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Eco-Efficiency and Its Evolutionary Change under Regulatory Constraints: A Case Study of Chinese Transportation Industry

Author

Listed:
  • Zhiqiang Zhu

    (Research Institute of Highway, Ministry of Transport, Beijing 100088, China)

  • Xuechi Zhang

    (Research Institute of Highway, Ministry of Transport, Beijing 100088, China)

  • Mengqing Xue

    (School of Economics, Capital University of Economics and Business, Beijing 100170, China)

  • Yaoyao Song

    (School of Economics, Capital University of Economics and Business, Beijing 100170, China)

Abstract

The transportation industry is characterized as a capital-intensive industry that plays a crucial role in economic and social development, and the rapid expansion of this industry has led to serious environmental problems, which makes the eco-efficiency analysis of the transportation industry an important issue. Previous research paid little attention to the regulatory scenarios and suffered from the incomparability problem, hence this paper aims to reasonably estimate the eco-efficiency and identify its evolutionary characteristics. We measure the eco-efficiency and the corresponding global Malmquist–Luenberger productivity index using a modified model of the data envelopment analysis framework, in which different regulatory constraints are incorporated. Based on the empirical study on the transportation industry of thirty provinces in China, we find that the eco-efficiency of Chinese transportation industry experienced a slight increase during 2015–2016, a sharp decline during 2016–2017, and a continuous rise since year 2017. The Middle Yangtze River area was the best performer among the eight regions in terms of eco-efficiency, while the Southwest area was placed last. The global Malmquist–Luenberger productivity index showed an earlier increase and later decrease trend, which was quite consistent with the reality of the variation of inputs and outputs and the emergence of COVID-19. Moreover, the best practice gap change was found to be the main driven force of productivity. The empirical results verify the practicability of our measurement models and the conclusions can be adopted in guiding the formulation of corresponding policies and regulations.

Suggested Citation

  • Zhiqiang Zhu & Xuechi Zhang & Mengqing Xue & Yaoyao Song, 2023. "Eco-Efficiency and Its Evolutionary Change under Regulatory Constraints: A Case Study of Chinese Transportation Industry," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7381-:d:1136035
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    References listed on IDEAS

    as
    1. Yao-yao Song & Xian-tong Ren & Guo-liang Yang, 2023. "Capacity utilization change over time," Journal of Productivity Analysis, Springer, vol. 59(1), pages 61-78, February.
    2. Fare, Rolf & Grosskopf, Shawna, 1996. "Productivity and intermediate products: A frontier approach," Economics Letters, Elsevier, vol. 50(1), pages 65-70, January.
    3. Murty, Sushama, 2010. "On the theory of a firm : The case of by-production of emissions," The Warwick Economics Research Paper Series (TWERPS) 934, University of Warwick, Department of Economics.
    4. 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.
    5. 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.
    6. Sheu, Jiuh-Biing, 2004. "A hybrid fuzzy-based approach for identifying global logistics strategies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(1), pages 39-61, January.
    7. Hao Zhang & Jianxin You & Xuekelaiti Haiyirete & Tianyu Zhang, 2020. "Measuring Logistics Efficiency in China Considering Technology Heterogeneity and Carbon Emission through a Meta-Frontier Model," Sustainability, MDPI, vol. 12(19), pages 1-18, October.
    8. Cui, Qiang & Li, Ye, 2015. "An empirical study on the influencing factors of transportation carbon efficiency: Evidences from fifteen countries," Applied Energy, Elsevier, vol. 141(C), pages 209-217.
    9. Zhang, Yue-Jun & Jiang, Lin & Shi, Wei, 2020. "Exploring the growth-adjusted energy-emission efficiency of transportation industry in China," Energy Economics, Elsevier, vol. 90(C).
    10. Sueyoshi, Toshiyuki & Goto, Mika & Ueno, Takahiro, 2010. "Performance analysis of US coal-fired power plants by measuring three DEA efficiencies," Energy Policy, Elsevier, vol. 38(4), pages 1675-1688, April.
    11. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    12. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
    13. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2018. "Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach," European Journal of Operational Research, Elsevier, vol. 269(1), pages 35-50.
    14. Yongrok Choi & Haohao Wang & Fan Yang & Hyoungsuk Lee, 2021. "Sustainable Governance of the Korean Freight Transportation Industry from an Environmental Perspective," Sustainability, MDPI, vol. 13(11), pages 1-14, June.
    15. Hualin Xie & Guiying Liu & Qu Liu & Peng Wang, 2014. "Analysis of Spatial Disparities and Driving Factors of Energy Consumption Change in China Based on Spatial Statistics," Sustainability, MDPI, vol. 6(4), pages 1-17, April.
    16. Gohari, Adel & Ahmad, Anuar Bin & Balasbaneh, Ali Tighnavard & Gohari, Ali & Hasan, Razi & Sholagberu, Abdulkadir Taofeeq, 2022. "Significance of intermodal freight modal choice criteria: MCDM-based decision support models and SP-based modal shift policies," Transport Policy, Elsevier, vol. 121(C), pages 46-60.
    17. Oh, Dong-hyun & Heshmati, Almas, 2010. "A sequential Malmquist-Luenberger productivity index: Environmentally sensitive productivity growth considering the progressive nature of technology," Energy Economics, Elsevier, vol. 32(6), pages 1345-1355, November.
    18. Juan Martín & Concepción Román & Augusto Voltes-Dorta, 2009. "A stochastic frontier analysis to estimate the relative efficiency of Spanish airports," Journal of Productivity Analysis, Springer, vol. 31(3), pages 163-176, June.
    19. Gupta, Pankaj & Mehlawat, Mukesh Kumar & Aggarwal, Usha & Charles, V., 2021. "An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry," Resources Policy, Elsevier, vol. 74(C).
    20. Lior, Noam, 2010. "Sustainable energy development: The present (2009) situation and possible paths to the future," Energy, Elsevier, vol. 35(10), pages 3976-3994.
    21. Liu, Hongwei & Yang, Ronglu & Wu, Jie & Chu, Junfei, 2021. "Total-factor energy efficiency change of the road transportation industry in China: A stochastic frontier approach," Energy, Elsevier, vol. 219(C).
    22. 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.
    23. Hailu, Atakelty & Veeman, Terrence S., 2001. "Alternative methods for environmentally adjusted productivity analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 211-218, September.
    24. 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.
    25. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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