Explore New Transportation Data on the State and Local Planning for Energy Platform (Text Version)

This is a text version of the video for Explore New Transportation Data on the State and Local Planning for Energy Platform presented on Aug. 4, 2021.

Sandra: Welcome, everyone. Welcome to today's webinar. I'm Sandra Loi from the National Renewable Energy Laboratory. Today's webinar will focus on the State and Local Planning for Energy platform, or SLOPE for short. The DOE-supported SLOPE model provides jurisdictionally resolved data on energy efficiency, renewable energy, and now sustainable transportation opportunities and the potential to support data-driven energy planning.

Our presenters will share new state and county-level scenarios of vehicle adoption, vehicle fuel and electricity consumption, and vehicle miles traveled by field type under reference and high electrification scenarios through 2050, which is hosted on the platform.

As a point of reference, transportation data for SLOPE were generated using NREL's Transportation Energy and Mobility Pathway Options, or TEMPO model, which enables exploring pathways to produce long-term scenarios that reach strategic transportation energy environment objectives and assess synergies with energy supply.

Before we get started, I'd like to review a few items so you know how to participate in today's webinar. All attendees are muted and will remain so for the entirety of the webinar. Audio connection options for today's webinar include listening in through your computer or by calling in on your phone. For the best audio connection, we recommend calling in through a phone line. Be sure to mute your computer if you're calling in over the telephone to avoid possible feedback. We also recommend disconnecting from VPN and other third-party connections.

There will be a Q&A session at the conclusion of the presentation. We encourage you to submit questions as the presentation's taking place. You can do so by typing them into the Q&A pane. The Q&A pane is noted as a question mark icon, which should be located on the right-hand side of your control panel or screen. Once we arrive at the Q&A portion we will address as many questions as time allows.

Should you have any technical difficulties please reach out to me via the chat feature, which appears in your control panel as a comment bubble. We are recording today's webinar and it will be available on the Clean Cities website within the next week or so.

So as I just noted, let's quickly read through this disclaimer. This webinar is being recorded and may be posted on DOE's website or used internally. If you speak during the call or use video you are presumed to consent to recording and use of your voice or image.

Now I'd like to go ahead and formally introduce today's three speakers. Megan Day will kick us off by providing an overview of the SLOPE platform. Megan's a senior energy planner at NREL, where she applies the lab's tools, models, and expertise to support state and local planning for equitable energy transitions. She is the PI for the State and Local Planning for Energy, or SLOPE platform, and the Sustainability Communities Catalyzer, and the co-PI for the LA 100 Equity Strategies Project. Megan is a certified urban and regional planner with a background in climate action planning.

Following Megan will be Chris Hoehne. Chris is a postdoctoral researcher in the Center for Integrated Mobility Sciences at NREL. He received his Ph.D. in civil environmental and sustainable engineering with a focus in transportation systems from Arizona State University in 2019. His research interests include parking, land use, mobility, energy productivity, electric vehicle adoption, and impacts of emerging mobility technologies, and transportation and energy systems.

The presentation will be rounded out by Arthur Yip. Arthur is postdoctoral researcher in the Center for Integrated Mobility Sciences at NREL as well. He received his Ph.D. in engineering and public policy at Carnegie Mellon University in 2020. His research interests include transport electrification technologies, economics of policies with expertise in consumer trace modeling and spatial desegregation, and energy and transportation system analysis.

Now I'd like to turn it over to Megan. Megan, you may begin.

Megan: Thank you. If you could advance the slides, we'll get going and tell you about the agenda here. Chris, do you want to jump in on the agenda or should I do that, too?

Chris: Sure. I'll just briefly go over the agenda. So I'll turn it back over to Megan here just in a minute so she can go over the SLOPE platform and then after that I'll review the TEMPO transportation model and the methodology for that model, which is sort of how we have created this data on the SLOPE dashboard. I'll go a little bit over some of our efforts for calibration and validation, and then Arthur will talk about our efforts to disaggregate the TEMPO model to the county level, which is how we were able to provide data on the SLOPE platform. And then finally we'll wrap up with some of the results and demo the dashboard and then have some Q&A.

Megan: All right. Thanks, Chris.

Oh, here we go. I'll give you a little bit of a brief introduction to the SLOPE platform. If you could go to the next slide.

So SLOPE is a collaboration and is supported by eight Department of Energy technology offices and the National Renewable Energy Laboratory, or NREL. So we're thrilled to be with you here today. And the purpose of SLOPE is to enable more data-driven state and local energy planning and comprehensive energy planning by delivering jurisdictionally resolved data and analyses on efficiency, renewable energy, and now sustainable transportation. So today we'll be focusing on the sustainable transportation data that Arthur and Chris have worked so hard on, and I'm really thrilled that they're going to be sharing that with you.

So it's meant to be an easy-to-access online platform giving you clear visuals that you can cut and paste and use in your reports with attribution and to support your energy planning. It's not a tool where you have to go around and find lots of data and enter it. No, we deliver the data to you as model data and hopefully it can help you with more data-driven state and local energy planning.

So I put the link to SLOPE in the chat if you want to link to it there. And just to give you a little bit more broader overview, we started off with the phase one beta in 2020 where we provided some state and locally-specific projection and potential data. Then we launched the generation scenarios and user phase settings. You can actually save like your jurisdiction, so when you open it back up it will go back to that and other settings like the date and some of the time periods, etcetera. And we added in transportation in 2021.

We are now developing a scenario planner module, and that's going to pull all of the data from energy demand, energy generation, and including transportation such that we can provide different scenarios, including high electrification, to help folks understand the carbon energy and cost impacts of different scenarios across sectors over time to help you inform your decision-making. So we're really excited and working really hard on that right now, that we hope will be launched on January 1, 2022. So please be looking for that.

And with that I will turn it over to Chris.

Chris: Thank you, Megan. So now that you have a little bit of background on the SLOPE platform, I'm going to go over the TEMPO model, which is the model that we used to generate the transportation data now available on the SLOPE platform.

So just really briefly before going over the model I want to just acknowledge the great team that has helped build this model and develop it over the last couple years, where all the great work has been done by our team here at NREL.

So why do we trade another model? So the TEMPO model is constructed as a way for us to help address a number of different pathways and potential futures. As we know, the future is very uncertain and there is many competing and rapidly advancing transportation and energy technologies on the horizon. And so a range of possibilities is expanding of technological change, behavioral change, policy change. And so the combination of these different possibilities can manifest in a variety of different ways, creating pathways—different pathways to achieving goals like decarbonization of our transportation sector. And so we developed TEMPO, Transportation Energy Mobility Pathways Options model to help address modeling these different pathways and simulate different potential futures.

So it fills a research gap on sector-wide transportation modeling and with a focus on exploring long-term scenarios and integrated large multisectoral studies. So this figure just kind of exemplifies the various different ways in which the future—we may see different fuels, we may see different policies that are enacted, different new technologies, whether it's on the infrastructure, on the vehicle side, connecting vehicles, autonomous vehicles—there's quite a lot of different things that may arise.

So this slide is giving you a little bit of an overview of the high-level sort of architecture of the model. This flowchart here on the right is just showing a representation of how we actually model different pathways. And we're using sort of innate demand for mobility. You know, people need to travel for various reasons, and we want to be able to determine what choices travelers will make in terms of which mode they would use and which technology they would choose in order to complete different types of trips given various factors, the cost of the technologies, and so on.

And I'll also just acknowledge that we constructed this model at a national level initially, and we have since been able to apply the model at a more disaggregate scale to simulate individual counties. And so for the SLOPE dashboard and some of the results that we'll be showing a little bit later in this presentation I'll just—you know, we'll be focusing mostly on passenger light-duty vehicle travel for this effort, but we have constructed the model to be able to also look at the entire transportation sector, so not just light-duty vehicles and even freight travel at the national level, but due to just many, many difficulties and being able to simulate that at a very high resolution, we aren't currently doing that at a county level.

And so the core modeling choices that we look at is sort of what modes will people choose, what technology or what type of vehicle would they use to satisfy their needs for mobility, and then how factors might influence that specifically at like the household level, you know, what's the different income levels people have and how does that sort of constrain their decisions. And then we were able to look at the outputs in terms of miles traveled, energy use by different modes, and shares of different modes.

So here's another figure that just sort of gives you a high-level overview of what kind of inputs are used as levers for us to simulate potential pathways of travel into the future. So the inputs include travel demand, which we use from survey data that describes a collection of how people travel across the U.S., and we also include information on travel costs. So the cost to own different types of vehicles now and into the future, the cost of different fuels, such as gasoline versus the cost of electricity for charging electric vehicles, obviously the time it takes to travel between different locations. So some modes are slower than others; I mean walking five miles is going to take you a lot longer than driving. We also include attributes on performance of the different technologies and the availability of different options. And not everybody has access to the same travel modes.

And of course, we include projections into the future and these projections can vary a lot. So part of the way we're able to evaluate different pathways is by using different data sources or assumptions about how there might be an evolution of things like battery costs for electric vehicles. We might assume aggressive or less-aggressive trajectories for different costs.

And then finally, we're going to put outputs for energy use by different types of modes and technologies, the overall mode shares that will happen, and then the vehicle stock compositions, emissions, etcetera.

And so again, just to reiterate for the SLOPE platform, we applied the TEMPO model to each individual county and only focused at this time on vehicle miles traveled, vehicle stock, and vehicle energy use for personal light-duty vehicles. And so this framework is sort of envisioning to be able to couple with other tools as well and to be able to assess different pathways.

So the coverage and resolution of the model, as I mentioned, it was initially constructed for looking at national-scale representation of the transportation sector. So with that comes this implicit geographical representation where we do look at how people living in different regions might have different access to different modes or might have different costs levied on them for different options. The model is we usually look at long-term assessments, so for example, out to 2050, and we do include pretty much the entire transportation sector, passenger and freight in terms of the full capabilities. Again, when we look at down at the county level, we tend to exclude freight, as freight is usually made on a much more aggregate and sort of wider spatial resolution, such as large regions in the U.S., as individual counties don't usually make decisions based on freight travel.

And then we were also able to incorporate the potential for different types of technologies into the future and how they may evolve. So for instance, we could look at new technologies that may not exist today, so long as we were able to represent how they might cost and how efficient they might be into the future.

Some of the key aspects for us to be able to simulate travel across different regions and across the U.S. is to understand and categorize travel demand. And so the approach we take for this modeling is to bin different types of travel demand based on sociodemographics and geography and also time, so that we can represent the diversity of travel demand and therefore choice for peoples' different decisions to travel. So this table here on this slide is showing how we represent the different household dimensions and the time dimensions.

So on the left side the household bin dimensions that we use, three primary categories. One is the household composition, so how many people in a household consider themselves drivers. So in a household where nobody is driving you're not probably going to be owning a car. We also consider income as a—if you have a lower household income your decisions to adopting a vehicle, for instance, might be more constrained or might look at different types of options than those who have a lot more disposable income and are less financially constrained. And then last we also look at the classification in terms of the urbanity or how dense a region is in terms of development because this influences how many trips people will take and how long those trips will be. So in a very urban region, like within a city or near a central business district you can imagine that people will take a lot more shorter-distance trips, whereas in a rural region people are taking a lot longer trips, especially if you need to go to the grocery store you can imagine it might take a 10-mile trip just to get to the nearest grocery store if you live in a pretty rural area.

And then finally, we do look at some binning of different times of days and different times of the week, just because of the way, you know, work schedules work and just observationally we tend to travel a little bit differently on the weekend versus the weekday and across different times of the day.

So now I'm just going to kind of briefly touch on a couple of the efforts we had over the last year to sort of calibrate and "validate" the model. So first I'll touch on some of our efforts to validate the model against mode choice on the passenger side of travel. And as I had mentioned before, we used survey data, we specifically used the National Household Travel Survey from 2017 to understand how people across the U.S. in different types of households are traveling. So this figure here on your screen is showing some of the results comparing our modeled outputs in 2018 to the inputs that we're basing them off of for comparison. So on the left side is the modeled results from the TEMPO model and on the right side is the observational survey data, and then the top row is showing the total amount of person miles traveled across different trip distances, and the bottom row is showing the comparison of the percentage of those person miles that are traveled within each of these bins.

And so you can see that we represent this sort of heterogeneity of passenger travel by distance really well. And you also can see that the green-colored bars are sort of dominating and so that green color is personal light-duty vehicles. Again, big focus for this effort for the SLOPE platform.

So we did look at two different scenarios and one of the sort of key aspects that we wanted to do when modeling was to be able to replicate a comprehensive and widely accepted—or compare against a comprehensive and widely accepted projections. So we looked at comparing to the Annual Energy Outlook, which provides projections not just for transportation and energy use but for all sectors in terms of energy use and key metrics. And so we can't validate the future, so we wanted to be able to ensure that if we use similar assumptions and the same sort of data inputs in terms of different technology costs for electric vehicles and projections for how efficient those vehicles might be into the future, we wanted to be able to replicate another well-vetted model and compare that we could represent the same outcomes as in this case the Annual Energy Outlook.

And so this figure here is showing that sort of the results from our model TEMPO, which are the right bars in each of these categories compared to the Annual Energy Outlook's energy use, which are the left bars in three different years, 2020, 2035, and 2050. And you can see that across the major modes, so, you know, air travel, light-duty vehicles, and so on, they match really well in terms of the fuel breakdown. And again, this is for a reference case, so this is sort of a fairly conservative case into the future where there isn't a huge adoption of electric vehicles, but it's meant to serve as less as a what's likely to happen or a truly predictive reference case but more as sort of an effort for us to be able to verify that we can replicate a well-vetted and sort of widely accepted projections from another energy model.

So just to give a little bit more context to this scope for the data that we have modeled and provided on the SLOPE platform, we focused on passenger light-duty vehicles. And the big reason we're focusing there for the current time being is because it dominates energy use and emissions. So I mentioned two slides ago you could see all those green bars was light-duty vehicles that dominate across most types of trip distances, and when you look at the total use in the transportation sector, if you look at the left figure here you can see the light trucks box and the automobiles box in light blue, you know, are the majority of 65% of passenger travel. So a little less than two-thirds of all energy use in the transportation sector is actually coming from personal light-duty vehicles.

And then on the right side here there's a figure just giving you an idea of what share of the pie across all industries are the emissions—greenhouse gas emissions—from light-duty travel. So if you look at the blue is the transportation sector and then passenger cars plus light-duty trucks is about 50% of that one-third, so about one-sixth of all emissions from all of the sectors comes from passenger light-duty travel. So that's kind of a huge point of focus for us.

And so with that, just kind of an overview of the methodology of the approach of TEMPO, I'm going to turn it over to Arthur Yip so he can give you some more information on how we're actually able to disaggregate TEMPO to different counties.

Arthur: Thanks, Chris. Next slide.

So as Clean Cities coordinators you know that state and local circumstances matter and that local factors have large and complex effects on transportation energy use. So for example, determinants include technology adoption, travel behavior, mobile choices, and the choices and preferences of different households, in addition to vehicle types and attributes. So in TEMPO we capture these heterogeneities and apply it to our national model to get results for SLOPE.

Next slide, please.

So for example, here's a look at the 2018 landscape. We see that counties have very different adoptions of hybrid electric vehicles on the left here, and we're starting to see similarly the plug-in electric vehicles being adopted differently in different counties. So TEMPO was used to simulate forward these types of maps for vehicle adoption and also vehicle miles traveled and energy use.

Next slide.

So as I was saying, some of the determinants of transportation energy use include vehicle ownership and vehicle age, shown here on these maps. We can see that they vary by county. More vehicles typically means that the household has more light-duty vehicle options and they're less likely to take other modes of transportation. And older vehicles tend to be less energy efficient, so that's going to affect the energy use in that county.

Next slide.

And then at the national level we see that—sorry, at the county level we also see that the mixes of households are very different across the country. So TEMPO, as we heard from Chris, TEMPO sorts the households by income, household composition, and urbanity. And we do this for every county using detailed census data because different households have different travel needs, they make different decisions. So for example, in these mostly rural households in these counties as shown on the top two graphs or maps, trip distances are going to be much higher in these counties where there are a lot of rural households. And then preferences and decisions in TEMPO are very different for households that are in the low-income household bin compared to households in higher income bins. And you can see counties have a wide distribution of income levels.

All right, next slide, please.

Finally, here we're showing that counties have different adoption and preferences of different vehicle types. So in TEMPO we sort vehicle types into these four different categories: compact cars, midsized cars, SUVs, and pickup trucks. And some counties prefer or have a lot more pickup trucks than other parts of the country. And the relevance is that pickup trucks use a lot more energy, so we incorporate that into our model.

Next slide.

So we know that many of these determinants of transportation energy use are heterogeneous, so we disaggregated these inputs, which are two main categories, the household counts and the demographics, and the vehicle counts and attributes, and that is how we produce location-relevant results for SLOPE users.

So back to you, Chris.

Chris: Thank you, Arthur. So that was a nice overview of some of the data and how we apply for the heterogeneity across different counties in terms of household compositions and vehicle compositions, to be able to simulate all these counties differently under the same modeling framework.

So now I'll go over some of the results. So just to give an overview of what we've actually done and what you can see on the dashboard here—or what you will see on the dashboard, we've simulated two different scenarios, and with a sort of focus on matching the electric vehicle adoption. So one is in line with the AEO reference case, the Annual Energy Outlook, which I talked about a little bit earlier, is sort of a key—provides key modeling projections for energy use. So we wanted to match to this reference case as sort of a more conservative or lower—somewhat of a lower bound in terms of electric vehicle adoption. And then the other scenario is a high electrification scenario where we're matching to the total adoption of electric vehicles in NREL's Electrification Futures Study. You can see the citation at the bottom if you want to read more about that. But this study made some assumptions about how in a more aggressive scenario we might see electric vehicles be adopted across the U.S. And so with the TEMPO model we were able to take sort of the high-level projections and use data inputs to be able to understand how that might manifest down to the county level and which counties might adopt a little sooner than others.

So the data that we're providing includes the vehicle energy consumption and the vehicle miles traveled in terms of the total—and the total vehicle stock itself by county and by state, and the key different—the different technologies that we're looking at are conventional gasoline vehicles, hybrid vehicles, non-plug-ins, plug-in hybrids, and then battery electric vehicles.

So I'll just give you one quick figure to sort of do a little bit of a comparison of the results. So there's two maps here showing a comparison of the two scenarios for the projected light-duty vehicles in terms of greenhouse—or total gasoline gallon equivalents per household level in 2050. So on the left you're seeing the reference case and the right you're seeing the more high electrification case, so you can see quite clearly that there is a significant reduction in the energy use at the household level in a more aggressive electrification scenario. And in some cases this can be as much as by 50% of households' energy for travel. So very significant. And this is primarily due to high adoption of battery electric vehicles and their significant increases in energy efficiency that we're anticipating over the next 30 years.

So here's just a snapshot of what the dashboard looks like. But I'm going to go ahead and switch gears really quickly and actually navigate the physical dashboard itself. So if you'd like to go to the dashboard right here you can see the link at the top of my screen. And so just to give you an idea of how we're able to see some of this data, go to the data viewer. And then the top-left of this page you'll see this little menu selection here, and we'll go down to transportation and we'll look at the vehicle stock.

So first it will probably load the state data by default so you can kind of hover over this interactive map and see some of the high-level vehicle counts in this case. I'll go ahead and switch to the county on the left-hand side. And let it load. So you can kind of see what I just showed you on the slide is sort of the diversity of the number of vehicles. And so this of course is just sort of showing a little bit of a population density here, but we'll zoom in a little bit. And so in this case I'll just go to somewhere in our sort of backyard to give you an idea. So if you actually click a county you'll be able to see a figure that will populate on the right-hand side and it will give you a plot of the metric you're looking at.

So in this case we're looking at the total vehicle count by time out to 2050, and if I scroll down a little bit you can see the full legend. So we've got all the different types of technologies; we can hover over these and turn them off or on by clicking on them. So I'm just going to go ahead and turn off everything really quickly except for battery electrics, so you can kind of see a comparison. I'll turn on the conventional gasoline vehicles, and so you can see that in these two different lines, one's dotted, one's solid, these are the two different scenarios we're looking at. And you can see that there's a pretty noticeable impact in terms of these two different scenarios about how many vehicles will be adopted in a specific county. We're looking at Boulder County here in Colorado.

So we'll just go a little bit further west and you can see that now we're seeing a noticeably different level of adoption in this. So the difference between Boulder County here in this case is more densely populated, you know, there's high employment centers, where if we go over into this Grand County we can see that it's a lot more rural in this case, which does affect how fast adoption occurs.

Let's go ahead—we'll also go ahead and switch to vehicle miles traveled. You know, you'll see similar trends, of course. But again, being able to notice that there is pretty significant differences, and a lot of the times the most noticeable differences will happen between highly urban versus highly rural counties, whereas electrification might be a lot more difficult to meet the demands of consumer preferences and the travel needs for longer trip distances.

So feel free to explore this. You can download the data directly through the dashboard. I believe it's up here; there's a download button. You can also filter, see the map update with different summary data.

So I think with that I'll maybe—we can start taking questions and maybe I'll quickly just show our e-mails here in case anybody wants to be able to reach out. But yeah, I don't have the chat box pulled up for questions; I'm not sure.

Sandra: Yeah. No problem, Chris. And I would encourage everybody to go ahead and submit questions. I know we had a question come in; I think it was answered, and if not, Sam, let us know and we can dive into it a little bit more. I think Megan responded.

But thank you to Chris, Megan, and Arthur for the presentation. This is an excellent tool and really interesting as you're doing a dive into that. So please go ahead and submit questions in our Q&A pane and we can talk through them.

I don't have anything right now, but everyone is welcome to still go ahead and input questions; we have a little bit of time. So if you have questions for Chris, Arthur, Megan, now's a good time to kind of dive into them. So go ahead and input them; we'll give everybody a minute.

So our first question here—let's see. Can you speak to the 2018 IHS market baseline data the numbers are based off of?

Chris: Arthur, do you want to take that question? You're a little bit more familiar, I think.

Arthur: So those are vehicle registration data that NREL has access to for its projects, and we start the vehicle stock numbers at those levels and then we project forward based on those. Yeah, we get them at a quite detailed resolution, but I'm guessing that may be—there's some interest in it, but it's unfortunately NREL-specific data.

Chris: Yeah, it is proprietary data that we have an agreement with the data provider to use, so sometimes it can span multiple projects, and so we have an agreement to be able to use that data for these purposes.

Sandra: Great. Thank you. I had another question come in, "At what point do state regulatory decisions begin to enter the model?"

Chris: That's a great question. I think for these two scenarios we didn't make any sort of specific scenarios—or sorry, specific assumptions in terms of policies, especially at individual county levels. But the model framework does have the capability to implement an array of different types of policies, whether it is we simulate a single county or a collection of counties under different assumptions. So it really just depends on the kind of policy that we would want to sort of test out and see what kind of outcomes that might have given the different preferences across different counties. So for instance, we could implement something like a carbon tax by increasing sort of the cost penalties that consumers would be levied for to see how that might affect their decisions to buy different vehicles. And we could also model restrictions on how much access they might have to charging infrastructure. But for these simulations we did not make any specific policy assumptions, but instead looked at if we have targets for electric vehicles that we're trying to meet at a national level, how will those sort of break down at the county level and across the U.S.

Sandra: Great. Thanks. I'm not seeing any questions, so go ahead and I encourage everyone to go ahead and type in a question if you have one. Chris, I just wanted to ask, I know something you mentioned to me, you said there's a publication coming out. Did you want to touch on that? And what the anticipated release date would be and what that's going to cover?

Chris: Right, there is. So it will be a detailed publication that summarizes the methodology. So even more detail, if you're really interested in learning even more about some of the very specific data sets and methodological approaches we use in terms of how we determine consumers might make different choices for different modes, for different technologies, and sort of how we actually set up the framework. That paper will be published very soon. We're actually just reviewing and submitting the proofs, so it's been peer-reviewed and accepted. And so we're waiting for that sort of to be officially online, which should be within hopefully about a week. And once we have that we'll be providing the link on the slides so that we can share the slides in case anybody has interest to sort of dig a little bit deeper into the background of the model.

Sandra: Great. Thank you. Yeah, and as Chris mentioned, the slides and also the recording will be made available, and you'll get that link to the report as well, once that's released.

Let's see. So kind of a question—okay, hold on, let me go back here. So another question, "How could this tool assist a city make decisions to address household energy equity? Is the data available by census tract?"

Chris: Unfortunately it is not available by census tract. I think to get to that kind of level you might need a different approach. So the TEMPO model is inherently sort of more of a top-down model, where we're looking at—you know, we're not simulating individual people or individual trips; we're sort of looking at aggregate distributions of how people are traveling based on their household characteristics within each county. So we simulate only a single county or a single region at a time based on their unique travel demands.

So if you wanted to get a little bit more specific into understanding some aspects of equity, I think the one thing you could look at is how vehicle adoption might be able to differ by the different types of households. We don't currently provide that data. I think we'd like to eventually. So if you have more interest I would encourage you to reach out.

Sandra: Okay. Great. Let's see, we have another one here, "I'm wondering about the decision to use AEO as a baseline scenario for EVs. Many folks consider that too conservative on EVs to be very insightful." Thoughts on that?

Chris: Yeah. So I would agree with that statement. We don't necessarily think it's what I would call realistic or likely. So the purpose I think was more along the lines of—and I guess in theory we could've chose one of the more aggressive scenarios from, you know, Energy Outlook. We chose the reference case, however. It's just more sort of the—you know, one of the more conservative and sort of pessimistic cases. I think in this case it gives us sort of more of a perspective of what's sort of the range, right? So the high electrification scenario that we're modeling is definitely a much more aggressive scenario in terms of electric vehicle adoption, whereas this AEO reference case is much more pessimistic. So this kind of gives you, at least initially, a very good scope of what is sort of the range of possibilities in terms of high to low. But yeah, I would agree that we're not thinking of it as a likely scenario, but more of maybe a pessimistic bottom case.

And in terms of why we also validated against it, it's just being able to ensure that we can replicate the same outcomes as they simulate in one of their scenarios through our different modeling approach.

Sandra: Great. Thanks, Chris. Our next question here—actually it was Megan had a comment. Megan, did you want to speak your question or comment that you had for the group?

Megan: Sure. Thanks. So we have users here who might be interested in this data, and I would really love to hear any of you, how you might be interested in applying this data, what further data would be of use to you. We're here to serve this audience and your needs. So if anyone wants to share comments about how they foresee using this data I would really love to hear it. And as folks might be—I don't know, can they take themselves off mute or does that have to go in the chat or Q&A?

Sandra: Yeah, it would have to go in the chat or Q&A, which is absolutely fine. Or if they want to think about it and reach out to you afterwards that's also another option. So it's kind of open, but I'll keep an eye on the Q&A and chat to see if we have any kind of responses to that question. But that's a good format for that. Yeah, we'd love to hear how you use it, what else you might need, and let us know—let the team: Chris, Megan, and Arthur know and maybe that's something that can be edited or built in if not already there.

Megan: Yeah. And thank you also for the equity question. If you're looking at sort of individual household equity, I did put the link to the LEAD tool, that's the Low-Income Energy Affordability Data tool, which does have energy burden, or the percentage of household income spent on utility bills by census tract and by housing type and renter versus owner-occupied, etcetera. It has a wealth of data that you can try to get down to the household level as far as energy bills. We do know that electric vehicle adoption is highly inequitable, including the subsidies and rebates and tax credits that are associated with them.

So for example, in looking at the tax credit claim for electric vehicle adoption, it was taken—so let's see if I can quote the data correctly: 90% of the people who took advantage of that tax credit were in the top income quintile, the top fifth of household earners as far as income.

So just another reminder of how to think about—consideration as you're thinking about energy planning and transportation energy planning. But if we're focused on solely owner-occupied vehicle adoption, buying new vehicles, that is going to be highly skewed towards high-income households, and there's also some differences along racial lines. And rural versus urban, right, in terms of your opportunity to adopt electric vehicles. So something to consider as they're doing charging planning, EVSE planning and the like, and also looking at maybe there are other types of transportation that you can electrify to help increase equity, whether it be transit or other forms: marine, aviation, etcetera.

So do we have any responses here as to how people might use these?

Sandra: Not yet.

Megan: Okay. All right.

Sandra: Not yet. So I did move the–

Megan: I'm sorry. [Laughs]

Sandra: No, no problem. And, you know, we certainly can reach out to us, you know, kind of as we go here, and also afterward. And I did put that link to the LEAD tool, Megan, in the chat, so that everybody could access that. So that's there now for anybody looking for it. So thank you for that.

Megan: Thank you.

Sandra: If there's any other questions or comments in response to Megan's question or inquiry about how you might use the data, or if there's anything else that maybe isn't there that you could use. Please let us know. Don't be shy. You know what, this is not a shy group. So certainly don't hold back, and feel free to e-mail us afterwards if you feel more comfortable doing that.

Okay, if anyone has any other questions, go ahead, you can still continue to type them in. Chris, is there anything else you want to know? You've been kind of going back and forth in the tools. Is there anything else you wanted to maybe that you didn't call out earlier you wanted to call out that might be interesting for folks?

Chris: No, I've just been clicking around. I don't think I have anything specifically in mind. So, you know, but I'd encourage people to check it out for themselves and let us know if they have any questions about features that they might want to look at or might have. Yeah.

Sandra: Okay, great. We'll give everyone another minute.

Megan: Okay, do you want to–

Sandra: Oh, go ahead.

Megan: Yeah. Do you want to show the fuel consumption and kind of give the qualifiers of how the chart might be a bit misleading at first glance?

Chris: Sure. Yeah.

Megan: [Laughs] I know, it's sticky territory, 'cause we don't want to mislead folks, but this is a good audience and highly educated in this area audience, that I think would be good to explain that, the right access and varying scales issues. If we can pull it up; just not quite there yet.

Chris: Yeah, maybe I should switch to the states. Or maybe my internet is just….

I don't know, maybe we have some other folks on the dashboard now, so I don't know if it's slowing down for just me. But I just guess just to describe them.

So when you're looking at fuel consumption or energy use for the different vehicles or the different geographical resolutions—okay, here we go—one thing that's important to note is that there's a difference between the gasoline consumption and the electricity consumption. So the energy that's reported for battery electric vehicles, for instance, will actually be energy going to the plug, while the energy being reported for conventional technologies or sort of the gasoline consumed by a hybrid plug-in vehicle will be the on-road fuel consumption.

And the other thing that's important to note is that the axis via the left side and the right side are two different metrics; they're both units of energy, but they're not necessarily considered to be sort of the equivalent translation in terms of how many gallons of gasoline equivalent to how much electricity in terms of peak watt hours. So it might look like there's a similar amount of energy being used by battery electric vehicles in a high electrification case as a—you know, compared to the reference case for a gasoline vehicle. But in reality, as I had shown earlier, it in fact is maybe not quite as obvious when you look at it like that. But battery electric vehicles do reduce the energy use significantly pretty much across all counties in a high electrification scenario.

So just keep that in mind and make sure to pay attention to the use as it might not be completely obvious on this single chart what's kind of happening between the two different types of vehicle electricity use versus conventional gasoline consumption. So that's clear.

Sandra: All right. Thanks, Chris and Megan. So, Megan, we did have a response—oh, thank you, Kiani, for submitting it, our coordinator in Hawaii just did this as an example in case anyone's thinking about how they might use it. Kiani noted that Hawaii's primary utility, the Hawaii Electric, is currently engaged in an IRP process with the PUC, and there's concern the high electrification scenario they're using is too aggressive and may be used as an excuse to keep the coal-burning plants going. She'd like to use this model as another projection reference for them. I think her one sticking point is the base vehicle data from 2018 that’s being used, because it's a bit off from what they're getting from the state. So she just wants to make sure the thing is as accurate as possible for them, but definitely looking to use this as a reference point for this particular scenario.

So it's an interesting scenario. Thank you, Kiani, for sharing that. Again, if anyone else wants to share now or afterwards, please do. But I thought that was a good example.

Megan: Thank you. It's great to know.

Sandra: Okay. Not seeing any other questions. We have about eight minutes. Again, Chris, Megan, or Arthur, did you have anything else you wanted to add?

Arthur: Nothing here.

Megan: Nope.

Sandra: I see you pulled up Hawaii, so that's good. Kind of get a feel for what that looks like.

Okay. So I'm not seeing any other questions, so I think we can go ahead and wrap up for today. Just double checking here. Yep, not seeing anything else.

Oh, here we go. Someone asked if they could see what percent of vehicles would be EVs under a scenario.

Chris: So I don't know that you can visualize that directly currently in the dashboard, but I mean that's maybe something to consider for some future sort of features. However, you can download the data yourself, so if you have the ability look at the Excel files, I think they're probably just some CSV files. You could probably be able to determine that pretty easily. I'm trying to think if I know off the top of my head, though. And I don't. Maybe Arthur knows. It's going to vary pretty significantly by county.

But in the high electrification case we do see pretty significant shifts to electric vehicles. So if you look at—I think we're still highlighted on Hawaii so let’s see.

Arthur: Yeah, I agree, that could be an additional feature, but it is dividing these numbers. I guess you choose the types of vehicles you want and divide by the total.

Chris: So I guess in the case of Hawaii here we see it's very much dominated by battery electrics in the high electrification case, maybe upwards of 80%. Just ballparking the numbers in my head here, maybe even more. And this is in 2050, so pretty aggressive adoption of battery electric vehicles in the high electrification case.

Sandra: Okay, great. Well, I think we'll go ahead and wrap up. I'm not seeing any other questions or comments. I just wanted to thank everyone for participating today. Thank you to Chris, Arthur, and Megan for your presentation and for this information—super valuable, interesting.

So again, if you have ideas on how you plan to use it or if you have any suggestions for additions/edits to the tool and the platform, please don't hesitate to reach out. And again, the recording of these slides as well as a link to the publication, when ready, it should be in the next week or so it sounded like, pretty soon, will be posted on the Clean Cities site within the next week or two. So I'll likely get the recording of today's webinar up first, and then you'll see the presentation and link to the publication after that.

So thanks again, and if you need anything reach out anytime. So thanks, everyone. Thanks for participating. And this concludes today's webinar. Thank you so much.

Chris: Thank you.

Sandra: Thank you all.

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