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Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

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  • Decai Tang

    (Institute of Climate Change and Public Policy, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Tingyu Ma

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Zhijiang Li

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Jiexin Tang

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Brandon J. Bethel

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

According to the economic and energy consumption statistics in Jiangsu Province, we combined the GM (1, 1) grey model and polynomial regression to forecast carbon emissions. Historical and projected emissions were decomposed using the Logarithmic Mean Divisia Index (LMDI) approach to assess the relative contribution of different factors to emission variability. The results showed that carbon emissions will continue to increase in Jiangsu province during 2015–2020 period and cumulative carbon emissions will increase by 39.5487 million tons within the forecast period. The growth of gross domestic product (GDP) per capita plays the greatest positive role in driving carbon emission growth. Furthermore, the improvement of energy usage efficiency is the primary factor responsible for reducing carbon emissions. Factors of population, industry structure adjustment and the optimization of fuel mix also help to reduce carbon emissions. Based on the LMDI analysis, we provide some advice for policy-makers in Jiangsu and other provinces in China.

Suggested Citation

  • Decai Tang & Tingyu Ma & Zhijiang Li & Jiexin Tang & Brandon J. Bethel, 2016. "Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020," Sustainability, MDPI, vol. 8(10), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1018-:d:80396
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    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    3. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    4. Tan, Xianchun & Dong, Lele & Chen, Dexue & Gu, Baihe & Zeng, Yuan, 2016. "China’s regional CO2 emissions reduction potential: A study of Chongqing city," Applied Energy, Elsevier, vol. 162(C), pages 1345-1354.
    5. Ma, Peizheng & Wang, Lin-Shu & Guo, Nianhua, 2014. "Modeling of hydronic radiant cooling of a thermally homeostatic building using a parametric cooling tower," Applied Energy, Elsevier, vol. 127(C), pages 172-181.
    6. Kais, Saidi & Sami, Hammami, 2016. "An econometric study of the impact of economic growth and energy use on carbon emissions: Panel data evidence from fifty eight countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1101-1110.
    7. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    8. Chen, Lin & Lin, Weilong & Li, Junzi & Tian, Binbin & Pan, Haihong, 2016. "Prediction of lithium-ion battery capacity with metabolic grey model," Energy, Elsevier, vol. 106(C), pages 662-672.
    9. Lau, Lin-Sea & Choong, Chee-Keong & Eng, Yoke-Kee, 2014. "Carbon dioxide emission, institutional quality, and economic growth: Empirical evidence in Malaysia," Renewable Energy, Elsevier, vol. 68(C), pages 276-281.
    10. Wang, Qunwei & Hang, Ye & Zhou, P. & Wang, Yizhong, 2016. "Decoupling and attribution analysis of industrial carbon emissions in Taiwan," Energy, Elsevier, vol. 113(C), pages 728-738.
    11. Ertugrul, Hasan Murat & Çetin, Murat & Şeker, Fahri & Dogan, Eyüp, 2015. "The impact of trade openness on global carbon dioxide emissions: Evidence from the top ten emitters among developing countries," MPRA Paper 97539, University Library of Munich, Germany, revised 10 Mar 2016.
    12. Paul J. Burke & Md Shahiduzzaman & David I. Stern, 2015. "Carbon dioxide emissions in the short run: The rate and sources of economic growth matter," CAMA Working Papers 2015-12, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2015. "Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 51(C), pages 252-260.
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    Cited by:

    1. Sheng-Wen Tseng, 2019. "Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    2. Ming Meng & Manyu Li, 2020. "Decomposition Analysis and Trend Prediction of CO 2 Emissions in China’s Transportation Industry," Sustainability, MDPI, vol. 12(7), pages 1-20, March.
    3. Thi-Nham Le & Chia-Nan Wang, 2017. "The Integrated Approach for Sustainable Performance Evaluation in Value Chain of Vietnam Textile and Apparel Industry," Sustainability, MDPI, vol. 9(3), pages 1-21, March.
    4. Ying Wang & Peipei Shang & Lichun He & Yingchun Zhang & Dandan Liu, 2018. "Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?," Energies, MDPI, vol. 11(10), pages 1-32, October.
    5. Haojia Kong & Lifan Shi & Dan Da & Zhijiang Li & Decai Tang & Wei Xing, 2022. "Simulation of China’s Carbon Emission based on Influencing Factors," Energies, MDPI, vol. 15(9), pages 1-15, April.
    6. Zhengsong Lin & Xinyue Ye & Qian Wei & Fan Xin & Zhang Lu & Sonali Kudva & Qiwen Dai, 2017. "Ecosystem Services Value Assessment and Uneven Development of the Qingjiang River Basin in China," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    7. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.
    8. Li, Guohao & Chen, Xue & You, Xue-yi, 2023. "System dynamics prediction and development path optimization of regional carbon emissions: A case study of Tianjin," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

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