Environmental energy efficiency of China's regional economies: A non-oriented slacks-based measure analysis

Abstract

This paper proposes two environmental energy efficiency indices, the pure energy efficiency and scale efficiency based on the slacks-based measure of efficiency in data envelopment analysis (SBM-DEA). These two indices are used to measure energy efficiency by incorporating three undesirable outputs—carbon dioxide, sulfur dioxide, and Chemical Oxygen Demand—in China's regional economies between 2001 and 2010. The empirical results show most provinces were not energy efficient, due primarily to pure energy inefficiency. There exists a regional unbalance in terms of the environmental energy efficiency. Results support the Porter hypothesis, which indicates that stricter environmental regulations can improve efficiency and encourage innovation. In addition, undesirable outputs had a significant effect on energy efficiency measurements.

Keywords:

• SBM-DEA
• Energy efficiency
• China
• Undesirable outputs

Acknowledgements

The authors are very grateful to the Editor Prof. Scott Alan Carson and four anonymous reviewers for their constructive comments on the earlier version of this manuscript. We are also grateful to the financial support provided by Inha University.

Notes

1 Energy efficiency indicators are well defined in Patterson (1996), one can refers to this paper for more details.

2 Choi, Zhang, and Zhou (2012) uses the SBM model based on constant to scale (CSR) technology to measure CO₂ emission performance and abatement cost. Unlikely with that paper, this study focuses on energy efficiency measurement under different technologies (CRS and VRS). In addition, we present the decomposition of energy efficiency into pure energy efficiency and scale efficiency.

3 In Du et al. (2012), the authors also consider CO₂ emission from cement production and adjust the emission based on quantity of provincial electricity trade to get more accurate provincial CO₂ emission amount, one can refer to this paper for more details.

4 The mean Capital/GDP ratio in the study is about 1.21 which is lower than commonly recognized interval 2–3. Actually, in the previous studies, the Capital/GDP ratio showed various results. For instance, Wei et al. (2009) has a capital/GDP ratio of 1.97, in Hu and Wang (2006) the ratio is 6.69. Yeh et al. (2010) calculates the ratio as 1.41. One of the possible reasons for those various results comes from the different sources of capital stock.

5 As shown in Table 3, the correlation between energy and capital is relatively high to 0.87, indicating that during the research period, both energy and capital have kept a high growth rate to boost Chinese economy. The similar correlation can be found in Shi et al. (2010) with 0.89.

6 There are 12 regions in the west area, in this study. Tibet is not included because the energy data is not available. Therefore, we get 11 regions in the west area in the empirical analysis.

7 The reasons are mainly come from different capital stock and area classification. Wei et al. (2009) also discussed about the reason of different area results from Hu and Wang (2006). They concluded it came from different capital stock selection and different regional classification.