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Logistics Efficiency under Carbon Constraints Based on a Super SBM Model with Undesirable Output: Empirical Evidence from China’s Logistics Industry

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  • Yongrong Xin

    (Business College, Jiangsu Open University, Nanjing 210036, China)

  • Kengcheng Zheng

    (School of Finance and Taxation, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Yujiao Zhou

    (School of Economics, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Yangyang Han

    (School of Finance, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • P. R. Tadikamalla

    (Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA)

  • Qin Fan

    (Business College, Jiangsu Open University, Nanjing 210036, China)

Abstract

As world resources and environmental constraints have increased, environmental cost has become a concern that affects the sustainable development of the logistics industry in various countries. Carbon emissions are an important part of any environmental cost assessment. How to scientifically and rationally evaluate the green GDP impact and regional efficiency in the logistics industry, especially when under carbon emission constraints, is of great significance to the realization of green and sustainable development. This study evaluated the logistics efficiency of 30 provinces in China from 2003 to 2016 by constructing a super SBM (Slack Based Model) model with undesirable output to explore provincial efficiency and its regional differences. The input–output ratio of the regional logistics industry was optimized through the calculation of the frontier slack variables. The research results showed that, first, it was more reasonable to adjust efficiency under carbon constraints, and it was consistent with the actual performance of the logistics industry. Second, technological progress and deeper capital investments promoted the development of the logistics industry, but technological barriers and low-scale efficiency between regions often limited technological efficiency. Therefore, decision-makers in the logistics industry should reconsider the challenges presented in each reason, encourage industrial technological innovation between regions, and especially promote energy-saving and emission-reduction technologies, so as to maintain the sustainable growth of the logistics industry.

Suggested Citation

  • Yongrong Xin & Kengcheng Zheng & Yujiao Zhou & Yangyang Han & P. R. Tadikamalla & Qin Fan, 2022. "Logistics Efficiency under Carbon Constraints Based on a Super SBM Model with Undesirable Output: Empirical Evidence from China’s Logistics Industry," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5142-:d:801216
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    References listed on IDEAS

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    1. Ali Pazirandeh & Hamid Jafari, 2013. "Making sense of green logistics," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 62(8), pages 889-904, October.
    2. Frota Neto, J. Quariguasi & Bloemhof-Ruwaard, J.M. & van Nunen, J.A.E.E. & van Heck, E., 2008. "Designing and evaluating sustainable logistics networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 195-208, February.
    3. Chia-Nan Wang & Jen-Der Day & Nguyen Thi Kim Lien & Luu Quoc Chien, 2018. "Integrating the Additive Seasonal Model and Super-SBM Model to Compute the Efficiency of Port Logistics Companies in Vietnam," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    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. F. Fumero & C. Vercellis, 1999. "Synchronized Development of Production, Inventory, and Distribution Schedules," Transportation Science, INFORMS, vol. 33(3), pages 330-340, August.
    6. Kaiyang Zhong & Chenglin Li & Qing Wang, 2021. "Evaluation of Bank Innovation Efficiency with Data Envelopment Analysis: From the Perspective of Uncovering the Black Box between Input and Output," Mathematics, MDPI, vol. 9(24), pages 1-18, December.
    7. Hamdan, Amer & (Jamie) Rogers, K.J., 2008. "Evaluating the efficiency of 3PL logistics operations," International Journal of Production Economics, Elsevier, vol. 113(1), pages 235-244, May.
    8. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    9. Lee, Der-Horng & Dong, Meng & Bian, Wen, 2010. "The design of sustainable logistics network under uncertainty," International Journal of Production Economics, Elsevier, vol. 128(1), pages 159-166, November.
    10. Sotiris P. Gayialis & Evripidis P. Kechagias & Grigorios D. Konstantakopoulos, 2022. "A city logistics system for freight transportation: integrating information technology and operational research," Operational Research, Springer, vol. 22(5), pages 5953-5982, November.
    11. Färe, Rolf & Grosskopf, Shawna & Pasurka, Carl A., 2007. "Environmental production functions and environmental directional distance functions," Energy, Elsevier, vol. 32(7), pages 1055-1066.
    12. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    13. 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.
    14. 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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    1. Hongtao Jiang & Jian Yin & Yuanhong Qiu & Bin Zhang & Yi Ding & Ruici Xia, 2022. "Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces," Land, MDPI, vol. 11(8), pages 1-22, July.

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