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Many climate policies adopt improving equity as a key objective. A key challenge is that policies often conceive of equity in terms of individuals but introduce strategies that focus on spatially coarse administrative areas. For example, the Justice40 Initiative in the United States requires 518 diverse federal programs to prioritize funds for “disadvantaged” census tracts. This strategy is largely untested and contrasts with the federal government’s definition of equity as the “consistent and systematic fair, just and impartial treatment of all individuals (Executive Office of the President, Federal Register, 2021).” How well does the Justice40 approach improve equity in climate adaptation outcomes acrossindividuals? We analyze this question using a case study of a municipality that faces repetitive flooding and struggles to effectively manage these risks due to limited resources and public investment. We find that the way the Federal Emergency Management Agency implements the Justice40 Initiative can be an obstacle to promoting equity in household flood-risk outcomes. For example, in this case study, ensuring the majority of benefits accrue in “Justice40 Communities” does not reduce risk for the most burdened households, does not reduce risk-burden inequality, and produces net costs. In contrast, we design simple funding rules based on household risk burden that cost-effectively target the most burdened households, reduce risk-burden inequality, and accrue large net benefits. Our findings suggest that “disadvantaged community” indicators defined at coarse spatial scales face the risk of poorly capturing many climate risks and can be ineffective for meeting equity promises about climate-related investments.
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Agent‐based Modeling to Evaluate Human–Environment Interactions in Community Flood Risk Mitigation
This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.
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- PAR ID:
- 10313253
- Date Published:
- Journal Name:
- Risk Analysis
- ISSN:
- 0272-4332
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/risa.13854
