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Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

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  • Jianwei Ren
  • Bin Gao
  • Jiewei Zhang
  • Chunhua Chen

Abstract

In China, the transportation sector contributes about 18% of the total carbon emissions. This research contributes to measuring the energy and carbon emission efficiency (ECEE) of regional transportation systems (RTS) in China considering uncertain carbon emissions. A radial chance-constrained data envelopment analysis (DEA) model is developed to estimate the overall efficiency, and a nonradial chance-constrained DEA model is presented to evaluate the pure energy efficiency (PEE) and the pure carbon emission efficiency (PCEE). We prove that the proposed chance-constrained DEA models can effectively address the uncertain carbon emissions when measuring efficiency. We find that most of China’s RTS have low ECEE and the inefficiencies are mainly due to the lower gasoline utilization efficiency and the lower kerosene utilization efficiency. In addition, east China performs better than central China, and central China performs better than west China. In China, the unbalanced regional development of the ECEE in transportation corresponds with the unbalanced regional economic development. We provide some valuable suggestions based on the evaluation of the potential cuts in each kind of energy and the potential decreases in carbon emissions.

Suggested Citation

  • Jianwei Ren & Bin Gao & Jiewei Zhang & Chunhua Chen, 2020. "Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, April.
    • Handle: RePEc:hin:jnlmpe:9740704
    DOI: 10.1155/2020/9740704
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    Cited by:

    1. Shaojian Qu & Yuting Xu & Ying Ji & Can Feng & Jinpeng Wei & Shan Jiang, 2022. "Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China," Sustainability, MDPI, vol. 14(20), pages 1-26, October.
    2. Khodadadipour, M. & Hadi-Vencheh, A. & Behzadi, M.H. & Rostamy-malkhalifeh, M., 2021. "Undesirable factors in stochastic DEA cross-efficiency evaluation: An application to thermal power plant energy efficiency," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 613-628.
    3. Zhao, Pengjun & Zeng, Liangen & Li, Peilin & Lu, Haiyan & Hu, Haoyu & Li, Chengming & Zheng, Mengyuan & Li, Haitao & Yu, Zhao & Yuan, Dandan & Xie, Jinxin & Huang, Qi & Qi, Yuting, 2022. "China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model," Energy, Elsevier, vol. 238(PC).
    4. Zhang, Qi & Gu, Baihe & Zhang, Haiying & Ji, Qiang, 2023. "Emission reduction mode of China's provincial transportation sector: Based on “Energy+” carbon efficiency evaluation," Energy Policy, Elsevier, vol. 177(C).
    5. Qizhen Wang & Qian Zhang, 2022. "Foreign Direct Investment and Carbon Emission Efficiency: The Role of Direct and Indirect Channels," Sustainability, MDPI, vol. 14(20), pages 1-23, October.

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