A computational tool developed by researchers at the MIT Joint Program in the Science and Policy of Global Change identifies specific counties within the United States that are particularly vulnerable to economic distress resulting from the shift from fossil fuels to low-carbon energy sources.
By combining county-level data on employment in fossil fuel industries (oil, natural gas, and coal) with data on residents below the poverty level, the tool identifies locations with a high risk of economic hardship resulting from the transition. It turns out that many of these high-risk counties are located in the south central United States, with a large concentration in the lower portions of the Mississippi River.
The computational tool, which the researchers call the Risk Screening System from Environmental and Socioeconomic Stressors (pressure), almost instantly displays these risk groups on an easy-to-read visual map, revealing those counties that would benefit most from targeted green job retraining programmes.
rely on data characterizing land, water and energy systems; Biodiversity; demographics. environmental justice; and transportation networks, the STRESS platform allows users to assess multiple, evolving, and compounding risks within a US geographic area from the national to the county level. Because of its comprehensiveness and accuracy, this scan-level visualization tool can identify ‘hot spots’ of risks that can later be examined in more detail. Decision makers can then plan targeted interventions to enhance resilience to site-specific physical and economic risks.
The platform and its applications are highlighted in a new study in the journal limits in climate.
“As risks to natural and managed resources—and the economies that depend on them—become more complex, interconnected, and complex amid rapid environmental and societal changes, they require more and more human and computer resources to understand and act on them,” MIT Joint Program Deputy Director C Adam Schlosser, lead author of the study. “The STRESS platform provides decision makers with an efficient way to consolidate and analyze data on those risks that are most important to them, identify ‘hotspots’ of compounding risks, and design interventions to reduce those risks.”
In one demonstration of the capabilities of the STRESS platform, the study shows that national and global actions to reduce greenhouse gas emissions can simultaneously reduce risks to land, water, and air quality in the upper Mississippi River basin while increasing economic risks in the lower basin, where poverty and unemployment are non-existent. They really fit. In another demo, I found the platform on “hot spots” where flood risks, poverty, and non-white populations coincide.
The risk screening platform builds on an emerging system called Multisector Dynamics (MSD), which seeks to understand and model compound risks and potential tipping points across interconnected natural and human systems. Tipping points occur when these systems cannot withstand multiple, co-evolving stresses, such as extreme events, population growth, land degradation, lack of safe water, air pollution, aging infrastructure, and increasing human demand.
MSD researchers use observations and computer models to identify key preliminary indicators of these tipping points, providing decision makers with critical information that can be applied to mitigate risks and enhance resilience in natural and managed resources. The MIT joint program has been working since 2018 to develop and use MSD expertise and modeling tools to explore compounding risks and potential tipping points in select regions of the United States.
Current STRESS platform data includes more than 100 county-level risk metrics, but data collection is ongoing. MIT joint program researchers continue to develop the STRESS platform as an “open science tool” that welcomes input from academics, researchers, industry, and the general public.
C. Adam Schlosser et al., Assessing Compound Risks Across Multiple Systems and Sectors: A Social and Environmental Systems Risk Screening Approach, limits in climate (2023). DOI: 10.3389/fclim.2023.1100600
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