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Can China's industrial sector achieve energy conservation and emission reduction goals dominated by energy efficiency enhancement? A multi-objective optimization approach

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  • He, Yong
  • Liao, Nuo
  • Lin, Kunrong

Abstract

Whether China could meet its 2030 energy conservation and emission reduction goals depends on whether energy efficiency serves as the most important measure of carbon emission reduction. This study has explored how industrial sector could achieve the goals on the premise of maintaining certain economic growth from the perspective of energy efficiency enhancement. The industrial correlation model and multi-objective optimization model are combined to find out the path of energy efficiency improvement, with the decision variables being economic output and energy intensity of various sub-sectors. Through intelligent optimization algorithm, nine paths of energy efficiency improvement are obtained and the corresponding optimal path is found out. The results indicate that regardless of the expected high-speed or medium-speed economic growth, the optimized path could achieve the goal of carbon emission peak, mainly depends on energy efficiency improvement. In the scenario of high-speed economic growth, the amount of CO2 emission could be reduced by 58.31% through energy efficiency enhancement, and further reduced by 28.22% through industrial restructuring; while in the medium-speed growth scenario, the CO2 emission amount could be reduced by 57.88% employing improving energy efficiency, and further be reduced by 29.44% employing industrial restructuring.

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  • He, Yong & Liao, Nuo & Lin, Kunrong, 2021. "Can China's industrial sector achieve energy conservation and emission reduction goals dominated by energy efficiency enhancement? A multi-objective optimization approach," Energy Policy, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:enepol:v:149:y:2021:i:c:s0301421520308193
    DOI: 10.1016/j.enpol.2020.112108
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    as
    1. Fazlollahi, Samira & Mandel, Pierre & Becker, Gwenaelle & Maréchal, Francois, 2012. "Methods for multi-objective investment and operating optimization of complex energy systems," Energy, Elsevier, vol. 45(1), pages 12-22.
    2. Zhang, Yue-Jun & Peng, Yu-Lu & Ma, Chao-Qun & Shen, Bo, 2017. "Can environmental innovation facilitate carbon emissions reduction? Evidence from China," Energy Policy, Elsevier, vol. 100(C), pages 18-28.
    3. Choi, Jun-Ki & Eom, Jiyong & McClory, Emma, 2018. "Economic and environmental impacts of local utility-delivered industrial energy-efficiency rebate programs," Energy Policy, Elsevier, vol. 123(C), pages 289-298.
    4. Ning, Yadong & Chen, Kunkun & Zhang, Boya & Ding, Tao & Guo, Fei & Zhang, Ming, 2020. "Energy conservation and emission reduction path selection in China: A simulation based on Bi-Level multi-objective optimization model," Energy Policy, Elsevier, vol. 137(C).
    5. Malinauskaite, J. & Jouhara, H. & Ahmad, L. & Milani, M. & Montorsi, L. & Venturelli, M., 2019. "Energy efficiency in industry: EU and national policies in Italy and the UK," Energy, Elsevier, vol. 172(C), pages 255-269.
    6. Azaza, Maher & Wallin, Fredrik, 2017. "Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden," Energy, Elsevier, vol. 123(C), pages 108-118.
    7. Li, Xiaoyan & Xu, Hengzhou, 2020. "The Energy-conservation and Emission-reduction Paths of Industrial sectors: Evidence from Chinas 35 industrial sectors," Energy Economics, Elsevier, vol. 86(C).
    8. Gielen, Dolf & Taylor, Michael, 2007. "Modelling industrial energy use: The IEAs Energy Technology Perspectives," Energy Economics, Elsevier, vol. 29(4), pages 889-912, July.
    9. Liao, Nuo & He, Yong, 2018. "Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model," Energy, Elsevier, vol. 158(C), pages 782-795.
    10. Ang, B.W. & Mu, A.R. & Zhou, P., 2010. "Accounting frameworks for tracking energy efficiency trends," Energy Economics, Elsevier, vol. 32(5), pages 1209-1219, September.
    11. Baboo Lesh Gowreesunker & Savvas Tassou & James Atuonwu, 2018. "Cost-Energy Optimum Pathway for the UK Food Manufacturing Industry to Meet the UK National Emission Targets," Energies, MDPI, vol. 11(10), pages 1-19, October.
    12. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    13. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    14. Ganjehkaviri, A. & Mohd Jaafar, M.N. & Hosseini, S.E. & Barzegaravval, H., 2017. "Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction," Energy, Elsevier, vol. 119(C), pages 167-177.
    15. Yuan, Jiahai & Xu, Yan & Hu, Zheng & Zhao, Changhong & Xiong, Minpeng & Guo, Jingsheng, 2014. "Peak energy consumption and CO2 emissions in China," Energy Policy, Elsevier, vol. 68(C), pages 508-523.
    16. 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.
    17. Shao, Shuai & Yang, Lili & Gan, Chunhui & Cao, Jianhua & Geng, Yong & Guan, Dabo, 2016. "Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO2 emission changes: A case study for Shanghai (China)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 516-536.
    18. Hasanbeigi, Ali & Morrow, William & Sathaye, Jayant & Masanet, Eric & Xu, Tengfang, 2013. "A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry," Energy, Elsevier, vol. 50(C), pages 315-325.
    19. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    20. Zhi-Fu Mi & Yi-Ming Wei & Bing Wang & Jing Meng & Zhu Liu & Yuli Shan & Jingru Liu & Dabo Guan, 2017. "Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030," CEEP-BIT Working Papers 103, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    21. Oikonomou, V. & Becchis, F. & Steg, L. & Russolillo, D., 2009. "Energy saving and energy efficiency concepts for policy making," Energy Policy, Elsevier, vol. 37(11), pages 4787-4796, November.
    22. Huang, Ying & Liao, Cuiping & Zhang, Jingjing & Guo, Hongxu & Zhou, Nan & Zhao, Daiqing, 2019. "Exploring potential pathways towards urban greenhouse gas peaks: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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