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Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei

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  • Jianguo Zhou

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

  • Baoling Jin

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

  • Shijuan Du

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

  • Ping Zhang

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

Abstract

This paper utilizes the generalized Fisher index (GFI) to decompose the factors of carbon emission and exploits improved particle swarm optimization-back propagation (IPSO-BP) neural network modelling to predict the primary energy consumption CO 2 emissions in different scenarios of Beijing-Tianjin-Hebei region. The results show that (1) the main factors that affect the region are economic factors, followed by population size. On the contrary, the factors that mainly inhibit the carbon emissions are energy structure and energy intensity. (2) The peak year of carbon emission changes with the different scenarios. In a low carbon scenario, the carbon emission will have a decline stage between 2015 and 2018, then the carbon emission will be in the ascending phase during 2019–2030. In basic and high carbon scenarios, the carbon emission will peak in 2025 and 2028, respectively.

Suggested Citation

  • Jianguo Zhou & Baoling Jin & Shijuan Du & Ping Zhang, 2018. "Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei," Energies, MDPI, vol. 11(6), pages 1-17, June.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1489-:d:151190
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    References listed on IDEAS

    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Uduak Akpan & Ovunda Green & Subhes Bhattacharyya & Salisu Isihak, 2015. "Effect of Technology Change on $$\hbox {CO}_{2}$$ CO 2 Emissions in Japan’s Industrial Sectors in the Period 1995–2005: An Input–Output Structural Decomposition Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(2), pages 165-189, June.
    3. Salim, Ruhul & Yao, Yao & Chen, George & Zhang, Lin, 2017. "Can foreign direct investment harness energy consumption in China? A time series investigation," Energy Economics, Elsevier, vol. 66(C), pages 43-53.
    4. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Multiplicative structural decomposition analysis of energy and emission intensities: Some methodological issues," Energy, Elsevier, vol. 123(C), pages 47-63.
    5. Jimenez, Raul & Mercado, Jorge, 2014. "Energy intensity: A decomposition and counterfactual exercise for Latin American countries," Energy Economics, Elsevier, vol. 42(C), pages 161-171.
    6. Mladenović, Igor & Sokolov-Mladenović, Svetlana & Milovančević, Milos & Marković, Dušan & Simeunović, Nenad, 2016. "Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 466-476.
    7. Xu, X.Y. & Ang, B.W., 2014. "Multilevel index decomposition analysis: Approaches and application," Energy Economics, Elsevier, vol. 44(C), pages 375-382.
    8. Deviren, Seyma Akkaya & Deviren, Bayram, 2016. "The relationship between carbon dioxide emission and economic growth: Hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 429-439.
    9. Hatzigeorgiou, Emmanouil & Polatidis, Heracles & Haralambopoulos, Dias, 2008. "CO2 emissions in Greece for 1990–2002: A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques," Energy, Elsevier, vol. 33(3), pages 492-499.
    10. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    11. Jinchao Li & Yuwei Xiang & Huanyu Jia & Lin Chen, 2018. "Analysis of Total Factor Energy Efficiency and Its Influencing Factors on Key Energy-Intensive Industries in the Beijing-Tianjin-Hebei Region," Sustainability, MDPI, vol. 10(1), pages 1-17, January.
    12. Xu, Shi-Chun & He, Zheng-Xia & Long, Ru-Yin, 2014. "Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI," Applied Energy, Elsevier, vol. 127(C), pages 182-193.
    13. Su, Bin & Ang, B.W., 2014. "Attribution of changes in the generalized Fisher index with application to embodied emission studies," Energy, Elsevier, vol. 69(C), pages 778-786.
    14. Ang, B.W. & Liu, F.L. & Chung, Hyun-Sik, 2004. "A generalized Fisher index approach to energy decomposition analysis," Energy Economics, Elsevier, vol. 26(5), pages 757-763, September.
    15. de Boer, Paul, 2009. "Generalized Fisher index or Siegel-Shapley decomposition?," Energy Economics, Elsevier, vol. 31(5), pages 810-814, September.
    16. Xu, Jin-Hua & Fleiter, Tobias & Eichhammer, Wolfgang & Fan, Ying, 2012. "Energy consumption and CO2 emissions in China's cement industry: A perspective from LMDI decomposition analysis," Energy Policy, Elsevier, vol. 50(C), pages 821-832.
    17. Zhang, Lin, 2017. "Correcting the uneven burden sharing of emission reduction across provinces in China," Energy Economics, Elsevier, vol. 64(C), pages 335-345.
    18. Yin, Yanhong & Mizokami, Shoshi & Aikawa, Kohei, 2015. "Compact development and energy consumption: Scenario analysis of urban structures based on behavior simulation," Applied Energy, Elsevier, vol. 159(C), pages 449-457.
    19. Kaivo-oja, J. & Luukkanen, J. & Panula-Ontto, J. & Vehmas, J. & Chen, Y. & Mikkonen, S. & Auffermann, B., 2014. "Are structural change and modernisation leading to convergence in the CO2 economy? Decomposition analysis of China, EU and USA," Energy, Elsevier, vol. 72(C), pages 115-125.
    20. Bhattacharya, Mita & Paramati, Sudharshan Reddy & Ozturk, Ilhan & Bhattacharya, Sankar, 2016. "The effect of renewable energy consumption on economic growth: Evidence from top 38 countries," Applied Energy, Elsevier, vol. 162(C), pages 733-741.
    21. Mrabet, Zouhair & Alsamara, Mouyad, 2017. "Testing the Kuznets Curve hypothesis for Qatar: A comparison between carbon dioxide and ecological footprint," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1366-1375.
    22. Torrie, Ralph D. & Stone, Christopher & Layzell, David B., 2016. "Understanding energy systems change in Canada: 1. Decomposition of total energy intensity," Energy Economics, Elsevier, vol. 56(C), pages 101-106.
    23. Xinxuan Cheng & Longfei Fan & Jiachen Wang, 2018. "Can Energy Structure Optimization, Industrial Structure Changes, Technological Improvements, and Central and Local Governance Effectively Reduce Atmospheric Pollution in the Beijing–Tianjin–Hebei Area," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    24. Zoundi, Zakaria, 2017. "CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1067-1075.
    25. Román-Collado, Rocío & Colinet, Maria José, 2018. "Is energy efficiency a driver or an inhibitor of energy consumption changes in Spain? Two decomposition approaches," Energy Policy, Elsevier, vol. 115(C), pages 409-417.
    26. Zhou, Xiaoyan & Zhang, Jie & Li, Junpeng, 2013. "Industrial structural transformation and carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 57(C), pages 43-51.
    27. Román, Rocío & Cansino, José M. & Rodas, José A., 2018. "Analysis of the main drivers of CO2 emissions changes in Colombia (1990–2012) and its political implications," Renewable Energy, Elsevier, vol. 116(PA), pages 402-411.
    28. Cansino, José M. & Román, Rocío & Ordóñez, Manuel, 2016. "Main drivers of changes in CO2 emissions in the Spanish economy: A structural decomposition analysis," Energy Policy, Elsevier, vol. 89(C), pages 150-159.
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    Cited by:

    1. Jialing Zou & Zhipeng Tang & Shuang Wu, 2019. "Divergent Leading Factors in Energy-Related CO 2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    2. Natalia Iwaszczuk & Jacek Wolak & Aleksander Iwaszczuk, 2021. "Turkmenistan’s Gas Sector Development Scenarios Based on Econometric and SWOT Analysis," Energies, MDPI, vol. 14(10), pages 1-18, May.

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