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Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China

Author

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  • Jinying Li

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Jianfeng Shi

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Jinchao Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Carbon emissions are the major cause of the global warming; therefore, the exploration of carbon emissions reduction potential is of great significance to reduce carbon emissions. This paper explores the potential of carbon intensity reduction in Beijing in 2020. Based on factors including economic growth, resident population growth, energy structure adjustment, industrial structure adjustment and technical progress, the paper sets 48 development scenarios during the years 2015–2020. Then, the back propagation (BP) neural network optimized by improved particle swarm optimization algorithm (IPSO) is used to calculate the carbon emissions and carbon intensity reduction potential under various scenarios for 2016 and 2020. Finally, the contribution of different factors to carbon intensity reduction is compared. The results indicate that Beijing could more than fulfill the 40%–45% reduction target for carbon intensity in 2020 in all of the scenarios. Furthermore, energy structure adjustment, industrial structure adjustment and technical progress can drive the decline in carbon intensity. However, the increase in the resident population hinders the decline in carbon intensity, and there is no clear relationship between economy and carbon intensity. On the basis of these findings, this paper puts forward relevant policy recommendations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:615-:d:75325
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    References listed on IDEAS

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    Cited by:

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    2. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "A Relational Analysis Model of the Causal Factors Influencing CO 2 in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model," Energies, MDPI, vol. 11(7), pages 1-16, July.
    3. Zhenghai Liao & Dazheng Wang & Liangliang Tang & Jinli Ren & Zhuming Liu, 2017. "A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network," Energies, MDPI, vol. 10(2), pages 1-11, February.
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    5. Li, Wei & Gao, Shubin, 2018. "Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China's cement industry," Energy, Elsevier, vol. 165(PB), pages 33-54.
    6. Huiru Zhao & Guo Huang & Ning Yan, 2018. "Forecasting Energy-Related CO 2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China," Energies, MDPI, vol. 11(4), pages 1-21, March.
    7. 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.
    8. Weijun Wang & Weisong Peng & Jiaming Xu & Ran Zhang & Yaxuan Zhao, 2018. "Driving Factor Analysis and Forecasting of CO 2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method," Energies, MDPI, vol. 11(10), pages 1-17, October.

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