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.

(PDF transcript)

(Active transcript)

Text transcript:

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.