Volts podcast: Fran Moore on how to represent social change in climate models

In this episode, UC Davis assistant professor Fran Moore discusses her research team’s effort to construct a climate model that includes (instead of ignores) effects from the interplay of social conditions and policy change.

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David Roberts

One of my long-time gripes about the climate-economic models that outfits like the IPCC produce is that they ignore politics. More broadly, they ignore social change and the way it can both drive and be driven by technology and climate impacts.

This isn’t difficult to explain — unlike technology costs, biophysical feedbacks, and other easily quantifiable variables, the dynamics of social change seem fuzzy and qualitative, too soft and poorly understood to include in a quantitative model. Consequently, those dynamics have been treated as “exogenous” to models. Modelers simply determine those values, feed in a set level of policy change, and the models react. Parameters internal to the model can not affect policy and be affected by it in turn; models do not capture socio-physical and socio-economic feedback loops.

But we know those feedback loops exist. We know that falling costs of technology can shift public sentiment which can lead to policy which can further reduce the costs of technology. All kinds of loops like that exist, among and between climate, technology, and human social variables. Leaving them out entirely can produce misleading results.

At long last, a new research paper has tackled this problem head-on. Fran Moore, an assistant professor at UC Davis working at the intersection of climate science and economics, took a stab at it in a recent Nature paper, “Determinants of emissions pathways in the coupled climate–social system.” Moore, along with several co-authors, attempted to construct a climate model that includes social feedback loops, to help determine what kinds of social conditions produce policy change and how policy change helps change social conditions.

I am fascinated by this effort and by the larger questions of how to integrate social-science dynamics into climate analysis, so I was eager to talk to Moore about how she constructed her model, what kinds of data she drew on, and how she views the dangers and opportunities of quantifying social variables.

Without further ado, Fran Moore, welcome to Volts. Thanks so much for coming.

Fran Moore:

Thanks for having me.

David Roberts: 

In climate modeling, we put in values for what we think is going to happen to the price and then watch the model play out. I've been looking at climate modeling my whole career, and I've always thought that what's actually going to determine the outcomes are our social and political processes, which are not in the model. So really, the models amount to a wild guess, we're all wallowing in uncertainty, and we just have to live with it. 

You confronted the same situation, and being a much more stalwart and ambitious person than I, said, “I'm going to try to get the social and political stuff into the model to make the model better.” 

In conventional climate modeling, these sociopolitical variables are treated as exogenous. What does it mean for them to be exogenous to the model?

Fran Moore:

Exogenous means that they come in from outside, so as the researcher using the model, you have to specify that. In particular, when we're thinking about climate change, those really important exogenous variables are the ambition of climate policy, whether that be in terms of trajectories of carbon prices, or target for temperature, or target for emissions levels. Typically, those are things that you set and they appear exogenously in two ways.

One is, in climate modeling, you take some radiative forcing trajectory, or some greenhouse gas concentration, and you ask, what does the climate system do in response to that? But it also comes up in other types of modeling, like energy modeling, where these policies appear exogenously as constraints on the model. So you're asking an energy model to tell you what's the least cost pathway for getting to a 2° temperature target, or to a certain carbon concentration limit in the atmosphere. Those are both versions of exogenous inputs of policy into climate-relevant modeling.

David Roberts: 

The upshot is that the modeler is basically specifying the trajectory of policy and then asking the model: given that, what will happen? What it means to make it endogenous, then, is allowing social and political factors to be affected by other variables and to affect them in return inside the model. What does it look like for something like this to be endogenous? What does that mean to us?

Fran Moore:

The way it works in our model is that climate policy becomes endogenous. We don't specify what it does; it arises from modeling of more fundamental social-political processes that we think are going to drive or enable climate policy as it might play out over the future. By taking that step back we see this policy not just as something that we're going to specify and ask what happens, but actually something that emerges from the system itself, that comes out of a model’s structure and parameterization.

David Roberts:

On one hand, that seems like exactly what we want: let’s specify some initial conditions, then ask the model what people will do on policy later. On the other hand, intuitively, it sounds impossible, like trying to predict the future. 

When I think about social and political forces and variables, there are an infinite number of ways to conceive of them. Of all the social and political forces you could imagine, how do you narrow down to something manageable? Which variables are you choosing?

Fran Moore:

Let me take a minute to say that it's actually not obvious that this is what you want to do. A lot of climate modeling has taken the view that the goal of this is to inform policy, and the goal of the modeling is to say to policymakers “if you do X then Y will happen and if you do Z then W will happen.” If that is the goal of your modeling exercise, then you don't want policy to emerge endogenously; you want to be able to specify some possible counterfactuals so you can take the results of your model and tell policymakers about just how bad climate change will be under these different cases. 

I see two main reasons why that is unsatisfactory as the only approach. One, scientifically, it seems unsatisfying, in that human decisions are the single most important determinant of how the climate system is going to evolve and if we just exclude them from our modeling, we don't really understand the system as a whole. 

But there's also a practical application, in that we're not just here trying to inform mitigation decisions. Increasingly, we're trying to help adaptation. And not being able to tell adaptation decision-makers about the probabilities of different emission trajectories when your single largest uncertainty is between different emissions pathways – that's really unsatisfying and not what we need for adaptation. We want to be able to put probability bounds over there in order to support much better adaptation decision-making. That was part of the motivation.

David Roberts: 

When you're choosing these variables, these feedback loops that you're trying to include in the model, where do you start? Where do you look? Is there existing literature or existing loops you can adapt and put in, or are you just starting with a blank piece of paper?

Fran Moore:

It was definitely a process. One of the starting points was the observation that we do want to be focused on feedback loops. This is a certain style of modeling, sometimes called system dynamics modeling, where you're focused on the coupling between different feedback loops because they tend to be really important in driving the dynamics of the system over long time periods. If you have reinforcing feedbacks, particularly if they're coupled to each other, you can get very complex, nonlinear behavior emerging from the model. We wanted to make sure that we were allowing for that, so we did have a focus on trying to identify the feedback loops. 

Essentially, an interdisciplinary team of people started brainstorming, based on our knowledge, what theories around psychology, social psychology, sociology, and political science might be relevant here. Then we did a literature review across different potential feedback loops, looking for evidence within a really diverse range of literatures about dynamics that might be relevant to the system, that we could take and incorporate into this model.

David Roberts: 

Give us an example of how a change in one thing might trigger a change in another thing that might trigger a change in policy.

Fran Moore:

One that we incorporate into our emissions or energy component is learning-by-doing feedback; it’s represented in a lot of energy-system models these days. This is a phenomenon where new technologies tend to be really expensive, but you tend to get cost reductions with increased deployment, so your technology gets cheaper, so it gets deployed more, so it gets cheaper, so it gets deployed more. That's a reinforcing feedback where there's quite a lot of evidence across different energy technologies about how large that effect tends to be. 

Some of the ones where the evidence is more qualitative or perhaps more debatable would be things like, we have a feedback from policy change to public opinion. This is the idea of the normative force of law or expressive force of law, which is described in some legal literature. It's the idea that policy change itself can signal to people what is desirable behavior or desirable outcomes, so you can get this reinforcing feedback where you get some change in the law that later drives public opinion in that direction because it's signaling something. That's the kind of feedback that we allow for in the model.

David Roberts: 

The learning curves are a socioeconomic process, but there's tons of data on them; they’re very well-understood and well-modeled and quantified. I can imagine getting those in the model relatively smoothly. But something like the extent to which passing a policy serves as a social proof that then shifts public opinion, which then makes the next policy slightly more likely – I can conceptualize that loop easily, I understand what it means on a qualitative level, but how do you begin to quantify that? What are the data sources that would even feed into that?

Fran Moore:

This is an issue that we ran into in designing the model, is that a lot of the evidence here is coming from more qualitative disciplines like legal literature and political science literature. That doesn't mean it's not evidence; in some cases, we have quite rich case studies showing some of these feedbacks in operation. But it does make it challenging when you're trying to take that and put an equation on it.

One thing is that we allowed for a lot of uncertainty. In our final set of runs, we sample over a lot of uncertain parameters in the model and we try and say, given the fact that we don't know a lot about this particular parameter that describes the strength of the feedback, or even the existence of this feedback, what can we say probabilistically about where emissions might go?

The other thing that we do is a hind-cost exercise to jointly constrain these parameters. Even though we don't have data that is allowing us to say “this feedback in particular,” we can take a subset of the model – in this case, I think it was our opinion, policy, adoption, and cognition modules – and we can start it in the past. I think 2010 was when we first had data for distribution of public opinion as well as carbon pricing. Then we can run the model forward using, again, sampling over a very large set of the parameter space, and look at how well that evolution of opinion and that evolution of policy actually matched what happened over 10 years. Based on the match under different parameter combinations, we can probabilistically say “this set of parameter combinations is more likely true than this set of parameter combinations” just because it seems like it generates a better match in the model over the last 10 years. 

We do two versions of that for different parts of the model, these hind-cost parameter-constraint exercises, and that's primarily how our empirical evidence comes into the model. It would be great if we could use other data from other fields to constrain some of these parameters more precisely, but for some of these ideas, that doesn't exist at the moment.

David Roberts: 

One of the things that's done with climate physical models to test them out is, as you say, backcast – meaning if we went back in time and used this model, would it accurately predict what actually happened? Do you think a model like this, with social features, some of which are fuzzier than others, could ever accurately backcast? What did you find when you backcasted? Are you comfortable that you have a set of feedback loops now that at least accurately captured the last 10 years?

Fran Moore:

It is tricky in that some of the feedback loops play out. Ideally, we would have much better historical data on some of these social measures that allow us to go back much further. Because we only have data over about 10 years, and it might even be less than that, we're able to say that over this relatively short time period, the model seems like it’s not going completely crazy. But part of the goal of incorporating feedback is so that you have the potential for things like tipping points and things like that, so you don't want to over-constrain.

Sometimes you see critiques of energy models that they over-constrain in order to precisely match historically what's happened; but if some of those constraints can change in the future, and we're projecting out a long way here, then you want to allow for that to happen too. So it's a balance between those: what do we have evidence for, in a broad sense of the word, in terms of the structure of the feedback; as well as, can we use the evidence as we have it to constrain it? 

There's uncertainty, and we can get a wide range of behavior, but more or less it can track this gradual expansion of support for climate policy in OECD countries, which is our focus, as well as relatively slow increase in average carbon price, which is our measure of policy. Between those two things, they can constrain some of the parameters in the model, but not all of them.

David Roberts: 

When it comes to social and political stuff regarding climate change, by definition, there aren’t data sets going back a long way, because the issue itself is relatively new to society and politics – a couple of decades, which in modeling terms is a relatively short period of time. So what data do we have? Of all the kajillions of social and political factors you might imagine trying to get in here, do some have data available and some don't? Do you end up biasing yourself toward factors where there is data available just because there is data, and overlooking things that might be important because there is no data? 

Fran Moore:

On that latter question, because we built into the model the potential for the feedback loops where we don't necessarily have strong quantitative data, we're deliberately trying to avoid that problem. We're allowing those feedback loops to operate in probabilistically. We can't constrain them directly with data, we recognize that; there are only limited model outputs and parameters that we can actually match to stuff that’s measurable in a defensible way. But that doesn't mean that we don't include them in our model. We still allow for those effects to operate, because they're potentially really important in driving the dynamics, and just because we don't measure them super well doesn't mean that they shouldn't be in there. 

In terms of the exact data, what's important is to have repeated data on repeated measures over time because that helps you constrain these dynamic systems. That is tough, because you have opinion surveys that’ll be for one country in one year and a different country in a different year, or the question changes. 

The Yale Program on Climate Change Communication has something for the US, so originally we used that. Then we wanted to be representative of more countries, so we used a Pew question that has been asked repeatedly across about nine OECD countries since about 2010. I don't think they do it every year, but it gives us a time series of how opinion is shifting on average across these countries. 

We have two other measures. One is on policy, so that measures carbon pricing. That’s fairly straightforward – well, it’s kind of straightforward.

David Roberts: 

I was going to ask about it, because explicit carbon pricing policies are a very small fraction of total climate policy. Are you taking all those other climate policies and trying to translate them into an implicit carbon price, or are you just looking at explicit carbon pricing?

Fran Moore:

That is exactly the caveat I was about to add. Ideally, we would like to do exactly what you said, which is take all these climate policies around the world that have some associated shadow cost and that can be quantified in terms of effective carbon price at the margin, and add it all up. We just can't do it. That has not been done by other people. So instead, we use just explicit carbon pricing: cap-and-trade systems and carbon taxes, essentially. Those are pretty well-documented.

David Roberts: 

Don't you worry that you're only capturing a fraction of policy? How do you compensate for that?

Fran Moore:

The important question is, do we get the change right? We're not able to say more than that. We can say we seem like we're matching the rate of change of explicit carbon pricing. 

How that matches up to other measures that would include things like renewable portfolio standards and so on is not clear, but those get complicated too, because there’s a bunch of reasons why you might do them. It's not just carbon – things like CAFE standards have a climate component, but they've also got air pollution and fuel economy and saving people money at the gas tank and all those things. It'd be great if someone else wanted to come up with the shadow cost of all these different regulations; we would definitely use it.

David Roberts: 

One of the significant types of findings that might come out of a model like this is, given the current social-political trajectory, when might we see some sort of tipping point when the gradual build flops over into sudden action? Even physical tipping points are incredibly hard to pin down because of emergent effects that are difficult to predict from initial circumstances; my intuition is that social tipping points would be even more difficult to predict. Do you get any firm predictions about tipping points out of this model, and how confident are you?

Fran Moore:

Part of the reason we built a model like this was this idea that you can get these tipping-like, nonlinear behaviors in the social, political, and technical systems that produce emissions.

David Roberts: 

If you look back on history, most progress comes out of something like that punctuated equilibrium model – things were the same for a while, then whoosh, a bunch of stuff changes. Actual policy history does not do these gently, upwardly sloping lines that you see in models so often.

Fran Moore:

The first step in trying to understand that is to actually have a model that can generate those. Our model can definitely do that. To some extent, it was designed to do that; we went around looking for these feedback loops and we coupled them all together, and that's inherently going to be a system that under certain conditions is going to give you this tipping-style behavior. 

The reason why some modeling communities don't like this type of modeling is exactly that reason, that you allow for this complex, rapidly changing, accelerating behavior under certain conditions. It’s not necessarily super well constrained what the future looks like, because you're allowing for rapid changes in ways that are going to produce futures that maybe we can't really imagine right now. When we look out we tend to extrapolate from the trajectory we're on, rather than accounting for the accelerating feedbacks that we're capturing here.

David Roberts: 

We don't necessarily know what public opinion will do in response to literally unprecedented conditions.

Fran Moore:

Yeah. So the goal is to try and draw on the theory that we have already in the social and political sciences and put them together.

David Roberts: 

Your paper mentions trying to learn from past episodes of rapid social change, previous tipping points. Can you pull durable lessons out of those past examples?

Fran Moore:

There are other strategies that might do a case study example of past social change. Here we're trying to abstract from that a little further and say, what are the underlying dynamics and more fundamental prophecies that revolve around things like social networks and information and political institutions and power? 

If you recognize the uncertainty, and that's what we're doing with our 100,000 runs, then the other thing you can do is query the model – to say, what combinations of parameters put us in a world where we get positive rapid transformation and what sets of parameters put us in a world that we don't? You can start to ask those types of questions. 

These models with tipping points are not necessarily fully predictive; that's not necessarily what they're trying to do, in the sense of “we're going to have a tipping point in 2042.” But they're still informative about the system, and they're still potentially informative to management of that system.

David Roberts: 

You're just constraining the field of possible outcomes. You don't have to get to a single prediction to be helpful.

Fran Moore:

That's really important. People are nervous of this style of models sometimes, because you can get rapid changes that maybe make us a little uncomfortable about making predictions like that.

David Roberts: 

Right, you don't want to bet on those things.

Fran Moore:

But we're actually able to constrain the set of, say, 2100 temperatures compared to looking at the range of representative concentration pathways and saying “well, it could be 8.5 and it could be 2.6 and we just can't put probabilities over those.” That is a huge range. We can really say “both those ends are pretty unlikely, and probably we’re somewhere in the middle.”

David Roberts: 

The balance of your model runs that try to capture sociopolitical processes end up with lower emissions than business as usual, which I take to mean that on balance, these social-political feedback loops are moving us in a positive direction. How do we know there won't be loops pushing in the other direction? One of the big things people are talking about these days is the looming possibility of eco-fascism, where climate impacts cause people to get into a lifeboat mentality and build walls and hoard the rest of their fossil fuels. You can imagine feedback loops pushing us in the wrong direction. How do you think about the possibility of negative loops?

Fran Moore:

If I was going to expand the model further, we would maybe pay more attention to those types of potential negative feedback loops, or balancing feedback loops. It’s fair to say we built a model to be tippy because we were looking for tipping points and we wanted to make sure we had the potential to capture them, and we definitely do. But thinking a bit more about what some of these balancing effects might be and how they might slow down that tippiness is a way we would want to expand the model.

The one you talked about is definitely one we thought about, this idea that with mitigation, you're trying to provide a global public good, and that’s difficult at the best of times. Maybe as things get worse or are perceived to be getting worse, it becomes more and more difficult to provide global public goods, and instead, maybe we would focus on much more local public goods, or no public goods at all, and switch more into an adaptation focus. That's definitely a dynamic that you could imagine playing out, and that could potentially have some effects on the model, depending exactly how it was parameterized.

The other important balancing feedback that we don't have in there at the moment is reaction against carbon pricing. That probably is important given that you can have higher energy prices, but you cannot have them quickly. If you're raising carbon prices very, very quickly, you're probably going to get negative reactions to that and public opinion that will slow that down. We could definitely incorporate more of that into the model.

David Roberts: 

It's on my mind these days, because I look around the world and it seems like reactionary backlash against progressive movement is very real.

Fran Moore:

We're clearly also no longer in a business-as-usual world. We have carbon policies in many countries, and we have accelerating reductions in the cost of energy technologies. What this model gets is spillover effects where you can drive down reduction in cost with just a little bit of policy. So if you do have reinforcing feedback loops, you don't necessarily need really fast climate policy to get some big reductions, potentially. 

By acknowledging that you can have accelerations in directions you're not necessarily focused on, it can show where there are positive places; and clearly, things are stuck in some places as well at the moment.

David Roberts: 

The social cost of carbon is an attempt to put a number on the total economic damages wrought by a ton of carbon emissions. It’s useful, for example, if you're going to make climate policy; you need to know on some level how much it hurts to emit a ton of carbon so you can calibrate your cost-benefit analysis and whatever else. In trying to capture all the damages, you are inevitably getting into difficult-to-quantify areas like the worth of a species, the value of intact ecosystems, the value of a human life. The decisions you make on these fuzzy variables have practical real-world effects insofar as they show up in the social cost of carbon, so it matters quite a bit how you quantify these things. 

There's been a lot of critique of the social cost of carbon lately on a couple of measures. One is that by the time you make all these value judgments, by the end, it's faux precision. The other general line of critique is that because certain things are so much easier to quantify than others, those are more likely to be incorporated in the social cost of carbon, and the things that are difficult to quantify tend to be on the damage side, so by restricting your vision to what you can quantify, you are undercounting the damages. 

I'd love to hear you talk a little bit about the social cost of carbon and how you balance this.

Fran Moore:

Your point about undercounting is definitely true. There are effects of climate change that probably we are always going to be unable to put dollar values on: things like effects on conflict risk, loss of cultural heritage, migration. 

In my head, it's always helpful to distinguish between the social cost of carbon – the number that we come up with that is legally defensible and can survive as it’s dragged through the courts, which is done as soon as the US government comes out with whatever number it's coming out with; we need to be able to go into court, to show defensively that this number came from sound scientific and economic processes that were transparent and other people can agree with them – and then the actual costs of climate change that are potentially and probably unboundedly large above that. 

Those two things are not the same thing. But we can do the best job we can at the former, getting it as comprehensive and up-to-date and with as sound science as we can. Why wouldn't we do that? We spend an awful lot of time and money documenting climate change impacts, and the only formal way in which those get into considerations of climate policy and US regulatory analysis is via something like the social cost of carbon. It seems somewhat crazy to me that we would do a lot of this work on documenting what climate change impacts are and not make that final step of actually trying to incorporate it into regulatory analysis, as and when we can.

David Roberts: 

Even given all the uncertainties and fuzziness, it's better to have a number than not have a number.

Fran Moore:

Everyone is very willing to give you numbers on how costly climate policy is going to be, and how many jobs it’s going to cost, and how much it’s going to raise energy prices. It seems pretty important to have on the other side of that some well-done accounting on what we’re getting for this. Those numbers don't have to just be in dollar terms, which is what the social cost of carbon does. But that is the language in which a lot of policy operates. You're fighting from a losing position if you're not able to provide that measure of the benefits of these kinds of policies.

As a legitimate critique of exactly how this modeling has been done over the last 30 or so years, it's definitely fair to say these models got stuck at a certain place, in particular in terms of representing what we know about climate change damages. They were really not where we needed them to be to have real confidence that they're telling us about what we know about climate change damages. 

But there's been a lot of work to fix that, some of which I've contributed to over the last 10 years. The US government is in the process of updating this number, and I think you'll see a lot of those benefits being reflected in the revised versions.

David Roberts: 

Every critique I've ever heard of it from climate scientists says it's too low. It makes me think that there might be some danger in having this too-low figure getting stuck in practice.

Fran Moore:

It's good to recognize that it misses stuff. But also, if we had a global carbon tax of $50 per ton, which is what the current number is, we would be in a totally different place than we are right now. Maybe it's low, but even if we just took what we're currently counting seriously as a guide to policy, we'd be in a really different place. 

It is not the fault of these models that we're not in that place. The models have been saying for a long time that the costs of climate change are real and are positive in the sense that we should be doing something about climate change. Then we get into debates around “is it high enough to justify 2°” or whatever, but that's not the place we're in right now in the policy sphere. 

David Roberts: 

Another critique is that a lot of the key variables that produce the social cost of carbon are, at root, value questions. The famous example here is discount rates – how much do we value future costs and benefits relative to present day costs and benefits? There's a long literature of people arguing over what the right discount rate is, and in the end, there's no empirical way to resolve that argument. Ultimately, you are making a value judgment about how much we value the future. And what you decide that figure is absolutely shapes, in a very fundamental way, the values that you end up with. 

Do you ever worry that we ought to be having that debate over values out in the open? In terms of values, do you worry that putting a precise number on it obscures the fact that there's a values debate at all?

Fran Moore:

As you might have gathered, I tend to take a more practical view of the matter rather than get into philosophical debates. When people say “my personal value judgment is this” that's fine, and you can plug that into the FTC models and calculate what that does to the FCC, but as an input into regulatory analysis, the way in which we carry out these values debates is through government, through political engagement. When the EPA and the interagency working group come up with the social cost of carbon, they are applying these discount rates, which do represent something about how we're going to value the future under various different epistemic arguments, and that's part of our democratic decision-making process. It’s not divorced from that. Just because there are values involved doesn't mean that it's not something that belongs in policy, because policy is a representation of our values. So I don't really see the tension there, in terms of how it's actually applied.

David Roberts: 

You wrote a recent comment in Nature with Zeke Hausfather that comes from a very different direction than your paper about the social determinants of climate change, but arrives at a very similar destination. Can you explain what that comment was about and the research it was describing?

Fran Moore:

This was an accompanying comment on a recent paper by Malte Meinshausen, which looked at what countries have pledged for their net zero commitments. In that paper, they add them all up, estimate what that does in terms of emissions, and show that if fully realized, these long-term pledges get us really close to that 2° Paris Agreement target.

David Roberts: 

That's a very big deal. It doesn’t seem like that news has really gotten out yet. 

Fran Moore:

I agree. When I give talks, I try to make sure to say that we're making progress. We're bending things. One thing economists often think about is that expectations are important; if businesses and investors and planners expect things to be going in a certain direction, then the capital allocations will flow accordingly. They bring themselves into actuality in some ways, although not fully, obviously. 

What we did in our comment on this paper, and I have to give Zeke the vast majority of the credit for this, was to pull together not just the current Meinshausen study but also a number of other papers, including my paper that we've been talking about, that have also tried to look at probabilities of temperature outcomes under different emission scenarios by 2100. 

There are a number of different approaches: some of those might just look at the effects of current policy; some might look at 2030 pledges; some are more fully probabilistic, like there's a recent study out of Resources for the Future that does some various expert elicitations combined with statistical modeling work to look at distributions of emissions and temperatures. Collectively, they do provide a much tighter temperature bound than if you were to just look at the range of, say, RCP scenarios.

David Roberts: 

When you gather all these models up and average them out, what range of temperature can we reasonably say we are headed toward now?

Fran Moore:

We find, and it matches what the other studies have found using very different methods, that we put a lot of probability math in this range between 2° and 3°. That can definitely go up, particularly on the high end, based on uncertainties in the climate system – so if we're more unlucky on carbon cycle feedbacks, or on what the climate sensitivity looks like, we could definitely be above 3°, even getting toward 4°. But the probability math right now is 2°-ish on the low end – maybe below that, depending on what happens with carbon capture, say – and then between 2° and 3°, essentially.

David Roberts: 

Ten or 20 years ago, 4° or 5° or 6°, even 8° were on the table. I don't know if there's common agreement on this, but I think once you're getting up above 4°, that's where you get into “does advanced human civilization persist?” type of questions, whereas between 2° and 3° is bad, but potentially non-catastrophic. 

There was an IPCC paper that talked about the difference between 1.5° and 2° in very helpful, clear terms; I feel like we need that same thing for the gradations between 2° and 3°, because that now looks like where we're going. How are we supposed to feel about this, Fran? Are we supposed to be optimistic? happy? still filled with dread? What does “between 2° and 3°” mean?

Fran Moore:

I think you have an appropriately nuanced and mixed set of feelings about this. The impacts we've seen so far, the extreme events – heat extremes, rainfall intensity extremes – that have even taken climate scientists by surprise are certainly enough, I think, to worry about at this range of 2° to 3°. But obviously, there would be an awful lot more to worry about if we saw we were getting up to 4° and 5° of warming by 2100. The fact that we can, with increasing confidence, start to rule out the really extreme rates of temperature increase is definitely good news. But there's plenty to worry about at this more moderate range of warming as well.

David Roberts: 

It’s such a complicated thing to explain to a public.

Fran Moore:

That's why things like the social cost of carbon can be really helpful. It's designed to think about the margin – for that additional ton of carbon dioxide, how bad is it? You don't have to say “climate change is a disaster” or “it’s solved” – we're always going to be at this margin of “should we do more?” The social cost of carbon can help you balance that, recognizing that it's uncertain and there's a lot missing from it. In those real-world cases, where we’re in the middle somewhere and we should probably do more – but how much more? – it does help you. 

The other point is that, given this increasing sense from various directions in the literature that we can narrow down this range of warming, that should be informing what our climate modeling looks like and what these climate impact studies are doing. We have a lot that look at RCP 8.5, which we think is probably quite unlikely now; we have a lot that look at lower levels of warming; and we need something more in-between if we're going to start providing serious advice to planners and governments about adaptation.

David Roberts: 

One of the big debates in climate science is how to treat what are called “tail risks” – ends of the spectrum where you have low probability but extremely high-impact possibilities. Martin Weitzman's work famously made the case that we’re misleading ourselves when we make policy based on the middle of the bell curve; we need to be making policy based on foreclosing these risks, because even if it's a small risk, the catastrophe would be so complete that in a sense, it’s worth almost anything to avoid it. 

In the context of that argument, it looks like our modeling is reducing those tails, at the very least. So how should we think about tail risks? Is the possibility of 4° or higher low enough at this point that I, as an average citizen, should breathe a sigh of relief? Or is it still high enough that it activates these Weitzman-y do-anything-to-avoid-it reactions? 

Fran Moore:

When I think about Weitzman’s writing on things like fat tails and the more catastrophic end of climate damages, the importance there is on the distribution of damages. That is both about emissions and what the climate system does, but in my looking at these systems and what drives damages and models like the social cost of carbon, what's even more important is, what does temperature do to human society and the things we care about? The tails on that, I think, are really large. That is not super well-constrained. You can get quite heavy probability mass at some quite large damages at moderate levels of warming under plausible scenarios of just how sensitive human systems are to changes in climate. 

Even if we think we're narrowing in the temperature range, that’s not giving us a huge amount of confidence in that we’re not necessarily narrowing in on constraining the damages, because the uncertainty bounds on those are still really enormous – for some people. These are not distributed equitably. There are going to be catastrophic consequences of this level of warming for some communities, perhaps many communities. So when we look at that distribution, we don't treat it as just a central estimate. We do look at a full uncertainty and that uncertainty is large. That right tail does pull out the mean. 

The question of how exactly that translates into policy is, again, a values question. How much you weight these unlikely but very bad outcomes is essentially a question of risk aversion and preferences over risk, in the same way that the discount rate is about preferences over time. That's something that can operate through the political system as well. Just trying to keep that uncertainty and that full distribution in the regulatory analysis as far as possible is good, although those processes do tend to be relatively adverse to uncertainty.

David Roberts: 

To summarize: the work we've done so far to address climate change and the work we’ve done so far in climate modeling has somewhat narrowed the possible range of outcomes, so there's some comfort to take in that; but on the flip side, the remaining uncertainty about damages to society and at least the possibility of truly large and catastrophic damage to society are still very much there, so there's no reason to reduce our sense of urgency about policy. Is that fair?

Fran Moore:

Yes, I think that's true. If you look at even just the social cost of carbon we have right now, we're so far short of it. Even that by itself, you don't even need to get to a fat tail; we should definitely be doing more than we're doing right now on a purely cost-benefit basis. We're definitely in a place where we're going to get benefits by doing more. Once we do a lot more, we can argue about that margin, but right now, the net benefits are definitely in terms of more ambition.

David Roberts: 

Well, that seems like a great place to close. Thanks so much for coming on. And thanks for all your research.

Fran Moore:

Thanks so much for the great questions.

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