IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i19p8961-d1767544.html
   My bibliography  Save this article

Model Construction and Scenario Analysis for Carbon Dioxide Emissions from Energy Consumption in Jiangsu Province: Based on the STIRPAT Extended Model

Author

Listed:
  • Ying Liu

    (Jiangsu Mineral Resources and Geological Design and Research Institute, China National Administration of Coal Geology, Xuzhou 221006, China)

  • Lvhan Yang

    (Sichuan Tianshengyuan Environmental Services Co., Ltd., Chengdu 610213, China)

  • Meng Wu

    (Jiangsu Mineral Resources and Geological Design and Research Institute, China National Administration of Coal Geology, Xuzhou 221006, China)

  • Jinxian He

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Wenqiang Wang

    (Jiangsu Mineral Resources and Geological Design and Research Institute, China National Administration of Coal Geology, Xuzhou 221006, China)

  • Yunpeng Li

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Renjiang Huang

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Dongfang Liu

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Heyao Tan

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Against the backdrop of China’s “dual carbon” strategy (carbon peaking and carbon neutrality), provincial-level carbon emission research is crucial for the implementation of related policies. However, existing studies insufficiently cover the driving mechanisms and scenario prediction for energy-importing provinces. This study can provide theoretical references for similar provinces in China to conduct research on carbon dioxide emissions from energy consumption. The carbon dioxide emissions from energy consumption in Jiangsu Province between 2000 and 2023 were calculated using the carbon emission coefficient method. The Tapio decoupling index model was adopted to evaluate the decoupling relationship between economic growth and carbon dioxide emissions from energy consumption in Jiangsu. An extended STIRPAT model was established to predict carbon dioxide emissions from energy consumption in Jiangsu, and this model was applied to analyze the emissions under three scenarios (baseline scenario, low-carbon scenario, and enhanced low-carbon scenario) during 2024–2030. The results show the following: (1) During 2000–2023, the carbon dioxide emissions from energy consumption in Jiangsu Province ranged from 215.22428 million tons to 783.94270 million tons, with an average of 549.96280 million tons. (2) The decoupling status between carbon dioxide emissions from energy consumption and economic development in Jiangsu was dominated by weak decoupling, accounting for 91.304%, while a small proportion (8.696%) of expansive coupling was also observed. (3) Under the baseline scenario, the carbon dioxide emissions from energy consumption in Jiangsu in 2030 will reach 796.828 million tons; under the low-carbon scenario, the emissions will be 786.355 million tons; and under the enhanced low-carbon scenario, the emissions will be 772.293 million tons. Furthermore, countermeasures and suggestions for reducing carbon dioxide emissions from energy consumption in Jiangsu are proposed, mainly including strengthening the guidance of policies and institutional systems, optimizing the energy consumption structure, intensifying technological innovation efforts, and enhancing government promotion and publicity.

Suggested Citation

  • Ying Liu & Lvhan Yang & Meng Wu & Jinxian He & Wenqiang Wang & Yunpeng Li & Renjiang Huang & Dongfang Liu & Heyao Tan, 2025. "Model Construction and Scenario Analysis for Carbon Dioxide Emissions from Energy Consumption in Jiangsu Province: Based on the STIRPAT Extended Model," Sustainability, MDPI, vol. 17(19), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8961-:d:1767544
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/19/8961/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/19/8961/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Griffin, Paul W. & Hammond, Geoffrey P. & Norman, Jonathan B., 2018. "Industrial energy use and carbon emissions reduction in the chemicals sector: A UK perspective," Applied Energy, Elsevier, vol. 227(C), pages 587-602.
    2. Hala Abou-Ali & Yasmine M. Abdelfattah & John Adams, 2016. "Population Dynamics and Carbon Emissions in the Arab Region: An Extended Stirpat II Model," Working Papers 988, Economic Research Forum, revised Apr 2016.
    3. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    4. Li, Ke & Lin, Boqiang, 2015. "Impacts of urbanization and industrialization on energy consumption/CO2 emissions: Does the level of development matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1107-1122.
    5. P. Hammond, Geoffrey & O' Grady, Áine, 2017. "The life cycle greenhouse gas implications of a UK gas supply transformation on a future low carbon electricity sector," Energy, Elsevier, vol. 118(C), pages 937-949.
    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 & Li, Zheng, 2020. "Is more use of electricity leading to less carbon emission growth? An analysis with a panel threshold model," Energy Policy, Elsevier, vol. 137(C).
    2. Yongxia Ding & Wei Qu & Shuwen Niu & Man Liang & Wenli Qiang & Zhenguo Hong, 2016. "Factors Influencing the Spatial Difference in Household Energy Consumption in China," Sustainability, MDPI, vol. 8(12), pages 1-20, December.
    3. Wang, Qiang & Lin, Jian & Zhou, Kan & Fan, Jie & Kwan, Mei-Po, 2020. "Does urbanization lead to less residential energy consumption? A comparative study of 136 countries," Energy, Elsevier, vol. 202(C).
    4. Wang, Shaojian & Li, Guangdong & Fang, Chuanglin, 2018. "Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2144-2159.
    5. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    6. Koçak, Emrah & Önderol, Seyit & Khan, Kamran, 2021. "Structural change, modernization, total factor productivity, and natural resources sustainability: An assessment with quantile and non-quantile estimators," Resources Policy, Elsevier, vol. 74(C).
    7. Nguyen Quan & Makoto Kakinaka & Koji Kotani, 2017. "How does urbanization affect energy and CO2 emission intensities in Vietnam? Evidence from province-level data," Working Papers SDES-2017-8, Kochi University of Technology, School of Economics and Management, revised Jun 2017.
    8. Chun Liu & Gui-hua Nie, 2021. "Identifying the Driving Factors of Food Nitrogen Footprint in China, 2000–2018: Econometric Analysis of Provincial Spatial Panel Data by the STIRPAT Model," Sustainability, MDPI, vol. 13(11), pages 1-23, May.
    9. Lapatinas, Athanasios & Garas, Antonios & Boleti, Eirini & Kyriakou, Alexandra, 2019. "Economic complexity and environmental performance: Evidence from a world sample," MPRA Paper 92833, University Library of Munich, Germany.
    10. Wang, Shaojian & Xie, Zihan & Wu, Rong & Feng, Kuishang, 2022. "How does urbanization affect the carbon intensity of human well-being? A global assessment," Applied Energy, Elsevier, vol. 312(C).
    11. Zhang, Ning & Yu, Keren & Chen, Zhongfei, 2017. "How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis," Energy Policy, Elsevier, vol. 107(C), pages 678-687.
    12. Sheng, Pengfei & Guo, Xiaohui, 2018. "Energy consumption associated with urbanization in China: Efficient- and inefficient-use," Energy, Elsevier, vol. 165(PB), pages 118-125.
    13. Nan, Shijing & Huo, Yuchen & You, Wanhai & Guo, Yawei, 2022. "Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity?," Energy Economics, Elsevier, vol. 112(C).
    14. repec:diw:diwwpp:dp1812 is not listed on IDEAS
    15. Rasool, Samma Faiz & Zaman, Shah & Jehan, Noor & Chin, Tachia & Khan, Saleem & Zaman, Qamar uz, 2022. "Investigating the role of the tech industry, renewable energy, and urbanization in sustainable environment: Policy directions in the context of developing economies," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    16. Yuxue Zhang & Rui Wang & Xingyuan Yang & He Zhang, 2023. "Can China Achieve Its Carbon Emission Peak Target? Empirical Evidence from City-Scale Driving Factors and Emission Reduction Strategies," Land, MDPI, vol. 12(6), pages 1-21, May.
    17. Weibo Zhao & Dongxiao Niu, 2017. "Prediction of CO 2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    18. Shoufu Lin & Ji Sun & Dora Marinova & Dingtao Zhao, 2017. "Effects of Population and Land Urbanization on China’s Environmental Impact: Empirical Analysis Based on the Extended STIRPAT Model," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
    19. 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).
    20. Jiang Qingquan & Shoukat Iqbal Khattak & Manzoor Ahmad & Lin Ping, 2020. "A new approach to environmental sustainability: Assessing the impact of monetary policy on CO2 emissions in Asian economies," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(5), pages 1331-1346, September.
    21. Zhibo Zhao & Tian Yuan & Xunpeng Shi & Lingdi Zhao, 2020. "Heterogeneity in the relationship between carbon emission performance and urbanization: evidence from China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1363-1380, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jsusta:v:17:y:2025:i:19:p:8961-:d:1767544. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.