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Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis

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  • Xu, Guangyue
  • Schwarz, Peter
  • Yang, Hualiu

Abstract

The global community and the academic world have paid great attention to whether and when China's carbon dioxide (CO2) emissions will peak. Our study investigates the issue with the Nonlinear Auto Regressive model with exogenous inputs (NARX), a dynamic nonlinear artificial neural network that has not been applied previously to this question. The key advance over previous models is the inclusion of feedback mechanisms such as the influence of past CO2 emissions on current emissions. The results forecast that the peak of China's CO2 emissions will occur in 2029, 2031 or 2035 at the level of 10.08, 10.78 and 11.63 billion tonnes under low-growth, benchmark moderate-growth, and high-growth scenarios. Based on the methodology of the mean impact value (MIV), we differentiate and rank the importance of the influence factors on CO2 emissions whereas previous studies included but did not rank factors. We suggest that China should choose the moderate growth development road and achieve its peak target in 2031, focusing on reducing CO2 emissions as a percent of GDP, less carbon-intensive industrialization, and choosing technologies that reduce CO2 emissions from coal or increasing the use of less carbon-intensive fuels.

Suggested Citation

  • Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.
  • Handle: RePEc:eee:enepol:v:128:y:2019:i:c:p:752-762
    DOI: 10.1016/j.enpol.2019.01.058
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    1. Zhang, Xiliang & Karplus, Valerie J. & Qi, Tianyu & Zhang, Da & He, Jiankun, 2016. "Carbon emissions in China: How far can new efforts bend the curve?," Energy Economics, Elsevier, vol. 54(C), pages 388-395.
    2. Fergus Green & Nicholas Stern, 2017. "China's changing economy: implications for its carbon dioxide emissions," Climate Policy, Taylor & Francis Journals, vol. 17(4), pages 423-442, May.
    3. Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
    4. Kriegler, Elmar & Petermann, Nils & Krey, Volker & Schwanitz, Valeria Jana & Luderer, Gunnar & Ashina, Shuichi & Bosetti, Valentina & Eom, Jiyong & Kitous, Alban & Méjean, Aurélie & Paroussos, Leonida, 2015. "Diagnostic indicators for integrated assessment models of climate policy," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 45-61.
    5. Auffhammer, Maximilian & Carson, Richard T., 2008. "Forecasting the path of China's CO2 emissions using province-level information," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 229-247, May.
    6. Azevedo, Vitor G. & Sartori, Simone & Campos, Lucila M.S., 2018. "CO2 emissions: A quantitative analysis among the BRICS nations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 107-115.
    7. 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.
    8. Wang, Zheng & Zhu, Yanshuo & Zhu, Yongbin & Shi, Ying, 2016. "Energy structure change and carbon emission trends in China," Energy, Elsevier, vol. 115(P1), pages 369-377.
    9. Tang, Baojun & Li, Ru & Yu, Biying & An, Runying & Wei, Yi-Ming, 2018. "How to peak carbon emissions in China's power sector: A regional perspective," Energy Policy, Elsevier, vol. 120(C), pages 365-381.
    10. repec:cdl:ucsbec:31-98 is not listed on IDEAS
    11. 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.
    12. Elzen, Michel den & Fekete, Hanna & Höhne, Niklas & Admiraal, Annemiek & Forsell, Nicklas & Hof, Andries F. & Olivier, Jos G.J. & Roelfsema, Mark & van Soest, Heleen, 2016. "Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030?," Energy Policy, Elsevier, vol. 89(C), pages 224-236.
    13. Katsuya Ito, 2017. "CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries," International Economics, CEPII research center, issue 151, pages 1-6.
    14. Ito, Katsuya, 2017. "CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries," International Economics, Elsevier, vol. 151(C), pages 1-6.
    15. Messner, Sabine & Schrattenholzer, Leo, 2000. "MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively," Energy, Elsevier, vol. 25(3), pages 267-282.
    16. 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.
    17. Wang, Tao & Watson, Jim, 2010. "Scenario analysis of China's emissions pathways in the 21st century for low carbon transition," Energy Policy, Elsevier, vol. 38(7), pages 3537-3546, July.
    18. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    19. John Weyant, 2017. "Some Contributions of Integrated Assessment Models of Global Climate Change," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 11(1), pages 115-137.
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