IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i14p8791-d866596.html
   My bibliography  Save this article

Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example

Author

Listed:
  • Feipeng Guo

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
    Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Linji Zhang

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
    Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Zifan Wang

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
    Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Shaobo Ji

    (Sprott School of Business, Carleton University, Ottawa, ON K1S 5B6, Canada)

Abstract

Driven by China’s peak carbon emissions and carbon neutrality goals, each region should choose a suitable local implementation path according to local conditions, so it is of great significance to mine and analyze the critical influencing factors of regional carbon emissions. Therefore, this paper integrates grey relation analysis (GRA) and an improved STIRPAT model and selects the Yangtze River Delta region of China as the research object to analyze the factors affecting carbon emissions in four provinces in the region. Firstly, it uses the IPCC method to calculate the energy carbon emissions of each province. Secondly, according to the existing research, the relevant influencing factors of carbon emissions are sorted and summarized as candidate sets and this paper uses GRA to calculate the correlation degree of the above candidate sets. On this basis, this paper combines with the characteristics of the improved STIRPAT model to determine the index selection criteria and filter out the critical factors of each province. Thirdly, an improved STIRPAT model is constructed for each province to explore the influence of critical factors and analyze the influencing factors of carbon emissions in detail. The empirical results show that during the period from 2005 to 2019, the carbon emissions of the four provinces in the Yangtze River Delta are significantly different in structure and trend. At the same time, the critical influencing factors of each province are different and the influence of the same factor on different regions is significantly different. Finally, the policy suggestions for the provinces to achieve their peak carbon emissions and carbon neutrality goals are precisely tailored to the different carbon emission influencing factors.

Suggested Citation

  • Feipeng Guo & Linji Zhang & Zifan Wang & Shaobo Ji, 2022. "Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example," IJERPH, MDPI, vol. 19(14), pages 1-20, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8791-:d:866596
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/14/8791/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/14/8791/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shi, Anqing, 2003. "The impact of population pressure on global carbon dioxide emissions, 1975-1996: evidence from pooled cross-country data," Ecological Economics, Elsevier, vol. 44(1), pages 29-42, February.
    2. Chuanhe Xiong & Shuang Chen & Liting Xu, 2020. "Driving factors analysis of agricultural carbon emissions based on extended STIRPAT model of Jiangsu Province, China," Growth and Change, Wiley Blackwell, vol. 51(3), pages 1401-1416, September.
    3. Ang, B.W & Zhang, F.Q & Choi, Ki-Hong, 1998. "Factorizing changes in energy and environmental indicators through decomposition," Energy, Elsevier, vol. 23(6), pages 489-495.
    4. 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.
    5. Liddle, Brantley & Lung, Sidney, 2010. "Age-Structure, Urbanization, and Climate Change in Developed Countries: Revisiting STIRPAT for Disaggregated Population and Consumption-Related Environmental Impacts," MPRA Paper 59579, University Library of Munich, Germany.
    6. Omri, Anis & Nguyen, Duc Khuong & Rault, Christophe, 2014. "Causal interactions between CO2 emissions, FDI, and economic growth: Evidence from dynamic simultaneous-equation models," Economic Modelling, Elsevier, vol. 42(C), pages 382-389.
    7. Feipeng Guo & Zifan Wang & Shaobo Ji & Qibei Lu, 2022. "Influential Nodes Identification in the Air Pollution Spatial Correlation Weighted Networks and Collaborative Governance: Taking China’s Three Urban Agglomerations as Examples," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
    8. Mohammad Reza Lotfalipour & Mohammad Ali Falahi & Morteza Bastam, 2013. "Prediction of CO2 Emissions in Iran using Grey and ARIMA Models," International Journal of Energy Economics and Policy, Econjournals, vol. 3(3), pages 229-237.
    9. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    10. Wang, W.W. & Zhang, M. & Zhou, M., 2011. "Using LMDI method to analyze transport sector CO2 emissions in China," Energy, Elsevier, vol. 36(10), pages 5909-5915.
    11. Yuan, Baolong & Ren, Shenggang & Chen, Xiaohong, 2015. "The effects of urbanization, consumption ratio and consumption structure on residential indirect CO2 emissions in China: A regional comparative analysis," Applied Energy, Elsevier, vol. 140(C), pages 94-106.
    12. Baek, Jungho, 2016. "A new look at the FDI–income–energy–environment nexus: Dynamic panel data analysis of ASEAN," Energy Policy, Elsevier, vol. 91(C), pages 22-27.
    13. Jing Li & Pingyu Zhang & Kevin Lo & Meng Guo & Mark Wang, 2015. "Reducing Carbon Emissions from Shopping Trips: Evidence from China," Energies, MDPI, vol. 8(9), pages 1-15, September.
    14. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    15. repec:ipg:wpaper:2014-542 is not listed on IDEAS
    16. Matthew A. Cole & Eric Neumayer, 2003. "Examining the Impact of Demographic Factors On Air Pollution," Labor and Demography 0312005, University Library of Munich, Germany, revised 13 May 2004.
    17. Shahbaz, Muhammad & Loganathan, Nanthakumar & Sbia, Rashid & Afza, Talat, 2015. "The effect of urbanization, affluence and trade openness on energy consumption: A time series analysis in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 683-693.
    18. Zhang, Yu & Zhang, Sufang, 2018. "The impacts of GDP, trade structure, exchange rate and FDI inflows on China's carbon emissions," Energy Policy, Elsevier, vol. 120(C), pages 347-353.
    19. Chen Li & Heng Li & Xionghe Qin, 2022. "Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities," IJERPH, MDPI, vol. 19(3), pages 1-29, January.
    20. Ji, Xi & Yao, Yixin & Long, Xianling, 2018. "What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective," Energy Policy, Elsevier, vol. 119(C), pages 458-472.
    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. Ruixia Suo & Qi Wang & Qiutong Han, 2024. "Driver Analysis and Integrated Prediction of Carbon Emissions in China Using Machine Learning Models and Empirical Mode Decomposition," Mathematics, MDPI, vol. 12(14), pages 1-16, July.
    2. Hongqiang Wang & Wenyi Xu & Yingjie Zhang, 2023. "Research on Provincial Carbon Emission Reduction Path Based on LMDI-SD-Tapio Decoupling Model: The Case of Guizhou, China," Sustainability, MDPI, vol. 15(17), pages 1-20, September.
    3. Jia Peng & Xianli Hu & Xinyue Fan & Kai Wang & Hao Gong, 2023. "The Impact of the Green Economy on Carbon Emission Intensity: Comparisons, Challenges, and Mitigating Strategies," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    4. Kerong Zhang & Liangyu Jiang & Yanzhi Jin & Wuyi Liu, 2022. "The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China," IJERPH, MDPI, vol. 19(24), pages 1-28, December.
    5. Qianru Guo & Xiuting Lai & Yanhong Jia & Feili Wei, 2023. "Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors," Sustainability, MDPI, vol. 15(21), pages 1-14, October.

    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. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    2. Zhang, Chuanguo & Tan, Zheng, 2016. "The relationships between population factors and China's carbon emissions: Does population aging matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1018-1025.
    3. Liddle, Brantley, 2013. "Population, Affluence, and Environmental Impact Across Development: Evidence from Panel Cointegration Modeling," MPRA Paper 52088, University Library of Munich, Germany.
    4. Fang, Wen Shwo & Miller, Stephen M. & Yeh, Chih-Chuan, 2012. "The effect of ESCOs on energy use," Energy Policy, Elsevier, vol. 51(C), pages 558-568.
    5. 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.
    6. Shafiei, Sahar & Salim, Ruhul A., 2014. "Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis," Energy Policy, Elsevier, vol. 66(C), pages 547-556.
    7. Menz, Tobias & Welsch, Heinz, 2012. "Population aging and carbon emissions in OECD countries: Accounting for life-cycle and cohort effects," Energy Economics, Elsevier, vol. 34(3), pages 842-849.
    8. 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.
    9. Phetkeo Poumanyvong & Shinji Kaneko & Shobhakar Dhakal, 2012. "Impacts of urbanization on national residential energy use and CO2 emissions: Evidence from low-, middle- and high-income countries," IDEC DP2 Series 2-5, Hiroshima University, Graduate School for International Development and Cooperation (IDEC).
    10. Shahbaz, Muhammad & Loganathan, Nanthakumar & Muzaffar, Ahmed Taneem & Ahmed, Khalid & Ali Jabran, Muhammad, 2016. "How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 83-93.
    11. Martínez-Zarzoso, Inmaculada & Maruotti, Antonello, 2011. "The impact of urbanization on CO2 emissions: Evidence from developing countries," Ecological Economics, Elsevier, vol. 70(7), pages 1344-1353, May.
    12. Lisa Gianmoena & Vicente Rios, 2018. "The Determinants of CO2 Emissions Differentials with Cross-Country Interaction Effects: A Dynamic Spatial Panel Data Bayesian Model Averaging Approach," Discussion Papers 2018/234, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    13. Wang, Shaojian & Zeng, Jingyuan & Huang, Yongyuan & Shi, Chenyi & Zhan, Peiyu, 2018. "The effects of urbanization on CO2 emissions in the Pearl River Delta: A comprehensive assessment and panel data analysis," Applied Energy, Elsevier, vol. 228(C), pages 1693-1706.
    14. Shaojian Wang & Chuanglin Fang & Guangdong Li, 2015. "Spatiotemporal Characteristics, Determinants and Scenario Analysis of CO2 Emissions in China Using Provincial Panel Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
    15. Vanesa Zorrilla-Muñoz & Marc Petz & María Silveria Agulló-Tomás, 2021. "GARCH model to estimate the impact of agricultural greenhouse gas emissions per sociodemographic factors and CAP in Spain," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 4675-4697, March.
    16. Ronald R. Kumar & Peter J. Stauvermann, 2019. "The Effects of a Revenue-Neutral Child Subsidy Tax Mechanism on Growth and GHG Emissions," Sustainability, MDPI, vol. 11(9), pages 1-23, May.
    17. Pani, Ratnakar & Mukhopadhyay, Ujjaini, 2011. "Variance analysis of global CO2 emission – A management accounting approach for decomposition study," Energy, Elsevier, vol. 36(1), pages 486-499.
    18. Olaronke T. ONANUGA, 2017. "Elasticity of CO2 emissions with Respect to Income, Population, and Energy Use: Time Series Evidence from African Countries," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 4, pages 651-670, December.
    19. Wang, Qiang & Wu, Shi-dai & Zeng, Yue-e & Wu, Bo-wei, 2016. "Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1563-1579.
    20. 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.

    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:jijerp:v:19:y:2022:i:14:p:8791-:d:866596. 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.