Modeling improvements needed to provide more reliable guidance to decision-makers, finds MIT-led study
Mark Dwortzan | MIT Center for Sustainability Science and Strategy
Amplified by climate change, January’s wildfires in Los Angeles destroyed more than 15,000 buildings and displaced more than 100,000 people. The weather forecasting service AccuWeather estimates that total economic losses incurred by the fires will exceed $250 billion. As wildfires, droughts, floods and other extreme weather events become more frequent and intense across the globe amid a warming climate, such losses are only expected to grow in coming decades. But just how much they will grow is a matter of scientific debate.
Today’s projections of the impacts of climate change on the global economy vary widely depending on the methods used to model the Earth’s interconnected physical and societal systems. In two recent studies, estimates of the global gross domestic product (GDP) loss due to the same level of global warming ranged from 3 percent to 60 percent. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report suggests that such large discrepancies must be reconciled to provide more robust guidance for decision-makers who seek to reduce the economic and human costs of climate change.
To that end, a team of researchers at the MIT Center for Sustainability Science and Strategy (CS3), MIT Energy Initiative (MITEI) and Electric Power Research Institute (EPRI) is working to better understand and more precisely model the pathways by which climate change impacts the economy. In a study appearing in Nature Climate Change, the team compares three common methods for estimating economic impacts of climate change: structural modeling, in which the climate change-economy relationship is specified for different impact categories ranging from temperature-related mortality to agricultural productivity; statistical modeling, in which historical data and statistical methods are used to directly estimate how the global economy evolves with climate change; and meta-analysis, which derives the climate change-economy relationship from structural and statistical estimates.
The researchers find that overall, statistical modeling produces significantly higher global GDP loss estimates than structural modeling. Based on these results, they call for further studies to pinpoint the mechanisms that lead to this discrepancy, and to determine how to incorporate additional climate impacts, and adaptive responses, into models and analyses. These include the economic consequences of climate impacts on ecosystem services and recreation, species loss and biodiversity, crime and conflict, and mass migration.
“There is a clear need for better understanding of the mechanisms behind widely divergent estimates of climate-driven global economic losses, and more comprehensive representation of their drivers in estimation methods,” says MIT CS3 and MITEI Principal Research Scientist Jennifer Morris, the study’s lead author. “These improvements would produce a more reliable range of estimates that decision-makers could use to guide their efforts to mitigate and adapt to climate change.”
