灾害脆弱性

Lisa Grow Sun, B. Daniels, D. Spencer, C. Sloan, Natalie J. Blades, Teresa Gomez
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引用次数: 9

摘要

脆弱性驱动了灾难法则——定义了它的成功,也说明了它的失败。虽然理解脆弱性对灾害法和学术至关重要,但文献缺乏对脆弱性不同方面的总体分析,也缺乏对形成灾害结果的因素的细致检查。本文试图填补这些空白。尽管脆弱性在灾难法律和政策中处于中心地位,但它往往潜伏在灾难的阴影中,只有在最糟糕的时刻过去、死亡人数被统计之后,脆弱性才会显现出来。COVID-19大流行是这一历史模式的一个明显例外:从大流行开始,很明显,该病毒对不同的人构成不同的风险,取决于不同的脆弱性变量。因此,最近的大流行经验为以更细致的方式考虑脆弱性提供了有益的有利条件,并阐明了数据驱动的脆弱性方法如何能够更普遍地改善灾害政策。根据新的经验数据以及过去灾害的经验,我们介绍并发展了脆弱性的三个维度及其对政策制定者的影响。首先,我们探讨脆弱性的地理分布。通过统计分析和GIS制图,我们的公共卫生、统计和法律专家团队开发了可能是迄今为止用于了解灾害脆弱性的最复杂、最详细的经验工具——创新的COVID-19脆弱性指数,该指数利用丰富的数据集,并使用病死率的统计建模来准确识别该国最脆弱的县。然后,我们展示了如何利用这一脆弱性指数为两项关键且有争议的政策决策提供信息,这些决策从大流行开始就占据了决策者的注意力:口罩授权和2020年选举期间的选民安排。在吸取2019冠状病毒病的教训的基础上,我们展示了类似的建模和思维如何使灾害管理更具前瞻性——能够更好地预测需求并优先考虑减灾和响应资源。结合我们对脆弱性的地理维度的探索,我们接着探索了灾难脆弱性的第二个方面:竞争或冲突的脆弱性。在这些情况下,政策制定者必须做出选择,需要优先考虑一个弱势群体的需求,而不是另一个群体的需求,或者一个群体脆弱性的一个方面高于另一个方面。为了说明这些问题,我们考虑在大流行期间对政策制定者提出挑战的另外两个重要问题:学校关闭和疫苗分发。最后,我们探讨政治脆弱性。这一分析涵盖了灾难使本已脆弱的群体更容易受到某些伤害的各种方式,包括政治忽视、污名化、剥夺公民权、流离失所和其他形式的剥削。特别是,我们考虑脆弱性数据如何既是一个无意的开发路线图,也是对灾难不平等的重要检查。总而言之,本文借鉴了2019冠状病毒病给最弱势群体带来的惨痛教训,提出了一个更健全的学术和政策框架,以评估和应对未来灾害中的脆弱性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disaster Vulnerability
Vulnerability drives disaster law—defining its successes and illustrating its failures. Although understanding vulnerability is critical to disaster law and scholarship, the literature lacks both an overarching analysis of the different aspects of vulnerability and a nuanced examination of the factors that shape disaster outcomes. This paper attempts to fill those holes.Despite its centrality to disaster law and policy, vulnerability often lurks in the shadows of a disaster, evident only once the worst is past and the bodies have been counted. The COVID-19 pandemic is a notable exception to this historical pattern: from the beginning of the pandemic, it has been clear that the virus poses different risks to different people, depending on different vulnerability variables. This most recent pandemic experience thus provides a useful vantage point for considering vulnerability in a more nuanced way and for illuminating how a data-driven approach to vulnerability could improve disaster policy more generally.Drawing on new empirical data, as well as experience from past disasters, we introduce and develop three dimensions of vulnerability and their implications for policymakers. First, we explore the geography of vulnerability. Using statistical analysis and GIS mapping, our team of public health, statistics, and legal experts develops perhaps the most sophisticated and detailed empirical tool ever deployed to understand disaster vulnerability—an innovative COVID-19 vulnerability index that draws on a rich dataset and uses statistical modeling of case fatality rates to accurately identify the country’s most vulnerable counties. We then demonstrate how this vulnerability index could have been used to inform two critical and contentious policy decisions that occupied decision-makers from the onset of the pandemic: mask mandates and voter accommodations during the 2020 elections. Building upon the lessons of COVID-19, we then show how similar modeling and thinking could make disaster management more proactive—better able to anticipate needs and prioritize disaster mitigation and response resources.Incorporating insights from our exploration of the geographic dimension of vulnerability, we then explore a second aspect of disaster vulnerability: competing or conflicting vulnerabilities. These are situations in which policymakers must navigate choices that require prioritizing one vulnerable group’s needs over another or one aspect of a group’s vulnerability over another. To illustrate these issues, we consider two other important problems that have challenged policymakers during the pandemic: school closures and vaccine distribution.Finally, we explore political vulnerability. This analysis encompasses a variety of ways that disasters make already vulnerable groups even more vulnerable to certain kinds of harms, including political neglect, stigmatization, disenfranchisement, displacement, and other forms of exploitation. In particular, we consider how vulnerability data may be both an unintended roadmap for exploitation and an important check on disaster inequity. In sum, this Article draws upon the costly lessons of COVID-19 for the most vulnerable to suggest a more robust academic and policy framework for assessing and responding to vulnerability in future disasters.
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