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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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
