Analysis of product efficiency of hybrid vehicles and promotion policies

The key aim of this study is to evaluate the product efficiency of current hybrid vehicles and suggest effective policies to promote hybrid vehicles in the Korean automobile market and development trends of hybrid vehicles. The efficiency levels for car models sold in Korea, including hybrid ones, were measured using the recently developed discrete additive data envelopment analysis (DEA) model that reflects consumer preference. The result of the analysis shows that current hybrid vehicles on the market are still at lower competitive advantage than traditional car models with conventional combustion engines and we can suggest a mix of incentive policies to promote the competitiveness of hybrid vehicles. In addition, we also identify two distinctive trends of hybrid vehicle development: environment-oriented hybrid vehicles and performance-oriented hybrid vehicles. It implies that the government should take account of development trends of hybrid vehicles to achieve the policy goals in designing support schemes and automobile companies that are willing to develop hybrid vehicles can also gain some insights for making strategic decisions.     

Does regulation stimulate productivity? The effect of air quality policies on the efficiency of US power plants

This research examines the effect of air quality regulations on the productivity of US power plants based on both economic and environmental outputs. Using data envelopment analysis (DEA) to estimate an efficiency measure incorporating both economic and environmental outcomes, we look at changes in efficiency in US power plants over an eleven-year time period (1994–2004) during which several different regulations were implemented for the control of nitrogen oxides (NO_x) and sulfur dioxide (SO₂). The paper then models how estimated efficiency behaves over time as a function of regulatory changes. Findings suggest mixed effects of regulations on power plant efficiency when pollution abatement and electricity generation are both included as outputs.     

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.     

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.     

Renewable energy and macroeconomic efficiency of OECD and non-OECD economies

This article analyzes the effects of renewable energy on the technical efficiency of 45 economies during the 2001–2002 period through data envelopment analysis (DEA). In our DEA model, labor, capital stock, and energy consumption are the three inputs and real GDP is the single output. Increasing the use of renewable energy improves an economy's technical efficiency. Conversely, increasing the input of traditional energy decreases technical efficiency. Compared to non-OECD economies, OECD economies have higher technical efficiency and a higher share of geothermal, solar, tide, and wind fuels in renewable energy. However, non-OECD economies have a higher share of renewable energy in their total energy supply than OECD economies.     

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.     

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.     

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.     

Performance evaluation and improvement directions for an Indian electric utility

This study evaluates the performance of 29 Electricity Distribution Divisions (EDDs) of an Indian state – Uttarakhand – deploying Input oriented Data Envelopment Analysis (DEA). The results indicate that the performance of several EDDs is sub-optimal, suggesting the potential for cost reductions and possible reduction in employees number. In the DEA method more than one unit are identified as efficient. Therefore, this study suggests a method for ranking the efficient units by their importance as benchmarks for the inefficient units through benchmark share measure. The bigger the benchmark share, the more important an efficient division is in benchmarking for inefficient ones. Result reveals that plain area divisions are relatively efficient and have higher potential to influence the performance of inefficient EDDs. This study is envisaged to be instrumental to policy makers and managers to increase the operational efficiency of inefficient EDDs and thereby increase the competitiveness in the face of restructuring and liberalization of Indian electricity sector.     

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.     

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.     

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.     

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.     

Comparison of Turkey's performance of greenhouse gas emissions and local/regional pollutants with EU countries

Turkey signed the Kyoto Protocol on February 17th, 2009. Therefore, to fulfill the liabilities of the Kyoto Protocol, she has to review her status and make strategic plans to hold the emissions at the levels specified in the protocol. For this purpose, in this study employing data envelopment analysis (DEA), Turkey's greenhouse gas (GHG) emissions and local/regional pollutants compared to the European Union (EU) countries have been put in place. Since the data on Malta and Cyprus was inadequate, these two nations were excluded from the evaluation. As a candidate for EU, Turkey is required to reduce the emissions by 8% until 2012 within the framework of the Kyoto Protocol.     

An integrated DEA–COLS–SFA algorithm for optimization and policy making of electricity distribution units

This paper presents an integrated data envelopment analysis (DEA)–corrected ordinary least squares (COLS)–stochastic frontier analysis (SFA)–principal component analysis (PCA)–numerical taxonomy (NT) algorithm for performance assessment, optimization and policy making of electricity distribution units. Previous studies have generally used input–output DEA models for benchmarking and evaluation of electricity distribution units. However, this study proposes an integrated flexible approach to measure the rank and choose the best version of the DEA method for optimization and policy making purposes. It covers both static and dynamic aspects of information environment due to involvement of SFA which is finally compared with the best DEA model through the Spearman correlation technique. The integrated approach would yield in improved ranking and optimization of electricity distribution systems. To illustrate the usability and reliability of the proposed algorithm, 38 electricity distribution units in Iran have been considered, ranked and optimized by the proposed algorithm of this study.     

Assessing the power generation,pollution control,and overall efficiencies of municipal solid waste incinerators in Taiwan

This paper evaluates the productivity of municipal solid waste incinerators (MSWIs) by addressing the following questions: (1) to what extent should one further increase the production of power generation while maintaining the emission of noxious air at the current level?; (2) To what extent should one further decrease the emission of noxious air while maintaining the production of power generation at the current level?; and (3) To what extent should one increase the production of power generation and decrease the emission of noxious air simultaneously? To effectively address these questions to improve performance, the power generation and pollution control efficiencies are evaluated using TODEA (two-objective data envelopment analysis), as well as the overall efficiency evaluated using Tone’s NS-overall model (slacks-based measure with non-separable desirable and undesirable outputs for evaluating overall efficiency). A MSWI case study in Taiwan with the panel data covering the period of 2004–2008 reveals that the power generation and overall efficiencies of build-operate-transfer are more efficient, on average, than those of public-own-operate and build-own-operate. However, the three building and operation types do not significantly differ in pollution control efficiency.     

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.     

Using climate classification to evaluate building energy performance

Traditional benchmarking of building energy performance usually starts by considering a wide range of different factors and giving these factors different weights to help reach one general indicator measuring a building’s overall energy performance. For obtaining more specific information in building energy management performance, this paper proposes an adjustment to the traditional approach by using climate classification and data envelopment analysis (DEA). The study first adopts cluster analysis to classify the evaluated buildings into different climate clusters. Secondly, scale factors are identified by regression analysis. DEA is then employed to assess the energy management efficiency of the evaluated buildings. The samples of 122 office buildings in Taiwan in summer are classified into three climate clusters (warm and long rain hour, hot and middle rain hour, and hot and short rain hour). Research results indicate that the average indicators of energy management performance in each of the three climate clusters are 0.5, 0.56, and 0.56 respectively. The lower value indicator of energy management performance, resulted from the comparison between the energy consumption of the evaluated building and the minimum energy consumption among buildings in the same scale and similar climate conditions, indicates a more potential in energy saving.     

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.