Assessing the Influence of Hazard Mitigation Planning on Disaster Recovery

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

This research project fills a critical gap in knowledge about the influence of pre-disaster hazard mitigation planning on post-disaster recovery, and reductions in long-term risk from natural hazards like hurricanes and floods. Common sense holds that mitigation planning done before a major disaster should help reduce disaster impacts and shape recovery, including whether decision-makers leverage the window of opportunity that opens after a disaster to avoid repeating decisions from the past such as concentrating development in floodplains that increase long-term risk. This project overcomes two barriers that have previously limited research on linkages between mitigation planning and recovery decision-making: 1) lack of comprehensive datasets on hazard mitigation planning and 2) datasets that do exist do not cover areas hit by major disasters. The research team's prior work in North Carolina and with Hurricanes Matthew and Florence provide unique opportunities to resolve both barriers by first collecting data on post-disaster recovery decision making, outputs like planning documents, and outcomes like decisions to steer development out of known hazardous locations. And second, coupling these recovery data with the researchers' data on hazard mitigation from earlier in the 2000s in quantitative analysis and case studies. In addition to new scientific knowledge, the research activities will generate timely and novel policy and practice-relevant information, extend the foundation of datasets needed for longitudinal and comparative analyses, and strengthen the hazards research community. This scientific research contribution thus supports NSF's mission to promote the progress of science and to advance our national welfare. In this case, the benefits will be insights to improve community planning and mitigation for disasters which can save lives and reduce economic losses. Theoretically, pre-disaster hazard mitigation planning can improve post-disaster decision-making by 1) improving local decision-making processes by fostering a collaborative network of local stakeholders, 2) generating plans that organize information and prioritize approaches to reduce long-term risks, and 3) increasing implementation of locally prioritized mitigation approaches, including non-structural approaches like prohibiting rebuilding in floodplains and restoring wetlands. Hurricanes Matthew and Florence create a natural experiment aligning with a multi-dimensional dataset on hazard mitigation planning generating as part of a national evaluation of the Disaster Mitigation Act of 2000. The overarching hypothesis for this project is that communities with higher quality pre-disaster hazard mitigation planning and implementation will have reduced disaster impacts, and will better capitalize on the post-disaster window of opportunity to pursue non-structural mitigation approaches in recovery than communities with lower quality pre-disaster hazard mitigation efforts. This proposed research will collect data on decision making, planning outputs, and planning outcomes during hurricane recovery through content analysis of mitigation and recovery documents, surveys and interviews with key stakeholders, field visits to matched pairs of case counties, and from secondary sources. Multivariate regression modeling will identify the strength and directions of relationships between pre-event hazard mitigation and post-disaster recovery decision-making, outputs, and outcomes, controlling for the external and internal influences, such as hazard intensity and social vulnerability. Detailed case studies will complement and extend the knowledge generated from the regression modeling by examining the role of local champions in advancing support for non-structural mitigation among local stakeholders and decision-makers and conducting fine-grained analysis of whether variation in pre-event implementation of mitigation approaches (e.g. non-structural vs. structural) was associated with reduced damage and disruption. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.