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On China's energy intensity statistics: Toward a comprehensive and transparent indicator

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  • Wang, Xin

Abstract

A transparent and comprehensive statistical system in China would provide an important basis for enabling a better understanding of the country. This paper focuses on energy intensity (EI), which is one of the most important indicators of China. It firstly reviews China's GDP and energy statistics, showing that China has made great improvements in recent years. The means by which EI data are released and adjusted are then explained. It shows that EI data releases do not provide complete data for calculating EI and constant GDP, which may reduce policy transparency and comprehensiveness. This paper then conducts an EI calculation method that is based on official sources and that respects the data availability of different data release times. It finds that, in general, China's EI statistics can be considered as reliable because most of the results generated by author's calculations match the figures in the official releases. However, two data biases were identified, which may necessitate supplementary information on related constant GDP values used in the official calculation of EI data. The paper concludes by proposing short- and long-term measures for improving EI statistics to provide a transparent and comprehensive EI indicator.

Suggested Citation

  • Wang, Xin, 2011. "On China's energy intensity statistics: Toward a comprehensive and transparent indicator," Energy Policy, Elsevier, vol. 39(11), pages 7284-7289.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:11:p:7284-7289
    DOI: 10.1016/j.enpol.2011.08.050
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    References listed on IDEAS

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    4. Ma, Ben & Zheng, Xinye, 2018. "Biased data revisions: Unintended consequences of China's energy-saving mandates," China Economic Review, Elsevier, vol. 48(C), pages 102-113.
    5. Yu, Yuqing & Wang, Xiao & Li, Huimin & Qi, Ye & Tamura, Kentaro, 2015. "Ex-post assessment of China's industrial energy efficiency policies during the 11th Five-Year Plan," Energy Policy, Elsevier, vol. 76(C), pages 132-145.
    6. Jiang, Lei & Folmer, Henk & Ji, Minhe, 2014. "The drivers of energy intensity in China: A spatial panel data approach," China Economic Review, Elsevier, vol. 31(C), pages 351-360.
    7. Ma, Ben & Liu, Min, 2022. "Improved statistical consistency: The effect of data revisions on the energy use gap between China and its provinces," China Economic Review, Elsevier, vol. 73(C).
    8. Le, Thai-Ha & Chang, Youngho & Taghizadeh-Hesary, Farhad & Yoshino, Naoyuki, 2019. "Energy insecurity in Asia: A multi-dimensional analysis," Economic Modelling, Elsevier, vol. 83(C), pages 84-95.
    9. Zhibo Zhao & Tian Yuan & Xunpeng Shi & Lingdi Zhao, 2020. "Heterogeneity in the relationship between carbon emission performance and urbanization: evidence from China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1363-1380, October.
    10. Li, Huimin & Wu, Tong & Zhao, Xiaofan & Wang, Xiao & Qi, Ye, 2014. "Regional disparities and carbon “outsourcing”: The political economy of China's energy policy," Energy, Elsevier, vol. 66(C), pages 950-958.
    11. Zheng, Heran & Shan, Yuli & Mi, Zhifu & Meng, Jing & Ou, Jiamin & Schroeder, Heike & Guan, Dabo, 2018. "How modifications of China's energy data affect carbon mitigation targets," Energy Policy, Elsevier, vol. 116(C), pages 337-343.

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