 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. Let you introduce yourselves and then kick off to some questions. 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? And I can let you guys, you know, chime in as you pipe up or I can call on names if we need that. 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 how much natural gas is being used, how much electricity demand is changing. 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, you know, the challenging part is that, you know a lot of the plans are, you know, 20, 20 years, you know, out and they are baking in emissions and transportation behaviors. 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. You can't, just like you can't draw geographical boundaries around air emissions or GHG emissions, you can't draw geographical boundaries around economies either. 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. Could you go back for just a second? 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 we make an argument to cities or encourage and support them to use it. So I think that we are under estimating emissions in important ways we've seen that at the city level we're not able to use tools to evaluate the efficacy of climate actions we're talking about. We have to measure their effect and then as I said how do we do more with less. I also just want to say I think we're undervaluing the power of data for public engagement and that's where cities as we know are powerful and that they're so close to residents and it's an opportunity to do a lot more. A few things I'd like to just talk about too is we do have and let's talk later about some things. We do have city net zero commitments. We also have big new compliance regimes in the private sector and we should think about how those interrelate. So when it comes to city and urban settings how can we create this enabling environment and how do we create a support for this. The uptake of more comprehensive GHG data these hybrid approaches. So we do have a lot of information on that. We do have a lot of information that people cannot take in reams of different research and expect their staff to figure out what makes the most sense. We certainly need standardization and as the report says institutionalization we need gold standard third party verifiers that make it official easy for a city government to feel comfortable taking a scarce approach. We need to move beyond action plans and recognize climate action plans were something that cities did at the behest of the climate and environmental community and we need to help them figure out how these new reporting mechanisms and approaches support that effort because we are going to face the fact that those baselines may not be accurate in all cases and so we have to help figure out new regulations at the city level around particular drivers things like waste and buildings and transportation have already been identified. What can be our verticals around that and how do we use the sensor technology and other kind of atmospheric approaches that can really make that work. So a few other things I don't think we should take off the table state broader enforcement regimes and incentivize and not just compliance markets but also bring resources to cities when they're able to incorporate more comprehensive GHG emissions also just want to say I do think equity is a critical driver at the city level so we can show how GHG emissions comprehensive approaches in you know support equity will be making great inroads and lastly I don't think we should take markets off the table I think we should be monetizing avoided emissions and I think we've been a little too singular in looking at points of source you know pollution by producers so I think that's another place where comprehensive GHG emissions really makes a difference so looking forward to the conversation. Thank you that's great Catherine thanks a lot all right next we'll turn to AJ and correct me if I'm mispronouncing your name sorry thank you Kevin so I just want to briefly introduce myself again so before joining Princeton I was a director at World Resources Institute I was developing a clean election plan for cities and during my work in WRI I formulated the policies local policies and as well as try to implement policies on the ground but please go to the next slide so this is a recent paper and for this type of work what we are doing first we are just first identifying like how you know different variables are affecting the greenhouse gas emission as well as the air pollution and most importantly in acting we are trying to assess like how different socio-economic categories of population are affecting the population of the population of the categories of population are responsible for different infrastructure activities as well as the air pollution and JG contribution so that is the our first goal and after that we are identifying the neighborhoods where we need to focus a particular kind of strategies I feel that we cannot I think you know that frame like one kind of strategy for the entire city because city has a different neighborhood so for that kind of analysis it's very important to understand the city at the micro level so that's what we are doing for both air pollution and JG emission and most importantly city is more concerned about the activities you know that because they are not that much because other sectoral people they are more concerned about like you know what kind of activity they are doing and what how can they improve so could you please go to the next slide so and next piece so I think by identifying the contribution of the different socioeconomic segments and the you know that how these different policies are going to benefits both air pollution and the JG benefits so we have I think this is I think one of the paper like we have recently published where we are trying to identify the policies where we can get the both kind of benefits you know so you know for example what generally will be happening like you know that we are trying to highlight the city level strategies but this work we have found like this if you are only going to improve the 10% efficiency of the rich people that how it is going to affect the JG and air pollution emission in the city and if you want to develop some kind of awareness program or something like that it's very important to just consider these kind of things because that could improve the efficiency of the implementation and it will give us the practical solution so that is the thing which we are doing yet another important thing is that like whatever policies we are formulating or action plans we are trying to implement in the city first important thing is to understand the stakeholders know like mindset that is the most important and that when I say stakeholder it doesn't mean like people who speak more you know the leaders are very important to talk to the ground level people and which we did in like our previous work and could you please go to the next slide so before joining here I think I had developed a clean action power and one of the Indian cities where you know that first time I think I was able to convince the construction industry like you know that how to mitigate air pollution on JG emission on your site and they we develop a clean construction guideline and you know that generally people are very you know that they these industry people I think sometimes they don't want to just try for some kind of new things but I think we were able to convince like you know that these construction people and they have given one site to us and on that site we try to implement the you know the whatever the strategy we have suggested and on that site and that way we were able to understand the practicality of the policy you know because theoretically it's very easy to do this do that but yeah practicality of this policy so we did that on that site and could you please go to the last slide yeah so these kind of intervention we have done the next slide please and what we found like with very simple intervention we were able to I think reduce I think because the construction activities emission by 70 to 80 percent and it was not require a lot of cost or money or something like that so these kind of benefits which we can get if we consider the you know that stakeholder you know that what they are thinking what is their priority and we can just try to match our priority with their priority and so in that way we will get the option benefits with this thank you so much thank you okay that was great so we'll we'll turn over to getting some of these questions in and remind everybody if you have questions put them in Slido we have a two or three here I'll start off with those for Leslie question is what is the temporal resolution of the data that you will be sharing on the cloud do you imagine that this data could be used for acute exposure assessments and maybe public health yeah that's a really good question and it and I'll say it varies by product so I so I won't go into every detail we're putting up on the cloud but just to say that for some of the model based analyses that have the closest analog to air quality you're looking at something like maybe hourly to three hourly diagnostics but there's a latency right now in delivering that data so it's not available in near real time but that would still put it in the ballpark for health assessments what I think is actually more challenging than the temporal resolution is the spatial resolution that a lot of times when we're dealing with model grid cells even fine resolution model grid cells you're talking about something that might be a few kilometers to right now you know tens of kilometers across a variety of conditions within a within a model grid cell so the approach that's being taken a lot with air quality and I think there is an analog to some of the work that particularly Ron showed earlier is being able to make fine scale measurements within a city that tell you how air quality is varying they can't be spatially complete and in some cases they're made with low cost sensors they may not be the most accurate but those data sets help us downscale what might be very good quality information but over a broad area and so I think that's where you're seeing this combination of the satellites giving you a global perspective combined with models to get every information everywhere that can be downscaled with the combination of local sensors to me that's the power of combining those kinds of approaches but I'd welcome any other opinions on that. You know that's a really interesting comment about the sort of multi-tiered system but the other thing I was thinking when I was commenting about the spatial resolution and the limitations because they are challenging but I must say you know the recent work with methane and a lot of these very spot imagers you know is impressive and is actually at the scale at suburban scales I mean they're seeing hotspots of oil and gas wells leakage landfills things like that and that's a I think has been an impressive evolution in the last decade around methane from space which I think is just amazing. So yeah go ahead and I'll just say yeah I think there's some energy there on the methane side if you had a very large concentration that's causing a public health impact on the methane itself I think there's a lot of opportunity with those measurements if you're talking about something that is more the greenhouse gas or quality co-benefits sometimes there's challenges with the accuracy of those data sets that haven't been vetted at more subtle concentrations and so I think there's still a lot of work to be done to figure out how you use the data directly but absolutely it's a big opportunity. Great next question this is for Catherine can you explain more what you mean by having decision makers move past action plans what framework would you replace these action plans with how to incorporate the new technologies to inform more specific local actions? Yeah thanks that's a great question and what I would say is maybe not move beyond but both end I think what is important to recognize is that and the last speaker said it so well in understanding who your stakeholders are when we're talking about cities we're talking about city governments who have struck out ahead of many others globally and created climate action plans according to the GHG protocol and the community-wide protocol so they've done what they were asked to do in terms of creating a climate action plan and then they brought along stakeholders to get behind that and then to make changes based on their baselines and over time the changes and made commitments in some for some cities these net zero commitments and I guess what I want to say is when you bring in much more comprehensive GHG data there may be there it may end up being that those actual inventories are not as accurate as we thought they were it could be a plus it could be a minus but and I think we have to recognize the political reality of that and so to be I think sensitive to that which is we want those cities to be rewarded for getting out there on climate action and so that to my point is just that we need to be at the table with them to figure out like how to do that and that some of these vertical strategies which are really the drivers you know we said waste and buildings and transportation maybe places were you know more comprehensive and atmospheric data resources could be used in a way that doesn't require them to like put their climate action plan on the shelf and start over again but it could be we're taking deeper dives we're going to use like best in class data to help us think about that any any other comments because that question is is a general one anybody else want to chime in on that okay let's see now general question for everybody in pursuit of just transitions areas with energy poverty have been identified for certain us cities other areas with measure ghd data vary with areas of energy poverty or lesser energy consumption so that's sort of co mapping of energy poverty and emissions feel free to jump in I shared a map with income distribution so I guess I should probably jump in on this one I think that's you know a really great great point in question and I think one of the things that we've learned through our work on the city climate intelligence initiative has been that importance of contextualizing the greenhouse gas emissions that we're seeing across cities and especially making sure that you know when we see a neighborhood and when we are getting down to these granular levels we're providing that information about potentially why that those those communities might be seeing higher greenhouse gas emissions so for instance as was mentioned in this comment if you're a neighborhood or a community that isn't near a public transit system then you're going to see higher transportation emissions in your community and that's not necessarily because you know you you want to drive it's because that's probably the only choice that's available for you and so having having those layers of different contextual maps I think is a really big part of you know responsibly sharing this data but it's also a very big part of the opportunity of being able to make this data actionable for a wide variety of city stakeholders. Yeah that's really true and Emma while we're with you a question specific to you what were the expectations of cities you met about near real time and location specific GHG emissions information. Yeah it's a great question so I think you know we we met with a large array of cities throughout this project both in Europe and the US and we heard a lot of different answers and so you know for many cities it wasn't necessarily about how granular can you get but how actionable can you be and so I think that's where a lot of kind of the analysis that I showed really shows that opportunity because a lot of cities are you know they might have one data science a handful of data scientists that can really go deep and really dig into hourly building emissions data but for the vast majority of cities you know that's probably too much information and especially when we start talking about you know non-policy makers like community activist organizations or others that might want to use this information being able to have that very granular data and then conduct analysis to be able to present it to decision makers and community makers who might want to use that information in you know digestible formats and digestible insights is really kind of what we heard really clearly from our stakeholder engagement. Anybody else with thoughts on that? I just wanted to you know commend RMI for this work because I saw those slides and it's really exciting and and I think it's like I think the question is how do you scale RMI so that you have that capacity in all the you know in these cities for that kind of data but I do want to say I think when it comes to the equity issues I think that will drive the support for more comprehensive GHG emissions technologies and approaches and I think in the same way that you know we're looking at nations that have created all of you know spent all the carbon wallet and now want somebody else to make you know stop doing what they're doing that's happening in you know in the United States in communities so I think that also looking at consumption based accounting and some of the other ways we look at that we're you know in the same way low income communities are not the ones driving our carbon footprint so I think these are really really interesting opportunities. Yeah and that's a really good point about that consumption based it's always worth noting that when we talk about the atmospheric approaches and our activity based approaches the atmosphere is obviously not built to look at consumption based accounting which is fundamentally supply chain so it's something that we're going to have to I think we're going to have to grapple with given how important supply chain perspectives are because they go to control and governance purview that sometimes immediate landscape emissions don't necessarily right and that's going to be something we're going to have to take on. Here's a question for AJ how significant were costs to carry out the mitigation and the construction example that you provided and then how would you recommend engaging the larger sector versus you know individual stakeholders an individual building owner for example versus the construction sector I think is what that means Yes I think you know when I think we started this program so when I think we started discussing with the construction people so the environmental thing that's what they said to us and after that I think another misconception among this industry whenever anything comes to the environment you know if you think in an industrial perspective or an industrial perspective they think it's very costly up there you know it's not in their priority that we all need to understand everybody wants to earn money so that's the thing then they were assuming that there would be implementing any kind of mitigation action it would be very costly so then firstly we convinced them that we are going to use very economic things on your site so in that way because that site was to just to show other builders and fortunately after doing that site now I was working in the Indian state Gujarat so now Gujarat the entire state is saying that please follow I think they are requesting their other industrialists and businessmen and for their construction purpose to follow these things because it's not that much that's how because we have to convince in a way mostly in an economic way how it's going to affect their project cost their project cost is going to be very high no one is going to use it they'll show it to you in a different way but they are not properly going to use it so you have to be very practical so in that way I think we tried in that way and we were successful yeah it does seem cost is something that we haven't I don't think grappled with a lot but we've seen you know three different tools here and so I'm wondering what your thoughts are on cost as so we think about maybe moving these tools into real practical application I mean Emma showed an example from Los Angeles Leslie you know very big but impressive system how are we going to what point should we are we already thinking about cost and I guess fundamentally who's going to pay so for example you know that I can tell you example there is a smoke gun kind of thing you know that that's like spread of the smoke into the atmosphere and the cost of that smoke gun if you are doing any kind of construction activities you have to use that big smoke gun it's very costly they're running cost very expensive you know that that's also for builder perspective it's very costly he will sometime follow like because of the regulation you know that force but in I think internally he don't want to do that so for that I think if we can use simple sprinkling thing you know that like water sprinkling that is also giving us similar kind of benefits and we tried in that way on because and also especially you know that when it comes to the things you know that the costly small builders you know they don't have that much money so for them I think anyway we have to develop some kind of practical solution and these practical solution are working for example truck was going from like one pavement and the pavement was I think you know there was no stone or something pebbles on it so what we did we simply just put pebbles on it that's it you know or some kind of water tank before I think you know before trucks enter into the site so that would wash the you know that truck tires so that's that kind of small small intervention we were doing and that was very useful you know and another challenge which we also like whenever we are asking something to the builders they were in denial mode always so we we have to convince them it is going to be I think not that much costly it would be cheaper so that's how we were going to and hopefully we will have a one piece of paper on it so we are going to compare the different cost okay so we have a couple minutes and I have one question that I wanted to pose and get everybody's quick thoughts on in the two minutes we have left which is in your experience developing tools and perhaps interacting with decision makers what do you see as the largest barrier adoption right now and jump in however knowing that we just got a couple minutes before we need to close okay I will start I think for us one of the challenges that we see is at the federal government level we have done a good job developing new and different approaches they are not all aligned and so this idea of moving from different approaches to consensus to authoritative information I think the central challenge that we are trying to address with these systems and I think if we could speak with one voice we would do a lot better in surveying the needs of stakeholders as we have heard throughout the day today yeah next so I feel that somehow we as environmental people are not able to convince them or to translate what we are saying to the city for example when you say emissions are increasing instead of that we can say that these activities we can work on my travel reduction or in their language so that is the important thing if you want to drive something on the ground you have to talk in their language not in our language that's what I understood my experience communication yeah both of the ideas that were already presented and take it a little bit higher of just user experience you know I think what I encountered quite frequently was a lot of decision makers getting a little bit wary about all the different tools and quantification methodologies that are out there and so just a little bit of kind of apathy of like oh there's so many how am I supposed to know which one to use and I can completely relate with that but also you know for many people you know we use the language that is very different and so especially when we are talking with community stakeholders being clear on the terms and you know the key messages that we're trying to drive I think is really important as well so I kind of just stole two ideas but I think they're very important so I wanted to elevate them yeah those are all those are amazing I'm like writing them down this is going to be a little tick list I would just add to this also just understanding the political realities that you know what makes a political win for them to do something and also what makes it financially viable and I think obviously financing for all of this is huge so I think those are two nets we have to crack at the same time as we address these other drivers okay that was great thank you to each of you for excellent presentations and being so both on time with presentations and succinct in answers that was wonderful that I will close this session and now we go into a 30 minute break before we head into our final synthesis wrap up session so thanks again come back to our national academy greenhouse gas emission information for decision making and now we are going to begin our synthesis discussion so we brought back a number of the panelists that we had earlier in the day and we want to try to facilitate a conversation now focused on opportunities for urban greenhouse gas information moving forward and Kevin and I will moderate this discussion again looking forward to hearing the questions from our audience so please use Slido to submit your question up vote questions and we'll bring those into the discussion and then we'll introduce the speakers yeah we're going to let them introduce themselves so if you're just tuning in as Ann Marie said we've got panelists from certainly the last two sessions and I think even the first so I'm going to run through everybody's name give just a brief intro for those that are tuning in and let's start with Phillip Fine Hi everyone I'm Phillip Fine I'm the executive officer of Bay Area Air Quality Management formerly an EPA and in the Southern California version of the Air Quality Management District Robert Stupka Hi Robert Stupka Head of Climate Action Implementation for North America at C40 Cities I worked with our 17 member cities in Canada and the United States in both achieving their greenhouse gas reduction targets and supportive actions and supports will be provided to the region Michael Ogletree Michael Ogletree I'm the director of the air pollution control division for the state of Colorado prior to this I was at the city and county of Denver overseeing their air quality program Leslie Ott Hi everybody I'm Leslie Ott from NASA's Goddard Space Flight Center I work in a place called the Global Modeling Assimilation Office where we're working to develop both computational models and information systems that help improve the dissemination of information of both greenhouse gases and atmospheric composition Emma Lewin Hi everybody my name is Emma Lewin and I work at RMI working specifically on our cities team to advance an equitable low carbon transition in urban areas globally Catherine Atkin Thanks Catherine Atkin I'm from the Stanford Law Schools Center for Environmental Informatics an attorney focused on climate data policy glad to be here AJ Nagpur Is AJ not with us? AJ are you out there? If not okay let's go to because I know we have some of the speakers from session one so maybe Ron Cohen Ron Cohen a professor at University of California Berkeley and I work on observations on greenhouse gas information systems for cities Kim Mueller Hi this is Kim Mueller I'm sorry my internet is not working so great so I have my camera off just to keep in with at a manageable level but anyways I work at the National Institute of Standards and Technology and the greenhouse gas sorry in the greenhouse gas program Yeah sorry about that I interrupted you and I don't think Yvonne is on Yvonne are you there? Okay so let's start off panel we reminding everybody to put questions in the Slido but maybe both Anne Marie and I had a few things that we thought would be interesting maybe to return to that came up as sort of themes and the really in all three sessions but maybe I'll start with a question just start to stimulate thinking here I'm going to go back to the thoughts on financial models for a greenhouse gas information tool for cities what might this look like from your perspective what are the challenges or opportunities in building some sort of model and maybe you know how do we finance this if we need to and I'll open it up to everybody just jump in I can jump in I think one idea would be that there's a role of federal government to pay for it given that all the collective actions roll up to achieving national level targets and there's a critical role of national governments to fund infrastructure and energy policies and certainly with the inflation reduction act providing incentives so being able to have the right data to work with standardized framework for cities to use as they're applying for funding and those actions being recognized helps create that beneficial relationship between federal commitments and city action and so yeah I think there's rationale for federal government to pay for this and certainly as well spur private sector from relying on that data as well. Yeah and maybe I can chime in and just say that I think one of the challenges we're faced with in trying to spin up these initiatives at NASA is we're doing our best to spin things up quickly to get information out which I think is good and so that means leveraging existing systems trying to find commonality with other initiatives so for example we're using cyber infrastructure that's common across projects at NASA so we're not paying for all of that cyber infrastructure we're sharing the cost across different programs we're also trying to leverage the connections between weather and air quality models both to provide more complete information but also for efficiency because that's allowing us to give people the most complete information and to share costs on the supercomputer data systems but one of the things that I think we're faced with right now is figuring out we can't possibly meet customized deployments for each city so how do we understand the attributes that get to the kind of information that cities, states and federal stakeholders all need but also allow some room for communities great initiatives like RMI to make their own tools that go a step further and I think that's really where finding the right balance the federal government is doing a lot in trying to take ownership of its part of the problem but we also recognize we're not the only player in the room so figuring out what that balance is how do you share and demonstrate this range of tools and recognize that there's going to be different components led by different groups I think that's one of the key challenges in near-term needs is figuring out how those different components of the system all work together I just wanted sorry I didn't mean to jump in the queue Catherine but I just wanted to mention this from the afar the abyss picture abyss but I wanted to mention that there was a federal strategy to advance an integrated US greenhouse gas monitoring and information system an RFI that was put out a couple of months ago by the White House for request for information and this is an active point of discussion that is being bantered around at the highest levels of the US government right now and it's certainly within the federal radar so I would expect that strategy to come out sometime in the end of the summer after the responses have been addressed so I guess I'm just putting out there that there is some hope there I can't I'm low level in the totem pole I just know that that's out there but certainly something that's being considered if I could just jump in there and add I do think this question of deployment and scale is huge and I do think putting some pressure I'm not one of the people here creating the data to let the best data scientist win if you can create scalable models at a certain cost I think we need to build that in to the innovation pipeline and also I would just say it's affordable also we've heard this from different panelists and I brought it up we do need city governments need confidence in the data and in the standard and in the pipeline and if we're going to put forth new next-gen solutions we need a process we need a standard we need a repository we need a place that says these are ready to go so I think that that's part of the foundation yeah that's a great idea to turn to next Catherine I think we did hear that from many of our speakers about the desire to have maybe more consensus and authoritative data so that they know this is reliable and usable and also some sharing of lessons so that people don't end up having to reinvent the wheel Ron I'm going to go to you and follow up probably with a little question for Michael and Phil so go for it Ron so I want to remind everyone that there are lots of things out there where they're important and engaging the wider community that are not authoritative there's a vast amount of carbon trading having to do with putting carbon into forests that the average scientist would say is nonsense and yet it's it's taken over in the financial markets there's a tremendous amount of investment driven by it so we shouldn't be letting the perfect be the enemy of the good in what we're doing because we might move the needle quite a bit with things that are more reliable than what's out there without saying we're all standardized and on the same thing and I think that also points to another audience that might pay for this so I've at one point had conversations about could we measure the output of every power plant in some geographic region with hourly frequency and I investigated who would make money on that and the answer is people who are day trading and carbon permits could make money on that and then we could release the data every six months and make it public at that point and they would already have made their money and that might be a way to finance something like that so there's other kinds of finance that might be important to the extent that markets are involved in changing the way we emit CO2 Yeah that was a good reminder Ron of I guess the perfect is definitely the enemy of the good and this is an arena where that really really rings true Michael and Phil I just wanted to tap on to you guys in this discussion of the sharing of information sharing of lessons learned you know any thoughts given what you heard here today are there some tools or information that you were unaware of that you see could have benefit or forums that researchers you're hearing from today might get engaged with that are sources of information for you and your community that we maybe could connect with and just having at this point I don't necessarily have a direct answer but I think even earlier today that cities counties, air districts, states are really underfunded for this type of work it's very hard to find even the expertise to do this type of work so whatever tools or forums that could exist need to be extremely accessible and easy to find it shouldn't require traveling halfway across the country for a conference for a week a lot of us don't have that type of those resources I mean there's lots of good examples of peer-to-peer learning models I think Kevin talked about some of those already that it's just websites clearinghouses I think set tools meaning like models and things like that some areas may have expertise to learn and adapt those most areas will not but it's just it could get even more basic than that it's just like you have some questions about emission factors you just have some questions about sort of the economic implications of certain things getting a place where that could all be shared and where the debates have being able to tap into debates that have happened in other places but it's just like it's just like sharing to enter your own local debate and I don't know how to connect all the dots maybe there are some kind of relational databases just for information that can do that but it just has to be easy I have to be able to tell my staff like go to this place you'll find all the information you need and they can do it in a day or two versus a six month training what are your thoughts given your experiences in Colorado I mean I'd echo Phil's comments and for us in Colorado it's something I mentioned during our panel discussion earlier we have different challenges at the state level than a lot of cities so a lot of the tools that we're looking to employ are some of the ones that were previously discussed but then also developing our own tools it's something that is really required and as Phil mentioned most entities don't have as much funding fortunately we're in a place where we do have significant funding to be able to support in-house development but also leveraging what's being provided and being developed in other areas as well I'm just going to add one more thing and this gets to maybe not exactly the question but just putting in a plug for the financial models there's definitely a financial model for life-cycle assessments because every private consulting company has their own model now and it's like the Wild West you can go model shopping that has to at some point be reeled in I don't see anyone who could do that other than the federal government good topic Phil Kevin you have some other questions queued up actually one that I'm curious about particularly for people in the practitioner space there's been a lot of development and emphasis on real-time data or very near real-time data particularly in the scientific community I'm wondering how important is that at the practitioner level and by real-time do you need last week's information is that helpful or is that not something that can be used I can start yeah thanks Michael yeah I mean for us you know the real-time or near real-time data is less useful because for us a lot of the changes that we're making are at the regulatory level so more accurate historical data to inform policy is more useful for my team certainly tracking some of the progress but I mean the progress tracking it's not week to week you know it's year to year or every two years so the near real-time data is less useful for for us but I can I would I would guess like depending on you know the individual entity and how small scale you get that week to week data may be more valuable as you know you're looking to make decisions about a specific building if you're the building owner or something like that you might be able to learn something you know based on you know what's going on in the environment during any given period of time which will help you make you know decisions in the future but but less so at the state level I agree with one exception and that would be you know for incidents if you did have a big incident either just to quantify what happened during that incident it could have been an hour a day but that that ends up being quite important and then in the terms of compliance and if a problem that you don't know about could be identified a leaky pipeline or something like that where you can have a faster response than waiting you know weeks for it to be discovered having that type of real-time data could be very useful yeah I can jump in I mean we just came out of COVID and there's a number of interventions that have experimented with and so you know the question is discrete you know interventions what is the impact of those on behavior and can you quantify impact on air emissions and greenhouse gases right and so you know the other aspect of it is just our energy use is shaped by peak demand and so if we're able to really understand what is happening during those peak demand usages what is happening when we have concerns about grid resilience you know what is the impact what you know energy facilities are coming online when our grid is strained you know those are the types of things that really do have a time aspect of it that needs to be at a higher resolution to really make meaningful you know decisions and make it matter and I think you know ultimately those because you design systems for those peaks have an impact in terms of the design of our future energy infrastructure in this key to actually getting to that zero emissions energy system any other thoughts maybe I'll say just on the transportation side of it would be the other one in terms of trip behavior and so again like if we're looking at any specific city policy level interventions specifically looking at shortening trips or you know mode shifts ultimately that affects commuting patterns and how those trips are being done and so impacts and then like the wildfires now we're seeing on a regular basis in many cities as well you know those extreme events have a big impact and how poor that air quality is you know and the compounding impacts of even you know weather and humidity and things like that that all has an impact to help inform you know building the case to better buildings and investment in grid resilience for example Thanks for those comments Robert Let's see we've got a question coming in from Slido We focus a lot of attention on having the right tools and technologies to reduce the greenhouse gases that are in a way consistent with state or city or organization goals and what approaches could be used to ensure that local government or organization set the best goals for their operations and this may I guess have a economic flavor to it or a co-benefit flavor or with thoughts on helping these organizations and governments set the best goals Catherine I see you're unmuted you want to give it a try Yeah I was unmuted this entire time but yes I'm happy to speak I was interested I'd love to hear what Robert from C40 has to say on this as well but I would I would say when you think about you know what these goals are and I think that we've got state and national we're focused here on the sort of this at the city level and as we know these net zero commitments are kind of they're broad right they're sort of a mile I wouldn't they're definitely a mile wide I don't know if I'll call them an inch deep but I think we should be thinking about those drivers of of GHG emissions in cities the as we know transportation buildings you know sewer systems and services and then I think really beginning to lean into what kind of do difference are going to be required to meet that net zero goal I guess my point is net zero is going to force that are going to be difficult and so I think that really focusing on what those major drivers are and what are the policies and financial arrangements and I think Ron made a great point about we've got to monetize this in different and creative ways whether that's you know green bonds or creating credit markets that make it more possible for cities to see an upside in making some of these changes which will certainly want federal and state money as well so it's going to be a combination of all that I'm happy to jump in so I mean we've seen examples of in Seattle and Denver is you're seeing cities leading on policy and being kind of testbeds and then states then adopting statewide regulations to kind of follow even Vancouver was given unique municipal powers to be able to test policies and put in their zero emissions building code requirements and basically reduce the risk of the state or the province from kind of being the first one in and so those are the opportunities for collaboration is recognizing that cities could big limiting factor in most cases the cities don't have that many powers and the powers need to be given to them from the state to be able to test these things but if there's a recognition of that then there's opportunities for willing cities to be able to act and see where they go and then to provide that broader based overarching politics needed to kind of move together faster and the other thing is a recognition is needed of the zero emissions commitments of cities by regulators when there is applications for either the electrical grade or the fossil gas system and this zero emissions by 2050 how does this fossil phase out planning or building performance standard actually factoring into the investment decisions of infrastructure there's a disconnect in that right now and it is a real question in terms of these actions need to be recognized no different than a mining company going to an electrical utility and saying we need you to include us in your resource plan for our future energy needs these are how policies and we plan energy systems and there needs to be a recognition of where the cities want to go and their commitments thanks Emma any thoughts on this topic from your perspective and your work yeah I mean I think just to kind of echo a bit of what's already been said especially around kind of the boundary issue that's came up quite a bit in our engagement on developing the city climate intelligence initiative of just you know what is in a city toolbox and what isn't and you know there's that question but then there's also the question of well how can we use this kind of data to help advocate for what Rob just kind of suggested of empowering cities and giving cities those power to take a little bit more control over you know emission sources that are in there are you know technically in their boundary but aren't jurisdiction in their boundary and so that's something that you know has come up quite often and is really an interesting challenge that I think any kind of data system or greenhouse gas emissions monitoring or measurement is going to have to be you know flexible and taking into account for sure well just sorry one dynamic that I play here is as a local entities doing climate planning they're often assuming certain things are going to come at the state or federal level or vice versa and sometimes whether you put that in your baseline assumption or not whether it's an actual law regulation versus just a lofty goal there's a lot of questions about how these things thread together and sometimes you know even within a single state and among state agencies there's a lot of inconsistencies around the assumptions for different types of planning efforts in energy and transportation and air quality so I think that's a big question where if everyone's assuming something's going to happen but nobody's really responsible for making it happen then everything it sort of falls apart and you might have to do your planning a little bit differently right yeah yeah yeah right that's the beauty in the downside to the multi prong approach that we take to most everything we do in our government it's just a little bit of a little bit of a cool question and Slido if you want to I can read this one out this is interesting to what extent are cities using offsets to reach net zero goals and would a federally blessed or federally ordained offset standard be helpful and likely to be widely adopted by cities if one existed anybody I'll just jump in that our framework doesn't really allow the use of offsets at this point and so the message really is that you know cities need to do everything within their control and influence to be able to get to that zero mission pathway you know I'm still not clear in terms of the real you know offsets that really can be achieved you know that that doesn't include just passing on emissions kind of from one entity to the next particularly if the end point is going to be zero emissions from everybody I find the offset discussion more of a distraction from some of the more challenging things that need to happen just direct mitigation this is Kim I can't speak to the federal a federal standard for offsets because we would be very far away from that but I am more interested about Ron since you deal with you know specific activities that have to do with infrastructure projects or so on and so forth and measuring the impact from those that's probably the closest thing that I know of that has tried to get to some sort of measure of some sort of try to get to that offsetting question so I think there's sort of maybe three different things there would be projects that are mitigation so those are of the variety that Rob just talked about that we could have a uniform way of vetting the greenhouse gas reductions associated with them and that requires some sort of model of what would have happened absent the project and that's the ends up being the trickiest part there are projects that are sequestration and so that's a category of net negative CO2 that might be or mitigation that would be different than some of the biological mitigation and then there are some I would say there are some mitigation projects that could be monitored in a way that seem to be working so the folks who are looking at using urban compost in a way that increases soil carbon uptake in rangelands seem to have much stronger evidence than some of the other kinds of projects that are getting more press so I think looking a little more carefully at what's out there might lead us to at least look at some class of the projects and be able to monitor them more effectively and trust them more agreeing that most of the existing projects that are getting press are nerve wracking at best that comment makes me, it reminds me that it's often it's not enough to have just data for even history or now is that you need to have some modicum of scenario capability to be able to look to paths in the future and that poses its own set of demands since that can be a difficult thing to do and maybe more importantly than just projecting out into the future in some baseline and you know sort of IPC scenario type of thing is the fact that a lot of policies will tend to be sector or technology specific but they'll have lots of interactions in a complex environment like a city and some of the we want them to all become benefits but they're not always right they can have lots of interactions with other things I'm thinking about social justice and equity and have really unintended consequences about policies relating to that and that does pose some you know significant knowledge and technological demands to be able to fulfill something like that but it does seem important any thoughts on that just to quickly add on to that I definitely and I think that's kind of where it's also important to acknowledge the limits of what modeling and data can do and where you need to kind of start talking to the people on the ground and understanding and having that kind of community engagement and so I think in a lot of these opportunities you know we have to be careful not to think of just our data and greenhouse gas emissions data in a silo and the opportunity to be able to bring that to those that are kind of living in these cities and experiencing these cities and kind of showing them the data and then also getting their reaction to it and it seems like it's a you know a two-way conversation for sure. Yeah I would just jump in on this one and just say that I think that equity as a driver for public policy is a very strong one in cities and I think there's a real opportunity if that is done and well to garner support and political buy-in you need to be able to do that because it scares public resources so I'm really you know you mentioned this idea the co-benefits and I think the idea that we can look at mitigation alone solutions and expect to like you know have people roll out the red carpet you know just that isn't that isn't and shouldn't be the way it goes I think we have to hold ourselves to a higher standard and so I think that thinking about extreme heat and the fact that low-income communities are often the ones experiencing the effects of climate change in our own communities and is really powerful so I think that's as a level of complexity but if we don't get that right at the beginning I think we'll won't have the impact we want. That's a good point. Awesome, thanks Catherine. Kevin shall I move to a little discussion of information? Sure. So we had a question from the audience which ties into some of the discussion we had earlier today about the spatial resolution of information so they're thinking about what is the value of information that say at the census block when you're trying to develop policy or when you really need to drive down to these smaller scales like Emma showed us some really information at the building level, street level so just let's talk a little bit about where it is you need to have that very fine resolution and where more aggregate will serve our needs. Certainly when trying to deploy energy efficiency programming the fine resolution really is helpful because you have maybe a certain buildings of certain types and vintage you're trying to figure out what is the split of energy sources oftentimes you don't have the breakdown between space heating and hot water with electrical demand so just being able to be targeted in terms of what are the various interventions required in certain neighborhoods and how you engage with those communities is really helpful and I think in earlier discussion as well we spoke on the transportation side of it and the real opportunity as well to reduce air pollution from transportation and do some targeted neighborhood scale interventions. And Ron I know your program really does get that data collection at a very fine resolution across the city so what are some of the biggest surprises you found in having that new kind of information? So I think there's sort of two things you might think about one is work that is the approach of a grid inventory or colleagues who are driving around I think there's a class of sort of finding the sort of leaks that Phil was talking about and things that are not in the inventory whether it's industry or it's a place where you're not tracking the fact that dozens of idling trucks show up contributing to poor air quality and high local greenhouse gas emissions and that's the kind of the kind of thing you get from high resolution observations and modeling and then it's intersection with air quality and I think the other thing you get is a process scale information so on the electricity side maybe the sorts of things we just heard about are straightforward knowing whether a building is using natural gas or electricity for its heating and once you know that in a class of buildings you can think about that whole class together and what you're trying to know is where in space are buildings of type A or type B but there are other aspects of the greenhouse gas emission system that we don't have good tests and we can't track necessarily at the meter and so observations can help us classify and characterize those so that even if there's only a subset of them in the census tract we can take action across multiple census tracts knowing something new about the process I don't have a good example of that but we did try and do some work for example measuring the speed dependence of emissions on the highway we're able to verify that the usual models for speed developed at the individual vehicle level hold for the fleet and so that's a good point the process learning is a good example of that we maybe don't need this information in every location everywhere but enough so you start to understand underlying drivers yeah I mean just as an example Ron touched on it there's been a lot of success in mobile monitoring of natural gas leaks in neighborhoods and sort of rapid response to fixing those I haven't necessarily seen I'm sure it's been done the emission reductions associated with those types of programs have been but that's very fine spatial scale very quick action I think more generally though it's not so much the GHG emissions at that scale it's more about all that other related data that we just talked about household characteristics income other types of things that come along with the GHG policy intervention that are really important to have even if the emissions themselves can be handled on an active advocate yeah I want to maybe I'll ask Kim to speak up because I recall this example in Baltimore Kim I don't know if you want to talk about that I think you know that had you aggregate information you might not understand why some areas had high CO2 that intersected with low income right Kim I don't want to steal your thunder you're very good at explaining things keep going what was interesting about this is that there were low income parts of Baltimore they had very high CO2 levels but it wasn't CO2 coming I mean it was CO2 from large interstates of throughput traffic so it had nothing to do with the activity in the neighborhoods themselves effectively imported CO2 and without again having the granularity and sector separation you can be very misled by what you would see and that's why kind of getting down into this detail starts to combined with the texture of the city starts to really give you insights into you know who's responsible or not responsible or how you're going to allocate mitigation and mitigation responsibility becomes really crucial go back to that social justice angle well said Kevin well said okay I'm glad I did a good job let's see how we any other comments on that there's a couple questions in Slido that we can turn to one thing I do have one thing to that it has nothing to do with Baltimore but it does have to do the fact that we have done comparisons between cities and it comes back to somebody saying cities are alike and not all available data from cities are alike and so for example when I showed in my presentation we did an intercity comparison during COVID between Los Angeles mega cities area in the Washington Baltimore DC area and what was interesting is that the types of data that we could get for granted we have to go to publicly available data sets we didn't we don't have to go to the city area of getting utility data or those types of data sets that other stakeholders people in the cities might have so but the amount of the types of data that we could get available that we were able to get our hands on for Los Angeles with very different than the types of data that we could get our hands on in the Baltimore DC area and so the question then comes down to a levels if you want to do this across cities whether or not you want the best available data depending on where you go and then you have an interpretation question so I just we found that to be really interesting and it was definitely a choice we had to make when we did that specific study just food for thought more than anything else let me just kick in one area I think it's very specific where there's some variables in terms of you know when we're trying to figure out energy usage at the building level and trying to figure out what the policies are like the difference between you know real energy bills real energy used versus modeled energy for reporting and you know the differences can be quite vast it's hard to predict things like uncontrolled air leakage in buildings and usage factors and things like that and so you know how do we get high resolution utility data is the elephant in the room it exists but you know various policies require a certain level of aggregation and you know what is an appropriate level of resolution and aggregation to be able to address privacy concerns but also make have meaningful information to actually advance policy and action I think in that light the New York City law on building energy is really transforming how building owners are going after that very question of how leaks in their building and efficiency and there's a bunch of small companies that have sprung up to work at the building scale with individual owners and so that's a place where policies spur to market and the market is actually you know even despite the crazy high cost of carbon in those New York we're driving down carbon emissions quite a bit I've often wondered even though you can't break through the privacy barrier of ratepayers could you have a platform that encourages the voluntary submission of utility billing data even though you're not going to get the population but even samples could be quite powerful something that you know imagine these days a lot of ratepayers might be willing to do if it was easy if you could just click a box and then you have AI could figure out the rest of it as more information is gathered I think it's conceivable to start to get way better information I think one of the biggest problems is everybody's a rate payer or customer in their own house in individual circumstance and they really have no clue being oriented how good or bad they are relative to their neighbor and so I think especially what you're describing if you're in a condo unit and you're able to disclose your own usage and your neighbor sees it or you're able to get a number of points I think that could be really powerful the dashboard effect a question came up in slide that kind of goes back to the supply chain which does seem to be an important topic the question is deep economic decarbonization involves understanding greenhouse gas emissions through activities life cycle how can the consumption based emissions accounting be part of the broader context to inform decision making so a lot wrapped up inside that and we've touched on it a bit before but it does seem to be just increasingly important particularly at the smaller scales at the urban scale for example anybody want to weigh in on that well you know I think this is again one of those where it has to be at both end because I'm a I think consumption based accounting is a very powerful tool and as we know especially it's going to uncover that higher income areas you know at any level are consuming more than low income areas and I think I think that the difficulty with the city level is you've got these climate action plans that we're not they're not based on that approach to GHG accounting so I think we have to create some on ramps for cities and I do think it gets then to some really great databases of life cycle analysis so that you can make it easy because we're not all like you know you read Sweden's report and they did you know it's like they had whole you know wings of higher education like focused on this and I think those kinds of things are you know you talk about cities doing something that leads to greater action I think when it looks that difficult you're not going to get it to happen at scale so I but I think it's it is very important and I'd like to say I think the compliance market for corporations is a win for us I mean between an SEC rule climate rule that's supposed to be finalized soon and then you and countries we do have I think a sea change on the recognition that supply chain is the driver for you know climate impact so I'm I'd like to see those things sort of you know cross-pollinating a little bit too and Robert what did you I thought you brought this up in the context of the C40 consumption based work was am I correct yeah I mean probably requirements is going to be consumption based emissions actions in two areas by 2024 so it's coming you know where cities are going to dip their toe in is you know up to them certainly food is is one of those you know big ones where they have opportunity but I mean I look at it from the perspective of you know yeah the global energy and right now there's a lot of discussion around you know where's our energy going to come from export of LNG for example to Asian countries to displace coal supposedly and we know a lot of the production of goods is coming from other countries with high intensity energy so how do we make that connection so certainly on the consumption based emissions side of it and now scope 3 reporting of companies you know can we start to really take ownership of those emissions so it's not just kind of what I'm making in my backyard but really what is the influence of what we are doing in terms of the emissions on a global scale I think the conversations around this are masoned and there's a lack of awareness of this and I think it's important policy leaver certainly I could say from my perspective in Canada you know we have the highest per capita emissions of any country but we see ourselves as you know a little baby in terms of the big pie chart on total global emissions but we need to own it from the perspective of that consumption that is where you're actually going to get maybe more public to get behind action because recognizing there's a real impact on the global emissions you know from the higher remaining countries yeah good point okay let's see now we have 10 minutes left and maybe we'll before we do a little wrap up with maybe have time for a couple more questions I don't know if Ann Marie you have anything on your mind that's come up I have one that I you know and it sort of picks up on a couple of things that have just been said which is in thinking about tools and strategies particularly the urban scale is it even feasible to have a common tool I mean Emma showed CCI looks like a sort of a fairly common platformish tool I think some of the observation systems that we saw you know can be applied anywhere but then going back to this point about consumption based accounting where some cities are producer cities some are consumer cities I mean they're very very different is the room for it a common tool a common data or or are we always sort of chasing kind of tailored approaches you know within urban science people say no every city is different and that's true to a certain extent but they share lots of things in common as well so I'm just curious what people's thoughts are on that but I mean obviously depends on what kind of tool you're talking about well yes greenhouse gas information tool yeah but still there's a lot of different types of those too I mean it doesn't necessarily have to be a tool but maybe we could start working towards common frameworks you know the way like the scope one scope two scope three is a common framework for considering things ensures that things aren't getting left out that certain things are being at least considered you know you don't need you don't need the data all the data to create an equation other people have to figure out what to plug into it but at least agreeing on the equation is a step forward right so I think there's a lot of room but in some cases I think some tools can be common you can always be customized making them open source is really really important so people can customize them and build modules and share I'd put a strongly encourage that so that gets back to the financial models that generally isn't a favorable financial model but yeah I mean I think it just really depends we've talked about you know a dozen just in the last hour of different types of tools that could be useful so I think it just depends yeah I'm just gonna say from a practical perspective like what cities really struggle with is you know staff turnover changes are there's a lot of information factors over time updating old models which you know they want to move forward on and so like what is is there an automated approach is there an approach that can be applied consistently and doesn't kind of bog you down in terms of chasing data and reporting when really kind of what matters here is how is it informing you know action more than yeah those are the current approaches are require a lot of legwork and there's a real challenge in terms of when updates are required and making sure you're measuring the same things even when you're trying to advance approaches so when we're seeing these new you know potential hybrid options coming on especially maybe things like transportation emissions which was kind of challenging we need to ask the how does that work with their existing commitments and past inventories and what is appropriate to accounting them I mean as well as what's appropriate for the time scale now that we're kind of targeting 2030 and 2050 around things like methane emissions and she'll be looking at shorter time scales for reporting is an important factor I just wanted to say that in the federal government I think there is this coalescence around the fact that we are whether or not it's necessarily specific to a one stakeholder need or one stakeholder need that we are coalescing around the idea that this information is going to need to be provided at disaggregated scales I can't tell you to what scale that will be or what spatial scale or what temporal scale and that it will largely have to be combined with with air pollutants and you already see the EPA right now disseminating information on greenhouse gases at the state level we can argue about what that means but there's obviously a push within the federal government to do this so again how that data is useful to each and every one of the stakeholder needs I can't say but it's going to happen I think at the federal level I'll chime in I think there is going to be progress on the there will be progress on the federal level but it's going to be an evolving conversation I think some of Phil's suggestions start with common frameworks we know there's a lot of basic things that need to be done on interoperability everybody says data formats are a challenge there's a lot of things that are technical barriers that we can make progress on near term while we have these dialogues and figure out and it's going to be a system of systems I don't think there's going to be one magic tool that can address all of the needs or really should because the federal government has limits in terms of what it's doing in terms of making space for private industry for other types of decision makers there's always going to be this range of approaches and I think having those conversations early to figure out how do we make sure people are moving forward but ideally growing together right and some of that is figuring out scope in terms of who is doing what but some of that is a very technical challenge of making sure that the data systems talk to each other making sure that we have common definitions formats that we're moving towards those things and actually listening to the people who are going to put into place some of those tools I think that's really important the other thing that I think we hear a lot from stakeholders in other kinds of venues and you know Robert just made this point people are busy there's a lot of staff turnover you don't have time for another meeting you don't have time to go across the country for a conference so to the extent that tools can be built into work clothes that are existing and are compatible with the tools people are already using that's critical it doesn't matter if you spin up the best new thing if somebody doesn't have time to train on it right and so I think that's where some early conversations that you know this is a great start I think that's going to be valuable as you see these growing to make sure that they're all growing together in a productive ecosystem of tools that was awesome Leslie Kevin and I were just discussing how we need to find some summary statements recommendations as we get to the closing minutes of our conversation here but that was a good good starting point for that sort of summary of some of the threads that we pulled today so I feel like we covered a lot of territory in this conversation we've had today maybe some unanticipated some expected ranging everywhere from what financial economic aspects of the problem scales of data consensus building information sharing etc but my challenge to our speakers as we bring things to a close just very brief you have any sort of either theme or recommendation that feels like it kind of is a good closing point for the day from your perspective given what we've heard and discussed today and we may need to organize ourselves to do this so I'm actually going to call names and start with Phil and then go to Michael I would just say this has been great and the more times we can lock policy makers and researchers in the same room and not let them out until there's an increase in understanding the better so this doesn't happen often enough and it can't happen too much because I mean my career has been back and forth and I've noticed this disconnect is constant as a constant theme okay that was a good one Phil I wrote that one down Michael thoughts yeah no I mean certainly what Phil said that's exactly what we're trying to do as well right get all of these different groups in the same place I would just add from a regulatory perspective like listening to all the conversations today is helpful as we think about regulatory frameworks that allow for flexibility and one of the challenges we have as a regulatory agency and a state who's put forth some of these different goals is how we continue to evolve and change the technologies we use to meet those goals while also considering how we go back and reset baseline based on the changes that we now know yeah good thank you here we're going to reach out to Kim we don't have video but I know you've got her on audio any closing thoughts Kim gosh I didn't expect you to call on me with since my video was turned off I thought I was going to dodge the bullet there but anyways I'm going to go back to my final thoughts and my session is that you know we're going to be pushing for standardizations and standardization is really important whether or not we're doing things in a slightly separate way across cities or in a similar way and I would encourage everybody who's on this call or listening in to support the standardization process because without that then nothing is comparable and like I said before we'll all be operating in the wild west so that's my last word Henry alright like the NIST perspective Ron, thoughts from you so I want to say that I'm pretty excited about the opportunity in front of us I think we've collectively built a set of tools that we can use to drive progress and I want to volunteer to be locked in a room with my colleague Phil anytime he's ready that's good well you guys are even in the same town quite possible excellent Emma, some thoughts yeah I mean I think the main theme I'm taking away today is just optimism there's so many really interesting and important things that are moving and I would say agree moving quickly and so kind of taking you know an optimistic approach to being able to have these conversations with cities or other implementers of carbon reduction actions and being able to kind of yeah come from a place of applied hope as we would say at RMI so yeah that's a good one applied hope I like that Emma thanks Robert yeah I mean I think it shows that you know what we need a data that we need depends on the actions and we need to focus on getting the right evidence that we need to support the actions that we need to do which might be different how we may have looked at inventories in the past if we want to do inventories with purpose we really need to understand that and let that lead so that could be a very localized disagree level it could have a temporal or spatial aspect of it and yeah so the long list of potential ways to get that you know all of the above I'd say and we just need to be intentional about how we use that data great thank you and Catherine just love what Rob said I mean I think it's you know fit for climate and so I think you know I think we're going to need to focus on the purpose data and I really appreciated that this group didn't come in with its report and our only job is to figure out how to scale like the like nirvana that we all see but to really think about where we could go next and what what's going to actually accelerate decarbonization and so anyway and I think you all I know you're done with your work but it would be just I think that would be a great plus up on this report great alright while we're running out of or early or out of time I'm going to pass it back to our committee chair Don for closing your remarks thanks for you thank you very much all of our panelists really appreciated participation yeah thanks everyone well I want to thank you all as well what a wonderful workshop and I think the panelists for discussion and I know I certainly learned a lot about from it for anyone that doesn't have our report you can download it from the National Academy site just put in you know the name and beginning of the name and you can find it quickly and one last message is that the recording of today's meeting will be available through the event website web page so thank you all again and this was really cool