IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0293763.html
   My bibliography  Save this article

What influences the performance of carbon emissions in China?—Research on the inter-provincial carbon emissions’ conditional configuration impacts

Author

Listed:
  • Weidong Chen
  • Dongli Li
  • Quanling Cai
  • Kaisheng Di
  • Caiping Liu
  • Mingxing Wang

Abstract

The severe global warming issue currently threatens humans’ existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments’ configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government’s carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China’s eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region’s resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

Suggested Citation

  • Weidong Chen & Dongli Li & Quanling Cai & Kaisheng Di & Caiping Liu & Mingxing Wang, 2024. "What influences the performance of carbon emissions in China?—Research on the inter-provincial carbon emissions’ conditional configuration impacts," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-26, April.
    • Handle: RePEc:plo:pone00:0293763
    DOI: 10.1371/journal.pone.0293763
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293763
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0293763&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0293763?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lu, Shibao & Wang, Jianhua & Shang, Yizi & Bao, Haijun & Chen, Huixiong, 2017. "Potential assessment of optimizing energy structure in the city of carbon intensity target," Applied Energy, Elsevier, vol. 194(C), pages 765-773.
    2. Su, Chi Wei & Liu, Fangying & Stefea, Petru & Umar, Muhammad, 2023. "Does technology innovation help to achieve carbon neutrality?," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1-14.
    3. Camilo Mora & Daniele Spirandelli & Erik C. Franklin & John Lynham & Michael B. Kantar & Wendy Miles & Charlotte Z. Smith & Kelle Freel & Jade Moy & Leo V. Louis & Evan W. Barba & Keith Bettinger & Ab, 2018. "Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions," Nature Climate Change, Nature, vol. 8(12), pages 1062-1071, December.
    4. Hong Xu & Baozhen Liu & Kai Lin & Yunyun Zhang & Bei Liu & Mingjie Xie, 2022. "Towards Carbon Neutrality: Carbon Emission Performance of Science and Technology Finance Policy," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    5. Lee, Chien-Chiang & Wang, Fuhao, 2022. "How does digital inclusive finance affect carbon intensity?," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 174-190.
    6. Tu, Zhengge & Hu, Tianyang & Shen, Renjun, 2019. "Evaluating public participation impact on environmental protection and ecological efficiency in China: Evidence from PITI disclosure," China Economic Review, Elsevier, vol. 55(C), pages 111-123.
    7. Mirziyoyeva, Ziroat & Salahodjaev, Raufhon, 2022. "Renewable energy and CO2 emissions intensity in the top carbon intense countries," Renewable Energy, Elsevier, vol. 192(C), pages 507-512.
    8. Lupi, Veronica & Marsiglio, Simone, 2021. "Population growth and climate change: A dynamic integrated climate-economy-demography model," Ecological Economics, Elsevier, vol. 184(C).
    9. Yang, Xue & Su, Bin, 2019. "Impacts of international export on global and regional carbon intensity," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    11. Zhou, Yang & Liu, Yansui, 2016. "Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China," Applied Energy, Elsevier, vol. 180(C), pages 800-809.
    12. Habiba, Umme & Xinbang, Cao & Anwar, Ahsan, 2022. "Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions?," Renewable Energy, Elsevier, vol. 193(C), pages 1082-1093.
    13. Danish & Recep Ulucak & Salah‐Ud‐Din Khan, 2020. "Relationship between energy intensity and CO2 emissions: Does economic policy matter?," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(5), pages 1457-1464, September.
    14. Śmiech, Sławomir & Papież, Monika, 2014. "Energy consumption and economic growth in the light of meeting the targets of energy policy in the EU: The bootstrap panel Granger causality approach," Energy Policy, Elsevier, vol. 71(C), pages 118-129.
    15. Zhang, Wei & Li, Jing & Li, Guoxiang & Guo, Shucen, 2020. "Emission reduction effect and carbon market efficiency of carbon emissions trading policy in China," Energy, Elsevier, vol. 196(C).
    16. Sun, Chuanwang & Ding, Dan & Yang, Mian, 2017. "Estimating the complete CO2 emissions and the carbon intensity in India: From the carbon transfer perspective," Energy Policy, Elsevier, vol. 109(C), pages 418-427.
    17. Yue-Jun Zhang & Zhao Liu & Huan Zhang & Tai-De Tan, 2014. "The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 579-595, September.
    18. Bashir, Muhammad Adnan & Sheng, Bin & Doğan, Buhari & Sarwar, Suleman & Shahzad, Umer, 2020. "Export product diversification and energy efficiency: Empirical evidence from OECD countries," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 232-243.
    19. Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
    20. Zheng, Jiali & Mi, Zhifu & Coffman, D'Maris & Milcheva, Stanimira & Shan, Yuli & Guan, Dabo & Wang, Shouyang, 2019. "Regional development and carbon emissions in China," Energy Economics, Elsevier, vol. 81(C), pages 25-36.
    21. Liddle, Brantley, 2014. "Impact of population, age structure, and urbanization on carbon emissions/energy consumption: Evidence from macro-level, cross-country analyses," MPRA Paper 61306, University Library of Munich, Germany.
    22. Liu, Xiao & Hang, Ye & Wang, Qunwei & Chiu, Ching-Ren & Zhou, Dequn, 2022. "The role of energy consumption in global carbon intensity change: A meta-frontier-based production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 109(C).
    23. Liu, Yang & Luan, Lin & Wu, Weilong & Zhang, Zhiqiang & Hsu, Yen, 2021. "Can digital financial inclusion promote China's economic growth?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    24. Chen, Dengke & Chen, Shiyi & Jin, Hao & Lu, Yulin, 2020. "The impact of energy regulation on energy intensity and energy structure: Firm-level evidence from China," China Economic Review, Elsevier, vol. 59(C).
    25. Wang, Feng & Sun, Xiaoyu & Reiner, David M. & Wu, Min, 2020. "Changing trends of the elasticity of China's carbon emission intensity to industry structure and energy efficiency," Energy Economics, Elsevier, vol. 86(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Boqiang & Wang, Chonghao, 2023. "Does industrial relocation affect regional carbon intensity? Evidence from China's secondary industry," Energy Policy, Elsevier, vol. 173(C).
    2. Cheng, Xiaoqiang & Yao, Dingjun & Qian, Yuanyuan & Wang, Bin & Zhang, Deliang, 2023. "How does fintech influence carbon emissions: Evidence from China's prefecture-level cities," International Review of Financial Analysis, Elsevier, vol. 87(C).
    3. Wang, Zhenguo & Su, Bin & Xie, Rui & Long, Haiyu, 2020. "China’s aggregate embodied CO2 emission intensity from 2007 to 2012: A multi-region multiplicative structural decomposition analysis," Energy Economics, Elsevier, vol. 85(C).
    4. Haiqiao Wang & Li Shang & Decai Tang & Zhijiang Li, 2024. "Research Themes, Evolution Trends, and Future Challenges in China’s Carbon Emission Studies," Sustainability, MDPI, vol. 16(5), pages 1-22, March.
    5. Bu, Caiqi & Zhang, Kaixia & Shi, Daqian & Wang, Shuyu, 2022. "Does environmental information disclosure improve energy efficiency?," Energy Policy, Elsevier, vol. 164(C).
    6. Xu, Chengzhen & Zhu, Qingyuan & Li, Xingchen & Wu, Liangpeng & Deng, Ping, 2024. "Determinants of global carbon emission and aggregate carbon intensity: A multi-region input−output approach," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 418-435.
    7. Xu, Renjing & Xu, Bin, 2022. "Exploring the effective way of reducing carbon intensity in the heavy industry using a semiparametric econometric approach," Energy, Elsevier, vol. 243(C).
    8. Li, Shuoshuo & Liu, Yaobin & Elahi, Ehsan & Meng, Xiao & Deng, Weifeng, 2023. "A new type of urbanization policy and transition of low-carbon society: A "local- neighborhood" perspective," Land Use Policy, Elsevier, vol. 131(C).
    9. Muhammad, Tufail & Ni, Guohua & Chen, Zhenling & Mallek, Sabrine & Dudek, Marek & Mentel, Grzegorz, 2024. "Addressing resource curse: How mineral resources influence industrial structure dynamics of the BRI 57 oil-exporting countries," Resources Policy, Elsevier, vol. 99(C).
    10. Dong Jichang & He Jing & Li Xiuting & Mou Xindi & Dong Zhi, 2020. "The Effect of Industrial Structure Change on Carbon Dioxide Emissions: A Cross-Country Panel Analysis," Journal of Systems Science and Information, De Gruyter, vol. 8(1), pages 1-16, February.
    11. Zhao, Ruizeng & An, Yuchen & Tu, Hanyun & Song, Jiashan, 2024. "China's economic growth and low-carbon development under the background of resource curse: A new perspective based on digital finance," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    12. Antonello Maruotti & Pierfrancesco Alaimo Di Loro, 2023. "CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    13. Geng Peng & Yixuan Tang & Kaiyou Tian, 2023. "Understanding the Nonlinear Impact of Information and Communication Technology on Carbon Emissions in the Logistics Industry of China," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    14. Cai, Jinyang & Zheng, Huanyu & Vardanyan, Michael & Shen, Zhiyang, 2023. "Achieving carbon neutrality through green technological progress: evidence from China," Energy Policy, Elsevier, vol. 173(C).
    15. Yu, Shiyu & Liang, Yanpeng & Zhu, Zhe & Olaniyi, Oladokun Nafiu & Khan, Numan, 2024. "Dutch disease perspective of energy sector: Natural resources and energy sector nexus with the role of renewable energy consumption," Resources Policy, Elsevier, vol. 90(C).
    16. Li, Rongrong & Han, Xinyu & Wang, Qiang, 2023. "Do technical differences lead to a widening gap in China's regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    17. Lee, Chien-Chiang & Tang, Manting & Lee, Chi-Chuan, 2023. "Reaping digital dividends: Digital inclusive finance and high-quality development of enterprises in China," Telecommunications Policy, Elsevier, vol. 47(2).
    18. Wang, Yanpeng & Cui, Lianbiao & Zhou, Jie, 2025. "The impact of green finance and digital economy on regional carbon emission reduction," International Review of Economics & Finance, Elsevier, vol. 97(C).
    19. Qin Yuan & Ruiqi Wang & Huanchen Tang & Xin Ma & Xinyue Zeng, 2024. "A study on the potential of higher education in reducing carbon intensity," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-21, November.
    20. Xinchun Zhang & Dong Liang & Shuo Zhang, 2025. "The Impact of Digital–Financial Dual Pilot Policy on Carbon Emission Efficiency: Evidence from Chinese Cities," Land, MDPI, vol. 14(4), pages 1-31, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0293763. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.