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Impacts Analysis of Dual Carbon Target on the Medium- and Long-Term Petroleum Products Demand in China

Author

Listed:
  • Li Shang

    (CAS Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Qun Shen

    (CAS Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China)

  • Xuehang Song

    (CAS Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China)

  • Weisheng Yu

    (CAS Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China)

  • Nannan Sun

    (CAS Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Wei Wei

    (CAS Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Petroleum has become a strategic resource to the national economy, and forecasting its demand is a critical step to supporting energy planning and policy-making for carbon reduction. We first conducted a characteristic analysis of end consumption for petroleum products, and the key affecting factors are identified through an extended logarithmic mean Divisia index (LMDI) method. Afterwards, the long-range energy alternatives planning system (LEAP) was adopted to predict the petroleum products demand by considering the potential impacts of different policies on the identified key factors. Through comparative analysis of three scenarios including five sub-scenarios, the findings show that the dual carbon constraints are crucial to petroleum demand control. Under the enforcement of existing carbon peaking policies, the petroleum products demand will peak around 2043 at 731.5 million tons, and the impact of energy intensity-related policies is more significant than that of activity level. However, even if the existing policy efforts are continued, supporting the carbon-neutral target will not be easy. By further strengthening the constraints, the demand will peak around 2027 at 680 million tons, and the abatement contribution will come mainly from industry (manufacturing), construction, and transportation. Additional abatement technologies are necessary for the petroleum industry to achieve carbon neutrality.

Suggested Citation

  • Li Shang & Qun Shen & Xuehang Song & Weisheng Yu & Nannan Sun & Wei Wei, 2023. "Impacts Analysis of Dual Carbon Target on the Medium- and Long-Term Petroleum Products Demand in China," Energies, MDPI, vol. 16(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3584-:d:1128860
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    References listed on IDEAS

    as
    1. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    2. Jiang, Shan & Zhu, Yongnan & He, Guohua & Wang, Qingming & Lu, Yajing, 2020. "Factors influencing China’s non-residential power consumption: Estimation using the Kaya–LMDI methods," Energy, Elsevier, vol. 201(C).
    3. Chen, Jiandong & Wang, Ping & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2018. "Decomposition and decoupling analysis of CO2 emissions in OECD," Applied Energy, Elsevier, vol. 231(C), pages 937-950.
    4. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    5. Zhang, Wei & Wang, Nan, 2021. "Decomposition of energy intensity in Chinese industries using an extended LMDI method of production element endowment," Energy, Elsevier, vol. 221(C).
    6. Ren, Xiaohang & Li, Yiying & Qi, Yinshu & Duan, Kun, 2022. "Asymmetric effects of decomposed oil-price shocks on the EU carbon market dynamics," Energy, Elsevier, vol. 254(PB).
    7. Li, Guohao & Niu, Miaomiao & Xiao, Jin & Wu, Jiaqian & Li, Jinkai, 2023. "The rebound effect of decarbonization in China’s power sector under the carbon trading scheme," Energy Policy, Elsevier, vol. 177(C).
    8. Zhang, Dongyu & Liu, Gengyuan & Chen, Caocao & Zhang, Yan & Hao, Yan & Casazza, Marco, 2019. "Medium-to-long-term coupled strategies for energy efficiency and greenhouse gas emissions reduction in Beijing (China)," Energy Policy, Elsevier, vol. 127(C), pages 350-360.
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