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The optimal CO2 emissions reduction path in Jiangsu province: An expanded IPAT approach


  • Yue, Ting
  • Long, Ruyin
  • Chen, Hong
  • Zhao, Xin


This study aimed to determine the optimal CO2 reduction path for Jiangsu province to achieve the target of 40–45% reduction of CO2 emissions intensity by 2020 based on the 2005 level. Using the IPAT model combined with scenario analysis, we consider four parameters: economic growth, population growth, energy intensity and renewable-energy share. Each parameter is measured from different scenarios, and 54 kinds of scheme are set to forecast the CO2 emissions. The forecast results show that it is likely for Jiangsu province to achieve the target. Rapid economic growth is the main determinant that causes increase in CO2 emissions. Energy-intensity reduction and renewable-energy-share increase have beneficial influences on reducing CO2 emissions. The effect of energy-share increase is larger than that of energy-intensity reduction. As for the reduction of CO2 emissions intensity, energy-intensity reduction has a larger influence than renewable-energy-share increase. The optimal development mode until the year 2020 is as follows: the economy and population grow at appropriate rates, energy intensity reaches the level in developed countries, and the renewable-energy share increases to 15% in 2020.

Suggested Citation

  • Yue, Ting & Long, Ruyin & Chen, Hong & Zhao, Xin, 2013. "The optimal CO2 emissions reduction path in Jiangsu province: An expanded IPAT approach," Applied Energy, Elsevier, vol. 112(C), pages 1510-1517.
  • Handle: RePEc:eee:appene:v:112:y:2013:i:c:p:1510-1517
    DOI: 10.1016/j.apenergy.2013.02.046

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    4. Xiaofei Han & Jianling Jiao & Lancui Liu & Lanlan Li, 2017. "China’s energy demand and carbon dioxide emissions: do carbon emission reduction paths matter?," 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. 86(3), pages 1333-1345, April.
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    13. Bo Li & Xuejing Liu & Zhenhong Li, 2015. "Using the STIRPAT model to explore the factors driving regional CO 2 emissions: a case of Tianjin, 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. 76(3), pages 1667-1685, April.
    14. Feng Dong & Yifei Hua, 2018. "Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model," Sustainability, MDPI, Open Access Journal, vol. 10(11), pages 1-24, November.
    15. Yue, Ting & Long, Ruyin & Chen, Hong, 2013. "Factors influencing energy-saving behavior of urban households in Jiangsu Province," Energy Policy, Elsevier, vol. 62(C), pages 665-675.
    16. 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, Open Access Journal, vol. 11(10), pages 1-32, October.
    17. Shuai, Chenyang & Shen, Liyin & Jiao, Liudan & Wu, Ya & Tan, Yongtao, 2017. "Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011," Applied Energy, Elsevier, vol. 187(C), pages 310-325.
    18. Li, Qianwen & Long, Ruyin & Chen, Hong, 2018. "Differences and influencing factors for Chinese urban resident willingness to pay for green housings: Evidence from five first-tier cities in China," Applied Energy, Elsevier, vol. 229(C), pages 299-313.
    19. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.

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    CO2 emissions; IPAT model; Scenario analysis; Jiangsu province;

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