Evaluating the safety performance of China’s provincial construction industries from 2009 to 2017
Abstract
Performance evaluation in construction safety is of great importance to further improve upon safety management processes. This paper develops a data envelopment analysis (DEA) based framework to evaluate the construction safety performance at the macro level. The core of the method is to compare the output-input ratio of construction safety. Using the building practitioner, construction machinery and equipment, and construction area as the inputs, and value added of construction and death toll as the outputs, safety performance score is computed for the China’s provincial construction industries from 2009 to 2017. The results show that the number of benchmark provinces every year is between five and seven. The gap between the best-performing and underperforming province was relatively small in 2012 and big in 2014. Beijing, Qinghai, Hainan, Fujian, Chongqing, and Tianjin can be utilized as role models for the provinces that need to improve their performance in construction safety. The eastern region has the highest score in construction safety performance, followed by the western and central region. This study provides an effective solution to solve performance issue in regional construction safety and improves the tradition performance evaluation system to a certain extent.
Keyword : safety performance, performance evaluation, output-input ratio, data envelopment analysis, construction safety
How to Cite
Kang, L., & Wu, C. (2020). Evaluating the safety performance of China’s provincial construction industries from 2009 to 2017. Journal of Civil Engineering and Management, 26(5), 435-446. https://doi.org/10.3846/jcem.2020.12646
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kang, L., Wu, C., & Wang, B. (2019). Principles, approaches and challenges of applying big data in safety psychology research. Frontiers in Psychology, 10, 1596. https://doi.org/10.3389/fpsyg.2019.01596
Kang, L., Wu, C., Liao, X., & Wang, B. (2020). Safety performance and technology heterogeneity in China’s provincial construction industry. Safety Science, 121, 83–92. https://doi.org/10.1016/j.ssci.2019.09.005
Li, Y., Chiu, Y. H., & Lin, T. Y. (2019). Coal production efficiency and land destruction in China’s coal mining industry. Resources Policy, 63, 101449. https://doi.org/10.1016/j.resourpol.2019.101449
Liu, Q., Meng, X., Hassall, M., & Li, X. (2016). Accident-causing mechanism in coal mines based on hazards and polarized management. Safety Science, 85, 276–281. https://doi.org/10.1016/j.ssci.2016.01.012
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Nahangi, M., Chen, Y., & McCabe, B. (2019). Safety-based efficiency evaluation of construction sites using data envelopment analysis (DEA). Safety Science, 113, 382–388. https://doi.org/10.1016/j.ssci.2018.12.005
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Nazarko, J., & Chodakowska, E. (2017). Labour efficiency in construction industry in Europe based on frontier methods: Data envelopment analysis and stochastic frontier analysis. Journal of Civil Engineering and Management, 23(6), 787–795. https://doi.org/10.3846/13923730.2017.1321577
Pugalis, L., & Tan, S. (2017). Metropolitan and regional economic development: Competing and contested local government roles in Australia in the 21st century. In Proceedings of the 40th Annual Conference of the Australian and New Zealand Regional Science Association International. Melbourne, Australia.
Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20. https://doi.org/10.1016/S0377-2217(01)00293-4
Shen, Y., Hermans, E., Bao, Q., Brijs, T., & Wets, G. (2013). Road safety development in Europe: A decade of changes (2001– 2010). Accident Analysis & Prevention, 60, 85–94. https://doi.org/10.1016/j.aap.2013.08.013
Shen, Y., Hermans, E., Brijs, T., Wets, G., & Vanhoof, K. (2012). Road safety risk evaluation and target setting using data envelopment analysis and its extensions. Accident Analysis & Prevention, 48, 430–441. https://doi.org/10.1016/j.aap.2012.02.020
Stern, D. I. (2012). Modeling international trends in energy efficiency. Energy Economics, 34(6), 2200–2208. https://doi.org/10.1016/j.eneco.2012.03.009
Tatari, O., Egilmez, G., & Kurmapu, D. (2016). Socio-eco-efficiency analysis of highways: a data envelopment analysis. Journal of Civil Engineering and Management, 22(6), 747–757. https://doi.org/10.3846/13923730.2014.914079
Wanberg, J., Harper, C., Hallowell, M. R., & Rajendran, S. (2013). Relationship between construction safety and quality performance. Journal of Construction Engineering and Management, 139(10), 04013003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000732
Wang, B., Wu, C., Kang, L., Reniers, G., & Huang, L. (2018). Work safety in China’s thirteenth five-year plan period (2016– 2020): Current status, new challenges and future tasks. Safety Science, 104, 164–178. https://doi.org/10.1016/j.ssci.2018.01.012
Wang, L., Yan, G., & Cai, H. (2012). Evaluation of building industry safety situation based on synthetic equivalent mortality method. China Safety Science Journal, 22(9), 10–15 (in Chinese).
Xue, X., Shen, Q., Wang, Y., & Lu, J. (2008). Measuring the productivity of the construction industry in China by using DEA-based Malmquist productivity indices. Journal of Construction Engineering and Management, 134(1), 64–71. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:1(64)
Zhang, J., Zhang, W., Xu, P., & Chen, N. (2019a). Applicability of accident analysis methods to Chinese construction accidents. Journal of Safety Research, 68, 187–196. https://doi.org/10.1016/j.jsr.2018.11.006
Zhang, W., Zhang, X., Luo, X., & Zhao, T. (2019b). Reliability model and critical factors identification of construction safety management based on system thinking. Journal of Civil Engineering and Management, 25(4), 362–379. https://doi.org/10.3846/jcem.2019.8652
Zhou, Y., Liu, W., Lv, X., Chen, X., & Shen, M. (2019). Investigating interior driving factors and cross-industrial linkages of carbon emission efficiency in China’s construction industry: Based on Super-SBM DEA and GVAR model. Journal of Cleaner Production, 241, 118322. https://doi.org/10.1016/j.jclepro.2019.118322
Barak, S., & Dahooei, J. H. (2018). A novel hybrid fuzzy DEAFuzzy MADM method for airlines safety evaluation. Journal of Air Transport Management, 73, 134–149. https://doi.org/10.1016/j.jairtraman.2018.09.001
Bastos, J. T., Shen, Y., Hermans, E., Brijs, T., Wets, G., & Ferraz, A. C. P. (2015). Traffic fatality indicators in Brazil: State diagnosis based on data envelopment analysis research. Accident Analysis & Prevention, 81, 61–73. https://doi.org/10.1016/j.aap.2015.01.024
Boyd, G. A. (2008). Estimating plant level energy efficiency with a stochastic frontier. The Energy Journal, 29(2), 23–43. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol29-No2-2
Chen, Y., Liu, B., Shen, Y., & Wang, X. (2016). The energy efficiency of China’s regional construction industry based on the three-stage DEA model and the DEA-DA model. KSCE Journal of Civil Engineering, 20(1), 34–47. https://doi.org/10.1007/s12205-015-0553-3
Choudhry, R. M. (2017). Achieving safety and productivity in construction projects. Journal of Civil Engineering and Management, 23(2), 311–318. https://doi.org/10.3846/13923730.2015.1068842
Dou, E. W., & Zheng, X. Y. (2011, August). Evaluation of contractors’ safety performance based on DEA. In 2011 International Conference on Management and Service Science. Wuhan, China. https://doi.org/10.1109/ICMSS.2011.5998194
Du, K., Lu, H., & Yu, K. (2014). Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach. Applied Energy, 115, 491–501. https://doi.org/10.1016/j.apenergy.2013.10.046
El-Mashaleh, M. S., Minchin Jr., E. R., & O’Brien, W. J. (2007). Management of construction firm performance using benchmarking. Journal of Management in Engineering, 23(1), 10–17. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:1(10)
El-Mashaleh, M. S., Rababeh, S. M., & Hyari, K. H. (2010). Utilizing data envelopment analysis to benchmark safety performance of construction contractors. International Journal of Project Management, 28(1), 61–67. https://doi.org/10.1016/j.ijproman.2009.04.002
Feng, C., & Wang, M. (2017). The economy-wide energy efficiency in China’s regional building industry. Energy, 141, 1869–1879. https://doi.org/10.1016/j.energy.2017.11.114
Feng, C., Zhang, H., & Huang, J. B. (2017). The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis. Renewable and Sustainable Energy Reviews, 71, 859–872. https://doi.org/10.1016/j.rser.2016.12.114
Ganji, S. R. S., & Rassafi, A. A. (2019). Measuring the road safety performance of Iranian provinces: a double-frontier DEA model and evidential reasoning approach. International Journal of Injury Control and Safety Promotion, 26(2), 156–169. https://doi.org/10.1080/17457300.2018.1535510
General Administration of Quality Supervision, Inspection and Quarantine of PRC (GAQSIQ), Standardization Administration of PRC. (2017, June 30). Industrial classification for national economic activities (Revised ed.). Standards Press of China.
Geng, Z., Dong, J., Han, Y., & Zhu, Q. (2017). Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes. Applied Energy, 205, 465–476. https://doi.org/10.1016/j.apenergy.2017.07.132
Geng, Z., Zeng, R., Han, Y., Zhong, Y., & Fu, H. (2019). Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries. Energy, 179, 863–875. https://doi.org/10.1016/j.energy.2019.05.042
Han, Y., Long, C., Geng, Z., & Zhang, K. (2018). Carbon emission analysis and evaluation of industrial departments in China: An improved environmental DEA cross model based on information entropy. Journal of Environmental Management, 205, 298–307. https://doi.org/10.1016/j.jenvman.2017.09.062
Han, Y., Long, C., Geng, Z., Zhu, Q., & Zhong, Y. (2019). A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries. Energy Conversion and Management, 183, 349–359. https://doi.org/10.1016/j.enconman.2018.12.120
Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206–3217. https://doi.org/10.1016/j.enpol.2005.06.015
Hu, X., & Liu, C. (2018). Measuring efficiency, effectiveness and overall performance in the Chinese construction industry. Engineering, Construction and Architectural Management, 25(6), 780–797. https://doi.org/10.1108/ECAM-06-2016-0131
Ji, T., Wei, H. H., & Chen, J. (2019). Understanding the effect of co-worker support on construction safety performance from the perspective of risk theory: an agent-based modeling approach. Journal of Civil Engineering and Management, 25(2), 132–144. https://doi.org/10.3846/jcem.2019.7642
Kang, L., Wu, C., & Wang, B. (2019). Principles, approaches and challenges of applying big data in safety psychology research. Frontiers in Psychology, 10, 1596. https://doi.org/10.3389/fpsyg.2019.01596
Kang, L., Wu, C., Liao, X., & Wang, B. (2020). Safety performance and technology heterogeneity in China’s provincial construction industry. Safety Science, 121, 83–92. https://doi.org/10.1016/j.ssci.2019.09.005
Li, Y., Chiu, Y. H., & Lin, T. Y. (2019). Coal production efficiency and land destruction in China’s coal mining industry. Resources Policy, 63, 101449. https://doi.org/10.1016/j.resourpol.2019.101449
Liu, Q., Meng, X., Hassall, M., & Li, X. (2016). Accident-causing mechanism in coal mines based on hazards and polarized management. Safety Science, 85, 276–281. https://doi.org/10.1016/j.ssci.2016.01.012
McCabe, B. Y., Alderman, E., Chen, Y., Hyatt, D. E., & Shahi, A. (2016). Safety performance in the construction industry: Quasi-longitudinal study. Journal of Construction Engineering and Management, 143(4), 04016113. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001260
Ministry of Housing and Urban-Rural Development of PRC. (2016, July 9). Technical code for working safety at height of building construction. China Architecture & Building Press.
Nahangi, M., Chen, Y., & McCabe, B. (2019). Safety-based efficiency evaluation of construction sites using data envelopment analysis (DEA). Safety Science, 113, 382–388. https://doi.org/10.1016/j.ssci.2018.12.005
National Bureau of Statistics of PRC. (2018). China statistical yearbook 2018. China Statistics Press.
Nazarko, J., & Chodakowska, E. (2017). Labour efficiency in construction industry in Europe based on frontier methods: Data envelopment analysis and stochastic frontier analysis. Journal of Civil Engineering and Management, 23(6), 787–795. https://doi.org/10.3846/13923730.2017.1321577
Pugalis, L., & Tan, S. (2017). Metropolitan and regional economic development: Competing and contested local government roles in Australia in the 21st century. In Proceedings of the 40th Annual Conference of the Australian and New Zealand Regional Science Association International. Melbourne, Australia.
Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20. https://doi.org/10.1016/S0377-2217(01)00293-4
Shen, Y., Hermans, E., Bao, Q., Brijs, T., & Wets, G. (2013). Road safety development in Europe: A decade of changes (2001– 2010). Accident Analysis & Prevention, 60, 85–94. https://doi.org/10.1016/j.aap.2013.08.013
Shen, Y., Hermans, E., Brijs, T., Wets, G., & Vanhoof, K. (2012). Road safety risk evaluation and target setting using data envelopment analysis and its extensions. Accident Analysis & Prevention, 48, 430–441. https://doi.org/10.1016/j.aap.2012.02.020
Stern, D. I. (2012). Modeling international trends in energy efficiency. Energy Economics, 34(6), 2200–2208. https://doi.org/10.1016/j.eneco.2012.03.009
Tatari, O., Egilmez, G., & Kurmapu, D. (2016). Socio-eco-efficiency analysis of highways: a data envelopment analysis. Journal of Civil Engineering and Management, 22(6), 747–757. https://doi.org/10.3846/13923730.2014.914079
Wanberg, J., Harper, C., Hallowell, M. R., & Rajendran, S. (2013). Relationship between construction safety and quality performance. Journal of Construction Engineering and Management, 139(10), 04013003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000732
Wang, B., Wu, C., Kang, L., Reniers, G., & Huang, L. (2018). Work safety in China’s thirteenth five-year plan period (2016– 2020): Current status, new challenges and future tasks. Safety Science, 104, 164–178. https://doi.org/10.1016/j.ssci.2018.01.012
Wang, L., Yan, G., & Cai, H. (2012). Evaluation of building industry safety situation based on synthetic equivalent mortality method. China Safety Science Journal, 22(9), 10–15 (in Chinese).
Xue, X., Shen, Q., Wang, Y., & Lu, J. (2008). Measuring the productivity of the construction industry in China by using DEA-based Malmquist productivity indices. Journal of Construction Engineering and Management, 134(1), 64–71. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:1(64)
Zhang, J., Zhang, W., Xu, P., & Chen, N. (2019a). Applicability of accident analysis methods to Chinese construction accidents. Journal of Safety Research, 68, 187–196. https://doi.org/10.1016/j.jsr.2018.11.006
Zhang, W., Zhang, X., Luo, X., & Zhao, T. (2019b). Reliability model and critical factors identification of construction safety management based on system thinking. Journal of Civil Engineering and Management, 25(4), 362–379. https://doi.org/10.3846/jcem.2019.8652
Zhou, Y., Liu, W., Lv, X., Chen, X., & Shen, M. (2019). Investigating interior driving factors and cross-industrial linkages of carbon emission efficiency in China’s construction industry: Based on Super-SBM DEA and GVAR model. Journal of Cleaner Production, 241, 118322. https://doi.org/10.1016/j.jclepro.2019.118322