Incorporating Both Undesirable Outputs and Uncontrollable Variables into DEA: the Performance of Chinese Coal-Fired Power Plants

There are two difficulties in doing an objective evaluation of the performance of decision-making units (DMUs). The first one is how to treat undesirable outputs jointly produced with the desirable outputs, and the second one is how to treat uncontrollable variables, which often capture the impact of the operating environment. Given difficulties in both model construction and data availability, very few published papers simultaneously consider the above two problems. This article attempts to do so by proposing six DEA-based performance evaluation models based on a research sample of the Chinese coal-fired power plants. The finding of this paper not only contributes for the performance measurement methodology, but also has policy implications for the Chinese coal-fired power sector

Investment efficiency of the new energy industry in China

This paper evaluates the investment efficiency of the new energy industry in China and investigates factors that explain variations in investment efficiency across firms and over time. Applying a four-stage semi-parametric DEA analysis framework to a sample of listed new energy firms over the period 2012-2015, we find that the overall investment efficiency of the new energy industry is relatively low, with an average total technical efficiency of 44%, pure technical efficiency of 48%, and scale efficiency of 90%. We also find that new energy firms’ investment efficiency is affected by both macroeconomic conditions and firm-specific characteristics. Our results are robust and have significant implications for policy makers and firm managers

Environmental performance evaluation of Chinese industrial systems:a network SBM approach

In recent years, environmental problems caused by industries in China have drawn increasing attention to both academics and policy makers. This paper assesses the environmental efficiency of Chinese regional industrial systems to come up with some recommendations to policy makers. First, we divided each Chinese regional industrial system into a production process and a pollutant treatment process. Then, we built a scientific input–intermediate–output index system by introducing a new network slacks-based model (NSBM) model. This study is the first to combine NSBM with DEA window analysis to give a dynamic evaluation of the environmental efficiency. This enables us to assess the environmental efficiency of Chinese regional industrial systems considering their internal structure as well as China’s policies concerning resource utilization and environmental protection. Hence, the overall efficiency of each regional industrial system is decomposed into production efficiency and pollutant treatment efficiency. Our empirical results suggest: (1) 66.7% of Chinese regional industrial systems are overall inefficient. 63.3 and of 66.7% Chinese regional industrial systems are inefficient in the production process and the pollutant treatment process, respectively. (2) The efficiency scores for the overall system and both processes are all larger in the eastern area of China than those of the central and western areas. (3) Correlation analysis indicates that SO2 generation intensity (SGI), solid waste generation intensity, COD discharge intensity, and SO2 discharge intensity have significantly negative impacts on the overall efficiency. (4) The overall inefficiency is mainly due to inefficiency of the pollutant treatment process for the majority of regional industrial systems. (5) In general, the overall efficiency was trending up from 2004 to 2010, indicating that the substantial efforts China has devoted to protecting the environment have yielded benefits

CO2 emissions reduction of Chinese light manufacturing industries:a novel RAM-based global Malmquist-Luenberger productivity index

Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist-Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed

Regulation and efficiency incentives: evidence from the England and Wales water and sewerage industry

This paper evaluates the impact of the tightening in price cap by OFWAT and of other operational factors on the efficiency of water and sewerage companies in England and Wales using a mixture of data envelopment analysis and stochastic frontier analysis. Previous empirical results suggest that the regulatory system introduced at privatization was lax. The 1999 price review signaled a tightening in regulation which is shown to have led to a significant reduction in technical inefficiency. The new economic environment set by price-cap regulation acted to bring inputs closer to their cost-minimizing levels from both a technical and allocative perspective

Green Biased Technical Change in Terms of Industrial Water Resources in China’s Yangtze River Economic Belt

As a significant ecological corridor from west to east across China, the Yangtze River Economical Belt (YREB) is in great need of green development and transformation. Rather than only focusing on the overall growth of green productivity, it is important to identify whether the technical change is biased towards economic performance or green performance in promoting green productivity. By employing the biased technical change theory and Malmquist index decomposition method, we analyze the green biased technical change in terms of industrial water resources in YREB at the output side and the input side respectively. We find that the green biased technical change varies during 2006–2015 at both the input side and output side in YREB. At the input side, water-saving biased technical change is generally dominant compared to water-using biased technical change during 2006–2015, presenting the substitution effects of non-water production factors. At the output side, the economy-growth biased technical change is the main force to promote green productivity, whereas the role of water-conservation biased technical change is insufficient. The green performance at the output side needs to be strengthened compared to the economic performance in YREB. A series of water-related environmental policies introduced in China since 2008 have promoted the green biased technical change both at the input side and the output side in YREB, but the policy effects at the output side is still inadequate compared to that at the input side. The technological innovation in sewage treatment and control need to catch up with the economic growth in YREB. Our research gives insights to enable a deeper understanding of the green biased technical change in YREB and will benefit more focused policy-making of green innovation

Regional environmental efficiency and economic growth: NUTS2 evidence from Germany, France and the UK

This paper by applying nonparametric techniques measures spatial environmental heterogeneities of 98 regions from Germany, France and the UK. Specifically environmental performance indexes are constructed for the 98 regions (NUTS 2 level) identifying their ability to produce higher growth rates and reduce pollution (in the form of municipal waste) generated from regional economic activity. By applying conditional stochastic kernels and local constant estimators it investigates the regional economic activity – environmental quality relationship. The results indicate several spatial environmental heterogeneities among the examined regions. It appears that regions with higher GDP per capita levels tend to have higher environmental performance.Regional environmental efficiency; directional distance function; conditional stochastic kernel; nonparametric regression

The comprehensive environmental efficiency of socioeconomic sectors in China: An analysis based on a non-separable bad output SBM

The increasingly high frequency of heavy air pollution in most regions of China signals the urgent need for the transition to an environmentally friendly production performance by socioeconomic sectors for the sake of people's health and sustainable development. Focusing on CO2 and major air pollutants, this paper presents a comprehensive environmental efficiency index based on evaluating the environmental efficiency of major socioeconomic sectors, including agriculture, power, industry, residential and transportation, at the province level in China in 2010 based on a slack-based measure DEA model with non-separable bad output and weights determined by the coefficient of variation method. In terms of the environment, 5, 16, 6, 7 and 4 provinces operated along the production frontier for the agricultural, power, industrial, residential and transportation sectors, respectively, in China in 2010, whereas Shanxi, Heilongjiang, Ningxia, Hubei and Yunnan showed lowest efficiency correspondingly. The comprehensive environmental efficiency index varied from 0.3863 to 0.9261 for 30 provinces in China, with a nationwide average of 0.6383 in 2010; Shanghai ranked at the top, and Shanxi was last. Regional disparities in environmental efficiency were identified. A more detailed inefficiency decomposition and benchmarking analysis provided insight for understanding the source of comprehensive environmental inefficiency and, more specifically, the reduction potential for CO2 and air pollutants. Some specific research and policy implications were uncovered from this work

Environmental efficiency analysis of listed cement enterprises in China

© 2016 by the authors.China's cement production has been the highest worldwide for decades and contributes significant environmental pollution. Using a non-radical DEA model with slacks-based measure (SBM), this paper analyzes the environmental efficiency of China's listed cement companies. The results suggest that the average mean of the environmental efficiency for the listed cement enterprises shows a decreasing trend in 2012 and 2013. There is a significant imbalance in environmental efficiency in these firms ranging from very low to very high. Further investigation finds that enterprise size and property structure are key factors. Increasing production concentration and decreasing the share of government investment could improve the environmental efficiency. The findings also suggest that effectively monitoring pollution products can improve environmental efficiency quickly, whereas pursuit for excessive profitability without keeping the same pace in energy saving would cause a sharp drop in environmental efficiency. Based on these findings, we proposed that companies in the Chinese cement sector might consider restructuring to improve environmental efficiency. They also need to make a trade-off between profitability and environmental protection. Finally, the Chinese government should reduce ownership control and management interventions in cement companies

A material balance approach for modelling banks’ production process with non-performing loans

The aim of this to study is to examine how non-performing loans on the balance sheets of Japanese banks affect their performance by adopting a material balance principle. The paper outlines how the material balance conditions can be applied when modelling banks’ production process in the presence of non-performing loans. The paper utilizes the generalized weak G-disposability principle which accounts for the heterogeneity among banks’ input quality. We test how an input-oriented model (non-performing loans are treated as an input), the weak disposability assumption and the adopted material balance approach, affect banks’ performance levels. We apply our test on a sample of Japanese banks over the period 2013 to 2019. Our findings indicate that the input-oriented model and the material balance estimator even if they present similar distributions, they account differently the effect of non-performing loans’ fluctuations over the examined period. In addition, the results under the weak disposability assumption are found to be different compared to the material balance measures and less sensitive to banks’ non-performing loans variation levels. We also provide evidence that the generalized weak G-disposability assumption captures better banks’ performance fluctuations that has been caused by the restructuring of the Japanese banking industry