Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces

Zhonghua Cheng, Jun Liu, Lianshui Li and Xinbei Gu

Energy Economics, 2020, vol. 86, issue C

Abstract: Based on the non-radial directional distance function of the Data Envelopment Analysis model, this paper uses the meta-frontier method to measure the total-factor energy efficiency of 30 provinces in China from 1997 to 2016. We then incorporate spatial correlation into the traditional convergence test model, study the spatial convergence of energy efficiency and explore the reasons for regional differences in energy efficiency. The results show that total-factor energy efficiency has significant regional heterogeneity, with the largest in the Eastern region, the second in the Central region and the smallest in the Western region. The root cause of energy inefficiency in China is poor management. As regional technology levels improve, the proportion of energy inefficiency due to management inefficiency slowly increases, while the proportion due to technology gap inefficiency gradually decreases. The total-factor energy efficiency has absolute β convergence and conditional β convergence within the three regions, but there is no absolute β convergence or conditional β convergence at the national level. This is mainly because the technology gap ratio between the Central and Western regions and the Eastern region has been gradually expanding. Not only do different factors have different effects on the convergence of energy efficiency but there are also significant differences within these three regions.

Keywords: Energy efficiency; Super-efficiency; Meta-frontier; Convergence analysis; Spatial correlation (search for similar items in EconPapers)
JEL-codes: C61 D24 O13 O44 Q54 R11 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (56)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988320300414

DOI: 10.1016/j.eneco.2020.104702

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