本研究探討氣候變遷情境下，天氣變數對於臺灣農業與工商服務產業產出的影響與產業脆弱度分析，其中工商服務業又包括：水電燃氣業、社會及個人服務業、金融保險房地產與工商服務、商業、運輸倉儲及通信業、製造業、營造業、礦業，共八大行業。研究資料來源來自於民國70年到100年度《工商及服務業普查》、《農業統計年報》與大氣研究資料庫。本研究使用Chou et al(2007)的方法，將Cobb-Douglas生產函數模型，考量長期氣候變遷情況後的改良形式，稱之為Economy-Climate Model，稱為C-D-C模型，初步建立天氣與產業之間的聯結。且為了修正多重共線性問題，將經濟變數除以產業從業人數的形式呈現。而在實證模型中則分別控制時間、地區與加入逐步迴歸，使用4種迴歸方式分別檢視天氣變數對產業的從業人均產值的顯著性。結果發現天氣變數對農業的影響最大，而在工商業中，則是以營造業與運輸倉儲業對天氣變數最為敏感，因兩者產業特性不同而導致營造業與運輸倉儲對天氣變數的顯著方向相反。另外，農業、礦業與商業也對臺灣的極端天氣的變數較為敏感，如：豪大雨天數、日均溫小於10度天數等。最後，本研究透過臺灣氣候變遷推估與資訊平台(TCCIP)所提供的氣候變遷在A1B情境下的氣候推估模式，比對2020-2039年相較於1980-1999年的雨量與溫度變化與本迴歸實證結果結合，模擬出氣候變遷情境下，近未來臺灣各產業的從業人均產值變化率。

This study mainly discusses the relationship between weather and outputs ofindustries. We obtain data from Industry, Commerce and Service Census, Agricultural Statistics Yearbook and the Data Bank for Atomspheric Research. We follow the Chou et al (2007) method by using the improved Cobb-Douglas production model, which taking climate change into considerations, named C-D-C. With C-D-C we can initially establishedassociation between weather and industries. To fix the multicollinearity problem in C-D, we use the percapita form to eliminate it. And we then control the time and regions, and use thestepwise regression to buildmodels. Result shows that agriculture is the most affected by weather variables. Except agriculture, the construction industry and transport industry are the most affected in the study by weather variables. Study also shows that agriculture, business industry and mining industry are most affected by extreme weather. For example, the numbers of days which rain over 200mm, the numbers of dayswhich average temperature is lower than 10℃. Finally, we use the datas from Taiwan Climate Change Information Platform, to simulatethe rate of change of outputs of all industries in the future.

賴穎緻(2013)，「天氣事件對台灣不同產業股價報酬率的影響」，國立政治大學財務管理研究所碩士論文。

古明萱，「農藝學：第五章作物與溫度」，國立屏東科技大學線上講義，
網址：http://cec.npust.edu.tw/e-learning/agronomy/%B2%C4%A4%AD%B3%B9.pdf

許晃雄、吳宜昭、周佳、陳正達、陳永明、盧孟明(2011)，「臺灣氣候變遷科學報告2011」，國家災害防救科技中心。

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