um, to, to listeners. Um, if you're interested to work in this space, um, we are, we are looking for researchers to join us.

Um, there will be ads for a job for a senior research associate coming out into the future and Chris might be able to include

Chris Adams: Yeah, we'll be very happy to share links to this, especially if we get to use some of this research ourselves going forward. Because as you say, we've been struggling as a practitioner when I work in this field. It's very hard to figure out, okay, what am I going to do in the short term? And then what are the things I need to do that work on the longer or medium term?

Because I work in an organization which has a 2030 deadline for an entirely fossil free internet, but also we also have like science-based targets, like the science is informed, basically the industry is saying that we need to halve emissions globally by 50%, particularly in the tech sector by 2030. And there are some papers and things being published to give some indication of this.

But as you've described, Daniel, there's a lot more to it than that. And the more we can actually have to influence policy, the better in this way, in this way. Okay, Dan. I've enjoyed diving into the weeds with you like this. I just want to ask, as we round up, if people have found any of this interesting or would like to continue some of this research, where should they go online to find out more about either your work or some of the papers and things that you're describing?

So we'll add the show notes, but is there a particular place you would direct people to?

Daniel Schien: So, maybe a good starting point is our 2019 CHI paper on YouTube, where we carried out an evaluation of a design change to YouTube that can reduce carbon emissions. Now, this is, this has been done purely with publicly available data. YouTube was not part of this. And so this is, there are assumptions in there and some uncertainty, but methodologically it describes quite well the general approach.

It's also important to note that In this, in this model, we are using an allocation-based approach to estimating the energy consumption of the network and the changes. We are, we're transparent around this and the shortcomings of it. So, so it needs to be seen in perspective, but broadly, I think that's a good text to start with.

Chris Adams: Great, okay, thank you for that. All right, Daniel, I just realized that you are also the author of the Low Carbon Kubernetes Scheduler, a piece of work that has influenced a bunch of nerds inside the GSF. We'll have to talk about that another day, about going from Kubernetes to Low Carbonetes. But Daniel, thank you so much for spending the time on this.

I've really enjoyed nerding out with you on this, and I'm pretty sure we're going to have a bunch of really interesting links and papers for anyone who fancies doing some more research from here. So once again, thank you, Daniel. Alright. 

Daniel Schien: Thank you so much, Chris.

Chris Adams: Lovely seeing you again, and take care of yourself, have a lovely weekend.

Daniel Schien: Likewise. Thanks a lot. Bye bye. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts.

And please, do leave a rating and review if you like what we're doing, it helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

Thanks again, and see you in the next episode!