IDEAS home Printed from https://ideas.repec.org/a/spr/orspec/v40y2018i1d10.1007_s00291-017-0490-2.html
   My bibliography  Save this article

Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach

Author

Listed:
  • Feng Li

    (University of Science and Technology of China
    The State University of New York at Buffalo)

  • Qingyuan Zhu

    (University of Science and Technology of China
    University of Illinois at Urbana-Champaign)

  • Jun Zhuang

    (The State University of New York at Buffalo)

Abstract

Fire-related hazards and incidents are a very common phenomenon that affects human society heavily, so many organisations, over a long period of time, have made efforts to mitigate fires and the caused damages. It is widely acknowledged that an evaluation of fire protection performance is critical for such efforts. In this paper, we propose a new data envelopment analysis-based approach for fire protection efficiency analysis in the United States at the state level. For this purpose, the fire protection system is generalised as an innovative two-stage network process, in which the fire hazard defence subsystem in the first stage is followed by a fire incident fighting subsystem in the second stage. Further, both intermediate outputs and final outputs are all undesirable measures. The fire protection expenditure, a kind of shared resources, is modelled under managerial disposability since it can be intentionally used to reduce fires and fire damages. Based on an empirical study of data from 2010 to 2014, we find that: (1) Texas, California, New York, Florida, and Illinois are the top five states prone to fire incidents. (2) The United States as a whole has an average fire protection efficiency of 0.590, implying relatively low fire protection performances at the state level. (3) Wyoming, Vermont, and Rhode Island are the top three most efficient states, whereas Iowa, Arkansas, and Pennsylvania are ranked the least efficient. Further, (4) the fire incident fighting efficiency is more likely to be higher than the fire hazard defence efficiency for most states. (5) Looking at larger scale by grouping all states into eight areas, the Far West has the highest fire protection efficiency, while Southeast and Plains areas have the lowest efficiency scores. Interestingly, (6) the results show also that a 1% increase in the fire protection expenditure by states per capita will result in a 2.6893% improvement in fire protection efficiency.

Suggested Citation

  • Feng Li & Qingyuan Zhu & Jun Zhuang, 2018. "Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 23-68, January.
    • Handle: RePEc:spr:orspec:v:40:y:2018:i:1:d:10.1007_s00291-017-0490-2
    DOI: 10.1007/s00291-017-0490-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00291-017-0490-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00291-017-0490-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, March.
    2. Rolf Färe & Shawna Grosskopf & Gerald Whittaker, 2014. "Network DEA II," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 307-327, Springer.
    3. Du, Juan & Liang, Liang & Chen, Yao & Cook, Wade D. & Zhu, Joe, 2011. "A bargaining game model for measuring performance of two-stage network structures," European Journal of Operational Research, Elsevier, vol. 210(2), pages 390-397, April.
    4. Plácido Moreno & Sebastián Lozano, 2014. "A network DEA assessment of team efficiency in the NBA," Annals of Operations Research, Springer, vol. 214(1), pages 99-124, March.
    5. Song, Malin & An, Qingxian & Zhang, Wei & Wang, Zeya & Wu, Jie, 2012. "Environmental efficiency evaluation based on data envelopment analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4465-4469.
    6. Jie Wu & Qingyuan Zhu & Junfei Chu & Liang Liang, 2015. "Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 455-477, October.
    7. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
    8. Chen, Yao & Cook, Wade D. & Li, Ning & Zhu, Joe, 2009. "Additive efficiency decomposition in two-stage DEA," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1170-1176, August.
    9. Chen, Yao & Du, Juan & David Sherman, H. & Zhu, Joe, 2010. "DEA model with shared resources and efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(1), pages 339-349, November.
    10. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    11. Podinovski, Victor V. & Kuosmanen, Timo, 2011. "Modelling weak disposability in data envelopment analysis under relaxed convexity assumptions," European Journal of Operational Research, Elsevier, vol. 211(3), pages 577-585, June.
    12. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Slacks-based efficiency measures for modeling environmental performance," Ecological Economics, Elsevier, vol. 60(1), pages 111-118, November.
    13. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    14. Korhonen, Pekka J. & Luptacik, Mikulas, 2004. "Eco-efficiency analysis of power plants: An extension of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 437-446, April.
    15. Yang, Hongliang & Pollitt, Michael, 2010. "The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants," Energy Policy, Elsevier, vol. 38(8), pages 4440-4444, August.
    16. William W. Cooper & Lawrence M. Seiford & Joe Zhu, 2011. "Data Envelopment Analysis: History, Models, and Interpretations," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 1-39, Springer.
    17. He, Fei & Zhuang, Jun, 2016. "Balancing pre-disaster preparedness and post-disaster relief," European Journal of Operational Research, Elsevier, vol. 252(1), pages 246-256.
    18. Zhou, Peng & Poh, Kim Leng & Ang, Beng Wah, 2007. "A non-radial DEA approach to measuring environmental performance," European Journal of Operational Research, Elsevier, vol. 178(1), pages 1-9, April.
    19. Mahyar Eftekhar & Hongmin Li & Luk N. Van Wassenhove & Scott Webster, 2017. "The Role of Media Exposure on Coordination in the Humanitarian Setting," Production and Operations Management, Production and Operations Management Society, vol. 26(5), pages 802-816, May.
    20. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    21. Fuminori Toyasaki & Tina Wakolbinger, 2014. "Impacts of earmarked private donations for disaster fundraising," Annals of Operations Research, Springer, vol. 221(1), pages 427-447, October.
    22. Fukuyama, Hirofumi & Weber, William L., 2010. "A slacks-based inefficiency measure for a two-stage system with bad outputs," Omega, Elsevier, vol. 38(5), pages 398-409, October.
    23. Dapeng Huang & Renhe Zhang & Zhiguo Huo & Fei Mao & Youhao E & Wei Zheng, 2012. "An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(2), pages 1575-1586, November.
    24. Feng Yang & Qianqian Yuan & Shaofu Du & Liang Liang, 2016. "Reserving relief supplies for earthquake: a multi-attribute decision making of China Red Cross," Annals of Operations Research, Springer, vol. 247(2), pages 759-785, December.
    25. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    26. W. Liu & W. Meng & X. Li & D. Zhang, 2010. "DEA models with undesirable inputs and outputs," Annals of Operations Research, Springer, vol. 173(1), pages 177-194, January.
    27. Li, Yongjun & Chen, Yao & Liang, Liang & Xie, Jianhui, 2012. "DEA models for extended two-stage network structures," Omega, Elsevier, vol. 40(5), pages 611-618.
    28. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    29. Malin Song & Guijun Zhang & Kuangnan Fang & Jing Zhang, 2016. "Regional operational and environmental performance evaluation in China: non-radial DEA methodology under natural and managerial disposability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 243-265, November.
    30. Emrouznejad, Ali & Parker, Barnett R. & Tavares, Gabriel, 2008. "Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA," Socio-Economic Planning Sciences, Elsevier, vol. 42(3), pages 151-157, September.
    31. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    32. Cook, Wade D. & Liang, Liang & Zhu, Joe, 2010. "Measuring performance of two-stage network structures by DEA: A review and future perspective," Omega, Elsevier, vol. 38(6), pages 423-430, December.
    33. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    34. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiyang Lei & Yongjun Li & Alec Morton, 2022. "Dominance and ranking interval in DEA parallel production systems," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(2), pages 649-675, June.
    2. Yongjun Li & Feng Li & Ali Emrouznejad & Liang Liang & Qiwei Xie, 2019. "Allocating the fixed cost: an approach based on data envelopment analysis and cooperative game," Annals of Operations Research, Springer, vol. 274(1), pages 373-394, March.
    3. Li, Yongjun & Wang, Lizheng & Li, Feng, 2021. "A data-driven prediction approach for sports team performance and its application to National Basketball Association," Omega, Elsevier, vol. 98(C).
    4. Feng Li & Qingyuan Zhu & Liang Liang, 2019. "A new data envelopment analysis based approach for fixed cost allocation," Annals of Operations Research, Springer, vol. 274(1), pages 347-372, March.
    5. Zhao, Linlin & Zhu, Qingyuan & Zhang, Lin, 2021. "Regulation adaptive strategy and bank efficiency: A network slacks-based measure with shared resources," European Journal of Operational Research, Elsevier, vol. 295(1), pages 348-362.
    6. Li, Feng & Zhu, Qingyuan & Chen, Zhi, 2019. "Allocating a fixed cost across the decision making units with two-stage network structures," Omega, Elsevier, vol. 83(C), pages 139-154.
    7. Svetlana V. Ratner & Andrey V. Lychev & Vladimir E. Krivonozhko, 2024. "Data Envelopment Analysis-Based Approach to Improving of the Budget Allocation System for Decarbonization Targets," Economies, MDPI, vol. 12(7), pages 1-16, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Feng & Zhu, Qingyuan & Chen, Zhi, 2019. "Allocating a fixed cost across the decision making units with two-stage network structures," Omega, Elsevier, vol. 83(C), pages 139-154.
    2. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    3. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    4. Wu, Jie & Zhu, Qingyuan & Ji, Xiang & Chu, Junfei & Liang, Liang, 2016. "Two-stage network processes with shared resources and resources recovered from undesirable outputs," European Journal of Operational Research, Elsevier, vol. 251(1), pages 182-197.
    5. Jie Wu & Qingyuan Zhu & Junfei Chu & Liang Liang, 2015. "Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 455-477, October.
    6. Junfei Chu & Jie Wu & Qingyuan Zhu & Qingxian An & Beibei Xiong, 2019. "Analysis of China’s Regional Eco-efficiency: A DEA Two-stage Network Approach with Equitable Efficiency Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1263-1285, December.
    7. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    8. Chen, Ya & Li, Yongjun & Liang, Liang & Salo, Ahti & Wu, Huaqing, 2016. "Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures," European Journal of Operational Research, Elsevier, vol. 250(2), pages 543-554.
    9. Xiaohong Liu & Qingyuan Zhu & Junfei Chu & Xiang Ji & Xingchen Li, 2019. "Environmental Performance and Benchmarking Information for Coal-Fired Power Plants in China: A DEA Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1287-1302, December.
    10. Haibo Zhou & Hanhui Hu, 2017. "Sustainability Evaluation of Railways in China Using a Two-Stage Network DEA Model with Undesirable Outputs and Shared Resources," Sustainability, MDPI, vol. 9(1), pages 1-23, January.
    11. Barnabé Walheer, 2020. "Output, input, and undesirable output interconnections in data envelopment analysis: convexity and returns-to-scale," Annals of Operations Research, Springer, vol. 284(1), pages 447-467, January.
    12. Jeanneaux, Philippe & Latruffe, Laure, 2016. "Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric frameworkAuthor-Name: Dakpo, K. Hervé," European Journal of Operational Research, Elsevier, vol. 250(2), pages 347-359.
    13. Jie Wu & Qingyuan Zhu & Pengzhen Yin & Malin Song, 2017. "Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices," Operational Research, Springer, vol. 17(3), pages 715-735, October.
    14. Khoveyni, Mohammad & Fukuyama, Hirofumi & Eslami, Robabeh & Yang, Guo-liang, 2019. "Variations effect of intermediate products on the second stage in two-stage processes," Omega, Elsevier, vol. 85(C), pages 35-48.
    15. Fukuyama, Hirofumi & Matousek, Roman & Tzeremes, Nickolaos G., 2020. "A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system," Omega, Elsevier, vol. 95(C).
    16. Xiang Ji & Jiasen Sun & Qunwei Wang & Qianqian Yuan, 2019. "Revealing Energy Over-Consumption and Pollutant Over-Emission Behind GDP: A New Multi-criteria Sustainable Measure," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1391-1421, December.
    17. Huang, Chin-wei & Ho, Foo Nin & Chiu, Yung-ho, 2014. "Measurement of tourist hotels׳ productive efficiency, occupancy, and catering service effectiveness using a modified two-stage DEA model in Taiwan," Omega, Elsevier, vol. 48(C), pages 49-59.
    18. Ang, Sheng & Chen, Chien-Ming, 2016. "Pitfalls of decomposition weights in the additive multi-stage DEA model," Omega, Elsevier, vol. 58(C), pages 139-153.
    19. Xiyang Lei & Yongjun Li & Qiwei Xie & Liang Liang, 2015. "Measuring Olympics achievements based on a parallel DEA approach," Annals of Operations Research, Springer, vol. 226(1), pages 379-396, March.
    20. Tsolas, Ioannis E., 2011. "Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA," Resources Policy, Elsevier, vol. 36(2), pages 159-167, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:orspec:v:40:y:2018:i:1:d:10.1007_s00291-017-0490-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.