Environmental Concerns in Water Pricing Policy:Â Â an Application of Data Envelopment Analysis (DEA)

Water management is subject to conflicting economic and environmental objectives, and policymakers require a clear overview of the different outcomes derived from different water management options. The aim of this paper is to assess the efficiency of several irrigation water pricing policies with a special focus on their environmental implications. Irrigation is chosen here as a crucial sector of water use in large parts of southern Europe, where pressure on the resource is expected to increase due to climate change. A novel methodological approach for performing an ex ante analysis of alternative water pricing policies is proposed here, where environmental and technical performance are simultaneously considered. This approach takes place in two steps: the first is a simulation of alternative water policies through a mathematical programming model, and the second is the analysis of results by using the Data Envelopment Analysis (DEA) technique. A case study is applied in Puglia (southern Italy), where irrigation is the primary factor of strategic relevance for policymakers regarding water management. Our results show that on the one hand alternative pricing policies perform similarly in terms of technical efficiency and environmental efficiency. On the other hand, inefficiency appears to depend mainly on technical rather than environmental concerns. According to the assigned weights, through the DEA technique, the highest improvement for inefficient options may be obtained by better labour use. We conclude that the proposed approach may be a comprehensive and versatile framework for water policy analysis, offering a tool for supporting the decision-making process.Irrigation, Policy assessment, Efficiency, Data Envelopment Analysis, Linear Programming

Incorporating Both Undesirable Outputs and Uncontrollable Variables into DEA: the Performance of Chinese Coal-Fired Power Plants

There are two difficulties in doing an objective evaluation of the performance of decision-making units (DMUs). The first one is how to treat undesirable outputs jointly produced with the desirable outputs, and the second one is how to treat uncontrollable variables, which often capture the impact of the operating environment. Given difficulties in both model construction and data availability, very few published papers simultaneously consider the above two problems. This article attempts to do so by proposing six DEA-based performance evaluation models based on a research sample of the Chinese coal-fired power plants. The finding of this paper not only contributes for the performance measurement methodology, but also has policy implications for the Chinese coal-fired power sector

Using a modified DEA model to estimate the importance of objectives. An application to agricultural economics.

This paper shows a connection between Data Envelopment Analysis (DEA) and the methodology proposed by Sumpsi et al. (1997) to estimate the weights of objectives for decision makers in a multiple attribute approach. This connection gives rise to a modified DEA model that allows to estimate not only efficiency measures but also preference weights by radially projecting each unit onto a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of Multiple Attribute Decision Analysis the basic contribution of this paper is a new interpretation of the methodology by Sumpsi et al. (1997) in terms of efficiency. We also propose a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures which does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.Multicriteria Decision Making, Goal Programming, Weights, Preferences, Data Envelopment Analysis.

Using DEA to estimate the importance of objectives for decision makers

In this paper we establish further connections between DEA and Multi-criteria Decision Analysis by suggesting a particular way to estimate preference weights for different objectives using DEA. We claim that the virtual multipliers obtained from a standard DEA model are not suitable to measure the preferences of a decision maker. Our suggestion takes advantage of the parallelism between DEA and the methodology proposed by Sumpsi et al. (1997) by projecting each unit on a linear combination of the elements of the pay-off matrix. Finally, we make an application of the proposed methodology to agricultural economics in a case study with Spanish data.Data Envelopment Analysis, Multicriteria Decision Analysis, preferences, weights, virtual multipliers.

Reallocating Agricultural Greenhouse Gas Emission in EU15 Countries

This research work uses an alternative approach for modeling agricultural greenhouse gas emissions as an undesirable output, based on the zero sum gains DEA model (ZSG-DEA BCC model). This approach reallocates agricultural greenhouse gas emissions among EU15 countries. The reallocation analysis of greenhouse gas emissions permits countries that increase their emissions negotiate the emissions reduction with the others. This negotiation process might create a quota trade system for agricultural activity.DEA, Zero Sum Gains, Movement along the Efficient Frontier, Smoothed Frontier, Greenhouse Gas Emissions, Environmental Economics and Policy, Q54, Q56,

Integrating multiple criteria decision analysis and production theory for performance evaluation: framework and review

Accounting, life cycle assessment (LCA) and data envelopment analysis (DEA) are examples of various research areas that independently develop and apply diverse methodologies to evaluate performance. Though, many methods have in common that the results to be assessed are mainly determined by the inputs and outputs of the activities which are to be evaluated. Based on both production and decision theory, our comprehensive framework integrates and systematically distinguishes specific types of production-based performance assessment. It allows to examine and categorise the existing literature on such approaches. Our review focuses on sources which explicitly apply concepts or methods of multiple criteria decision analysis (MCDA). We did not find any elaborated methodology that fully integrates MCDA with production theory. At least, a basic approach to multicriteria performance analysis, which generalises the methodology of data envelopment analysis, appears to be well-grounded on production theory. It was already presented in this journal in 2001 and has rarely been noticed in the literature until now. A short overview outlines its recent insights and main findings. A key finding is that a category mistake prevails among well-known methodologies of efficiency measurement like DEA. It may imply invalid empirical results because the inputs and outputs of production processes are confused with resulting impacts destroying or creating values (to be minimised or maximised, respectively). We conclude by defining open problems and by indicating prospective research directions

Defuzzification of groups of fuzzy numbers using data envelopment analysis

Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships

Application of Multicriteria Decision-Making Methods in Railway Engineering: A Case Study of Train Control Information Systems (TCIS)

In order to improve its position in the transport market railway, as a complex system, it has to fulfill a number of objectives such as increased capacity and asset utilization, improved reliability and safety, higher customer service levels, better energy efficiency and fewer emissions, along with increased economic viability and profits. Some of these objectives call for the implementation of maximum values, while some of them require minimum values. Additionally, some can be expressed quantitatively, while some, for example, customer service, can be described qualitatively through a descriptive scale of points. The application of MCDM in railway engineering can play a significant role. Therefore, the major objective of this chapter is the review of the application of MCDM methods in railway engineering. As one of the means in achieving the objectives of railways and above all the utilization of capacity are Train Control Information Systems (TCIS). Based on that, the aim of this chapter is the evaluation of the efficiency of TCIS in the improvement of railway capacity utilization through defined technical-technological indicators. The non-radial Data Envelopment Analysis (DEA) model for the evaluation of TCIS efficiency in improvement of utilization of railway capacity using the selected indicators is proposed. The proposed non-radial DEA model for TCIS efficiency evaluation in using railway capacity could be applied to an overall network or for separate parts of railway lines