In this week’s episode, host Daniel Raimi talks with Silvia Pianta, a junior scientist at the sister institution to Resources for the Future (RFF), the RFF-CMCC European Institute on Economics and the Environment. Pianta discusses the influence of social and political factors on climate and energy policymaking, how incorporating these factors into models can help inform the process of climate policymaking, and the efficacy of emissions-reduction strategies at the global and national scales.
Listen to the Podcast
- Political factors help determine the success of climate policy: “Capacity … can be defined as the ability to reach a goal that you set. In the case of government … we often talk about governance capacity or institutional capacity. This is a term that can be used to define the ability to reach policy goals once we set these policy goals. If a government decides to have, for instance, a short- or long-term climate mitigation target, how likely is it that the bureaucratic structure, but also the economic structure of the country or the technological capacity of the society, of industries and companies in the country—how likely are they to contribute to reaching these specific policy goals?” (6:58)
- Public opinion shapes national responses to climate change: “If we look at survey data across the globe measuring support for climate policies or climate change concern over time, we see that public opinion is more and more supportive … governments will implement more ambitious policies, because they think this is more convenient also in electoral terms. The idea is that supportive public opinion can be an enabler of more ambitious climate policies.” (11:48)
- Quantifying the influence of politics on climate policy: “The idea is to just see what empirical data tell us about what’s happening in the world, and be able to capture these differences across different countries, and build some scenarios that incorporate these social and political dynamics a bit more by using existing empirical data.” (18:38)
Top of the Stack
- “Emissions Lock-in, Capacity, and Public Opinion: How Insights from Political Science Can Inform Climate Modeling Efforts” by Silvia Pianta and Elina Brutschin
- On Time and Water by Andri Snær Magnason
Daniel Raimi: Hello and welcome to Resources Radio, a weekly podcast from Resources for the Future. I’m your host, Daniel Raimi. Today we talk with Dr. Silvia Pianta, junior scientist at Resources for the Future’s sister institution, the European Institute on Economics and the Environment. Silvia is an author of a recent paper describing how researchers that use integrated assessment models, or IAMs, can incorporate political dynamics into their models to more accurately represent the world.
In today’s episode, I’ll ask Silvia which political dimensions are the most important to add and how they can affect the outcomes of modeling efforts. The conversation is a little wonky, but it’s really important. IAMs play a major role in shaping energy and climate policies, and making them more realistic can go a long way to improving our decisionmaking on this crucial topic. Stay with us.
Silvia Pianta from the European Institute on Economics and the Environment, our sister institution over in Italy, welcome to Resources Radio.
Silvia Pianta: Thank you. Thank you for having me.
Daniel Raimi: Silvia, we always ask our guests how they became interested in working on environmental issues, and if that interest started when you were young or whether it developed later in life. How did you get inspired to work on these topics?
Silvia Pianta: I would say I always wanted to work on these topics, and at the beginning it took me some time to figure out which perspective I wanted to take. At the beginning, I was doing things on international environmental law, and then I realized I wanted to switch to environmental policy. But the story that I always tell when they ask me this question is that my name is Silvia. The root of the name comes from “forest” and Pianta means “plant,” so it’s kind of in my destiny that I had to work on this kind of thing. Probably, it’s my parents’ fault somehow, although I don’t think they did it on purpose. But that’s how it started, probably.
Daniel Raimi: That is so funny. So, you’re green to the core.
Silvia Pianta: Exactly.
Daniel Raimi: That’s great. Well, Silvia, today we’re going to talk about your work on climate change and political science, and we’re going to focus on a really fascinating paper that you wrote with a colleague. The paper’s called “Emissions Lock-in, Capacity, and Public Opinion: How Insights from Political Science Can Inform Climate Modeling Efforts.” Of course, we’ll have a link to the paper in the show notes so people can check it out, but I think it would be great to start by asking you to help us understand the three major barriers that you describe in the paper that impede ambitious climate policy. Those three are listed in the title: lock-in, capacity, and public opinion. I’d like to take each of those in turn. Can you start off by helping us understand how this idea of emissions lock-in affects climate policy decisions?
Silvia Pianta: Yeah, sure. Before replying to this specific question, I would like also to say that I think that with these things, we don’t want to frame them only as constraints of ambitious climate policy—they can also be enablers. I think they are factors shaping the transition, and we should take them into account, but we shouldn’t have a pessimistic view in terms of them being only challenges for decarbonization.
We are political scientists—Elina Brutschin was the other coauthor with me—doing quantitative empirical work, and we have many colleagues who are actually modelers. They build these long-term climate mitigation scenarios. Then, looking at the literature, at the papers written by modelers and working on some papers with them, we realized that these models could incorporate some of these aspects. The thing that we wanted to do is make a review of what the political science and the socio-technical transition literature tell us about the political and social factors shaping the transition and then discuss how they can be incorporated into these long-term climate mitigation scenarios.
The first factor that we take into account, we call it “emissions lock-in”—and this actually builds on an existing term that’s a bit more used, “carbon lock-in”—that is used to refer to the fact that we have societies that are highly dependent economically, technically, and also politically on some polluting sectors. This can also shape the likelihood of implementing ambitious transition policies.
Of course, this is very intuitive when we think about the fossil sector, but we used a broader term—we didn’t use the standard term, carbon lock-in, but we used a broader term, which is emissions lock-in, because there are other sectors besides, for instance, the industrial sector, that are responsible for emissions that we should try to mitigate. And this is a bit more clear if we think about some concrete examples. For instance, in the case of Brazil, we have a lot of emissions coming from the agricultural sector—in particular, there is cattle production that is responsible for median emissions and also emissions of other greenhouse gases.
In these countries—for instance, in Brazil—of course these economic sectors will also think that this is completely legitimate to somehow delay the transition and try to continue their business in the way they’re doing already. These things, of course, can reduce social welfare, but these are normal phenomena in society—and, of course, they increase the share of the economy that is dependent on the emitting sector. They’ll increase the likelihood that these interests will try to slow down the transition, for instance, by lobbying efforts or, for instance, trying to influence public opinion. This is the first idea of how emissions lock-in can shape ambitious climate policymaking, or, in this case especially, slow it down.
Daniel Raimi: Right. That makes a lot of sense. That’s a great description of this idea of lock-in. Let’s move now to the idea of capacity. Can you define the term? What do you mean by capacity, and how does it shape decisionmaking about ambitious climate policy?
Silvia Pianta: Capacity, as a broader term, can be defined as the ability to reach a goal that you set. In the case of government, for instance, we often talk about governance capacity or institutional capacity. This is a term that can be used to define the ability to reach policy goals once we set these policy goals. If a government decides to have, for instance, a short- or long-term climate mitigation target, how likely is it that the bureaucratic structure, but also the economic structure of the country or the technological capacity of the society, of industries and companies in the country—how likely are they to contribute to reaching these specific policy goals?
If we set a specific policy goal, we also need the capacity to achieve this specific policy goal. In this case, we need a combination of the capacity of the government and also some enabling actors in society. This can mean enterprises that are able to contribute to this or also the society having sufficient technological capacity to implement this goal.
Daniel Raimi: That’s great. Can you give us an example of how that would play out in the real world—an example of a government that maybe has limited capacity or a sector that has limited capacity—or, on the other side of the coin, a government or a sector that has very strong capacity to achieve these goals?
Silvia Pianta: Absolutely. Thinking about the capacity of the government, we can think that some governments are more able to make sure that their goals are actually implemented because, for instance, there are countries with lower corruption levels, so it’s easier to actually achieve the goals that the bureaucracy decides. But also, we think about having knowledgeable, enabled bureaucrats or people working in public administration that can actually make sure that this policy is implemented in detail. If we think about the technological capacity—for instance, if we want to scale up the amount of energy that is produced from solar photovoltaics, we need people that have the technical abilities to contribute to that. That’s the idea behind that.
Daniel Raimi: That’s great. This idea of capacity is so front and center in the United States right now as the government tries to implement the Inflation Reduction Act and other recent laws. It’s a top issue here with the US Department of Energy, as they’re trying to implement really ambitious policies, and having capacity at the staff level to do that is a really big challenge when they’re trying to do such ambitious things.
Silvia Pianta: Exactly. This is particularly important now that we’re in this very fast transformation phase—sometimes it’s hard to figure out which capacities you need and what’s the best way to achieve a given goal that you have set. These are interesting times.
Daniel Raimi: The third item that you and your coauthor identify as affecting climate policy is this idea of public opinion. This is probably fairly intuitive for our audience, but can you just talk through it a little bit more and help us understand how public opinion shapes policymaking on climate?
Silvia Pianta: Of course. This is, as you said, pretty intuitive, but, as we know, especially in democracies—but not only—governments have to do things that the public opinion agrees on. In the case of democracies, governments need to be elected, and politicians hope to be reelected, so public opinion is an important determinant of the policies that a government decides to implement and considers feasible.
But this is true if we think about, for instance, air pollution in other political systems. For instance, in China, there are different papers showing that public opinion, and in particular, public opposition to very high pollution levels, were an important factor shaping changes in policy in China. This is particularly important and particularly a factor in the, let’s say, standard democracies. But it’s important across the world, and having a supportive public opinion is really important. If we want to be optimistic here, if we look at survey data across the globe measuring support for climate policies or climate change concern over time, we see that public opinion is more and more supportive.
This is also evident if we look at some Western countries or European countries that have governments that also now include the Green Party. We have some, let’s say, aspects that suggest that governments will implement more ambitious policies, because they think this is more convenient also in electoral terms. The idea is that supportive public opinion can be an enabler of more ambitious climate policies.
Daniel Raimi: That makes a lot of sense. I think we’ve got our three key pieces outlined, and now it’s time to get wonky and talk about how these elements can actually get incorporated into the models that experts use to project future energy systems and climate outcomes under different policies. Can you give us an example of how modelers might actually incorporate them into existing integrated assessment modeling frameworks? We might use this term “IAM”—IAM means integrated assessment modeling framework. Can you start us off by telling us what an IAM is, and then how modelers can start to incorporate these political dynamics into those models?
Silvia Pianta: Sure. I am happy to talk about that, because this is something that I learned in recent years, because I was not a modeler, as I probably said in the beginning. Sometimes it’s easier to teach something that you learned more recently rather than something that’s very familiar to you, so hopefully I can be relatively clear. These models are simplified mathematical models of reality, and they model at the same time the economic system, the energy system, the climate system, and the land system. The way they work is, usually, one can make different assumptions on how population will grow, how economies will grow, and then you can input these assumptions into the models. You can also make some assumptions about some possible policies that are implemented by governments. Then, these models build some scenarios telling you, with these assumptions, what will happen. For instance, we can use a very simple example. A model can be used to see what happens if the whole planet implements a carbon price of a given amount in a given year. You can feed this information in the model, and the model can tell you what will happen to emissions over time.
These models are long-term models. They model what happens usually until the end of the century, but you can use them in different ways, and they’re very salient in the public discourse, and they’re very central in IPCC reports—the reports of the Intergovernmental Panel on Climate Change, which puts together all the existing literature on climate change that we have.
To give you an idea of how policy-relevant these models and their scenarios are, I think many of us have heard of this net-zero commitment that many countries have made. Many countries—the European Union, the United States, China—announced that they want to become carbon neutral in a given year in the future. This idea of being carbon neutral in the middle of the century was probably inspired by one sentence in the IPCC report that was based on a review of all different scenarios built by integrated assessment models.
Most of these scenarios were saying that, if we want to reach the long-term goal of the Paris Agreement of keeping global warming well below 2°C compared to preindustrial levels, or even possibly 1.5°C, we need to become carbon neutral in the middle of the century. This review of the literature actually had an impact on policy announcements. This is to give you an idea of why it’s important to engage with integrated assessment models.
But, at the same time, these integrated assessment models were built originally for a different purpose—they were not initially built to incorporate social and political dynamics. So, what we see often in existing model scenarios is that these model scenarios are built to find the most cost-efficient solution. Often there is more mitigation where it’s cheaper to mitigate, and often it’s cheaper to mitigate in developing countries. But if we look at what is happening now, actually, most mitigation efforts are being made by developed countries, which are also, by the way, responsible for very high shares of historical emissions.
There’s also an ethical discussion of what is more fair to do: Should we, Europe and the United States, who have been historically responsible for more emissions, do more? But besides any normative consideration, it’s also not very likely that developing countries will implement more ambitious mitigation than developed countries.
By observing what was happening in existing scenarios, we were thinking that perhaps by also incorporating these social and political dynamics, we can build some scenarios that are a bit more plausible in terms of, for instance, having more mitigation in Europe and the United States, where we are likely to have more supportive public opinion, because perhaps our primary needs have already been satisfied and public opinion is more concerned about the environment rather than reducing hunger, for instance, or having basic well-being levels. But also in Europe and the United States, we are more likely to have the governance capacity or the technical capacity.
Although I think it’s important to say that it’s not really clear that people in developed countries care more about the environment—this is not true at all. It’s also not absolutely true that developing countries have less capacity than developing countries to implement policy.
The idea is to just see what empirical data tell us about what’s happening in the world and be able to capture these differences across different countries and build some scenarios that incorporate these social and political dynamics a bit more by using existing empirical data. To go a bit more to the question that you asked, how can we actually incorporate these factors in existing models, and what can we do in these models?
There are some functions—there are some ways to model emissions reductions, for instance. What we can do is make sure that in the model emissions are not only shaped by, for instance, economic and technical factors, but you can also include in the functions defining how emissions trends will go. You can make sure that emissions are also shaped by these other social and political factors. You can either constrain emissions or enable emissions reductions in different ways in countries that have more or less capacity, more or less supportive public opinion, or more or less carbon lock-in.
Daniel Raimi: That’s great. To follow up and ask a very practical question about how these ideas get operationalized, is the idea that, in the model, different countries or different regions would basically have different coefficients or different quantitative levels of public opinion, and then those quantitative levels of public opinion would shape the way that the model projects future emissions reductions in those countries or different energy mixes in those countries? Is that the basic idea?
Silvia Pianta: Exactly. That’s the idea. The idea is to use existing empirical data to look at the relationship between, for instance, lock-in, capacity, and public opinion on the one end and emissions reductions or policy decisions on the other end. Then, you can incorporate into the models the coefficients that you derive from empirical analysis. You can do so especially when you also have some projections of these political drivers. If we have some projections of lock-in, some projections of public opinion, or some projections of capacity, and these projections exist—for instance, there are existing governance capacity projections that can be used and we are, with our colleagues, already using them. We can use these projections together with projections of GDP and population to define future emission pathways, not only based on these economic and technical drivers and population, but also based on governance capacity, for instance.
Daniel Raimi: That’s great. One last question on this—I imagine our audience is imagining how complex this could potentially get—I’m wondering about a different layer of complexity, which is that in the real world, public opinion, I’m sure, interacts with emissions lock-in and with capacity, and these things don’t remain static over time. For example, if there’s a strong lobbying presence from the coal sector in a country, maybe they are able to influence public opinion through advertising or other ways. In your imagination for how these elements can be integrated into IAMs, would they ideally be interacting with each other over time, or would they remain isolated in the model and just have their own coefficients that don’t interact with each other?
Silvia Pianta: That’s a great question. We are discussing this a lot, and our approach at the moment is to start first with very simple efforts where they do not interact with each other. We know that, in reality, they interact with each other a lot. Since there is really very little incorporation of these dynamics into existing scenarios, we think it’s already a good step of improvement to try to incorporate these factors in an exogenous way. This is the first step, but for sure, you can also, as one can say, endogenize these things.
For instance, if you think about lock-in, in the model over time you will have different shares of the economy that are dependent on the fossil sector, and you can also make this change over time. This will also change over time. The impact of lock-in on emissions reduction or policy will reduce over time. Even later, you can also think about making this factor interact with one another.
The problem is that these models are already very complex, and it’s really hard to understand what is driving what. We think it’s a bit more transparent to start with an exogenous approach so that it’s a bit easier to understand what is happening there. Because, once you endogenize everything, it’s a bit hard to understand what’s happening, and your outcome probably is very dependent on your assumptions.
Daniel Raimi: Right. That’s so interesting. And for the non-economist listeners out there, that word “endogenize,” I think, is mostly referring to the idea that these factors would interact with each other within the modeling framework rather than have their own isolated pathways.
One last question, Silvia, which you discuss in the paper, is the idea of regional differences. In some, or maybe even many integrated assessment models, there are large geographical groupings. For example, the entire Organisation for Economic Cooperation and Development might be grouped together. That’s the United States and Canada and the European Union and Japan and Australia. But we know that these countries have very different political dynamics, very different capacities, and different potentials for lock-ins. Can you talk a little bit about the importance of regional disaggregation in the modeling frameworks that you are thinking about?
Silvia Pianta: That’s a key point, actually. This was also a bit puzzling for Elina and me at the beginning, because, as empirical researchers, we try to zoom in as much as possible. These models have these very aggregated, very simplified ways of modeling reality, which is, of course, something that you need for these complex modeling efforts. But then, as you said, you put together often very different regions of the world, and then you forget, or you cannot incorporate, some important heterogeneities within regions. This is very evident. Of course, just think about the United States and Europe—they’re very different. But also within Europe, if you think about Poland, which is a country that is very dependent on coal; and Sweden, for instance, which has almost two thirds of its electricity being produced by renewables. These countries are very different, and they’re very different in terms of public opinion, lock-in, and capacity. One thing that we included in our paper was to try to push a bit more for even more disaggregation—a more disaggregated disaggregation, let’s say.
This has already been done a bit. For instance, in the latest IPCC report, not long ago, most integrated assessment models—and especially these studies that try to put together outputs from different models—were dividing the world into five macro regions. These are very big, but lately, at least in the last IPCC report, the standard was to use 10 regions, and this was already an improvement. But we would say the more disaggregation, the better, because there are very important differences that we cannot incorporate when we have these very big regions.
One thing I wanted to add on the former question, when you were asking how to incorporate these dynamics into integrated assessment models—I also would like to add that there are different groups trying to work on this, and there’s also, for instance, Wei Peng in the United States who’s a modeler as her background, and she’s working a lot on the same topic. I think this is a very interesting field of research, because there are people from different backgrounds, and we try to talk to each other, and this is very interesting. We learn a lot, but at the beginning, it’s really hard, because sometimes it looks like we speak different languages. This is true also with my colleagues who are modelers here. At the beginning, it took some time to find a common vocabulary, because sometimes you use the same term for two different things, or the other way around. I really love interdisciplinary collaborations, and this is a field of research where you can do that more, but that’s also a bit challenging.
Daniel Raimi: It’s such an interesting topic. We actually had Wei on the show a few months ago talking about exactly that, and we also had Fran Moore on the show, who’s done some similar work. It’s definitely a really exciting area of research, and hopefully our listeners are getting a flavor for it, and I’m hoping that they will understand how valuable this could be to add these elements into our models, and maybe even get inspired to work on them themselves.
Silvia, this has been a great conversation, really fascinating. We’d love to end the show by asking you the same question we ask all of our guests, which is to recommend something that’s on the top of your literal or metaphorical reading stack—something that you’ve read or watched or heard that you think is really great and that you think our listeners would enjoy.
Silvia Pianta: I think I would recommend reading On Time and Water by Andri Magnason. I hope I pronounced his surname right. He’s an Icelandic writer, and he’s also a great science communicator. There’s also a TED Talk talking about the same topics of the book, and this is the best book I’ve read so far on climate change, I would say. It’s a combination of the story of his family and the relationship of his family with nature—and with snow and ice in particular—and a combination of that with insights from scientific evidence. In my case, it was a really good combination of telling us things that we don’t know, but also making us feel what climate change means for us as people and communities. I really recommend this book.
Daniel Raimi: That’s great. That looks so fascinating, On Time and Water. Great. We will have a link to that in our show notes so people can check it out.
Well, one more time, Silvia Pianta from the European Institute on Economics and the Environment, thank you so much for coming out to the show today and sharing your work with us. It’s been a fascinating conversation.
Silvia Pianta: Thank you so much for inviting me. It’s been nice to talk to you.
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