Developing the ‘machinery’ to solve wicked planetary problems
Solomon Hsiang combines data science, natural science, and social science to answer key policy questions about climate change and other fundamentally global problems.
As told to Josie Garthwaite
I knew I wanted to work on environmental issues when I started college, but I wasn’t sure how.
I took a class on preserving the Amazon rainforest, and understood for the first time that the forest was disappearing partly because there were people on the ground – families – who had needs that weren’t being met.
At that point, I started to realize that I had to learn more about the politics, economics, and policies that were shaping the choices being made by individuals. Before that, as a kid, I had thought that we had environmental issues because other people just didn’t care “enough.”
I decided to major in ocean and atmospheric physics, because I absolutely loved thinking about the entire planet as a single analytical problem. When I read the sci-fi book Dune, I was obsessed with the “planetologist” character, Kynes. I wished that was a real job I could get. That wish has kind of come true.
Climate change entered my field of view as a real concern later in college. I was in my apartment, studying my notes from a climate dynamics class taught by the pioneering climate scientist Peter Stone. I remember looking at warming projections from the Intergovernmental Panel on Climate Change (IPCC), and I converted the temperature from Celsius for the first time: more than 5 degrees Fahrenheit. My stomach dropped. Up until then, it had all just been a math problem. When I converted the units to the ones I used on a daily basis, I realized those were big numbers, and that this was a big problem.
Later, when I was starting grad school for a PhD in sustainable development at Columbia, a blockbuster report about the economic costs of climate change came out – the Stern Review. It was all we talked about in my classes, and it was fascinating. But when I opened it up to read it, I recognized there was no real evidence to back up any of the costs they were calculating.
I realized this was a huge gap in our understanding, and felt that we should try to systematically evaluate what the actual impacts would look like. At the time, I didn’t know how I was going to do that, but I thought we should break the problem down by sector and region, and then try to measure something with real data. I guess everything flowed from there.
In my lab, the Global Policy Laboratory, we bring together data science, social science, and natural science to answer policy questions about problems that are fundamentally global in nature. One country is not going to solve them on their own.
We have worked on a lot of different topics, ranging from how do you stop elephant poaching in Africa to the effects of the climate on people. These are wicked hard problems and, in many cases, we don’t even have the machinery to think about them very clearly. One reason is we don’t have a functional global government, so many issues just fall between the cracks of nation states.
This type of policy work is much less about critiquing existing policies, and more about helping people think through the policy questions of tomorrow before we get there. You’re trying to meet a moving train. That’s part of the challenge, but also what’s exciting.
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