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Projections in Various Scenarios and the Impact of Economy, Population, and Technology for Regional Emission Peak and Carbon Neutrality in China

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  • Song Wang

    (School of Business Administration, Northeastern University, Shenyang 110167, China
    The Key Laboratory of Carbon Neutralization and Land Space Optimization, Nanjing University, Nanjing 210023, China)

  • Yixiao Wang

    (School of Business Administration, Northeastern University, Shenyang 110167, China)

  • Chenxin Zhou

    (School of Business Administration, Northeastern University, Shenyang 110167, China)

  • Xueli Wang

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

Abstract

Owing to the surge in greenhouse gas emissions, climate change is attracting increasing attention worldwide. As the world’s largest carbon emitter, the achievement of emission peak and carbon neutrality by China is seen as a milestone in the global response to the threat. By setting different “emission peak” and “carbon neutrality” paths, this study compares the different pathways taken by China towards regional emission reduction to illustrate China’s possible contribution to global emission reduction, and analyzes the role that China’s economy, population, and technology need to play in this process through the Stochastic Impacts by Regression on Population, Affluence, and Technology model. In terms of path setting, based on actual carbon emissions in various regions from 2000 to 2019 and grid data on land use from 2000 to 2020, the model simulates three emission peak paths to 2030 and two carbon neutrality paths to 2060, thus setting six possible carbon emission trends from 2000 to 2060 in different regions. It is found that the higher the unity of policy objectives at the emission peak stage, the lower the heterogeneity of the inter-regional carbon emission trends. In the carbon neutrality stage, the carbon emissions in the unconstrained symmetrical extension decline state scenario causes the greatest environmental harm. Certain regions must shoulder heavier responsibilities in the realization of carbon neutrality. The economic development level can lead to a rise in carbon emissions at the emission peak stage and inhibit it at the carbon neutrality stage. Furthermore, the dual effects of population scale and its quality level will increase carbon emissions at the emission peak stage and decrease it at the carbon neutrality stage. There will be a time lag between the output of science and technology innovation and its industrialization, while green innovation is a key factor in carbon neutrality. Based on the results, this study puts forward policy suggestions from a macro perspective to better realize China’s carbon emission goals.

Suggested Citation

  • Song Wang & Yixiao Wang & Chenxin Zhou & Xueli Wang, 2022. "Projections in Various Scenarios and the Impact of Economy, Population, and Technology for Regional Emission Peak and Carbon Neutrality in China," IJERPH, MDPI, vol. 19(19), pages 1-31, September.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12126-:d:924737
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    References listed on IDEAS

    as
    1. Roca, Jordi & Padilla, Emilio & Farre, Mariona & Galletto, Vittorio, 2001. "Economic growth and atmospheric pollution in Spain: discussing the environmental Kuznets curve hypothesis," Ecological Economics, Elsevier, vol. 39(1), pages 85-99, October.
    2. Tinus Pulles & Hongwei Yang, 2011. "GHG emission estimates for road transport in national GHG inventories," Climate Policy, Taylor & Francis Journals, vol. 11(2), pages 944-957, March.
    3. Wang, Zhaohua & Huang, Wanjing & Chen, Zhongfei, 2019. "The peak of CO2 emissions in China: A new approach using survival models," Energy Economics, Elsevier, vol. 81(C), pages 1099-1108.
    4. Robaina-Alves, Margarita & Moutinho, Victor, 2014. "Decomposition of energy-related GHG emissions in agriculture over 1995–2008 for European countries," Applied Energy, Elsevier, vol. 114(C), pages 949-957.
    5. Dai, Shangze & Fan, Fei & Zhang, Keke, 2022. "Creative Destruction and Stock Price Informativeness in Emerging Economies," MPRA Paper 113661, University Library of Munich, Germany.
    6. Stephanie Roe & Charlotte Streck & Michael Obersteiner & Stefan Frank & Bronson Griscom & Laurent Drouet & Oliver Fricko & Mykola Gusti & Nancy Harris & Tomoko Hasegawa & Zeke Hausfather & Petr Havlík, 2019. "Contribution of the land sector to a 1.5 °C world," Nature Climate Change, Nature, vol. 9(11), pages 817-828, November.
    7. Liu, Jiaguo & Li, Sujuan & Ji, Qiang, 2021. "Regional differences and driving factors analysis of carbon emission intensity from transport sector in China," Energy, Elsevier, vol. 224(C).
    8. Haiqian Ke & Wenyi Yang & Xiaoyang Liu & Fei Fan, 2020. "Does Innovation Efficiency Suppress the Ecological Footprint? Empirical Evidence from 280 Chinese Cities," IJERPH, MDPI, vol. 17(18), pages 1-23, September.
    9. Tan, Xianchun & Lai, Haiping & Gu, Baihe & Zeng, Yuan & Li, Hui, 2018. "Carbon emission and abatement potential outlook in China's building sector through 2050," Energy Policy, Elsevier, vol. 118(C), pages 429-439.
    10. Wu, Rong & Wang, Jieyu & Wang, Shaojian & Feng, Kuishuang, 2021. "The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    11. Wang, Xueli & Wang, Lei & Zhang, Xuerong & Fan, Fei, 2022. "The spatiotemporal evolution of COVID-19 in China and its impact on urban economic resilience," China Economic Review, Elsevier, vol. 74(C).
    12. Paltsev, Sergey & Morris, Jennifer & Kheshgi, Haroon & Herzog, Howard, 2021. "Hard-to-Abate Sectors: The role of industrial carbon capture and storage (CCS) in emission mitigation," Applied Energy, Elsevier, vol. 300(C).
    13. Gao, Cuixia & Tao, Simin & He, Yuyang & Su, Bin & Sun, Mei & Mensah, Isaac Adjei, 2021. "Effect of population migration on spatial carbon emission transfers in China," Energy Policy, Elsevier, vol. 156(C).
    14. Yuan, Rong & Behrens, Paul & Rodrigues, João F.D., 2018. "The evolution of inter-sectoral linkages in China's energy-related CO2 emissions from 1997 to 2012," Energy Economics, Elsevier, vol. 69(C), pages 404-417.
    15. Kasman, Adnan & Duman, Yavuz Selman, 2015. "CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis," Economic Modelling, Elsevier, vol. 44(C), pages 97-103.
    16. Terry Barker & Annela Anger & Unnada Chewpreecha & Hector Pollitt, 2012. "A new economics approach to modelling policies to achieve global 2020 targets for climate stabilisation," International Review of Applied Economics, Taylor & Francis Journals, vol. 26(2), pages 205-221, October.
    17. Knapp, Tom & Mookerjee, Rajen, 1996. "Population growth and global CO2 emissions : A secular perspective," Energy Policy, Elsevier, vol. 24(1), pages 31-37, January.
    18. Kabir, Md Nurul & Rahman, Sohanur & Rahman, Md Arifur & Anwar, Mumtaheena, 2021. "Carbon emissions and default risk: International evidence from firm-level data," Economic Modelling, Elsevier, vol. 103(C).
    19. Song Wang & Jiexin Wang & Chenqi Wei & Xueli Wang & Fei Fan, 2021. "Collaborative innovation efficiency: From within cities to between cities—Empirical analysis based on innovative cities in China," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1330-1360, September.
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