IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v185y2022ics0040162522005959.html
   My bibliography  Save this article

Scenario prediction and decoupling analysis of carbon emission in Jiangsu Province, China

Author

Listed:
  • Dong, Jia
  • Li, Cunbin

Abstract

As a major energy consumption province in China, Jiangsu Province is a key area for carbon emission reduction in China. Grasping the future trends of carbon emission in Jiangsu Province will help to find effective ways to reduce carbon emission. This paper proposes the STIRPAT-IGWO-SVR model, including screening the carbon emission influencing factors based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, optimizing the parameters of the support vector regression (SVR) model using the gray wolf optimization (GWO) algorithm improved by the differential evolution (DE) algorithm, and sets five scenarios to predict and compare the carbon emissions of Jiangsu Province under different scenarios. In addition, the Tapio decoupling model is used to analyze the decoupling relationship of carbon emission and economic growth in each scenario. The results show that the STIRPAT-IGWO-SVR model proposed in this paper shows good performance compared with other models. For Jiangsu Province, improving the energy structure has the strongest inhibitory effect on carbon emission, stronger than reducing energy intensity, and far stronger than optimizing the industrial structure. Compared with a single plan, even if the measures are slightly weakened, the implementation of combined planning measures can more effectively control carbon emission.

Suggested Citation

  • Dong, Jia & Li, Cunbin, 2022. "Scenario prediction and decoupling analysis of carbon emission in Jiangsu Province, China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:tefoso:v:185:y:2022:i:c:s0040162522005959
    DOI: 10.1016/j.techfore.2022.122074
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522005959
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.122074?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhai, Mengyu & Huang, Guohe & Liu, Lirong & Guo, Zhengquan & Su, Shuai, 2021. "Segmented carbon tax may significantly affect the regional and national economy and environment-a CGE-based analysis for Guangdong Province," Energy, Elsevier, vol. 231(C).
    2. Zhao, Pengjun & Zeng, Liangen & Li, Peilin & Lu, Haiyan & Hu, Haoyu & Li, Chengming & Zheng, Mengyuan & Li, Haitao & Yu, Zhao & Yuan, Dandan & Xie, Jinxin & Huang, Qi & Qi, Yuting, 2022. "China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model," Energy, Elsevier, vol. 238(PC).
    3. Wang, Changjian & Wang, Fei & Zhang, Xinlin & Yang, Yu & Su, Yongxian & Ye, Yuyao & Zhang, Hongou, 2017. "Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 51-61.
    4. Tapio, Petri, 2005. "Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001," Transport Policy, Elsevier, vol. 12(2), pages 137-151, March.
    5. Pan, Xiongfeng & Guo, Shucen & Xu, Haitao & Tian, Mengyuan & Pan, Xianyou & Chu, Junhui, 2022. "China's carbon intensity factor decomposition and carbon emission decoupling analysis," Energy, Elsevier, vol. 239(PC).
    6. Xu, Haitao & Pan, Xiongfeng & Guo, Shucen & Lu, Yuduo, 2021. "Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis," Energy, Elsevier, vol. 228(C).
    7. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
    8. Huo, Tengfei & Xu, Linbo & Feng, Wei & Cai, Weiguang & Liu, Bingsheng, 2021. "Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050," Energy Policy, Elsevier, vol. 159(C).
    9. 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.
    10. Ma, Haoran, 2022. "Prediction of industrial power consumption in Jiangsu Province by regression model of time variable," Energy, Elsevier, vol. 239(PB).
    11. Li, Dezhi & Huang, Guanying & Zhu, Shiyao & Chen, Long & Wang, Jiangbo, 2021. "How to peak carbon emissions of provincial construction industry? Scenario analysis of Jiangsu Province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    12. 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).
    13. 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).
    14. Liu, Bingquan & Shi, Junxue & Wang, Hui & Su, Xuelin & Zhou, Peng, 2019. "Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier," Applied Energy, Elsevier, vol. 256(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhan, Jinyan & Wang, Chao & Wang, Huihui & Zhang, Fan & Li, Zhihui, 2024. "Pathways to achieve carbon emission peak and carbon neutrality by 2060: A case study in the Beijing-Tianjin-Hebei region, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Hui Li & Yanan Zheng & Guan Gong & Hongtao Guo, 2023. "A Simulation Study on Peak Carbon Emission of Public Buildings—In the Case of Henan Province, China," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    3. Yang, Yi & Qin, Huan, 2024. "The uncertainties of the carbon peak and the temporal and regional heterogeneity of its driving factors in China," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    4. Feng Dong & Guoqing Li & Yajie Liu & Qing Xu & Caixia Li, 2023. "Spatial-Temporal Evolution and Cross-Industry Synergy of Carbon Emissions: Evidence from Key Industries in the City in Jiangsu Province, China," Sustainability, MDPI, vol. 15(5), pages 1-27, February.

    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. Huo, Tengfei & Du, Qianxi & Xu, Linbo & Shi, Qingwei & Cong, Xiaobo & Cai, Weiguang, 2023. "Timetable and roadmap for achieving carbon peak and carbon neutrality of China's building sector," Energy, Elsevier, vol. 274(C).
    2. Chen, Qingjuan & Wang, Qunwei & Zhou, Dequn & Wang, Honggang, 2023. "Drivers and evolution of low-carbon development in China's transportation industry: An integrated analytical approach," Energy, Elsevier, vol. 262(PB).
    3. Miao, Ankang & Yuan, Yue & Wu, Han & Ma, Xin & Shao, Chenyu & Xiang, Sheng, 2024. "Pathway for China's provincial carbon emission peak: A case study of the Jiangsu Province," Energy, Elsevier, vol. 298(C).
    4. Zhang, Pan & Wang, Huan, 2022. "Do provincial energy policies and energy intensity targets help reduce CO2 emissions? Evidence from China," Energy, Elsevier, vol. 245(C).
    5. Zhou, Yiqi & Zou, Shan & Duan, Weili & Chen, Yaning & Takara, Kaoru & Di, Yanfeng, 2022. "Analysis of energy carbon emissions from agroecosystems in Tarim River Basin, China: A pathway to achieve carbon neutrality," Applied Energy, Elsevier, vol. 325(C).
    6. Chen, Changhua & Luo, Yuqing & Zou, Hong & Huang, Junbing, 2023. "Understanding the driving factors and finding the pathway to mitigating carbon emissions in China's Yangtze River Delta region," Energy, Elsevier, vol. 278(PB).
    7. An Cheng & Xinru Han & Guogang Jiang, 2023. "Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
    8. Regueiro-Ferreira, Rosa María & Alonso-Fernández, Pablo, 2023. "Interaction between renewable energy consumption and dematerialization: Insights based on the material footprint and the Environmental Kuznets Curve," Energy, Elsevier, vol. 266(C).
    9. Han Wang & Yujie Jin & Xingming Hong & Fuan Tian & Jianxian Wu & Xin Nie, 2022. "Integrating IPAT and CLUMondo Models to Assess the Impact of Carbon Peak on Land Use," Land, MDPI, vol. 11(4), pages 1-16, April.
    10. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    11. Yang, Yi & Yuan, Zhuqing & Yang, Shengnan, 2022. "Difference in the drivers of industrial carbon emission costs determines the diverse policies in middle-income regions: A case of northwestern China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    12. Huo, Tengfei & Ma, Yuling & Xu, Linbo & Feng, Wei & Cai, Weiguang, 2022. "Carbon emissions in China's urban residential building sector through 2060: A dynamic scenario simulation," Energy, Elsevier, vol. 254(PA).
    13. Chao-Qun Ma & Jiang-Long Liu & Yi-Shuai Ren & Yong Jiang, 2019. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO 2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model," Energies, MDPI, vol. 12(24), pages 1-16, December.
    14. 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.
    15. Liang, Xiaoying & Min Fan, & Xiao, Yuting & Yao, Jing, 2022. "Temporal-spatial characteristics of energy-based carbon dioxide emissions and driving factors during 2004–2019, China," Energy, Elsevier, vol. 261(PA).
    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. Lijun Zhang & Caiyun Kou & Ji Zheng & Yu Li, 2018. "Decoupling Analysis of CO 2 Emissions in Transportation Sector from Economic Growth during 1995–2015 for Six Cities in Hebei, China," Sustainability, MDPI, vol. 10(11), pages 1-15, November.
    18. Meng Yang & Yisheng Liu & Jinzhao Tian & Feiyu Cheng & Pengbo Song, 2022. "Dynamic Evolution and Regional Disparity in Carbon Emission Intensity in China," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    19. Yulia I. Pyzheva & Evgeniya V. Zander & Anton I. Pyzhev, 2021. "Impacts of Energy Efficiency and Economic Growth on Air Pollutant Emissions: Evidence from Angara–Yenisey Siberia," Energies, MDPI, vol. 14(19), pages 1-10, September.
    20. Huang, Yuan & Yu, Qiang & Wang, Ruirui, 2021. "Driving factors and decoupling effect of carbon footprint pressure in China: Based on net primary production," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

    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:eee:tefoso:v:185:y:2022:i:c:s0040162522005959. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.