伴隨近年全球氣候變遷、極端氣候所帶來的突發性幹擾事件頻發。太平洋與歐亞大陸交界地帶，深受海洋與大氣交互影響，加上緯度、季風與島內地形垂直變遷度大，氣候變遷對該區域影響甚為顯著，其中尤以颱風夾帶龐大水氣引發洪患使台灣地區與大陸沿海地區之經濟、環境、生命安全數度面臨嚴重損失。如何針對城市減災措施，近年來強調從工程治理到與非工程措施協同防災，但無論是何種治理方法，政府本質上都是希望實現防災資源的優化配置和高效利用，以減少災害損失，並提升政府災害防災的投入決策能力和防災管理。
本研究從都市颱風災害的防治效率角度切入，選取兩個地區，包括臺北都會區和江浙滬地區，進行防救災投入及災害損失資料之收集。並針對兩都市防災組織、都市發展環境、社會經濟人口數據進行收集比較。以此為基礎建構防災效率指標。再运用資料包括分析法（Data Envelopment Analysis，DEA）進行兩地區針對各自防災效率指標之效率比較，並就前述資源防救災組織、都市發展環境、社經屬性條件之投入因素面向與防災相關損失產出之關係，進行解析。

With the global climate change in recent years, sudden disturbances caused by extreme climate occur frequently. The border area between the Pacific Ocean and the Eurasian continent is deeply influenced by the interaction between the ocean and the atmosphere, coupled with the large vertical changes in latitude, monsoon and island topography, climate change has a significant impact on the region, especially the flooding caused by typhoons with huge water, which has caused serious losses to the economy, environment and life safety in Taiwan and the coastal areas of China. However, regardless of the management methods, the government essentially wants to achieve the optimal allocation and efficient use of disaster prevention resources in order to reduce disaster losses and enhance the government's disaster prevention input decision making capability and disaster prevention management.
In this study, data on disaster prevention and relief inputs and losses were collected from two regions, Taipei and Jiangsu, Zhejiang and Shanghai, from the perspective of urban typhoon disaster prevention and control efficiency. The data on disaster prevention organizations, urban development environment, and socioeconomic and demographic data were also collected and compared between the two cities. The data were used as the basis for constructing disaster prevention efficiency indicators. The data were then analyzed by using Data Envelopment Analysis to compare the efficiency of the disaster prevention efficiency indicators of the two regions, and to analyze the relationship between the input factors of the disaster prevention and relief organizations, urban development environment, and socioeconomic attributes, and the disaster prevention-related loss output.

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