A survey of data envelopment analysis in energy and environmental studies

https://www.grips.ac.jp/r-center/wp-content/uploads/07-08.pdf

Network DEA: A slacks-based measure approach

We present a method for decomposing wholesale electricity payments into production costs, inframarginal competitive rents, and payments resulting from the exercise of market power. The method also parses actual variable costs into the minimum variable costs necessary to meet demand and increased production costs caused by market power and other market inefficiencies. Using data from June 1998 to October 2000 in California, we find significant departures from competitive pricing, particularly during the high-demand summer months. Electricity expenditures in the state's restructured wholesale market rose from $2.04 billion in summer 1999 to $8.98 billion in summer 2000. We find that 21% of this increase was due to increased production costs, 20% was due to increased competitive rents, and the remaining 59% was attributable to increased market power.

/pdf/measuring-market-inefficiencies-in-california-s-restructured-1w2ag7tu82.pdf

Measuring Market Inefficiencies in California's Restructured Wholesale Electricity Market

In data envelopment analysis, there are several methods for measuring efficiency changes over time, e.g. the window analysis and the Malmquist index. However, they usually neglect carry-over activities between two consecutive terms and only focus on the separate time period independently aiming local optimization in a single period, even if these models can take into account the time change effect. In the actual business world, a long time planning and investment is a subject of great concern. For these cases, single period optimization model is not suitable for performance evaluation. To cope with long time point of view, the dynamic DEA model incorporates carry-over activities into the model and enables us to measure period specific efficiency based on the long time optimization during the whole period. Dynamic DEA model proposed by Fare and Grosskopf is the first innovative contribution for such purpose. In this paper we develop their model in the slacks-based measure (SBM) framework, called dynamic SBM (DSBM). The SBM model is non-radial and can deal with inputs/outputs individually, contrary to the radial approaches that assume proportional changes in inputs/outputs. Furthermore, according to the characteristics of carry-overs, we classify them into four categories, i.e. desirable, undesirable, free and fixed. Desirable carry-overs correspond, for example, to profit carried forward and net earned surplus carried to the next term, while undesirable carry-overs include, for example, loss carried forward, bad debt and dead stock. Free and fixed carry-overs indicate, respectively, discretionary and non-discretionary ones. We develop dynamic SBM models that can evaluate the overall efficiency of decision making units for the whole terms as well as the term efficiencies.

/pdf/dynamic-dea-a-slacks-based-measure-approach-2t9vup7bvp.pdf

Dynamic DEA: A slacks-based measure approach

Benchmarking and regulation: international electricity experience

We propose a dynamic DEA model involving network structure in each period within the framework of a slacks-based measure approach. We have previously published the network SBM (NSBM) and the dynamic SBM (DSBM) models separately. Hence, this article is a composite of these two models. Vertically, we deal with multiple divisions connected by links of network structure within each period and, horizontally, we combine the network structure by means of carry-over activities between two succeeding periods. This model can evaluate (1) the overall efficiency over the entire observed period, (2) dynamic change of period efficiency and (3) dynamic change of divisional efficiency. The model can be implemented in input-, output- or non-(both) oriented forms under the CRS or VRS assumptions on the production possibility set. Finally, we applied this model to a dataset of US electric utilities and compared the result with that of DSBM.

/pdf/dynamic-dea-with-network-structure-a-slacks-based-measure-7lpkd4jz04.pdf

Dynamic DEA with network structure: A slacks-based measure approach

An epsilon-based measure of efficiency in DEA – A third pole of technical efficiency

http://www.provedor.nuca.ie.ufrj.br/eletrobras/estudos/hattori1.pdf

Miki Tsutsui

Papers

Network DEA: A slacks-based measure approach

Dynamic DEA: A slacks-based measure approach

Dynamic DEA with network structure: A slacks-based measure approach

An epsilon-based measure of efficiency in DEA – A third pole of technical efficiency

Economic impact of regulatory reforms in the electricity supply industry: a panel data analysis for OECD countries