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Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry

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  • Jingwen Yi

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Yuchen Zhang

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Kaicheng Liao

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

Among China’s five major industries, the logistics industry is the only one in which carbon emission intensity is continuing to increase, so it is of great importance in developing a low-carbon economy for China. Thus, some scholars have learned about carbon emission efficiency (CEE) in logistic industry recently; however, few of them have considered the inner structure, regional differentiation, or dynamic items of CEE. To fill this gap, we first calculate the dynamic carbon emission efficiency of China’s logistics industry (CEELI) (2001–2017) using the three-stage DEA-Malmquist model, and then using the Dagum Gini coefficient method, the Kernel Density Estimation (KDE), and the panel vector auto-regression (PVAR) model to analyze regional differential decomposition and their formation mechanism. The results indicate that the dynamic CEELI is ‘inefficient’ overall; it shows a decreasing trend, and the decline of dynamic efficiency mainly comes from technical backwardness rather than efficiency decline. Moreover, the domestic differences are gradually narrowing; the Gini inequality between regions and the density of trans-variation between regions are the main reasons for the gap between different regions and different periods.

Suggested Citation

  • Jingwen Yi & Yuchen Zhang & Kaicheng Liao, 2021. "Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry," IJERPH, MDPI, vol. 18(24), pages 1-25, December.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13121-:d:700815
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    1. Yamaji, Kenji & Matsuhashi, Ryuji & Nagata, Yutaka & Kaya, Yoichi, 1993. "A study on economic measures for CO2 reduction in Japan," Energy Policy, Elsevier, vol. 21(2), pages 123-132, February.
    2. Ang, B. W., 1999. "Is the energy intensity a less useful indicator than the carbon factor in the study of climate change?," Energy Policy, Elsevier, vol. 27(15), pages 943-946, December.
    3. Kortelainen, Mika, 2008. "Dynamic environmental performance analysis: A Malmquist index approach," Ecological Economics, Elsevier, vol. 64(4), pages 701-715, February.
    4. Pretis, Felix & Roser, Max, 2017. "Carbon dioxide emission-intensity in climate projections: Comparing the observational record to socio-economic scenarios," Energy, Elsevier, vol. 135(C), pages 718-725.
    5. Ferreira, Ana & Pinheiro, Manuel Duarte & de Brito, Jorge & Mateus, Ricardo, 2018. "Combined carbon and energy intensity benchmarks for sustainable retail stores," Energy, Elsevier, vol. 165(PB), pages 877-889.
    6. Zhang, Xiliang & Karplus, Valerie J. & Qi, Tianyu & Zhang, Da & He, Jiankun, 2016. "Carbon emissions in China: How far can new efforts bend the curve?," Energy Economics, Elsevier, vol. 54(C), pages 388-395.
    7. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    8. Dong, Feng & Li, Xiaohui & Long, Ruyin & Liu, Xiaoyan, 2013. "Regional carbon emission performance in China according to a stochastic frontier model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 525-530.
    9. Wang, Zhaohua & Feng, Chao & Zhang, Bin, 2014. "An empirical analysis of China's energy efficiency from both static and dynamic perspectives," Energy, Elsevier, vol. 74(C), pages 322-330.
    10. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    11. Cai, Bofeng & Guo, Huanxiu & Ma, Zipeng & Wang, Zhixuan & Dhakal, Shobhakar & Cao, Libin, 2019. "Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data," Applied Energy, Elsevier, vol. 242(C), pages 994-1009.
    12. 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.
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    Cited by:

    1. Chong Wu & Jiahua Gan & Zhuo Jiang & Anding Jiang & Wenlong Zheng, 2022. "Ecological Efficiency Evaluation, Spatial Difference, and Trend Analysis of Logistics Industry and Manufacturing Industry Linkage in the Northeast Old Industrial Base," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    2. Jin, Baoling & Han, Ying & Kou, Po, 2023. "Dynamically evaluating the comprehensive efficiency of technological innovation and low-carbon economy in China's industrial sectors," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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