SAI: Integrating Equity in Emergency Management of Critical Infrastructure
SAI:将股权纳入关键基础设施应急管理
基本信息
- 批准号:2324616
- 负责人:
- 金额:$ 75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-15 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.Every year, wildfires in the United States cause many deaths and bring enormous economic loss. The frequency and harm caused by wildfires are projected to grow with changes in the climate and shifting population centers. Wildfires represent a significant threat to critical infrastructure systems. These challenges are compounded by the observation that rural and disadvantaged communities are often the most susceptible to wildfire disasters. Although significant progress has been made in predicting wildfire propagation, less is known about the complex interactions between wildfires, socially vulnerable populations, and emergency management practices. This SAI project focuses on strengthening the emergency management of critical infrastructure systems, with special attention to the disproportionate societal impacts of wildfires. Specifically, this project integrates social scientific theories with mathematical models to yield novel insights into the design and improvement of emergency management of critical infrastructure systems. The goal is to develop a new assessment framework and an integrative decision model that enhances the human-centered governance of such systems.One critical challenge in this area is understanding how essential services, such as electricity, water, and transportation, might fail due to their strong interdependencies when facing a wildfire. Another major challenge is that the needs of socially vulnerable communities residing at the interface between wildlands and urban areas are often unseen. As a result, the disproportionate impacts of wildfire-induced critical infrastructure failures on these communities are not adequately considered in emergency planning and practices. This SAI project aims to develop a human-centered, equity-focused, risk-informed decision-making framework to address these challenges. The research develops equity-aware and interpretable models and computational algorithms related to vulnerability assessment and efficient post-wildfire recovery strategies of interdependent critical infrastructure systems under deep uncertainties. It also evaluates the social burden of wildfire-induced critical infrastructure service disruptions on rural and disadvantaged communities and effectively integrates it with the new emergency management decision model. The project brings together expertise and resources from a network of researchers in the social sciences, engineering, and public policy, along with stakeholders from multiple institutions, public and private agencies, non-profit organizations, and local communities.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Mathematical and Physical Sciences.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)是美国国家科学基金会的一个项目,旨在促进以人为本的基础研究和潜在的变革性研究,以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门的创新,发展经济,创造就业机会,使公共部门提供的服务更有效率,加强社区,促进机会平等,保护自然环境,加强国家安全,并推动美国的领导地位。为了实现这些目标,需要来自科学和工程学科的专业知识。SAI侧重于人类推理和决策、治理以及社会和文化过程的知识如何使有效的基础设施的建设和维护能够改善生活和社会,并以技术和工程的进步为基础。每年,美国的野火都会造成许多人死亡,并带来巨大的经济损失。随着气候变化和人口中心的转移,预计野火的频率和危害将会增加。野火对关键基础设施系统构成重大威胁。农村和弱势社区往往最容易受到野火灾害的影响,这一观察结果使这些挑战更加复杂。尽管在预测野火传播方面取得了重大进展,但人们对野火、社会弱势群体和应急管理实践之间复杂的相互作用知之甚少。该SAI项目侧重于加强关键基础设施系统的应急管理,特别关注野火造成的不成比例的社会影响。具体而言,该项目将社会科学理论与数学模型相结合,为关键基础设施系统的应急管理设计和改进提供了新的见解。目标是开发一个新的评估框架和一个综合决策模型,以增强这些系统的以人为中心的治理。这一领域的一个关键挑战是了解电力、水和交通等基本服务在面临野火时如何因其强烈的相互依赖性而失效。另一项重大挑战是,居住在荒地和城市交界地区的社会弱势社区的需求往往不为人所知。因此,在应急规划和实践中没有充分考虑到野火引起的关键基础设施故障对这些社区造成的不成比例的影响。SAI项目旨在建立一个以人为本、以公平为中心、了解风险的决策框架,以应对这些挑战。该研究开发了与深度不确定性下相互依赖的关键基础设施系统的脆弱性评估和有效的野火后恢复策略相关的公平感知和可解释模型和计算算法。它还评估了野火引起的关键基础设施服务中断对农村和处境不利社区造成的社会负担,并将其有效地与新的应急管理决策模型相结合。该项目汇集了社会科学、工程和公共政策研究人员网络的专业知识和资源,以及来自多个机构、公共和私人机构、非营利组织和当地社区的利益相关者。该奖项由社会、行为和经济科学理事会(SBE)和数学和物理科学理事会支持。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
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会议论文数量(0)
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Sayanti Mukherjee其他文献
A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth
估算自然灾害引发的灾害对国家/地区经济增长影响的新方法
- DOI:
10.1007/s13753-017-0156-3 - 发表时间:
2018 - 期刊:
- 影响因子:4
- 作者:
Sayanti Mukherjee;M. Hastak - 通讯作者:
M. Hastak
A FRAMEWORK TO ASSESS NATURAL HAZARD-INDUCED SERVICE INOPERABILITY IN THE ELECTRICITY SECTOR
评估电力行业自然灾害导致的服务无法运行的框架
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Soojin Yoon;Sayanti Mukherjee;M. Hastak - 通讯作者:
M. Hastak
Evaluating socioeconomic factors for crime against women in developing countries: A data-centric statistical learning approach
评估发展中国家针对妇女犯罪的社会经济因素:一种以数据为中心的统计学习方法
- DOI:
10.1016/j.seps.2025.102255 - 发表时间:
2025-10-01 - 期刊:
- 影响因子:5.400
- 作者:
Esther Jose;Sayanti Mukherjee;Jose Swaminathan - 通讯作者:
Jose Swaminathan
Impact of geophysical and anthropogenic factors on wildfire size: a spatiotemporal data-driven risk assessment approach using statistical learning
地球物理和人为因素对野火规模的影响:使用统计学习的时空数据驱动的风险评估方法
- DOI:
10.1007/s00477-021-02087-w - 发表时间:
2021 - 期刊:
- 影响因子:4.2
- 作者:
N. Masoudvaziri;Prasangsha Ganguly;Sayanti Mukherjee;K. Sun - 通讯作者:
K. Sun
An integrated framework to improve the resiliency of electricity distribution systems exposed to wildfires
一个用于提高暴露于野火的配电系统弹性的综合框架
- DOI:
10.1016/j.ejor.2025.04.035 - 发表时间:
2025-11-01 - 期刊:
- 影响因子:6.000
- 作者:
Prasangsha Ganguly;Sayanti Mukherjee;Jose L. Walteros;Luis Herrera - 通讯作者:
Luis Herrera
Sayanti Mukherjee的其他文献
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