 We have heard the challenges from Cameron and the hazard from both the United States and Australia and They can talk forever, but the key thing is we need to do something we need to really execute and the following session are going to here is Stanford's version what we are going to do of applying AI machine learning to help energy transition and the climate Brazilians and We are very glad on the E trace leadership of pre-call we launch a program Some of you may heard on Monday or yesterday called the PPP pre-call pioneering project It's a great partnership between our home organization pre-call and the bits and wants and with our national lab body of Stanford slack and With another very famous Institute here at the Stanford in the human AI so for organization working together and Found this the first round of the PPP project. We're also very honored to have a Very distinguished scientists to join the review panel one of them I think some of them are here like professor Lin wall was the review committee and the each way was on the review committee as well Without the further deal, I'd like to invite a room to present to the first project Then in this will go next great Yeah, well, thank you Leon for that introduction of the Stanford pre-code pioneering projects So I'm really representing a whole team out here, which is led by Ram Rajagopal in civil environmental engineering and Then myself then we have in s co-PI as well as Andrew Ng in computer science Oh got the wrong one, okay Chad is a postdoc and then we have Immanuel out here Rob Buchler Natalie Diaz Taosan and Zicheng Wang who's going to be a speaker again later on and this is in partnership with Google We have Guha Out here in data commons, and we're going to say a little bit about that and then Jhangir Amjad who is in Guha's team and in Google and this is as was mentioned Sponsored by Stanford pre-code pioneering project and and thank you E4 starting that whole initiative Zicheng has a standard graduate fellowship. This is partly funded by the Department of Energy And then of course Google With the support for Natalie and then Immanuel has support from a fellowship from Chevron So what are we trying to do? This is in a climate change. I don't have to spend too much time on this Climate change is happening and it's here. There are lots of data that shows that the extreme events are really causing havoc and As I mentioned earlier The reason when I came out of the transition team in January 20, 2021 I was kind of exposed to all kinds of issues with environmental justice and this we got to do something about it and and there's a tool called EJ screen environmental justice screen and In the government which is EPA uses that but it is used for looking at pollutants around, you know refineries and things like that to see what is the ozone level what is the other toxic, you know VOC levels, etc. And they come up with demographic to show us through where the hot spots are But there is nothing on climate on climate extremes and While we talk about the global average temperature rise going up to 1.2 degrees or now We're trying to keep it bill or two degrees It is not the average it is the extreme that really matters It's like putting in your head in the freezer and you put in the oven and you're saying average is fine But you're killed right so it is the extreme that really matters and It is a distribution and the tail of the distribution is critical So while these and you can see it manifested in some of the events that happen and suddenly we say okay We got to do something about it So what what can we do? This is a classic example of what happened in Texas freeze where the the jet stream when you have global warming there the jet stream becomes unstable and Sometimes in the winter the jet stream comes down and you know, these are what are called rosby waves and you get a You know a freeze and these are we call it polar vortex Well, why it's happening in the United States? Well, this happened There was another heat wave somewhere else in the world because these are unstable and there are waves that are created so this is what happened and the The demand went up like crazy to about 69 gigawatts and there was not and this is Texas and the grid Is in its manage its Urquhart territory. They're not too many Thai lines There are few but there were not enough capacity to provide the Electricity and not only that it was like a triple whammy There was also the fact that some of the generation were not winterised. I heard I've learned the new word winterised And they were not winterised and even those turbines can run in not Dakota. They were not running out there winter rides a Nuclear plant got offline and it was chaos and now it's a market system. It's a deregulated market system Market system is great when supply and demand match up but when the supply drops and the demand goes there is no market and So these are situations that I think we're going to face more and more but nevertheless there were there were there was trouble and this is just Houston and When you click on it, you know, there were these power outages going on and the one that has to ask the question How were the power outages decided that this community is going to not get power in that community is going to get power There was no basis for that, right? And so you now you see issues of equity and environmental justice showing up in Decisions being made kind of randomly and this was you know going around all of Texas And it's not I mean we don't can't blame them because they were not it was not in there In a realm of possibilities that to consider that while making the decision and now hopefully it'll come back to this Come back to the decision-making process So as a result of that it was so timely that coming out of the This you know the whole transition team a few of us have found out a few things and we said that okay We should write an op-ed about it. So this is an op-ed that few first co-authored How can we better predict weather catastrophes and this came out February 25th? 2021 right after the thing and our first line out here was we are playing Russian to let with extreme climate events Okay, that's the you know that was that was set up the whole stage and this is where let me just you know Introduce our co-authors Dave Crisp is the head of the carbon observatory satellite system in NASA and Abhishek Chatterjee is a modeler in NASA who take the data and try to predict things and Bill Collins is one of the most well-known climate scientists in Berkeley LBL and the reason we wrote This is the is the following That why we have climate predictions which are 2030 40 years downstream ahead and they predict the average The weather community as you know can only predict about 10 days Maybe 14 if you're lucky, but 10 days. That's why on your in your phone You get the weather forecast maximum 10 days. You cannot forecast more so the climate community climate modeling community and the weather modeling community have not quite connected to predict a climate induced weather extremes and Of course nature doesn't care which community there they they've climate is inducing weather extremes So that's the dilemma that we are facing and there are some work that is going on in sub-seasonal modeling but it's too little and We kind of highlighted this and also highlighted the fact that our satellite system that produces the data on co2 temperature It's woefully inadequate and I thought in this transition that we're going to put some dollars and budgets and all that there must be a constellation of satellite measuring you know climate related environment related things and Measuring data in real-time. We're putting in a database and it's widely available Nothing of that sort is there there's only two satellites that that NASA has that measures co2 and They are beyond the life expectancy. There were no plans to renew them And and you know, they measure the earth's surface 1% per month Okay, so that's where we are So that of course highlights the lack of data. Hopefully that can be fixed The OSTP has now taken a thankfully Sally Benson is there to take this up and Actually push this along so that we can get more data on climate event prediction I'm just setting the stage for what we're trying to do. This is what we are doing is still work in progress So this is I learned this from my students how to look at things Climate threat these are climate-induced extreme events multiplied by vulnerability is climate risk Okay, it's very simple. And so what do we now we can break up the problem a little bit first is to find Climate threat that is climate-induced extreme weather prediction. Can we do that in a way that has not been done before? The vulnerabilities about population climate threat is not enough You got to look at the demographic of the population where they're living. What is the age group, etc? And the infrastructure so if you have adequate infrastructure, you're fine You know, maybe yeah, there's a heat wave going on, but you're air conditioning. The substation is fine There's enough capacity out there. You're okay. You can live through that but when you have a Event like 120 degrees in British Columbia They don't have air conditioners out there in Seattle, etc they don't have air conditioners and if you try to put air conditions quickly the capacity of the substation mod may be Inadequate and so that's infrastructure part is as important to be able to get through these climate threats And of course if you if you take both into account you get hotspot maps And I'll show you one of the hotspot maps that z-chang has been as developed the vulnerability So now if you know what the vulnerability is you can actually do reduction measures for the vulnerability that that can address the resilience and adaptation of communities and We bent into this thinking that there's a lot of work going on in mitigation as it should be But the issue of adaptation resilience should we reach two degrees should we reach two and a half degrees which could happen? In fact, it's probably you know We have to look at those risks and see if you can mitigate them So the adaptation resilience we are trying to elevate that issue as much as mitigation Okay, so very quickly the there are this data this IPCC models is seem up six There is modeling from deep learning techniques that I'll briefly talk about an adaptation risk aware I won't I won't go into the details this the there is dry bulb temperature and and humidity The both are important if you're trying to do cooling It's the combination of the two because the amount of load for humidity is equal or more than just the cooling part Because you'd you want to dehumidify the patterns of magnitude of heating and cooling will change the demand for electricity And so this is going to be very important and then changing extremes may impact the Frequency and duration of power outages which will have impact more some areas more than others So you've got to figure that out and of course who will bear the brunt of a one in in a hundred-year event that is happening now And once in ten years once once in 20 years or so who is going to so these are now equity issues that come into the picture as well If you haven't read this book I Would strongly urge you to This is a book in fiction Call the Ministry for the Future by Stan Robinson and I happen to get to know him and interview him on a few things on Stanford campus. It's the book starts off with a heat and humidity wave in India Where the wet bulb temperature exceeds 35 degrees Celsius? For those of you who are not familiar when the wet bulb temperature Is a combination of temperature humidity when the wet bulb temperature exceeds 35 degrees Celsius people die humans cannot survive Okay, I mean this is this is just biology humans don't survive when the wet bulb temperature exceeds 35 degrees Celsius and he starts off a fiction novel somehow too close to home frankly of What you know this heat wave humidity wave and then what does what do countries do when 20 million people die? And so this is how it starts and so we kind of said that let's find this out Where are the likelihood of wet bulb temperature exceeding 35 degrees Celsius in the future? So we I'm going to talk about that So how do we approach this? This is the work of Emmanuel and Rob With help from others. This is you know, you can do predictive modeling of the models And as some of you know when you try to model weather with Navier Stokes equation all that Be on a certain point you really cannot predict. It's a 10 days So how are you going to do like 10 years ahead where the climate gives you the average? And then you got to do the Navier Stokes to find other extremes you cannot So we we took the approach because we don't understand all the details of the physics We took the approach of machine learning and this is what it's called generative adverse modeling Gantz Techniques to find out what is very important is not the peak of the shape of the distribution But the tail of the distribution and and that is a long tail. It's not a Gaussian It's a long tail and it is that accuracy of the prediction of the tail. That's going to be very important So this is still work in progress in trying to find out. What is the energy climate risk map using this technique? And this is a prediction of you know, a hundred-year event where it's likely to happen In in in the in the United States and as you can see, Texas shows up brightly out here And there are other regions that are above and the why the the axis the color axis out here is the wet bulk Temperature 35 degrees and that's 42.5 degrees This is a once-in-a-hundred-year event This is now that the C-Mip 6, but this is a slightly different approach using the IPCC models to predict The wet bulb temperatures across the world and this is the work of Natalie out here and with Jahangir and others at Google To look at where the likelihood of wet bulb temperature exceeding 35 degrees and by the way I said 35 degrees people don't survive beyond 35 30 to 35 is pretty bad They are vulnerable populations who are elderly populations or young population who may not survive if you don't have the air conditioning or the other Adequate measures so 30 to 35 25 to 30. We have to look at all of them not just 35 and This is a prediction in Mexico By Natalie and her team looking at the probability of when you're gonna exceed a certain degree Celsius So the bottom one is 35 degrees in a region in Mexico and by 26 to your soul You will you will likely exceed 35 degrees wet bulb temperature But you can also see the red line on top is the 34 degrees than 32 33 32 And the chances of exceeding 30 degrees wet bulb temperature is 100% in that region of Mexico. This is pretty bad And so and you can now see the other parts of the world you can find yourself wherever you're from if you're international You can see in places in the Amazonian forest area in Africa. This is 2050 in India In a Chennai someone was telling me about Chennai Chennai India has a heat wave going on right now and these are heat and humidity waves that are that are likely to happen The risk of this happening are pretty high and and so This is you know, if you ask the question again the the lack of modeling Where is the next we heat wave going to happen this year? We have no idea. Okay, so again, this is the the Electricity consumption because of these extreme. I'm going to quickly go through this. This is style and Chad's work of the percentage increase in demand And then again coming back to that the vulnerability Let me show you one example of this. This is work done by z-chain We have an an algorithm called deep grid and What what z-chain has done is to look at street view Combination with satellite imagery and the algorithm then looks at street view to figure out where the distribution network is and He has mapped it out. He's used I think San Carlos as your ground truth, right? If I remember right and now he can map out the distribution network Not just out here, but anyway including in Africa is done this and very importantly found out He can map out where the undergrounding of the wire is some in some regions the wires underground very important Why because of fire risk the fire risk is much lower if the wires are underground than if it's overgrown Okay, so he has now figured this out and we are writing this paper right now of The deep grid on trying to figure out where the distribution network is which is sometimes oftentimes the more vulnerable than the transmission system And so as a result of that what he's found was the on the vertical axis out here is the undergrounding rate you know what what fraction of the mileage is underground and What you find it is highly correlated with median household income locally It has nothing to do with fire risk Okay, and that's why how decisions got made in California So this is now in the PG&E region. I don't know if the PG&E person Heather is still around So this is in the PG&E region where you find both the fire Rixie you overlay on top the map of the fire while fire risk as well as Overground or underground and then you find hot spots of where this is likely to happen and it is overgrown This is a tool we would like others to use in mapping out and what z-cheng has done It's not just a PG&E with Sun California Edison all of California as well to find out where the risk And then we looked into the policy and What is turns out that the policy of where the decisions are made for undergrounding has nothing to do with wildfire risk That's the California policy right now And so this needs to go to the utility commission to see if that could be changed in the future Because there's there's a cost issue as well This is z-cheng is going to talk about it. I'm going to very quickly talk about this This is a Algorithm that he wrote and he and Jeff on another student called deep solar to use satellite imagery To find out where the solar panels are and now we have the largest database of the GPS location in size of pretty much every solar panel the United States and this is now being used in all the parts of the world and This is the house the rooftop solar is highly correlated with household income as one would expect The question one asked we asked is what about commercial solar because rooftop solar? Yeah, you can make a decision But if the commercial solar is also proportion to household income then there's a real inequity issue and It turns out that it is not as Correlated with household income white commercial and white now I'm not utility scale with the utility skill You just throw it in the grid and you kind of lose the color of electricity out there But for commercial solar if it is behind the substation meter Then frankly you could use locally and it turns out that it is not correlated with family income So there's if you really want to address inequity, maybe one should be going for commercial solar And he's going to talk more about this I'm going to this is kind of a architecture of how we're thinking about Looking at you know, how does one build a decarbonized energy system that serves all the people? Not just you know some communities in and is resilient to climate change extreme and these are more maps We are also developing a algorithm called deep e.J. And this is the combination of Environmental justice issues communities as well as the climate risk and the infrastructure all combined and very Importantly we are putting this all in data comments that Guha is going to talk about later on and that's the platform a Open-source platform that Google has developed and to really enable others than to use the data and use the data to produce more data More interesting data out of it, and he's going to talk about it later Next steps. This is as you build extreme a climate event impact maps for policymakers researchers and industry based on open methodology We put all our algorithms on github and all the data into data comments so that others can use Model coupled infrastructure. This is very important. We're only looking at electricity But as we found in Texas, it's not just one infrastructure It is gas electricity transportation all together because at the end of the day It is the service the human services that matters it's not just the electricity system the service matters and that's a combination of all and If electricity goes and you have gas at least you have heating them But if you don't have gas and electricity you're really in trouble Then build adaptation response maps with a broader set of technologies But let me stop here. Have any answer questions and then we'll have it over the next So Bruton going back to some of the modeling and this is What I referenced earlier when we're thinking about the importance of us engaging with the disadvantage and vulnerable communities because the energy and the other critical Infrastructure that was built over the last century didn't take into account the needs of the communities it was often serving and I I just want to have a response to what you said about undergrounding and the risk models and the methodology We have a very robust methodology right now And I think most people in this room are probably familiar that we have a goal that we're going to underground 10,000 miles of lines in the next decade and we have spent months talking about What is the right model to decide where we will underground first? And it's that combination of wildfire risk because we're doing this to mitigate wildfire risk It's also to think about reducing the impact of of customers to public power shutoff events, and it's also Explicitly incorporating equity into this and so I just want to make it very clear that there are a lot of built in Equities into our system and we are working to address this so that we have a more equitable and resilient system in that future Fantastic. Thank you. See Cameron asked me to be controversial Which is what I was trying to do I don't thank you very much under as much I was from shell I was in the unfortunate situation to be in Texas on February 14th, 2021 and Appreciate you bringing this up because I think that's something many states can learn from and I hope California is one Is one of them doing that and glad to hear with the question I have is There is to be some intrinsic resistance to interconnect the the Grid networks of different regions in the United States and I've heard that technically if we could connect them and We run them properly that we could eliminate some of those shortages and Emergencies, what is your view about the possibility and the and the via political viability of doing that? great question I Agree with you on a more like a us grid approach and your systems approach The reality is that and Lynn will appreciate this The there I know of at least two secretaries of energy who have scars on the back trying to get a single transmission line built okay, and I'm trying to save the third secretary of energy with that scar Because this is part of the work that the Department of Energy is trying to do with the new infrastructure bill so that we can you know Rapidly get NEPA review and sort of prioritize certain lines and others and it's really the the getting together of the environmental reviews the permitting process Plus if you are doing interstate transmission lines Interstate is much easier because just the state the interstate transmission line It is a federal government FERC is has responsibility for the interstate Then you even though you may have right away you will have to consult the states and The local communities and they all have to line up the local communities along the transmission line and the business model For how you compensate for the middle state where the transmission line flies over and those are still to be Determined okay, so there are some systemic issues in how we build infrastructure. I'm hoping now That with the infrastructure bill and a lot of money for grid modernization that is going in and that is one of the focal points For our advisory board for the secretary right now is the working group on grid modernization Trying to streamline and prioritize how to get things done quickly Because otherwise it is if you just leave it to you know normal thing It's going to take a much longer than what we wanted to be Lynn so I'm Lynn or from Stanford. So Arun I your your presentation Struck me that that there's a real opportunity of here for how we think about Very complex Networks and systems of very complex systems and you mentioned the fact that you know Electricity and gas and water and transportation They're all linked and in ways the systems all have different latencies and different timescales for responses And they're they're connected in ways that are not always transparent so Your ability to to at least use these big data sets to look at these raises an upper time another Harder question, which is how do all these connect to markets because markets? I mean that you could view that the Texas situation as An assumption that the market made about the Whether you would winterize Because there would be high prices for electricity in that period and that clearly didn't work in that example in California we have relatively high electricity prices, which is another It makes it a challenge for the fact that we're going to try to electrify so many services So I'm just want I'm wondering. I'm hoping you can speculate a little bit on what's what's the role of some combination of data Artificial intelligence sophisticated modeling and understanding market structure And in a way to put all this together That's great question. So the integration of like Infrastructure of system of systems That kind of you know and cascading effects now in electricity system. We do n minus one In a contingency planning. Well, this is n minus one in one M minus one in the other and and and sort of the cascading effects There is some scholarship some research that has been done into it But not enough it's mostly been done in electricity and information technology because electricity goes It goes as well and then if it goes then in other things So there's a lot of that that has happened But I think and I'm not and maybe there's a lot of work that has happened But I'm not familiar with the cascading effects of Infrastructure going down and we saw that happen in Texas The issue of markets so in the electricity, you know, there's The longest market horizon is the capacity market that pgm and all use and the time horizon is three years That's it. That's about it We don't have a market that looks out at 20 or 30 year and these climate events are about that time scale And so I think the market structures either need to be modified with a futures market, which has climate risk introduced in it or We have to solve some things with market and some things without market and And that combination we have to get it right So it's we have to so I don't have a clear answer for whether markets can solve this and This is by the way the same thing happened in nuclear energy Where today's markets are not enough for nuclear but nuclear assets are there for 60 70 80 years now And so there's a the infrastructure time scale versus market time scale There's a mismatch and we have to figure this out. Yeah, I think I've exceeded the time. Sorry about that So this this project is called mesmerize and if you're wondering what it means because in the agenda We actually just included mesmerize This is a project that focus on macro energy system Model for we equity realism and insight in zero emissions So the overall goal of the project as Outline is really to have an holistic view on the changes that we need in our energy system All the way from the engineering and market base to decisions Here is the vision and the team so the team is composed by myself ram Professor Adam Brandt and Sally Benson John Wyant and Jack the Schellender Joining also the team and three students right now and and growing John Astor meals the Labo Mel and Drew Suri and The vision that we were proposing is really how can we accelerate? the deployment of effective and equitable energy solutions for Climate change mitigation and still under that umbrella by providing reliable information to decision makers About the implications of different types of pathways including their cost benefits and potential unintended Consequences and having a broader view than just Climate change including also implications in terms of air quality and distributional effects So the key large question is really what our realistic and implementable pathways for Sustainable and deeply decarbonized energy system and how can we make those decisions by including also features? From real policies so that we're actually making those in the context of real-world constraints and opportunities in light of people's decisions and behaviors and accounting for environmental justice so a room showed an opinion piece and so I just changed my slides a few seconds ago to show another one that that came out so keeping on learning on an opinion piece that a few of us did on Things to worry about and things that we shouldn't be wasting time on regarding climate debates and so here we really Outlines issues on some directions that are clear on the type of deployment that we need for the power sector and Increasing the size of renewables and storage and other things that We may not need as much time wasted right now in terms of what is a hundred percent? Renewable energy system look like what we need instead is try to accommodate the path as we are getting to Increased levels of renewables and ensuring that we have resilient systems So I won't spend more time on this opinion piece, but I'll welcome everyone. This was also on the New York Times So as goals for this project We do propose to overall create an ecosystem of both people and policy Centered computational hub for Stanford Looking at energy and climate change mitigation solutions Another goal is really to make this as so as to inform policy and find mechanisms to do so Both at the national level and international level The other goal is to develop modeling capabilities by both the developing and supporting interdisciplinary simulation and optimization modeling platforms You'll see some of those from the students on what is going on already right now in the first year of the project to identify effective technological financial and equitable solutions and Finally also this goal of providing resources to others as the team was developing the ideas for this project one of the things that became apparent is that Folks around Stanford are already doing a lot So there are a lot of tools and data and models that Can be useful for such decisions and that can be even more powerful if coupled together and Often research teams weren't aware of the efforts that we're doing and so highlighting That and understanding where there are opportunities for collaboration became also an important goal of the project Just to give you an example in EV 50. We have students looking at characterizing the future patterns of vehicle charging In California whereas at the same time in other groups. We had the characterization of marginal Emissions factors and damage factors from climate and from air pollution at the very detailed level So you see where this goes combining those two pieces of information can actually be a very powerful policy vote from climate and environmental purposes on when to Charge the cars and what sorts of nudges on policies can be pursued And finally also to in this collaboration across the research team This types of challenges are interdisciplinary by nature and no one research team can do it alone We really need Really a village with different perspectives all the way from engineering to social sciences So to move towards the carbonized sustainable energy system but this is really the most pressing issue of our generation and It is first and foremost an energy problem and so this begs the question of what sorts of energy systems models Do we have and what source of energy models do we need to inform these different types of decisions? And My own take is that there is no sort of holy grail model that will address all the different questions Will be different types of modeling strategies depending on the decision that we're trying to formulate but those energy models can go from fairly Static to quite dynamic and with more or less Insight on agents behavior. So all the way through Understanding just technology models. So I see him is one of those examples on just looking at the performance of Power plant weed and without CCS and the emissions implications for that You can move forward on life cycle analysis, which increases the scope Potentially to include upstream emissions and not only site related emissions But life cycle analysis has also traditionally been fairly static over time and not accounting for major changes that may occur in energy systems You can think about bottom-up technology integration Models such as the capacity expansion and operations models, which are very useful to guide some of the planning decisions But often miss how the agents actually make the decisions and behave and Lastly the family of models like the CG models this Compatitional general equilibrium models that explicitly may includes price and income and substitution elasticity is across sectors of the economy. So they provide more of that Agent-specific Behavioral and characterization, but they are competitionally really hard to run and to calibrate across all different sectors of the economy Most of the work that was done today In the groups at Stanford was based on a central planner optimization Perspective and that's not how the real world decisions are made If you think about this in the context of climate change most of the models in academia have this sort of formulation This is an example from the dice model But the way to plan what the optimal emissions reductions ought to be are a function of a utility function and consumption is used as a proxy for Utility over time So you see the function including consumption over time the population and then as assumptions about the pure rates of social time preference This really misses the point that we are making different types of decisions and investments that are not necessarily Geared to a central decision maker maximizing utility for all and based only on consumption It also misses very important distributional effects that may occur some of those are contemplating on your contemplated on the assumptions That are made on the discount rate, but not all In addition to that portion of the climate change pressing issue the current energy systems rely on fossil fuels and with that we have also implications in terms of air pollutants which lead to premature mortality Indeed fine particulate matter PM 2.5 is the single largest Environmental health risk it is responsible for about five million premature deaths globally annually and PM 2.5 is associated with increased mortality rates From different types of health consequences as shown here Now when you talk about air pollution in PM Just one of the things that is important to refer is that we have emissions from primary PM That the rise from tailpipes and a little bit from the stacks of coal power plants But the vast majority of the PM 2.5 health damages Comes actually from the secondary formation of PM 2.5 due to the emissions of SO2 and NOx those will react with ammonia in the atmosphere and creating that increased concentration of secondary PM 2.5 Why do I bring this up? Well in the both the literature and in the actual policy design climate change and air pollution I've both been treated quite separately. So we have climate mitigation goals and we may have air quality standards But they haven't been looked at Together in terms of what are the optimal Strategies for your fleet or for the transportation sector when you account for those two externalities the consequences and damages from climate change as well as the premature mortality from air pollution furthermore, whereas CO2 and methane will be like global pollutants with global dispersion and in the case of CO2 with Very long lasting lifetime in the atmosphere When we're talking about air pollution from PM 2.5 The effects are actually very localized the premature mortality will be dependent on where the emissions occur And so we are not all breathing the same air We have tremendously different concentrations of PM 2.5 across the US and across the globe Let's just see at what that implies in terms of environmental justice in the vertical axis I'm showing premature mortality per a hundred thousand people Associated with power plants operations in the United States So what you don't see in here is underlying this modeling We compute the change in emissions at every single power plant in the US the dispersion and reaction of pollutants in the atmosphere And then couple that with census level data to understand who is exposed to that pollution And so we see that on average across the United States we have a Premature mortality that is around 5.3 for the average American per hundred thousand people and we also see somehow unsurprisingly that as Folks are in higher income segments team. They tend to live in places where the air quality is better So we see a slight decline along that line Now with what we also see is that black African-American across all income segments are more exposed and Faced more with the damages from air pollution than other segments of the population so what I'm showing here is the The size of those bubbles is just a population size and on the vertical axis you see the Average premature mortality for that group and that that income level So as income locations increase we can see still those effects So some of this is what's the electricity mix in different locations and where do people live? But we do see that there is an important effect that seems underlying here to take into account as we design policies This is not only an issue across demographics It is also an issue that involves states. So though all pollution is more localized for p.m. 2.5 than CO2 It still travels large distances So what you see in this map is premature mortality. So annual deaths attributable to in each state From emissions that may occur anywhere in the United States from the stack of other power plants So just calling out that we have states like Pennsylvania Texas and Ohio that have very large numbers for the premature mortality Regardless of where plants are located So they are this is kind of the consequences to the state from having the electricity generation system that we have But we can decompose this into self damages. Those are the implications for premature mortality that arise from electricity generation between the state boundaries and Different picture emerges the numbers are not super visible, but the numbers decrease quite a lot for Pennsylvania and for Ohio meaning that the self-imposed damages from their electricity generation Do not account for the bulk of the consequences that they face between the state boundaries But here is another picture. This is the damage to others This is the premature mortality that is imposed in other states From generation between the state borders, so here we see that the Pennsylvania and Ohio again calling out a little bit on those states are imposing an enormous damage in other states not between their state boundaries and Finally the net effects and I'll call out just New York over here, which has a very small Distribution in terms of premature mortality due to electricity generation in the state But it is the state that suffers the largest net consequences in terms of premature mortality So this motivates the need to bring climate change and air pollution together as we think about all of those issues and in the context of policies I Mentioned that climate change and air pollution have been treated mostly separately One of the things that we did and this is still not ideal given that it is in an optimization framework is To look at a specific policy that aims at reducing CO2 by 30 percent from Base from the baseline across the United States. So we're just meeting a 30 percent reduction overall And then the question we ask is what's the cheapest way to get there? If you only account for this climate goal and track separately what happens to air quality and consequences for premature mortality Versus looking at the two things together and by that I mean explicitly optimize the plants that are being retired and replaced based on the Consequences the dollar value of imposed damages from climate change and air pollution and The three sets of scenarios easier in map and AP 3 You can disregard the focus of attention to that. Those are three different air quality models the systems for thinking just about the climate policy and let's see what happens to the health consequences from Air pollution and the H plus C means that we have an explicit objective function That looks at health damages plus climate damages So a couple of good news do the diamonds represent the net effects and all across As a societal output We would be better off So there will be net benefits when we incorporate the consequences for climate change and air pollution To make the transitions to a 30 percent carbon reduction and That's true across all scenarios But more importantly, we see that if we look at help plus Climate change damages together. We are Setting up a set of solutions that further maximizes the total net benefits associated with With these decisions to meet the 30 percent carbon goal That has very important implications for states here is just a map on With the cold generation across states and and they're lying that the color code shows the health damages that are incurred With the current system as we have it as well as what happens when we pursue a climate only policy versus climate plus air pollution explicit policy and this just highlights that The plants that would be retired would be very different depending on whether we draft a policy one way or the other So having very large implications to the cost of new infrastructure in the state Could talk more on that but just moving forward on What have you done we done on this sort of work and on the type of data that is needed We're building mesmerize us to include both a energy and climate mitigation solution model that will be a patchwork of several different modules that Align with the things that I just presented previously and more Having harmonized data sets that can be used by others as well as data products and the computational and AI tool kids So that's the goal and a few more results so far from this first year of operations So one of the things that I would like to focus on is really what are the costs and emissions reductions from different technologies for the electricity? generation fleet and We pose this problem at the global scale. So I'll just move So at the global scale the first piece of our homework is can we characterize the emissions from every single power plant? Across the globe and so we started parsing out data sets on Power plant generation Characteristics as well as emissions outputs, and we showed here just an Illustration of those with the goal of doing that both for co2 Which we have been moving forward on as well as other air pollutants and so here in the vertical axis You see the emissions of co2 from different power plants and the horizontal axis We are showing the age of the power plant since we also have that information and we show for three regions that contribute a lot to the emissions China India and United States as well as all the other The first panel shows the coal related emissions Second one gas and oil and please note that the vertical axis is changing in terms of the maximum unit across all of them We're still pushing on this But one of the things that we need to have if we want to think about the replacement of those power plants is What are the resources? That we can substitute that which meaning we would need very detailed Capacity factors or output for wind and solar in those same regions as well as an understanding on whether there's access to transmission and so Nils one of the other students involved in this project started digging up all the papers and data sets that assess resource potential for different renewable technologies and One of the things while the numbers are not a super visible or the names of the authors that I'll point out Is that for all of those the orders of magnitude? Very by a lot So really even just at the level of the technical potential globally for these resources There is still quite a little bit of uncertainty. So we're trying to identify what's the best Data source for each of those resources to be mapped out for every single country Here I'm showing the sort of outputs that we aim to have For here we already have for the United States and what you'll see is on the vertical axis Dollars per ton of CO2 abated and so what we're doing is going for to every single power plant fossil fuel based in the US and Trying to identify What's the most cost-effective? alternative In that location and so on the right you're we're illustrating. Okay We're shifting from a coal power plant and retrofitting it with natural gas or coal to wind and so on and so forth and We set up this model and code so that it's very flexible We can assume different types of discount rates as we make those decisions We can make different criteria on whether the plant is replaced with the same county or location or further away And so on and so forth So the hope is to do this, but globally another set of Stream of data and outputs that we're moving forward is that both Jack Shalander and and I have been working for the last few years on problems of identifying marginal generators and producing tools that have Marginal emissions and marginal damage factors by our for every single balancing area in the United States While I had done this on just a production level Jack took a further step of being able to identify The emissions intensity associated with consumption instead So defining an algorithm and a tool that allows to attribute emissions in a certain region Based on where imports come from and this is important in interconnected systems, and so Maybe not super visible, but you can see in the plot on the right how the emissions intensity is changing as a function of the hour of the day and Jack already put this tool available online. He keeps on being updated and Finally bringing all of this together with the work from Nora This paper was actually just accepted today And so though I know that impact may not be that just having publications out is actually lovely to see that it came through Which is combining a lot of these notions so Nora was able to couple the Sort of premature mortality analysis that I've showed previously We Jack's work on imports to try to understand what's the premature mortality And few to emissions jointly Associated with energy consumption rather than production in case we want to start thinking about policies that are more consumption based rather than production based and This this is my final slide and it was again an add-on in the last minutes Just due to lean or question about markets, but this is an example. This is Taking this with a degree of caution since we're just looking at this but what we're showing is a simulation of The Alberta ISO market, which has very interesting characteristics since generators can beat portions of their generation at different prices so they can be the portion of the capacity at zero and Another one at 999 sort of sort of distribution and they have Full say on the distribution of those beads. And so what you're looking at is And and those beads are publicly available and downloadable that provides a great publicly available data set to understand the behavior of those generators and as well as to compare how they are bidding versus what Economic tier will tell us that would happen in a competitive markets where beads would be placed at marginal cost I won't go into this because we're still looking at the results, but this is going to be quite interesting I'll I'll end with that. Thank you So I'm probably over time, but maybe you'll have time for one or two questions. You'll end. Yeah Congrats, that's really really interesting work It's really impressive and it's really important my question is and I guess it touches what I said to a rune before and I probably misspoke about being Controversial, I mean probably being academic or intellectually courageous and when you see that work That's going to be pretty uncomfortable for some people to see it's going to be quite confronting and I think it's really really important So how as you go into this sustainability school? Are you going to make sure that you get the impact you get that information to the right people in a non-partisan way so that you get Action taken from the really important work that you do you've got an accelerator That's going to help technologies get to market and get to scale How do you do the same thing from a policy point of view? So that's a wonderful question Cameron and so I'll I'll provide my Own take not necessarily the one from the sustainability school given that that's still under Development, but these these problems really are only as interesting as we get to Provide the information to actual decision makers At the same time, we're not in the business of doing advocacy one way or another, right? So we want to stop or at least I want to stop short of of doing that But I think we do need to create new mechanisms to be able to brief also policy makers in DC in the international setting to and definitely in California about the results and Implications so that they can do informed decisions as they think about that and that will go all the way from Really the industry sector as well as regulators. Yeah I have the other question Thanks, Inez. I'm really glad that this effort is happening at Stanford because you know equity the people who are most Effective are not likely to be in these kinds of rooms. So thank you for doing that. I'm curious the most rise effort What would it take for your work to become sort of the objective widely Accepted standard around the social cost of carbon and I'll give an example under the Biden administration The federal number for the social cost of carbon is 51 dollars a ton under the Trump administration It was between one and seven dollars So that seems completely arbitrary and you know any business leaders or investors who've ever run a techno economic Analysis knows that those two numbers will give you widely different solution sets for viable options So I'm curious, you know as mesmerized moves forward How do we how do we start to have conversations that are not necessarily political driven? but you know informed by numbers that that people can agree on So that's Really on in terms of the flux that the changes that we see on the value that the social cost of carbon can take depending on the administration and leadership and their takes and both of them Justified if needed by plausible assumptions assumptions on what this country to use and Assumptions on domestic versus international effects The models that we're using make use of a social cost of carbon range of assumptions and where we took the interagency working group numbers Pre-Trump administration actually and then keep on updating Those to the executive order of reverting back to the old numbers from the Biden administration But we don't run the integrated assessment models For climate damages, so on your question about First there's no right number I think that it's a fool's errand to keep on trying to run those models and coming up with one number that everyone settles on It's it's not gonna happen because one the uncertainty on damages is large All of this is driven by the discount rate that you assume in the first in the first place in the assessment so my take is maybe we can think about this differently and Thinking about it differently is really maybe a more of a cost-effectiveness approach Right, so let's assume that we need to get to a certain level and being mostly The carbonized which in itself it's going to be a challenging issue but instead of playing with those numbers just trying to identify what are the strategies that make sense all along and Not all of it is going to be cost-effective Obviously, but one of the good things is that at least the cost of some of the technologies are getting To decrease enough to keep us busy. I know this is probably not a satisfying answer That's a very difficult difficult one, but the other thing would be to just My hope would be that the climate science department here at Stanford will be engaging and working more With the folks in DC and with the EPA that have been Producing the models on the social costs of carbon or asking others across academia to run them To support at the very least information on what climate damages will look like