AI and Data Driving India’s Energy Transformation for Climate Solutions

Data .org is a connector, a convener, and a catalyst. Through five data capacity accelerators in the U .S., India, Latin America, Africa, and the Asia Pacific, our capacity accelerator network, or CAN, is building a global workforce for data and AI practitioners. While helping impact -first organizations unlock these tools in service of their missions, through CAN, we invest both in supply and demand, strengthening the pipeline and advancing the readiness of organizations to think, plan, and operate responsibly in an AI -driven world. Our work is globally informed and locally grounded through more than 100 cross -sector partners. In India, we focus on climate and its deep implications. We have many intersections with help, energy, productivity, and livelihoods. These domains may appear distinct, but they are fundamentally interconnected.

That insight on intersectionality gave rise to ClimateVerse while we’re here today. ClimateVerse, a vision to unlock climate and energy data, tools, and collaboration pathways by upskilling local talent and supporting digital transformation for organizations. Let me share a bit about what we’ve learned about the climate and energy data ecosystems during our discovery work. Reliable, usable data is essential for decision -making and policy. But today, many barriers persist. Fragmented ecosystems. Lack of shared language and standards. And a lack of accessible, hyper -local information, especially in emerging economies. In India alone, we conducted 50 -plus consultations. We reviewed 40 -plus data platforms and tools. So we’ve been talking to a whole lot of people and listening to a whole lot of people and learned alongside CAN partners like Junhagra, Civic Data Lab, and SEAS, amongst others.

What we heard was that data and tools must be easier to discover, more granular, interoperable, and supported by incentives and infrastructure, and paired with interdisciplinary capacity building and stronger multi -stakeholder collaboration. So it’s the listening and the hearing and joining. India is already doing important work in this space, but the real questions now are, how do we move from pilot… to system level change? How do we design ecosystems that drive adoption, not just innovation? And how do we build the interdisciplinary talent that can translate across climate and AI? To integrate climate and energy data into real decision -making, we need to build local capacity and advance organizational AI readiness and activate partnerships across academia, practitioners, industry, and government.

We all have a role to play. Today we want to share examples of what we’ve been building with our partners and invite all of you alongside our expert panelists, which you will see and hear from later, to help identify the gaps, the enablers, and the conditions needed to drive impact at scale for climate resilience and a global clean energy transition. Transition. With that, let me invite our first partner from Arthur Global for our first Climate Solutions Spotlight, Dr. Linan and Karan Shah, to share insights from their recent study on spatializing the impact of heat on human health and productivity across Delhi’s neighborhood with implications for grid planning. Welcome.

Okay. Thank you very much, Cormekki, and very good morning to all of you who are here today. Thank you for being there. At the outset, I need to thank our wonderful, lovely partners, Data .org and the entire team for not only facilitating the event but facilitating the study that we’re going to present today. My name is Karan. I’m the Chief Operating Officer of the India Office of Arthur Global. We’re a policy organization that works with governments, philanthropists, multinationals and other policy stakeholders to improve the design and implementation of policy making. I’m here with my colleague Neelanjan Sircar Sarkar who’s the director of the Centre for Rapid Insights which is our rapid insights unit that aims to support governments and partners with providing policy relevant feedback in a rigorous but timely manner.

So with that I just like to talk a little bit about our work that we recently did. So we know that being in Delhi extreme heat is no longer episodic, it is a structural phenomena that we’re dealing with. We’re not talking about heat waves as shocks anymore, we’re talking about a significant rise in the baseline. When Delhi records its warmest night in six years we know something is going wrong. There is no relief, nights are no longer providing that relief anymore. And the invisible part of all this is not the temperature, right? The invisible part is the impact on health burden, productivity, and grid management, right? Today, we know that 76 % of our population actually lives in districts that are classified as high to very high heat risk, and close to 50 % of India’s population actually works in the outdoors.

So if India needs to think about its productivity and competitiveness, and cities are going to be the engines of economic growth, and cities are going to be dependent on labor markets, then we know that heat no longer is just a meteorological variable, but is now a significantly important macroeconomic variable. So our work on heat actually has been going on for several years. So back in 2024, between the months of May and June, ARSA actually conducted… India’s largest survey to try and integrate the impact of heat on the health of citizens. We surveyed 27 ,500 Indians across 20 plus states and about 490 plus assembly constituencies to try and discover three things. What is the impact of heat on health and how are citizens coping both at home as well as their workplace?

The results, as you will see, are startling. Close to 45 % of respondents actually reported to have one member of their household ill in the last one month because of a heat -induced issue. And close to two -thirds of those actually felt sick for more than five days. Now you can just sort of try and understand the impact on productivity here. And when you start digging into the data, you realize that heat has very, very uneven disturbances, actually impacting the less privileged population. Significantly more, right? Even coping gave us a lot of insights. So greater than 30 % of people actually said that they are uncomfortable in their own home. and even from the ones that said that they are comfortable, more than 40 % relied on either air conditioners or coolers.

Now this tells us that cooling has become a private adaptation strategy. We still don’t have a public one. So that was the motivation of our study and what made it very clear that heat has very, very widespread impact and that impact is not evenly distributed. So we said, okay, how is heat distributed then? And we looked at cities as a critical part to identify that. Now we all know about the urban heat island effects in cities. Cities amplify heat, distribute it even more unevenly. Concretized areas are causing heat traps. Building materials are actually keeping heat much longer. The lack of adequate tree cover is causing natural ventilation and natural cooling to actually disappear. We know all of these things are actually impacting heat very, very much.

Now here as well, we found that that our response architecture is failing, right? Most heat action plans in the country today are made either at the state level or the district level. But heat is significantly experienced at the neighborhood level, right? And that’s the scale mismatch that we wanted to highlight with the study to try and see if heat action plans can be more granularly informed, right? Now, we began our hypothesis just on three parameters. And we said the way in which heat is experienced actually rests on three parameters. The first parameter is who you are, right? What’s your occupation? What are your daily routines? What appliances do you own, right? What sort of economic background do you belong to?

We said that has a significant impact on the way you will get exposed to heat as well as deal with heat. So that was the most important contribution to the study, is to bring the voice of citizens and layer that with other forms of data. The second question we asked is, how is your neighborhood built? And this is not your district or your city, this is your immediate neighborhood. Is it well planned, is it formal, is it informal, is it dense, does it have a lot of tree cover, does it not have enough tree cover? Those are the aspects that we looked at. And third is where you live. So even where you live actually makes a big difference because temperature, humidity, pockets of airflow and ventilation can make a substantial difference and cause pockets of uneven heat across cities.

So the hypothesis was that these are the three pillars on which we will be able to understand the impact of heat on households. And that’s what led to the study. So with that, I’d just like to welcome Professor Neelan to walk us through what some of these findings were and talk about what implications does this have on plans as well as grid management.

so just taking over from that great introduction from my colleague Karan so let me just talk you through what the data problem here is because that’s a large part of what we’re here so we have good data from satellites on green cover, on built area we have good measures from the Indian Meteorological Department on air temperature land temperature, humidity what we don’t have is the third piece of the puzzle which is how are people experiencing heat we know that experiencing heat has a substantial amount to do with behavior do you have an air conditioner do you work in the heat do you have comorbidities these are pieces of information that you need to be able to triangulate with these other administrative data sets now if this data does not exist in any system in a systematic way then how do you make claims about health heat action plans, energy overload, right?

You need this piece of data. So our empirical problem was the following. If I go to a person’s household, right? I need to be able to construct the built environment for that person, I need to construct what kind of heat that person’s experiencing, but I also need to construct what that person is doing throughout the day, right? I need to know whether that person’s turning on the air conditioner, at what time, I need to know when that person is working, where that person is working. So that’s where the surveys come into place. Now our infrastructure at the Center for Rapid Insights basically uses that geographic information, that spatial information, figures out where to sample, and in this case we sampled 2 ,400 households broadly across the city of Delhi, and collect that data very quickly, because heat waves don’t last for very long, so we did this all in two weeks, right?

So that’s the kind of technology that one needs to be able to do with data collection. Just very quickly going through some of the results. You can see that there are huge differences between when an area is more spatially planned and not. This difference is about a degree, right? So if you happen to live on the right side where there’s more green space, you are experiencing a degree less of heat in the middle of a heat wave than somebody living in a more densely populated area. This is the area right around the airport, so many of us will be coming in and out of this area. This is just a snapshot of what’s happening there, where you can see that a large part of this story is actually the amount of green cover.

If I just increase the green cover by 5 to 6 percentage points from 4 % to 10%, we’re talking a degree of cooling. We also wanted to demonstrate that actually heat and how people are experiencing heat have very, very significant economic impacts on productivity. So you can see that there’s a 50 % increase in work loss in the middle of a heat wave. Just for a 3 degree Celsius increase in experience heat, right? So this is actually not uncommon. If you look back at some of these initial maps, you can see it’s going from 39 to 46. so actually the variation is 7 to 8 degrees of Celsius in terms of what people are feeling in Delhi and just 3 degrees Celsius is increasing work loss by 50 % so we’re talking about very very significant economic productivity effects so how are people coping with this kind of heat well it turns out and this is something that exists in literature more generally beyond cooling that exists in the environment and across much of India you have environments that look densely densely concretized like what we have on the left without green cover, people are having to turn on their ACs people do report being having 3 times better sleep if they’re turning on the air conditioning but they also report consuming twice as much energy so as the world gets hotter if people are going to require turning on the air conditioning to get better sleep to be able to show up to work the next day we know it’s going to have an impact on the grid.

And I just want to make one quick point here. Without doing this kind of measurement I might be able to look at energy flows over the last two years and guess what the next month of grid load will look like. But it’s going to be very hard to predict three years down the line, five years down the line unless you know who’s using an AC, how much they’re using the AC. So that kind of grid load management is what’s important. So just finishing up here. So what I want to demonstrate here and I think what we want to demonstrate at ARCA Global individual characteristics, built environment characteristics are so determinative of how people experience heat that without very localized heat action plans that integrate all of this data we can’t really get to people and address their needs.

The other thing is when it comes to grid planning yes I might be able to plan for the electricity grid tomorrow or maybe a year down the line but if I need to have planning for 5 years down the line, 10 years down the line without this kind of data, how individuals are using air conditioners, when they’re using air conditioners, how they’re cooling how the world is changing for them, you won’t be able to come up with adequate grid planning. Thank you.

Thank you Karan and Neelan for those great insights Next up we would like to share another example of a use case in the AI and energy space from ClimateDot and I invite Akhilesh Magal to talk about their work on open data architecture and how it will shape multiple use cases for India’s energy stack Thank you

Thank you All right, good morning, ladies and gentlemen. Am I audible? Yes? Okay. It’s great to be here. Thanks to data .org, who we’re working with extensively on reshaping some of energy power sector data, actually, in India. And it’s also nice to see familiar faces in the auditorium. So I think this is going to be a short but sweet, hopefully sweet presentation. Happy to interact with some of you if you have some questions after this. All right, so what we’ve been doing as ClimateDart is trying to get a grip on India’s power sector data, which is significant, large, and often disorganized. We have data. We have granular data. The issue is, of course, getting it into usable formats.

And so over the last three or four years, we’ve been, as an organization, trying to organize some of this data. We’ve been trying to get some of this data at the state level, and trying to build learnings that can be scaled up to the national level. and I’ll talk about some of the collaborations that we have in this regard. So what is the problem? As I said, India’s power sector, we have a lot of data, significant number of data points, but it’s largely unstructured and non -interoperable. And this is a problem especially when we want to talk to each other between states, for instance, or between the center and the states, but also within the states.

We’ve noticed discrepancies between years. For example, you’ll see on the right side, you’ll see two rather simple examples. We have many more, but given the paucity of time, I’m focusing on two examples. You’ll see that, for example, in the first table, you’ll see O &M being the acronym being used, but on the right side, you see in the earlier year, in 2016, we have it being fully the expanded version of that. Now, that may seem a very small issue for us as humans, but when you have machines reading this, you already have the first stumbling block, and that would require… I think it’s a very big issue. I think it’s a very big issue. significant man hours or woman hours or people hours in terms of, to be accurate, to be in order to make sure that the machines read this and we can, you know, build AI tools and so on on top of this.

So one of the problems is on data nomenclature, but we also have problems in data granularity. And what does that mean? For example, those of you in the power sector will recognize these terms. Fixed charge and variable charges have standard reporting metrics for the power sector. And in 2022, we had that data, so it’s pretty granular. But in 2023, we noticed from the regulatory filings, this has suddenly disappeared and it’s been lumped into a single cost head. Now, for people working in the power sector, this may be okay, and we may be able to do some simple math and get these numbers out. But for machines, this is already a significant problem. So as we built out the databases, standardized databases, we realized that these are some of the problems that we could already begin to share with regulators.

With regulators, with policy makers, with data scientists, et cetera, so that we begin to organize this. already. And so what we’ve worked on for the last two and a half years also with support from our partners, data .org, our funders and so on, we’ve tried to build a unified and scalable data architecture for India’s power sector that works across states and within states as well. And I’ll tell you why the within states is so important. So what we want to do is we want to get the data from various plethora of input sources. We have PDFs and scanned reports. Sometimes these are handwritten reports in government files that have been digitized or scanned, often from a mobile phone.

So you need to use some sort of character recognition, some basic form of intelligence to be able to read that. And we’ve run into significant challenges there. Most of the other data is on spreadsheets and databases, easier to read. But the challenge is that these aren’t really organized in the way we would like them to be organized. So they’re not consistent. of course now the government has worked significantly in putting out a lot of data in the public domain, in portals and so on, each department having their own portals, but the problem is most of these portals don’t really talk to each other so they are, not just the front end is different but also the back end is very different, so smiling so I know this is a problem and of course we have significant number of data silos that we just don’t know how to access sometimes for good reasons because these are isn’t data that you can make public but sometimes this is publicly available data that is sitting in silos, so can we begin to have a discussion on making this accessible so what we’ve done is really over the last 3 or 4 years, built scripts intelligent scripts that can sort of scout the internet verse, get this data that we want, scrape the data and aggregate this data, this is not efficient some of you have a computer science background, you know that this is typically not a very efficient way to do this, what would be efficient is to have an API, API access to this and so what we’ve done with all the scraping is built a standardized data acquisition method but also an architecture for the power sector the key point in the outcome is to make this standardized and machine readable what this means is if we can get this data read by machines with very little human interaction that’s the best because it really increases the pace at which we can begin to bring various state related data onto a single homogenized architecture we of course can the applications from this are multi, there’s a multiverse of what we can do with these applications what we’ve been doing is building analytical dashboards so power sector dashboards at the state level and I’ll show you on the next slide what we’ve done but we can also build AI insights so any AI engine today requires machine readable data, data is extremely important so once we have these databases which various tools can plug in and building AI tools on top of this becomes really, really easy.

I mentioned the API aspect and I think that’s critical but then all of this can go into making better policies and effective decision making which is what we do as an organization. So a small example of what we did for the state of Goa. Goa, we’re working with the state to understand how to bring up bring in all the power sector data into a single portal. This is 15 year data goes back in history and that’s just an example on the right side of one of the pages of that portal where we were tracking their renewable power obligation something very important especially from a climate and an energy transition perspective. The QR codes are up there so if some of you are interested you can scan the QR codes it should take you directly to the website and it’s a very interactive very visually built dashboard.

And I have one minute left so I’m… just wrapping up. Thanks. so essentially walking through this process of automation, standardization and visualization so we need automation we need to reduce manual intervention we need to standardize a lot of this which we believe we’ve done for at least two or three states and of course then build interesting tools that are usable not just tools that look at past data but perhaps modeling tools and predictive tools that look at what the past sector might be in the next five years so extremely crucial from a policy perspective so that leads us to the India energy stack and I’ll talk very little about this because there are people who are leading this initiative Shweta, my colleague is here so it’s an initiative built or rather led by the Ministry of Power the RAC and FSR Global Shweta is here it’s essentially the digital public infrastructure for India’s energy sector very similar to the UPI country this but I is for power and I is for power what UPI is for banking in India, unlocked a trillion dollar, two trillion dollar economy something like this, so can we do something similar for the Indian power sector where someone from Tamil Nadu can sell electricity from their rooftop power plant to someone in Ladakh, if this is possible I think our work as researchers is really would come to fruition so we can certainly take questions on this in our panel discussion but I will wrap up here and I thank you very much for your attention Thank

Thank you so much for that Thank you so much for that, you’ve heard some great presentations about what’s possible with data, but remember the data is about the people at the end of the day so there are many more such climate and AI solutions that innovators in the room will be able to share but for the next segment of this session I want to invite my colleague Priyank Hirani, Director of Capacity Building at data .org to explore the enabling conditions to accelerate the climate and energy data ecosystem for sustained public impact with an esteemed panel of global experts. Priyank.

Thank you, Cormekki, and thank you to our wonderful speakers. We’re going to be quick on this one. We’re running out of time. But I quickly want to bring on key experts so that my talking is minimal on this panel and you get a chance to listen to these global visionaries. So let me first invite Dr. Srikanta Panigraha. So please join us. Mr. Srinivas from Vasudha Foundation, Dr. Priya Donte from MIT, and Shweta Ravikumar from FSR Global. Thank you so much. So, today’s panel is going to focus on not just technology, but thinking about the enabling conditions. We heard about two use cases, and I’m sure a lot of you in this room are working on climate and AI use cases, and you have several examples.

But as Kurmiki mentioned in an opening remark, sort of how do we move from pilots to permanents? How do we move from having just dashboards to ensuring decisions and sustained decisions with those things? And how do you help these innovations to be institutionalized? So that’s the goal for us to cover in the next 25 to 30 minutes. We want to think about these enabling conditions, whether they are on the governance side, what are the incentives, what are the digital public infrastructure needed, what sort of coordination mechanisms that might be needed, and most importantly, what’s the capacity within organizations and as a country that we need to develop? So what’s the talent pipeline that we need to think about?

So we’re going to start with the talent pipeline. and how do essentially we start measuring these things both quantitatively and qualitatively so that we are able to track the progress. So with that, let’s begin with the big picture. My first question that I’m going to ask all the panelists to quickly reflect on is from your vantage point, what is the single most critical institutional shift or enabling condition that might be needed to ensure that these solutions become embedded in both the core organizational or say government decision making rather than remaining as one of innovations. So maybe we’ll go around in this order. Srinivas. And please feel free to quickly introduce yourself or tell us about your organization.

is incredible. So we need to leverage that and that can be leveraged if we have the data. Now in terms of institutional and governance I would say that let’s take India today. We have multiple agencies that have been tasked in compiling and collecting the data. At the national level you have the Bureau of Energy Efficiency that compiles data on all efficiency related aspects. You have the Central Electricity Authority. You have the Ministry of Statistics and Planning Implementation. At the state level you have the State Planning Board. So on and so forth. But then what is still lacking is the granular data collection and compilation. That’s something that is still lacking I would say. And so that’s where institutions need to gear up to ensure that we have more granular collection and compilation of data at a higher frequency of sharing.

So that’s how I would put that.

Thank you so much. That’s very insightful. Dr. Srikanth, what’s one critical institutional shift that you think is needed?

I am Dr. Srikanth K. Panigrahi, Director General, Indian Institute of Sustainable Development and Distinguished Research Fellow. I am basically a policymaker working on scientific policies because of my interest for last 37 years. Now I am leading this institute which is a public policy think tank and scientific research organization, Indian Institute of Sustainable Development. So coming to the questions, in public policy when you are insurable to people and you are insurable to planet and you are insurable to the growth of the nation, sustainability rests in all three of them. You need, you have to be very particular. that analysis -based decision -making has to be adopted. And analysis -based decision -making is only possible when you are adopting tools, scientific tools like AI is a wonderful tool and which has the precision and it helps you with the exact information and the data you are looking for.

If wrong data will be fed to the tool, the wrong decisions will be indicated. So as it has been told by my colleague, we need so far is quality of the data, relevance of the data and all these has to be in alignment with the objective which we are looking for. And so for that we need for the right public policy, we need right data strategy and there are many examples. to which I am not getting into. And in IAST, we have a wonderful research project where we are studying apiculture. That is the behavior of bees, honey bees. And these bees are generating, through pollinisation, they are generating honey, which is a good livelihood source for the poor tribal women.

So I will explain this study in my later round.

Thank you, sir. So I’m hearing sort of ensuring coordination between departments, ensuring thinking about the data strategy. What more do you have to add, Swetha?

Thanks, Priyank. I’m Swetha, head of FSR Global, currently leading the India Energy Stack Program. So I’m going to share some learnings from there. You used the word coordination, and that’s literally on every slide that I have on IES, which is coordination at scale. We’re talking about designing systems for… Billionaires. so I think the government has already started to take its steps in terms of this whole of a government approach what we have done through this initiative is taken that to a whole of an ecosystem approach because we need in such a multi -sector multi -stakeholder projects we need all of them at the design board if we don’t articulate what is in it for me for every stakeholder from early on the question that you asked can we move from pilots to scale would be a recurring question so to have them in the drawing board is very important and in terms of actually scaling in the AI unlock I think inclusivity is a very important aspect we need to consider Akhilesh was just talking about can I trade from Tamil Nadu to another place in fact two days ago in this very room we facilitated that trade and showed how a farmer Arun from Meerut was selling to a garment owner in Lakshmi in Delhi across state borders and they did it through very simple WhatsApp based interfaces because they didn’t want to understand all of this complicated AI.

That’s for all of us engineers who love to work with complicated things. As a consumer, they could talk in their local language to an AI bot in WhatsApp and trade power. It needs to be made as simple as that for the stakeholders. Ultimately, all of the best ideas in this room need to scale in countries like ours and beyond.

Got it. Thank you. I like the phrase coordination at scale, thinking about the billions. Dr. Priya.

Hi, everyone. I’m Priya Donti. I am an assistant professor at MIT working on developing AI for power grid optimization and renewables integration. I’m also a co -founder of Climate Change AI, which is a nonprofit focused on large -scale democratization and coordination of skills and expertise in AI and climate. I agree with everything the other panelists have said. The two things I will add. One is being principled about defining what success means. The other is being principled about defining what solutions are. I think often we’re building without doing that. And it leads to things like when we have, let’s say, a pilot innovation, we don’t know where we’re headed. We don’t know what that intermediate success is leading to a final success since we don’t set up stages for actually moving things forward.

We also kind of defining what success means means having metrics that are kind of stated that are measured. It means thinking about what is the role of the technical system versus the human who’s making a decision around it. So I think basically kind of anchoring in that notion of what is success, how do we measure it, how do we get there, what is an intermediate success, I think drives a lot of really important thinking and infrastructure around this. The second thing I would say is having, I think we’ve heard a lot about quality. Coordination, but I think also being principled about what kind of cross -functional skills are necessary to actualize and measure solutions in the long term and kind of what that means in terms of gaps, in terms of what kinds of actors exist in the broader ecosystem to make that happen.

Right now, there’s a little bit of a dichotomy where kind of. you know between kind of can we build capabilities in -house versus can we procure externally and when it comes to external procurement often there’s some sort of generic notion of there’s like some notion of a solutions provider that does generic data that does generic AI and yet in many places kind of solutions are very specific right we heard about power system related like data standardization that kind of effort is really important but it also looks very different if you’re doing that in health if you’re doing that in buildings and if you don’t have kind of specialized solutions providers that are really able to contend with this the kind of nuanced aspects of knowing the data and knowing the methods in a particular domain then I think there’s often sort of a gap where there isn’t enough capacity to upskill internally nor is there actually a good procurement option so I think from a public policy perspective kind of you know I think there’s often sort of a gap where there isn’t enough capacity to upskill internally nor is there actually a good procurement option so I think from a public policy perspective kind of enabling this more diverse ecosystem of solutions providers that are also more tuned towards the needs of specific sectors is also important

that’s wonderful and that’s core to sort of the philosophy of data .org that we think about where it’s essentially putting people at the center of the problem and so thank you for rounding us up Priya because as all the things I was hearing it’s ultimately about do we have the skills do we have the institutional capacity to be able to engage with these things and that is something that we need to look at from the cross functional skilling perspective the lens that you talked about and that’s what at data .org we often talk about as socio -technical skills so how do we think of people as bilinguals in terms of domain understanding but also a data or AI understanding and then they are able to work across.

So continuing with that thought I want to come back to Swetha and Swetha you talked about IES and the coordination that you’re doing with sort of multiple kinds of stakeholders bringing everyone together from a regulatory and governance perspective thinking about this ecosystem of the energy sector What ecosystem design choices, it could be sort of standards, interoperability, it could be things around incentives. Do you think most influence whether stakeholders meaningfully adopt data -driven tools? The one thing that you talked about, which I really love, is ensuring that they are there at the table from the get -go. They’re not an afterthought. No one wants to be an afterthought. But amongst these things of standards, interoperability, incentives, what do you think ensures sustained adoption of tools?

Thank you. I’m going to break it down through what we call the AAA framework at the Indie Advertising Stack. So first is the architecture, which is all of the technical specifications. I’m not using the word standards because standards means it’s an authoritative stamp, right? So it’s a combination of standards and specifications and new things coming where the old cannot sort of adapt to. So it’s going to be a suite of specifications and standards that allow for all of us to have sort of a common data language. Let’s put it that way. so that if you and I want to exchange information, we know what and how to do that. If two systems need to do as we saw in the use cases, they know how to do that.

And the power sector is quite complicated. You have millions of assets and millions of people interacting, so we need to have a basket of solutions that need to come together and be interoperable at the core. Then, of course, the second one is the adoption because not all of us are at the same level playing field as stakeholders. There are some DISCOMs who have certain systems built in, some ready to build in, which might be an advantage in their case because you can leapfrog. You don’t have to think about integrating into legacy systems. So we’ll have to create these different pathways for different stakeholders to harness this data AI layer or digitalization wave that’s coming about in the sector.

And that’s being done through what we call in the accelerator, the third A, wherein you’re building use cases so that everyone can plug and see what value extraction that they can have. And some descoms might want to focus on grid phasing use cases. Some might want to look at market side. Some might want to look at societal impact. So there has to be this pieces of puzzle that could sort of fit in for each of them. And it’s not something that you do over a year and close. It’s a continuous process of building. And so through the accelerator, which is a sandbox environment, we’re building certain reference implementation architectures demonstrating the idea into action. And then it’s for the ecosystem to take and scale with the stakeholders.

And that’s where I said the articulation of what is in it for me. And that’s where incentives come in. and we also have the regulators on board co -designing with us and the policy makers in parallel. Ministry of Power is bringing in a new national data policy framework for the power sector because we’re talking about critical infrastructure here. We need to also look at who gets to access what kind of data and what should be sort of the safeguards we have within the ecosystem. So it’s truly a 360 -degree view on this particular project and hopefully we will have some best practices out of this, learnings from here that could help other projects.

Got it. Thank you. I love the AAA framework. We’re going to keep coming back to it. I wanted to bring in Srinivas into the conversation now and your work at Vasudha over so many years has supported the NITI IO through the India Climate and Energy Dashboard, which is now adopted and institutionalized. So you’ve seen sort of this coordination piece, getting everyone aboard. Getting the adoption sustained. that the full cycle in practice, apart from all the other work that all of you do with the state government. So from this experience, like what strengths did you find in India’s climate and digital architecture while working on that dashboard or working with the government? And what and I’d be remiss not to ask, like what gaps do you think currently are preventing further coordinated action?

So I would I would start off by saying that the data that is there in the India climate and energy dashboard is not new. It’s there and multiple reports of various ministries and agencies. It is there in multiple dashboards of various ministries and agencies. But what the ICD does, it brings together data from all these various reports, all the various dashboards in one unified manner. And it actually marries. This is from the entire power sector and energy and power sector value chain. And it marries the data with the climate. Data and key economic indicators. so what it actually does is it gives you a holistic picture of what are the trends and developments in India’s power sector, power and energy sector but viewed from a climate and a development lens so that’s what the ICED does second what it does is that the visual architecture in a way has been designed that it brings out the nuances of the trends so it’s not just about aesthetics, yes we did take care of aesthetics, we did want good looking graphs but we also wanted graphs and infographics that brings out the key nuances that one is looking at to give a holistic picture of what is happening in this entire sector if you are looking at energy transition you can actually get what are the trends now if you look at the kind of users of the ICED from a low of about average of 2000 hits per day we get as much as 5000 hits per day with roughly how many I would say 5 lakh users across multiple stakeholder groups and from 170 countries, so virtually the entire world.

Okay, we have 195 countries, so we have 170 countries, we have hits from 170 countries. Now, that’s the kind of impact that the ICD has had. So it’s not just in India, but it’s also global. Coming to the second point on the challenges. I think the biggest challenge that we still have today is that we still have dedicated staff who have to do manual entry of the data. I think in today’s time and age, I think we should have digital integration. We should have APIs that Akhilesh talked about. I think that is something that is still lacking. Yes, for some of the data sets, we are able to digitally scrap it. But then by and large, we have, and Rahul is here, and you can see we have a dedicated team who are just into this manual entry.

Thank you. and that’s a pain because not only does it mean that errors tend to seep in and we have to do a lot of quality checks but also means that the ICT still remains near real time and we want to make it real time. So now we have a 3 to 4 days gap but we would like to ideally make it real time. The third, the second challenge I would say is that there is still a reluctance even for non -sensitive data to share the data. I would say a combination of reluctance and a combination of sluggishness. Sometimes when you are dealing with getting the data it’s like pushing a wet sponge. It’s as sluggish as that and that sometimes gets a little tricky because we are very conscious that we want to have this as a real time and so when the sluggishness seeps in then things tend to get a little slow.

I would like to add one other point. now if you look at how do we avoid duplication of efforts in which I think I would one thing that we at Vasudha have always been endeavouring and not just with ICD but all the dashboards that we created with states whether it’s a Gujarat Climate Action Tracker, Tamil Nadu Tracker, whether it’s a Kerala Dashboard or even the predecessors of the ICD which was Vasudha Power .in or Vasudha EMI one thing that we made very clear is that the data is available in open domain. Anybody can use it there are no paywalls. The whole idea was to reduce duplication of efforts and also ensure that people can share the data.

Thank you so much. I think that idea of reducing barriers to access and making any tool user friendly is super critical. I want to bring Dr. Shrikanta into the conversation and think about the aspects of equity, just transition, long term resilience. From your experience, you’ve been a key global climate negotiator for India, you’ve been part of the IOC for many years. What operational governance and human capacity factors do you think most enable and ensure not just technically robust solutions are integrated, but also then are leading to those decisions within those systems?

A very important question indeed. When in the public policy, the equity is extremely important. And equity means the entire planning has to be inclusive. Like in UN SDGs, we have a slogan, nobody should be left behind. We have to carry everyone along with us. So the Gandhi, the Gandhi, the Gandhi, the Gandhi, the Gandhi, the Gandhi, the Gandhi, these talismans also tell the same thing. So coming to the very fundamental, the kind of the energy transition that is taking off. India is doing excellent in enhancing its renewable energy capacity, which is geometrically increasing. Say it’s solar, we are getting into wind, or say it’s other new form of energy, renewable energy, like in Ladakh, geothermal, wave energy, there is a huge investment, new projects are coming up.

So India is considered as one of the most serious nation who is heavily investing in renewables. And trying to make the transition rapid. And if you see our achievements, it is also very impressive so far. Coming to the fact, when someone is switching from coal -based fossil energy to renewable energy, the kind of the workers, the technology, everybody goes through a transition. And… And for a country like India, where the use of machine is less, and more and more people like laborers and wage -based laborers, they work at the bottom of the pyramid. Those who are engaged in coal -based work, they don’t have alternative. They are not trained in renewable energy space. So, they are very much afraid of losing their job and livelihood security.

Coming to the electric vehicles also, in mobility transition, the similar challenges are aired. In ISD, we have a separate transition research cell, where both the mobility as well as energy transition, while happening, how the transition can be taken up. of enabling the bottom line of the pyramid for giving them right training and capacity building and bringing to the mainstream of livelihood, ensuring their security has been assured. For all these things, technology plays a very big role and we need to plan and do this with precision, with optimization of time and a very focused strategic approach. The program has to be initiated for tools like different tools of AI is of great importance. Given the time, I would like to explain our B project.

which is extremely impressive, which we are taking up with National Anusandan Research Foundation and this project ensures the the pollination rate of the bee enhances more and more honey is collected from the flowers and gives better livelihood option to the poor tribal women and more honey you cannot collect unless there is more greenery so for that the more plantation densification of forest and agriculture enabling carbon credits through sequestration thank you

on that note I wanted to bring in Dr. Priya in thinking about how do we build this workforce at scale how do we get the collaboration between these different practitioners

absolutely and I will keep my remarks brief I realize we need to wrap up and so I guess the one thing I will say is that it is incredibly important that we really think about AI literacy at much larger scale among kind of policymakers, NGOs, industry, so forth. We’re having a whole AI summit, and I think the number of people who could actually define what AI is and what an AI pipeline looks like is extremely small. And this trickles down in many ways because then decision makers who are making decisions about AI at an organizational level, at a policymaker, it’s very hard to pinpoint what’s actually needed if you don’t have that basic literacy. So I will make a plug.

Climate Change AI is running an open registration virtual summer school towards the end of this year, kind of focused on trying to provide some of these AI basics as well as climate basics to those coming from an AI background to try to spur collaboration. So whether through that or something else, I would just encourage everyone, take a couple of hours to take AI 101.

Got it. Thank you so much. Thanks, everyone. Thank you so much to our panelists, and thank you for being here. We’ll pass it on to the next session. Thank you. Thank you. Thank you.