Efficiency assessment of Turkish power plants using data envelopment analysis

The performance of electricity generation plants in Turkey are analyzed and compared. The data set contains inputs from 65 thermal, hydro and wind power plants, owned by private and public sectors. Data envelopment analysis (DEA) is used as the primary mathematical tool. Two efficiency indexes, reflecting operational and investment performance, are defined and pursued. Constant returns to scale, variable returns to scale and assurance region type DEA models are used in the analysis. Scale efficiency is also considered. Performance comparisons include public versus private sector plants, and natural gas versus coal versus oil fired plants. Also, relationships between efficiency scores and various input/output factors are investigated and some interesting trends are identified.     

Efficiency analysis of Turkish natural gas distribution companies by using data envelopment analysis method

This paper analyzes the performance of 38 Turkish natural gas distribution companies by using a non-parametric method, Data Envelopment Analysis (DEA). The results are used to determine the most proper model specification, to detect the important criteria affecting the efficiency levels, and to find the common characteristics of the most inefficient firms. We find that public firms compared to private firms, non-tender firms compared to tender firms, large firms compared to small firms and firms operating in more developed areas compared to firms operating in underdeveloped areas that utilize resources and manage costs more efficiently. However, we cannot reach a certain conclusion about the comparison of old firms versus the new firms. Lastly, we try to detect the common characteristics of the most inefficient firms and find that most of these firms are immature and low scale companies. Therefore, we think these firms can increase efficiency level either by increasing their delivery amounts through higher penetration rates or by merging or doing both together.     

Do undesirable output and environmental regulation matter in energy efficiency analysis? Evidence from Indian Cement Industry

Energy use in the production of desirable output often results in the generation of undesirable emission with detrimental impact on the environment, and whose disposal is frequently monitored by public authorities. Previous studies, however, paid little attention to undesirable output and environmental regulation related to its disposal while estimating energy use efficiency. Analysis of energy efficiency ignoring undesirable output could result in biased estimates of efficiency. Thus the aim of this paper is to estimate energy use efficiency in the presence of energy related undesirable emission by taking Indian cement industry as a suitable context of my analysis. Depending on the presence and absence of undesirable output and environmental regulation, three measures of efficiency have been estimated at the state level from 2000–01 to 2004–05 by applying Data Envelopment Analysis. Energy efficiency is defined as the ability of the producer to reduce the energy input to the largest extent possible, conditional on the given level of output and non-energy inputs. Empirical results reveal that energy efficiency estimates are biased if only desirable output is considered. Results also demonstrate that environmental regulation has a reinforcing effect on energy use efficiency.     

Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies

This paper presents Data Envelopment Analysis (DEA) model with uncertain data for performance assessment of electricity distribution companies. During the past two decades, DEA has been widely used for benchmarking the electricity distribution companies. However, there is no study among many existing DEA approaches where the uncertainty in data is allowed and, at the same time, the distribution of the random data is permitted to be unknown. The proposed method of this paper develops a new DEA method with the consideration of uncertainty on output parameters. The method is based on the adaptation of recently developed robust optimization approaches proposed by Ben-Tal and Nemirovski [2000. Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming 88, 411–421] and Bertsimas et al. [2004. Robust linear optimization under general norms.     

Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis

Electricity is essential in the economic development of a nation. Due to the rapid growth of economy and industrial development in Taiwan, the demand for use of electricity has increased rapidly. This study evaluates the power-generation efficiency of major thermal power plants in Taiwan during 2004–2006 using the data envelopment analysis (DEA) approach. A stability test was conducted to verify the stability of the DEA model. According to the results, all power plants studied achieved acceptable overall operational efficiencies during 2004–2006, and the combined cycle power plants were the most efficient among all plants. The most important variable in this DEA model is the “heating value of total fuels”. Findings from this study can be beneficial in improving some of the existing power plants and for more efficient operational strategies and related policy-making for future power plants in Taiwan.     

Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis

In this study, efficiency analyses of the eleven lignite-fired, one hard coal-fired and three natural gas-fired state-owned thermal power plants used for electricity generation were conducted through data envelopment analysis (DEA). Two efficiency indexes, operational and environmental performance, were defined and pursued. In the calculation of the operational performance, main production indicators were used as input, and fuel cost per actual production (Y) was used as output (Model 1). On the other hand, in the calculation of the environmental performance, gases emitted to the environment were used as output (Model 2). Data envelopment analysis (DEA) is the main instrument for the measurement of relative performances of the decision making units with multiple inputs and outputs. Constant returns to scale (CRS or CCR) and variable returns to scale (VRS or BCC) type DEA models were used in the analyses. The relationship between efficiency scores and input/output factors was investigated. Employing the obtained results, the power plants were evaluated with respect to both the cost of electricity generation and the environmental effects.     

Measuring efficiency and productivity change in power electric generation management companies by using data envelopment analysis: A case study

This paper provides an empirical analysis of the determinants of energy efficiency in 32 power electric generation management companies over the period 2005-2009. The study uses non-parametric Data Envelopment Analysis (DEA) to estimate the relative technical efficiency and productivity change of these companies. In order to verify the stability of our DEA model and the importance of each input variable, a stability test is also conducted. The results of the study indicate that average technical efficiency of companies decreased during the study period. Nearly half of the companies (14) are below this average level of 88.7% for five years. Moreover, it is shown that the low increase of productivity changes is more related to low efficiency rather than technology changes.     

Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach

In this study energy use pattern for canola production in Golestan province of Iran was studied and the degrees of technical and scale efficiency of producers were analyzed using a non-parametric data envelopment analysis technique. The study also helped to identify the wasteful uses of energy by inefficient farmers and to suggest reasonable savings in energy uses from different inputs. Further, the effect of optimization of energy on energy ratio and energy productivity was investigated. Data used in this study were obtained from 130 randomly selected canola farms from Golestan, the most important center of canola production in Iran. The inputs were human labor, diesel, machinery, fertilizers, chemicals, water for irrigation, seeds and electrical energies; while the yield value of canola was considered as output. The results revealed that, the total energy of 17,786 MJ ha⁻¹ was consumed for canola production; about 15% of farmers were found to be technically efficient and the mean efficiency of farmers was found to be 0.74 and 0.88 under constant and variable returns to scale assumptions, respectively. The results also suggested that, on average, a potential 9.5% (1696 MJ ha⁻¹) reduction in total energy input could be achieved provided that all farmers operated efficiently.     

Linear programming models for measuring economy-wide energy efficiency performance

Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as by-products of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring economy-wide energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 21 OECD countries and the results obtained are presented.     

Energy use efficiency of Indian cement companies: a data envelopment analysis

The present paper aims at measuring energy use efficiency in Indian cement industry and estimating the factors explaining inter-firm variations in energy use efficiency. Within the framework of production theory, data envelopment analysis has been used to measure energy use efficiency. Using firm-level data from electronic PROWESS database for the years 1989–1990 through 2006–2007, the study first estimates energy use efficiency of the firms and then compares the efficiency scores across. Empirical results suggest that there is enough scope for the Indian cement firms to reduce energy uses, though this potential for energy saving varies across firms. A second-stage regression analysis reveals that firms with larger production volume have higher energy efficiency scores and that age of the firms impacts differently on energy use efficiency obtained from two different models. Also, higher quality of labor force associates with higher energy use efficiency.     

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.     

Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model

Data envelopment analysis (DEA) has gained much popularity in performance measurement of power industry. This paper presents a slack-based measure approach to investigating the relationship between fossil fuel consumption and the environmental regulation of China's thermal power generation. We first calculate the total-factor energy efficiency without considering environmental constraints. An environmental performance indicator is proposed through decomposing the total-factor energy efficiency. The proposed approach is then employed to examine whether environmental regulation affects the energy efficiency of China's thermal power generation. We find that the environmental efficiency plays a significant role in affecting energy performance of China's thermal generation sector. Decreasing the discharge of major pollutants can improve both energy performance and environmental efficiency. Besides, we also have three main findings: (1) The energy efficiency and environmental efficiency were relatively low. (2) The energy and environmental efficiency scores show great variations among provinces. (3) Both energy efficiency and environmental efficiency are of obvious geographical characteristics. According to our findings, we suggest some policy implications.     

A dynamic multi-stage data envelopment analysis model with application to energy consumption in the cotton industry

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of homogenous decision making units (DMUs) with multiple inputs and outputs. In this paper, we present a dynamic multi-stage DEA (DMS-DEA) approach to evaluate the efficiency of cotton production energy consumption. In the proposed model, the farms which consume resources (i.e., fertilizers, seeds, and pesticides) to produce cotton are assumed to be the DMUs. Inputs not consumed during a planning period are carried over to the next period in the planning horizon. Initially, a DMS-DEA model is used to determine the overall efficiency of the DMUs with dynamic inputs. Next, the efficiency score of each DMU is calculated for each time period in the planning horizon. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedures and algorithms with a real-life case study of energy consumption in the cotton industry.     

Environmental efficiency evaluation based on data envelopment analysis: A review

The paper aims to investigate the achievements of the theoretical and practical basis of environmental policy analysis in order to study their works and point out the future possible research direction. It sorts out researches about environmental efficiency assessment and reviews the works about the theory and application of efficiency analysis around the world. It is suggested that environmental efficiency evaluation theory under small samples and DEA method with undesirable outputs will further extend the research on environmental efficiency evaluation. Also, this review confirms that more studies in methods and their applications in this area are in urgent need.     

Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach

The environmental performance of regions and largest economies of the world – actually, the efficiency of their energy sectors – is estimated for the period 2010–2030 by using forecasted values of main economic indicators. Two essentially different methodologies, data envelopment analysis and stochastic frontier analysis, are used to obtain upper and lower boundaries of the environmental efficiency index. Greenhouse gas emission per unit of area is used as a resulting indicator, with GDP, energy consumption, and population forming a background of comparable estimations. The dynamics of the upper and lower boundaries and their average is analyzed. Regions and national economies having low level or negative dynamics of environmental efficiency are determined.     

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.     

A comparison for Turkish provinces’ performance of urban air pollution

The objective of this paper is to assess the environmental performance of Turkish provinces by using a non-parametric method, Data Envelopment Analysis. The results of ranking are based on the provinces’ ability to produce the largest equi-proportional increase in the desirable output-gross domestic product and decrease in the undesirable output namely, air pollutants. The results indicate that 7.41% of the sample provinces are relatively efficient. The results also show that, regions with the highest level gross domestic product per capita have the highest efficiency scores. New industrial districts have lower efficiencies in spite of relatively higher income per capita. In the second stage of the study the possible relationship between environmental efficiency scores and input, output variables are investigated by a regression analysis. According to the results, there is a positive relationship between gross domestic product and efficiency scores.     

Comprehensive evaluation of effects of straw-based electricity generation: A Chinese case

Greater use of renewable energy is being aggressively promoted to combat climate change by the Chinese government and by other governments. Agricultural straw is the kind of renewable energy source that would become a pollution source if it is not well utilized. We select the Shiliquan straw-based electricity generation project in Shandong Province, China as a case and assess environmental externalities of straw utilization in power plants by using life-cycle analysis. Results show that straw-based electricity generation has far fewer greenhouse gas (GHG) emissions than that of coal-based electricity generation. Improvement in the energy efficiency of equipment used for straw’s pretreatment would lead to a decrease of GHG emissions and energy consumption in the life-cycle of straw-based electricity generation. In case 400 million tonnes of wasted straw in China could be used as a substitute for 200 million tonnes of coal, annually the straw 291 Terrawatt hours (TWh) of electricity could be generated, resulting in an annual total CO₂ emissions savings of 193 million tonnes. Straw-based electricity generation could be a high-potential alternative for electricity generation as well as an incentive for utilizing wheat straw instead of burning it in the field.     

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.     

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.