DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation

This study discusses a new DEA (Data Envelopment Analysis) approach to measure the unified (operational and environmental) efficiency of energy firms. It is widely known that they produce not only desirable (good) outputs (e.g., electricity) but also undesirable (bad) outputs (e.g., CO₂) as a result of their plant operations. The proposed approach incorporates an output separation (desirable and undesirable outputs) for the performance evaluation of energy firms. In addition to the output separation, this study separates inputs into energy and non-energy inputs. Consequently, the proposed approach incorporates not only the output separation but also the input separation within a computational framework of DEA non-radial measurement. This study compares the proposed approach with other previous DEA approaches used for the performance evaluation of energy firms. After the methodological comparison, this study applies the proposed approach for measuring the unified efficiency of Japanese fossil fuel power generation. This empirical study confirms that the implementation of Kyoto Protocol (2005) has not been effective on the unified efficiency of Japanese fossil fuel power generation during the observed period (2004-2008). Although the empirical result is inconsistent with the current Japanese environmental policy under Kyoto Protocol, it contains policy implications for guiding the future direction of Japanese environmental policy on the electric power industry.     

Congestion measurement for regional industries in China: A data envelopment analysis approach with undesirable outputs

The industry in China has been developing extensively in the last few decades. Large investments in China's industry may cause congestion because congestion is a widely observed economic phenomenon in such a scenario. In order to know the performance and allocate resources well, it is necessary for the Chinese government to measure congestion of the industry. Many scholars have studied this topic by means of data envelopment analysis (DEA). However, previous studies only pay attention to the framework of desirable outputs. In fact, undesirable outputs often accompany desirable outputs in production. Thus, in this study, a new approach for measuring congestion with undesirable outputs is proposed and applied to analyzing congestion of the industry in 31 administrative regions of China. The results show that five regions have congestion in their industry in 2010. Besides, the regions located in the east of the country perform the best in ecological efficiency, followed by regions in central and west China. Based on these findings, this paper proposes some political schemes to improve regional industrial efficiency.     

Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement

Nowadays, majority of organizations are seeking to achieve sustainable development with respect to “green” concept. One of the main criteria for assessing green performance is eco-efficiency. To identify all aspects of the eco-efficiency, inputs should be divided into energy and non-energy and outputs should be divided into good and bad outputs. To deal with this issue, a data envelopment analysis (DEA) model is developed to divide inputs into both energy and non-energy and outputs into both desirable (good) and undesirable (bad) outputs. Likewise, variables are separated into both discretionary and non-discretionary factors. Accordingly, a bounded adjusted measure (BAM) based on green indicators is developed to calculate the eco-efficiency of decision making units (DMUs). Besides, energy saving potentials and undesirable output abatement potentials are calculated to show correlation coefficient between energy consumption and undesirable output. Finally, proposed model is validated by assessing the eco-efficiency of some selected members of organization for economic cooperation and development (OECD). Australia, Finland, Ireland, New Zealand, and Switzerland are recognized as eco-efficient countries and the rest of countries are inefficient in terms of the eco-efficiency. High and positive Spearman correlation coefficient between energy consumption and undesirable outputs addresses that the more use of energy inputs, the more undesirable outputs.     

Environmental efficiency of the Indian cement industry: An interstate analysis

Coal combustion, for the production of cement, generates considerable amount of environmentally detrimental carbon dioxide as an undesirable by-product. Thus, this paper aims at measuring environmental efficiency within a joint production framework of both desirable and undesirable output using Data Envelopment Analysis and Directional Distance Function. Carbon dioxide is considered as an input in one context and as an undesirable output in the other with the environmental efficiency being defined accordingly. Using 3 digit sate level data from the Annual Survey of Industries for the years 2000–2001 through 2004–2005, the proposed models are applied to estimate environmental efficiency of Indian cement industry. Empirical results show that there is enough potential for the industry to improve its environmental efficiency with efficiency being varied across states. Results also show that Indian cement industry, if faced with environmental regulation, has the potential to expand desirable output and contract undesirable output with the given inputs. However, regulation has a potential cost in terms of lower feasible expansion of desirable output as compared to unregulated scenario.     

Methodological comparison among radial,non-radial and intermediate approaches for DEA environmental assessment

This study compares three DEA approaches for environmental assessment, all of which are designed to examine the level of simultaneous achievement on economic prosperity and environmental protection, so measuring the degree of sustainability development. DEA, standing for Data Envelopment Analysis, has been widely applied for performance assessment in the past five decades. A new type of applications is referred to as “DEA environmental assessment” and it measures the performance of many organizations that utilize inputs to produce not only desirable outputs (e.g., electricity) but also undesirable outputs (e.g., CO₂). In the previous studies, DEA-based performance evaluation for environment assessment is methodologically classified into radial or non-radial category. Recently, a new “intermediate” approach, analytically locating between the radial and non-radial measures, has been proposed as the third alternative. A use of the intermediate approach has several unique features, all of which cannot be found in the radial and non-radial ones. The new approach measures the degree of unified inefficiency on each production factor and determines the level of total unified inefficiency from the average of the sum of these inefficiency scores. This study discusses the analytical features by comparing the intermediate approach with the radial and non-radial ones. The methodological comparison attempts to convey a message that DEA is indeed an important methodology, but not perfect. Rather, it is an approximation approach to examine the performance of various organizations. Many DEA applications on energy and environment often suffer from a methodological bias, implying that different approaches produce different empirical results. Thus, in guiding a large policy issue such as the global warming and climate change, it is necessary for us to compare several different approaches (e.g., models and concepts) to derive a reliable empirical suggestion. The importance of such a message is applicable to not only DEA but also the other types of empirical research in natural and social sciences. Therefore, this study discusses the methodological bias issue from the practicality of the three DEA approaches in assessing various concerns on energy and environment.     

DEA environmental assessment in time horizon: Radial approach for Malmquist index measurement on petroleum companies

The climate change and various pollutions have been influencing our societies and economies. The environmental assessment, to be discussed in this study, is increasingly important because it serves as an initial step toward pollution prevention. Corporate leaders, policy makers, researchers and individuals who are interested in environmental protection have been paying attention to the assessment so that they can prepare policy suggestions on the global warming and climate change. As a methodology for the assessment, this study proposes a use of Data Environment Analysis (DEA) in a time horizon. Most of data sets on the climate change are sampled in a time series where the performance of organizations fluctuates every moment. In applying the DEA environmental assessment to such a data set, it is necessary for us to classify outputs into desirable (e.g., oil production) and undesirable (e.g., CO₂ emission) categories because all organizations usually produce not only desirable but also undesirable outputs as a result of their economic activities. To unify the two different types of outputs, this study incorporates the concept of natural and managerial disposability into the computational framework of DEA and extends them into a time horizon. For the research purpose, this study incorporates Malmquist index into the proposed DEA environmental assessment to examine an occurrence of a frontier shift among multiple periods. The frontier shift indicates a technology progress and/or managerial innovation during an observed period. The index is further separated into four subcomponents in a time horizon. These subcomponents are differently expressed under the natural and managerial disposability. Thus, eight different subcomponents on the Malmquist index are proposed to measure the frontier shift. As an application, this study uses the proposed DEA approach to examine whether the frontier shift (due to technology progress) occurs or not in the petroleum industry from 2005 to 2009. Our empirical study finds that the industry has not exhibited any major frontier shift under natural disposability, but showing a considerable frontier shift under managerial disposability. In other words, the petroleum firms have improved their environmental performance by eco-technology to reduce an amount of CO₂ emission during the observed annual periods.     

Energy efficiency measurement of Chinese Yangtze River Delta’s cities transportation: a DEA window analysis approach

In this paper, the transportation energy efficiency of Yangtze River Delta’s 15 cities is studied in the period from 2009 to 2013. To measure transportation’s dynamic performance, the window analysis of data envelopment analysis (DEA) is used to estimate the efficiency in cross-sectional and time-varying data. Capital inputs and labor inputs are selected as the two non-energy inputs, energy consumption as the energy input, passenger volume and freight volume are selected as the two desirable outputs, and carbon dioxide is chosen as the undesirable output. The empirical study shows that Shanghai had the highest transportation energy efficiency, followed by Zhejiang province, and the average efficiency of Jiangsu province was the worst. A regression analysis on the efficiency values and their three influencing factors was carried out. The results show that per capita gross domestic product and the per capita area of paved roads in a city negatively influenced the efficiency values, while the number of public transportation vehicles per 10,000 persons positively influenced the transportation efficiency values. Lastly, some policy recommendations, conclusions, and suggestions for further research are given.     

Performance analysis of US coal-fired power plants by measuring three DEA efficiencies

Data Envelopment Analysis (DEA) has been widely used for performance evaluation of many organizations in private and public sectors. This study proposes a new DEA approach to evaluate the operational, environmental and both-unified performance of coal-fired power plants that are currently operating under the US Clean Air Act (CAA). The economic activities of power plants examined by this study are characterized by four inputs, a desirable (good) output and three undesirable (bad) outputs. This study uses Range-Adjusted Measure (RAM) because it can easily incorporate both desirable and undesirable outputs in the unified analytical structure. The output unification proposed in this study has been never investigated in the previous DEA studies even though such a unified measure is essential in guiding policy makers and corporate leaders. Using the proposed DEA approach, this study finds three important policy implications. First, the CAA has been increasingly effective on their environmental protection. The increased environmental performance leads to the enhancement of the unified efficiency. Second, the market liberalization/deregulation was an important business trend in the electric power industry. Such a business trend was legally prepared by US Energy Policy Act (EPAct). According to the level of the market liberalization, the United States is classified into regulated and deregulated states. This study finds that the operational and unified performance of coal-fired power plants in the regulated states outperforms those of the deregulated states because the investment on coal-fired power plants in the regulated states can be utilized as a financial tool under the rate-of-return criterion of regulation. The power plants in the deregulated states do not have such a regulation premium. Finally, plant managers need to balance between their environmental performance and operational efficiency.     

Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs

Data envelopment analysis (DEA) has recently become a popular method in measuring energy efficiency at the macro-economy level. However, previous studies are limited in that they failed to consider the issues of undesirable outputs and minimisation of energy consumption. Thus, this study considers both factors in measuring Chinese industrial energy efficiency and investigates the maximum energy-saving potential in 28 administrative regions in China. The results show that industries in the east area have the best average energy efficiency for the period 2000–2006, followed by the central area. Further, after comparing the industrial energy overall efficiency, pure technical efficiency (IEPTE), and scale efficiency of the 28 administrative regions examined, the study finds that in most regions of this study, the two main reasons causing the wastage of a large amount of energy during the industrial production process are that the industrial structure of most regions still relies on the massive use of energy in order to support the industrial-based economy and the IEPTE is too low. Based on these findings, this paper correspondingly proposes some policies to improve regional industrial energy efficiency.     

A comparative study among fossil fuel power plants in PJM and California ISO by DEA environmental assessment

This study compares among fossil fuel power plants in PJM and California ISO by their unified (operational and environmental) performance. DEA (Data Envelopment Analysis) is used as a methodology. For comparative analysis, DEA incorporates strategic concepts such as natural and managerial disposability into the computational process. This study explores both how to measure Returns to Scale (RTS) under natural disposability and how to measure Damages to Scale (DTS) under managerial disposability. This empirical study obtains two implications on US energy policy. One of the two policy implications is that California ISO outperforms PJM in terms of the three unified efficiency measures. The result implies that strict regulation on undesirable outputs, as found in California, is important in enhancing the performance of US fossil fuel power plants. Thus, it is necessary for federal and local governments to regulate the fossil fuel power plants under the strict implementation of environmental protection. Under such a policy direction, it is possible for US fossil fuel power plants to attain economic prosperity (by enhancing their operational efficiencies) and to satisfy environmental regulation (by enhancing their environmental efficiencies). The other policy implication is that coal-fired and gas-fired power plants in PJM and California ISO need to reduce their operational sizes or introduce technology innovation on desirable and undesirable outputs and/or new management for environmental protection within their operations. Meanwhile, oil-fired power plants may increase their operational sizes if they can introduce technology innovation and new management on undesirable outputs.     

DEA window analysis for environmental assessment in a dynamic time shift: Performance assessment of U.S. coal-fired power plants

This study discusses a new use of window analysis for DEA environmental assessment in a time horizon where DEA stands for Data Envelopment Analysis. The data sets on environmental protection are often structured by time series. In applying DEA to environmental assessment, it is necessary for us to examine a frontier shift between different periods because it indicates a technology progress on desirable and undesirable outputs. An important feature of the proposed approach is that it incorporates the concept of natural and managerial disposability into the computational framework of DEA and extends the two disposability concepts in a time horizon. To capture the frontier shift, this study proposes a new type of DEA window analysis for environmental assessment. This study applies the proposed DEA window analysis to a data set on U.S. coal-fired power plants during 1995–2007. The application finds that the coal-fired power plants have gradually paid attention to environmental protections under Clean Air Act (CAA). Consequently, their performance under managerial disposability has increased from 1996 to 2007. This indicates the importance of CAA and regulation on industrial pollutions. Thus, it is necessary for the United States to extend the scope of CAA for controlling the amount of CO₂ emission because current regulation has a limited policy influence on the source of global warming and climate change in our modern society.     

Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon’s entropy

Data envelopment analysis (DEA) has been widely used in energy efficiency and environment efficiency analysis in recent years. Based on the existing environment DEA technology, this paper presents several DEA models for estimating the aggregated efficiency of resource and environment. These models can evaluate DMUs’ energy efficiencies and environment efficiencies simultaneously. However, efficiency ranking results obtained from these models are not the same, and each model can provide some valuable information of DMUs’ efficiencies, which we could not ignore. Under this situation, it may be hard for us to choose a specific model in practice. To address this kind of performance evaluation problem, the current paper extends Shannon-DEA procedure to establish a comprehensive efficiency measure for appraising DMUs’ resource and environment efficiencies. In the proposed approach, the measure for evaluating a model's importance degree is provided, and the targets setting approach of inputs/outputs for DMU managers to improve DMUs’ energy and environmental efficiencies is also discussed. We illustrate the proposed approach using real data set of 30 provinces in China.     

Energy efficiency in Swedish industry: A firm-level data envelopment analysis

This paper assesses energy efficiency in Swedish industry. Using unique firm-level panel data covering the years 2001–2008, the efficiency estimates are obtained for firms in 14 industrial sectors by using data envelopment analysis (DEA). The analysis accounts for multi-output technologies where undesirable outputs are produced alongside with the desirable output. The results show that there was potential to improve energy efficiency in all the sectors and relatively large energy inefficiencies existed in small energy-use industries in the sample period. Also, we assess how the EU ETS, the carbon dioxide (CO₂) tax and the energy tax affect energy efficiency by conducting a second-stage regression analysis. To obtain consistent estimates for the regression model, we apply a modified, input-oriented version of the double bootstrap procedure of Simar and Wilson (2007). The results of the regression analysis reveal that the EU ETS and the CO₂ tax did not have significant influences on energy efficiency in the sample period. However, the energy tax had a positive relation with the energy efficiency.     

Social sustainability measured by intermediate approach for DEA environmental assessment: Chinese regional planning for economic development and pollution prevention

This study examines the level of simultaneous achievement on economic prosperity and environmental protection, so measuring the level of sustainability by a newly proposed intermediate approach. Conventionally, DEA (Data Envelopment Analysis) for performance evaluation was used to assess various economic activities of organizations that utilized inputs (e.g., the number of employees) to yield desirable outputs (e.g., the number of products). The methodology was usually classified into radial or non-radial category. In an effort of extending it to environmental assessment, DEA needs to incorporate undesirable outputs (e.g., the amount of CO₂) into the computational framework. This type of assessment is referred to as “DEA environmental assessment”. The proposed intermediate approach develops a new type of DEA environmental assessment by combining the analytical features of radial approach with those of non-radial approach. The new approach measures the level of unified inefficiency on each production factor and determines the level of total unified inefficiency from the average of the sum of these inefficiency scores. A rank sum test and a mean test are additionally incorporated into the proposed intermediate approach. Such a combination enhances the statistical capability. As an application, this study examines the level of sustainability related to 30 provinces in China from 2003 to 2014. The application finds four policy implications. First, it is important for China to allocate its resources to western provinces at a level that it has used for coastal developments. Second, the government continues to reinforce the policy making effort on environmental protection by paying attention to central and southern regions. The Chinese industries need to transfer toward “green” energy. The transfer should be efficiently executed for their future developments. Third, the government has focused on the sustainability development in urban provinces and then has gradually shifted the policy influence toward rural areas. Finally, the policy implementation needs a time lag until it becomes effective. The time lag on economic policy is longer than that of environmental policy. The economic policy has historically produced a regional imbalance, often leading to educational and income imbalances, among Chinese provinces.     

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.     

Should the US clean air act include CO2 emission control?: Examination by data envelopment analysis

This study proposes a new use of Data Envelopment Analysis (DEA) to measure the operational, environmental and both-unified efficiency measures of US coal-fired power plants. The power plants produce not only desirable outputs (e.g., electricity) but also undesirable outputs (e.g., CO₂ and NO_x) as a result of their plant operations. A Range-Adjusted Measure (RAM) is used as an original non-radial DEA model. Then, it is reformulated for handling undesirable (bad) outputs. The proposed use of DEA models measures the environmental and unified performance of power plants under two variable alternatives (with and without CO₂ emission control) in order to examine both the influence of US Clean Air Act (CAA) on the acid rain causing gases (NO_x and SO₂) and its extension to the CO₂ regulation. This study finds that the acid rain program under the CAA has been effective on the emission control of SO₂ and NO_x produced at US coal-fired power plants. Moreover, additional regulation on CO₂ may enhance their environmental and unified performance. Thus, it is recommended that the US federal and state governments need to expand the legal scope of CAA to the emission control on CO₂ because the gas is considered as a main source of global warming and climate change.     

A multiplicative environmental DEA approach to measure efficiency changes in the world's major polluters

We propose a multiplicative environmental data envelopment analysis (ME-DEA) approach to measure the performance of 46 countries that generate most of the world's carbon dioxide (CO₂) emissions. In the model, we combine economic (labour and capital), environmental (freshwater) and energy inputs with a desirable output (GDP) and three undesirable outputs (CO₂, methane and nitrous oxide emissions). We rank each country according to the optimum use of its resources employing a multiplicative extension of environmental DEA models. By computing partial efficiency scores for each input and output separately, we thus identify major sources of inefficiency for all sample countries. Based on the partial efficiency scores obtained from the model, we define aggregate economic, energy and environmental efficiency indexes for 2002, 2007 and 2011, reflecting points in time before and after the official enactment of the Kyoto Protocol. We find that for most countries efficiency scores increase over this period. In addition, there exists a positive relationship between economic and environmental efficiency, although, at the same time, our results suggest that environmental efficiency cannot be realized without first reaching a certain threshold of economic efficiency. We also find support for the Paradox of Plenty, whereby an abundance of natural and energy resources results in their inefficient use.     

Eco-efficiency of the world cement industry: A data envelopment analysis

Chemical reactions and the combustion of dirty fuels, such as coal and petroleum coke (petcoke), that are used in cement production processes generate a significant amount of CO₂ emissions. In this paper, we provide an eco-efficiency measure for 21 prototypes of cement industries operating in many countries by applying both a data envelopment analysis (DEA) and a directional distance function approach, which are particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account. To understand whether this eco-efficiency is due to a rational utilization of inputs or to a real carbon dioxide reduction as a consequence of environmental regulation, we analyze the cases where CO₂ emissions can either be considered as an input or as an undesirable output. Empirical results show that countries where cement industries invest in technologically advanced kilns and adopt alternative fuels and raw materials in their production processes are eco-efficient. This gives a comparative advantage to emerging countries, such as India and China, which are incentivized to modernize their production processes.     

Energy and GHG emission efficiency in the Chilean manufacturing industry: Sectoral and regional analysis by DEA and Malmquist indexes

Global warming produced mainly by the emission of greenhouse gases is currently a worldwide concern. In the last few decades since the 1950s many of the changes observed in the world climate have been meaningful. This paper presents an analysis of energy efficiency and of greenhouse gas emissions in the Chilean manufacturing industry by region and sector taking into consideration time sequences. Data Envelopment Analysis (DEA) models are used for the analysis. Three ways to handle undesirable outputs are compared, the source of inefficiency in each decision making unit (DMU) is calculated using scale efficiency, and the evolution over time is analyzed using the Malmquist index. The results indicate that the industries located in the Chilean regions of Coquimbo, La Araucania and Aysen were the most efficient while the industries in the regions of Tarapaca, Antofagasta and Biobio were less efficient. The most efficient industrial sectors were those involving communications equipment, base metals, and clothing; the least efficient were those concerned with food and beverages, textiles and nonmetallic minerals. Due to the treatment of the undesirable outputs, differences were found in the efficiency indexes obtained by the three models. This finding suggests using a model better adapted to the characteristics of the outputs in question and the viability of improving industrial practices.     

Energy efficiency development in German and Colombian non-energy-intensive sectors: a non-parametric analysis

This paper measures energy efficiency development in non-energy-intensive sectors (NEISs) in Germany and Colombia from a production-based theoretical framework using Data Envelopment Analysis (DEA). Using data from the German and Colombian Annual Surveys of Industries from 1998 to 2005, the analysis compares energy efficiency performances in German and Colombian NEISs at two levels of aggregation and then applies several alternative models. The results show considerable variation in energy efficiency performance in the NEISs of both countries. Comparing the results across models, it was found that in the German and Colombian NEISs, the measures of energy efficiency are similar, indicating that an appropriate combination of technical efficiency and cost minimisation are necessary to improve energy efficiency. However, energy efficiency based on cost minimisation is greater in both countries, demonstrating that energy prices in this sector are not the key variable for improving energy efficiency. This is due to the low share of energy costs, making it preferable to change other inputs rather than energy. A second-stage regression analysis reveals that in the German and Colombian NEISs, labour productivity and investments are fundamental to changes in energy efficiency. Finally, the energy efficiency measures of the DEA models show significant correlations with the traditional energy efficiency measure, indicating that energy efficiency as measured through DEA could be complementary to measures of energy intensity when analysing other key elements of energy efficiency performance in the industrial sector.