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Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis

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Abstract

Energy efficiency improvement has been a key objective of China’s long-term energy policy. In this paper, we derive single-factor technical energy efficiency (abbreviated as energy efficiency) in China from multi-factor efficiency estimated by means of a translog production function and a stochastic frontier model on the basis of panel data on 29 Chinese provinces over the period 2003–2011. We find that average energy efficiency has been increasing over the research period and that the provinces with the highest energy efficiency are at the east coast and the ones with the lowest in the west, with an intermediate corridor in between. In the analysis of the determinants of energy efficiency by means of a spatial Durbin error model both factors in the own province and in first-order neighboring provinces are considered. Per capita income in the own province has a positive effect. Furthermore, foreign direct investment and population density in the own province and in neighboring provinces have positive effects, whereas the share of state-owned enterprises in Gross Provincial Product in the own province and in neighboring provinces has negative effects. From the analysis it follows that inflow of foreign direct investment and reform of state-owned enterprises are important policy handles.

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Notes

  1. We analyze panel data of 29 Chinese provinces over the period 2003–2011. Tibet, Hong Kong, Macau and Taiwan are excluded because of lack of data. Guizhou is not included in the analysis because of incomplete data.

  2. We similarly consider spatially lagged controls in model (2).

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Authors and Affiliations

  1. School of Economics, Zhejiang University of Finance and Economics, Hangzhou, 310018, Zhejiang, China

    Lei Jiang

  2. Department of Economic Geography, Faculty of Spatial Sciences, University of Groningen, Landleven 1, 9747 AD, Groningen, The Netherlands

    Lei Jiang & Henk Folmer

  3. Department of Agricultural Economics, College of Economics and Management, Northwest A&F University, Yangling, Shaanxi, China

    Henk Folmer

  4. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Minhe Ji

  5. Centre for Public Health, Queen’s University Belfast, Belfast, BT12 6BJ, Northern Ireland, UK

    Jianjun Tang

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  1. Lei Jiang
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Correspondence to Lei Jiang.

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Appendix

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Table 5 Energy efficiency scores by province over the period 2003–2011
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Jiang, L., Folmer, H., Ji, M. et al. Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis. Ann Reg Sci 58, 301–319 (2017). https://doi.org/10.1007/s00168-016-0782-5

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