 This panel will be moderated by Margo Garrison, Professor of Energy Resources Engineering at Stanford. She's also a co-founder and co-director of the International Organization Women in Data Science. Please join me in welcoming to C3E, Margo Garrison. Over to you, Margo. Thank you so, so much Naomi. And welcome everyone to this panel discussion. I'm so excited to be here with four wonderful female colleagues and all of you in the audience. And we're going to be talking about the intersection of equity, energy, and data. And I can't wait. I'm very excited. And I know I'm going to be learning a lot from my very esteemed panelists. We'll be here with Chiara Bills, with Jessica Granderson, Noelle Bakhtian, and Jin Ma. And shortly I will introduce them to you. We have an hour together. We'll spend the first little bit, getting to know the panelists a little bit better. You have their bios as well. So please have a read through that. They're all extremely accomplished and have very long records of impact and contributions. And after that, we will dive into some questions. We first will talk about equity, what we mean with equity in this context. Then we'll introduce energy and then we'll go to the data questions. What can data help us with? What additional challenges will data actually give us? And how can we move forward? So I hope by the end of this panel discussion, you will have a pretty good idea about outstanding challenges, about contributions that you can make, about what is possible and what is not, and get some really good messages also from this panel. Before we start though, I'd love, we'd love to know a little bit more about you and where you're calling in from or where you're from. So there is a little poll here to just get you to interact as well. So look at your polling underneath at the bottom of your screen or scan the Q code and you can put in where you're from here. And we'll see the results coming up almost immediately. While we're waiting for that, I'm calling in today from Bend, Oregon. So if there's anyone here in Oregon, I'd love to know. All right, well, can anybody see the results now? I'm assuming so. Okay, so we see that we're really all over the US and also outside of the US, which is just wonderful. So welcome everyone. Okay, so we'll go to our wonderful panelists. And like I said, I will introduce each of them individually and we'll have a little chat about their current occupation and also their background. And I'd like to start with Jessica Granderson. So Jessica, welcome very much. You have a very intriguing title right now. You're the Director of Building Technology at the White House Council on Environmental Quality. And this council coordinates, so you've told us the federal government's effort to improve, preserve and protect America's public health and environment. So I'd like to ask you a little bit about that later, what that exactly means and what you do. But I do want to mention also first that you were actually a C3E 2015 Research Award winner. So you're back in home territory. Now, before you joined the council, you were Chief Scientist and Deputy Division Director for Building Technology and Urban Systems at Lawrence Berkeley National Laboratory. And you've spent a lot of time working on energy management and savings initiatives of all kinds of different organizations, nations, largest enterprises, national accounts, public sector entities, as well as small commercial buildings. I particularly liked hearing about Sensor Suitcase, which is an application that you've been working in. Now we recently met and I'm just delighted because I'm going to be learning so much from you. But one of the questions I wanted to ask you here for the audience also to get to know you a little bit better, what does the Director of Building Technology do? Well, thank you very much. Thanks to the C3E community for including me in today's panel. It was such an experience for me to win the 2015 Research Award and just so inspiring, all the great work being done across the board from the award winners, the ambassadors and the students. So it's really my deep pleasure to be here. Director for Building Technology and what do I do? In my federal capacity now, I'm working on the building emissions and community resilience team at the Council on Environmental Quality. At the White House, looking across the mission, broadly speaking to decarbonize the nation's building sector and that's across all of our homes and businesses, we work from the federal level to the state and local level and back again in coordinating efforts to really bring a whole of government approach to the climate crisis, spanning decarbonization of buildings into our justice initiatives. In my capacity at Lawrence Berkeley National Laboratory, my focus is more on the R&D programs and portfolio, whether that's residential or commercial, high-tech and industrial facilities and our programs from earlier stage development all the way into demonstration and deployment. What is your biggest dream as Director for Building Technology? What do you hope to achieve in your time there? Oh my goodness. You know, I really have an aspiration, just kind of a blend of also just my personal passions to see data really much more ubiquitously integrated into the delivery of low-carbon climate resilient homes and buildings across the nation. And we really know that to do that effectively will require equity in all of its dimensions, many of which we will unpack today across the panel's conversation. That's great. Thank you so much, Jessica, for joining us today as well. Let's go to Noelle. Noelle Bakhtian. Now Noelle and I go back quite some time. I know her from Stanford. She did a PhD at Stanford a long time ago and she and I met at that time. And I'm just delighted to find her again so many years later as a leader in this field at this intersection of energy and equity and data. She is now the Executive Director of the Berkeley Lab Energy Storage Center. She is the Founding Executive Director actually of this initiative. And this is a lab-wide center that wants to accelerate the translation of basic and applied research into real-world energy storage solutions. She's also a board member for Q-Site, which is the Institute for the Quantitative Study of Inclusion, Diversion and Equity. And Q-Site leverages quantitative methods to reveal and analyze big data in support of grassroots organizations and that all with a goal to catalyze systemic change. She's also on the Advisory Council for the Duke University Energy Initiative as she just told me that. So thank you so much, Noelle, for reconnecting and being on this call with us today. Tell us a little bit more about that big new initiative that you're sending up in the area of energy storage and energy justice. Absolutely. Thanks, Margo, for having me. It's really a thrill to be associated with C3E and just love the mission here. So as Margo said, I am the Executive Director for this new center out of Berkeley Lab, one of the Department of Energy's 17 national labs. It's all about energy storage. And with this new administration's focus on energy equity, energy justice, it's really opening the aperture as far as what the national labs are empowered to do and what we could be doing in this space. And so I'm a convener in my roles at DOE and the White House previously, that's something that I've really enjoyed doing. So what we've done is we've created a new community of practice across all of the DOE complex, this 30 billion plus annual investment in the national labs and broader with headquarters to bring people together at the nexus of energy justice and energy storage so that we could be sharing best practices and how do you incorporate energy justice and research and development? How do you build relationships with communities? Making sure that we have a sense of all the research that's going on in this space, energy storage plus equity and going a step further, identifying the gaps and the opportunities that exist so that we're doing the right thing and accelerating this work as fast as we can for the benefit of the nation. You have a PhD in aeronautics and astronautics. And so it may be really fun for the audience also to hear about how you ended up where you are right now. That is a good story. Yep, my PhD is in aerospace at Stanford and I was doing a lot of work at NASA at the time and around the last year of my PhD I was working on Mars landings and the administration decided to shut down the Spatial Program. And so I was like, oh my gosh, how does government make decisions like this? So I decided to go off and do a year in DC to learn about science policy. Of course that year turned into five years and I went from Congress to Department of Energy to the White House but I really learned about science policy. And during that time I started focusing on energy and environment and climate and firmly believe that climate is the biggest problem and biggest challenge of our generation. So I switched from aerospace. Now I'm a climate person for the last decade and every day I wake up trying to make a difference on climate change and equity is a big part of that. Yeah, so you go for climate rather than SpaceX. I know. Great, well, thank you again. Let's go to chair of bills. Jared and I met each other quite recently and she gave this wonderful seminar recently in my department here in energy resources engineering. And one of the reasons why we connected is because we overlap a little bit in some of the research that we're doing and that surround equity and transportation. And I was just so impressed with Tierra that I thought all the people need to know about her. And so that's why she's here and thank you for accepting our invitation. She's currently still an assistant professor in civil and environmental engineering at Wayne State but she's soon moving to UCLA also in civil and environmental engineering and the Luskin School of Public Affairs. Before that she was at Michigan as assistant professor and also a Michigan society fellow. And she worked as a research scientist at IBM Research Africa as well. Now, Tierra investigates social impacts of transportation projects. She works to design transportation systems that provide more equitable returns to society and investigates individual and household level transportation equity effects. You have many different areas of interest, Tierra, public transit, transit reliability, emerging data sources for travel demand modeling. And like I said, we have some common interests here. One of the projects that I'm running is a transport and equity related project for Sonoma County. So one of the projects as you're working on now looks at quantifying data error from under-representation of vulnerable communities and how they affect travel model predictions. And I find that particularly interesting. So tell us a little bit more about that, Tierra. Yeah, so first and foremost, thanks so much for having me. Thank you so much to the C3E community for this invitation. I do think that there's a lot for me to learn from this community. So I'm quite excited about this discussion. So yes, I have a couple of different projects that are focusing in this area of quantifying errors associated with collecting household travel data from disadvantaged communities. These are low income travelers, disabled travelers, elderly travelers, travelers who in general struggle to access opportunities using the transportation system for a variety of complex reasons. And the reality is that it's very challenging to collect representative data from these communities because of a variety of reasons and reasons that we don't necessarily understand very well in practice. And so I've been looking into traditional data collection strategies that are implemented at the regional level and coming up with the measures for characterizing how well these data sets reflect the travel behaviors and various statistics of vulnerable communities. And by and large, what we're able to tell is that we're not doing a very good job. And it's something that we sort of know in the back of our minds, but we still don't have very good practices for that will improve representation. And so this is one of the areas of interest. And then on the other side, it's really important to know sort of how far off we are in our predictions of travel behavior, given that we're applying models that ingest this data that does not reflect these groups well. And so if we're trying to understand how our systems perform for vulnerable and disadvantaged communities, it's important to know sort of what the gaps are. And so if we're able to sort of characterize this, then we can inform our decision-making going forward. And so one part of it is making sure we can collect better data. The other part of it is understanding how poor data impacts our ability to predict behavior. Yeah, and we'll have some opportunity also later to dive a little bit deeper into some of those aspects that are so critical and that you've just mentioned. So now you've heard so far from somebody from government, we have national labs represented, we have someone from academia, we've heard about housing, storage, energy storage and transportation. So we really have a very wide and diverse representation here in this panel. And that gets even better when we go to the next person, Jin Ma, welcome so much, Jin, to this panel, because you're representing industry also outside of the US and business opportunities also in this space. So Jin works for TotalEnergies. I think you just changed your name, right? This company from Total to TotalEnergies. She's the founder and the managing director of the Asia platform at TotalEnergy Ventures TV. And she's been with the company for 12 years. Her expertise is in venture capital investments, business strategy and corporate governance, as well as sectorial experience in energy, mobility and sustainability investments. You're really strongly embedded in the venture capital and the private equity ecosystem in Europe and Asia. And it's super interesting, I think to quite a few people on this call as well. Now Jin is quite the powerhouse. For example, recently ranked fourth at the Global Corporate Venturing Rising Stars Awards for 2021, so congratulations, Jin. Now, Jin and I go back a little ways because she appeared in a wonderful panel just recently in the Women in Data Science Conference earlier this year. And she and I got to know each other there and it was really delightful to hear you. I'm really interested in hearing from you a little bit more about how equity meets business opportunity. You've made some interesting investments in your career. For example, in a company called Canopy Power and I was looking at Canopy a bit. And one of the things I like is that they invest in and develop microgrids and energy solutions for remote areas and also disadvantaged communities. You know, disadvantaged communities. So tell us a little bit more about how, you know how you thrive at this intersection in business of solutions that really put equity central and at the same time also seek these business opportunities. Yeah, thank you so much, Margo. It's so great to see you again and thank you for the invitation. Honored to be part of the C3E community. As you said, you know, our day-to-day job is doing investment in startups that is proposing the new business model or technology in the energy mobility as well as the sustainability field. And increasingly I realize that's actually the data, the information, the digital means, the sensors jointly play a very important role to actually transform the energy landscape and it's actually bringing energy and mobility to a lot of population that hasn't have access to those services before. And because those are normally very capex heavy industry needs a lot of government support. And for a long time, it's difficult to bring them to some faraway communities or communities don't have the access or capacity to pay. But then with the new digital solutions and data tools, those are possible. So you have mentioned about Canopy. So Canopy is one of the examples that we have made investment in the company in the startup. What they do is they propose a PV panel as well as battery solutions as a package with a remote control system and data analyst solutions so that they can actually provide micro mobility as a package to communities that does not have access to energy before. So that actually changed the entire story and people have access to energy. And with that household can buy more electric appliance and they can start to do more business on top of that. And we just realized that is actually a door opener for more and more opportunities afterwards. Some other examples are the mobility field. We can see that for some households they don't have access to the public bus services because it just doesn't justify the economic skills. But with, again, with the data and with a sensor and with a smart mobility solutions, it is possible to design an on-demand route to really specifically each time adjusting with the demand and to allow these people to have access to the mobility. So we are very thrilled coming from energy industry. Traditionally, we see heavy cash investment in order to lift the people out of poverty and bring energy and mobility to these people. And we see that more and more opportunities are opened up actually digital and data opportunities to really helping people to have access to those solutions. And every day I'm very happy to work on this field and bring more opportunities, not only commercially viable but also serving equity costs in that. Thanks very much, Jen. Well, you've heard a little bit about the panel members and you've seen that they cover a wide range of areas of expertise. We'd like to hear a little bit more from you. So we have another poll. We'd like to see what field you're from. Are you a data scientist interested in moving into the direction of energy and equity? Are you studying equity? Or are you maybe an energy specialist or a student? So please fill out the poll, same thing. Go to the bottom of your screen and select poll or scan the QR code and we'll get the answers given to us as we go along. Unless you're doing that, what we're gonna do next with the panel is jump into the question of how we all define equity. We've heard equity mentioned a lot today. I mean, the whole conference of course is about it. And every time you talk to people in a particular sector they have their own views on how equity is defined. So we'll do that first, then we'll bring in energy and we'll look at the nexus of energy and equity and then towards the end we'll bring in data. So instead of doing a data first approach we'll do a data last approach in this panel and then we'll end with these questions about what can modern data science give us and maybe not give us and what challenges are open in this field. So we start looking at some of the results here. If you can put this up, you see that there's a wide variety of people in this audience as well. So that's great. Thank you very much for that. So let's move to this whole question about equity. You know, when I work on this transportation project we think about equity as having many different facets. You know, very often in what I see is when I review papers or when I hear people they talk about equity as meaning equal access to benefits of particular policies or other mechanisms or solutions that are put into place. They also look at burdens maybe very carefully thinking about what a policy of a new approach can mean in terms of unintended burdens of additional burdens on particular populations. We also think about agency. Do populations or groups have the agency to control whether or not they can reap these benefits or can they alleviate the burdens that they may have additionally? Scale comes into this as well. You know, what scale are we looking at equity? Are we looking at marginalized communities in the region? Are we looking at this much more broadly in the world? So I'm really curious to see how each of you would define equity and how you have worked with that in your own projects. And I'd love to start with Jessica. Jessica, let me go to you and talk about what that means for you and your council. Absolutely, thank you. We think about first a little context on the building sector and the role that it plays in climate and environment. I certainly was surprised to discover at first that buildings are accountable for a good third of our energy related greenhouse gas emissions in contrast to say industry or transportation, which is where I think we somewhat more intuitive to think about the implications there. Across the country, we have well over 100 million homes and some six million businesses and commercial buildings that comprise the sector. And typically when we think of, there's so many dimensions of equity, I really appreciate this question because I feel like it's so necessary to really unpack specifically what are we talking about? So in my domain, we could think about equity across the workforce, technology and service providers who comprises that workforce, whether we have established versus up and coming businesses, demographics of ownership and staff as one realm to consider. We might think about rural versus urban issues that might have implications for things like data access, access to wifi, exposure to different fuels and different utilities that may be available. Third, we think about environmental justice and frontline communities where we see a convergence of say race and income. And if we get back to the fabric of the buildings themselves, we can think about who's in them, owners, occupants or tenants, class A versus class B type of buildings or even the businesses that occupy them, maybe small versus large businesses and the resources that they can bring to bear in creating a productive, healthy and safe environment that's actually benefiting the climate and questions of community resilience. Yeah, I appreciate you focusing on all these various stakeholders that you may have, depending on what problem you're hoping to address which is really important. Noel, let me go to you. You're looking at energy storage solutions here. What are you thinking about when you talk about equity in that context? Yeah, well, I think you hit on a lot of them and Jessica did too. Access, burdens, agency, representational justice, et cetera. When I think about energy storage in particular, I think about like a range of things. One of the major tenants or the strengths of energy storage is we can help get more renewables on the grid and more solar and wind on the grid if we have storage to hold that energy when the sun's not shiny and when the wind's not not blowing. And what that means is that we can do away with some of these dirty power plants that tend to affect certain communities, lower income communities who happen to be next to a power plant. So there's a geographic piece there, there's an income piece there. Another thing I think about when I think about energy storage is resilience. When we have these big storms or the polar vortex or wildfires or whatever it is that makes the grid go out, energy storage is what could enable like a microgrid or people to stay online and that affects hospitals and other critical infrastructure like energy infrastructure. But also what we know is that a lot of times it's the poorer communities that have their electricity go out first and there's data on this. There needs to be more data that we collect on this. But on the resilience side, again, it comes down to income geographic and there's other factors as well. And then I'll just mention one more thing that's energy storage related which is the story about fast charging. So for those of you who aren't aware, obviously there's electric vehicles that are really taking off which is exciting as we're striving for a clean energy economy by 2050. But right now it could take a few hours to charge an electric vehicle. So the way I heard it was that this concept of fast charging so charging in the same amount of time it takes to maybe fill up your car with gas came about because there was a recognition that there were certain communities that didn't have their own garage or the means of putting in their own charger. They didn't have time to sit in a parking lot even if there was a charger available for a few hours because they don't have a few hours because they're working, they've got kids, they've got the multiple jobs, they're working, et cetera. So this concept of fast charging has part of its foundation in the equity conversation which I really love. And Chira, when we talked earlier you mentioned various places where there really are severe compromises made in terms of equity in the transportation systems. Can you give us some examples of that to try to put this equity definition also in perspective? Right, so I spent a lot of time thinking about how to define equity and the definition itself, they are these things, these goods that are distributed and so they are benefits, they are opportunities, they are costs, they are a number of things. And the idea is that they'd be distributed fairly and also that we pay close attention to what the needs are because I think that fair really means providing people what they need and recognizing that some people need more than others. In terms of examples in transportation, one, there's a long history of looking at the fairness of transportation benefits and costs, dating back to the environmental justice movement, dating it back to original investments in the Eisenhower Interstate system. There are numerous examples of minoritized communities being displaced and made to be worse off in order to roll out the interstate highway system, communities being displaced, homes being taken away for right of way. And these were largely low income, black and minoritized communities. And you don't actually have to go back 50 years, you can still see very clear examples today when it comes to transportation opportunities, when we think about ride sharing services and how, excuse me, ride sourcing, when drivers are deciding where to locate and which trips to take, which trips not to take, there's quite a bit of evidence that these choices might be subject to racial discrimination. And what that means is that you have for some communities lower levels of accessibility to ride sharing, to ride sourcing in a systematic way. And this has ramifications for their quality of life, for their ability to reduce their emissions by adopting more environmental friendly, more choices. And also has long-term ramifications as well in terms of our ability to quickly move to a place where we're emitting fewer emissions. So these are really important examples to consider. I think to summarize, when we're thinking about equity, we need to be thinking about the needs and we need to be thinking about how we can incentivize provisions or solutions or innovations that will provide greater needs to those who are most vulnerable in our society. Thanks, Chair, I know it resonates so much with me, what you're saying, and one of the things that I've heard people say, but I don't always see in practice that when you think about equity, that needs to be front and center. That needs to be right there from the start of your solution design process. And right at the end and all the way through. And in our meeting beforehand, one of you said so beautifully, don't let equity get lost during the process. Sometimes people start with equity at the beginning, they have all the best intentions, they work on solution approaches, but somehow it get lost along the way. Sometimes also in solutions, you see equity come in as an afterthought and there's a quick adjustment to put equity a little bit more central at the end of the design process. Jessica, I just wanted to ask you about this, you know, when you're addressing an energy problem and you're putting equity front and center, you know, what kind of approaches are you using and how do you make sure that this equity piece stays central? Is the main, you know, the main, most of the attention is paid to that throughout the design process and not just at the very start or as an afterthought at the end? Well, absolutely. And, you know, I think if we're honest with ourselves and thinking about the energy field at large, we don't necessarily have the best playbook that's universally known on how to deliver in the ways that we would aspire to. I can give, you know, one example of where I think this is coming up in a very compelling way in some emerging policy that we're looking at for building sector emissions and climate, which is in the development of building performance standards which are new policy that address a minimum bar of performance for existing buildings throughout their life cycle. This is a case where we have a very compelling opportunity to leverage requirements in a way that we can bring about material improvements across our stock, across our, you know, multifamily homes and businesses, but we quickly get to a place where we have to really dig in to figure out how can we really correct historic underinvestment, increase uptake of incentives where they're most needed, how can we marry requirements with programs that will address poor condition and poor environmental quality and balance costs and benefits across stakeholders. We don't wanna just design policies that only the most resourced can really comply with and then say otherwise we will say slap you with a fine. And I think the way to do that is really to emphasize out of the gate early on in the engagement at the community level in a way that we haven't necessarily done so well in the past to think about how we're bringing in workforce locally, interests of real estate, needs of local programs and policy shops. I think this is a place where I'm very excited because I'm seeing these principles really begin to take hold and be delivered on the ground in a way that hasn't necessarily been present in our more pinpointed and siloed approaches in the past. Yeah, beautiful explanation there, Jessica. Thank you for that. It's time I think to bring data in. We've been talking about equity and we've been talking about the interface between energy and equity, but how does data come in? Can data help us? Is that giving us the possibility to generate solutions that previously we couldn't generate and where is it not exactly helping us? What additional challenges with respect to equity does data actually bring up? And there are so many different facets to this. One of the things that we heard so far is that stakeholder engagement is extremely important to understand the needs of stakeholders. So that immediately of course raises this awareness that we cannot just look at quantitative data, that qualitative data are also very important. But there are so many aspects to this and I'd like to go to Chinn because you work at a large scale and you've been looking at solutions, data-driven solutions in very large areas of the world, for example, in Asia and mobility challenges where you're really developing data-driven solution approaches or you're sponsoring companies that do make that like Grab and other companies in this space. So what is your, what have you seen that really works at this intersection of data, equity and energy and solutions that you're very positive about and very optimistic about? Yeah, thank you. So really instead of for question, I think for data definitely it is playing a very important role in the sectors that we are looking at. And give one example is plastic recycling in India and actually plastic use a lot of energy to be produced and data can just helping people, helping the collectors, the informal collectors who earn a very little amount of money each day and before they just spend their days earning very low just barely enough to survive their day. But with data actually they can be guided to work on days when there are more plastic to be collected for example, there is festivals or because of the certain kind of weather conditions there is less plastic to be collected so they can be guided and they can also be guided to collect the best suitable kind of plastics in that sense the large quantity of data can actually extract a lot of valuable things to helping the people before who doesn't have all this access to information therefore doesn't have access to guide their activity to the most valuable added parts of the value chain now it become possible. Similar for the mobility as you mentioned grab and it's actually possible for them to propose in some on demand bus services and some other on demand carpooling services to serve communities that is otherwise not being served the same for energy access for the canopy because the data is possible capable of assess the condition of the battery, condition of PV condition of the weather to forecast the energy so it become possible to have some remote micrograde solution. So it's definitely an open up doors for a massive amount of kind of solutions and bring power to the people who is otherwise suffered from the equity before but I think what is to be really thought of and to be monitored is there anybody else anybody being overlooked in the design of the data system? For example, there's a lot of business now is based on the data therefore the mobile network is very helpful for people to, for example, run their own business buy a little two wheeler scooter and run the passenger transport or logistics services but this is on condition this person is literate can read and can use the app. Actually some community that is not able to read our elderly citizens actually are even worse than before because they couldn't have access to this new data solution. I think those are things we probably need to carefully look at in the design of the systems and investors or governments can play a role to ask startups and companies to have a checklist make sure that as you said in our region those questions have been carefully thought of. So as investor, have you been on the lookout for companies that addressed a particular challenge that you think can be advanced very well using data and you just haven't seen that yet? I know there are people in the audience who are really interested in moving in this space and thinking about good startup ideas so maybe this will be interesting to hear. Now, what are you hoping for that you haven't seen yet but that you're very positive and optimistic about? I think really a large quantity of data and carefully engineered algorithm and assessment of those data can really make it possible for the whole system to be more decentralized and customized and give one example. For example, the ammonia and fertilizer are a bigger source of greenhouse gas emissions before which can create some climate disasters and actually impact the most unfavorable people in the world. But with large quantity of data, actually the soil data, the weather data, the air data it's actually possible to design at scale commercially viable decentralized system. For example, small system with PV with a smaller production unit locally on the field to produce a fertilizer tailor-made customized. And this system become viable well before people couldn't imagine because before everything has to be at scale to be able to work because energy is a commodity you have to make price low and cost low. So that's where massive, massive opportunity can really be driven and value being extracted out of the data. Okay, well, if anyone in the audience is interested in trying that out, they can connect with you, Jin, about this. I'd like to go to Noel because she had another example of solar combined with demographic data that she is quite positive about Noel. Yeah, so we actually have a big publication out about big data for solar and going back to really centralizing the equity equation. So they took data from about 2 million installed solar PV panels around the country on people's rooftops. And what they did is they merged it with demographic data and they started to look at the equity pieces of this. And it really takes people who are interested in honing in on that or at least making the data available so that others can. But the basic story is there's a very distinct gap as you can imagine in equity and deployment of rooftop solar but starting to look now at what can be done about it and having that concrete data is really helpful but looking at the policies and they're figuring out it's about the financing and how can we make the financing easier and lower that? I also just wanted to mention that there's some interesting other ways you could be thinking about data for equity as well. For instance, we've got a whole center at Berkeley Lab that looks at lithium for energy storage and other uses. And we're very involved with the Lithium Valley Commission which is the California Energy Commission that brings together the communities with the industry that's pulling lithium out of geothermal brines. And it's great because it brings the communities together. But what our team is doing is they're actually using the transcripts to code the community reactions to what's going on. So we can be more responsive to what the community thinks. Other ways that we're working on this is we actually use natural language processing to look at how communities have reacted to other types of projects like this, mining or otherwise across the world so that we can understand what the challenges are and be learning from those best practices because we don't wanna just be starting fresh. So I just wanted to throw those two examples out there. Yeah, thanks for that. So when we think about data, you all mentioned data that we can gather, data we can collect. We need to think about who does this, who owns that data. Is the data that we're collecting useful? Is it really reflecting what we're looking for, what we're after? And Tierra, I wanted to go back to you and talk a bit about that. You in your work, you've certainly encountered data collection challenges as well as bias in data. And so I'd love to hear your thoughts about the additional challenges that data collection, generation and data use for making decisions really bring when you look at things for the strong equity lens. Yeah, so I am on the, I guess on the second half of a NSF grant where we are looking into how to collect data using community-based approaches. And that is combining the implementation of things like mobile apps and online surveys with traditional data collection, including paper surveys and also centering all of these around a community interface. And so these are workshops that we do and this is centered in Bitton Harbor, Michigan, but we've learned a lot of interesting things, mostly about what the data tells us may not necessarily align with how people actually experience the transportation system. We learned a lot about the different types of barriers that might exist, that are, they don't necessarily, or they're not necessarily things that one would expect. For example, in these community workshops, we would have participants to come, ready to participate, ready to tell us about how they travel and what they think about the current transportation system. And they would say, hey, here's my cell phone. You know, I'm ready to download this smartphone app, perhaps the cell phone, while GPS enabled requires some updates. And so without those community workshops and being able to actually orchestrate the downloading of smartphone apps, these are the types of people who would not be able to generate data. You know, other things, you know, people perhaps being eager to contribute, but they don't have the appropriate eyewear to read the questions and understand the information. And so learning, you know, hands on about these types of barriers, learning about literacy challenges, you know, there's a disconnect between people being excited about or wanting to contribute and being able to contribute. And so, you know, part of our, you know, that our study allows for us to highlight some of these things. Also, you know, just thinking about the nature of the data and that they reveal what people are doing. They reveal people's preferences and how they make choices given some constraints, but we don't necessarily observe the constraints that they are, you know, that are governing their choices. And so, you know, we call this, you know, this challenge with stated preference data. And that's the nature of the data that we have. The data one that we get from Google and Streetlight and AirSage and all of these different companies that basically take, you know, cellular traces and, you know, different types of perhaps Google information or information from cell towers and produce these large-scale datasets that tell us about hyperbole traveling. You know, this is observed data, what people are doing under constraints. I think that the challenge is that just because we observe people doing various things, for example, we observe them biking or we have, excuse me, we don't observe them biking or we don't observe them using transit in a particular area. It doesn't necessarily mean that they don't want to. And so I think that we need to get to a place where we're actually thinking about the nature of the data and what is telling us about behavior and also taking a look at or using other, as you mentioned, qualitative approaches to understand what the constraints actually are and trying to marry these various approaches to get at a true picture of what people are doing and also what they want to do. That makes sense. Yeah, thanks, Jera. You know, what is increasingly evident is that if you really want to put Equity Central, then the whole solution approach becomes very complex. Very interesting also, very worthwhile doing. And we can really make great contributions, but you have to look at all aspects of this design process. You know, the data gathering, the data generation that you do yourself, how you measure impacts of any program that you're suggesting as well, you know, and does it really do what you said it out to do? We've seen some really interesting examples of that in the EV adoption programs, for example, in California, where the outcomes are not as great as they were hoping when they started with these policies. All these things are incredibly important, right? And throughout this whole big optimization problem, and some of the questions that have come in from the audience relate to that sense of optimization also and balancing that comes into play when we're working on these very complex challenges. And Jessica, I wanted to go to you with some of the questions that are coming in from the audience, and thanks everyone for putting those questions in. It's wonderful to get them, so please keep them going. One of them is, you know, how do we actually get better data? So we just talked about some of the shortcomings of data that we may have, so how do we get that? And here is a question related to getting better data on how low income communities use energy and how they may get increased burdens from surface shutoffs, for example, who benefits from efficiency programs in clean energy investment? And that seems to be in your neck of the woods, Jessica. So I'd really like to ask you about that. No, my goodness, what a great question. What a way to get at our aspirations as well as our pains as researchers working at the intersection of energy and data. So this whole question of data access and availability and ownership is just such a pressing one. Who can get their hands on what data to solve which problems and how does that really drive the solutions that are emerging and being developed? I think it's really something that we're grappling with. We've many times been confronted with just not, you know, having a great idea for something we want to develop but not really having the ability to access the information that we need. So I think here is where, you know, in my field, this is there's as in so many, you know, so much data locked up in either legacy systems or proprietary systems or with companies whose business may not be contingent upon making data available as a public utility. So I think we have huge opportunities if we think across the private sector, research, multiple levels of government to think about what problems are we trying to solve and how can we open up diverse data sets to be brought to bear in solving those problems. Some efforts that I personally have been involved in over the last several years are all around the development of open ground truth verified data sets that can be used to catalyze the development and innovation of new algorithms and software-based approaches to do just that kind of optimization of efficiency delivery and building operations. So I would encourage everyone who's interested in this to think about ways that we can work together and what are those partnerships going to be that will unlock, develop, maintain and sustain the data pools that we really need to move the needle forward. Yeah, thanks. You know, one of the difficulties and challenges that's also why it interests me so much in this field is that these energy systems that we're developing and creating or trying to improve touch so many different stakeholders in different ways. And so when you look at equity, you always end up having to make choices, right? And this whole idea of how the choices that you're making contributes to equity or inequity is a good one. Sometimes you solve one, what you perceive to be strong equity problem to create another and several of the questions by the audience are in that space, right? How, what does fair mean here? How do we balance one stakeholder need versus another? And so let me just go for some final comment on that before we wrap up with your last thoughts and messages for the audience to Noel. Noel, what's your take on that? Let's see, quick take on that. You know, it's an important question. How you're collecting the data, what your metrics are, what characteristics you're looking for, what, you know, how disadvantage is defined. This is all important and it's gonna make a difference in your final answer. So I'll just mention an example. We all know that a lot of effects are non-linear. So if you start to use averages, for example, you could end up with a completely wrong answer. And so disaggregating data and trying to get it as granular as possible and showing, you know, regional effects or whatever that is is important. And so the example is there's a whole program and a tool at Berkeley Lab called BEAM. And I'll, I think if you just search BEAM in Berkeley Lab, it'll come up. But Anna Sprelak, who's helping run this has really been throwing out the point that the whole purpose of having an agent-based model where you literally are modeling little simulator people, you know, these are the agents making their own decisions and they're all imbued with their own characteristics based on census data or whatever that is so that you get a distribution of answers in a disaggregated way so that you can figure out what the distributional impacts are instead of just getting a single average answer which can be completely wrong based on those non-linear effects is really important. So I just wanted to bring that up. Yeah, thanks Noel. Well, you know, this topic is so intriguing and complex. We could talk about this for many hours but I'm hoping that the audience got at least a taste of all the different important aspects to it. And we're very close to being out of time very quickly going around one word that comes to your mind now something you want to leave the audience with one word. Jessica. Persist. Chin. Resistance. Noel. Hope. And Chira. While you're muted still. I'm gonna cheat and use a phrase, change our way of thinking. Fantastic. Thank you all so much for joining us today. Thanks to all of the panelists and thanks Naomi and the organizers for having us on.