Collaborative Research: SAI-R: Decision-making Under Evolving and Conditional Risk Associated with Coastal Flood Barriers

合作研究：SAI-R：与沿海防洪屏障相关的不断变化和条件风险下的决策

• 批准号: 2228486
• 负责人: Jeremy Bricker
• 金额: $ 30.95万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228486&HistoricalAwards=false
• 关键词: Collaborative; Research; SAI; Decision; making

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.People make decisions on where to live based on many considerations. In addition to budget constraints, this may include such features as availability of public goods, the environment, and local climate. One very important consideration is the risk associated with flooding. Locations vary by flood risk. Such risk evolves because of changing climate and because of government investments in flood control infrastructure. This SAI research project examines the decision making of people residing in coastal communities regarding their residential location choices when threatened by changing flood risk conditions. Investments in flood control infrastructure can turn previously risky areas into safer locations. The safety provided by coastal flood barriers may, in turn, increase the demand for local housing. A societal consequence is that real estate prices are driven up, and low- and middle-class residents are driven out. This outcome may further exacerbate disparities in home ownership across income strata and lead to the emergence of a new source of social inequity. This project develops a framework integrating risk modeling with economic decisions and social behavioral models. The integrative approach brings clarity to various coastal protection options in the face of climate uncertainty. It also helps to understand how decisions regarding different risk reduction strategies can best address the needs of racially, ethnically, and socioeconomically diverse populations.The design of flood control infrastructure is often driven by hydraulic and structural engineering models to assess and validate baseline conditions as well as simulate future risk at the parcel level. This project extends that approach by integrating two additional perspectives drawn from the social and behavioral sciences. One is an econometric sorting model that uses risk profiles to model preferences for location choices based on sales data of owner-occupied residential properties. The other is based on social behavior models that identify residents’ risk perceptions and uncover how risk preferences are affected by beliefs, information and other factors not captured by the economic sorting model. These approaches are integrated to quantify the change in welfare of the total population and the distributional equity for a set of risk profile scenarios involving different storm surge barrier types and current and future storm climatology and sea levels. The risk profiles and welfare metrics are further refined to address the needs of end-users and key stakeholders involved in making decisions about the design of a coastal barrier system. This engagement seeks to help policy makers evaluate a range of possible interventions, from investment in public housing to more accessible participatory planning, that may address inequities emerging from conditional and evolving risk associated with flooding.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Geosciences.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.

加强美国基础设施 (SAI) 是一项 NSF 计划，旨在促进以人为本的基础性和潜在变革性研究，加强美国的基础设施，为社会经济活力和广泛的生活质量改善提供坚实的基础。私营部门创新、发展经济、创造就业机会、提供更多公共部门服务、加强社区、促进平等机会、保护自然环境、增强国家安全并增强美国的领导力。要实现这些目标，需要来自各个领域的专业知识。 SAI 专注于人类推理和决策、治理以及社会和文化过程的知识如何能够建设和维护有效的基础设施，从而改善生活和社会，并以技术和工程的进步为基础。除了预算限制外，决定居住地点的因素还包括公共物品的可用性、环境和当地气候等，其中一个非常重要的考虑因素是与洪水相关的风险。这些风险因气候变化和政府而演变。该 SAI 研究项目研究了沿海社区居民在受到洪水风险条件变化的威胁时做出的居住地点选择决策。沿海防洪堤可能反过来会增加对当地住房的需求，社会惩罚是房地产价格上涨，中低阶层居民被赶出，这一结果可能会进一步加剧各地住房拥有率的差距。收入阶层并导致出现该项目开发了一个将风险模型与经济决策和社会行为模型相结合的框架，该综合方法使面对气候不确定性的各种沿海保护方案变得清晰。防洪基础设施的设计通常由水利和结构工程模型驱动，以评估和验证基线条件并模拟地块层面的未来风险。项目通过整合两个额外的视角来扩展该方法一种是基于社会和行为科学的计量经济学分类模型，该模型使用风险概况来根据业主自用住宅物业的销售数据对位置选择偏好进行建模，另一种是基于社会行为模型，该模型可识别居民的风险认知和风险偏好。揭示风险偏好如何受到经济分类模型未捕获的信念、信息和其他因素的影响，这些方法被整合以量化总人口福利的变化以及涉及不同风暴潮的一组风险状况情景的分配公平。屏障类型以及当前和未来的风暴进一步完善风险状况和福利指标，以满足参与沿海屏障系统设计决策的最终用户和主要利益相关者的需求。干预措施，从公共住房投资到更容易参与的规划，可能会解决与洪水相关的有条件和不断变化的风险所产生的不平等问题。该奖项得到了社会、行为和经济 (SBE) 科学理事会和社会科学理事会的支持地球科学。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。