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Industrial Energy-Related CO 2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016

Author

Listed:
  • Linlin Ye

    (School of Geography Science, Nantong University, Nantong 226000, China)

  • Xiaodong Wu

    (Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Northwest Institute of the Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China)

  • Dandan Huang

    (School of Geography Science, Nantong University, Nantong 226000, China)

Abstract

As the world’s largest developing country in the world, China consumes a large amount of fossil fuels and this leads to a significant increase in industrial energy-related CO 2 emissions (IECEs). The Yangtze River Economic Zone (YREZ), accounting for 21.4% of the total area of China, generates more than 40% of the total national gross domestic product and is an important component of the IECEs from China. However, little is known about the changes in the IECEs and their influencing factors in this area during the past decade. In this study, IECEs were calculated and their influencing factors were delineated based on an extended logarithmic mean Divisia index (LMDI) model by introducing technological factors in the YREZ during 2008–2016. The following conclusions could be drawn from the results. (1) Jiangsu and Hubei were the leading and the second largest IECEs emitters, respectively. The contribution of the cumulative increment of IECEs was the strongest in Jiangsu, followed by Anhui, Jiangxi and Hunan. (2) On the whole, both the energy intensity and R&D efficiency play a dominant role in suppressing IECEs; the economic output and investment intensity exert the most prominent effect on promoting IECEs, while there were great differences among the major driving factors in sub-regions. Energy structure, industrial structure and R&D intensity play less important roles in the IECEs, especially in the central and western regions. (3) The year of 2012 was an important turning point when nearly half of these provinces showed a change in the increment of IECEs from positive to negative values, which was jointly caused by weakening economic activity and reinforced inhibitory of energy intensity and R&D intensity.

Suggested Citation

  • Linlin Ye & Xiaodong Wu & Dandan Huang, 2020. "Industrial Energy-Related CO 2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016," IJERPH, MDPI, vol. 17(16), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5880-:d:398604
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    References listed on IDEAS

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    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. Ren, Shenggang & Fu, Xiang & Chen, XiaoHong, 2012. "Regional variation of energy-related industrial CO2 emissions mitigation in China," China Economic Review, Elsevier, vol. 23(4), pages 1134-1145.
    3. Weishi Zhang & David I. Stern & Xianbing Liu & Wenjia Cai & Can Wang, 2017. "An analysis of the costs of energy saving and CO2 mitigation in rural households in China," CCEP Working Papers 1704, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
    4. Zhaosu Meng & Huan Wang & Baona Wang, 2018. "Empirical Analysis of Carbon Emission Accounting and Influencing Factors of Energy Consumption in China," IJERPH, MDPI, vol. 15(11), pages 1-15, November.
    5. Zhang, Weishi & Wang, Can & Zhang, Long & Xu, Ying & Cui, Yuanzheng & Lu, Zifeng & Streets, David G., 2018. "Evaluation of the performance of distributed and centralized biomass technologies in rural China," Renewable Energy, Elsevier, vol. 125(C), pages 445-455.
    6. Shao, Shuai & Yang, Lili & Gan, Chunhui & Cao, Jianhua & Geng, Yong & Guan, Dabo, 2016. "Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO2 emission changes: A case study for Shanghai (China)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 516-536.
    7. Ang, B.W. & Liu, Na, 2007. "Handling zero values in the logarithmic mean Divisia index decomposition approach," Energy Policy, Elsevier, vol. 35(1), pages 238-246, January.
    8. Wang, Juan & Hu, Mingming & Rodrigues, João F.D., 2018. "The evolution and driving forces of industrial aggregate energy intensity in China: An extended decomposition analysis," Applied Energy, Elsevier, vol. 228(C), pages 2195-2206.
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