Suppr超能文献

量化美国各州和领地在灾害缓解方面的支出与重大灾害声明之间的关系。

Quantifying the relationship between predisaster mitigation spending and major disaster declarations for US states and territories.

作者信息

Renken Katharina, Jackman Andrea M, Beruvides Mario G

机构信息

Senior Researcher, Professor, Kühne Logistics University GmbH, Hamburg, Germany.

Data Engineer, Public Sector, IBM Corporation, Pittsburgh, Pennsylvania.

出版信息

J Emerg Manag. 2020 Jul/Aug;18(4):341-347. doi: 10.5055/jem.2020.0478.

Abstract

Since the Stafford Act of 1988, the process of obtaining a formal Major Disaster Declaration has been codified for national implementation, with tasks defined at the smallest levels of local government up to the President. The Disas-ter Mitigation Act of 2000 (DMA 2000) placed additional requirements on local government to plan for mitigation ac-tivities within their jurisdictions. The goal of DMA 2000 was to not only implement more mitigative actions at the local level, but also initiate a process by which local governments could set up ongoing conversations and collaborative efforts with neighboring jurisdictions to ensure continuous, proactive measures were taken against the impacts of disasters. Based on the increased attention paid to mitigation and planning activities, a reasonable expectation would be to see a decline in the number of major disaster declarations since DMA 2000. However, simple correlation analy-sis shows that since DMA 2000, the number of major disaster declarations continues to increase. This article is in-tended as a preliminary study to encourage more detailed analysis in the future of the impacts of federal policy on local-level disaster prevention.

摘要

自1988年《斯塔福德法案》以来,获得正式重大灾难声明的程序已被编纂以供全国实施,任务从地方政府的最基层一直明确到总统。2000年的《减灾法案》(DMA 2000)对地方政府在其辖区内规划减灾活动提出了额外要求。DMA 2000的目标不仅是在地方层面实施更多减灾行动,还启动了一个程序,通过该程序地方政府可以与相邻辖区建立持续对话和合作努力,以确保针对灾害影响采取持续、积极的措施。基于对减灾和规划活动的更多关注,合理的预期是自DMA 2000以来重大灾难声明的数量会下降。然而,简单的相关性分析表明,自DMA 2000以来,重大灾难声明的数量持续增加。本文旨在作为一项初步研究,鼓励未来对联邦政策对地方层面灾害预防的影响进行更详细的分析。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验