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Decomposition and Attribution Analysis of Industrial Carbon Intensity Changes in Xinjiang, China

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  • Xinlin Zhang

    (School of Geographic Science, Nanjing Normal University, Nanjing 210023, China
    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Yuan Zhao

    (School of Geographic Science, Nanjing Normal University, Nanjing 210023, China
    Ginling College, Nanjing Normal University, Nanjing 210097, China
    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China)

  • Qi Sun

    (School of Geographic Science, Nanjing Normal University, Nanjing 210023, China
    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Changjian Wang

    (Guangzhou Institute of Geography Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China)

Abstract

Xinjiang’s industrial sector accounted for more than 80% of the total energy-related carbon emissions. A further understanding of each industrial sub-sector’s carbon intensity is very necessary to make differentiated policies and measures. This paper applied index decomposition analysis and attribution analysis to examine the influencing factors and each sub-sector’s contributions to the changes in influencing factors. The results demonstrated the following: (1) energy intensity effect contributed most to the decreases in industrial carbon intensity, and mining and quarrying, foods and tobacco , and other manufactures were the most representative industrial sub-sectors; (2) energy structure effect showed a positive effect on industrial carbon intensity, but its effect was not significant, and fuel processing , smelting and pressing of metals, metal products, and textile were mainly responsible for the increases in energy structure effect; (3) industrial structure effect showed significant fluctuations, but its accumulative effect promoted the increases in industrial carbon intensity, and fuel processing, mining and quarrying, and textiles were the main sub-sectors, which exerted negative effects on the decreases in industrial structure effect; (4) fuel processing, smelting and pressing of metals, and mining and quarrying significantly influenced these three decomposed factors from 2000 to 2014; (5) since 2009, energy-intensive sub-sectors increased rapidly, and the energy structure was not optimized, while attention was not paid to controlling the energy efficiency, thus all decomposed factors promoted the increases in industrial carbon intensity; and (6) mining and quarrying, textiles, fuel processing , and transport equipment were primarily responsible for the increases in energy structure effect. Fuel processing, chemicals, and smelting and pressing of metals were primarily responsible for the increases in energy intensity effect. Fuel processing , chemicals, smelting and pressing of metals , and other manufactures were primarily responsible for the increases in industrial structure effect.

Suggested Citation

  • Xinlin Zhang & Yuan Zhao & Qi Sun & Changjian Wang, 2017. "Decomposition and Attribution Analysis of Industrial Carbon Intensity Changes in Xinjiang, China," Sustainability, MDPI, vol. 9(3), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:3:p:459-:d:93538
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