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Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?

Author

Listed:
  • Ying Wang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Peipei Shang

    (Editorial Department, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Lichun He

    (School of Public Administration, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Yingchun Zhang

    (School of Economics, Qingdao University, Qingdao 266071, China)

  • Dandan Liu

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

Abstract

To mitigate global warming, the Chinese government has successively set carbon intensity targets for 2020 and 2030. Energy restructuring is critical for achieving these targets. In this paper, a combined forecasting model is utilized to predict primary energy consumption in China. Subsequently, the Markov model and non-linear programming model are used to forecast China’s energy structure in 2020 and 2030 in three scenarios. Carbon intensities were forecasted by combining primary energy consumption, energy structure and economic forecasting. Finally, this paper analyzes the contribution potential of energy structure optimization in each scenario. Our main research conclusions are that in 2020, the optimal energy structure will enable China to achieve its carbon intensity target under the conditions of the unconstrained scenario, policy-constrained scenario and minimum external costs of carbon emissions scenario. Under the three scenarios, the carbon intensity will decrease by 42.39%, 43.74%, and 42.67%, respectively, relative to 2005 levels. However, in 2030, energy structure optimization cannot fully achieve China’s carbon intensity target under any of the three scenarios. It is necessary to undertake other types of energy-saving emission reduction measures. Thus, our paper concludes with some policy suggestions to further mitigate China’s carbon intensities.

Suggested Citation

  • Ying Wang & Peipei Shang & Lichun He & Yingchun Zhang & Dandan Liu, 2018. "Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?," Energies, MDPI, vol. 11(10), pages 1-32, October.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2721-:d:175022
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    References listed on IDEAS

    as
    1. Barry Eichengreen & Donghyun Park & Kwanho Shin, 2012. "When Fast-Growing Economies Slow Down: International Evidence and Implications for China," Asian Economic Papers, MIT Press, vol. 11(1), pages 42-87, Winter/Sp.
    2. Wang, Xiaoyu & Luo, Dongkun & Zhao, Xu & Sun, Zhu, 2018. "Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation," Energy, Elsevier, vol. 152(C), pages 539-548.
    3. Jinying Li & Jianfeng Shi & Jinchao Li, 2016. "Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China," Energies, MDPI, vol. 9(8), pages 1-17, August.
    4. Fei Ye & Xinxiu Xie & Li Zhang & Xiaoling Hu, 2018. "An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China," Energies, MDPI, vol. 11(1), pages 1-16, January.
    5. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    6. Yue, Ting & Long, Ruyin & Chen, Hong & Zhao, Xin, 2013. "The optimal CO2 emissions reduction path in Jiangsu province: An expanded IPAT approach," Applied Energy, Elsevier, vol. 112(C), pages 1510-1517.
    7. Xu, Lei & Chen, Nengcheng & Chen, Zeqiang, 2017. "Will China make a difference in its carbon intensity reduction targets by 2020 and 2030?," Applied Energy, Elsevier, vol. 203(C), pages 874-882.
    8. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    9. Su, Bin & Ang, B.W., 2014. "Input–output analysis of CO2 emissions embodied in trade: A multi-region model for China," Applied Energy, Elsevier, vol. 114(C), pages 377-384.
    10. Yuan, Jiahai & Xu, Yan & Zhang, Xingping & Hu, Zheng & Xu, Ming, 2014. "China's 2020 clean energy target: Consistency, pathways and policy implications," Energy Policy, Elsevier, vol. 65(C), pages 692-700.
    11. Tan, Zhongfu & Li, Li & Wang, Jianjun & Wang, Jianhui, 2011. "Examining the driving forces for improving China’s CO2 emission intensity using the decomposing method," Applied Energy, Elsevier, vol. 88(12), pages 4496-4504.
    12. Nan Xiang & Feng Xu & Jinghua Sha, 2013. "Simulation Analysis of China’s Energy and Industrial Structure Adjustment Potential to Achieve a Low-carbon Economy by 2020," Sustainability, MDPI, vol. 5(12), pages 1-19, November.
    13. Du, Kerui & Lu, Huang & Yu, Kun, 2014. "Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach," Applied Energy, Elsevier, vol. 115(C), pages 491-501.
    14. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
    15. Li, Hongmin & Wang, Jianzhou & Lu, Haiyan & Guo, Zhenhai, 2018. "Research and application of a combined model based on variable weight for short term wind speed forecasting," Renewable Energy, Elsevier, vol. 116(PA), pages 669-684.
    16. Yong Wang & Yu Zhou & Lin Zhu & Fei Zhang & Yingchun Zhang, 2018. "Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions," Energies, MDPI, vol. 11(5), pages 1-29, May.
    17. Gambhir, Ajay & Schulz, Niels & Napp, Tamaryn & Tong, Danlu & Munuera, Luis & Faist, Mark & Riahi, Keywan, 2013. "A hybrid modelling approach to develop scenarios for China's carbon dioxide emissions to 2050," Energy Policy, Elsevier, vol. 59(C), pages 614-632.
    18. Decai Tang & Tingyu Ma & Zhijiang Li & Jiexin Tang & Brandon J. Bethel, 2016. "Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020," Sustainability, MDPI, vol. 8(10), pages 1-15, October.
    19. Baker, Bruce D., 2001. "Can flexible non-linear modeling tell us anything new about educational productivity?," Economics of Education Review, Elsevier, vol. 20(1), pages 81-92, February.
    20. Stern, David I. & Jotzo, Frank, 2010. "How ambitious are China and India's emissions intensity targets?," Energy Policy, Elsevier, vol. 38(11), pages 6776-6783, November.
    21. Wang, Run & Liu, Wenjuan & Xiao, Lishan & Liu, Jian & Kao, William, 2011. "Path towards achieving of China's 2020 carbon emission reduction target--A discussion of low-carbon energy policies at province level," Energy Policy, Elsevier, vol. 39(5), pages 2740-2747, May.
    22. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    23. Dong, Feng & Li, Xiaohui & Long, Ruyin & Liu, Xiaoyan, 2013. "Regional carbon emission performance in China according to a stochastic frontier model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 525-530.
    24. Yi, Wen-Jing & Zou, Le-Le & Guo, Jie & Wang, Kai & Wei, Yi-Ming, 2011. "How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development," Energy Policy, Elsevier, vol. 39(5), pages 2407-2415, May.
    25. Ping Wang & Bangzhu Zhu, 2016. "Estimating the Contribution of Industry Structure Adjustment to the Carbon Intensity Target: A Case of Guangdong," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
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