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Can China realise its energy-savings goal by adjusting its industrial structure?

Author

Listed:
  • Shiwei Yu
  • Shuhong Zheng
  • Guizhi Ba
  • Yi-Ming Wei

Abstract

To investigate whether China can realise its energy-savings goal by 2020 through adjustments to its industrial structure, this study proposes a dynamic input--output multi-objective optimisation model. According to this model, the objectives to be achieved include the maximum gross domestic product and employment, and the minimum energy consumption, where the constraints are the sectoral dynamic input--output balance, labour and energy supply, and sectoral production capacity. The four best solutions are screened from the Pareto-optimal front. The study findings show that the energy intensities in 2020 would decrease by 42.8%, 43.5%, 42.9%, and 43.4% in the four scenarios when compared to their 2002 levels. This means that China can fully achieve its planned energy-savings target for 2020. In order to ensure that the industrial structure is optimised for the future, sectoral capital investments should be regulated by China's government and efforts to improve energy efficiency should be maintained.

Suggested Citation

  • Shiwei Yu & Shuhong Zheng & Guizhi Ba & Yi-Ming Wei, 2016. "Can China realise its energy-savings goal by adjusting its industrial structure?," Economic Systems Research, Taylor & Francis Journals, vol. 28(2), pages 273-293, June.
    • Handle: RePEc:taf:ecsysr:v:28:y:2016:i:2:p:273-293
    DOI: 10.1080/09535314.2015.1102714
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    Cited by:

    1. Nagashima, Fumiya, 2018. "The sign reversal problem in structural decomposition analysis," Energy Economics, Elsevier, vol. 72(C), pages 307-312.
    2. Wu, Rui & Dai, Hancheng & Geng, Yong & Xie, Yang & Tian, Xu, 2019. "Impacts of export restructuring on national economy and CO2 emissions: A general equilibrium analysis for China," Applied Energy, Elsevier, vol. 248(C), pages 64-78.
    3. Liu, Yajuan & Wang, Yutao & Mi, Zhifu & Ma, Zhongyu, 2018. "Carbon implications of China’s changing economic structure at the city level," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 163-171.
    4. Fu, Xue & Lahr, Michael & Yaxiong, Zhang & Meng, Bo, 2017. "Actions on climate change, Intended Reducing carbon emissions in China via optimal industry shifts: Toward hi-tech industries, cleaner resources and higher carbon shares in less-develop regions," Energy Policy, Elsevier, vol. 102(C), pages 616-638.
    5. Yingxue Rao & Min Zhou & Chunxia Cao & Shukui Tan & Yan Song & Zuo Zhang & Deyi Dai & Guoliang Ou & Lu Zhang & Xin Nie & Aiping Deng & Zhuoma Cairen, 2019. "Exploring the quantitive relationship between economic benefit and environmental constraint using an inexact chance-constrained fuzzy programming based industrial structure optimization model," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2199-2220, July.
    6. Xi Zhang & Zheng Li & Linwei Ma & Chinhao Chong & Weidou Ni, 2019. "Forecasting the Energy Embodied in Construction Services Based on a Combination of Static and Dynamic Hybrid Input-Output Models," Energies, MDPI, vol. 12(2), pages 1-26, January.
    7. Zheng, Shuhong & Yang, Juan & Yu, Shiwei, 2021. "How renewable energy technological innovation promotes renewable power generation: Evidence from China's provincial panel data," Renewable Energy, Elsevier, vol. 177(C), pages 1394-1407.
    8. Song, Xiaoling & Wang, Yudong & Zhang, Zhe & Shen, Charles & Peña-Mora, Feniosky, 2021. "Economic-environmental equilibrium-based bi-level dispatch strategy towards integrated electricity and natural gas systems," Applied Energy, Elsevier, vol. 281(C).
    9. Maria Markaki & Stelios Papadakis & Anna Putnová, 2021. "A Modern Industrial Policy for the Czech Republic: Optimizing the Structure of Production," Mathematics, MDPI, vol. 9(23), pages 1-20, November.
    10. Feng Wang & Changhai Gao & Wulin Zhang & Danwen Huang, 2021. "Industrial Structure Optimization and Low-Carbon Transformation of Chinese Industry Based on the Forcing Mechanism of CO 2 Emission Peak Target," Sustainability, MDPI, vol. 13(8), pages 1-26, April.
    11. Alexander Melnik & Irina Naoumova & Kirill Ermolaev & Jerome Katrichis, 2021. "Driving Innovation through Energy Efficiency: A Russian Regional Analysis," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
    12. Jiang, Meihui & An, Haizhong & Gao, Xiangyun & Liu, Donghui & Jia, Nanfei & Xi, Xian, 2020. "Consumption-based multi-objective optimization model for minimizing energy consumption: A case study of China," Energy, Elsevier, vol. 208(C).
    13. Xi Zhang & Zheng Li & Linwei Ma & Chinhao Chong & Weidou Ni, 2019. "Analyzing Carbon Emissions Embodied in Construction Services: A Dynamic Hybrid Input–Output Model with Structural Decomposition Analysis," Energies, MDPI, vol. 12(8), pages 1-23, April.
    14. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    15. Yu, Shiwei & Zheng, Shuhong & Zhang, Xuejiao & Gong, Chengzhu & Cheng, Jinhua, 2018. "Realizing China's goals on energy saving and pollution reduction: Industrial structure multi-objective optimization approach," Energy Policy, Elsevier, vol. 122(C), pages 300-312.
    16. Yu, Shiwei & Liu, Jie & Hu, Xing & Tian, Peng, 2022. "Does development of renewable energy reduce energy intensity? Evidence from 82 countries," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    17. Xing, Menglin & Liu, Xiaojun & Luo, Fuzhou, 2023. "How does the development of urban agglomeration affect the electricity efficiency of resource-based cities?—An empirical research based on the fsQCA method," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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