I'm delighted to welcome you all to our meeting for the recent release National Academy Report on reading gas emissions information for decision-making. And my name is Don Wubbles. I served as the chair of the study committee that wrote that report. And I will briefly be introducing the study and the plans for today's meeting. Next slide, please. The National Academy's released the consensus study in October of 2022. The reading of charge to the study committee is shown on the right side here. I won't go through all of that. The committee did consider approaches used to develop greenhouse gas emission inventory as well as their strengths and limitations and ultimately developed a framework for evaluating greenhouse gas emissions information and to make recommendations for the use of that framework. The event webpage, shown here, includes the links to the report. Next slide, please. This is the committee that put together the report and wrote the report. Some of the fellow committee members will be moderating today's meeting. And on top of this, by the way, we had excellent help from the National Academy staff led by Rachel Silver and many people who helped her. They were wonderful to work with. Thank you, please. Next slide, please. Today's meeting is an opportunity for the committee to submit recommendations from a report and also for us then to extend it even further by looking at how this report could be used at the urban scale because our report mostly focused on the global and national scales. And so we'll be exploring approaches to quantifying emissions at the urban scales today, stakeholder information needs, and the tools available to aid decision-making. Next slide, please. This is an agenda for today's meeting. In a moment I will hand things over to Kevin Gurney who will provide an overview of the Academy's report. Next, we'll have a session of approaches for quantifying and report urban greenhouse gas emissions where I'll be moderating. After a break, we'll have a panel discussion on urban stakeholders and a pretty extensive discussion about what all this means. Next slide, please. So start sharing a logistics with you and engaging for the meeting. You're watching a stream of the meeting itself, live stream. Below your video player, you will see Slido, which is a platform we use to take questions for our speakers. Please enter the question for speakers directly into Slido and you can find our meeting through the event code on the screen. Now I'm going to close this session and turn things over to Kevin who as I mentioned before was on the study committee. Is it Kevin as an atmospheric scientist, ecologist and policy scientist at Northern Arizona University? Where is he as a professor in the School of Informatics Computing and Cyber Systems? Kevin, it's all yours. All right, thanks Don. I'm going to give a quick overview of the report. As Don mentioned, here's our committee listing. I also want to give a shout out to the staff at the National Academy, Rachel, Rita, Rob, Bridget, Sabha, Patricia and Amanda, they were indispensable in putting this report together, especially given the time constraint we were under. Next slide please. So motivations to develop criteria for evaluating greenhouse gas information. There were really three converging trends that motivated the report. There's been a rapid increase in demand from a large variety of users for information about greenhouse gases across multiple scales, sectors and scales. Also the development of many new approaches have emerged in the last few years. And then finally, there's a growing and rapidly evolving institutional landscape, including public, private and academic entities that are very much busy within this space. So three key motivations to try to put this information together and synthesize what's out there. Next slide please. So the charge of the study committee, Don already mentioned this. I won't necessarily go through it again, though I do want to note the study sponsors and that we really were not aiming at evaluating individual inventories or trying to build a framework that could be exercised in the evaluation of inventories or other data products, GHD information that's out there. Next slide please. So there are three basic approaches to generating greenhouse gas information in particular emissions flux information. And they come in these three flavors and I'll quickly describe each of them. The first is we refer to as activity based. This is probably the approach or method that most people are familiar with. It's what's utilized in the UNFCCC reporting done for the UN and done by practitioners at multiple scales. The second is atmospheric based approaches which use measurements in the atmosphere typically to infer fluxes or somehow determine fluxes from atmospheric measurements. And then finally a sort of a new category that is a catchall for increasingly integrated methods that both combine the previous two and utilize new techniques in this more hybridized or integrated form. Next slide. So as I said, activity based approaches in their most rudimentary form really are comprised of taking activity data, some measure of vehicle miles traveled or some other activity measure multiplying that by an emissions factor to come up with an emissions amount. I said in the most rudimentary form because these now span a very wide variety of techniques and increasingly sophisticated approaches to generating this information though it's still can all be covered by this idea of using activity data. Next slide. Here, just a couple of examples to show that spectrum on the right is kind of your typical sort of table that might be generated by a nation for the UNFCCC putting different emission categories often by fuel or by sector and then estimating the emissions amount based on some broad measure of activity data. On the left then might be a more sophisticated approach where they're going down to finer and finer scale more deterministic approaches to estimating emissions more combinations of activity data to try to both increase the scale accuracy and rigor of the activity based approach and many estimations in between those two. Next slide. Atmospheric based approaches, as I said, use atmospheric measurements. They can use those measurements directly in what's referred to as maybe mass balance approaches or other approaches, but you can take the information from atmospheric measurements and directly estimate of flux. You can also use atmospheric transport model systems which has been referred to as the inverse approach. This will take the atmospheric data often combined with an activity based estimate but utilizing atmospheric transport. In other words, trying to connect the link between a flux at the surface and a measurement in the atmosphere. And the inverse mode means you're really starting with the atmospheric measurement working backwards to best estimate the flux at the surface. A relatively complicated process but now been used for three decades and increasingly performed at finer and finer scales. Next slide. This is just an example of that in sort of pictorial form on the right-hand side here are a variety of observations, typically concentrations or column amounts, occasionally fluxes that you measure directly in the atmosphere. That could be with aircraft, satellites, ground-based instruments on the very left-hand side would be a prior or what I referred to as the sort of initial guess with an activity-based approach. You put those together in an inversion or an atmospheric assimilation system and you adjust the prior according to the atmospheric measurements to get the best estimate which is often referred to as a posterior. This utilizes kind of a Bayesian setup hence the prior posterior idea. Very commonly deployed as I said now and multiple scales. Next slide. Finally, this hybrid approach which starts to really just conceive of the problem as many, many different observational constraints. They could be fluxes, they could be activity data, atmospheric data, all constraining some sort of model system. And though that could include atmospheric transport models as in the inverse approach, it could also include process models of let's say anthropogenic or industrial emissions, process models of biospheric fluxes, process models of ocean fluxes and trying to put all that modeling sort of at the center to best utilize the observations. And it also increasingly is using some new techniques such as using optical or infrared, high resolution imagery from space combined with machine learning to best estimate fluxes as well. So again, it's kind of a catchall of some new techniques but focusing on the integration often of multiple approaches bringing them together in some singular systemic estimation approach. Next slide. This is just an example of that taking that previous picture that I showed the previous diagram of the inversion and sort of opening it up now. Now you have all these observations surrounding a central process model. They're all providing constraints and indeed those constraints might be adjusting parameters within this larger process model. The advantage is you get more information, more learning about the processes and hence can come up with both a better estimate and one that includes a lot more information that stakeholders and decision makers are increasingly interested in. Next slide. So in reviewing all these approaches, it's clear that there are some structural and technical limitations of what's out there. There's obviously institutional structural barriers where information that's being developed is highly distributed in many different places from the decision makers point of view. It can be a confusing landscape of information and often not inter-compared, not standardized or harmonized. Each of the three approaches I referred to also have their own strengths and weaknesses. Atmospheric based, sorry, excuse me, activity-based approaches have underlying activity data and emission factors. They might not be accurate, they might not be representative, they're often data collected by regulatory agencies that may not have uncertainty built in, things like that. Atmospheric-based approaches might not be continuous, they may be spot measurements. Certainly the transport model has errors associated with it that are well-known. And it's often difficult to get much, either granularity or separation of emissions into individual sectors or even sub-sectors of information. And then finally, hybrid approaches have their own suite of challenges. Digital technologies that are being utilized based challenges of interoperability, transparency, data quality and algorithmic bias. So they all have some structural limitations. Next slide. So in building a framework to evaluate, the committee settled on a series of what we call pillars. And these were just almost self-evident desirable qualities that you want in information that you're going to use in the decision-making space. And I'm going to come back across these a couple of times. So I'll go through real quick right now. First, you want the information to be usable. That might be almost the most important of these attributes. Has to be usable by decision-makers, by stakeholders for the purposes that they need that information for. Needs to be timely instead of the long latencies that we've seen in the past. Needs to be transparent. It'd be publicly available, traceable back to original data forms. It needs to have evaluation and validation procedures so it's trusted comparison to other independent estimates is crucial. Needs to be complete. Cover all sources, all greenhouse gases for whatever relevant piece of geography you're examining. Needs to be inclusive. Who's involved in the GHG information creation? Is that representative of the communities that might be impacted by it? And finally, needs to be well communicated. It needs to show methods. It needs to be clearly communicated so that people from all different backgrounds can understand what the information's about. Next slide. So in building these pillars, we also started to just show an example of the evaluation framework when we look across those three broad approaches just to start to set up the idea of this framework. And in each of these on the left, activity, atmospheric and hybrid, the three that I reviewed, we broke this up into methods and data and gave some general outline of right now, given the status of what's out there across these three approaches or anticipated approaches in the case of hybrid, how they might score in a low, medium, high across the six pillars. And the point here is that, again, you see their strengths and weaknesses. And fortunately, in some cases, they're complementary. One approaches weakness as another one's strength and vice versa. And so you can already begin to see that by integrating more will overcome some of the weaknesses. Next slide. Sorry, and I'm just pointing out again that we demonstrated the use of this evaluation framework in a series of example use cases. I won't go through those here. We went through many in the report at multiple scales just to show how this framework might be applied to some of those examples to help encourage the use and demonstrate the use of the evaluation framework that we built. Next slide. So ideally in those six pillars, support the sort of this cycle where you develop a piece of greenhouse gas information inventory or an information suite that gets used in decision-making processes of which we identify three phases the sort of planning phase that is what you're going to mitigate, who's that, what sector, what fuel, how big, how small, tracking, so once you start a mitigation policy tracking that over time so that you know if you're on or off course. And then finally an assessment or verification of your mitigation policy. Did it meet its goals or did it miss them? And then finally that feeds into identifying actions that mitigate and then that can also once you learn a bit about using the information feedback into adjusting or further tweaking the original greenhouse gas information that you use. Next slide. So I'm going to go through a few of the final recommendations that came out of the report. The first again just reiterates these pillars and again they're crucial for building what we think is the best greenhouse gas information for the purposes of decision-making. Next slide. We also recommended a rather practical outcome which is something like a clearinghouse or maybe a federation of clearinghouses just because there are so many inventories, data sets, information systems that have been developed by many, many players both within academia, private sector, the NGO space that there needs to be perhaps some place where they can be brought together. There can be some harmonization comparison. And by doing that you give stakeholders and decision-makers a really good overview of what's out there in one location or federation of locations. Next slide. And this just goes through again critical characteristics that need to be part of a clearinghouse. The information needs to be traceable, standardized data formats and metadata, something that people that build data repositories know all about, good documentation and non-technical and multiple languages, evaluation metrics that evaluate each of these data products updated and accessible databases of the key input data so that you can trace the information back. Governance mechanisms that are coordinated, trusted and inclusive and even education modules or videos that can help with capacity building so that more and more people can both use them and better understand them. And finally, collaborations with very parallel efforts that have lots of synergies and lots of co-benefits or trade-offs. And for example, collaborations with air quality information, which is crucial at a lot of scales across the planet. Next slide. Transparency, greenhouse gas information providers should clearly communicate underlying data's methods and associated uncertainty. Everybody knows this is important. It's not always easy to do, but it's fundamental to building better greenhouse gas information. Next slide. Granularity and accuracy. Increasingly finer both spatial scales and finer functional information is becoming important to decision makers. And by functional, I mean things like not just numbers in a box or in a location, but what fuel, what sector, maybe what technology. Other just useful bits of information that give decision makers not just a magnitude, but attributes that are very important about making particular decisions or engaging in particular policies. And the accuracy and representativeness of all the underlying data used to estimate emissions should be further improved. Next slide. Operationalization, there's been a lot of development within the scientific and academic community on building all of those approaches that I showed to generate greenhouse gas information. And I think there's enough prototypes that the committee agreed that we're very close to operationalizing this, taking it out of laboratories, taking it out of individual maybe businesses or other entities, and making some sort of operational system that has underlying support is delivering information in an ongoing updated basis with all of those attributes so that it can be used widely by stakeholders. That's not easy to do, but there's already lots of discussions here in the US about building more something that's akin to an operational system for greenhouse gas information, same as occurring in other parts of the world, particularly in Europe. Next slide. And then finally, this hybrid recommendation. It's clear that though the approaches have often been functioning in their own space, just sometimes out of necessity, sometimes out of complexity, but more and more it's clear that further integration of those approaches will lead to more functional use, better accuracy, availing of all the information that's available. And so the committee felt that encouraging more integration through this catch all hybrid idea will really make a big difference and also bringing in some of the newer digital techniques with machine learning, artificial intelligence that are being built just in the very last few years that show a lot of promise. And that might be the end, next slide. Nope, sorry, one more. And then it's clear that, sorry, thank you. It's clear that in that cycle that I described earlier where you have all your different approaches, your construction of information, having that used within these different phases of the decision-making process, hitting your, going after your policy goals and then iterating and then feeding that back into improved greenhouse gas into the cycle requires lots and lots of iteration. It's not the type of thing that occurs through one cycle that we have all learned a lot both in building the technical systems to deliver this information. Interacting with decision makers and decision makers are also learning about the capacity as well. So, and that interaction just requires iteration not only because it requires understanding between the two worlds, but they both evolve. And they'll co-evolve as they learn about the capacity of each other or better understanding of each other. So we strongly encourage this iterative cycle through development, decision-making, policy goals and back again. And that might be the last slide, if I'm not mistaken. Yes. So, I mean, I close this session. I think I've left five minutes for questions and I'm seeing one question that came in. So I'll see if I can respond. Activity-based estimations are averaged over a longer time period. Atmospheric measurements use spot measurements. How does the hybrid approach recommended bridge the large difference in temporal scales and in spatial scales of these two approaches? Good question. I mean, I think that one possibility is that with something like an assimilation system, which is commonly deployed in, let's say, weather forecasting with numerical weather prediction, those systems move through space and time, availing of observations whenever they come in. And so that can include spot measurements. It can include ongoing observational information. But the core is a model that will have, you know, physics, chemistry, it has processes. And so where you don't always have an observed quantity, you have a model that at least is healing to those observational constraints when they occur. And so I think the following that model, because again, using numerical weather prediction as an example where we don't have observed quantities all the time, sometimes every three hours, sometimes six, sometimes only twice a day, the model has to fill in as it moves through space and time. And I imagine in these hybrid approaches, the same type of thing would ideally occur. One more question given the need for more and better data, are there any particular industry sectors or processes that should be prioritized? I think I'll give just my opinion and having done some of this work. I think that some of the biggest challenges probably come in the industrial sector only because it's a sector where there's probably less information available publicly for understandable reasons. But it's probably the sector that I think poses some of the biggest challenges in trying to estimate emissions out of, you know, many different sectors. I think next are things like, you know, buildings dispersed sources are difficult, both from an atmospheric observing perspective and from an activity-based perspective. That's difficult as well. Any other questions? Okay, well, I've bought us a couple of minutes. I'm happy to close this session if there are no other questions and turn it over to Don again, I think to introduce the next session section. Very good. So we're gonna start what officially is session one, I guess, the approaches for quantifying and reporting greenhouse gas emissions. We'll have one hour in total with three speakers and then we'll have about a half an hour for discussion afterwards. So we'll have a panel of experts who will share examples of approaches used in their work to quantify and report greenhouse gas emissions at the urban scale. Each of the three speakers will have 10 minutes to provide their remarks. I will give a verbal two minute queue at eight minutes. And once all the speakers are done, we will then have a larger group Q and A. So if you have a question, please submit your questions via slide on. So the first speaker is Kim Mueller. Kim is a member of the Greenhouse Gas Measurement Program at NIST, the National Institute for Standards and Technology where she focuses on characterizing urban and regional carbon dioxide and methane emissions. Kim, turn it over to you. Thanks, Don. And hello, everybody. I'm honored to be here today. And I wanted to thank the Academy staff and the committee members for inviting me to speak on how we here at NIST are using atmospheric observations to estimate urban to regional scale greenhouse gas emissions. And I'd like to know that NIST works in partnerships with many other federal and state agencies, the private sector, other academics and researchers. And we really don't try to go it alone. And instead we love the bevy of experience among these different communities. So today I'm gonna quickly explain who we are, what we do and can do and demonstrate capabilities in the value of estimating urban scale emissions. And before I begin, I'd just like to acknowledge my fellow colleagues at NIST that are listed on this slide in orange. Next slide, please. So the National Institute of Standards and Technology is the US National Metrology Institute. And our mission is to promote US activities by advancing measurement science, standards and technologies. So it's important to underscore that NIST is a non-regulatory agency and we are rooted in aiding commerce through standardization. Next slide, please. So as noted by Donna, I work in the greenhouse gas measurement program where our purpose, which is really not that surprising, aligns with the overall goal of NIST. So here we look to using greenhouse gas measurement and methods to develop standards for mapping urban and regional greenhouse gas emissions. And what we're really looking towards is we want low latency information and that high granularity that Kevin talked about that can speak to a variety of stakeholder needs. We have several different components within our program but today, just next slide, just the next tab, I'm gonna forward focus on the urban piece of our measurement program. Next slide. So before I move forward, I'm gonna use a different diagram than Kevin used in his slides to explain and diagrammatically show how we use atmospheric measurements to infer greenhouse gases. So for the most part, first we observe the amount of greenhouse gases in the atmosphere, like carbon dioxide and methane, that is their mass, although we do observe other constituents in the atmosphere. And we also take, as Kevin mentioned, a finely resolved, that is something that's very granular in space and time, emissions, information that is developed using statistics like energy data from the EIA. And we also use this another component that Kevin mentioned, which is this atmospheric transport model that simulates how sensitive observations are to the ground based on a bunch of meteorological parameters like wind direction and wind speed. It's a very important component. When we combine all this data together, and we like to call this the hybrid approach, it might be as little different than what Kevin mentioned, we estimate emissions. And I should note that these are territorial emissions. Next slide, please. And so although there are many parts of this system, some that are more important depending on the method people use, it generally comprises of two different components. One being the atmospheric measurements, and this is very important tied to international standards. And a spatially explicit, oh, please go back to the previous slide. A spatially explicit emissions information. And our goals here are really to estimate urban emissions that are consistent, and that's really important consistent from the urban to the continental scale. And we really want to get to that building and street level resolution in terms of our emissions with uncertainty goals at the whole city scale of one to 3%. And we're hoping, this is our goal, is that this will lead to transparent methods and standardization that support policy and reporting. Next slide, please. So to this end, we've developed three urban testbeds across the United States, one around Indianapolis, one in the Los Angeles mega city area, and the other, which is a regional testbed in the northeastern part of the United States, which is focused on Washington DC and Baltimore. And these are places, these are like many laboratories that we can test different types of methods and measurements, but the backbone of our testbeds are a dense set of permanent sites that consistently or continuously observe carbon dioxide and methane with a great amount of accuracy and precision. Next slide, please. I should note that some of our testbed are also constrained by other types of observational platforms like aircraft measurements, and we can routinely make aircraft observations around Indianapolis and the Washington DC area. So now we can move on to some of the things we've learned with this particular study where we're able to infer trends in a whole city of missions around Washington DC and the Baltimore area, shown in the red blobs on the left-hand plot. So you can see in this example where aircraft observations led us to determine that there is a downward trend of carbon monoxide, which is closely related to carbon dioxide. And this wasn't really that unexpected to us, as we all know, fuel efficiencies are improving. But we are also able to detect or infer abrupt changes like the drop during the onset of behavioral changes associated with COVID-19. Next slide, please. We are also able to infer these drops associated with COVID-19 across cities for carbon dioxide, in this case for the Washington DC and Baltimore area, along with Los Angeles. And using our permanent measurement sites, we did observe this previous, can you go back to the previous slide, please? We were able to observe this relative decrease in emissions in April, 2020, compared to the other April's of previous years. And the drop was about 30 to 35%. Next slide, please. We're also able to infer a whole city methane emissions and changes over time in the Baltimore and DC area and point to those sectors that contribute to these changes. In this case, we show a decreasing trend in methane over the past five years in Washington DC and Baltimore. And we, previous slide, please. And the estimated emissions correlate with the natural gas sector, which makes sense because our estimated emissions are higher in the winter than they are in the summer. And in the winter, we expect more people to use natural gas to heat their homes. Other urban studies have published similar results like those of our colleagues in the Los Angeles region. Next slide, please. Next slide, please. So, but we can't talk about our science and move down to standardization space until we explore the stakeholder value of needing such information. So we've explored what our data means when we compare it to other pieces. And in this case, policy where the data is available. So we've compared estimates with those at the whole city scale. We've demonstrated how emissions correlated in terms of greenhouse gases to air pollutants at fine spatial scales where you can really get co-beneficial information or co-benefit impacts if you mitigate one or the other. And we've compared emissions to other economic drivers because you could imagine that policymakers wanna know what types of policies that they can use compared to other things that may affect their emissions and they need data to do that. So next slide, please. Finally, I wanna end that effort to cross the US government to make finely resolved emission information that is aligned with air quality data. And this effort is called the greenhouse gas and air pollutant emission systems for grapes. And in the end, we hope it will help enable the hybrid approach that I explained earlier and that Kevin explained that will utilize atmospheric observations that will help us provide transparency and accuracy and precision of the data that a variety of stakeholders can use. So with that, I'd just like to say thank you. And I think I ended a little bit earlier then, which is great. Very good. So next speaker, so we'll go through all three speakers is Yvonne Alboros. And Yvonne is a, and I hope I'm pronouncing her name correctly, is a junior researcher affiliate with France's renowned laboratory of climate and environmental sciences. Her PhD research focuses on the intersection of urban, climate politics and atmospheric monitoring. Yes, hello, and thank you for the introduction. I'm really thrilled to be here. I also would like to thank the Academy to be invited and acknowledge my colleagues, especially to Malovo, for accompanying me on a daily basis and for the valuable insights and help on that study. So I will be talking to you about the short term versus the long term monitoring of urban GHG missions. And to see how we can adapt with that. Next slide, please. So in Europe, we have that project that's called ICA-Cities. It's a project within the Green Deal. It started about 18 months ago. And it's one of the world's first coordinated networks that's bringing together 15 different cities from different, from 15 European countries. And it concentrates on the testing of a comprehensive urban GHG measurement technique, where it wants to provide actually data services that have a societal impact. So it's kind of comparable to what Kim just mentioned in the urban testbed from NIST. And next slide, please. And here you can see a little more in detail one of the three pilot cities that are in that project. From left to right, you have Paris, Munich and Zurich. And this is actually showing here the setup of an atmospheric monitoring network if everything would be as a scientist would wish for. So for example, in Munich, you can see it's really screwed. But then we're actually facing reality while setting up that network, because we don't get permission to set up anywhere. We want it to be where we see that it's best because the footprint is best or the sampling height is best or the influence area is best there, et cetera. But we're actually facing that where you want it to be and we think that we in the end rather have an opportunity-based setup of a network. And also what's currently going on is that what we do take into account is current information, right? It's what we have right now. We don't look into what's happening in the future like urban sprawl or socioeconomic developments that are influencing future emissions. That's something that we wanted to see. So what we try to do is what would future emissions look like in the future and what would an optimal network would look like if it takes into account such information that influencing future emissions. Next slide, please. So we first looked at the city self-reported inventories and tried to see if they're on track to reaching their climate targets that they are mentioning. And then we tried to have a look at the climate plans and see in how far are they actually influencing the spatial distribution of the future emissions. And then in third, we wanted to link that to the atmospheric monitoring networks and to see how far are they actually able to track those long-term emission trends that are really getting to a very fine scale. Next slide, please. So to answer that question, like I said, we looked at the city self-reported inventories. Here we have plotted the sectoral GHG emissions for Munich and Paris. Munich is in blue and Paris in red. I won't go too much into detail. We don't have time. What's interesting here is that we did see that both cities are showing a decreasing trend in their GHG emissions. But however, on the right-hand side, you can see the two flashes in blue and in red that are actually showing the gaps that are in between the city's climate targets and where they are heading for if they continue the efforts that they are mentioning in the climate plans. So, yes, they do have a decreasing trend, but it's not enough in order to reach the climate targets, for example, climate neutrality by 2050, for example, for Paris. Next slide, please. So, what we wanted to see is also what Kevin has mentioned before, the validation is very important. We wanted to see if it's really true what they're saying, their decreasing trends since the self-reported inventories and they do have a certain amount of uncertainty. So, I have the colleague in Jingwilian, she ran an inversion for six years over the Paris area and it did confirm or validate the decreasing trend of the self-reported emission inventories. And so, we were comfortable with using that information. Now, the question is, how will emissions evolve in the future? Next slide, please. So, here I'm showing you a very simplified way, what we did in order to see what emissions would look like in the future. So, first step is that we have a problem. So, I will just continue then. The first step is that we took the emissions inventories that are coming from the city or wherever, which we take as a baseline. Then in the second step, what do you want me to stop? I don't know what happened. I don't see the slides anymore. No, we don't either. So, like I said, so the first step is to have the baseline and specialized emission inventory. In the second step, we take a look at the climate action plan that the cities are issuing. So, there we try to quantify all the action items in order to mitigate the C2 emissions that are mentioned in that climate plan. That taken, we subtract them from the baseline in order to have then the projected emission maps for each sector, for we took 2030 and 2050, in a specialized way. Next slide, please. So, it much more looks like this. Don't worry. I will go through each one of the steps with you. Next slide, please. So, first step, like I said, have a baseline and emission inventory. We had the chance that the French startup origins earth had provided us with a high resolution dynamic inventory. This is built on a massive amount of data, which is actually then combined with several proxies, a lot of different activities that really give us a, oh yeah, and then of course data about the dynamization, like for example, the energy usage, which gives us a temporal profile. And then we mix this all together, which gives us a very detailed information for every sector, for every neighborhood, and also on a temporal scale. We can go through to the hourly level. This is rather comparable to the Vulcan or the Hestia emissions product that you might already know from the S. Next slide, please. This is what it would look like for next slide. Yeah, this is what this high resolution dynamic inventory looks like for France for one year. On a two meter scale. Next slide. Two minutes left. Thank you. So here you can see the same information, but as time series, so it's from 2018 to 2022 for the Great Dimitropolis in area of Paris and for every emission sector. And this really gives us the opportunity to have a close look at special events like the COVID lockdown, energy crisis, and also having the Parisians. So we can see the Parisians leaving for the summer holidays where traffic emissions are already going lower. And then also on the bottom level, you can see the annual emissions where it's interesting to see that the 2022 annual GT emissions are actually lower than the 2020 ones, which indicates that the energy crisis due to the Ukraine war is actually influencing much more emissions than had the COVID crisis. Just rather interesting to see. Next slide, please. So after that first step of building the emission inventory had a closer look at the climate plans. Since in Paris, the building sector and the traffic sector are comprising about 80% of the whole city emissions. We concentrated on those sectors. So you can see a smaller view of the residential sector. In the climate plan in Paris, they are mentioning, for example, that they want to renovate 45,000 dwellings per year. And so I mapped here where those high energy consuming buildings. And as you can see, it's really in the city center with some of the surrounding areas. And if we zoom into that black area, then we see that there's a really high correlation between the CO2 emissions, the buildings age, and the share of all that is used for heating. So what's interesting is here that they are concentrating in their climate plan actually on energy efficiency and not on GHG emissions. Although the final goal is actually to have climate neutrality. So they have a GHG emissions at zero. Next slide, please. For the traffic sector, kind of the same, they're concentrating on air quality factors. And here they advocate the willingness to ban fuel powered cars by the year 2030. So no more fuel powered cars are allowed in that reddish area on the left-hand side. And this is why you can see on the right-hand side the drop in emissions. It's really concentrated only in that area, which actually really shows that it's very important to have a inter-regional collaboration instead of having only focused on your own city and not look anywhere else. We really need, thank you. So we really need to have that core hand picture. Next slide, please. So you have the total emissions for Paris. What's interesting here is really the spatial heterogeneity that's observable at that very fine scale, which is important for the dimensioning of an optimal atmospheric monitoring network for the future. Next slide, please. And so the take-home messages are really that the cities, like I said, are heading the right direction, but still are a little too slow. You have that spatial heterogeneity and future urban networks. We really need to consider future GGM emissions and not only urban expansions. Thank you. Next slide, you can show the left, right? Thank you. Okay. Thank you very much. Our next speaker is Professor Ron Cohen from the atmospheric chemistry department at the atmospheric chemistry expert at the University of California, Berkeley. And he's also the chief scientist at Secured Carbon, a company aiming to provide financing for projects coupled to verified greenhouse gas emission reductions. Ron? Thank you, Don, and thank you all for coming to hear from us today. So I wanna reinforce the points you heard in the previous two speakers and from Kevin in his overview of this report, that providing detailed information about greenhouse gas emissions in cities as possible and also urgent. And I'll offer some opinions from my own work in the context of trying to build, as Kevin described, a hybrid approach. So in the next slide. So just to repeat that, we really do have the tools to instrument and report out in greenhouse gases in every US city in this day one policy memo, we made an argument that you could, every city over a hundred thousand people could have greenhouse gas emissions reporting. It's important that we do this with an eye to environmental justice. Many of the public issues related to exposure to air pollutants are associated with where we have large greenhouse gas emitters and we should be paying close attention to the intersection of these two important objectives related to climate and public health. And we've been talking about cities, but as you just saw at the end of Yvonne's talk, the way cities are gonna reduce their emissions is with individual projects at individual buildings. And those projects need to be financed and so we should be aspiring not just to whole cities, but to verification of the emission reductions of specific projects. And that sort of verification would enable the creation of financial instruments that would accelerate the goals that we have of reducing greenhouse gases. Next slide. So again, we're gonna pursue a hybrid approach, the instrument that we've built is called beacon, the Berkeley environment air quality and CO2 network. And as you've heard from everyone before me, you have some sort of an emission inventory, which includes all the different sources of greenhouse gas emissions. The one on the left here is a CO2 inventory and you see the roads and you see individual point sources. And then we have a set of observations and those green dots show where we're making measurements in the San Francisco Bay Area. And then you have a synthesis and analysis showing how that combination of an inventory and observations tells you something about the emissions and something that's different from your inventory. And here you can see a series of four aprils, the white is our prior, our guests and the colored bars are the difference from that guests in each of the four aprils. Next slide, please. So it's important to, as you heard in the previous speakers, it's important to be able to disentangle the different sectors of emissions. So here's an example of four sectors for the San Francisco Bay Area. There's an industrial sector that has, is about a third of the total emissions, but comes in small geographically isolated point sources, the passenger vehicles, which are distributed along the roads primarily, but also going to and from people's homes, the residential heating, as you just saw it so important in the Paris example, and then the diesel trucks, which are largely on the same places as the passenger vehicles, but have a very different mix of pollutants and a very different impact on public health. Next slide, please. So the way we've been going about this is to put together an observing system. The observing system has hardware costs, which tend to be what's eye-catching, but as with most things we do, it's really the people to maintain and interpret that drives the true price of it. So you'll hear lots of things about low-cost sensing. Really, you should be paying attention to the number of people involved in interpreting those measurements. And if we're not driving down the number of people involved, then you're not really changing the costs because the hardware costs don't really drive what we do. So in this case, the initial hardware costs for a network that's based on about a mile covering a city is somewhere, but depending on the area of the city you're thinking about is hardware costs of between two and four person years. And then ongoing analysis and maintenance would have significant economies of scale if we were thinking about many cities. So you would have less than a person per city per year on average. Next slide, please. Actually, if you could go back for one second. I just wanna emphasize that one of the things that's unique about the beacon approach is that we're measuring CO2, but also the air quality gases, CO, ozone, NO and NO2 and PM 2.5 in every location. So we bring, you heard a little at the end of Kim's talk about grapes. We're trying to drive this synthesis of air quality and CO2. The two communities, the greenhouse gas community and the air quality community have been proceeding in parallel where learning from one community doesn't translate to learning from the other community. And that's really a tremendous waste of effort. And so bringing the two together fundamentally at their original inventory and with observations can really be a hidden cost savings and a hidden efficiency in everything we do together. Next slide, please. So we've been exploring this model. The beacon idea is being implemented in four urban centers. You see here in the Bay Area in Providence, Rhode Island with in collaboration with Meredith Hastings at Brown in Los Angeles in collaboration with Will Berrelson at USC and in Glasgow, Scotland in collaboration with Craig Mickey at the University of Strathclyde. And so we have the identical hardware and identical calibration approaches and software but different people trying to think about each of their cities differently. I'll try to see if we can build a community together that could advance the utility and ease of use of this idea. Next slide, please. And again, we're unique in the simultaneous emphasis on greenhouse gases and air quality and everything we do, we're making all those measurements together. Here's just an example of one time series showing and you can see about seven days in you see a spike in NO that doesn't show up in any of the other traces and where in other places you see strong correlation for example between CO and CO2. Most of the carbon monoxide comes from cars and a tremendous amount of the daily variation in CO2 was also from cars, for example. Next slide, please. So in addition to their separate importance for air quality, the air quality gases provide a very important route to attribution of the CO2. So here's just one example of that. You see the passenger vehicles have much, much higher carbon monoxide per unit CO2 emission than industrial emissions diesel trucks or residential heating. So by a simultaneous use of the different kinds of information from the air quality gases, you can have much finer attribution. In the next slide, please. So I'll just show you an example. You already saw some of this, the COVID period. So here you see in blue what CO2 or CO looked like before the COVID related shutdown and in orange afterward and two things happened. One, we all stayed home and two, spring came. And so it's important to disentangle the effects of springtime from the effects of the shelter in place. So next slide. So here's an example of an inverse just as described by my colleagues, a weather model that combined a prior and our observations to get an estimate. And here it is disentangled by sector. So you see that on average before the shelter in place, our emissions were about 400 tons of carbon per hour. And then after that shelter in place, there were almost always less than that 400 tons shown in that red line and that the big change was in the traffic. But you also see that in the green that the trees were more active. And so carbon was being removed from the system more by the biosphere than earlier. Next slide, please. If you go back just one, here we have for carbon monoxide. You see again that the emissions dropped, they dropped especially at night and from the traffic sector. Next slide, please. So I just wanna emphasize this point that air quality and greenhouse gases have much in common and that we could be much more efficient if we bring these two things together. So I'm excited to see this new effort called grapes that Kim told you about at the end of her talk. And the next slide, please. So yeah, I'll be done in just a second. So I already showed you that we can track emissions within a month and we can also track process level things. So I show you in this other figure here that the fuel efficiency of the average vehicle has improved by about 3.3% per year over time. And that's another thing, kind of thing we can get from these observations and inventories. And then the next slide, so yeah, to close out here, I just wanna say that really the important thing is the people. So I show you here an example of three of my close collaborators in establishing this project, Alexis Schusterman who was a grad student who worked on this with me in the early days, Alex Turner who did much of the development of our modeling strategy and Naomi Azamo who's working on the CO2 trends right now. We're on a steep trajectory as a community learning how to do this, but we're in a position to deliver greenhouse gas and air quality information in every city in a cost effective and sustainable way. So thank you for listening. Okay, thank you, Ron. And now we'll go into the Q&A. Please do ask your questions on Slido. Meanwhile, I'll ask a couple of questions to our speakers. First of all, I was really curious what kind of response policymakers are giving you relative to the work you're doing. Why don't I start? I'd say policymakers are focused on the UN prescription. So in my initial conversations with policymakers the idea that we're bringing measurements to the table is not yet of deep interest because they think their instructions are to fill out a spreadsheet per the UN and their recommendation to me was that I should go to the UN and try and change the UN prescription. I think the kinds of things you heard from the three of us should pretty rapidly change that, that the existence of the possibility of using observations to show that inventories are changing in the ways that cities expect or not changing in the way that cities expect should bring things to a different point with those policymakers. But I think cities are short-staffed and they're struggling too. And so their idea that we would bring new tools to them is not yet socialized. I think that's largely true. I think the city of Chicago may be an exception because I think they would be... I just wanted to know that at least we tried to interface with policymakers partially because we wanna know how our data is useful. But I also wanted to know that unless they are at the state level, at the city level, a lot of the city policies are voluntary. So I think that that... I don't think that means they're not... I mean, I can't speak about their policies but I could say that that may have an impact in terms of how much engagement people have. And then until we see some things that are more... Not voluntary, there might be more engagement. Yeah, Yvonne, did you have something? Yeah, well, I'm working very closely with the city of Paris. They're very much interested in our work and the project that I presented, I Could Cities, is actually looking at what cities need, what they want and what can they include into their policies. And what we do see is that although they're very interested and we have a very common get-together, we exchange on a regular basis. It is really hard to get things in a written phase into the policies. It's really right now it's more about, let's say like get to know each other, how do you work, what can we do, what can we get from you or really to have it down to policy-making to trigger it down. This is really difficult right now though. So yeah, they are interested and I think we will get there but it does take some time also to speak the same language. There are a couple of things that we need to overcome. We're starting to get some questions from the audience but one more quick question before we do that. I wonder if you could just talk, and I know Ron already kind of dealt with this but could you talk a little bit more about how the tools and methods you used in your work could be applied more broadly to other cities? I guess I could make a plug from one of my colleagues who uses aircraft observations. I showed a little bit of his work. I think that beyond the sensors that Ron uses and some of the permanent in situ sites that we talk about a lot and some of the variety of different things. I think aircrafts or measurements could be used more often if they're used on a routine basis. I think they can provide a lot of value. Anybody else? I think just to reiterate what I said, I think we have some pretty good ideas for how to do this at scale. There's a lot of commonality in what the ICOS European cities are trying and what we're trying and what the four cities and the test beds, the NIST test beds are trying. And so I think any one of those approaches is primarily people driven. And so we could choose a set of hardware and move forward really trying to figure out what the cost savings are at scale, which is really where some of the interesting challenges will be and the interesting opportunities that the ability to do this in multiple cities at once and really learn from the different plan forms of different cities, how to be effective would accelerate what we're all trying to accomplish tremendously. Yeah, I think we're from the technological aspect, I think we're there. We can do that, we can expand, we can scale it. What we need is, as Kevin and also Kim have already said, we need this coordinated approach, we need some kind of standards that really make sure that the data that we provide are comparable and that also the cities between one another can compare themselves in order to see where do we actually stand compared to a city that is comparable to mine. I should just caution to say that there are some things that do not necessarily work yet that are in research and development. For example, if we think that satellites are going to be able to be able to provide city scale information and anytime in the very near term, I think that that is really getting yourself over the wheels of the bicycle. I just, it's gonna be a long time before that type of satellites are going to be able to provide that type of information in the short, at least in the intermediate term. No, that's a good point. So we have a question for Ron. Do sensors at one mile intervals in beacon sufficiently identify industrial point sources? So I think the answer for that is, depends on the scale of those industrial point sources for industrial point sources that are large compared to the emissions from other things in that grid cell. The answer is yes. And for things that are at the same scale of the emissions in their grid cell, I think it should be yes, but we have yet to demonstrate that. Yeah. So we have a question for Yvonne. Really interesting to see the level of detail and connection to the climate plans is there feedback where policymakers are updating policy based on what you are finding? Certainly answered that, Ron. This, yeah, I would hope for that. That's actually what really drives my research to have it applied into reality and see it become, yeah, something living. Right now, like I said, we are in that exchange phase with Paris and also with me and Ike, we had several exchanges with the policymakers but it's not always the technical people who are interested in what we are providing and who understand what we are providing that are actually pushing through the policy makings. So there is also inside the city administration that they need to get aligned with one another and then, yeah, of course, I would just hope for that, that those findings get pushed their way through. I'd just like to add that it's easy to get John to the policymakers because they're individual decision makers with authority over a whole city in concept but a lot of the change that we're gonna try and drive will be driven by individual decision makers in smaller projects and our ability to connect to the finance community and there's a ton of energy and so-called green bonds and the idea that those bonds could be verified is a really interesting one that might change things. So I, you know, full disclosure, I joined this small startup that's trying to sell those things but I did that because it was the first place that where I felt like my science could really contribute to change in a way that was fundamentally different than other folks who were trying to convince me to join their small companies. Yeah, I'm trying to do the same thing with the company. I'm part of Earth's knowledge and in which we're trying to work ostensibly with corporations and help them deal with these issues. So we have, the next question actually is again for Ron, how do we access, how do we get access to land to put the sensors? Does that limit where you can put them? High-rise building owners might not let you have the space to place the sensors. Well, that's you the data. So that is of course one of the challenges. We've been proceeding by working with large distributed landowners. The most convenient for us has been local school districts. So almost all of our sensors are on top of a school. We can make a deal with those folks, both that will come into their classroom and give them materials and develop materials at scale that could be used by teachers to talk about the measurements that are occurring on the roof of their building, but also you can make an arrangement with a school district and then get access to 15 or 20 sites all from one landowner. And that greatly reduces the overhead of that part of the project. Other building by building, it's extremely difficult. Everyone has their own set of legal requirements and tricks. And so we try very much not to do things building by building by agreements with a large landowner. I just wanted to piggyback on what Ron said, because that's true for any in-situ site. It doesn't have to be a low-cost sensor, that like I said, the backbone of our test beds are high-precision pocaros and finding places to put those are very, very challenging. And so I think that that's a logistical hurdle that anyone is going to have to jump through. And that's why things that you can observe from space or air on these mobile platforms are very attractive because you don't have to look for the leases. You don't have to look for those locations and deal with those nitty gritties. So there are certainly more than just the aircraft I mentioned, that's why satellites are interested. People are interested in satellites, drones, other type of high-prospectral instruments, partially because of this logistical issue of how do you site and fund and deal with the legal requirements of site or having a permanent sensor. Yeah, then you deal with timing issues with the fact one's continuous. Right, exactly. There's these trade-offs you have to make. Yep. Okay, another question for Yvonne. Can you shortly explain the calculation basis and information sources are used for converting a 3% year renovation plan into energy reduction and then into greenhouse gas emission reduction? It's short, in a short way. Very short. So yeah, the 3% that are actually what's written in the climate plan. So I was a little quick on that part, maybe. So that's saying that they have 45,000 dwellings that they want to renovate and that's 3% of the overall dwelling budget, let's say. So that corresponds to that. And then what I've done is that I took the most energy used, the dwellings that are the high energy consumers. So the biggest ones in France, we actually have labels for each one of them. So I took the words and my simulated, what would it be if we renovated them and had them instead of a 330 kilowatt hour per square meter per year energy consumption, I renovated them and had them afterwards in a 50 kilowatt hour energy consumption per square meter per year. So a really high impact on energy consumption, as you can see. And then based on what they're using as fuel type, so I did not change that, I just renovated like for example, from simple glazed to triple glazed when those stuff like that or the roofs. So I took the same energy used for heating and then I translated that simply through the emissions factor and then got back to my GHD emissions. So this is how the approach basically is done. Okay, we have a question for Kim. The WMO prescribes concurrent air quality and greenhouse gas analyses and measurements. At the US federal level, how can we truly develop and implement such systems of monitoring, reporting, and verification nationally? For example, phone weather apps now report air quality. When will they do that for CO2? That's a really good question. I can't answer that question with a good air bar, but I can say that we recognize this need that Ron rightly articulated in his presentation that doing these two things in parallel in silos doesn't make any sense. And that's why this initiative called Grapes came into being just recently of trying to bring that information together. Once we move forward with that and that information is available, I suspect that there will be data providers that will be able to take that information and do things like make apps or package it in a way that a lot of the weather apps are able to do today. But I think the first step is to acknowledge it. We have, I just mentioned NIST and NOAA as being a part of this, but we've recently had some discussions across many different agencies, NASA, other parts of NOAA, DOE. I know I'm leaving some agencies out, so I apologize to those folks. So there's a lot of interest to do this across the federal landscape. And I think that that's a real positive step forward. And I think we're all excited to work towards that end goal. Very good. I was just part of a report to NOAA that basically suggested that they get on to exactly that kind of thing. So there's a lot of interest out there. So we had a question for Ron from a Bay Area resident who's interested in his thoughts on how his work is being integrated into the San Francisco Climate Action Plan. So I'd say that at this moment, our work is not we're responding to that Climate Action Plan by trying to observe whether things are changing as predicted by it, but we're not driving the Climate Action Plan. And so we, you know, with the comfort level, we have now and our ability to observe things, we're looking forward to a closer collaboration with our local colleagues in developing plans that can be verified and demonstrated and adjusting those plans when things don't go as expected, which of course will happen. Okay. So we have a question for Yvonne. Were the presented data sets where residential emissions were shown to be higher than commercial? Do the lack of reporting from the commercial sector? Utilities, nope, I just lost one, was it? Usage commercial or utilities or usage commercial real estate? No, I could not say that the residential sector has less, has more detailed information on emissions than the tertiary sector has. They are both influenced by gas consumptions, by stuff like that. We do have what I was just explaining, those energy labels for the residential sector that is not being done for the tertiary sector, but this is not something that's really influencing a detailed question about the emissions. So no, the question was why is it in the end lower? I guess that's really the reality and that a lack of emissions that are reported in our inventory. Okay. Kim, I have a question for you. What is the scale for air platform measurements? Do these measurements provide more granular data than ground-based sensors at one-mile intervals? I'm not sure how to answer that question. I just want to reiterate that along with Kevin and others here that it's really a system. So when we use aircraft observations, we use this, we try to use it as a spatially explicit, I don't want to say prior, that's very much a cottage term in our industry, but emissions information. And then we try to see how the atmospheric observations inform that spatially explicit and temporally explicit emissions information. And I think that the answer to that question depends on how good that coupled system really is. Generally, if you use something like aircraft observations, I think even if you didn't use something spatially explicit, you still could get to whole city emissions. You wouldn't be able to get to that detailed level of information. However, people like CarbonMapper, which is a private company, they're able to use hyperspectral imaging to be able to get down to plumes and point sources for methane. And so I think it really depends on what you're going for and using the right measurement and techniques to try to constrain the emissions that you're interested in. Oh, sorry, Kevin just mentioned CarbonMapper. It is a nonprofit. Thank you for correcting me, Kevin. Yes. Another question for Ron. And I've kind of seen this myself in the observations that are being made by Argonne in Chicago area. How does the air quality data you collect compared to the EPA network, air quality network? What air quality events do you capture and what are missed with low cost sensors? Are the low cost air quality sensors more precise and accurate than low cost greenhouse gas sensors? Okay, so I would say that in general, we're seeing the same things as the regulatory network on the air quality side and we're seeing it with more spatial granularity. So we see differences in different locations. The sensors are not as precise as the higher cost ones that are the EPA regulatory standard, but they can be just as accurate. In terms of what you should be thinking about in terms of the precision and accuracy is the relative accuracy needed to be useful. So for CO2, you're not particularly useful unless you're good to about a part in 400 or better, whereas for NO2 or Ozone, if you're good to, five or 10% you're contributing, especially if you have 50 locations and the square root of N is your advantage. So the two kinds of needs drive different sensor requirements to be useful, but the low cost sensors in both cases are capable of the kind of accuracy and precision that can provide a useful map and those maps can be interpreted in a useful way. Very good. Okay, so we're gonna close this session, but before we do that, we're gonna give a chance, first of all, to thank each of the speakers, but also to ask each speaker for a closing thought. Who wants to go first? Go ahead, Kim. Oh, great. I was gonna just say, go ahead, Ron. Well, from being from NIST, I'm just going to have to push for this idea of moving toward, I didn't really make it in the question and answer portion of the talk, but I think what's really needed is us to move towards standardization and without that type of, without moving towards standardization, I think what's going to still be sort of a little bit of a wild west out there in terms of, in terms of how reliable you think that your estimates are, you have to, you're gonna always have to kind of keep evaluating it. And so we're really focused on that and interested in taking the science and moving it along into the standardization process. So I'm gonna have to wave our flag to say that, this is what we're about and we're excited about moving this forward. I'll add that completely concurring with what Kim said, we're excited to partner with her and the rest of our NIST colleagues in reaching some standardization. I just refer back to the pillars of this report that Kevin highlighted in his opening. We're committed to open data. If you go to our website, the data can all be downloaded. And you can ask us for the raw form and the calibrated form is open to anyone without asking. And I'd say the other thing that we didn't emphasize, but that is true for all of these approaches is we've established a pretty timely systems of analysis. So you can get an analysis of the greenhouse gas emissions for a city within three months of when those emissions happen, maybe sooner. And so the kinds of lags that we struggle with and other kinds of reporting in the greenhouse gas systems are can be eliminated using direct observations in this hybrid approach. Okay, Yvonne? Yeah, I can just say that I totally agree with you. For me, it's really important to have, like I said already, those standards in order to have something comparable. And I did not go too much into detail in my presentation, but what we're actually using, that the company is using Orange and Earth is a hybrid approach as well. And I really think that's where we should be going because we have those two approaches and they're very much complementary. We can learn from the two of them. They're both having their advantages, of course. Also, they have those advantages. But I think picking the best of each one of them and then trying to get them to work together is really where, yeah, we should be going. And for France, at least we're at the building scare on an hourly basis. We can have the emission of entry from last month. So we are really, yeah, capable of telling policymakers that to see if they're going in the right direction or not and to guide them also to see where they can be doing better, maybe in next year or over the future. So I think that it's really something, this work together also with the policymakers is really interesting and important. Well, I want to thank you all. That was an excellent session. We'll take a break now for 30 minutes before we go into the afternoon sessions. So it's 12.30 Eastern time. So we'll start again at 1pm Eastern time with the next session. All right, hi everyone. Welcome back from our break. My name is Ann Marie Eldring and I will be moderating our next session. I'm a scientist at NIST working in the greenhouse gas measurement group. And session number two is about urban stakeholder information needs for decision-making. And we've got a great selection of folks for our panel here today. They'll be doing some introduction of themselves but just in contrast to the previous panel, if you were here, the folks that are in this session are more focused on actual policy-making in the art of decision-making, et cetera. So a little different perspective to share. And the way we're gonna run this session is entirely a moderated discussion. We're gonna let folks introduce themselves and then start them off with some questions. And I very strongly encourage our audience to help us out and submit your questions in Slido. You can upvote questions so we can see what it is. People really wanna hear the discussion be about. So really please engage, ask questions and that'll help us make the most of the opportunity you have for this discussion today. And I understand we have a pretty big audience out there so we wanna hear what you're interested in. All right, I'll stop talking and let our panel introduce themselves. We'll just go through folks in the order here with Phil, Robert, Michael Berger, Mike Ogletree.  Thank you. I guess I'll start. Good morning, good afternoon, everybody. My name is Phillip Fine. I'm the executive officer at the Bay Area Air Quality Management District. That is the Regional Air Quality Agency in the San Francisco Bay Area covering about nine counties. I've only been in the job for a little over two months. Prior to that, I was working in DC at ETA as a principal deputy associate administrator for policy as a political appointee for two years coming in with the Biden administration. Prior to that, I was at the South Coast version, the Southern California version of the Bay Area AQMD, the South Coast AQMD for almost 15 years working on a variety of technical and policy areas. And as Marina was prior to that, I was in academia doing research. So I have been on sort of both sides of this equation doing research that is designed to feed policy makers and help in that decision making but also being on the receiving side. So maybe that brings a unique perspective. The Bay Area AQMD, again, a local air agency traditionally focused on criteria pollutants and air toxics and protecting folks from the negative health effects of air pollution. More recently, last five to 10 years, like many local air districts and states have been focusing more on greenhouse gases both because of the enormity of the problem but also because there's a lot of synergies and policies between greenhouse gas reductions as well as reducing air pollution and protecting public health and also the crossover between the fact that our air quality, despite emissions being reduced in terms of traditional air pollution, because of higher temperatures, because of events like wildfires, our air quality is not improving as fast as we would like. So we have a vested interest in making sure that we minimize the effects of climate change in order to do our core mission. I look forward to the discussion and answering any questions. Thanks, Anne-Marie. Great, thank you, Phil. Robert, will you go ahead and introduce yourself for us? Hi there. Robert Stipka, I'm head of Climate Action Implementation for the North American region of C40 cities. I sit here actually in Kelowna, British Columbia, Canada. So coming north of the border, but I work with our 17 C40 cities in Canada and the United States. C40 is a global collaboration of climate mayors who are committed to achieving the Paris targets. And to be part of C40, cities need to achieve five of our leadership standards. And among the leadership standards are having GPC compliant emissions inventories updated every two years and Paris compliant climate action plans. And so certainly those come in hand-in-hand. Another area that we're working on is consumption-based emissions inventories as well and action on consumption-based emissions. And so we're very closely looking at, not only what are the emissions actually sourced from within cities, but also what are the emissions as a result of activities that happened within cities? And I think there's a variation there in terms of what we are measuring prior to my stint at C40, which I've been here for one year. I worked for the local energy company here in British Columbia, supported cities and partnerships around implementing, supporting their climate action plans as well as implementing performance-based building codes. And prior to that, quite a bit of consulting with cities and supporting their climate action inventories and plans. So I look forward to the discussion. Excellent. Thanks, Robert. So glad to have you here. Mike Berger, let's hear a little bit about you. Sure, thanks, Emery. So my name is Mike Berger. I'm the Executive Director of the Savings Center for Climate Change Law at Columbia Law School. The Savings Center is a think and do tank that's housed at the law school and does the sorts of things that you would expect to go along with that across a wide range of climate change issues on the mitigation and adaptation fronts up and down the scales of government from the local to the global. One of our initiatives is our Cities Climate Law Initiative, through which we not only conduct independent research and promote what we hope is thought leadership in the field, but also engage directly with cities, cohorts of cities and sort of organizations and associations like C40 and others that sort of work with cities to address their climate commitments. The purpose of the Cities Climate Law Initiative was to, we set it up to fill a gap in the provision of sort of legal expertise to cities that have made ambitious climate commitments 80 by 50 net zero, 100% clean energy, whatever it may be, and to address the naughty legal issues that come up when pursuing those policies, some of which are not necessarily fully considered at the time that the policy, the commitments are made. That's not our only engagement in cities. We also have over a number of years, I actually started my career as an environmental and land use attorney for the city of New York under Mike Bloomberg's mayoral administration and worked on some of the early climate change action in the city, back in the early aughts and sort of as a consequence of that as well as a number of other things. Over the years I've developed a sort of habit, I guess I would say of filing amicus briefs in big climate cases at the DC Circuit Court of Appeals and at the US Supreme Court along with other jurisdictions on behalf of the US Conference of Mayors, the National League of Cities, the International Municipal Lawyers Association and also sometimes cohorts of individual cities who will sign onto these briefs. So we've weighed in in a number of cases. The final thing I'll just flag quickly on my and our work at the Saban Center when it comes to cities in greenhouse gas emissions is we are also a member of a new coalition called the Smart Surfaces Coalition, which will be working hopefully with nine pilot cities here in the US as well as cities in India to implement a suite of policies around smart surfaces referring not only to green infrastructure but also to other forms of carbon sequestered concrete and cement, gray infrastructure, white roofs and a wide range of other policies to address not only the heat impacts of climate change but also as a way to help reduce greenhouse gas emissions. So I'll just close by saying I'm also excited to be on this panel. My expertise may be a little bit less technical than the others that are here and a little less expert in many regards but I'm happy to play the lawyer in the room for this panel. Excellent, thank you, Mike. Appreciate that. And Michael Ogletree, thanks. Yeah, my name is Michael Ogletree. I'm the director of the Air Pollution Control Division for the state of Colorado. My background prior to coming to here, I've been in this role about a year and a half now prior to that I was at the city and county of Denver running their air quality program for about five years and then prior to that as an analytical chemist looking at stationary sources. You know, here at the division, at the state level, you know, we created in 2019, you know, it was through legislation, statewide greenhouse gas reduction goals. So pretty aggressive goals, pretty aggressive goals. We started initially with four FTE in that space but through the last couple of years we've increased that to about 18. We haven't yet filled all of those roles but, you know, certainly with a lot of the legislation and hitting a lot of those targets and really needing to kind of staff up. We've been hiring a lot lately so if anyone out there is interested, you know, that program itself, you know, we've passed, you know, in the last couple of years a handful of different rules, you know, around light duty vehicles phasing out of FHC products and then additional greenhouse gas reduction requirements for the oil and gas sector as well as different sectors. You know, we've really tried to also include a lot of environmental justice outreach and components throughout all of our rule makings and, you know, different goals. So making sure that we are doing those in our work. We actually have a dedicated person in that climate unit to help support a lot of that just to make sure that we are listening to communities as we're passing a lot of these rules and regulations. So with that, I'll hand it back over to Anne-Marie. Great, thank you so much. Well, I'm really excited. We have an excellent panel here. Different perspectives represented. A lot of depth of experience. So maybe just let's dive into some details. So if folks could tell us a little bit more, what are your greenhouse gas information needs? Is it really just emission estimates or are there other type of information you require and maybe give us a little hint about spatial scales, temporal scales, what is it you really need to know?  But I see fields unmuted, so we'll let you go. Thanks, is that the signal? It's just unmuted. Yeah, so local air districts, and I'm sure states too are generally have pretty good activity data because we've been doing, you know, traditional air quality emissions and authorities for a long time. So we probably have access to better data than any national study or global study in terms of traffic, location of traffic, you know, our industrial activity, land use, all of those things. So we're pretty good at bottom-up inventories, although, you know, to the extent that our emission factors are good. And often some are good and some are not good. So there's a few areas where we could use some help, obviously around methane. There's a lot of questions around landfill, methane emissions, distribution, pipeline leaks, and then just more generally, the whole life cycle emissions of those, not just locally, and refrigerants are another one. But I think what would really be helpful is not just another published paper with another number, because you can cherry pick those numbers on one side or the other, more authoritative numbers that we don't need, when we're doing a policymaking or a rulemaking, we don't need another debate over which number to use. I think that could be a role that the National Academies can play is some, you know, consensus reports or synthesis reports that really settle on it, on the best number that we can rely on. It's not something, again, we're gonna be debating enough during some of these policymakings that, so we don't wanna do that. But it's not just one number either. And getting to spatial scales, obviously there's different emission factors and even different life cycle assessments that happen regionally or locally, to the extent that whatever is developed can have regional or sub-regional data, but again, blessed with the academies and a consensus report or something like that would avoid one of many, many, many arguments and debates we have when we're engaging in a rulemaking. Great, that's helpful. And I'm gonna turn over to Michael Ogletree. Does that resonate with you, Michael, in terms of your work in Colorado and what the agency has and needs? Yeah, you know, it does. I mean, for us, as I mentioned, we do have these targets. You know, for us, we're looking to reduce 26% by 2025 from 2005 base levels and then 50% by 2030. You know, we do our statewide GHG inventory at least every two years and we're working towards tracking the progress through those projects. You know, as we think about what that looks like in terms of what Phillip mentioned, you know, for us, we're trying to work with sources to create an intensity value for the work that they're doing. So we're trying to do it on a source by source basis and we actually have a rulemaking coming up in a couple of months here at the state to help better get better data to better inform, you know, some of the rules that we'll be putting out after that. Sort of, to Phil's point, you know, the more data and the better we can get around it, you know, the better information, the better informed policy we can have in the future as well. What we've learned through that stakeholder process is that it's not easy. So, you know, we've had a lot of discussions both virtually and in person and we've found, you know, working with academics and experts, getting people in the room to help identify the best way to create, you know, very, very well-ground truth information and data has just been really important. So hopefully we'll have a better understanding of what that looks like in the next couple of months. Great, helpful comments, thank you. Robert, in the C40 work, do you see common needs across all the cities you're working with or there's a lot of variation in what they're after? I think, I mean, there's the needs that respect to kind of the current reporting mess out there, then there's kind of what are the needs that you need to, you know, drive from the actions that you're looking for, right? And so, you know, from the city's perspective, there is, you know, the traditional activity-based inventory work that's taking place and the challenges that they have in, you know, obtaining data from utility companies. For example, from understanding, you know, methane leaks from landfills or from leakages, even from the fossil gas infrastructure within their own cities. But then there's, you know, the linkages between, you know, cities, climate actions and the influence they may have on business within their city, on the influence of regional governments and then kind of state and national. And so how do we integrate the data and the emissions? And hopefully then the actions that are needed to be able to help support actions on all levels. And certainly with, you know, businesses within within a jurisdiction, as you can imagine, you know, if they're reporting on task force climate disclosure and have their own greenhouse gas reductions and net zero journeys, certainly the success of the city being able to provide infrastructure to be able to help them reduce their emissions, there's an interdependence there and we see similar interdependence then on the cities and what the states are doing with respect to energy grid in particular and what the federal government is doing. And so with the Inflation Reduction Act right now, we are seeing kind of how do we downscale what is, you know, plan and the reductions coming from that to the city level and how do you kind of determine what levers you need to pull. We look at what cities can do within their direct control but then we also look at what's within their influence and what can they advocate for. And so that's where it's really important that whatever we're doing that it basically is able to be consistent in terms of being applied. And so that means we've got to be looking at things from data from different scales and really on the city side of it it really is having that localized scale as possible to be able to inform policy and action. And so you need the emissions but you also need some attached spatial information and disaggregated information to make meaning of that. So there's a lot in there. And I guess lastly on the consumption-based emissions front of it, I think that's really important because it is the question of what is the influence that again, back to what is within direct control rather than what is just simply happening within the city and certainly on the consumption side of it. And we talked about things like clean construction where's your steel coming from? Where's your concrete coming from? Being able to actually have real data to be able to understand those emissions and where they're coming from and be able to put policies to reduce those and then recognize those, I think is really important. Yeah, I'd say on the last front is really regional emissions. And it's not just kind of our large cities the fastest growing parts of population growth and development is happening in the suburbs. And so how are we able to now start to kind of make action in a cohesive way with regional governments to be able to see those productions that are required because the actions are not on the island of any one city we need to be looking at it on a regional basis. Yeah, now those are some interesting different perspectives Robert in the kind of information and then that connectedness of the different scales I think we're gonna hear that theme all throughout our discussions today. Yeah, Mike, some comments from you from your different view. Sure, I mean, I think that all of these points actually as you've underscored sort of touch on the key issues. I think that the usability and implementability of the data is a key issue from a legal perspective and in our engagements with cities that's often where we come in less on the sort of construction of the inventory and the benchmarking process itself and more on, okay, what are we gonna do with this information and how can we actually use this in order to take measures that will have the effect of reducing emissions. And in that regard, I think I would just flag maybe two thematic points. One is variability. There is a tremendous variability among cities in the United States and the approaches that they're taking to greenhouse gas emissions reductions. Even Robert can probably speak more specifically about like variation within the C40 network and well, you're dealing with things from Ithaca, New York to Portland, Oregon to New York City. It is a wide range and they're each operating within a different state context and the relationship between the cities and the state governments and the relationship between the authority of the municipality within the context of a state constitutional legislative framework does define to a large extent what cities and local governments can do and can't do. So I just think that obviously there's always gonna be the quest for the one number, the one methodology to rule them all but query whether or not that's realistic and feasible and think more about how our different places are gonna use different approaches. The issue of what's under your control I think is really central in thinking about this from a legal perspective because there is a great deal spatially that will appear as within a city's jurisdiction because it appears within the city's territory that it's not under its authority to do very much about it all and consumption based emissions sort of as a test case for how far authority can go in that direction. I think of that as kind of one extreme example where cities may seek to push the envelope a fair amount in trying to reduce those consumption based emissions depending on how far they wanna go but transportation is like a less extreme but much more visible and sort of currently existing cross cutting problem because all of those cars are trucks, ships, freight are moving through these jurisdictions but the cities have very limited authority to do anything at all about that. And to the extent that they can influence state action that's one thing in the extent to which the federal government's vehicle emissions standards aircraft standards, shipping emissions standards and so forth and so on those directly impact how emissions appear in city inventories and in city satellite imaging. So the hybrid approach that focuses a hybrid approach or one of a number of hybrid approaches that focuses on giving information to cities that they can use in order to take meaningful action seems to me to be the key. Awesome. Thanks for those thoughts, Mike. And just one moment to encourage our audience again if you feel free to submit questions in Slido and get engaged in our conversation. Thank you. I see Michael you wanna throw in one more comment here, go for it. Yeah, no, just wanted to comment a little bit on what Mike had mentioned around this jurisdiction and authority. I think that's a very important thing to consider and think about. I know us here in Colorado, Denver put out building performance standards a couple of years ago. We're now at the state level doing similar statewide building performance standards. And there is conflict in what those look like. And as a regulatory authority at the state level, we're trying to work with the city and county of Denver and learn from their experience, but it's hard to be perfectly in alignment with what their rules are, just because we have a much broader context we need to think about. And there is this overlapping jurisdiction, but as much as possible, we need to have at least some level of alignment so that we're all comparing apples to apples as we're looking at ways to really measure the impact of these GHGs and not be in conflict if we can. But we just have different considerations as a state government. Yeah, interesting. I'm gonna realize a little bit back on the information tract again. We have a question from the audience and they're asking about city climate plans. And sometimes we get started in activity, we don't have perfect information, but we don't wanna wait till the perfect information's there to start moving forward. So if a city is trying to create a baseline climate plan and how can they get the kind of information that they need and what are your thoughts about setting intermediate goals, right? Maybe they don't have the information to know how to set a good goal for 2050 or 2070, do they set some intermediate goals? So maybe from your experiences or the type of information you know you can get and you know you can't get, what are some thoughts about how cities might be able to create climate plans, even if information's imperfect that they get the start? I might as well kick in then from a city's perspective. Yeah, I mean, we're right now working through a review of various data collection around inventories and climate action plans with global covenant of mayors and CDP. And really what we're looking at is trying to figure out like what are good enough proxies of data to be able to provide evidence for action, right? And so my suggestion is like you don't need a perfect greenhouse gas inventory to create a solid climate action plan. We know what the main levers are and sources of emissions in most cities and it's transportation, buildings and waste. And so really an inventory should not really change what it is that is the right action for doing what a city should take. It gives you a way to track of your progress as you're coming along ideally, but it really shouldn't change the nature of action that needs to be taken. And so if our goal is zero emissions by 2050 then I think regardless we're looking at a steep decarbonization pathway and actions that are gonna be supporting that. Bill, what do you think as a man who's been in a few of these air quality management decision organizations, how do you make plans and what are your thoughts? Well, I agree with Robert. You don't need perfect information in most cases, especially in the context of climate planning. And this gets back to sort of a legal authority discussion we just had is that a lot of the actions that cities can take, regional agencies can take are using authorities that might not be directly related to GHG reductions, but have other co-benefits, congestion relief, increasing transit, public transit ridership, land use policies, all of these things that have other benefits as well but can be done in a way that maximize the climate benefits are things that you don't need a comprehensive climate plan or inventory to actually do. You know, you're heading in the right direction and there shouldn't be enough technical information to make sure you're giving GHG emissions full treatment. Yeah, thanks. Others, all right, I'm not seeing hand raising or unmuting from our other panelists so we'll bring in another question. I guess I'll just chime in quickly and sort of looking at it from a legal perspective. There is, others correct me if I'm wrong with this. I'm unaware of like a particular criteria that information must meet in order to justify cities taking action to reduce its greenhouse gas emissions. For the most part, these plans or pledges or commitments are embodied in policies that are not legislated, certainly not based in the city's charter or something like that. And so there are policy commitments that are being made and I think that the tracking point is really the key. We kind of know where we need to get to, right? We need to get to 50% by 2030 and net zero by 2050 that's the goal. And so we know what the pathways are to do that in cities, go after buildings, go after transportation through the multi-layered approaches that are available and go after waste and maybe after consumption as well, all with an eye to equity and justice. So the numbers are helpful because they might direct policy towards the higher emitting areas or locations, like depending on how granular it really does get, but as a general matter, we kind of know the pathways. And there's nothing about the data that's going to stop a city from seeking to decarbonize its building sector. All the data can do is sort of help specify where and exactly how quickly it should happen, I guess. Right, yeah, no, I mean, I would almost turn this questions on its head. And for example, if interest in citizens wanted to work with their city government and really try to advocate for change, it's like you guys were saying, you shouldn't be held back by saying, oh, we don't know enough just yet that there's steps one can take, there's large-scale sort of areas of focus. And I like what you said, Phil, about the co-benefits are another great area to think about, right? Congestion relief, air quality reduction, all these benefits that come from making a plan to address, say, transportation. Yeah, all right, we're gonna go ahead. I was just gonna say, it's important to understand, what are the regulatory levers that cities actually do have? And so then what are the right kind of metrics that maybe you should be attaching your actions to? And so that's where air quality is a really valuable one in being able to attach air quality, air quality being a proxy for greenhouse gas emissions. Maybe that is the one where you need that better level of quantification to be able to make that case. But there's other metrics like congestion or like affordability and cost. It all depends on what the action is. And so it's important to understand what are the key actions that these can take to reduce greenhouse gas emissions and what are the various kind of co-benefits to that? And through that, that's where you can kind of start to design the policy interventions. Thank you, Robert, appreciate the comments. Taking another question from our audience on a slightly different topic. So let's say we've got these climate action plans and changes are happening. Is there sort of real-time or prior quarter economic information that's helpful? Like construction, installation of infrastructure or heat pumps, et cetera, are those well-tracked as we move towards climate goals? I think a lot of our conversation is focused on concentration measurements, but clearly other information can help us see if change is happening. I'll jump in. Definitely, we have pretty good data on the production side.  We know gasoline sales, diesel sales. So we have pretty good information on being able to track it. But I also don't want to discount the need for doing some top-down analysis to be able to show that there's been overall change. It gets to some of the trade-off questions that we often get into and need data for in terms of some of the shorter-term impacts and if you ban purchases of, let's say, a certain type of appliance, does that mean people are just stockpiling or putting off making changes or are estimates of the transition accurate or is there delays? There's lots of things to look at to see if our policies were designed correctly, which is another area of research that I think we would all benefit from is looking across all the cities and all the different policies that are tailored to different legal authorities in different situations. But there's probably enough commonality where if we had a forum or research or something to show what's working, what's not working and we could learn from each other's mistakes, I think that could be very helpful. Thanks, Phil. Robert? Yeah, I mean, I think with policies like building performance standards, it's pretty, you know, it's in the regulation in terms of like what the outcome needs to be like. And I think that obviously needs to be evaluated. It's more so where there's not the regulation you're relying on voluntary or incentivized based approaches that, you know, you're really not sure, you know, what the outcome is going to be. And so I think there is a need to find a way to be able to track it, but oftentimes the outcome is going to be found in the utility data that you're going to see, hopefully a response in what's happening. So I think, you know, we can monitor and evaluate the effectiveness of certain policies and interventions. I'd argue one of the most challenging ones would be around land use planning and transportation because that is more of a lagging type outcome you're going to have and a lot harder to kind of characterize what is the exact change you're going to be able to see. And then you have, you know, the effects of, you know where people are fueling up, whether or not it's within the jurisdiction or outside the jurisdiction and things like that. So it gets really messy when you're kind of talking outside of buildings and looking at land use planning.  And so how do we, you know, get good data and good information as these plans are being developed and updated to really be able to, I highlight, you know the outcomes of various scenarios such that it actually can influence, you know how we kind of build our cities and where we put our money in terms of infrastructure. That's a really challenging one. Interesting, yeah. All right. If our other panelists don't want to chime in, we've got plenty of questions now queued up. Thank you. Our audience has come alive. I'm going to take this next question from, what they're asking is regarding the comments on limited need for accurate or specific information for policy, what are your thoughts if reductions generate credits in a market? Would accuracy be important then to establish tradable credits? And I think this is again, bringing up this idea of what if there really is a carbon market and then we're going to have to better quantify, monetize what's happening. So any thoughts on that? Mike, go for it. Yeah, I can jump in first, I guess. I mean, the types of projects that are going to generate credits, carbon credits for participation in a voluntary or regulated market are going to be happening at a scale where measurement is much more localized and individualized and will have to be demonstrable. You can't get the credit and put it on the market unless you can verify the reduction. And those projects are happening really at a micro scale within an urban context to the extent that they're happening within urban context. So you're talking about particular replacement of, or a particular project that's going to result in a particular set of reductions in one particular place, not citywide policy or statewide policy for cities and local governments. So I guess I don't know enough about it to say whether or not it's a different kind of analysis, but it does strike me that it may be a different type of analysis than the one in this report, so the types of analyses that are the focus of this report, and it's a much more sort of tailored individual accounting of particular reductions from some one or several actions. Yeah, I understand that. Other thoughts from panelists? I mean, I'll chime in. I think, you know, for us, when we're trying to create something like this, which we're trying to do for our intensity rulemaking, that's for oil and gas, which is maybe not this audience, but it certainly is, right? We're trying to find how we can specifically quantify and create a rule around that, and that's been one of the bigger challenges. It's hard to create very specific metrics for a lot of these different emissions, and how do we tie that in an accurate way that is a way that's replicable from source to source to be able to actually create a method for doing that trading? Because if you don't have a way that every source can actually do it in a way that's replicable to your point, like, you can't do that. Yeah. Yeah, and these comments are great. Like, as you say, Michael, some of the report was focused on a different, perhaps, motivation, and you guys are talking about practical, actual things that people are trying to get done and the kind of information gaps you have, so it's really informative. Bill? I was just going to pile on there for a lot of the trading or offset generating programs. There's protocols that run hundreds of pages long that set up exactly what you have to do, and it also creates the huge need for enforcement around that. I mean, some of these protocols are requiring activities to continue 20, 30, 50 years into the future. You can imagine if this was a path we went on and we upscaled the amount of projects to the level that would be needed, the amount of sort of bureaucratic infrastructure needed to maintain that would be quite large. Not to mention we'll learn something in those 20, 30, 40 years that could change those value of those credits that we didn't consider before. Yeah. Yeah. Thank you, Bill. All right. I think we're going to squeeze in one more question before we run out of time here. So one of our participants asked, with the role of combustion sources, land use policy, and other kind of factors, you have this interplay of greenhouse gases and air quality, and what are your jurisdictions and your organizations thinking about when they try to optimize GHG reductions but also thinking about the environmental justice benefits? So tell us a little bit about the interplay of these GHG goals and environmental justice objectives. I could start. I mean, they're hugely connected because when we're looking at, you know, as well some of the market transformation and the actions that are required and means oftentimes government funding attached to it, it just, this is the opportunity to attach those subsidies and those benefits to those environmental justice communities. And, you know, we're seeing the opportunities, for example, for zero mission freight and where you basically can improve localized air quality. There is most vulnerable in EJ communities are often where there's a lot of those fleets being housed. And so we are doing targeted air quality measurement in those communities. We're seeing cities looking at what can they do within their jurisdiction to be able to help reduce, support rapid electrification of vehicles in those neighborhoods. But we're also seeing targeted building retrofits and building decarbonization programs targeted for those communities. So, you know, I think we're, we are seeing things moving in the right direction in terms of the supports and the opportunities led by government happening within EJ communities with the hope that then you're, you know, that also supports building capacity of industries to be able to support more broader based, you know, decarbonization and specifically buildings and transportation. Thanks, Robert. Phil, go for it. Yeah. Well, I would agree with Robert that the, the, you know, it mostly aligns what we would be doing in terms of GHG reductions and improving exposure in communities. It's not always the case. And there's, I can give you many examples. I mean, but one we're dealing with right now is composting. For example, a lot of the composting facilities end up being in underserved communities. There's lots of issues around trucking and odors and other types of emissions. So I think air quality, air quality regulation historically has not been designed to address the concerns of these really local impacts. So as we're looking at climate change planning, I think we can do a better job from the beginning as the air quality world is also moving towards looking at these local impacts in a better way. But it doesn't always align. And we often find that in some communities, you know, while local air quality, they may be their biggest priority. Perhaps GHG emissions are not. We have to respect that. A lot of the work we do in communities needs to be really community driven and listen to what the, the community members vision for their own community is. But I do think we can lean into the areas where, where the, where the outcomes do align, which, which is what we've been doing in the Bay Area. Thanks for those comments, Phil. Mike, anything you want to know? All right. Well, we're just a few minutes away from the wrap up. This has been really interesting discussion with the range of perspectives. Any last minute comments folks might want to share with the, maybe with the perspective of, given what we talked about in the greenhouse gas information report and the pillars that Kevin mentioned earlier. If you could have a magic wand and get the community provide you two key pieces of information in the next year, what might that be? What can we do for you? And I can't change law. This is more, you know, technical measurement interpretation data. I'm not sure I'm going to answer your question, Anne Marie, but there's one point I did want to get into for the end. And one of the things I think we struggle with. And maybe outside the scope of this conversation or outside the scope of some of the, even the Academy's research is really the micro and macroeconomics of the transition. We cannot do policymaking without doing extensive analysis of sort of the economics of the, again, not just micro and macro, but actually household economics. It's some things we probably all do in different states and localities independently. But if there's any research or effort that can help do that at a national scale, but be able to scale it down. So we don't have to reinvent the wheel every time we're analyzing a policy that would be super helpful.  So that might help us get to some of the leakage issues. So if that is in the wheelhouse of the Academy's, it's be something we'd be very interested in. Interesting. All right. I'm going to go over to Michael Ogletree. Any thoughts on information gaps that could actually practically be addressed by the broader science community? I mean, I think that there were, you know, some kind of uniform, some kind of development of like a uniform inventorying method so that we can, you know, have a standard that's more widely adopted. It'll provide us, you know, better information. And I think just larger data sets that we can, that are more accurate to better inform policy. Yeah. No, I think you actually answered my question and you're starting comments, but thank you. How about you, Mike? Any thoughts? No, I mean, not really. I think that we've touched on a number of things that are obviously are some gaps, but it does seem like, you know, greater refinement and particularity that's driven by potential use and implementation is what's in order. Great. Great. And Robert, I'm going to give you our last 30 seconds, thoughts on practical information we can provide. Well, just the information that can be applied at the local scale, so building level, city scale, state and national scale that they all agree with each other so that, you know, when we're talking about actions, we are talking about the same, you know, CO2 that, that, you know, that, that we're looking at both mitigating and that we can have consistent actions among them. And I think, you know, with now, corporations now increasingly need to come up with credible pathways that that agreement is really important. And we certainly want to see that recognition of city actions with national commitments and national actions so that then the funding can flow to cities as well in order to who rely on who are going to be the drivers of implementation on this. So seeing that opportunity to really have everybody kind of collectively working together and what are the information needs to be able to, we're all on the same page of, of what those emissions are and where they are going. Yeah, great. Thank you again to our panelists. I'm sorry, I'm already run a minute over. So we're going to wrap up this session, appreciate your comments. And then we'll see a number of you back in our final wrap up at the end of the day. Thank you again. And I'll hand this back to Rachel and the NASA crew. Rachel, I think I'll pick up the baton if that's okay. So we're going to launch into our sessions three identifying tools to better inform urban decision makers in the U.S. And it really is a nice follow on to the excellent discussion we just had. So our goal in this session is to discuss existing or potential assets and tools in the public private and research sectors at urban scales to aid local decision making in the U.S. We have a panel of experts to discuss some of the tools used in their work. They represent, I think, a diversity of backgrounds and scales and we're lucky they could take time to share their thoughts. Just logistics for our session. We have 45 minutes in total. Each of our speakers will have five minutes to provide their remarks. I'll give a verbal one-minute cue at four minutes. Once all speakers have gone, we'll have, as we've done in the previous couple sessions, a larger group Q&A. So everybody out there, if you have a question, please submit it via Slido. What I'll think I'll do is I'll give a one-sentence introduction of each of the speakers and then we'll start at the top. And if I don't give an adequate description of your background, I'll finish that before you share your thoughts and slides. So first up is Leslie Ott. Leslie is a climate scientist at NASA where she currently leads the carbon group within the global modeling and assimilation office. Next up is Emma Lewin, is a senior associate with RMI, an independent nonpartisan nonprofit organization of experts across disciplines working to accelerate the clean energy transition. Catherine Atkin is an attorney, climate entrepreneur and urban planner focused on building the policies and enabling the environment needed to drive global decarbonization and sustainable development. Catherine will not be presenting slides. And then AJ Nagpur is an urban system scientist at the Department of Civil and Environmental Engineering at Princeton University. So why don't we go ahead and start with Leslie, you have five minutes Leslie. All right, thanks Kevin. Thanks for the great introduction into everybody who's been coordinating for a great meeting so far. So I'm gonna in five minutes try to tell you about a few of the things we're doing at NASA to both create better greenhouse gas information and to deliver that to people in new ways. Are NASA, are you guys showing slides? I think so. If not, I'm just gonna go for it. So first up, people probably know that NASA launches a lot of satellites, it makes a lot of observations to help tell us about greenhouse gas concentrations. But the other important step is to take all of the information that we get from satellites, things like observations of the land surface, things like nighttime lights, air quality species and of course greenhouse gas concentrations and integrate that into a holistic picture that tells us about how sources and sinks and concentrations are varying. And so early on in Kevin's introduction, you heard some of the discussion of how we need to operationalize those data sets to be able to deliver them more reliably on a global sense. Thank you. All right, there we go. So this is our little schematic of information flowing through all the way from observations to integration with models to a 40 atmospheric state of greenhouse gas concentrations. So we're able to deliver this now with a few months latency and the important advances that this enables are things like independent verification with aircraft data, ability to detect anomalies quickly and ability to serve regional models more regularly. That's really important because at the global scale, we're producing good quality products but that do serve all urban areas. But the hook is that there's limitation in spatial scale. Right now we're at something like a 50 kilometer scale which is really challenging for urban stakeholders to use. So we're working to get that down to something closer to 10 kilometers. We'd love to be at the single kilometer scale but there's still always going to be a need for this tiered modeling approach to help bridge that gap. So I want to say we're making progress and this is one piece of that with a lot of need to connect to some of the local scale efforts that we're discussing here. So next slide. So the next piece that I want to hit on is not just making new and better data from combination of observations and observations and models but being able to deliver that data in newer ways. And so on the busy right-hand side of this plot, left-hand side of this plot, sorry, you're seeing something that looks a lot like the previous slide. On the right-hand side, you're seeing something that looks more like a diagram of data systems. And this is emphasizing some of the new cyber infrastructure initiatives that NASA has really working to get all of our data up in the cloud and make sure that conforms with new open data policies. That's something that Ron hit on that's very important. Doing our science, not just good quality science but science out in the open.  Thank you. So making the data available in the cloud is important but also connecting that to shared analytic tools that allow scientists to collaborate on methods and visualization tools that allow both scientists and lay people, practitioners, to be able to access the data in new ways to facilitate their use of greenhouse gas information. And so in this pilot project, which has been going on for about a year, we've been working really hard to move NASA greenhouse gas data to the cloud to be able to develop new tools to work with it so that we can improve the reliability and transparency of the work that we do. And to demonstrate interoperability of some of the many data sets that we have because that's one challenge. We have a wealth of information and we need to be able to use those data sets together to understand the whole picture of greenhouse gases. So the last slide, go ahead and go to the next one now, is really making sure that when we develop these new cyber infrastructure tools that that serves both science and applications users. So these use cases that were developed through this project called the Earth Information System were co-developed with colleagues at the EPA to help inform inventory planning and also to make sure that the inventory data that were produced at the national scale are more accessible. And those priority use cases include things like gridded inventories making those data sets available more reliably making them more quickly complimenting anthropogenic emissions information with the best quality information on natural sources and sinks so that we again get the big picture of greenhouse gases and also really delivering new tools for detecting and mitigating hotspot emissions using combinations of aircraft and satellite data. And so I want to say that we're making a lot of progress connecting with colleagues at EPA and colleagues at NIST and Noah like you heard about earlier to understand what the federal landscape should look like but one of the real challenges and opportunities here is hearing and integrating input from the urban perspective because as we heard before there's a variety of perspectives a variety of technical capacities and so we're looking forward to showing you more as this evolves and to hearing more from all the panelists. Thank you. Thank you Leslie that was great right on time and I'll hand it over to Emma Lewin for her five minutes. Thanks so much Kevin. Hi everyone I'm really excited to be participating in this discussion today and thank you to the National Academy for inviting me to speak about the City Climate Intelligence Initiative and so as we've heard from the preceding panel discussion there are still many greenhouse gas emission information and usability gaps that need to be addressed to support climate action at the urban level and it's with this challenge in mind that RMI in partnership with Carbon Monitor the Hestia project out of Northern Arizona University next and I guess out of the world meteorological organization have gotten together and developed and piloted a next generation open data methodology to deliver near real-time high resolution greenhouse gas emissions data for cities globally and so on the screen here you can see our piloted data for Los Angeles but before we dive in I'd really just like to take a moment to talk about the City Climate Intelligence or CCI initiative more broadly. So there are increasingly large amounts of previously untapped public data that can help us to develop estimations for greenhouse gas emissions in cities and the CCI initiative leverages this untapped data by developing a methodology which includes both bottom-up data sources such as local data sets and top-down data sources such as remote sensing to establish a novel approach for emissions estimation and recognizing that there currently exists a varied level of greenhouse gas emission capabilities and access across cities globally we've developed three tiers of granularity for urban greenhouse gas emissions estimates and so tier one provides daily sectoral estimates across seven emitting sectors at 10 kilometer granularity and tier two and three provide emissions estimates at the neighborhood and asset scale respectively and this tiered approach is intended to meet the varying needs of all cities. Advanced slide please. So the tier one provides a starting place for emissions information for cities that currently have limited access to emissions data and can be a very helpful source of emissions information for developing inventories or tracking carbon budgets. Advanced slide please. While tier two and three data were developed for cities that are familiar with greenhouse gas emissions data and likely already have GHG emissions inventories but are looking for a more granular data to help inform, deploy and track climate action at the urban level and so to date we've developed tier one data for an estimates for 53 cities globally and that's available on our project website and we've piloted our more granular tier two and three data in Paris, Los Angeles and Copenhagen. Next slide please. So just to give a quick example of how tier two and three data can be useful for cities to drive climate action. This slide demonstrates the opportunity of leveraging granular greenhouse gas emissions data with other data sources such as household income, building roof size, energy build data among others to identify high emitting low income neighborhoods that could be targeted by a low income rooftop solar program to see the greatest climate and equity impact in neighborhoods across the region of L.A. And so the map on the left demonstrates the emissions impact potential from installing rooftop solar on households earning less than $50,000 per year with the red areas being the highest emissions reduction potential and the blue being lower emissions reduction potential. And while the table on the left provides us with some of the key impact statistics under two scenarios targeting households earning under $25,000 per year and under $50,000 per year. As we can see toggling the income threshold for rooftop solar deployment does not substantially change the individual energy savings per household but a rooftop solar program targeted at homeowners earning under $50,000 versus under $25,000 per year does allow for a larger intervention and corresponding emissions savings. And so this is just one example of how the tier two and tier three data in combination with other data sets can drive insights for cities to better understand the emissions reduction potential of policies and programs in addition to targeting those programs where they are most needed and can have the biggest impact. Next slide please. One minute. So I'd just like to quickly wrap up with highlighting that a core tenant of this work is data transparency and accessibility. The CCI initiative is really about developing greenhouse gas emissions data as a public good that is accessible to all. We liken it to the public weather data service so emissions data needs to be universally accessible updated with a frequency that is helpful to its users and provided by independent third party. And we believe that this sort of data can be leveraged not only by policymakers but community members to understand how their community is accessible to all. So I would just like to thank you for joining me today. Thank you so much for joining me as well as the private sector to identify and trace emissions reductions opportunities and investment projects in cities. So thank you for the time to speak about our CCI initiative and I'm looking forward to the broader discussions with my colleagues. Perfect. Thanks Emma that was great. Next in Catherine I don't know if you're doing five minutes of discussion. Yeah. No, no, please make your points. I just wasn't sure since I know I'm showing slides. Typical lawyer comes in with no slides. Nothing really to detail. So I really want to say super appreciate being a part of this day of reflection and being with these amazing cutting edge and just high impact scientists. And I think what we're here today to do and what the what the report was really driving was to create higher impact, more comprehensive GHG data that can drive more effective climate action decarbonization and how do we create and what I want to talk about is how do you create the enabling environment for that because as we've seen in the some of the earlier panelists and the sort of disconnect between the technologists and the advancements that we're making in creating the environment that we're looking for. At the climate data policy initiative that I run at the Stanford law school we look at what is the enabling environment what are the sets of levers both standards, statutory regulatory regimes and financing and markets that can actually drive the kind of decarbonization that we're looking for. So the study calls for a hybrid approach and at the end of the day we're looking at the two different types of technologies. So all of this you know work around machine learning and sensors and DLT holding this real promise for getting more granular data and I'd also like to posit that also data that is less resource intensive and I think that's a real driver and important part of deployment and scale so this complete and atmospheric development. We have entities making commitments really shining a light on are we actually understanding what the GHG emissions are is critical and drives change I think what we have to the net we have to crack at the city level is there these are voluntary enterprises and we have to figure out what is the way in which we incentivize and actually support GHG data usage of the kind that we're looking at. We're also looking at what is that minimum available data product when do we need it and how do