China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis
Introduction
In recent years, energy performance and environmental performance evaluation issues has attracted increasing interest since they are considered a crucial approach to save energy, reduce greenhouse gas emissions, protect environment and mitigate global climate change. Despite the major energy performance improvements achieved by China during the last two decades, the rapid development of economy has substantially increased China’s primary energy consumption and leaded to serious environmental problems due to the yearly increasing emissions of CO2 and other pollutants [1]. In addition, since different regions of China have different energy consumption structures, different economic growth modes, and different energy saving and environment protection policies, the regional energy and environmental performance of China may vary significantly across different regions. Hence, it is meaningful to evaluate China’s regional energy and environmental efficiency, which can assist the energy and environmental policy making for Chinese government both at the national and regional levels.
The evaluation of energy and environmental performance is often in the form of energy or environmental efficiency indices which can be constructed through mathematics programming methods such as conventional data envelopment analysis (DEA) models [2], [3], non-radial DEA models [4], Range-Adjusted Measure based DEA (RAM-DEA) models [5], and directional distance function (DDF) models [6], [7].
At the macro-economy level, DEA approach has recently been widely applied to studying the energy and environmental efficiency for it provides an appropriate method to deal with multiple inputs and outputs in examining relative efficiency [3]. For instance, Hu and Wang [8] proposed a total-factor energy efficiency evaluation DEA model and measured the energy efficiency of 29 regions in China. Zhou et al. [9] developed several environmental DEA technologies and measured the carbon emission efficiency of eight world regions. Yeh et al. [10] calculated the technical efficiency of energy utilization in Chinese mainland and Taiwan by using the traditional BCC model [11]. Wang et al. [12] developed a mixed energy economic-environmental efficiency model to measure the environmental efficiency, economic efficiency, and economic-environmental efficiency of 28 provinces in China for the period of 2001–2007. Bian and Yang [13] proposed several DEA models to simultaneously measure resource and environmental efficiency and applied their models in efficiency evaluation of 30 Chinese provinces. Shi et al. [14] presented three extended DEA model to calculate the energy and environmental overall technical efficiency, pure technical efficiency, and scale efficiency of 28 administrative regions in China. Wang et al. [4] established several DEA window analysis models to measure the energy and environmental efficiency of 29 administrative regions of China using cross-sectional and time-varying data and proposed a dynamic evaluation result.
Since Färe and Grosskopf [15] and Zhou and Ang [16] incorporated undesirable outputs in measuring efficiency, many researchers have considered undesirable outputs in energy efficiency evaluation. Here, we point out that most of the previous researches are built upon the concept of weak and strong disposability in environmental performance evaluation [17]. If we consider X as an input vector and G as a desirable output vector, then, production technology can be considered as P: X → P(X), where the set P(X) denotes that the output vector G is producible by the input vector X. The weak disposability on G can be specified by G ∊ P(X) ⇒ θG ∊ P(X) for all 0 ⩽ θ ⩽ 1, and the strong disposability on G can be specified by . However, this concept, associated with radial DEA model, is not sufficient, since the weak disposability assumes that all the decision making units (DMUs) yield a unified abatement on input factors for using only one efficiency score [18]. In addition, the previous researches never measured the returns to scale and damages to scale under the energy and environmental efficiency evaluation framework. Therefore, in this study, following Sueyoshi and Goto [19], [20], [21], we evaluate the regional energy and environmental efficiency of China by applying Range-Adjusted Measure based DEA models. The RAM-DEA models are non-radial models and they measure the energy and environmental efficiency by slacks, therefore, the evaluation framework in this study is different from the previous ones and not only the regional efficiency levels for China are measured, but the types of returns to scale and damages to scale for each region are explored, which is considered more meaningful for policy-making regarding how to improve the effect of energy conservation, greenhouse gas emission reduction, and environment protection for each region of China.
The rest of this paper is organized as follows: Section 2 proposes the RAM-DEA model and several indicators for integrated energy and environmental efficiency measurement under natural disposability and managerial disposability, respectively. The related approaches for determining returns to scale and damages to scale are also presented in Section 2. In Section 3, the regional energy and environmental efficiency, the types of returns and damages to scale, as well as the RAM-DEA based energy conservations and CO2 emissions reduction potentials for China’s 30 regions during 2006–2010 are evaluated and analyzed. Furthermore, the strategies and policy implications for integrated energy and environmental efficiency improvement are also discussed in Section 3. Section 4 concludes this paper.
Section snippets
Methodology
In this study, we apply the Range-Adjusted Measure based DEA (RAM-DEA) models, which is first proposed by Cooper et al. [22] and then further developed by Sueyoshi and Goto [20] in environmental strategy, to measure China’s regional energy and environmental efficiency. Since the RAM-DEA models can easily combine both energy performance and environmental performance for each DMU under a unified treatment, this non-radial method is considered to be more appropriate than the traditional radial DEA
Empirical studies
In this section, we first describe the regions and areas of China, the input and output variables selected, and the associated data for integrated energy and environmental efficiency measurement. Then, the RAM-DEA models are applied and the indicators of IEND, PE, IEMD, and EE are calculated and analyzed for China’s 30 regions. In addition, the types of returns to scale and damages to scale, as well as the energy saving potential and CO2 emissions reduction potential for each of these regions
Conclusions
In recent years, increasing studies have focused on the measurement of energy efficiency or environmental efficiency applying DEA based approaches. In this paper, we applied the RAM-DEA approach to evaluate the integrated energy and environmental efficiency and determine the types of returns to scale and damages to scale, under the economic concepts of natural disposability and managerial disposability, respectively, for China’s 30 regions during the period of 2006–2010. Then, we proposed
Acknowledgements
We gratefully acknowledge the financial support from the National Natural Science Foundation of China (71101011 and 71020107026), the China Postdoctoral Science Foundation (20110490298 and 2012T50049), and the National Basic Research Program of China (2012CB95570004).
References (28)
- et al.
Measuring the efficiency of decision making units
Eur J Oper Res
(1978) - et al.
A survey of data envelopment analysis in energy and environmental studies
Eur J Oper Res
(2008) - et al.
Performance analysis of U.S. coal-fired power plants by measuring three DEA efficiencies
Energy Policy
(2010) - et al.
Directional distance functions and slacks-based measures of efficiency
Eur J Oper Res
(2010) - et al.
Energy and CO2 emission performance in electricity generation: a non-radial directional distance function approach
Eur J Oper Res
(2012) - et al.
Total-factor energy efficiency of regions in China
Energy Policy
(2006) - et al.
Measuring environmental performance under different environmental DEA technologies
Energy Econ
(2008) - et al.
A comparative study of energy utilization efficiency between Taiwan and China
Energy Policy
(2010) - et al.
Efficiency measurement with carbon dioxide emissions: the case of China
Appl Energy
(2012) - et al.
Resource and environment efficiency analysis of provinces in China: a DEA approach based on Shannon’s entropy
Energy Policy
(2010)
Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs
Energy Policy
Modeling undesirable factors in efficiency evaluation: comment
Eur J Oper Res
Linear programming models for measuring economy-wide energy efficiency performance
Energy Policy
Methodological comparison between two unified (operational and environmental) efficiency measurements for environmental assessment
Eur J Oper Res
Cited by (153)
Assessing renewable energy efficiency to identify improvement strategies: A network data envelopment analysis approach
2023, Energy for Sustainable DevelopmentDoes carbon trading mechanism improve the efficiency of green innovation? Evidence from China
2023, Energy Strategy ReviewsThe carbon and production performance of water utilities: Evidence from the English and Welsh water industry
2023, Structural Change and Economic DynamicsAssessing integrated coal production and land reconstruction systems under extreme temperatures
2022, Expert Systems with Applications