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Sectoral comparison of electricity-saving potentials in China: An analysis based on provincial input–output tables


  • Meng, F.Y.
  • Zhou, D.Q.
  • Zhou, P.
  • Bai, Y.


China's electricity generation is mainly dependent on fossil fuel. Promoting electricity conservation in different sectors and regions plays a vital role in reducing China's energy-related CO2 emissions and other pollutants. This paper contributes to identify the sectors and economic regions with larger electricity-saving potential and make policy recommendations for promoting electricity conservation. Two indicators, namely TEC (technical elasticity coefficient) and PSC (price sensitivity coefficient), are constructed within an input–output analysis framework to measure the electricity-saving potential. The proposed indicators are applied to analyze the electricity-saving potentials of 20 sectors and 30 provinces in China. We also use EFC (electricity forward correlation coefficient) to examine the relationship between power consumption and electricity-saving potential. Our empirical results show that Mechanical industry, Construction industry and Chemical industry have the largest electricity-saving potential. Meanwhile, the east region of China also shows greater electricity-saving potential. It is suggested that Chinese government adopt pricing regulation and technology innovation policies jointly to promote China's energy conservation. Some important policy implications according to our empirical study are also proposed.

Suggested Citation

  • Meng, F.Y. & Zhou, D.Q. & Zhou, P. & Bai, Y., 2014. "Sectoral comparison of electricity-saving potentials in China: An analysis based on provincial input–output tables," Energy, Elsevier, vol. 72(C), pages 772-782.
  • Handle: RePEc:eee:energy:v:72:y:2014:i:c:p:772-782
    DOI: 10.1016/

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    References listed on IDEAS

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    Cited by:

    1. Özkara, Yücel & Atak, Mehmet, 2015. "Regional total-factor energy efficiency and electricity saving potential of manufacturing industry in Turkey," Energy, Elsevier, vol. 93(P1), pages 495-510.
    2. Ling Li & Ling Tang & Junrong Zhang, 2019. "Coupling Structural Decomposition Analysis and Sensitivity Analysis to Investigate CO 2 Emission Intensity in China," Energies, MDPI, Open Access Journal, vol. 12(12), pages 1-23, June.
    3. Zhen, Wei & Qin, Quande & Zhong, Zhangqi & Li, Li & Wei, Yi-Ming, 2018. "Uncovering household indirect energy-saving responsibility from a sectoral perspective: An empirical analysis of Guangdong, China," Energy Economics, Elsevier, vol. 72(C), pages 451-461.
    4. Jiang, Xuemei & Zhu, Kunfu & Green, Christopher, 2015. "China's energy saving potential from the perspective of energy efficiency advantages of foreign-invested enterprises," Energy Economics, Elsevier, vol. 49(C), pages 104-112.
    5. Cortés-Borda, D. & Guillén-Gosálbez, G. & Jiménez, L., 2015. "Assessment of nuclear energy embodied in international trade following a world multi-regional input–output approach," Energy, Elsevier, vol. 91(C), pages 91-101.
    6. Mardones, Cristian & Baeza, Nicolas, 2018. "Economic and environmental effects of a CO2 tax in Latin American countries," Energy Policy, Elsevier, vol. 114(C), pages 262-273.
    7. Choi, Jun-Ki & Morrison, Drew & Hallinan, Kevin P. & Brecha, Robert J., 2014. "Economic and environmental impacts of community-based residential building energy efficiency investment," Energy, Elsevier, vol. 78(C), pages 877-886.
    8. Duarte, Rosa & Langarita, Raquel & Sánchez-Chóliz, Julio, 2017. "The electricity industry in Spain: A structural analysis using a disaggregated input-output model," Energy, Elsevier, vol. 141(C), pages 2640-2651.
    9. ORALHAN Burcu & ALTAY TOPCU Betül & SÜMERLİ SARIGÜL Sevgi, 2016. "Determination Of Key Sectors In Turkish Economy By Using Input-Output Analysis," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 68(1), pages 178-192, June.


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