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Energy conservation in China: Key provincial sectors at two-digit level

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  • Liao, Hua
  • Du, Jian
  • Wei, Yi-Ming

Abstract

In March 2011, China’s central government set a new challenging target of reducing its energy intensity by 16% during 2011–2015, after it had achieved a reduction of 19.1% during 2006–2010. And this new target was assigned to provincial authorities in August 2011. However, China’s provincial energy-economic developments are unbalanced and different provinces have different key sectors for energy conservation. Most previous studies focused on provincial energy efficiency at the aggregate level, or the three-industry level (or one-digit level). However, whether for policy decision or academic research, it is necessary to further subdivide the sectors. In this paper, we use three indicators (Gini Coefficient, energy consumption share and energy intensity) to compare provincial energy conservation potentials at the two-digit sector level. To our knowledge, this paper is the first one to identify the keys for energy conversation across the 31 provinces×65 sectors. And the results are shown in visualized maps and matrix tables to help identify the key province×sectors for energy conservation easier. This also helps the central and provincial governments to distinguish key sectors when they monitor the energy conservation progress.

Suggested Citation

  • Liao, Hua & Du, Jian & Wei, Yi-Ming, 2013. "Energy conservation in China: Key provincial sectors at two-digit level," Applied Energy, Elsevier, vol. 104(C), pages 457-465.
    • Handle: RePEc:eee:appene:v:104:y:2013:i:c:p:457-465
    DOI: 10.1016/j.apenergy.2012.11.036
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    1. Chen, Yaping & Guo, Zhanwei & Wu, Jiafeng & Zhang, Zhi & Hua, Junye, 2015. "Energy and exergy analysis of integrated system of ammonia–water Kalina–Rankine cycle," Energy, Elsevier, vol. 90(P2), pages 2028-2037.
    2. Jingjing Qu & Aijun Li & Morié Guy-Roland N’Drin, 2023. "Measuring technology inequality across African countries using the concept of efficiency Gini coefficient," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4107-4138, May.
    3. Li, Zheng & Pan, Lingying & Fu, Feng & Liu, Pei & Ma, Linwei & Amorelli, Angelo, 2014. "China's regional disparities in energy consumption: An input–output analysis," Energy, Elsevier, vol. 78(C), pages 426-438.
    4. Yang, Yuan & Cai, Wenjia & Wang, Can, 2014. "Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces," Applied Energy, Elsevier, vol. 131(C), pages 117-127.
    5. Zheng, Bo & Zhang, Qiang & Borken-Kleefeld, Jens & Huo, Hong & Guan, Dabo & Klimont, Zbigniew & Peters, Glen P. & He, Kebin, 2015. "How will greenhouse gas emissions from motor vehicles be constrained in China around 2030?," Applied Energy, Elsevier, vol. 156(C), pages 230-240.
    6. Herrerias, M.J. & Joyeux, R. & Girardin, E., 2013. "Short- and long-run causality between energy consumption and economic growth: Evidence across regions in China," Applied Energy, Elsevier, vol. 112(C), pages 1483-1492.
    7. Milin Lu & Zhaohua Wang, 2017. "Rebound effects for residential electricity use in urban China: an aggregation analysis based E-I-O and scenario simulation," Annals of Operations Research, Springer, vol. 255(1), pages 525-546, August.

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    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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