08 10 Quantifying Eco-Efficiency

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<ul><li><p> 1Georg-August-Universitt Gttingen Professur fr Produktion und Logistik Platz der Gttinger Sieben 3 (Raum 1.223) D-37073 Gttingen 2Universitt Karlsruhe (TH) Institut fr Industriebetriebslehre und Industrielle Produktion (IIP) Hertzstr. 16 D-76187 Karlsruhe </p><p>Quantifying Eco-Efficiency with Multi-Criteria Analysis </p><p>Prof. Dr. Jutta Geldermann1 </p><p>Dr. Martin Treitz2 </p><p> Research Paper Nr. 5 </p><p>Gttingen, Oktober 2008 </p></li><li><p> ISSN 1867-0121 Kontakt: </p><p>Herausgeber: Prof. Dr. Jutta Geldermann Schwerpunkt Unternehmensfhrung Professur fr Produktion und Logistik Wirtschaftswissenschaftliche Fakultt Wirtschaftswissenschaftliche Fakultt Georg-August-Universitt Gttingen Georg-August-Universitt Gttingen Prof. Dr. Jutta Geldermann (Produktion und Logistik) Platz der Gttinger Sieben 3 Prof. Dr. Lutz M. Kolbe (Informationsmanagement) D-37073 Gttingen Prof. Dr. Klaus Mller (Controlling) Tel. +49 551 39 7257 Prof. Dr. Olaf N. Rank (Organisation) Fax +49 551 39 9343 Platz der Gttinger Sieben 3, D-37073 Gttingen Mail produktion@wiwi.uni-goettingen.de Web www.wiwi.uni-goettingen.de/man Web www.produktion.uni-goettingen.de </p></li><li><p>- 1 - </p><p>Quantifying Eco-Efficiency with Multi-Criteria Analysis </p><p>Abstract </p><p>Based on the efficiency definition by (Koopmans, 1951) a case study is presented in this paper comparing the results of a multi-criteria method and an eco-efficiency analysis for emerging technologies for surface coating. Multi-criteria analysis aims at resolving incomparabilities by incorporating preferential information in the relative measurement of efficiency during the course of an ex-ante decision support process. The outranking approach PROMETHEE is employed in this paper for the case study of refinish primer application with data from an eco-efficiency analysis presented by (Wall et al., 2004; Richards and Wall, 2005). Comprehensive sensitivity and uncertainty analyses (including the first implementation of the PROMETHEE VI sensitivity tool) elucidate the variability in the underlying data and the value judgements of the decision makers. These advanced analyses are considered as the distinct advantage of MCA in comparison to the eco-efficiency analysis (Saling et al., 2002), which just comprises various types of normalisation of different criteria. </p><p>1 Introduction </p><p>The term eco-efficiency was coined by the World Business Council for Sustainable Development (WBCSD), comprising almost 200 international companies in a shared commitment to sustainable development through economic growth, ecological balance, and social progress (WBCSD, 2006). The concept of eco-efficiency has emerged as one of the crucial themes linking the economy and environment and presenting opportunities for joint improvement in economic and environmental performance. However, methods for quantified eco-efficiency analyses for the comparison of the sustainability of different alternatives are in their early stages of development although the need for comprehensive evaluations of different technological options is well acknowledged. Such analyses require the simultaneous consideration of different mass and energy flows and economic performance, leading to a multi-criteria problem cause by various units of measurements and goal conflicts. This became most obvious in the European Union when the Directive on the Integrated Pollution Prevention and Control (IPPC 96/61/EG) was adopted, aiming to achieve a high level of protection for the environment taken as a whole (art. 1). The assessment and comparison of effects of industrial installations call for suitable approaches to gauging the effectiveness of these measures. A special technical challenge is to avoid the shift of environmental problems from one medium to another. Thus, an information exchange on Best Available Techniques (BAT) was organised by the European Commission for all industrial activities with a significant contribution to environmental pollution as listed in Annex I of the Directive. BAT covers all aspects of the technology used in production and in the way that installations are </p></li><li><p>- 2 - </p><p>designed, built, maintained, and decommissioned. BAT means using the most effective economically and technically viable means to achieve a high level of protection for the environment and for human health and safety. In this way, BAT delivers a comprehensive description of aspects relevant for eco-efficiency. </p><p>The first round of 31 BAT reference documents (BREFs for short) has been completed by end of 2006, with the last three BREFs related to ceramic manufacturing, large volume inorganic chemicals and surface treatment using organic solvents. Altogether, around 55.000 installations are covered by the IPPC Directive, encompassing an immense economic dimension. It is a significant challenge both in terms of environmental protection and competitiveness to regulate and operate all these industrial installations in a successful way. In spite of some criticism, (Hitchens et al., 2001) come to the conclusion that the IPPC Directive did not hamper the competitiveness of the European Industry but rather promoted innovation and deployment of environmentally friendly technologies. Especially linkages between environment and energy savings are important aspects given the current developments on energy markets. </p><p>While the definition of BAT is focused on available techniques1, the information exchange also includes sections on so-called Emerging Technologies, being developed either by companies or institutes and having the potential to become available in the near future. As companies keep their innovations confidential for competition reasons, institutes provide an open policy in publication but might lack practical experience in scale-ups. Thus, if companies want to introduce innovative technologies in order to improve the eco-efficiency of their production processes, it is important to have credible and reliable information for prospective analyses. Since innovative or emerging technologies have to compete with existing technologies on economic, technical, ecological, and social aspects, the effectiveness in all these dimensions needs consideration. </p><p>This paper describes and discusses the quantification of eco-efficiency by Multi-Criteria Analysis (MCA), especially for emerging technologies. Section 2 gives an introduction to the problem of defining eco-efficiency. The application of MCA is illustrated in Section 4 with a case study about emerging technologies for surface treatment. Six types of primers and their application techniques in vehicle refinishing are being compared on the basis of data delivered by (Wall et al., 2004; Richards and Wall, 2005) with an eco-efficiency analysis. Preferential information is modelled by weighting factors and preference functions based on paired comparisons within the Outranking approach PROMETHEE. Special emphasis is put on comprehensive sensitivity analyses. Finally, Section 5 summarises the findings. </p><p> 1 Article 2, para. 11 gives the following definition: 'available' techniques shall mean those developed on a scale which allows implementation in the relevant industrial sector under economically and technically viable conditions, taking into consideration the costs and advantages, whether or not the techniques are used or produced inside the Member State in question, as long as they are reasonably accessible to the operator. </p></li><li><p>- 3 - </p><p>2 The Problem of Defining Eco-Efficiency </p><p>The optimal allocation of resources to maximise the desired output for the given input is the core question in business economics (Koopmans, 1951; Koopmans, 1975). In the context of thermodynamic processes (which underlie many environmentally relevant production processes) the input and output parameters are often limited to energy quantities, such as the transferred or converted energy compared to the employed energy (for example in a power station, wind turbine, etc.). However, this definition considers only the heat quantity and not the quality, e.g. temperature, which is relevant for defining its convertibility in, for example, refrigeration systems and thus for its economic value (for a discussion see (Grassmann, 1950)). If it is not possible to define a single common denominator, such as the heat content, the definition of the degree of efficiency is quite difficult and can only be based on relative comparisons. Consequently, the allocation of resources is efficient if no improvement (i.e., an addition to the output of one or more goods at no cost to the others) is possible. This relative efficiency definition is called Pareto efficiency and a possible improvement is referred to as a Pareto improvement or Pareto optimisation (cf. e.g., (Moffat, 1976)). Mathematically, every Pareto efficient point in the commodity space is equally acceptable. Trade-offs and compromises are to be made when moving from one efficient point to another. </p><p>The definition of eco-efficiency in the context of technique assessment is complex since ecological, economic, technical and social parameters must be considered and representative ones selected. As the discussion for public goods and external costs shows, no competitive markets exist which could guide resources to their maximised utility (cf. e.g. (Rabl and Eyre, 1998; Schleisner, 2000)). Hence, no common denominator for eco-efficiency exists, and only relative comparisons can lead to value judgements. Relative efficiency measurements are the starting point for the Data Envelopment Analysis (DEA) and Multi-Criteria Analyses (MCA), two different approaches to resolving incomparabilities in a technique assessment, which are briefly introduced in the following. After that, approaches for Life Cycle Assessment (LCA) and particularly the so-called eco-efficiency methodology are being compared to the more formal MCA approach. </p><p>2.1 Data Envelopment Analysis (DEA) </p><p>The Data Envelopment Analysis (DEA) is an approach to comparing the relative efficiency of so-called decision making units (DMUs) in general. The decision making units are characterized by their vector of external inputs and outputs. By using scalarizing functions, the inputs and outputs are aggregated to an efficiency measure for each unit (Charnes et al., 1978; Belton and Stewart, 1999; Kleine, 2001; Cooper et al., 2004; Kleine, 2004). DEA has been developed for the evaluation of non-profit organisations, whose inputs and outputs can hardly be monetarily valued with market prices and are therefore more difficult to compare. </p></li><li><p>- 4 - </p><p>In general, DEA assumes that inputs and outputs are goods, but from an ecological perspective also pollutants with negative properties have to be considered. Thus, ecologically extended DEA models have been derived by incorporating a multi-dimensional value function (Dyckhoff and Allen, 2001). The fact that no explicit weights are needed to aggregate the indicators is seen as an advantage. Nevertheless, it is possible to integrate preferential information into DEA (Korhonen et al., 2002; Mavrotas and Trifillis, 2006). </p><p>Recently, there are more and more applications of DEA for technique assessment or technology selection and environmental performance measuring (Sarkis and Weinrach, 2001; Keh and Chu, 2002; Zaim, 2004; Zhou et al., 2006; Kuosmanen and Kortelainen, 2007). Especially in the context of the regulation of the energy market and particularly the electricity distribution, DEA benchmarking has been tested for various large samples (Korhonen and Luptacik, 2004; Estellita Lins et al., 2007). </p><p>It can be concluded that the application of DEA is useful for the ex-post evaluation of many similar organisations but is less suitable for the comparison of few emerging technologies. A crucial point of the DEA is the determination of the efficiency frontier, and thus the virtual efficient production process to which the real existing organisations are compared. Such a virtual efficient technology cannot be constructed by any combination of existing technologies and will never exist in reality. </p><p>2.2 Multi Criteria Analysis (MCA) </p><p>The efficiency definition is also the starting point for Multi Criteria Analysis (MCA). In a decision problem, all non-dominated alternatives are called efficient.2 Through special focus on the dominance relation multi-criteria methods seek to reduce incomparabilities by explicitly incorporating preferential information of the decision maker (Brans and Mareschal, 2005). The research field of Multi Criteria Analysis comprises methods for Multi Attribute Decision Making (MADM), covering the assessment of a finite set of alternatives (discrete solution space), and Multi Objective Decision Making (MODM) focussing on alternatives restricted by constraints (continuous solution space). The comparison of emerging technologies calls for MADM, for which two main streams exist (Belton and Stewart, 2002; Figueira et al., 2005): </p><p> the classical approaches, which are based on the assumption that clear judgements exist about utility values of the attributes and their weightings, which can be formalised within the multi-criteria technique. Examples are the Multi Attribute Value/Utility Theory (MAVT/MAUT) or the Analytical Hierarchy Process (AHP). </p><p> 2 Alternatives are dominated if there is another alternative that is not worse in any attribute and better in at least one. </p></li><li><p>- 5 - </p><p> the Outranking approaches, which suppose that the preferences are not apparent to the decision maker, and therefore the decision support aims at giving insights into the consequences of different weightings. The main difference to the classical MCA methods lies in the consideration of weak preferences and incomparable criteria. The most prominent Outranking models are ELECTRE and PROMETHEE. </p><p>Both classical and Outranking approaches structure the decision making process and thus support the understanding of preferences. During the last decades, behavioural aspects of decision making became more important (French et al., 1998; Pyhnen et al., 2001; Hodgkin et al., 2005), while comparisons of different algorithms are no longer in the focus of the scientific debate (Lootsma, 1996; Simpson, 1996). Thus, MCA can be considered as mature, which explains its wide use in environmental contexts (Miettinen and Hmlinen, 1997; Seppl et al., 2002), in technique assessment (Geldermann et al., 2000; Geldermann and Rentz, 2001), and in technology foresighting (Gustafsson et al., 2003). It can provide support for the decision maker in his/her quest for better understanding of the interdependencies in the weighting of environmental criteria. However, this discussion is highly controversial and it is important to note that some authors favour a more technical approach, whilst others stress the importance of detailed stakeholder involvement because of context sensitivity and the significant influence on the overall re...</p></li></ul>

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