• Energy Research

Abstract This study proposes a use of Data Envelopment Analysis (DEA) for environmental assessment. All organizations in private and public sectors produce not only desirable (good) but also undesirable (bad) outputs as a result of their economic activities. The proposed use of DEA determines the level of unified (operational and environmental) efficiency of all the organizations. A contribution of this study is that it explores how to measure not only RTS (Returns to Scale) on desirable outputs but also a new concept regarding “DTS: Damages to Scale” (corresponding to RTS for undesirable outputs). This study discusses how to measure RTS under natural disposability and DTS under managerial disposability by DEA. The measurement of RTS and DTS is formulated by incorporating “Strong Complementary Slackness Conditions (SCSCs)”. As a result, this study can handle an occurrence of multiple reference sets and multiple projections in the RTS/DTS measurement. The incorporation of SCSCs makes it possible both to restrict DEA multipliers in a specific range without any prior information and to identify all possible efficient organizations as a reference set. Using the unique capabilities of SCSCs, this study discusses the use of DEA environmental assessment by exploring how to classify the type of RTS/DTS with SCSCs. Such analytical capabilities are essential, but not previously explored in DEA environmental assessment for energy industries. As an illustrative example, this study applies the proposed approach for the performance evaluation of Japanese manufacturing industries. This study finds that these firms need to introduce technology innovation to reduce an amount of greenhouse gases and wastes. The empirical result confirms the importance of measuring RTS/DTS in DEA environmental assessment.

Abstract This study proposes a use of Data Envelopment Analysis (DEA) for environmental assessment. All organizations in private and public sectors produce not only desirable (good) but also undesirable (bad) outputs as a result of their economic activities. The proposed use of DEA determines the level of unified (operational and environmental) efficiency of all the organizations. A contribution of this study is that it explores how to measure not only RTS (Returns to Scale) on desirable outputs but also a new concept regarding “DTS: Damages to Scale” (corresponding to RTS for undesirable outputs). This study discusses how to measure RTS under natural disposability and DTS under managerial disposability by DEA. The measurement of RTS and DTS is formulated by incorporating “Strong Complementary Slackness Conditions (SCSCs)”. As a result, this study can handle an occurrence of multiple reference sets and multiple projections in the RTS/DTS measurement. The incorporation of SCSCs makes it possible both to restrict DEA multipliers in a specific range without any prior information and to identify all possible efficient organizations as a reference set. Using the unique capabilities of SCSCs, this study discusses the use of DEA environmental assessment by exploring how to classify the type of RTS/DTS with SCSCs. Such analytical capabilities are essential, but not previously explored in DEA environmental assessment for energy industries. As an illustrative example, this study applies the proposed approach for the performance evaluation of Japanese manufacturing industries. This study finds that these firms need to introduce technology innovation to reduce an amount of greenhouse gases and wastes. The empirical result confirms the importance of measuring RTS/DTS in DEA environmental assessment.

Abstract This study discusses a combined use of DEA (Data Environment Analysis) and DEA–DA (Discriminant Analysis) to determine the efficiency-based rank of energy firms. This type of performance evaluation is important because we often have a difficulty in accessing a large sample on energy firms to derive reliable empirical results. The proposed approach is useful in dealing with such a limited number of energy firms, often found in previous DEA studies on energy industries in the world. The proposed approach uses DEA to classify energy firms into efficient and inefficient groups based upon their efficiency scores. Then, it utilizes DEA–DA to assess their efficiency scores and ranks. In this stage, we can find an adjusted efficiency score for each energy firm. The proposed approach provides us with the following analytical capabilities, all of which cannot be found in a conventional use of DEA in assessing energy firms. First, the proposed DEA approach can avoid zero in all multipliers on efficient energy firms by incorporating SCSC (Strong Complementary Slackness Condition) so that it can handle an occurrence of multiple reference sets and multiple projections. The DEA result classifies all energy firms into efficient and inefficient groups. Second, DEA–DA, applied to the two groups, evaluates all energy firms by an industry-wide evaluation, not depending upon a limited number of efficient energy firms in a reference set, as found in a conventional use of DEA. The analytical capability can reduce the number of efficient energy firms. Third, the proposed approach can provide their efficiency-based ranking scores. Finally, we can conduct a rank sum test based upon their ranking scores to obtain a statistical inference. As an application, this study uses the proposed approach to examine the performance of Japanese electric power industry. We find two economic implications. One of the two implications is that no major change has occurred in the operational performance of Japanese electric power industry because of Japanese sluggish economy from 2005 to 2009. The other implication indicates that there are strategic differences in the operation of Japanese electric power firms after the liberalization.

Abstract This study discusses a combined use of DEA (Data Environment Analysis) and DEA–DA (Discriminant Analysis) to determine the efficiency-based rank of energy firms. This type of performance evaluation is important because we often have a difficulty in accessing a large sample on energy firms to derive reliable empirical results. The proposed approach is useful in dealing with such a limited number of energy firms, often found in previous DEA studies on energy industries in the world. The proposed approach uses DEA to classify energy firms into efficient and inefficient groups based upon their efficiency scores. Then, it utilizes DEA–DA to assess their efficiency scores and ranks. In this stage, we can find an adjusted efficiency score for each energy firm. The proposed approach provides us with the following analytical capabilities, all of which cannot be found in a conventional use of DEA in assessing energy firms. First, the proposed DEA approach can avoid zero in all multipliers on efficient energy firms by incorporating SCSC (Strong Complementary Slackness Condition) so that it can handle an occurrence of multiple reference sets and multiple projections. The DEA result classifies all energy firms into efficient and inefficient groups. Second, DEA–DA, applied to the two groups, evaluates all energy firms by an industry-wide evaluation, not depending upon a limited number of efficient energy firms in a reference set, as found in a conventional use of DEA. The analytical capability can reduce the number of efficient energy firms. Third, the proposed approach can provide their efficiency-based ranking scores. Finally, we can conduct a rank sum test based upon their ranking scores to obtain a statistical inference. As an application, this study uses the proposed approach to examine the performance of Japanese electric power industry. We find two economic implications. One of the two implications is that no major change has occurred in the operational performance of Japanese electric power industry because of Japanese sluggish economy from 2005 to 2009. The other implication indicates that there are strategic differences in the operation of Japanese electric power firms after the liberalization.

This study examines the cost structure of Japanese electric power industry to investigate whether a structural reform on the industry really enhances a cost-saving benefit to consumers. A composite cost function model, using a panel data set, is used for this study. The data set consists of nine electric power companies from 1990 to 2008. Based upon the estimation results, this study examines whether economies of scale and vertical economies exist in the industry. Then, this study conducts a cost subadditivity test that is a necessary condition of natural monopoly. The empirical results indicate that the electric power firms exhibit the status of economies of scale in their transmissions and distributions and the operation as a whole. However, they do not exhibit economies of scale in their generations. Thus, the transmission operation, by integration, in a large area can improve its economic efficiency. Furthermore, the industry should introduce more competition in both generation and wholesale power markets where more firms can participate in their power trades. This study also empirically confirms that vertical economies have existed in the industry. Moreover, this study confirms that all the estimates in the cost subadditivity test satisfy the necessary condition of natural monopoly, where each estimate indicates cost saving in cost subadditivity. The test does not guarantee a sufficient condition of natural monopoly. However, it clearly indicates that the functional separation between generation and transmission will increase total production cost in the industry. The complete separation may result in a net loss of economic efficiency if a competition benefit does not exceed an expected economic loss. Consequently, this study suggests that the industrial structure of future Japanese electric power industry should be evaluated from not only an expected benefit by introducing competition but also an unbundling cost that occurs with a loss of vertical integration.

This study examines the cost structure of Japanese electric power industry to investigate whether a structural reform on the industry really enhances a cost-saving benefit to consumers. A composite cost function model, using a panel data set, is used for this study. The data set consists of nine electric power companies from 1990 to 2008. Based upon the estimation results, this study examines whether economies of scale and vertical economies exist in the industry. Then, this study conducts a cost subadditivity test that is a necessary condition of natural monopoly. The empirical results indicate that the electric power firms exhibit the status of economies of scale in their transmissions and distributions and the operation as a whole. However, they do not exhibit economies of scale in their generations. Thus, the transmission operation, by integration, in a large area can improve its economic efficiency. Furthermore, the industry should introduce more competition in both generation and wholesale power markets where more firms can participate in their power trades. This study also empirically confirms that vertical economies have existed in the industry. Moreover, this study confirms that all the estimates in the cost subadditivity test satisfy the necessary condition of natural monopoly, where each estimate indicates cost saving in cost subadditivity. The test does not guarantee a sufficient condition of natural monopoly. However, it clearly indicates that the functional separation between generation and transmission will increase total production cost in the industry. The complete separation may result in a net loss of economic efficiency if a competition benefit does not exceed an expected economic loss. Consequently, this study suggests that the industrial structure of future Japanese electric power industry should be evaluated from not only an expected benefit by introducing competition but also an unbundling cost that occurs with a loss of vertical integration.

Abstract 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.

Abstract 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.