The latest climate science makes clear that climate change will have vastly different impacts depending on where you live. Areas around the poles, where few people live now because it’s too cold, will fare the best. Areas around the equator, where it’s already very hot, will fare the worst. But with 75 percent of the world’s population and 91 percent of world GDP concentrated in only 10 percent of the world’s land—some of which is expected to be severely impacted by climate change—could reshuffling population and economic activity be an important adaptation strategy?
Studying the economic impacts of climate change must examine how people, goods, capital, technology, and other key factors, move across space, argues a new study that explores the importance of modelling the economic costs of climate change and climate adaptation.
“Robust spatial modelling is crucial to accurately measuring climate change impacts and adaptation mechanisms,” says co-author Esteban Rossi-Hansberg, Glen A. Lloyd Distinguished Service Professor, Kenneth C. Griffin Department of Economics at the University of Chicago. “Spatial integrated assessment models can combine everything we know about the workings of the global economy and its relation to climate, making them a key tool to assess the local and global costs of climate change and the policies that could be used to help us adapt.”
The study, by Rossi-Hansberg and Klaus Desmet at Southern Methodist University, surveys the literature and offers key considerations for future modelling of the impacts of climate change. In an ideal scenario, Rossi-Hansberg and Desmet say that climate assessment models should have three key characteristics: be global, have highly-localized geographic information, and show change over time. A global approach to modelling the economic impacts of climate change is crucial because all local emissions generate changes in global temperatures, which in turn affect local temperatures. Highly-localized geographic models allow for the understanding of heterogeneous impacts of climate change across regions and for the possibility of testing adaptation approaches that would vary in effectiveness across space. Lastly, dynamic models that show change over time are important because the impact of today’s emissions will be experienced in the future.
The authors provide several ways the models could be used to better understand different adaptation mechanisms and policies. For example, they discuss trade, migration, and innovation as methods for adapting to climate change that can impact costs, local and global economies, and the need for policies to facilitate such changes. Similarly, modeling economies with multiple sectors and policies such as clean energy, carbon taxes, and electric transportation can provide a better sense of emissions intensity in the future.
“Although much progress has been made in developing these sophisticated models over the last decade, we hope to see new studies incorporating more impacts of climate change, refining parameters, including new adaptation mechanisms, and designing policies more fitting for the needs of specific locations,” says Rossi-Hansberg.