Sara Bergman, a Senior Software Engineer at Microsoft and co-author of Building Green Software, joins the discussion on sustainable data practices in relation to AI and cloud computing. They tackle the environmental impact of data storage, questioning cloud providers' carbon reporting transparency. The conversation highlights the importance of responsible AI use, optimizing cloud resources, and finding greener solutions in digital practices. Listeners are encouraged to consider the ecological implications of their technological choices and explore low-impact AI applications.

Olivier Corradi, CEO of Electricity Maps, and Íngrid Munné Collado, Tech Lead, dive into the complexities of carbon intensity data and its critical role in decarbonizing electricity grids. They explore the origins of their company and the importance of precise weather forecasting in renewable energy management. Discover how geographical dynamics shape electricity pricing and the nuances between average and marginal electricity signals. Their mission aims at empowering citizens and corporations to optimize energy use for a sustainable future.

Host Chris Adams is joined by Asim Hussain to dive into The Week in Green Software, exploring the environmental impacts of artificial intelligence and how the growing adoption of AI technology affects carbon emissions, as well as the growing complexities in the measurement and reduction of these. They discuss a primer on AI's direct environmental footprint, regulatory trends in Europe and the US, and the complexities surrounding the renewable energy credits tech companies use to offset emissions. The conversation touches on real-time cloud data initiatives, carbon accounting in AI, and the future challenges of balancing sustainability with technological innovation.Learn more about our people:Chris Adams: LinkedIn | GitHub | WebsiteAsim Hussain: LinkedIn | WebsiteFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterNews:The Environmental Impacts of AI -- Primer | Hugging Face[03:12]How Tech Companies Are Obscuring AI's Real Carbon Footprint | Bloomberg [22:25]AI analysed 1,500 policies to cut emissions. These ones worked | Nature [32:48] Events:Green IO Conference 2024 London Dates and City Locations - Sustainable Tech Partner for Green IT Service Providers Resources:Does the EU AI Act really call for tracking inference as well as training in AI models? | Chris Adams [12:21]Simon Willison on openai [14:15]EnergyStarAI (AI Energy Star Project) | Hugging Face [16:12]Meta-Llama-3.1-8B-Instruct · Hugging Face [21:28]Jevons paradox and greening software—why increasing efficiency makes sense | ASIM.DEV [21:51]Olivier Corradi [27:43]Real-Time Cloud | GSF [28:41]GitHub - Green-Software-Foundation/real-time-cloudReviewing the evidence we accept for Green hosting verification | Green Web Foundation [31:06] The Week in Green Software: Modeling Carbon Aware Software | TWiGS with Iegor Riepin [37:18] If you enjoyed this episode then please either:Follow, rate, and review on Apple PodcastsFollow and rate on SpotifyWatch our videos on The Green Software Foundation YouTube Channel!Connect with us on Twitter, Github and LinkedIn!TRANSCRIPT BELOW: Asim Hussain: Three, four years ago, everybody treated all carbon offsets the same. They didn't realize there was nuance between them. Now that's changed. Everybody needs to now pay attention to the same thing in terms of renewable energy. If you do not pay attention to the fact that there is a lot of variability in a lot of this stuff, it's all going to get tarnished with the same brush in the future and any renewable energy claim is not going to be trusted.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 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. This is our news roundup show in the Green Software Foundation podcast. So we aren't doing a domain expert news roundup. Deep dive where we go into a deep narrow subject, but rather we're taking a more broader view.So we'll try to add some context and commentary to the stories that have been shared with us that we discuss with our guests. With me today is my friend, colleague and mushroom enthusiast Asim Hussain of the Green Software Foundation. Asim, it's really good to see you again. How are your holidays?Asim Hussain: Yeah, it's been quite a long time off and just gently dipping my toes back into the swing of things. Glad to be on the show. It's a nice gentle introduction back into the world of green software. So, glad to be here again, Chris. Asim Hussain, I'm the executive director of the Green Software Foundation.And yeah, I spend my day probably similar to you thinking about how to advocate for green software. What do we need? What do we want? What are the questions that need to be answered and what are the levers that we need to pull to get action taken in this space?Chris Adams: Reduce the environmental impact of digital services. Yeah. Okay, cool. Thanks. I should introduce myself as well. Hello, folks. My name is Chris Adams. I am the executive director of the Green Web Foundation. That's a smaller, a different organization. We are a Dutch non profit focused around reaching a fossil free internet by 2030.And we do that using open as a lever. So we do loads and loads of stuff with open source, open culture, and things like that. As a quick reminder, we're going to share all the stories that we have and any projects or things that come up, we'll add to the show notes. So if you want to continue your quest to learn more about green software and how to reduce the environmental impact of digital services yourself, just look at podcast.greensoftware.foundation to see this. If you're looking in Spotify or some of the other podcast tools, you might not be able to see the links. So please do go to the website to see the show notes and you will be rewarded with diligently prepared links and helpful notes, and a transcript as well.Alright then, so, I guess, Asim, I've introduced our News Roundup format, we kind of know what we're going to do, we've done this a few times, I assume you're sitting comfortably, shall we begin?Asim Hussain: Yeah. Yes. Yeah. Let's go for it.Chris Adams: Alright, okay, so the first story I see here is actually a post from, from Sasha Luccioni, Bruna Trevelin, and Margaret Mitchell, Hugging Face the Environmental Impacts of AI - A Primer.So, this is one thing we had shared, and, Asim, I'm going to ask you, you've had a chance to look at this, what made you think this was actually worth discussing, and what would you draw people's attention to if they'd heard about this? And how would you, like, persuade people this is worth a read, for example?Asim Hussain: Well, I suppose if you're new to the space and I think there's a lot of people out there who, for whom are kind of surprised to find out that AI has an environmental impact. So this is, I mean, A lot of this stuff, obviously we've been talking about different components of it over the last couple of years, but I think it's a really good, it's actually a great summary of the different components of what makes environmental impacts of software.It's also got information there about what are some of the legislation coming down the pipeline? What are the, some of the actions that you can do? And some some things there. So I think it's a really good kind of primer for people. I think it's the title, is the title Primer? ItChris Adams: Yes,Asim Hussain: Yeah. So it does what it says on the tin. And I think it's probably could do as a really great introductory piece of information. It's got some great links there as well.Chris Adams: Yeah, the thing that it might be worth just focusing on briefly, or one thing that leapt out at me when I looked at this is that it talks about the direct environmental impacts of AI specifically. So rather than talking about AI for good, or like "isn't it great that you could use AI to, say, make it easier to deploy renewable energy, or do this, or do that?" Right?They're talking about, "no, there's an environmental impact that you still need to address regardless of whether you use something for good or for bad," and it seems to be focusing primarily on that kind of stuff. So as a responsible engineer, these are the things to think about. These are the kind of, what you might call an impact criteria, like there's carbon emissions, there's water, there's other things like that.And generally this is from one of the kind of most trusted hands-on group who are like at the coalface for all this stuff. I mean, maybe, is coalface the correct term?Asim Hussain: Yeah. I always use that term as well. And then I'm like, actually, that's aChris Adams: Maybe if you're in, maybe if you're in US AWS East, it's coalface and then if it's on AWS west, it's hydro face.I dunno. Asim Hussain: I use coalface and then if I don't, then I sometimes use front lines and I'm like, "actually, that's not a good term as well." We need a term which doesn't have a war MetaphorChris Adams: or industrial revolution connotationsAsim Hussain: Metaphor.But anyway, it's at the cutting edge!Chris Adams: Yes.Asim Hussain: Cutting edge. There you go.Chris Adams: Okay. That, that, that will do for me. Alright then. Okay. That's the first thing. I think on a following from this, when I was reading this. I quite like that it actually touched on some of the regulatory drivers that you have, because it's very common for people to talk about the AI Act, because that's probably the first piece of legislation, but it also calls out stuff taking place in Spain, and taking place in the US, and it shows that there's a kind of growing, I guess, regulatory trend to basically say, "well, If you're going to have this piece of technology in society, then we need to have a data informed discussion about what impacts it might actually have."So we can talk about, okay, where is it responsible to deploy this? But also just like, okay, how do we actually mitigate this? Because, I don't know, it seemed like cars are useful. And the fact that cars are useful doesn't mean that we don't talk about seatbelts, right? You still have to talk about them being safer, regardless of how useful they are, basically.So it's really nice to have a trusted organization sharing some information like this in a relatively roundabout in a, in my view, quite concise fashion. But if you look at the set of footnotes, wow, there's so much stuff that you can dive into if you wanted to kind of go down that rabbit hole, basically.Asim Hussain: Yeah it's a very well researched, almost a state of the art paper. And also say like, I think it's also good to know, because people don't, yes, there's regulation coming down the pipeline, and some of that stuff is more mature than other regulation. But I think when you're working inside large enterprise organizations, this is the kind of stuff that gets people to pay attention.You can be talking for ages about, "hey, look, there's the carbon impact, it's having a... we should be looking at our AI usage" and sometimes that can land and sometimes that can't, but a regulation or the threat of a future regulation is something that I've seen personally open a lot of doors. It doesn't kind of complete the internal sale of, "we need to invest in mitigating the impacts of our AI use." But it's certainly, I've seen it kind of open up a lot more doors because regulation is something that a lot of organizations pay significant attention to, and it's also something that they will, Invest time before the regulation comes out to look at it and put effort into it.So I, I look at that, right. And it's kind of, it's great to see those regulations that I wasn't aware of. So I'll be obviously using this as it's a great primer for me as well, but this is a really good way of capturing people's attention. So then you can have that more refined conversation about, well, how are you using your AI?Let's have a conversation about it. How is it, how is future regulation going to affect you? So it's a really good way of opening that door. And if you're inside an organization and you are a little bit concerned about the consumption of AI that you're having, I think for me personally, like pointing out the regulation that's coming down the pipeline does open a lot of doors, enables some conversations with leaders.Chris Adams: This actually might be a kind of somewhat appropriate time to mention some of the kind of policy stuff we're doing, because I, so in addition to doing the podcast here, I help co-chair the policy working group. And we've, I think we've, we, we're likely to be getting quite a I'm going to put a policy radar out to see precisely this kind of stuff coming up.Because, yeah, if you didn't know, I mean, okay, today is the 6th of September, and, you've seen this whole energy efficiency directive thing in Europe, right? So, in nine days time, every datacenter that uses more than 500 kilowatts of power draw has to start reporting and posting in public all of their absolute energy use, absolute water use, the amount of energy coming from renewable energy, how much of it comes from the kind of credits that you might buy which are unbundled, how much is coming from a power purchase agreement, so the kind of green energy that you've purchased directly.There's all this new stuff. There are some caveats around this. So not every single organization will have to, we'll probably publish, but we basically have a regulation that's saying, "look, this has to happen now in nine days at the time of this recording. So when this goes out, it probably will have already happened.And like this gives you an idea that if you didn't know this is happening, then you probably will, we do need to know this because this is written into the law in lots and lots of countries now. Well, all across Europe, for example, and I suspect this is the kind of thing we might see in other parts of the world because when you look at the figures and look at the data that people are currently basing policy on, it's really hard to figure out what the environmental impact of, say, data centers might be or what the growth is going to be.And if you want to plan for a grid or plan for hitting some climate targets, this is the kind of stuff you need to actually be knowing about.Asim Hussain: But it's also useful because we've been having these conversations for a while about, I think we spoke on the podcast a couple of times in the past and when we were developing the SCI specification, it came up a lot like do you include the data? How much of the data center do you include? But the biggest problem was, is that you don't know.If you decided to put into the specification, you've got to include certain, the concrete or whatever it is that goes into data center. If that data is not public, then what's the point of putting into specification? That's why these regulations, that specific regulation is so interesting. I'm interested to see what actually happens in nine days time and the quality of data that comes out.The conversations I've had in the past, because when this first started being discussed, I was chatting to a lot of, not data center operators, but people that worked with data center operators. And obviously it kicked up a storm and everybody's like, well, I need this data. How do I get this data?What is the minimum level of information I can provide to like meet? And that's where it gets really interesting for me. What is the minimum level of information I need to provide to meet the regulation? And I think in nine days time, we're going to find out what is the minimum level of information that people have figured out that theyChris Adams: Yeah, they can get away with,Asim Hussain: that they can get away with? Because when I had that conversations, I don't know where it landed now, to be honest with you, but they were like, is it at the building level? Is it at the rack level? You know, it's, and it was like, it's at the building level is where I was left at. So I think the more and more this regulation comes along and it kind of surfaces this data to us, then we can then use that data to make more informed choices, hopefully not from a consumer level.I think it should be from a, not from an end user level, but from the people who use data centers and make make different choices.Chris Adams: I think you're right. Okay, what we'll do, we'll share some, a couple of links to this, because this is something we've discussed in a few places, there's one or two working groups where this has come up, in particular, because there's also, on the, just, just before we move on from this, there's a whole, there's a current kind of, in my view, an interesting discussion going on about, okay, with this, in Europe at least, with this AI act, yes, it says that you need to talk, you need to disclose the training data, the energy used for training a model, right?But it's not totally clear if you need to also track the inference, right? So if you think about the training part, and I've shared a link to a blog post where I've basically highlighted the bits of the law that make me think that you might need to track inference, or at least disclose some information about likely inference because you can think of like the training part as like the energy going into making a car and then the inference figures as a bit like the car's mile per gallon for that model, for example. And well yeah, well it's not totally clear yet and we've seen the law passed and we'll figure out yeah.Asim Hussain: Cause I assume the only, I'll be honest with you. I assumed included inference. The only time I did it was when I just like read your article and I was like, oh, hang on. That's and that's where these things get very interesting to me. I mean when I was in working in enterprise like organizations and I've one of the things that was always interesting to me was whenever I asked questions or got meetings together, like "let's talk about, we've got some questions about the, how do we calculate this figure to meet with this specification?" And there's almost always legal got involved and lawyers got involved. And I was always kind of, I'm like, "I don't need to speak to a lawyer. I need to speak to an engineer."Why am I speaking to a lawyer? Because it's all about, "let me read this text. What can we interpret from this text? What do we need to give?" So I assume just because everybody just, we know inference is where most of the emissions are these days, I just assumed it was that, but you've now actually read the text and gone, the text has, is interpretable.Chris Adams: Well, yeah, because you think about like when this initiative was written ages ago, like a few years ago, it's gone through this massive kind of gestation process, right? And a couple of years ago, when we hadn't really got to this point where AI is being deployed in the same way, like, was it November 2023?That was like 100 million users. OpenAI had gone from zero to 100 million users in five months. And maybe last week? We should share a link to Simon Willison's blog post because he wrote about this quite eloquently. He's like, well, OpenAI have just mentioned that they're now at 200 million weekly users. So that's like doubled in a single year. So we've gone from, so inference is now a significant part of the story in a way that it wasn't previously, basically. So it may be that the law, when it was written two years ago and began that process through it, it might not have been such a concern. And this is the thing that we're, this is why it seems a bit unclear, and I think we'll probably end up with a test case that will set a precedent for people to figure out what they should be sharing or what you might need to share if you're building new foundational models in future.Asim Hussain: I mean, I can tell you that, that preChatGPT kind of announcing, it was very well known that inference was significant, as in way more than training for any, like, I can't reveal too much, but you know, it was known very much that that was the case and a lot of effort had been put into mitigating, not from a carbon emissions perspective, just from a cost and energy, but just all of that stuff.So it's kind of known, it might not have been in the zeitgeist, it might not have been in the kind of the public discourse because it's so much easier to talk about this big training runner. Maybe there's just more public data about that because inference in a way, if you think about it, is going to be pretty private.Because that's inference is basically telling everybody the business end of your where you're making money from, and they'll probably keep that pretty private. So yeah, maybe it just wasn't well known, but it was true and well known, I think, to anybody in the kind of the AI space that inference was a pretty big deal prior to this.But yeah, it makes sense. Yeah, these acts take a long time. So yeah, a couple of years ago, all we were talking about was training. It was a good, it was a good headline to discuss. Yeah.Chris Adams: Useful insight from the inside tracker team, alright. So there's one thing you mentioned actually, we're just on the subject of inference. There is this, in my view, really interesting project right now, EnergyStarAI, which is a project which is, you see a few names associated with, so, Sasha Luccioni, Sara Hooker, Régis Pierrard, Emma Strubel, Yacine Jernite, I think, Carole-Jean Wu, and one of our own at the Green Software Foundation, Boris Gamazaychikov.He's at Salesforce and he's been one of the people who's writing publicly about a bunch of this stuff and also about like, quite, the small models as well as large models. And it's, I was really pleased to see his name actually. So. Hi Boris, if you're listening to this. This is a really good story to look at, because this essentially is talking about inference and saying, "well, let's find ways to make this visible for people" two years later now, basically, and say, "well, let's see if we can find ways to introduce some of the incentives to go for more efficient inference," the same way we've done with Energy Star in other kind of industries, for example.Asim Hussain: There's really interesting, I'll try and get her on the podcast, actually, she's from IBM, her name's slipping me, so I'm not as good as names with you, you can just rattle them off, but I'll make sure to put it in. Because yeah, there's Energy Star for AI, but we're also, there's conversations inside the foundation as well now, kind of looking at SCI and how do you apply SCI to AI and kind of, there's a lot of overlap with a lot of this work as well.But what's interesting is there's this real question about what to do when it comes to inference and training. Like if you were to report that. How do you report that for a model? And the point that was raised, and I thought it was so, because I never thought of it before, which is, if you've got a foundational model, you've done the training, you've done a big training run for a foundational model, and you're now then running inference on that, when you report, let's say, I don't know how any, I think Energy Star is just going to be like a good bad kind of label, where SCI is more like a score, like a carbon per prompt or something like that.How do you apportion the training into the, do you include the training every time you call it? Because if you do, there's a really interesting thing that happens, which is these foundational models, like if you're using an open source model and you just, that costs 10 000 to run. Do you include that in your AI solution and then just say like, "Oh, I'm three, three grammars per prompt?"Then somebody else uses that foundational model. Do you then divide that by two and say, well, now you're, youChris Adams: talking about double counting kind of question, right?Asim Hussain: And then like, if that is how the measurement eventually lands, then if you're an unscrupulous organization, all that you would do is try and get as many people to use this foundational model as possible to then dilute your numbers.And so I think one of the, one of my little bugbears, and it comes up quite often, is the assumption that something that works in the physical realm will work in the digital realm. And one of the things I try and educate people as much as possible is that stop trying to take something that has worked in the physical realm and apply it to digital because there's so many ways it just doesn't work.If you're thinking about training like a scope 3 embodied carbon physical device thing. You can't divide a chip in two and I give you half and you, it's like, that chip's yours, but you can do that in the digital realm. So there's this whole supply chain accounting aspects of digital emissions, which, it just needs to be thought of differently in the world that we're in. And if you don't think about it differently, you can then have, I call them unintended, we used to call them gaming, like when people were developing the SCI, like one of the conversations we'd have on the calls is, how is somebody going to game?How is somebody going to take SCI,Chris Adams: Carbon Intensity here, right? Yeah, okay.Asim Hussain: how is somebody going to game the side? That's kind of a lot of the conversations we had at the start. Cause obviously everybody was like, we want to make sure if we develop a standard, people aren't going to thenChris Adams: Abuse it,Asim Hussain: it. And therefore the standard has no respect in the world.And so like a lot of how, like a lot of how I kind of work with the standards projects here is I'm a bit of an annoying devil's advocate. I love it actually because I kind of walk in and go "here are 10 ways I can hack this standard to present a better score without actually doing anything." And so I think that's some of the things we need to think of as we think of SCI for AI, as you think of Energy Star, as you think of these other things is yes, there's this happy path that everybody's a good actor,it will work and it will give you the right signal, but we need to think about the non-happy part, where people might not even necessarily be bad actors. It's just death by a thousand cuts. You're working in an organization, you've got a deadline, you've got a bonus you have to meet, there's a customer that you're going to have to get or you lose your business.And so you're just death by a thousand cuts. So yeah, we have to be very careful as we explore like SCI for AI and Energy Star and anything really in this space, which is talking about measuring emissions. Because if you don't think through those unintended consequences, that's a problem. And that's one of the ones I have is like, is if you're including training, how do you apportion that?You might not actually want to include training. You might actually want a separate. You actually, you might actually only want to measure the inference, because that gives a truer figure.Chris Adams: So to cap this off, I'm going to, as we move to the next story, I'm going to link, share two links which might be useful for this. So the first is the link to the Meta Llama 3.1 8B, their model card. They literally say "the methodology used to determine energy use and greenhouse gas emissions can be found here."They've linked to it and they said "since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others." So they're basically saying you don't need to count that part. That gives an example. We'll share a link to that. The other thing we'll share a link to for the show notes is actually the Asim, for this software, carbon, yeah, this isAsim Hussain: Yeah, this is my unintended.Chris Adams: This is a good example because this is where you've basically said, this is me, like, red teaming this approach, and these are the ways I can basically, in bad faith, try and engage with this example.And this will probably be useful for people who are looking at this, to get an idea of, like, how some of these standards or some of these conventions are being developed. Alright, shall we move to the next story? Because it kind of does relate a little bit to, basically, tweaking numbers to present a view of the world, basically.So this story is now, how tech companies are obscuring AI's real carbon footprint. This is a story from Bloomberg, I believe. Asim, do you want to introduce this one?Asim Hussain: So yeah, so I thought, well, the reason this kind of popped up on my radar was, I forgot what I posted on LinkedIn, but I was posting, I started posting a bunch of information on LinkedIn about the use of, the use of RECs and the effectively like kind of renewable claims that organization makes and how it's in a really frustrating way, it actually puts us in what we're doing in kind of competition with this very important energy transition, because the argument I'm making is, look, you can either do two things and then we'll talk about AI.Let's keep it to AI. You can either make your AI model more efficient, so it consumes less energy. Or, you can do absolutely nothing, and just buy offsets, energy offsets, RECs, whatever you want to call them, to mitigate, theoretically, your energy offsets, your energy consumption. And that's kind of like being this "Are we friends?Are we not friends?" How do we like, we want to support the energy transition, but at the same time, like we really want to advocate for more energy efficiency. So, and I think one of the things we've spoken about is that there's, when you do make these renewable energy claims, like one of the things that you do with all types of offsets to kind of avoid a greenwashing claim, you have to have that additionality component to your offset, which for the audience means that how do you, if I'm saying this thing is offsetting your emissions.What is it a litmus test to say that is a true statement and is basically, are you actually adding? So for a renewable energy credit, it's like, if you weren't about this renewable energy, would that thing have happened?Chris Adams: So you're talking about the counterfactual here, right? So you're trying to compare something against. This is, you see a load of this in the hydrogen circle, in the hydrogen, in the world of hydrogen, because Just like datacenters, hydrogen electrolysis, like the electrolyzers use loads and loads of energy, right, and one way that you can do that is just by plugging them into the grid, right, and there's various people doing various things to say, well, I'm just going to buy a bunch of, say, renewable energy credits, right, and that's going to make that count as green, and there's, that's, in some ways, that's kind of somewhat problematic because, essentially,Asim Hussain: coal to make hydrogen.Chris Adams: Yeah, that's not exactly what all, you're, in many cases, you're a, you're burning coal to make hydrogen, so the actual net, it's a net loss in climate terms. But also, the, there's been a big fight in the kind of hydrogen world of, to have like this notion of three pillars. Where you basically, if you're going to have something, if you're going to count something as green hydrogen, then you need to be talking about new infrastructure being added to the grid to provide that new supply.You can't just use, you can't just take from the existing stock of green supply and then count that as green. And this is one of the things that we've seen, like, I don't, Amazon made the news, I think a few weeks, a while, because they basically acquired a data center from a company called Talon, I believe, where they're right next to a nuclear power station, right?So this, that you, there are some people saying, "oh, this is great, isn't it good that Amazon's using a bunch of clean power," but then you've got to think about, well, okay, who was that clean power going to before? Was it going to the grid? Like, there's a whole discussion there about this. Yeah, so there's a whole set of things to be talking about and this is why this is such a kind of fraught area, basically.Asim Hussain: I mean, but I think the way to bring it back to something that people understand is when we talk about carbon offsets, I think now it's more understood that it's kind of like you, you have carbon removal offset. So you can plant a tree. Right? And then you planted the tree, that tree will grow, and there's issues there.That tree will grow and also suck carbon from the atmosphere. And you can say that's a carbon credit of planting a tree. Or, there's carbon avoidance offsets. And there's many various, and there's actually very good variants of carbon avoidance offsets. But there is a variant of a carbon avoidance offset where I've got a tree.And you pay me not to cut it down. And so where's the additionality? If I'm actually planting a tree, it's happening. I'm planting a tree. I'm adding additional kind of capacity in carbon removal. And in the renewable energy markets it's exactly the same. You can have renewable energy. Which if you buy means a renewable power plant is going to get built and you can have renewable energy which is just kind of sold and if you buy it or you don't buy it there's no change nothing's going to happen there's no more new renewable plants going to get built. Only one of them has that additionality component and so, therefore, only one of them should really be used in any kind of renewable energy claims.But both of them are allowed in terms of renewable energy claims. So in terms of what this article is talking about, when they're saying "tech companies obscuring AI's real carbon footprint," they're actually talking about companies using what's called those unbundled RECs, which is those RECs which do not have that additionality component.And then use buying them and then saying, "well, that's mitigating my environmental impact." And what the article is talking about is really, you should be looking at higher quality, Renewable Energy Credits, ones with more additionality components to it. And I think that's really interesting. There's actually also really, Olivier Corradi from, don't know if I'm pronouncing the second name correctly.Electricity maps. Yeah, he, when I was talking about, he shared a really interesting article he'd written a year ago, which I thought was interesting as well. I'll share that here if you've got it. Yeah. He's actually advocating for like a more nuanced approach to looking at renewable energy in that there's additionality, then there's additionality, and then there's additionality.There's like different levels of additionality. There's like, "this definitely 100 percent would never have been built unless you bought this renewable energy credit." And there's other ones like, "we may not have been able to build it, but we had some funding from here and there." So there's kind of different levels of additionality here as well, which I thought was really interesting also.I'd never thought of additionality more than just a binary yes/no. And he was saying it's actually more of a score for a renewable energy credit. ButChris Adams: Yeah, there's totally a continuum there. So the thing I might share for people who are looking for something actionable to work with here is basically the numbers that you often see reported by technology firms. There's all this, there's all this nuance hidden behind it. And there's one project called the Real-Time Cloud Project inside the Green Software Foundation, which essentially is a data set of the largest three providers.So that's Amazon, Google, and Microsoft. And they've got the figures shown in both the kind of location-based figure, which is the closest thing you might think to, like, the physical location, the physical impact on the ground. They also talk about some of the market-based figures, which is what lots of firms like to use, like market-based on an annual basis.But they also provide a few other details and a few other ways of talking about it, because some firms are now talking about hourly, basically hourly green energy versus annual green energy, with the idea being that you, it's a way to try and avoid making claims about saying, "I'm running a data center at night with certificates coming from a solar farm." This is inherently a little bit silly. So they address that stuff. So there's a, there's now, I think it's in the final stage of what's referred to as consistency review, where every member in the GSF is able to just say, "Hey, I object to this, or I'm not sure about this." And then, yeah, there'll be an open data data set for every single region from the three largest providers, which make up more than two thirds of the entire cloud market.So you have some meaningful numbers that have come from the actual big providers themselves that you can actually, that we can work with.Asim Hussain: And I think, like, I think basically my, I think the point I'm generally raising out, out there with another kind of, one of the reasons this article was very interesting to me, and especially the work that Realtime Cloud is actually interesting. Three, four years ago, everybody treated all carbon offsets the same.They didn't realize there was nuance between them. Now that's changed. Everybody needs to now pay attention to the same thing in terms of renewable energy. If you do not pay attention to the fact that there is a lot of variability in a lot of this stuff, it's all going to get tarnished with the same brush in the future and any renewable energy claim is not going to be trusted.So I was, I'm kind of a guiding and advising organizations to pay very close attention to kind of the type of renewable energy that you're buying. And be aware that because of podcasts like ourselves, there's generally, it's a Bloomberg article talking about this right now. It is now becoming very aware in the minds of a lot of people who care about this space, who listen to our podcasts, who are paying attention, that there is nuance here.They're paying attention. And so as an organization, you need to pay attention to this as well.Chris Adams: Cool. Asim, I'm just going to add this one thing because I realised I should have mentioned this. So I work in an organisation where we do track some of this stuff. We track the transition of the internet away from fossil fuels to greener energy. And, I've shared a link for the show notes. Because we're basically reviewing our own evidence that we accept for green hosting.And we've linked to a couple of papers. And specific reports, which dive into this a bit more, which have kind of also prompted us to start looking at this. So, organizations like the Science Based Targets Initiative, we mentioned there. We talk about some of the other things that we, some of the nuances around RECs.And yeah, this is, this will be something we'll be doing. So we're going to be essentially figuring out how to do this ourselves in the open over the next few months. So, Yeah, I guess it applies to small firms as well as large firms.Asim Hussain: Yeah, yes, absolutely.Chris Adams: All right, should we move to the next story? So, this is a story.Researchers analysed 1 500 climate policies to find what works. And these are the lessons for Australia. I think this is the link you shared with me, Asim. There's a very kind of Australian centric kind of point of view, which, as someone born in a small mining town in Australia called Prospect, because what else would you name a mining town other than Prospect, because it's full... you? Yeah,Asim Hussain: I didn't know you were born in Australia.Chris Adams: Ah yeah, born in Australia, small mining town.Yeah, I was literally born in a mining town called Prospect, and it's near One Tree Hill. Can you guess how many trees are on that hill? And it's next to Dry Creek. Can you guess the conditions of that river? Yeah, it's descriptive rather than creative, is the term I've heard people in Germany who do similar things talk about, actually.Okay, so you shared this story, maybe you can introduce this one here, because I think it's quite relevant in this discussion, what we were just talking about in the previous two stories, actually.Asim Hussain: Hmm. So I think it was just really interesting. It was an analysis of like 1500 climate policies and just really looking at what worked and what didn't work. And I thought it was interesting because we spoke a lot about, we've spoken a lot about things like carbon levies and things like that on this podcast.But what I found interesting about this article was they, again, brought nuance to the discussion and saying, "actually there's different, different policies seem to work for different types of organizations and also combinations of policies seem to work better than individual policies." So a couple of interesting ones.So one of the highlights I got, so some of the, in developed economies, some of the most successful cases were the results of two or more policies working together. So that could be like a ban or something, as well as like a carbon tax, kind of pulling those things together. Oh, for instance, like a great example they had here was like, for instance, example, a fuel efficiency mandate for vehicles combined with subsidies for developing like charging stations and things like that. So then you've kind of got the pressure on both sides. And another thing that was really interesting was cause we spoke about kind of carbon levies and pricing was particularly effective policy in sectors dominated by profit orientated companies, such as electricity and industry.So I just think it was really interesting to kind of think through it from that perspective.Chris Adams: So there's a really nice example, there's a few really good examples. Good concrete examples of this to make this, like, something you can, like, get your hands around. So in America, right, we've seen the Inflation Reduction Act. So that, in many ways, are kind of it's all carrot, no stick. So the idea is that there's massive amounts of subsidies for building out, like, for, like, EVs or building out new, kind of, battery gigafactories, all this stuff like that, or things which are essentially make deployment of renewables much, much more profitable than they otherwise would be, because they're gonna, because you have all these kind of subsidies saying, well, we're going to give you a production tax levy.So for every unit of energy you produce, you'll be able to get, you'll be, you'll get a credit that you can actually apply. So your project over the entire length of it will end up being slightly more profitable. That, there's stuff like that, that you see, which is coming from one end. But we've also seen, In America, the EPA, the Environmental Protection Agency, they've got, they've now come in with a stick, or they're coming in with a stick now, to basically say, well, we're going to have to regulate carbon emissions.And this now means that it's going to be all new kind of coal fired power stations or gas fired power stations, there'll be all these restrictions on how you should, how you can use them. And this is particular, the reason I raise this in America specifically is we were talking a little bit about AI before, right?Now these regulations, I don't think that many technologists are aware of right now. They basically say if you're going to run a gas-fired power station, you need to fit loads of carbon capture and storage onto it, which is, broadly speaking, if it does work, it's not something that's really used in large amounts right now.And what you currently see right now is you see lots of utility companies basically saying, "Oh, the only way we can possibly meet demand for AI is to build all this gas right now." And the problem with that is that ends up locking in all kinds of emissions. Because once you've built something, you have this incentive to kind of try and get your return back on building this in the first place.And this feels like," I don't think people have realized just how much of a stick this is going to be, because as far as I can tell, all the laws from the EPA basically say, look, you can't build gas like this, and you can't actually do this." So we're going to have, we've got like this case of massive build out of AI coming up against all these regulatory forces as well.And it's going to be quite a significant fight in the next 6 to 12 months, I think, because yeah, this is, we've now had the honeymoon period of all carrot. Like you said in this piece, and now we're coming up to the stick, which is the other part, to kind of make sure that you can, make sure the significant part of the US grid is going to be decarbonized by, I think it's the mid 2030s, basically, is what they're doing, that they're aiming for with this.But we have the same thing in the UK as well, like, UK right now, we've got a target for, the UK has agreed to try and decarbonize the grid entirely by 2030, which is great for us as an organization because we, we want a fossil free internet by 2030. So we're like, "oh, thank God the UK is doing this." The UK government, one of the big kind of manifesto policies from Labour coming in, who've just won the election is "we're going to have a clean grid, entirely clean grid by 2030."So five years, basically five years time, they're going to get rid of all the fossil, almost all the fossil gas generation, right?Asim Hussain: How are they going to do that? Chris Adams: That's what we'll find out. But the thing I found out when I spoke to some people who, basically, this is actually all based on some modeling using a piece of software that we interviewed a chap called Iegor Riepin, he was talking about this in one of the episodes, we'll share a link.That software was, basically, these kind of things were put together by some analysts on our laptop saying, well, this is what you can do. There's a report from Ember Climate where they, the report is called Escape from Gas, I think, or A Path Out of Gas. And this was one of the things that was written in 2022, when gas was super expensive, to say, "well, this is one thing you could plausibly do for this."And yeah, when, the thing about policy, people reach for what's there. This turned into one of the things that one of the parties led on, and now we're going to see if we do see a fossil free grid and fossil free internet in the UK by 2030. Because, yeah, it's fascinating. I'm so, this is the most exciting, most excited I've been about UK politics in a very long time.Asim Hussain: I don't know. I might dampen it for you. I'm just not, I'm just not very, I'm just, there's a lot of manifestos that come out from governance when they join and there's a lot of disappointment in the years later when they, when it doesn't manifest, when their manifesto doesn't manifest.Chris Adams: So this is the final thing that might come in, might be relevant. So the modeling that was used for this as the basis to say, "yeah, we can do this." This one thing that ends up being, so I'll share a post to it, which I end up doing a bit of research and speaking to some of the people about this. It's actually very conservative, more conservative than the National Grid's ownestimates about, specifically in our industry, demand size and batteries. So, these are the two big things that we're likely to see a massive increase in.Asim Hussain: That's what gas is used for more like this is specifically to get rid of gas.Right. Chris Adams: yeah, so the,Asim Hussain: peaker plants and then therefore you can do a little demand, demand responsible.Chris Adams: Partly that, the thing that they said is like, the, their plans basically are relatively conservative about the ability for demand side of reduction, making your, you know, Carbon Aware in stuff like that, right? And there is another thing that we've seen is that the UK government is actually being quite gung ho about deployment or deploying all these new data centers.So I'm kind of thinking, is there a chance to actually say, "well, okay, if you're going to have this deployment of all these data centers, and you know that one thing you're going to need to have is a much more responsive grid, is there a path for all this kind of carbon aware infrastructure to actually serve some of the roles that you wouldn't have to typically rely on peaker gas plants to actually fit, to like kind of fit?"There's a bunch of stuff there and I think we'll learn basically because, yeah, this has been a really ambitious goal and you've also got this other idea to like bring in something which, can be quite flexible, but only if you incentivize infrastructure to be flexible, because for the most part, we don't see an economic incentive passed down to the consumers of infrastructure to be using this right now.So, yeah, maybe this is a help of one piece. Yeah.Asim Hussain: Yeah, I mean, I think there's been, there was some really good work done kind of several years ago, and this will be really good because one of the things I've seen is that the, all the positive moves I saw kind of three years ago regarding new data center rollouts, hydrogen fuel cells, kind of building kind of a much more advanced data center seems to have gone back a little bit.And yeah, You're right, I think data centers could lead the way in terms of demand response. I'm not even talking about compute demand response. You can just take batteries, you can fill data centers with batteries and then they can store and then they can do their own sucking from the grid when it's clean and powering their own infrastructure when it's dirty.You know, there's, there's other solutions, which doesn't even necessarily need kind of a software,Chris Adams: Yeah, exactly. I mean, this is one thing that we've seen in Ireland. There's precedent in Ireland where people have said, "if you're going to be connected to the grid, you need to be prepared to be kind of complementary or sensitive to the needs of the grid for this." So, I think there's actually room for this, and it will be really nice.I think that this feels like, given such an ambitious target, it does feel like a role where you could actually tell a good story about green Software, and be part of the solution as opposed to part of the problem, because a lot of the discussions around like rolling out of digital infrastructure is basically saying we can't possibly meet this demand.But if we accept that demand is dynamic, then there is a chance to actually fit this in, and that feels like definitely worth going for, particularly to kind of maintain this kind of social license for operation, particularly for technology firms.Asim Hussain: And I think a lot of what you've just said over the last couple of minutes runs very counter to what we were saying before about, I mean, everything you just described, this is all related to that whole idea of additionality. It's all about how do we transition, truly transition the grid to be fossil free?And you need solutions like this. Not necessarily buying unbundled RECs, but you need to actually, like, think through, "well, how do I how do I be a better citizen in the grid infrastructure, do demand response, be sensitive, not demand energy when everybody needs it and therefore we have to spin up a gas power plant or something like that."So these are the kind of things you need to actually transition the grid.Chris Adams: Asim, I think we might have fallen down a bit of a grid rabbit hole,Asim Hussain: Yeah, we've done it again, haven't we?Chris Adams: Yeah, so, we're gonna have to move on, I think we've come up to time, but Asim, it's really nice to see you again, I'm glad you had a nice holiday, and I guess we've got a bunch of new things to do this quarter, right? With various projects we have inside the Green Software Foundation, and in the other member organizations related to it.Alright dude, it's Friday, so have a lovely weekend, and for those listening, we'll put all the links to everything we've discussed in there, and if there's something you didn't see, Please do let us know, and we'll make a point of adding it. Alright, thanks a lot folks.Asim Hussain: Thanks, Chris. Bye.Chris Adams: See you around soon. Bye! 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.

Philipp Wiesner, a research associate and PhD student at TU Berlin, dives into the future of carbon-aware computing. He discusses the innovative Vessim project, which models energy consumption scenarios linked to renewable resources. Topics include the challenges and advancements in federated learning, optimizing workload management to shift energy use toward cleaner sources, and the complexities of measuring carbon intensity. Listeners will gain insights into dynamic simulations that help enhance energy efficiency in computing systems.

This week we are joined by two PhD researchers, Silke Kaiser and Chiara Fusar Bassini, from the Hertie School in Berlin. With host Chris Adams they discuss their use of data science and machine learning and how they are using them to tackle some of today’s most pressing environmental challenges. Silke shares insights into her research on predicting cycling traffic in cities to better inform urban planning and promote sustainable transport, while Chiara discusses her work on analyzing European energy data to support the renewable energy transition. Together, they explore the intersection of technology, data, and policy, highlighting the importance of data-driven decision-making in achieving sustainability goals.Learn more about our people:Chris Adams: LinkedIn | GitHub | WebsiteSilke Kaiser: LinkedIn | WebsiteChiara Fusar Bassini: LinkedInFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterNews:From counting stations to to city wide estimates: data driven bicycle volume extrapolation | Silke Kaiser [09:46] Pedalling Towards a Greener Future: The Impact of Cycling and Active Transport on Climate Change and Public Health - Catalyse [12:46]Chapter 10: Transport | IPCC [14:10]Estimating Coal Power Plant Operation From Satellite Images with Computer Vision [24:11]Does the EU AI Act really call for tracking inference as well as training in AI models? [38:27]What is the methodology used to measure the carbon footprint of training Llama 3.1? [41:12]Climate policies that achieved major emission reductions: Global evidence from two decades | Science [44:27]Microsoft employees spent years fighting the tech giant's oil ties. Now, they’re speaking out. | Grist [46:51]A review of the ENTSO-E Transparency Platform Resources:How does AI and ML Impact Climate Change? | EV Ep 5 [05:38]French Revolution: Cyclists Now Outnumber Motorists In Paris [07:25]Berlin’s Efforts to Reduce Driving Stalled by German Car Culture [07:42]Emissions of Carbon Dioxide in the Transportation Sector | Congressional Budget Office [16:47]Electricity Maps [21:59]Machine Learning for Sustainable Energy Systems | Annual Reviews [30:41] CodeCarbon [40:49]Light bulbs have energy ratings — so why can’t AI chatbots? [42:16]The Chronic Potentialitis of Digital Enablement | Vlad Coraoma [46:10]Blackout (Elsberg novel) - WikipediaInternational Energy Agency - an overview | ScienceDirect TopicsTaxation - an overview | ScienceDirect TopicsHow fast is Germany cutting its greenhouse gas emissions? – DW – 07/10/2023Tackling Climate Change with Machine Learning | ACM Computing Surveys If you enjoyed this episode then please either:Follow, rate, and review on Apple PodcastsFollow and rate on SpotifyWatch our videos on The Green Software Foundation YouTube Channel!Connect with us on Twitter, Github and LinkedIn!TRANSCRIPT BELOW:Silke Kaiser: I like the term of fighting fire with fire. You know, you're trying to make it better, but you're making it maybe even worse. But I think if we make some smart choices along the way, I rather like to compare it to the idea of fighting a forest fire with a controlled burn. What I'm trying to say is that there are different approaches that we can actually also reduce the emissions caused by AI.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 Environment Variables, where we bring you the latest updates and news in the world of sustainable software development. I'm your host Chris Adams. It's important to understand that when we talk about sustainability and technology, it's easy to mix up sustainability of software with sustainability through software. Sustainability of software development is about understanding the direct impacts of technology and doing as much as we can to reduce it without delivering a worse experience for people using the software. Here, we care about the impact of code, like making it more efficient or making sure the energy we use is cleaner and coming from the cleanest possible sources. So when we talk about green software or green IT, this is what we're talking about. Sustainability through software development, this occurs through the application of software to solve a specific sustainability problem or provide us with insights that we didn't have previously to help us meet some of our sustainability goals. To make this really concrete, you can use green software and talk all day long about the sustainability of software whilst helping people drill for oil and gas. Now you can do that, but it's really not a good idea if you wanna hit any kind of societal climate goals. And if you're listening to this podcast, I think you probably don't wanna do that either. So while we usually cover the sustainability of software. It can also be helpful to look up from our keyboards sometimes to talk about the effects of software, the effects it can have for helping us reach our climate goals. So in this episode, we'll be diving deep into the work of PhD candidates who are pushing the boundaries of what's possible in sustainability ad software. In this episode, we're joined by two researchers from Berlin Institutions, the Berlin School of Economics and the Berlin Hertie School of Governance. So what insights can we gain from their research? How are they using technology to address some of the challenges around sustainability today? Let's find out. So, first of all, Silke, can I just give you folks a bit of time to introduce yourselves? I'll hand over to you, Silke, and then hand over to you, Chiara, to introduce and give you the floor. So, yeah, Silke, thank you very much for joining. The floor is yours.Silke Kaiser: Thank you very much, Chris. I'm Silke Kaiser. I'm a PhD researcher at the Berlin School of Economics and at the Hertie School Berlin. I'm excited to be here today. My research focuses on the analysis of sustainable transport data, with particular emphasis on cycling data. I utilize various tools from machine learning, data science, and spatial statistics to explore this field.Maybe more on a personal note, outside of academia, I would say I'm a bit of an auto enthusiast. Just earlier this week, I came back from a vacation in France, but I'm happy to be back and to join you for this episode today.Chris Adams: I'll be Back just in time for the weather to be nice in Berlin, right?Silke Kaiser: Exactly.Chris Adams: Cool. Thank you for that, Silke. And, Chiara, can I do the same to give you some space to introduce yourself as well?Chiara Fusar Bassini: Yes, thank you for that. My name is Chiara Fusar Bassini. I'm a PhD researcher at Hertie school in Berlin. I'm very excited to be here for my very first podcast. My own research focuses on the analysis of European energy markets. And I use data science and machine learning to analyze time series of dispatch of single power plants and try to look at how power plant dispatch has changed in the context of an evolving energy system. Beside being an academic, I'm also a rather lousy actor in an amateur theater group here in Berlin.Chris Adams: Cool, thank you. So you said you're working in a theatre. Are there any particular roles you play or anything like that? Because I think you may be the first actor who's come onto the podcast actually, Kiara. Chiara.Chiara Fusar Bassini: Well, I have a tendency to take on the roles of either mad people or police men or like with gender changes. So anything in between is business, usually like mad people it has been, like the latest role I've had was a police officer. And before that I was a mentally ill person.Chris Adams: Wow. I did not, I was not expecting that. Okay. All right. Well, welcome onto the show. And I guess that maybe we'll see some, productions in future. Folks, if you're new to the podcast, my name is Chris Adams. I am the executive director of the Green Web Foundation. That's not the same as the Green Software Foundation.It's one of the members of the Green Software Foundation. I'm also one of the chairs of the Policy Working Group and also one of the hosts of this podcast. Okay, so before we talk in depth about your research, I just want to check for people who are also listening to this, you've been doing this research under the supervision of Professor Lynn Kaack, I believe, is that the case?Chiara Fusar Bassini: Precisely. We've been working with her for a few years now, each of us.Chris Adams: Brilliant. Okay, so what we've, the reason I'm sharing this for listeners is that we did an episode 5, where we spoke to Lynn and another person, Will, oh his name has changed, I think it's Will Alpine now, talking all about climate change and AI, two years ago. So if you enjoyed this, I would suggest looking at that to learn a little bit more.And that might provide some extra context for this discussion. All right, then. Are you two folks sitting comfortably? Happy to go ahead with this?Silke Kaiser: I think it's good to go and share insights on our research.Chris Adams: Good stuff. Thank you. For anyone who is listening as well, the thing I'll just share is that we will share show notes with links to all the projects and papers that come up to this. So if any of this is interesting to you, then yes, we, you can continue your quest for more knowledge and insight outside of this podcast. All right. Silke. Let's start with you. Your research focuses on predicting cycling traffic in cities using data from bike sharing systems. And this is something I believe you worked with Lynn and another researcher, Nadja Klein, on from this. Could you maybe just explain a little bit about how this actually helps people when they're trying to design how people move around in cities like, say, Berlin or Paris or things like that?Silke Kaiser: Yes, I'd love to. So what we generally see when we think about transport in cities is that public space in cities is limited. Whether it be in Europe, the USA, or any other place. Generally, then when we think about how we want to redistribute the space among different mode shares in city, we see that there often tends to be a heated debate.And especially as we work towards promoting more sustainable modes of transport and therefore reducing the CO2 footprint of our cities, conflicts often arise. And the question then remains, how do we actually want to prioritize these different modes of transport and allocate the space and also financial resources among them?So, for example, take Paris as an example. I've lived in the city for several years during my studies. And what you can see in the city that in the past few years, they made a lot of changes to prioritize cyclists, which has improved the uptake of cycling, but which has also led to quite heated debates.The same we can see here in Berlin, the city that we're currently both located in, is that we had a re-election last year here in February, and a lot about the debate actually hinged in part on the choice between prioritizing cycling and individualized motorized transport. So what I do try to do in my research is actually to provide more data to this debate, because what I see as the main challenge in these kinds of debates is that we don't have accurate data on how much cycling traffic we actually have in cities.Chris Adams: Ah, okay. So maybe just kind of dive into there. So it's basically, we don't have the data to really have a data informed discussion, basically. That's one of the things that is the challenge here. And for context, so the three of us live in berlin. We saw, basically, the new government and the new mayor come into power on a very kind of pro car platform, basically.So this is what you're referring to, right?Silke Kaiser: Precisely. So, answering maybe the first part of your question, so for example, in Berlin, we have only 40 locations where we count cyclists. I think in Paris, it's around 53, in New York, it's 41. And then in Berlin, for example, we have around eight times more locations where we count motorized traffic. So we just have much more data and much more information on motorized traffic than we do have on cycling.And then yes, in Berlin, actually, there was quite a bit of a heated debate pretty much between let's say the inner city, which was more pro cycling and in the suburbs, which were more likely pro cars. The government switched from a green to a more conservative government, which actually decided to suspend actually just this month quite some, projects, long distance commuter paths, and both bicycle parking houses in cities.But that's just more really on the political side of the debate. And then what I really see as the main challenge is that just this data and the information is missing on where we actually would need the infrastructure most, given how much we want to prioritize cyclists.Chris Adams: Okay, so maybe I should ask, where is the data coming from then, for this?Silke Kaiser: So, we do have these 40 counting stations in Berlin, that's the case study that I'm looking at, and what we then figured is, well, we don't have that much precise data on cycling, but we do have an abundance of other data on cycling. So, for example, we do have, as you mentioned, the bike sharing data. We do have as well data from Strava.That's an app popular to record yourself while doing sports. We do have data on infrastructure, we do have data on weather, we do have data on socio economic factors. And we figured, well, why don't we use all this available data to actually extrapolate from these few isolated locations to actually obtain city wide estimates.And that's what I did in this research with Lynn Kaack and Nadja Klein. So what, precisely we do is that we train various machine learning algorithms to use all this kind of data in combination with the cycling counting station data that we have to obtain citywide estimates. And what we actually found is that only using this data is a bit tricky.It provides us estimates for completely new locations 32%, which is you know, rather good in comparison to having no data at all, but still 32 percent is an error I take seriously. And what we then simulated, continuing with this research was, what if we make some sample counts for new locations? So for example, if I want to estimate the traffic in front of your house, Chris, the cycling volume in front of your house, we figured that if we would maybe put you, someone else, on an automated machine there to count the cyclists, and we were to count the traffic for 10 days.And then combine it with our models, we're able to get estimates with an error of only 17%, so really rather low for complete new locations. And this gives us a good estimate of how much cycling traffic we actually have in every single street of a city.Chris Adams: Ah, I see. Okay, so you're using the machine learning model, basically, to make the extrapolations when there's, there might not be so much data, give you more accurate so you can say, "well, I'm more confident that this many people are trying to get around using an active, non gas burning form of transportation," for example, right?Silke Kaiser: Precisely,Chris Adams: Ah, okay. All right. Thank you for clearing that up. And I understand. And just, we'll come back to this a little bit later, but you said you're using ML. not the same as generative AI or something like that. That's a different, there's, a whole flavor of different things you might be using there, right?Silke Kaiser: precisely. I mean, there's many different models out there. In this paper, we used rather simple machine learning algorithms, nothing comparable to maybe what most people think of when they think about chat GPT or whichever generative AI you might think about. Those are really rather simple models making usage of the data we have.And those models So, I just tend to have, I sometimes, you know, sometimes I think about, I mean, you can target these problems with very complicated algorithms, but sometimes using rather simple algorithms might just be sufficient. And that's what we actually found in this research.Chris Adams: Okay, alright, thanks. When I was doing a bit of research, I realized that you also wrote a piece around, I think in a publication called Catalyse, where you're talking about active transport and why it's important for greenhouse gas emissions, because you've spoken about like mode shift, which I assume means basically moving from a Being in a car to moving and maybe a active transport, which people use like scooters, bikes, stuff like that.That's what I think you're referring to there. Could you maybe talk a little bit about how this research actually contributes to like the adoption of cycling as one of the potential modes, because you spoke about it in Paris and I went to Paris and was being pretty transformational when I was there compared to being a few years back.And I also bike around Berlin too. So I have a vested interest in learning this. Maybe you could like lay out why that's actually benefiting and how active transport helps. Basically, us meet our climate goals.Silke Kaiser: Absolutely. So, when I, Referring to other research that I've read, research that I haven't done myself, but it is out there and it's been cited a lot, is that we do see that cycling has numerous benefits. It benefits your individual health. If you cycle, it's good for your physical health and then all the sicknesses or illnesses related to insufficient physical health.We can also see that if you cycle, it's also good for me because then generally we see a reduction in noise and air pollution in cities. So it really benefits the public health, the broader public. And then yes, absolutely. I mean, I did read the IPCC report, which is a report on climate change and it comes out, the last one came out in 2023.And what they found is that actually 15 percent of net global greenhouse gas emissions are related to transport. This of course includes all kinds of transport, but also, one of them is urban transport. And then switching, there are many levers how to tackle this, right? And within general, as in climate change, there's no one solution fits all, but switching from motorized traffic to cycling is one of those means to actually reduce those greenhouse gas emissions.And coming a bit back to my research as well, what we find also in research and science is that all you can think about, you know, talk to your friends and family and gather some anecdotal evidence, you'll probably find that one of the biggest deterrents that keeps people from cycling is that they actually are afraid because there's not enough cycling infrastructure.They're afraid of accidents. And that's a relevant fear. We do see many accidents in cities right now, mainly between motorized traffic and cycling, but also all kinds of other accidents. And what we do can do in cities is, to actually promote cycling, is to build more attractive infrastructure for cyclists.This can include bicycle lanes, a better design of roundabouts. And all this attracts people to cycle, but, it actually also reduces the risk of serious and fatal accidents. So what I really try to do with my research is that again, if we have these heated debates in cities, how we want to distribute space among cyclists, cars, delivery trucks, etc.I'm trying to provide data on where we actually have how many cyclists in which street, so that when policy makers or transport planners come around, they can use my data and actually make fact driven decisions when and where infrastructure benefits the most, the greatest number of people. And that's how I hope my research can contribute Chris Adams: Ah, I see. Okay. So that's really helpful. And I think there are some kind of comparisons I can make, which make to maybe help me understand if, and some of the listeners. So you know how a couple of years ago, in the middle of the pandemic and COVID, one way to reduce the number of COVID cases was just to reduce the number of people taking tests, right? You know, that's not necessarily the best way to solve that, know, and it feels likewe've got a similar situation. We've got a data asymmetry problem here it looks like you're doing some work to address for that. I mean, Also, you've spoken about, as I understand it, there are various parts of, like, our economy which are easier to decarbonize than other ones.Like if, for example, in Germany and in America, transport's the biggest, one of the places where we've seen not so much progress on reduction, on carbon emission reductions compared to things like the energy sector and stuff, which is decarbonizing relatively quickly. So this is what some of this is a reference to. Okay, so what we'll do is we'll share some links to Catalyst, I'm sorry, Catalyse, the paper there, and also some of the papers that you have. So we spoke about Paris, and we've spoke about Berlin, where we both live. Are there any other places you would point people to as examples of, okay, this is what good might look like, and this is one place which actually has quite good data to show where you've actually seen quite effective policymaking to kind of change the environment to make it easier to cycle? Because, yeah, not everyone wants to become a MAMIL, like middle aged man in Lycra wearing the helmets and everything like that.Silke Kaiser: So, I mean, there are definitely some cities that you know that are popular for cycling, for example, just earlier this spring, I did a research day for some months in Copenhagen. And obviously Copenhagen is a bit of a dream for cyclists, right? I'm not the first one to mention this. And then there are other cities, Amsterdam, you name it, but generally I do have to admit that in my research, I haven't really come across cities that do have much better data.I would say it's a grasping problem across different cities that data is missing. Copenhagen and Amsterdam have taken political decisions to prioritize cycling, but I do have to admit that I didn't, I haven't come so much across that they've made this as a data-driven decision, but this was more of a political decision.Chris Adams: Ah, okay. And there's one thing I'll just ask before, Chiara, I'm, okay, I am a closet energy, well, not very closet energy nerd. I'm totally gonna, looking forward to talk about that. But Silke, I was just going to ask you, so you mentioned use of Strava and you mentioned the use of, Okay. It's useful to have these new sources of data, but there's also a question about the provenance of that data and like the circumstances under which it's collected.So for example, we've seen Strava used in lots of other places and if you're using Strava, you tend to be a bit richer, a bit younger, a bit healthier than most people. Maybe you could talk a little bit about some of that, because there are various sources available to inform these policy sessions, and like, Strava is one.But like, where else, like, assuming you had, you were suddenly queen of the world, where would you wish you could get some of the data from to kind of inform this in future?Silke Kaiser: So you're absolutely right, Strava definitely is quite biased. It's the data, for example, for Berlin, I definitely know that they're all male, they're mainly male, young, and they do tend to do very sporty biking in comparison, for example, what I probably do to commute work. So it is true that some of the data we use is biased and we're trying to balance this off with the other data sources that we're having.We're also taking socioeconomic factors into account because obviously we do not want to have, infrastructure is meant to be there for everyone and not for privileged or less privileged people. It's, meant to be equal for everyone. But then obviously I thought about a lot, well, how could we actually improve the data availability in cities?And I definitely see two levers that we have. Well, first of all, we can place more cycling counting stations. That is a bit challenging because, for example, we have so many kilometers of roads and it's hard to track them all. There are cheaper options than the ones that we're currently employing. So this might be our one option.Some of them are then using cameras, for example, that are just much cheaper to put them out there. And then the other question, and that's something I'm also looking forward to, to answer this question is because I'll be looking at this in my future research, is that actually how can we place the sensors that we have better across a city? Because currently look it up again for your city.You'll probably find a similar image is that we do tend to place these censuses as very busy and scenic roads. And the question is actually, can we maybe place them at more diverse spots within a city? And if yes, how can we choose those streets to place those sensors at to actually get a more comprehensive image of the cycling traffic and then also of all kinds of socioeconomic areas and a more equal data image.Chris Adams: Cool. Thank you for that, Silke. All right. We'll come back to you a little bit later about some of the specific techniques that we were using, because we spoke a little bit about ML and there's a lot more we might dive into there. Chiara, if it's okay, can I ask you a little bit about your research analyzing European energy data?Because you didn't hint a little bit about how this can affect renewable energy transition, and one of the, one of the things that Germany has in particular is a target to have 80 percent of the grid running on renewables by in, wow, in five years. So that's not much time, and we've also spoken about on the grid, we've spoken about things like time shifting and location shifting as a kind of carbon-aware software, particularly in changing how data centers, like, fit into the grid, I suppose, or the energy they use. Can you maybe talk a little bit about some of the challenges you've found actually working with this data hands on? Because we At best, most of us developers, we might use it in a really nice, pretty fashion from electricity map or Watttime or in an SDK, but it sounds like you're pretty much at the front end having to figure out how the sausage gets made. So yeah, if I can ask you, maybe you could tell us a little bit more about how this data comes about and what are some of the challenges.Chiara Fusar Bassini: I mean, if you've been using Electricity Maps, you probably have been using an application that in the back uses NSOE data. So you, in Europe, we are rather lucky because. There have been two regulation that have been released in 2011 and 2013, which forced in a way transmission operators to publish a variety of a time series of energy data from the grid and from the markets in an effort to increase transparency. And we have a number of data in a central, on a central repository, which is called the Transparency Platform. We have load data, we have generation data, we have transmission data about the grids, we have balancing data, balancing markets, but also a lot of information on individual power plants. This data is overall extremely useful, but unfortunately it's not Always accurate and it's not always complete and not all the data is not always published in a timely manner.That very much depends on the type of data, the country itself. We are still very much better off than other markets where there's no data at all. But it's still an issue of like how good and like what the data quality actually is. Because you mentioned time and location shifting. To do time and location shifting, most likely you will be working with aggregated data.For example, load data, load data, load forecast data. And. One could analyze, for example, a load to decide whether to shift more energy consuming activities at night or at moment where there are off peak time windows. And on the other hand, one could look at aggregated renewable generation data to try to relocate some more of energy consuming activity to time of the day where the grid is actually greener and there are a lot, there's a lot of interest in academia, but also in, in the industry sector to provide us information, to have an estimate of carbon intensity. there are a number of startups out there. You mentioned Electricity Maps, but also academics have come up with top, bottom up, top down and bottom up approaches to compute really at hourly or quarter hourly level, these carbon intensity estimates. The trick here is that you are working with, the aggregated data and aggregate the quality of aggregated data and the timeliness of aggregated data is rather high, the situation is a little bit different when you move to a more geographically granular, like a higher geographical granularity.Chris Adams: Okay, so from Germany going to like Berlin or Germany going to another part like Frankfurt, for example, something like that. Yeah? Chiara Fusar Bassini: Rather, when you're looking from, the aggregated generation to the generation of individual power plants, because in that sense, you might be interested to know which power plants are actually generating right now. And you might be interested to know which areas are generating more solar, for example, which areas are generating more wind. Unfortunately, we don't have data on all power plants. Which would be rather impossible in terms of like amount and extent of the data, but we have only data for power plants that are at least 100 megawatt. It's mostly conventional power plants. So, for example, we have no individual information or very little information on wind farms, for example, because Some of them are not big enough to qualify for this criteria. And also this data get published with a significant delay of four days so that you can't really use it todo anything operational. It's also not conceived for that. And we can or we cannot use the data to properly, like we cannot do it for, we use it more for analysis than for forecasting, but nonetheless, we can use this data to understand a lot on individual power plant data, why they decide to dispatch on how they are dispatched, and especially in the context of conventional power plants, how their dispatch has changed over time because of political reasons, but also because of the increase in cycling of fossil power plants, because they have to adapt to the renewable energy generation, to more renewable energy generation.Chris Adams: Can I just quickly stop? I just want to check I understand some of the terms you've used for listeners who might not be familiar with load, cycling, some of these things here. So when you talk about loads, you talk about energy, like basically that's power draw, what people are trying to draw from the grid.You mentioned that. And then you also mentioned, I think, like cycling. So that's like basically scaling down a power station in response to there being loads of wind on the grid or stuff like that. Maybe is that about right?Chiara Fusar Bassini: Yes. Thanks for asking. So to clarify, so load means the demand and in the past, I mean, demand, especially from, industry, but also from household, it has been rather predictable. And the way we faced demand, or we satisfied demand, because well, in an energy system, demand and supply need to be equalized at any time. In the past, the most of the baseload, so the main bulk of consumption, have been satisfied using traditional fossil fuel technologies, so called dispatchable, because you can, decide when and how to switch them on and off.Chris Adams: Ah, okay.Chiara Fusar Bassini: But the thing is, as more and more renewables enter the grid, they cannot be dispatched whenever, they can only be dispatched, or they can't choose when to dispatch aChris Adams: Yeah, control the sun and the wind. We can just respond. Yeah.Chiara Fusar Bassini: They just respond to external weather factors, right? But that also implies that we still have these conventional power plants that aredispatchable, but we now have to operate them with increased flexibility. So they have to be able to ramp up and ramp down as the load is more and more satisfied by renewable energy sources.Sources when they are there. And we always make the assumption that say conventional power plants are a hundred percent flexible, but that's not actually the case. For example, some power plants, when they are turned on, they have to generate a minimal capacity. And if the demand for that capacity is not there, that might be an issue, or they might have some minimal times to be switched on and switch off.So there, there is a plenty of interesting question that arise from the increase of renewables. Like how will conventional power plants cope with more renewables in the grid?Chris Adams: Ah, I see. Okay. So one thing you're saying, she's like, yes, it's not like a computer. You can't turn it on straight away, like in milliseconds. And so that's one thing you mentioned and the fact they need to do that more is another issue. And if I understand it, what you described was quite a physical process.It's like, we're not using bits, we're using atoms, like burning coal, things are expanding and contracting. Like, is there a risk that, you know, the, a big power plant could be damaged some more, or does that introduce any wear and tear when people need to scale something back? Because I can imagine someone saying, "hey, you're making me change how I do things, and therefore you're introducing some risk into this.That's not what this was designed for in the first place."Chiara Fusar Bassini: Yes, that's actually a very interesting question. What I mentioned, cycling, meaning that you operate conventional power plants more flexibly, has some consequences on the lifetime of power plants, especially if you keep on turning it on and turning it off. There are some wear and tear indeed for thermal power plants, wear and tear consequences, some of some power plants may not even be able to do so because they have some agreements with O&M managers that tell them, you know, "you can do that, but then you'll have to pay more because you will have, we'll have to do more main maintenance." And also, there are a number of obstacles that arise, especially for older power plants that have not been conceived with this flexibility option in mind, but rather to satisfy baseload.Chris Adams: Ah, okay, thanks for clarifying that. And you're essentially doing some of the research to see how you might predict some of this better to either reduce or basically accommodate some of these changes that we might have when we've got a much more dynamic grid that is influenced by the sun shining and the wind and all the things like that, right? So maybe if we can talk a little bit about some of the techniques being used to track this and reduce the amount of, maybe, reserved capacity that needs to be done, or reduce the amount of wear and tear that might be imposed on the kind of entire system full of all different power generation. You said you spoke a little bit about using machine learning, and We spoke to Silke.Silke mentioned that she's using some ML models, which are not like generative AI. That's a very, it's a different kind of AI. Could you maybe talk a little bit about like how that gets used, because you hinted at it, and like what some of the barriers are for using some of that, because it, that sounded quite enticing and interesting.Chiara Fusar Bassini: Yes, before, before that, I might, I want to add on this. So there is some parallel research being done, especially like at engineering department of a lot of engineers trying to use machine learning to efficiently operate conventional power plants to reduce this wear and tear of wear and tear problems.And in general, like damages from cycling while still satisfying a change in demand. What I'm doing is and Rather different analysis of historical usage of power plants. So to see how power plants are, have actually been operated so far in the markets, how they're, how they, how flexible they actually are.Because sometimes we assume that they're 100%, again, we assume how they're 100 percent flexible, but how flexible are power plants that we already have in the grid? And also how available are there in cases of outages, for example, how, like. What's the percentage of time in the year that they actually could provide electricity, for example? And in terms of techniques, well, it's a lot of time series data, so most time series apt methods can be used here. It very much depends on the ultimate task, but one of the major obstacles I encounter is that this high granularity data is by far not as good as the aggregated data, especially, for example, an availability of power plants that has to be reported in a rather accurate way, but then is not one to one translatable to time series format because it's published as market messages, meaning that the data that we have is not in a format that makes it directly usable for a researcher. So there there are a number of obstacles that are really determined by the data quality rather than by the task itself.Chris Adams: Ah, okay, so it comes down to the data a lot of the time then, basically, yeah?Chiara Fusar Bassini: Again, like as Silke said, sometimes it's really just a matter of the data that you have, like the research that you can do is going to be determined by the quality of the data that you have.Chris Adams: Okay, we'll touch on that a little bit later, but I guess the, that does make me think about, particularly in Germany and countries where we've seen very rapid changes. Like, Germany, there's, you know, there's a massive craze of balcony solar, for example, or we've seen loads of battery coming onto the grid, or even Pakistan. We've seen, like, a third of the power, the new power introduced this year, was come from rooftop solar, and each one of those is individually less than 100 megawatts. That's an enormous chunk of power. So there's all this new stuff that we're not, don't necessarily have access to the data for to actually figure out, okay, how will the grid work and how can we make sensible predictions on this? That's useful to know. Brilliant. Okay. So we're speaking a little bit about the upsides and how, where some of the potential might be. We do speak about Green Software, about reducing the environmental impact of some of this, and obviously when we're doing some of this work, I've asked a little bit about the kind of models you might be using, partly because there's a question, whenever we start using technology to help us meet climate goals, it's when some of that energy is still coming from burning fossil fuels, for example, there's trade offs to be made. Does anyone want to go first, talking about how we think about these trade offs? Because as practitioners, I imagine you're at the coalface, but you're also working with some of the people who think about this every single day. And like, if you're working with Lynn, and like, Lynn was one of the founders of Climate Change AI, I reckon she probably has some reckons and you've probably had some conversations about this, right?Silke Kaiser: Absolutely. Actually, just, I think just the last group meeting we had, we just discussed about precisely this topic, because it obviously, it is a question that keeps coming, a question that we do want to answer. And it is also like, it's in our minds, right? Because if we want to do something positive for the climate, and then actually, the net result might be negative, because our models consume that much energy.This definitely is a topic that we think about a lot, I would say. I see Chiara nodding. I think she's agreeing with me, but, and I can see that maybe to the outside world, often this can seem a bit like, I like the term of fighting fire with fire, you know, you, you're trying to make it better, but you're making it maybe even worse, but I think if we make some smart choices along the way, I rather like to compare it to the idea of fighting a forest fire with a controlled burn. So right, that we do try, for example, in, in the models that I was employing, I did partially check how much, how big the energy usage was.I was using simpler models, as I mentioned earlier. So the energy consumption wasn't that high, but I think it's good for us and for everyone out there using, similar models. To track your energy consumption and there are very nice packages and libraries out there, tools, all kinds of things, open source, freely available that are very good in, in managing or in measuring the energy consumption you have.And then of course there are a whole bunch of other approaches that you can take. Right? I mean, you mentioned it's an issue if it comes from fossil fueled energy, but obviously you know, you can think about, I know that there are a lot of like. Service and data science centers, for example, out there in Iceland, where you tend to have more natural cooling, where a lot of the energy being produced is renewable.I'm not saying that at all perfect, but what I'm trying to say is that there are different approaches that we can actually also reduce the emissions caused by AI. Chris Adams: Ok, so there was one thing about the actual technique, like, AI is not a monolith. There's all different approaches within this, in some ways, not particularly helpful term, like the use of relatively small machine learning models, which are relatively simple, that's going to have a totally different footprint to the model used to generate SOAR, like a video or something like that.And that's something that we probably would benefit from having a better kind of intuition off as practitioners, for example, and you spoke a little about the carbon intensity. So there's two, two things that you have there. And you mentioned some software that you have. And you said that, Chiara, if I can kind of give the floor to you, because I think you mentioned you, you've spoken about some of this before about, yeah, there are some tools and I use them as well. Can I ask you a little bit about when you've been thinking, I mean, how do you think about these trade offs? Or is it a trade off? Or is there another set of dimensions you might be thinking on rather than like forest fires and controlled burns, for example?Chiara Fusar Bassini: Yeah, I think, there are two things that need to be thought through when using AI. Number one is like, how do you develop your model, and then what do you use your model for? So how do you develop your model? That's similar to what Silke said, for example, doing emission emission tracking while developing the code and while training the code. And at the moment, I think AI is missing some embedded indicator of the social environmental cost of the training. So. We kind of think of performance metrics such as accuracy, such as like classic cross entropy losses and so on, and we think only about precision. But sometimes we need to be a little bit more critical of whether an increase of accuracy of 0,1 percent is worth an increase in the training time of two hours or an increase in the size of the model of 25%. These are like actual numbers and scientists have coined for that the term green AI, meaning Okay, can you know, can we, in a way, embed this measure of the size of the model within the loss that we are trying to minimize in the training of our model? There is another, a good example, for example, is the Bloom model that is an alternative large language model to GPT. It is similar in size, but it required Like the CO2 emissions of the model are 20 times lower than GPT 3. And this has been made possible by, first of all, in smart usage of the training and also tracking of the carbon intensity of the grid. It system was trained, the model was trained mainly in France, which is, which has runs predominantly on nuclear power. So in like carb, much more carbon neutral system. So there are a number of things that one can consider while training their model. But also another thing that is very important, and I think that we sometimes don't really think through, is what are we using AI for? And in that sense, there is currently no standard assessment in place. Like, is this application really worth using AI? AI is, by its nature, ethically neutral. It can be used for anything from targeted advertisement that will have probably a negative impact on environment to detecting wildfires. So very positive impact. I think policymakers in that sense can make a great deal to really make a difference and start, for example, by providing a classification of which user cases are positive for environment and which are negative. It sounds, it may sound like science fiction, but it has already been done in the European AI Act in looking at the perspective of risks, like which application have a high risk and hence should be more controlled and which other have lower risk. And I think a similar classification would be also very useful for environmental purposes.Chris Adams: I'm really glad you mentioned that because I ended up reading through the AI act for research recently. And the idea of the risk that is, you're right, there doesn't seem to that much be that much reference to the use of AI for, let's say, you know, increasing the extraction of fossil fuels, right, versus that.That's, there doesn't seem to be much to mention about that, but there is some information about the transparency around training. And now that we've looked at it a bit closer, so within the Green Software Foundation, there is a group called the Real Time Carbon Group. We've been looking into some of the specific implications of this, and it looks like the AI Act, it also, it looks like it's probably going to suggest not just understanding the training, but also the cost of inference, like the use of the model rather than just the training of the model. If I can just quickly, you've, you, mentioned there are tools out there, and Silke also mentioned there are tools out there. If I did want to measure some of this, and if I did think there was some legislation coming for this, what tools are there available for me to measure the direct impact? So at least I know what the trade off might be.So we understand that the carbon footprint of decarbonizing transport, like Silke mentioned, that's going to be, you know, positive, but quite, but there's ways of calculating that, but for us as practitioners, are there any software or any tools you might recommend that are kind of common in the field now? Either goes. I'm happy to, whoever's more comfortable talking about this.Chiara Fusar Bassini: I'm thinking CodeCarbon is more probably a standard used by many scientists. I know there are more applications that might have a higher granularity, but I guess that's aChris Adams: That's the one that you folks have used, right? Okay, I hear CodeCarbon used a lot, and I, as I understand it, that's the one that's been used for the Bloom model when they wrote a paper about that. That's what I'm not sure Facebook have actually explained this because when I was looking at LLlama's model, so AI models have model cards, which basically, which I think, various responsible practitioners now say, "this model took this much carbon, or they had this much energy gone in to kind of create it," for example, if you go to the existing Llama 3.1 model card on Hugging Face, and you try to follow a link to the actual methodology, It's not actually explaining how it works. there's now a bug. I filed a bug to ask out, ask, well, how did you work these figures out? Because these feel like it's quite important, especially because when you look at the numbers, it's significantly larger than Bloom, basically. That's, so, so what you're referring to is CodeCarbon. That's one tool that people can use that will give you some idea that is in use in a few places already that's relatively safe to start off with. Great. Okay. And we spoke a little bit about some tools. So if someone is, wants to take their first steps, they might look at this.And there are various projects I'm aware of to make it a bit easier to understand the impact of one versus another. I believe there's one Energy Star AI or Energy Star, AI Energy Star or something like that. There's one person who I've spoken to who's involved with it. Boris. I'm so sorry I can't pronounce your surname, but I do know you're the AI lead, the AI sustainability lead at Salesforce. Boris G is one of the people who's been writing about this. He's not the only author, but he's the person I know, and we'll share a link to that as well, because that's the first thing I've seen of a useful, like, A kind of nice idea to give you an idea of what the inference, the usage as well as the training might actually be. If you were to look at this, we've still got this issue of data or having access to data like, and Silke, I ask you, if you were like queen of everywhere for a moment, how would I change it for here, right? Let's say that you want to be responsible AI practitioners, like what are the things that we need to see in the next, in the coming years to make it possible to be like responsible practitioners so that when we do use AI, we're using it in the kind of greenest possible fashion. Silke, I asked you first about Queen of Everywhere, so maybe you go first and I'll hand over to Chiara.Silke Kaiser: Well, that's a very good question. I definitely say, as in general, with all kinds of, you see, in more technical approaches, we do need reproducibility and traceability of what we do in our research. I mean, just as you mentioned with the Llama, I think it's important that other people are also able to understand what we did, what was the energy consumption of what did, how can they, how can we check the things that we've done and, see if, we did it properly, if it took a right approach?And then obviously, I think this is a bit less related to, the topic that I'm or that Chiara was working on, but also in the longterm, we do need to think about ethical concerns coming down to this. And then again, I think just, really, transparency. So I really think that transparency is a good way to address this.What take do you have on this Chiara? I think one of my major takes also from what Silke mentioned, and I'm really glad you mentioned, is the fact that when we were talking about policy making is that very often policy making is not data-driven. One problem is that we don't have the data and it can be addressed partly by regulators asking for those data, right? But another issue is also that we don't really do data-driven assessment of the policy that we implement then. And I came across very recently a paper that tries to systematically evaluate policies.And having been implemented in the last 25 years, this very recent paper has been published like a few days ago.And I thought it was very interesting to well, once again, the results of the paper is there's no one size fits all and some countries depending on their level of development might need different policies. And we have to keep that in mind that we can't use the same policies for a developed country whose energy consumption, for example, is no longer linearly dependent on its GDP from a different, from a developed country or a developing country that has very different issues. But I think in general, this approach of doing data-driven policymaking and science-driven policymaking is something that would really, we would really need in this space.Chiara Fusar Bassini: I think that's something I can really agree on. I often feel that as a scientist, we feel like we're trying to really produce clear results, objective results. And then often we feel there's maybe a bit of a lack between the two. The research that we do and how much this is sometimes uptaken, by policy.And obviously we hope that because we do really put so much effort into this and always try to be objective. We hope that this will eventually be more used more and more in the policy sector.Chris Adams: You've touched on a really interesting point, and I can think of some examples that just occurred to me. So, we had an interview with, oh, Vlad Kor, his first name is Vlad, I'm gonna mispronounce his surname, but we spoke a little about, all about the rebound effect, and Vlad Coraoma, that's it, Vlad Coraoma had this lovely post actually on LinkedIn talking about the curse of potentialities, potential itis, which is basically talking about, we have all these kinds of really exciting projects, but whether people follow through to check whether the actual gains materialised, or the benefits materialised, there's much less effort put into that.And we've been seeing, like you said, Chiara, from the last 25, we've been seeing predictions for things that would happen in 2020 or 2030. And 2020 is in the past now, we can check if this is, if these actually delivered, but a lot of the time we do not see that. And in our field, specifically as kind of cloud providers, or people who might be consuming services, there's some, there's a really, I'm thinking of a really good example. Microsoft has a whole thing about pushing for AI and everything like that. And we know that, as you mentioned, AI can be used for good, and can be used for bad, or used for Climate aware things, which are really helpful and things are not so good. And we've even seen like people who are workers really pushing for this. I'll share a link to an article in Grist where, written by Maddie Stone, where she talks about some of the sustainable connected community inside Microsoft, speaking to some of the management there. There's a guy called Darrell Willis. He's the vice president of energy. And they spoke and said, "hi, we are pushing for," you know, "can we please have a conversation about what we're using AI for inside our company, because we're one of the largest companies in the world and we're one of the leaders in various industries," right?And there was a commitment to say, we're going to produce, as the management said, "we're going to start releasing information about, okay, how much of our use of AI is coming from the fossil part of the industry versus the renewable part of the industry?" And this feels like a really important data point if we're going to be looking at tens of billions of dollars used on AI.I mean We know that it's an accelerant. If it's an accelerant of fossil fuel extraction and burning, that's a very different story to using tens of billions of dollars for renewable energy, for example. And if we've seen commitments at a management level, then it would be nice to see these. As we understand, these commitments were made, these were shared inside the team, but we don't have this, and we'll share a link to the specific terms, because actually, I'll just share the quote with you, because I think it's one thing that, if you're an employee of a cloud firm, or a customer of a cloud firm, it's the kind of thing you might want to know about, so on the call, "Darrell Willis, committed to providing employees with updates on net zero requirements as Microsoft continued to implement these energy principles. Committed to providing a breakdown of energy divisions revenue across six different sectors from oil, gas extraction, to all zero, low to zero carbon energy. So sharing this information internally." Now this feels like a thing that employees probably should be aware of or asking for. Also feels like something that if you're an investor of Microsoft or a customer, you might want to know.Because there's an impact inside your supply chain thinking about this. And if you're choosing one provider because they have really strong GSG credentials, this may make you view it somewhat differently. We'll share the links because it seems to be the best concrete example I can think of at significant scale that we might be talking about. And I'll get down on my soapbox because that's just the thing that really leapt out when I, when you spoke about that. So we coming up to time, and we've spoken about the different uses of AI, sustainability of software, as well as some of the Things you might want to use or be aware of as a practitioner. If people do want to find out about the work that the two of you are doing, where should people be looking? So Silke, if people are interested in your work, is there a LinkedIn page or is there a website that you direct people's attention to?Silke Kaiser: I normally try to direct people to my personal webpage, which is silkekeiser.github.io. Or you also, you can also find me on X or on LinkedIn. And I'm always happy to share news on my research as well as the articles that are out there. And I'd be happy to, if people were to look at those pieces of information.Chris Adams: Cool, thank you. Alright, and Chiara, if I just hand over for you?Chiara Fusar Bassini: I've seen Silke's website and you guys should really see it.It's a very nice animation. I don't have myself a website, but I'm very active on LinkedIn. You can find me under Chiara Fosar Fusar Bassini.Chris Adams: Chiara F U S A R, we'll put it in the link, we'll add it in show notes. So, Chiara Fusar Bassini. Brilliant. Thank you, folks. This has been lots and lots of fun. I've learned a lot from this, and this has been a really nice chat. Hopefully, we'll cross paths sometime in Berlin, but otherwise, thanks again for coming on, and have a lovely week.Silke Kaiser: Thank you very much for having us.Chris Adams: Ta ra! 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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