台灣位於西太平洋颱風區，因此颱風災害的發生次數較為頻繁，這也使得民眾的生命財產遭受威脅，並且隨著台灣經濟之發展，颱風災害對人民所造成的損失有擴大之趨勢。本篇試圖建立一套客觀的評估模式，並了解防災系統各階段其效率。本研究以西元2009年至2016年台灣本島縣市之數據，並採用資料包絡分析法，求得各縣市其颱洪災害防救效率值。其次，以拔靴法之Tobit迴歸進行分析，了解環境變數對效率值之影響。最後，將防災系統分為中央於地方減災及整備與地方政府救災應變兩個階段，並且分別求其績效，透過中央於地方減災及整備績效之排名，提供減災及整備相關之政策建議。除此之外，透過地方政府救災應變績效之排名，提供救災相關之政策建議。

本研究結果顯示環境變數中的減災預算與颱洪救災效率有顯著的負相關。有關中央於地方減災及整備與地方政府救災應變兩個階段其結果為台灣中北部地區在中央於地方減災及整備效率排名相對較高，並且發現省轄市與台灣東部地區之地方政府救災應變效率排名相對較高。

As Taiwan is located in the western Pacific typhoon area, typhoon disasters occur more frequently, which threatens the life and property of the people. With the development of Taiwan's economy, typhoon and flood disasters have caused an increase in losses to the people. The objective of research is to establish an objective assessment model and to understand the efficiency of the disaster prevention system at all stages. In this study, the data of the island counties and cities in Taiwan from 2009 to 2016 is used and the data envelopment analysis method is adopted to obtain the efficiency of typhoon and flood disaster prevention. Next, we use bootstrapped Tobit regression analysis to understand the impact of environmental variables on the efficiency. Finally, this study divides the disaster prevention system into two stages: the central government to the local disaster mitigation and preparedness (Stage1) and the local government disaster response (Stage 2). Through the rank of the central government to the local disaster mitigation and preparedness (Stage1), we can offer policy advice related to the disaster mitigation and preparedness. In addition, through the rank of the local government disaster response (Stage 2), it can also offer policy advice related to the disaster response.

The result of this study shows that the disaster mitigation budget has a negative and significant correlation with the efficiency of disaster response. The result of the two stages shows that the central and northern part of Taiwan is ranked the most efficient in the central government to the local disaster mitigation and preparedness (Stage1). And, the provincial city and the eastern part of Taiwan are ranked the most efficient in the local government disaster response (Stage 2).

王价巨 (2017)，《災害管理：13堂專業的必修課程》，五南出版。

內政部 (2017)，《災害防救法》。

方進義 (2015)，《DEA資料包絡分析法入門與應用》，三星課程網。

行政院 (2016)，《民國105年災害防救白皮書》。

行政院 (2016)，《行政院105年度災害防救業務訪評報告》。

陳恭平、林常青、張永健 (2016)，「法實證研究方法進階導論：設限資料」，《臺灣法學》，303，81-91。

高強、黃旭男、Toshiyuki Sueyoshi (2003)，《管理績效評估：資料包絡分析法》，華泰文化。

經濟部水利署 (2015)，《易淹水地區水患治理計畫第3階段實施計畫執行情形及績效報告》。

廖威彥、包匡 (2009)，「臺灣地區各縣市災害防救能力評估之研究」，朝陽科技大學建築及都市設計研究所碩士學位論文。

羅凱文、楊千 (2004)，「應用資料包絡分析法評估臺灣地區消防機關相對效率及探討環境因素之影響」，《警學叢刊》，35(2)，241-262。

Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.

Berg, S., Forsund, F., & Jansen, E. (1992). Malmquist indexes of productivity growth during the deregulation of Norwegian banking, 1980-89. The Scandinavian Journal of Economics , 94, 211-228.

Caves, D.W., Christensen, L.R., & Diewert, W.E. (1982). Multilateral comparisons of output, input, and productivity using superlative index numbers. Economic Journal, 92, 73-86.

Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

Coelli, T. J., Prasad Rao, D.S., & Battese G.E. (1998). An introduction to efficiency and productivity analysis. Boston: Kluwer Academic Publishers.

Dakpo, K.H., Jeanneaux, P., & Latruffe, L. (2016). Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric framework. European Journal of Operational Research, 250 (2), 347–359.

Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35–49.

Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A (General), 120 (3), 253-290.

Fare, R., Grosskopf, S., Lovell, C.A.K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. The Review of Economics and Statistics, 71, 90–98.

Fried, H. O., Lovell, C. A. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for environmental effect and statistical noise in data envelopment analysis. Journal of Productivity Analysis ,17, 157–174.

Garcı´a-Sa´nchez, I.-M., Rodrı´guez-Domı´nguez, L., & Parra-Domı´nguez, J. (2013). Yearly evolution of police efficiency in Spain and explanatory factors. Central European Journal of Operations Research.

Hahn, J. S., Kim, H. R., & Kho, S. (2011). Analysis of the efficiency of seoul arterial bus routes and its determinant factors. KSCE Journal of Civil Engineering, 15(6), 1115-1123.

Kao, Chiang. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949–962.

Kao, Chiang. (2016). Network data envelopment analysis: Foundations and extensions. 1st ed. Switzerland: Springer International Publishing.

Kao, C., & Hwang, S.N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429.

Lewin, A., & Minton, J. (1986). Determining organizational effectiveness: Another look, and an agenda for research. Management Science, 32(5), 514–538.

Li, F., Zhu, Q., & Zhuang, J. (2017). Analysis of fire protection efficiency in the United States: A two-stage DEA-based approach. OR Spectrum, 40, 23-68.

Liu, W., & Sharp, J. (1999). DEA models via goal programming, in: Westermann, G. (Ed.), Data envelopment analysis in the service sector. Springer Fachmedien Wiesbaden, German, 79-101.

Lovell, K., Pastor, J., & Turner, J. (1995). Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. European Journal of Operational Research, 87, 507-518.

Murty, S., Russell, R.R., & Levkoff, S.B. (2012). On modeling pollution-generating technologies. Journal of Environmental Economics and Management, 64, 117–135.

Pal, D., & Mitra, S.K. (2016). An application of the directional distance function with the number of accidents as an undesirable output to measure the technical efficiency of state road transport in India. Transportation Research Part A, 93, 1–12.

Peng, M., Song, L., Guohui, L., Sen, L., & Heping, Z. (2014). Evaluation of fire protection performance of eight countries based on fire statistics: an application of data envelopment analysis. Fire Technology, 50(2), 349–361.

Perez, K., Gonzalez-Araya, M.C., & Iriarte, A . (2017). Energy and GHG emission efficiency in the Chilean manufacturing industry: sectoral and regional analysis by DEA and Malmquist indexes. Energy Economics, 66, 290-302.

Pittman, R.W. (1983). Multilateral productivity comparisons with undesirable outputs. The Economic Journal, 93(372), 883–891.

Seiford, L., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142, 16–20.

Shyu, J., & Chiang, T. (2012). Measuring the true managerial efficiency of bank branches in Taiwan: A three-stage DEA analysis. Expert Systems with Applications, 39(13), 11494–11502.

Simar, L., & Wilson, P.W. (2007). Estimation and inference in two-stage, semi-parametric
models of production processes. Journal of Econometrics, 136(1), 31-64.

Timmer, C.P. (1971). Using a probabilistic frontier production function to measure technical Efficiency. Journal of Political Economy, 79(4), 776–794.

Zofio, J.L., & Prieto, A.M. (2001). Environmental efficiency and regulatory standards: The case of CO2 emissions from OECD industries. Resource and Energy Economics, 23, 63–83.