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Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing

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  • Zhi-Fu Mi
  • Su-Yan Pan
  • Hao Yu
  • Yi-Ming Wei

    (Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology)

Abstract

An optimization model is developed based on the Input-Output model to assess the potential impacts of industrial structure on the energy consumption and CO2 emission. The method is applied to a case study of industrial structure adjustment in Beijing, China. Results demonstrate that industrial structure adjustment has great potential of energy conservation and carbon reduction. When the average annual growth rate of GDP is 8.29% from 2010 to 2020, industrial structure adjustment can save energy by 39.42% (50.06 million tons of standard coal equivalent), and reduce CO2 emission by 46.06% (96.31 million tons) in Beijing in 2020. Second, Beijing had better strive to develop several low energy intensive and low carbon intensive sectors, such as information transmission, computer service and software, and finance. Third, energy intensity is possible to decrease without negatively affecting economic growth by reasonable industrial structure adjustment. Four, compared to 'intensity targets', 'total amount targets' are more effective on the energy conservation and carbon reduction, but have much greater negative effects on economic growth. Therefore, it needs to be balanced between 'total amount targets' and 'intensity targets'.

Suggested Citation

  • Zhi-Fu Mi & Su-Yan Pan & Hao Yu & Yi-Ming Wei, 2014. "Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing," CEEP-BIT Working Papers 51, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    • Handle: RePEc:biw:wpaper:51
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    File URL: http://www.ceep.net.cn/docs/2014-07/20140714181844971978.pdf
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    References listed on IDEAS

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    2. Miaomiao Liu & Yining Huang & Rosemary Hiscock & Qin Li & Jun Bi & Patrick L. Kinney & Clive E. Sabel, 2016. "Do Climate Change Policies Promote or Conflict with Subjective Wellbeing: A Case Study of Suzhou, China," IJERPH, MDPI, vol. 13(3), pages 1-16, March.
    3. Zhifu Mi & Hua Liao & D’Maris Coffman & Yi-Ming Wei, 2019. "Assessment of equity principles for international climate policy based on an integrated assessment model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 309-323, January.
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    6. Chen, B. & Yang, Q. & Zhou, Sili & Li, J.S. & Chen, G.Q., 2017. "Urban economy's carbon flow through external trade: Spatial-temporal evolution for Macao," Energy Policy, Elsevier, vol. 110(C), pages 69-78.
    7. Tao Yang & Rong Wang, 2023. "Research on the Impact of Green Finance and the Digital Economy on the Energy Consumption Structure in the Context of Carbon Neutrality," Sustainability, MDPI, vol. 15(22), pages 1-19, November.
    8. Zhi-Fu Mi & Yi-Ming Wei & Chen-Qi He & Hua-Nan Li & Xiao-Chen Yuan & Hua Liao, 2017. "Regional efforts to mitigate climate change in China: a multi-criteria assessment approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(1), pages 45-66, January.
    9. Wentao Lu & Zhenghui Fu & Yang Zhang & Yuxuan Qiao & Lei Yu & Yi Liu, 2021. "Integrated Planning for Regional Development Planning with Low Carbon Development Constraint under Uncertainty: A Case Study of Qingpu District, Shanghai," Sustainability, MDPI, vol. 13(19), pages 1-24, September.
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    More about this item

    Keywords

    Industrial structure; Energy consumption; CO2 emission; Input-output; Optimization model;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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