Assessing the Influence of Hazard Mitigation Planning on Disaster Recovery

该研究项目填补了关于灾前减灾规划对灾后恢复的影响以及减少飓风和洪水等自然灾害的长期风险的知识方面的关键空白。常识认为，在重大灾害发生前进行的减灾规划应有助于减少灾害影响和塑造恢复，包括决策者是否利用灾害后打开的机会之窗，以避免重复过去的决定，例如集中在洪泛区开发，增加长期风险。该项目克服了以前限制减灾规划和恢复决策之间联系研究的两个障碍：1）缺乏关于减灾规划的综合数据集; 2）现有数据集不包括遭受重大灾害的地区。该研究团队之前在北卡罗来纳州以及飓风马修和佛罗伦萨的工作提供了独特的机会，通过首先收集灾后恢复决策的数据，规划文件等输出，以及决策等结果来解决这两个障碍。第二，将这些恢复数据与研究人员在21世纪初的定量分析和案例研究中关于减灾的数据相结合。除了新的科学知识外，研究活动还将产生及时和新颖的政策和实践相关信息，扩大纵向和比较分析所需的数据集基础，并加强危害研究界。这一科学研究的贡献，从而支持NSF的使命，以促进科学的进步和提高我们的国家福利。从理论上讲，灾前减灾规划可以通过以下方式改善灾后决策：（1）通过促进当地利益相关者的协作网络改善当地决策过程;（2）制定计划，组织信息并优先考虑减少长期风险的方法;（3）制定计划，以减少长期风险;（4）制定计划，以减少长期风险。3）加强实施当地优先的缓解方法，包括非结构性方法，如禁止在洪泛区重建和恢复湿地。飓风马修和佛罗伦萨创建一个自然实验，与减灾规划的多维数据集相一致，作为2000年减灾法国家评估的一部分。本项目的总体假设是，与灾前减灾工作质量较低的社区相比，灾前减灾规划和执行质量较高的社区将减少灾害影响，并将更好地利用灾后的机会之窗，在恢复过程中采取非结构性减灾办法。这项拟议的研究将通过减灾和恢复文件的内容分析、对主要利益攸关方的调查和访谈、对配对案例县的实地访问以及二手资料来源，收集飓风恢复期间的决策、规划产出和规划成果数据。多元回归模型将确定事前减灾和灾后恢复决策、产出和结果之间关系的强度和方向，控制外部和内部影响，如灾害强度和社会脆弱性。详细的案例研究将补充和扩展从回归建模中产生的知识，方法是研究当地倡导者在推动当地利益相关者和决策者支持非结构性缓解方面的作用，并对缓解方法的事前实施变化是否（例如，非结构性与结构性）该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，优点和更广泛的影响审查标准。

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.