RAPID: Constraints on Policy Learning After Disaster

RAPID：灾后政策学习的限制

• 批准号: 1763218
• 负责人: Kristin Taylor
• 金额: $ 5.5万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763218&HistoricalAwards=false
• 关键词: RAPID; Constraints; Policy; Learning; After

The objective of this study is to understand why communities that have experienced a natural disaster decide to reduce the risk of a future disaster. Communities learn from the experience of disaster but often that learning does not lead to a policy change that reduces risks. Furthermore, communities vary in their capacity to mitigate future disasters. The broader impact of this study is to identify the limits on policy learning about disaster mitigation after a community has experienced a disaster. It is critical to understand why one community may be more vulnerable to a disaster than its neighbor. Hurricane Harvey presents a case for understanding the limits on policy learning after a disaster because of the range of communities it affected across Southeast Texas. Data will be collected using a series of semi-structured interviews over several months with local government officials - mayors, emergency managers and city planners - in communities affected by Hurricane Harvey. The intellectual merit of this project is the use of data collected directly from local government officials on policy learning after a disaster across time. This project asks what are the constraints on policy learning after disaster? The central theory of this project is that local governments will attempt to engage in policy learning after a natural disaster but those efforts will not necessarily lead to a policy change that reduces future risk. This theory is based on the idea that the process of policy learning can stall or halt when local governments are assessing information about mitigating natural hazards. This study examines policy learning after disaster in the context of Hurricane Harvey because of the range of communities it impacted across Southeast Texas. It hypothesizes that information is the critical factor for policy learning and that learning can be constrained by (1) the types of sources a community looks to for information, (2) whether the information is credible, (3) whether the local government officials tend to be myopic in how they consider the information. Data will be collected using a series of semi-structured interviews over several months with local government officials?mayors, emergency managers and city planners in communities affected by Hurricane Harvey. Previous studies of policy learning have relied on ex-post analyses of government documents to infer the presence and meaning of policy learning. However, this project will gather data in real time, thereby capturing the decision making process for how information is considered and used. Furthermore, it allows local government officials who are engaged in the process of policy learning to speak for themselves and their communities.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.

本研究的目的是了解为什么经历过自然灾害的社区决定减少未来灾害的风险。社区从灾难的经历中吸取教训，但通常情况下，学习并不会导致降低风险的政策变化。此外，社区减轻未来灾害的能力也各不相同。这项研究更广泛的影响是确定社区经历灾难后减灾政策学习的限制。了解为什么一个社区可能比其邻居更容易受到灾害影响至关重要。飓风哈维提供了一个案例，帮助我们理解灾难后政策学习的局限性，因为它影响了德克萨斯州东南部的社区范围。将在几个月内通过对受飓风哈维影响的社区的当地政府官员（市长、应急管理人员和城市规划者）进行一系列半结构化访谈来收集数据。该项目的智力价值在于使用直接从地方政府官员收集的数据来进行跨时期灾难后的政策学习。 该项目询问灾后政策学习的限制是什么？该项目的核心理论是，地方政府将在自然灾害发生后尝试进行政策学习，但这些努力不一定会导致政策变化，从而降低未来风险。该理论基于这样的想法：当地方政府评估有关减轻自然灾害的信息时，政策学习过程可能会停滞或停止。本研究考察了飓风哈维背景下灾后的政策学习，因为它影响了德克萨斯州东南部的社区范围。它假设信息是政策学习的关键因素，并且学习可能受到以下因素的限制：（1）社区寻求信息来源的类型，（2）信息是否可信，（3）地方政府官员在考虑信息时是否倾向于短视。数据将通过几个月内对受飓风哈维影响的社区的当地政府官员、市长、应急管理人员和城市规划者进行一系列半结构化访谈来收集。以往的政策学习研究都是依靠对政府文件的事后分析来推断政策学习的存在和意义。然而，该项目将实时收集数据，从而捕获如何考虑和使用信息的决策过程。 此外，它还允许参与政策学习过程的地方政府官员为自己和社区说话。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。