How Much Will the Inflation Reduction Act Reduce Emissions?, with Maya Domeshek and Nicholas Roy

Notable Quotes

• Current modeling projects a decline in emissions due to the Inflation Reduction Act: “The Inflation Reduction Act is likely to reduce US emissions. That’s robust across all of the models; they all found that there would be a fall in emissions. I think the range that we state in the paper is something like 33 to 40 percent below 2005 levels by 2030, which is not quite to the US Paris goals—which are 50 to 52 percent by 2030—but it’s on the way there and much better than in the absence of the [Inflation Reduction Act]. ” —Maya Domeshek (5:04)
• The social cost of carbon emissions drives the benefits of the Inflation Reduction Act: “Currently, the most up-to-date research says that the social cost of carbon is somewhere near $185 per ton of CO2 [carbon dioxide] added to the atmosphere. The idea is that, if you have another ton of CO2 emissions added to the atmosphere, it would cost society as a whole around $185. If your costs per [ton of] CO2 removed from the atmosphere (or not emitted into the atmosphere due to [the Inflation Reduction Act]) are, on average, lower than that value, then you could say that the benefits outweigh the costs.” —Nicholas Roy (8:50)
• The Inflation Reduction Act may help stabilize and reduce electricity prices: “The Inflation Reduction Act is likely to decrease the price of electricity generation relative to what it would’ve been in the absence of the Inflation Reduction Act. It’s also likely to decrease the volatility of the price of electricity, because the electricity sector as a whole is relying more on renewables and less on fossil fuels. Fossil fuels have notoriously volatile prices. For example, last year, when the price of gas really went up due to the war in Ukraine, electricity prices all over the world also went up. A grid that was more reliant on renewables would see less of that kind of impact.” —Maya Domeshek (11:57)
• Decisionmakers can use projections to check progress on emissions-reduction goals: “One way we could use these top-line findings … is to think about these results as benchmarks. We show three different years of outputs in this study: 2025, 2030, and 2035. We have a range of different models that have different structures and representations of the world. If, for example, by 2025, real-world emissions or real-world costs are a lot higher than any of these models predicted, then maybe it’s time for decisionmakers to say, ‘Well, why are we failing to achieve what these models have predicted we’re capable of achieving?’” —Nicholas Roy (26:14)

Top of the Stack

• “Emissions and energy impacts of the Inflation Reduction Act” by John Bistline, Geoffrey Blanford, Maxwell Brown, Dallas Burtraw, Maya Domeshek, Jamil Farbes, Allen Fawcett, Anne Hamilton, Jesse Jenkins, Ryan Jones, Ben King, Hannah Kolus, Joh, Larsen, Amanda Levin, Megan Mahajan, Cara Marcy, Erin Mayfield, James McFarland, Haewon McJeon, Robbie Orvis, Neha Patankar, Kevin Rennert, Christopher Roney, Nicholas Roy, Greg Schivley, Daniel Steinberg, Nadejda Victor, Shelley Wenzel, John Weyant, Ryan Wiser, Mei Yuan, and Alicia Zhao
• “Beyond Clean Energy: The Financial Incidence and Health Effects of the IRA” by Nicholas Roy, Maya Domeshek, Dallas Burtraw, Karen Palmer, Kevin Rennert, Jhih-Shyang Shih, and Seth Villanueva
• “The 45V Hydrogen Tax Credit: Considerations for US Treasury Guidance” RFF Live event
• After the Flood by Lydia Barnett
• Field Trip podcast

The Full Transcript

Daniel Raimi: Hello, and welcome to Resources Radio. I’m your host, Daniel Raimi. Today, we talk with Resources for the Future (RFF) Research Associate Maya Domeshek and Senior Research Analyst Nick Roy. Maya and Nick were part of a large team of authors that recently released an analysis published in the journal Science on the effects of the Inflation Reduction Act on the US energy sector. The authors estimate how the policy might affect carbon dioxide emissions, the energy mix, and energy costs over the next 10–15 years.

I’ll also ask Maya and Nick about the uncertainties and limitations that are inherent to any modeling analysis. Whether you’re an energy modeler or just energy model–curious, I think you’ll learn something from today’s conversation. Stay with us.

Nick and Maya, it’s great to have you here on Resources Radio and to be talking to you in person. Welcome to the show.

Maya Domeshek: Thank you for having us.

Nicholas Roy: Thanks for having us, Daniel.

Daniel Raimi: Nick, you’ve been on the show before as part of our 70th-anniversary series. Maya, you were on it very briefly in a kind of unusual episode that we did. We haven’t actually asked you to introduce yourself to the audience. Can you tell us about yourself and how you got interested in working on environmental issues?

Maya Domeshek: Of course. I’m Maya Domeshek. I work as a research associate here at RFF. I work primarily on the Haiku Electricity Model with Nick and Dallas Burtraw. I also do some work on the incidence of environmental policy across households.

Daniel Raimi: Great. Haiku is the name of the electricity model that we have at RFF. Does it stand for something, or is it just that we like poems?

Maya Domeshek: It does not stand for something, but it is an homage to an old model the government used to run called Poems, which was a much larger and slower-running electricity model. The idea was that RFF’s was faster and shorter.

Daniel Raimi: I see. It was more elegant or something, like a haiku. That’s nice.

Maya Domeshek: They could never find an acronym that had a “K” in it.

Nicholas Roy: I’m working on it, though.

Daniel Raimi: “Kilowatt-hour,” maybe.

Well, let’s talk about the study that the two of you were a part of—a major new study. It was published in the journal Science, and we’ll have a link to it, of course, in the show notes, so people can check it out.

Listeners of our show will probably have a good idea of the main provisions of the Inflation Reduction Act, which we’ll call the IRA—or maybe we’ll call it IRA, or maybe we’ll call it the climate bill, or maybe we’ll call it something else—so we don’t need to give a lot of background on the provisions of the bill. Can you tell us a little bit about this study that you were involved in? Who was involved, what did you try to do, and what about the methods of the study were interesting or notable?

Nicholas Roy: When the Inflation Reduction Act was released last year, a lot of different research teams came out with modeling studies to represent what they believed the bill would do. What this study has achieved is really trying to see what commonalities we have among all these different studies; usually, with policy analysis, we don’t really get that opportunity to compare such a broad set of studies. This particular paper has nine different teams involved, four of which were releasing studies on the Inflation Reduction Act right after the bill came out. We were one of those teams—as well as REPEAT, Rhodium, and Energy Innovation—that were trying to quantify the emissions impacts of the bill right after it came out.

Those studies are quick. The only reason we were able to do them was really because we were following the policy process so closely, going back to when it was part of the Build Back Better Act and was framed in that context. Since then, we’ve revisited our analysis, and all four of the teams that released those initial analyses revised their studies. Then, we also included five other groups from national labs, government agencies, and universities. This was all organized by John Bistline at the Electric Power Research Institute, or EPRI for short. He really did this heroic effort of coordinating all our different teams with all our different assumptions, all our different data inputs, and all our different modeling frameworks to see where you can get these common outputs and compare direct apples to apples across all these different models.

Maya Domeshek: I would really say that what makes this study unique is the ability to look across so many studies and try to figure out what we can learn that is robust across studies and what things are maybe not robust across studies.

Daniel Raimi: It sounds a little bit similar to the Energy Modeling Forum, which has been run out of Stanford for a long time. In some ways, it’s similar to what we do with our Global Energy Outlook here at RFF—different, of course, in lots of details, but a similar concept. We won’t get too bogged down in the methods, although we certainly could. Let’s just jump to the big-picture findings. What are some of the headlines that have come out of this work in terms of energy outcomes and emissions outcomes?

Maya Domeshek: The big takeaway from the paper is that the IRA is likely to reduce US emissions. That’s robust across all of the models; they all found that there would be a fall in emissions. I think the range that we state in the paper is something like 33–40 percent below 2005 levels by 2030, which is not quite to the US Paris goals—which are 50–52 percent by 2030—but it’s on the way there and much better than in the absence of the IRA.

I think the second main takeaway is also a little bit expected, which is that most of those emissions reductions are coming from the power sector. We see pretty dramatic decreases in emissions in the power sector across the models, something around 47–83 percent below 2005 levels by 2030. Then, there are more detailed results across models looking at how many renewables are built out, how much consumption increases, what happens to coal and gas capacity, etc.

I think the last thing that’s particularly interesting is the question of electrification, because some of the models were representing the entire US energy sector, and they tried to look at what the uptake of the vehicle tax credits would mean for electrification or what the uptake of other tax credits would mean for building electrification. They created some projections of what consumption might be in the future, whereas other models, like ours, had consumption as something parametric that we just had as an input. There’s a pretty wide range of consumption projections, as well.

Daniel Raimi: One quick follow-up. When you say you had consumption as something that’s parametric—for someone who hasn’t worked on these types of models or these types of analysis, what does that mean?

Maya Domeshek: That means we made an assumption about what consumption was going to be, and we just put it in the model, and we didn’t touch it after that.

Nicholas Roy: Well, I should add that we did get that assumption from the Energy Information Administration’s Annual Energy Outlook. We weren’t making it up completely, but we were getting it from a modeling team that had projected the demand for electricity in the US back in 2021—that was the number we used.

Daniel Raimi: That’s great. Those are the key outcomes for the energy system and for carbon dioxide emissions. How about costs? That’s a key talking point that many people care about a lot. What happens to energy costs under the IRA? Also, when we think about those costs and compare them to the benefits that the bill gives us, how do those stack up? What do the costs look like, and what does the cost-benefit ratio look like?

Nicholas Roy: There’re a bunch of different ways we can think about costs. You described one that we do in the paper: You look at the total energy system and sum up all the costs from the modeling exercise and see what those costs look like prior to the bill and after the implementation of the bill in these models. Something that you do when you have something like the Inflation Reduction Act—that is mostly subsidies driving decarbonization—is you subtract out those subsidies from the costs on the grid. That’s a big reason why we see a reduction in costs for retail prices, for example. Now, the prices of electricity are different from how much money is being spent by the government. If you look at the government, the government is spending money from just these climate provisions, and it’s raising money from somewhere else.

Like I said, we’re concerned about this cost on the energy system. When we want to compare those costs on the energy system to the benefits from reducing these emissions, we have to find some similar metric or comparable metric. What we do here at RFF—as well as in environmental economics more generally and in cost-benefit analysis in the government—is consider what’s called the social cost of carbon, which I know we have plenty of podcasts talking about. I won’t go too much in depth on how they’re calculated. Currently, the most up-to-date research says that the social cost of carbon is somewhere near $185 per ton of CO2 added to the atmosphere. The idea is that if you have another ton of CO2 emissions added to the atmosphere, it would cost society as a whole around $185. If your costs per [ton of] CO2 removed from the atmosphere or not emitted into the atmosphere due to this bill are, on average, lower than that value, then you could say that the benefits outweigh the costs.

What we find in this study is that when we’re putting the costs in terms of dollars per [ton of] CO2 abated or not put into the atmosphere, we find a metric of $27–$102 per ton as the range across the studies. That $27–$102 is a lot lower than the $185. We would say that the climate benefits of this bill far outweigh the costs on the energy system. You could look at older social cost of carbon models, such as the Obama administration’s, and you’d find that landing within the range of our costs where they’re just about the same as the benefits. If you want to go back to the Trump administration and look at their $7 per ton, you would see that the climate benefits are not worth the costs of this bill. But yes, in conclusion, across all these modeling studies, we do see that the benefits far outweigh the costs from implementing this bill.

Daniel Raimi: That makes a lot of sense. Just to clarify for people, we’re not going to go into the details on the social cost of carbon. There are lots of parts of the economy that we can reasonably expect will be damaged by climate change that actually are not accounted for in the current best estimates of the social cost of carbon. We’re also not talking about the social costs of other greenhouse gases associated with the energy system, like methane and nitrous oxides and things like that.

Maya Domeshek: We’re not including the other benefits of the bill, like the health benefits that we might expect from reduced fossil fuel usage.

Nicholas Roy: Maya is being humble by not mentioning the paper that we wrote that does try to get at that. We released that last October to measure the health benefits of the Inflation Reduction Act.

Daniel Raimi: Fantastic. All right. We’ll definitely have a link to that, as well, in the show notes, so people can dig in and admire more of your excellent work.

Nick, you’ve talked about system-wide costs, but when “normal” people think about government policy related to energy, their first question is going to be, How is it going to affect my energy costs? Is it going to do anything to gasoline prices? I know you all didn’t look at the fuels sector in your modeling analysis, but people will wonder that. They’ll also wonder, What will it do to my electricity bill? Is my electricity bill going to double, or something like that, because of these policies? What are some of the results that you and your coauthors found?

Maya Domeshek: Daniel, that’s a great question. The paper does not talk very much about electricity-price impacts, but almost all of the individual studies that contributed to the paper did look at price impacts, and so did we. In the paper that we published in October preceding this paper, we found that the Inflation Reduction Act is likely to decrease the price of electricity generation relative to what it would’ve been in the absence of the Inflation Reduction Act. It’s also likely to decrease the volatility of the price of electricity, because the electricity sector as a whole is relying more on renewables and less on fossil fuels. Fossil fuels have notoriously volatile prices. For example, last year, when the price of gas really went up due to the war in Ukraine, electricity prices all over the world also went up. A grid that was more reliant on renewables would see less of that kind of impact.

We found this decrease in volatility and decrease in electricity-generation prices. That’s all happening because the government is subsidizing electricity effectively and moving us to an overall—in the long term—cheaper and cleaner grid. Now, whether that means cheaper or more expensive household bills is a separate question, because, first of all, electricity-generation price is not the same as the electricity price that households are paying, because there are transmission and distribution costs.

Second of all, your bill is also about how much electricity you’re consuming. In fact, one of the things the IRA is trying to achieve is getting people to consume more electricity, because we’re trying to electrify the whole economy. Reducing the price of electricity makes it easier for people to consume more units of electricity. What this means for your bill, I don’t know. That remains to be seen. We do know that it is likely to decrease the price of electricity generation relative to what it would’ve been in the absence of the policy.

Daniel Raimi: That makes sense. Electricity prices (I’m not an electricity expert, so correct me if I’m wrong) are set by the marginal cost of electricity generation in whatever region you’re in. Do you think that marginal cost is more likely to be set by renewables in the future, or is the marginal cost still going to be set by gas? Because I’m imagining renewables—since there’re zero fuel costs—are going to generate whenever they can generate and supply energy at that margin. Can you just talk a little bit more about how the IRA might affect the marginal price of electricity generation?

Maya Domeshek: You’re absolutely right that electricity prices, especially in regions of the country with a deregulated electricity sector, are set by the marginal unit that’s generating. The price, on average, that you’re paying over the course of the year is reflecting the marginal price in a bunch of different hours. The more hours that are shifted away from having gas as the marginal unit, the cheaper your overall average annual price is going to be. The less gas and other fossil fuels we’re using in the grid overall, one would hope that lower demand for those goods also means those prices are lower, so the marginal gas unit is also not as expensive.

Nicholas Roy: Something that I really liked that you pointed out there was the temporal aspect of the cost of generation. Something that economists, especially energy economists, have really been interested in—especially those at Berkeley’s Energy Institute—is the idea of dynamic pricing. Because the Inflation Reduction Act subsidizes those renewable generating units, that means that if you’re going to implement something like dynamic pricing in the future, where people can basically get different prices at different hours—there’re already some aspects of that being implemented in some utilities—you’d actually be able to get an even cheaper price during certain hours than you would if you’re averaging out the price across all hours. It does also enable more benefits to the climate and more benefits to people’s electricity bills if something like that is implemented and leads to a more efficient system.

Maya Domeshek: I also want to return to this original question about what the impact on household electricity bills is. Because again, in our earlier study last October, we looked a little bit at the distributional impact of reducing electricity prices. We find that reducing electricity prices by subsidizing them with government funds is effectively a progressive—in the technical sense—policy, because you’re reducing the amount that households are having to spend on a crucial good and you’re paying for it with the tax system, which is somewhat more progressive than the flat quantity of electricity most households are consuming. That’s an important aspect and an important goal of the Inflation Reduction Act: to keep costs down for households.

Daniel Raimi: For sure. Let’s talk a little bit now about modeling, because you and your colleagues in this analysis carry out really excellent modeling work—top-of-the-line modeling work, best in class. But models are limited. Inherently, they are limited representations of the real world. You have to leave some stuff out. Can you talk a little bit about the things that you have to leave out from this type of modeling exercise? What are some of the most important things you have to leave out, or you can’t model for one reason or another, and how do you think they might affect the outcomes that, Maya, you described just a few minutes ago?

Nicholas Roy: I really appreciate you talking about our models as best in class, but also talking about the limitations of modeling. Because I think every modeler would say that they’re some of the last people to trust the results of models as something that you can guarantee.

Yesterday, at the hydrogen event that we had at RFF, I forget which of the panelists mentioned it, but they mentioned the cliche that “all models are wrong; some are useful.” I think that’s really important to keep in mind. What exactly are these models useful for? But to get at that question about what they aren’t useful for, I think there’re a lot of things that we described in this paper, as well as in the broader discussion on the Inflation Reduction Act that people have described, but that models aren’t capturing. These models represent a version of the world that is under textbook market conditions and is heavily reduced down and simplified so we can analyze these policies in a quick but also interesting and in-depth analytical manner.

Because of that, there’re certain things that don’t fit in that kind of framework and approach that we haven’t been able to implement directly. Some of these things you’ll hear about in the news right now are the interconnection delays for renewables. Some of the electricity markets have issues with getting their renewables online after they can get the project planned and the capital associated with that project ready to go. It’s difficult to actually get it set up and ready to connect to the grid. In a similar vein, you see the same thing with transmission. There’s all this money flowing to build renewables, and it’s ready to be done, but actually implementing that is difficult from an interconnection and transmission perspective. There’re all sorts of institutions that need to be made a little bit more efficient to be able to handle this level of build-out.

That’s one thing that we just simply don’t do in our models, because it’s an institutional question, not an inherently economic, analytical question. There’re other things that people, I think, are a lot more familiar with that could get in the way of the Inflation Reduction Act reaching the emissions targets we saw in this analysis. That would be something like supply chain backlogs or critical mineral shortages. During the COVID pandemic, a lot of people saw prices going up because of the supply chain issues. That’s probably a big part of the reason this bill was labeled as the Inflation Reduction Act and not the “Emissions Reduction Act.” Those are some resource constraints that get in the way of that process.

There’s also an aspect of human behavioral things that economic models have never really quite tried to implement. Labor shortages are already being talked about as big frictions to the implementation of the Inflation Reduction Act, as well as siting and permitting, which is really the big one that’s been discussed federally, as well as at the state level, which can sometimes just come from local opposition. Sometimes people don’t want wind turbines in their backyard. These models don’t model every backyard and don’t model every potential wind turbine. We just say where the costs make it possible to build these and make it more cost-effective to build them. These are things that we’re not including.

I mentioned the event yesterday about hydrogen, which was centered around the US Department of the Treasury releasing guidance on exactly how these tax credits are understood to be in the law and how the US Internal Revenue Service makes them available. That event yesterday on the hydrogen tax credits was really getting at how these tax credits can be made available and the rules that Treasury decides to make them available by. During that event, they talked about a range of very small amounts of electricity-demand increases. You could generate a little bit of hydrogen, or you could have rules that allow for a lot of hydrogen production that could lead to a lot more electricity demand, which would actually undo a lot of the emissions reductions that could be in the Inflation Reduction Act, because you could increase demand more than you could increase renewable generation.

How Treasury ends up deciding all these rules—you yourself talked about energy communities and how that was an important rule that took some time to be able to parse out, and electric vehicles was also an important rule that Treasury had to figure out. How that gets done is also a big uncertainty that these models are not trying to capture. There’re a lot of things that get in the way of implementation—frictions, as well as just some uncertainties that these models can’t actually represent.

Daniel Raimi: That’s great. When you think about the directional influence of those uncertainties, when I think about them, I usually—maybe it’s just because I’m a pessimist—think about the downside risks from these uncertainties: local opposition, problems with interconnection queues, problems with labor supply, problems with materials. Do you think I’m right to be thinking that most of these unmodeled aspects would tend to limit the benefits of the Inflation Reduction Act, or are there plenty of uncertainties that could go in the other direction?

Nicholas Roy: There’s definitely some that go in the other direction. I was tempted to bring an old output sheet from modeling we had back in 2008, because I was thinking about this. We saw the modeling in 2008 that was done by the same team that we’re on now, which projected that emissions would be a lot higher today than they are, and that electricity demand would be a whole 1,000 terawatt-hours greater in the United States. I don’t actually know what work they were doing; it was just a random spreadsheet I found. I thought it was really interesting that they also under-predicted how much solar and wind would get deployed, because the capital costs were so high back then for those technologies, and they really reduced down in the past decade. That kind of thing could happen for a lot of advanced technologies that are developing right now.

Those are the kinds of things that modelers don’t like to make bets on. You don’t want to make a bet on an optimistic outcome when it comes to costs. You’re definitely right that all those frictions and constraints that I was talking about before do go in the negative direction in terms of leading to potentially higher costs or potentially higher emissions. That’s something that modelers do sometimes try to proxy. One way we do it is we build a constraint around how much capacity can grow in a given year. Sometimes we’ll run a version of the model that says, if your capacity is going to go up four times today’s value in 2030, it better have gone up at least two times the year before. You’re not seeing random spikes in the build out of electricity generation, but rather a path that has to build up to it to actually capture some heuristic of incremental institutional capacity being built out for these things.

Maya Domeshek: Not to make a plug for our future work, but the same team that wrote this paper also looked a little bit at sensitivities you could run around each group’s central case. Those sensitivities often involved changing how fast the models think it’s possible to build out renewables. We are proxying some of these things. I would also add that I think it’s useful to pay attention to the downside risks, because you do more things in advance if you’re preparing for downside risks than if you’re just waiting for something great to happen.

Daniel Raimi: Totally. If we don’t know what the downside risks are, then we can’t try to deal with them before they happen and prevent those bad things from happening.

The next question I want to ask is related to this question of model outcomes and model uncertainty. It’s a big-picture question. When studies like these get released, the headline finding—the one that we talked about at the beginning of this conversation—is the thing that ends up as the headline. The minutiae about siting delays and interconnection queues and other downside risks—they are minutiae, and they’re maybe buried in the newspaper article, if they’re there at all. When you think about communicating results from modeling studies like this, what do you think is the right balance of communicating that top line and communicating the uncertainties that are there? As researchers, I know you’re transparent, you’re getting all the information out there, but you also have to think about how to talk about it in the world. We do press releases at RFF, and newspaper reporters talk to us. How do you think about that mix of top line versus uncertainties and assumptions?

Maya Domeshek: Well, I think you have to communicate both. Oftentimes, the uncertainties and assumptions are part of the top line in a really important way, because modeling is really about clarifying our thinking on any subject and making sure that we understand what we know and what we don’t know. It’s useful in a study like this to say, “Top-line takeaway—emissions go down. Second top-line takeaway—we don’t really know how much they’re going to go down, and here’s why we don’t really know how much they’re going to go down, because the models disagree about some inputs, and because there are some things we can’t model.” I think that’s the most useful thing the public can take away.

Nicholas Roy: Something I’d also like to point out is that we have a communications team at RFF that does a lot of this thinking about the best way to communicate this work, and this podcast is part of that. That’s also nice, because it liberates the researchers from a lot of those decisions. My own personal philosophy or belief on one way we could use these top-line findings—as well as the minutiae that should be top-line findings—in a unique or balanced way is to think about these results as benchmarks. We show three different years of outputs in this study: 2025, 2030, and 2035. We have a range of different models that have different structures and representations of the world.

If, for example, by 2025, real-world emissions are a lot higher or real-world costs are a lot higher than any of these models predicted, then maybe it’s time for decisionmakers to say, “Well, why are we failing to achieve what these models have predicted we’re capable of achieving?” If you’re a grid operator, maybe you have to spend a lot more time looking at your interconnection queues. If you’re a renewable developer, maybe you need to start to strategize better about your community outreach so you can overcome some of these siting issues. If you’re a government agency or a government official, maybe you’re thinking more seriously about permitting in 2025 when you see you’re missing this benchmark.

Similarly, if you’re looking farther out—2030, 2035—and you see that you’re missing these benchmarks (or if you’re lucky, you’re exceeding them) what policies do you have to put in place to complement this set of policy instruments and these incentives that exist to unlock the full capabilities of this policy?

One thing that I think is really important to think about in this benchmark framework is this idea of regulatory capture, which exists in the field of economics. You don’t want to design a bunch of subsidies that are simply captured by industry and they just make profits off it, and they don’t actually reduce the cost as the policy was intended to do. You don’t want all these inefficiencies in the market to be gamified for a bill that was labeled the Inflation Reduction Act and aimed to reduce prices and not increase profits. That kind of accountability, I think, can only really be achieved when you have these independent research teams putting out work that they have to be accountable to, but they’re not accountable to these corporations which might have different projections about the implementation of the IRA.

Daniel Raimi: It’s going to be so fascinating to watch how that plays out over the next several years. I know that there’s a lot of concern about how much of these subsidies will get passed on to consumers versus how much will go to investors. We’ll just have to wait to see how that plays out. Either way, hopefully we’ll have lots of emissions reductions, and we’re all being too pessimistic about this, but time will tell.

Last question, Maya and Nick, before we go to our Top of the Stack segment, is about variation across results. Maya, you mentioned this just a minute ago. There are multiple models running here, looking at multiple sectors of the economy. Even if you look within a sector, like the electricity sector, there’s quite a bit of variation between some of the models and what they find. Can you talk a little bit about that? What’s going on there? What are some of the assumptions under the hood that might lead one model to have a very different result from another model, even with a very similar set of input assumptions?

Nicholas Roy: I think Maya might talk more about the variation and input assumptions between these models. Something that’s really important about a multi-model study like this is that there’re just different structures for how we think about these models. Something that’s an economy-wide model or trying to represent the macroeconomy in not one particular sector or some subset of sectors might be framed as a continuous general equilibrium model. We have a fellow here at RFF, Marc Hafstead, who runs a model like that. Those use a lot of economic theory and concepts such as elasticities that are really crucial to those solutions that those models will put out.

One issue with those models is they’re really bad at fully capturing zero emissions from a sector, just with the nature of how elasticities work. The model we run falls under the label of a partial equilibrium model, which is more of looking at one particular sector and the market equilibrium that that sector can achieve. You’ll see other models in this study that have several partial equilibrium models of several sectors and link these, and that might be a linked partial equilibrium model. Sometimes an integrated assessment model falls under that framework, though that can be more interdisciplinary. These different frameworks can lead to different outcomes. You can really see it in the study, too–if you look at the supplemental materials, we categorize the models in this way.

When you look at their emissions pathways, the partial equilibrium models have a very similar pathway to each other. These continuous general equilibrium models or other kinds of models in this study might have a different one. Some ways that these models can differ from each other is this concept of perfect foresight, which is, Do you represent your model in a way that it solves for one particular year, takes that year, goes on to the next year, and solves that year? Or do you consider all the years as one big, long-planned decision and figure out a way to optimize your costs across a longer time horizon? That concept is called perfect foresight. We use it in our model, but it’s not used in every model. These different structural assumptions about how decisions are made can really lead to different outcomes in emissions and costs.

Maya Domeshek: In fact, when we’ve looked at sensitivities across the models, I think we found that, basically, the models differ from one another more than any given model does across sensitivities. Maybe we just need wider sensitivity ranges, but model structure really matters to the type of output you’re going to get. I think the biggest structural difference is the one-sector versus multi-sector models, because any model that was representing multiple sectors and trying to represent electrification was going to have a vastly different impact in the electricity sector, and vastly different build-out of renewables than a model that doesn’t really consider electrification.

On the input assumptions topic, I think the models were reasonably well-aligned on their capital cost and natural gas assumptions, but they did differ. I think the places where they really differed the most were around the implementation of carbon capture and storage, and again, on demand. Carbon capture and storage, in particular, was one that felt very assumption-driven across models, because we don’t know how easy it will be to site carbon capture facilities. We don’t know how easy it will be to build out a pipeline network for that or how rapidly it will be possible to start storing CO2. A lot of the models had to just pick a level that they thought wasn’t too much and just put that in. I think that there’s going to be a lot of work on that going forward.

Nicholas Roy: Similar to carbon capture and storage, we have hydrogen, as I was mentioning in the event yesterday. Some models have a representation of hydrogen and how that’s going to affect demand. Others, like ours, don’t represent hydrogen and how it interacts with the electricity sector. On that topic of demand, there’s all sorts of other tax credits, such as residential rooftop solar or the electric vehicle tax credits, which, if you represent them, you’re going to see increases in electricity demand. If you don’t represent those, you’re not going to see it. That can really drive a lot of the differences in emissions and costs, because you’re talking about a meaningfully different grid than you would be without those assumptions built in.

Daniel Raimi: Then let’s not even talk about all the incentives for energy efficiency, which is going to maybe change things going in the other direction. As folks can tell, there’s a ton to talk about here. There’s a ton to unpack. I hope our listeners will check out the study and check out all of the supplementary materials, which I’m sure you’re all aching to read to get into the weeds here with us.

Let’s close it out, Maya and Nick, with the last question that we ask all of our guests: to recommend something to our audience that you’ve read or watched or heard that you think is great. Maya, first, what’s at the top of your stack?

Maya Domeshek: Well, I’ve been reading this book After the Flood: Imagining the Global Environment in Early Modern Europe by Lydia Barnett. It’s a history of the way that Enlightenment European philosophers thought about Noah’s flood and how that allowed them to conceptualize the world. I think it’s an interesting book to read in the context of people who work on climate change, because it’s all about trying to understand how people in the past—in Europe, specifically—thought about weather and how much and in what way they could affect the world around them and what it would mean to change the world around them.

Nicholas Roy: For me, I was actually looking for something to say this weekend for this question. I was lucky that I came across a podcast called the Field Trip podcast by Washington Post journalist Lillian Cunningham. She basically got to do what a lot of people at RFF probably would say is their dream in a lot of ways, which is to get the Washington Post to fund your trip across the US national park system. She went to five really amazing national parks. The audio captures all the sounds of nature and all her meditations in it while she’s there. I guess the reason that this podcast is good and why she could justify asking for that kind of sponsorship is the fact that she goes really in depth on the history of these national parks and all the contentious issues that have come up with them.

In pretty much every episode, she talks to the Native tribe that was located in that area before it was turned into a national park and talks about the history of how the national park system interacted with those groups, as well as how some of them were better at preserving that area than the national park system. The fires in Yosemite was a really interesting episode that they started with, where they talk about how the Native people were better about letting the underbrush burn and preventing a lot of things from building up to allow for more fires.

One thing I really liked about that podcast was just this idea that, when trying to achieve a goal—as well-intentioned as it can be—it’s really important to think about the individuals who are going to be affected directly by it and to take those considerations into account as you build your governance structures. We spent a lot of time talking about federal climate policy on this podcast, but a lot of the way this is going to get done is through local governance and state government issues like that.

Daniel Raimi: So true. Well, we’ll be talking about, I think, those local governance issues in the years to come, and we’ll be talking about the modeling exercises that try to account for the big picture. I think we’ve got to think about both of them and try to keep both of them in our heads at the same time.

Maya and Nick, thank you so much for joining us on Resources Radio. Congratulations on the study. Thanks again, it’s been a great conversation.

Maya Domeshek: Thank you, Daniel.

Nicholas Roy: Thanks, Daniel.

Daniel Raimi: You’ve been listening to Resources Radio, a podcast from Resources for the Future (RFF). If you have a minute, we’d really appreciate you leaving us a rating or comment on your podcast platform of choice. Also, feel free to send us your suggestions for future episodes.

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Resources Radio is produced by Elizabeth Wason, with music by me, Daniel Raimi. Join us next week for another episode.