The varied contexts of environmental decision problems and their implications for decision support

Environmental Science & Policy 8 (2005) 378–391 www.elsevier.com/locate/envsci

The varied contexts of environmental decision problems and their implications for decision support Simon French a,*, Jutta Geldermann b a

Manchester Business School, University of Manchester, Booth Street West, Manchester M15 6PB, UK b DFIU-IFARE, University of Karlsruhe, Hertzstraße 16, Karlsruhe D-76187, Germany Available online 16 June 2005

Abstract Society today is faced with many environmental issues, and many decision analytic techniques and methodologies are offered to support their resolution. The literature to date, however, has not explored the appropriateness of the different methods in relation to the different contexts to which they might be applied. As a beginning, we take three categories of environmental decisions as exemplars with regard to contextual issues, such as problem dimensions, social circumstances and cognitive factors. Then, we turn to analytic methodologies and reflect on their suitability for application to the three exemplars. Finally, we will draw more general conclusions to help others in developing appropriate decision analysis and support processes for environmental problems. # 2005 Elsevier Ltd. All rights reserved. Keywords: Artificial intelligence (AI); Decision analysis; Decision-support systems (DSS); Emission reduction strategies (ERS); Environmental decisions; Environmental emergency management (EM); Life cycle assessment (LCA); Multi-criteria decision analysis; Operational research; Risk; Stakeholder involvement; Uncertainty

1. Introduction Environmental considerations are crucial in many decisions. Various tools for analysing the impact and the reduction of the environmental burden have been developed (Wrisberg et al., 2002; Graedel and Allenby, 2003). Recently, the need to apply decision analysis and support methodologies has been recognised (Munda, 1995; Hobbs and Meier, 2000). A survey of decision-analysis applications, including many environmental ones, is given by Keefer et al. (2004). Most environmental decisions have much in common, e.g. many stakeholders, uncertainties, multiple, possibly conflicting criteria; and impacts which extend far into the future. Conversely, there are often differences, e.g. in the quality of uncertainty or the number of alternative strategies to be evaluated. These differences mean that different problems may need different analytical approaches, a fact that is seldom recognised explicitly. Here, we explore different types of environmental decisions and consider appropriate approaches * Corresponding author. Tel.: +44 161 275 6401; fax: +44 161 275 7134. E-mail address: [email protected] (S. French). 1462-9011/$ – see front matter # 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsci.2005.04.008

for their resolution. Our goal is to provide guidance for selecting appropriate decision analytic methods to address a set of environmental issues. In order to give substance to our discussion, we focus on examples from emission reduction strategies (ERS), life cycle assessment (LCA) and environmental emergency management (EM). These are chosen because they have a strategic character and involve many stakeholders, and thus require sound, explicitly justified decision-making. The paper is structured as follows. In Section 2, we briefly describe the ERS, LCA and EM problems. We introduce a variety of decision-making contexts in Section 3, before turning in Section 4 to the decision analytic techniques which may be used and the levels of support that these may bring decision makers (DMs). We note the need to adopt multi-disciplinary perspectives on environmental issues and the danger that this may risk naı¨ve simplification if the analysing team has unequal skills in some disciplines. Against this background, we are able to discuss which tools and techniques may be more appropriate for certain environmental decision contexts, and how these might be deployed within the wider decision process to fit with a variety of political imperatives relating to

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stakeholder involvement. Finally, Section 5 summarises our main points and offers a discussion.

2. Three exemplar environmental decision problems Since the early 1970s, environmental issues have become increasingly important in many decisions. Many industrial sectors need to develop and adopt strategies, which are both economically and environmentally efficient. An obvious example is the need to develop feasible, cost-effective ERS. Companies do not simply seek to comply with emission limits, but rather set up their own environmental management targets. This influences investment decisions and product development, requiring a consideration of the full product life cycle and hence LCA. While ERS and LCA are concerned with the potential, future impacts of emissions on the environment and human health, EM deals with disasters, which may drastically and immediately affect people’s lives and livelihoods. Disasters may be ‘natural’, such as floods, fires, storms, earthquakes, droughts and volcanic eruptions or ‘man-made’, such as radioactive and hazardous materials accidents. All three contexts share a need to involve different disciplines, e.g. natural scientists for the modelling of the potential environmental consequences, engineers for the design and improvement of the production processes, and politicians, economists and managers to establish strategies. There are many stakeholders involved in all cases. On the other hand, they differ in urgency, levels of uncertainty and complexity. Some occur in many circumstances, whereas others are essentially one-off. 2.1. Emission reduction strategies Many environmental policies are geared towards integrated pollution prevention and obviously ERS play a major role. The necessary emission reductions for achieving

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targets can result from changes in technology or product mix and the implementation of emission abatement techniques. Also, changes in sector activities will influence the national emission level. In this context, a national cost function represents the minimum cost incurred by measures to be implemented in order to achieve a given emission reduction level. Fig. 1 presents an example set of cost functions. Decision analytic support involves the minimisation of the total discounted costs over the planning horizon including energy and mass flow optimisation (see e.g. Alcamo et al., 1990; Hordijk and Kroeze, 1997; Makowski, 2000; Fichtner et al., 2003; Geldermann and Rentz, 2004a). 2.2. Life cycle assessment LCA seeks to specify all the environmental consequences of products, services or processes ‘from cradle-to-grave’. LCA may provide quantitative or qualitative results. The latter makes it easier to identify problematic parts of the lifecycle and to specify what gains can be made with alternative ways of fulfilling the function (Wrisberg et al., 2002). Within LCA, four basic steps are identified in ISO 14040, Environmental Management-Life Cycle Assessment-Principles and Framework (see Fig. 4): (1) determination of objectives, scope, system boundaries and functional unit; (2) inventory analysis; (3) impact assessment (classification, characterisation and valuation of the emissions and consumptions); (4) interpretation (weighting by specific contribution as an indication of the quantitative relevance of the substances concerned, by environmental importance and ‘verbal– argumentative’ final valuation). The LCA methods and approaches developed to date differ mainly in the impact assessment step, which is still at an early stage of development (Guine´ e et al., 2002). Choosing between ecological profiles involves balancing different types of impact and is typical of a multi-criteria decision problem, when explicit or implicit trade-offs are needed to construct an overall judgment (Geldermann, 1999; Belton and Stewart, 2002; Seppa¨ la¨ et al., 2002). 2.3. Emergency management

Fig. 1. Example of National Cost Curve. The x-axis shows the remaining emissions, while the y-axis gives the total discounted costs, calculated beginning with the emissions in the base year at the origin of the diagram for different scenarios (e.g. short or long transition periods).

Environmental emergencies seem to occur all too often; such situations differ considerably, but they do share some common characteristics of sudden, unexpected events. In many cases, the events result from a completely unanticipated juxtaposition of circumstances and present the DMs with a unique situation. Initially, there is a need for urgent decisions to be made under stressful circumstances; subsequently, there is a need for decision-making on remediation strategies to bring the affected region back to a – not necessarily the pre-existing – ‘normality’. The teams

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of DMs who are faced with the handling of emergencies often have not worked together before, although they may have rehearsed some of the issues during exercises within other teams. Most importantly, they need to balance the needs of many stakeholders. For a variety of discussions of EM, see French (1995), Paton and Flin (1999).

3. Factors that affect decision-making Firstly, we need some terminology. Sadly, there is little agreement on this within the decision-making literature: (Keeney and Raiffa, 1976; French, 1986; Kleindorfer et al., 1993; Keeney, 1996; Roy, 1996; Bouyssou et al., 2000; French and Rios Insua, 2000; Ragsdale, 2001; Rosenhead and Mingers, 2001; Belton and Stewart, 2002; Denardo, 2002; Teale et al., 2003). For instance, the DMs may choose between actions, alternatives, options, policies or strategies. Some authors reserve the terms policies and strategies for courses of action which specify responses to potential events, i.e. contingency plans and use the other terms, e.g. acts, for more clear-cut alternatives. That distinction can be very useful in discussing the theory of sequential decisionmaking, but in the applications literature, it is far from universal. When discussing preferences or value judgements, authors may refer to criteria, factors, attributes or objectives. There is some agreement that an attribute is a dimension that is important in determining preferences, e.g. cost, whereas an objective is an attribute plus an imperative, e.g. minimise cost. Equally, authors may consider the uncertainties involved in a decision under many headings: lack of knowledge, randomness, stochastic variation, imprecision, lack of clarity and so on. Against this background of multiple terminologies, we make little effort to define terms precisely except when it is essential to our arguments. No two situations, which call for a decision are ever identical. They differ due to a wide range of factors, such as

Fig. 2. Factors that affect decision-making after Payne et al. (1993).

problem and social context and the cognitive abilities of the DMs (see Fig. 2). 3.1. Problem context Perhaps the most common distinction is that between strategic, tactical and operational decisions. In this paper we concentrate on strategic decisions. These tend to correspond to ill-formed problems, also called unstructured or nonprogrammed (Simon, 1960), and the first step is to formulate the problem through discussion often drawing in multiple perspectives from the stakeholders. Snowdon (2002) has argued recently for a different typology of decisions based on his Cynefin model, which identifies four decision spaces. In the known space, cause and effect are completely understood. Thus, decisions relate to actions the consequences of which may be completely and accurately predicted. Cause and effect is also understood in the knowable space, but insufficient data are immediately available to make complete forecasts of the consequences of an action. In the complex space, there are so many interacting causes and effects that predictions of system behaviours – often social-political behaviours – are affected by a wide range of uncertainty. Decisions must be made without a clear or complete understanding of their potential consequences. LCA may venture into complex space, since it aims to model potential environmental impacts (Guine´ e et al., 2002). The calculation of impact potentials largely removes spatial and temporal considerations, resulting in analytical and interpretative limitations. Some impact categories are defined using highly complex or unknown interdependencies such that the degree of uncertainty varies significantly between the impact categories (Owen, 1996). In the chaos space, things happen beyond our experience and we cannot perceive any candidates for cause and effect. Our lack of understanding of the full causes and ramifications of climate change is but one example of a chaotic context for some of the most important environmental decisions facing us. For discussions of the Cynefin contexts of EM, see Niculae et al. (2004), French and Niculae (2005). Environmental decisions almost invariably fall into the complex or chaotic domains, particularly as they involve many stakeholders and hence need to address many socialpolitical issues: yet much work on environmental decisionmaking seems to assume a known and knowable context. Problem contexts may be further differentiated by a number of more detailed structural issues. The majority of environmental decision problems involve uncertainty and risk. By their very nature, estimates and long-term forecasts, as required in LCA, are obviously uncertain; and an ERS considered optimal on the basis of particular assumptions made today is highly unlikely to turn out optimal in the actual situation of 2010 (Landrieu and Mudgal, 2000). For reviews discussing different types of uncertainty, variability and risk, see (French, 1995, 2003; Huijbregts, 2001; Geldermann et al., 2003a). The scale of the impacts and

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Table 1 Different problem contexts for the three examples Characteristic

Emission reduction strategies (ERS)

Life cycle assessment (LCA)

Emergency management (EM) Early phase

Later phases

Structured vs. unstructured

Structured (as far as the consecutive elaboration of cost functions for single substances) Repetitive Not urgent, lots of time

Structured

Structured

More unstructured

Repetitive Not urgent, lots of time

Very high

Very high

One-off Urgent, very little time Extremely high

One-off Less urgent, but more time Moderate

Long (mass pollutants)

Mixed (cf. impact assessment factors) Many Few

Very long

Very long

Few Few

Few Very many (many combinations)

Repetitive context vs. one-off Urgency of decision Uncertainty and imprecision in data and forecasts Time-span of environmental impacts Number of harmful substances Number of alternatives

Few (but also sum parameters) Many (depending on the concerned industrial sectors)

when they are incurred is also an important differentiator. In particular, there is little agreement on how to evaluate options with very long-term impacts (Atherton and French, 1997, 1998, 1999). Finally, alternative strategies need to be considered. In some decisions a high-level view may be taken by considering a few representative strategies that differ qualitatively, e.g. in EM one might consider evacuation areas without considering a plethora of operational details relating to order of clearing dwellings and escape routes. In other contexts, many more alternatives need be evaluated, e.g. in ERS different technical options for emission reduction within the various industrial sectors. Table 1 summarises the similarities and differences between LCA, ERS and EM in terms of problem context. 3.2. Social context Discussions of decision-making are also categorised by the number of DMs: individual, group, organisational and societal decision-making (Kleindorfer et al., 1993). The environmental context of our discussion implies that we are almost exclusively discussing the latter pair. There are many parties to such decisions. DMs are responsible for making the decision: they ‘own the problem’. They are accountable to some, but not necessarily all the stakeholders in the problem. Stakeholders share, or perceive that they share, the impacts arising from a decision. They have a claim, therefore, that their perceptions and values should be taken into account. Experts provide economic, engineering, scientific, environmental and other professional advice used to model and assess the likelihood of the impacts. The DMs may have technical advisors who are undoubtedly experts in this sense, but they are unlikely to be the only experts involved. Other experts may advise some of the stakeholders, thus influencing the stakeholders’ perceptions and hence shaping their decision-making. Analysts develop and conduct the analyses, both quantitative and qualitative, which draw together empirical evidence and expert advice to assess the likelihood of the outcomes. They will also be

concerned with a synthesis of the DMs’ and stakeholders’ value judgements. These analyses are used to inform the DMs and guide them towards a balanced decision. Whereas experts support decision-making by providing information on the content of the decision; analysts provide process skills, helping to structure the analysis and interpret the conclusions. This separation of roles is very idealised; some of those involved may take on several roles. Clearly, DMs are necessarily stakeholders because of their accountabilities; but they may also be content experts and may conduct their own analyses. Similarly, experts may be stakeholders and vice versa. Scientific knowledge is seldom unambiguous. Environmental problems often include issues at the frontier of research; thus, expert advice may be uncertain, riven by conflict of opinion. Moreover, stakeholders (and DMs) may be listening to other ‘experts’ with very different perceptions from established science. While DMs may wish to dismiss some dubious (pseudo-)sciences, if some stakeholders are persuaded by them, the DMs would be wise to take notice of them as well. Only then, will they understand the motivations of all the stakeholders and be able to engage in constructive dialogue. Stakeholders are not drawn from a homogeneous population; they differ in perceptions, motivations, attitudes, etc. It is helpful for analysts and DMs to have some cultural stereotypes in mind when designing the process and analysis so that they ensure that a representative set of perceptions and values is incorporated. Cultural Theory (Douglas, 1992; Thompson et al., 1990), one of many theories offering a perspective on ‘culture’, suggests that there are different stereotypes, each having a distinctive attitude towards risk (see Table 2). Other classification schemes include ecocentrists versus techno-centrists (O’Riordan, 1995), and environmentalists versus industrialists (Lave and Dowlatabadi, 1993). Claims for the universality of cultural theory (see Rotmans et al., 1994) contradicts what we know from behavioural decision studies (Bazerman, 2002; Gigerenzer, 2002). Risk attitude

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Table 2 The perspectives of the cultural stereotypes used by Hofstetter (1998) following Thompson et al. (1990) Archetype

Time perspective

Manageability

Required level of evidence

Hierarchists fear threats to social order and believe technological and environmental risks can be managed within set limits Individualists and Entrepreneurs see risks as opportunities, save those that threaten freedom of choice and action within free markets Egalitarians fear risks to the environment, the collective good and future generations

Balance between short and long-term

Proper policy can avoid many problems

Inclusion based on consensus

Short time

Technology can avoid many problems

Only proven effects

Very long-term

Problems can lead to catastrophe

All possible effects

and other judgements can be substantially affected by framing and other aspects of a problem, suggesting that cultural characteristics are far from dominant in defining attitudes and values. Rather, we see cultural stereotypes as helpful in designing broadly based societal decision processes that address the full range of stakeholder perceptions and values. For instance, see the discussion in Hofstetter (1998) of value-sphere models within LCA (see Table 2). Thinking about such viewpoints opens the minds of DMs to likely challenges and may help find alternative solutions to the decision problem. A further range of cultural issues relate to national and racial cultures, which may also play an important role in international environmental policy and decision-making. Hofstede (1980) emphasises the numerous characteristic differences between the culture of Latin Europe on the one hand, and the German and the Anglo–Saxon cultures on the other hand; though Pateau (1998) tones down these findings. Therefore, participatory approaches for technique assessment might differ from country to country and will require sensitivity to the needs of multi-cultural societies in many regions. In the context of LCA, cultural differences can be easily identified, e.g. the German scientific literature on technique assessment is fairly concentrated on risk assessment (cf. Bechmann, 1996; Beck, 1998; Hansju¨ rgens, 1999; Grin and Grunwald, 2000; Mai, 2001), whereas in the UK, battered by the impact of the poor management of bovine spongiform encephalopathy (BSE, so-called ‘‘mad cow’’ disease) (Phillips, 2000), there is a wide recognition of the need to include socio-political issues more explicitly into the decision-making (HSE, 1998). 3.3. Cognitive factors Problem and social contexts are external factors influencing the decision-making: there are also internal factors, namely the cognitive abilities of the DMs. Humans are loathe to admit their failings, but evidence shows that our intuitive analysis and decision-making are far from perfect (Kahneman et al., 1982; Bazerman, 2002; Gigerenzer, 2002). The behaviours, which they identified fly in the face of many of the theoretical models used within decision analysis, risk analysis and operational research, for instance:


Dramatic, easily recalled or imagined events tend to be judged as more likely than they actually are (availability bias).
Judgements tend to become fixed early in discussion clustering around the values first suggested (anchoring and adjustment biases).
Recent evidence overrides general knowledge of base rates of occurrence, whatever the relative reliabilities (insensitivity to base rates).
DMs’ risk attitudes can be changed simply by reexpressing the risks in either positive or negative terms (framing bias).
DMs and experts tend to be overconfident in the accuracy of their judgements (overconfidence). In nuclear EM exercises, French et al. (2000) found that DMs were very discomforted when faced with uncertainty and to some extent they assumed it away. Moreover, it is difficult in EM to frame descriptions of events in anything other than negative terms, yet doing so induces a greater willingness to take risks in DMs. Environmental issues generally are rife with uncertainties, conditions that engender biased judgements. Thus, there is a need to understand and address departures from the rules of probability and decision theory that may be present in expert and lay intuitive judgments, and which inevitably will be incorporated into the analysis. Especially, in decision contexts of ERS and LCA, the ecological and quantitative relevance of certain emissions over decades and their potential environmental impacts are rarely possible to imagine. In such circumstances, we must recognise the limits of both modelling and judgement, and take broad – not detailed – guidance from simple, robust methods, e.g. simple linear impact assessment factors are used in LCA to give a notion of the most prevailing environmental problems. The human brain has limits to its cognitive capacity (Miller, 1956). When faced with an assessment of the environmental impact of a system, we cannot rely on holistic judgement alone to predict and evaluate its consequences. We need to decompose the system into many subsystems, consider each separately and then assemble an overall synthesis. Thus, a chain of environmental models each modelling a different aspect often lies at the heart of any

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environmental decision-analysis. Similarly, decision analytic techniques help the user decompose the complex evaluation. In ERS, for instance, cost functions were first derived substance by substance, and then assembled to address the question of multi-pollutant multi-effects. LCA leads to an ecological profile comprising about a dozen impact categories, which are often difficult to judge against each other, so that that decision support is needed. Environmental decisions involve many people, and thus, much communication, inevitably requiring the explanation of potential risks and the steps that might be taken to mitigate them. This can be fraught with difficulty (Covello, 1993; ILGRA, 1998; Bennett and Calman, 1999; Slovic, 2001; Cox and Darby, 2003). Behavioural studies have demonstrated discrepancies between the information disseminated by technical experts and the interpretations of these messages by the general public. Within risk communication literature, this has led to ‘scientific’ or ‘expert’ perspectives being contrasted with ‘lay’ or ‘public’ perspectives. Experts are credited with the objectivity provided by scientific investigation and statistical principles; while the public are accused of forming subjective interpretations of risk, influenced by social networks, emotions and fear. This has led researchers to focus on lay ‘misperceptions’ of risk, and for many years attempts to improve risk communication focused on ways of conveying the statistical risk with the intention of countering the public’s ‘irrational’ perceptions. Recently, however, the legitimacy of a plurality of perceptions has become more recognised and there has been a move to address the concerns of the public and stakeholders in debates upon risk communications directly (Fischhoff, 1995; Bennett and Calman, 1999). Moreover, there is a current imperative to integrate the management of risk communication much more into the decision processes and analyses surrounding any major societal decision (Renn, 1998; French et al., 2005). Risk communication is a significant issue in the three contexts of EM, ERS and LCA; but there are differences. In EM, it would seem that the initial urgent need to act simply requires compliance on the part of the public; however, analysis of the long-term impacts of past emergencies has demonstrated that it is also vital to explain what is happening and why recommendations are being made. Doing so creates a public understanding and engenders trust. It can be argued that the confusion and lack of information surrounding the initial stages of the Chernobyl accident contributed significantly to its dreadful consequences, which in many ways were due more to the stress created by poor information management than radiological effects (Karaoglou et al., 1996). Trust is also an important topic in today’s discussions about shareholder value; once industries and shareholders understand the long-term environmental impacts and their economic consequences, their willingness to invest in long-term measures may become more apparent than their preference for short-term profits. For the

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development of ERS and LCA, risk communication helps to build a shared understanding. At the moment, there is discussion of the use of newer processes of public consultation, such as stakeholder panels, citizen juries and e-democracy (Renn et al., 1995; Levy, 1995; TED, 2003).

4. Tools and techniques 4.1. The analysis cycle We now turn from various possible decision contexts to the different tools and techniques that might be used in the analysis for supporting DMs. Fig. 3 gives an outline of the decision analytic cycle with the three phases: problem formulation, evaluation of options, and review of the decision models. Each phase involves many sub-activities, of which the main ones are shown in the figure. The analysis will seldom be purely cyclic. Rather, it will move backwards and forwards between phases with the predominant direction being clockwise, but with many short reversals. The process is complete when the DMs are comfortable with the conclusion of the analysis, i.e. when they feel the analysis is requisite (Phillips, 1987; French and Rios Insua, 2000). The ubiquity of this is illustrated in Fig. 4 in the context of LCA (Geldermann and Rentz, 2004c). We would emphasise that the analyst needs to sensitive to the cognitive issues indicated in Section 3.3. The process is prescriptive taking account of the cognitive limitations of the DMs in contrast to the ideals assumed in the underlying

Fig. 3. Overview of the decision process.

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Fig. 4. Mapping the decision-analysis cycle onto the phases of LCA (ISO14040).

normative decision model (e.g. French and Smith, 1997, French and Rios Insua, 2000). One cannot take judgements at face value; they should be challenged and explored to avoid many potential biases. Further, some uncertainties may be related to a lack of clarity rather than external randomness or lack of knowledge. The DMs might be unclear on what aspects of environmental impacts they should model. Such uncertainty needs to be addressed and resolved at the problem formulation stage through discussion and exploration of ideas (French, 1995). At the outset of the process, the DMs and their analysts must formulate the models and choose the methods of analysis needed in order to support the decision. They are usually faced with a jumble of issues and events in an illdefined context out of which they must localise their objectives, potential strategies, possible consequences, etc. There is a wide range of methods that the analysts working

first with the DMs and later the experts and stakeholders can use to identify objectives, key stakeholders, potential consequences, key uncertainties, contingencies and dependencies, constraints, confounding issues, etc. Many soft modelling methods known under various names have been developed over the past 20 or so years to help in problem formulation; see Keeney (1996), DeTombe (2001), Rosenhead and Mingers (2001), Belton and Stewart (2002) for recent general reviews. Hatfield and Hippel (2002) describe the use of systems theory for formulating and structuring a herbicide risk assessment on Alachlor. These methods not only help the DMs sort out their thinking before entering into detailed analysis; they also identify many issues that must be addressed in discussions with stakeholders and the public (French et al., 2005). The evaluation of options is the stage in which the models are analysed. This analysis requires a multitude of computations to support consequence modelling, statistical analysis and decision analysis (see Fig. 5). The first step in the modelling separates the science, predictions of what might happen as a result of possible actions, from the value judgements of how much each possible consequence matters. This separation corresponds to the difference in the roles of the experts and stakeholders in the decision. On the left hand branch in Fig. 5, the first step is the construction of one or more consequence models that predict the impacts of potential strategies. In environmental decision analysis, these consequence models may be extremely complex, predicting, say, the global warming potential of 1t CO2 emissions in 50 or 100 years. Even if in LCA studies simple linear conversion factors for the impact assessment are used, these factors are based on complex statistical analyses. Such models are based on scientific expertise in many disciplines

Fig. 5. The analysis underpinning the stage ‘‘evaluate options’’.

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from climate and atmospheric pollution modelling to epidemiology. For the calculation of cost curves in ERS, statistical analyses are required for the estimation of the future sectoral activities, taking economic key figures into account. It is crucial that the models recognise their inherent uncertainties; see Goossens and Kelly (2000) for a recent survey of this in the context of nuclear accident consequence modelling. Once the consequence models are built, they may be refined through the iterative analysis of further data, requiring statistical inference and forecasting techniques. The modelling on the right hand branch in Fig. 5 concerns the preferences of the DMs and their stakeholders—possibly the whole of society. Appropriate cost functions or more general objective functions, based perhaps on multi-attribute value and utility ideas, need be developed. Once this was the domain of cost benefit analysis (CBA); but now, this usually requires the more subjective methods of decision analysis to capture preferences for intangibles, such as ‘quality of the environment’ and ‘intergenerational equity’ (see Section 4.2). Finally, there is a need to combine the value models with the consequence models, making due allowance for the inherent uncertainties and rank of the alternative strategies in the stage of decision analysis. In theory, this requires the full machinery of the subjective expected utility (SEU) model, but in practice, more tractable, approximate methods are often used. For instance, the techno-economic optimisation model ARGUS, using linear optimisation, minimises an overall objective function which describes the minimisation of the combined operating costs for the production processes including end-of-pipe techniques over a number of time periods (Geldermann and Rentz, 2004b). 4.2. Modelling preferences and value judgements Environmental decisions inevitably involve value judgments. It is surprising how seldom these value issues are acknowledged and how much less explicitly they are debated in public discussions. While politicians may wish –

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and claim – that their decisions are based upon the ‘‘best available science’’, for many environmental issues there is simply not enough sufficient evidence for scientists to agree on the mechanisms that underlie the inherent risks or potential environmental effects. Thus, in deciding how to address such issues, the DMs will be driven by value judgements on, inter alia, the acceptable level of risk and their responsibilities to present and future stakeholders. In any case, as indicated in Fig. 5, both values and science enter the analysis. Therefore, there is a need to model the value judgements that the DMs feel are relevant. Several decades ago this was the area of CBA methodologies, calculating the net expected benefits minus net expected costs, both expressed in monetary terms. The stumbling block, however, lies in unambiguously determining all relevant consequences and ‘objective’ prices and probabilities (Fischhoff, 1977; Schleisner, 2000; French et al., 2004). Thus, it has adapted or been replaced by explicitly subjective decision analytic techniques in which value judgements are obtained and modelled through multi-attribute value and utility functions (Keeney, 1996). However, environmental problems necessarily involve the evaluation and weighting of various aspects, such as the protection of air, water, soil, conservation of nature and natural resources; at the same time taking economic, social and technical aspects into account. It is not surprising, therefore, that the representation and aggregation of such factors remain the subject of ongoing debate (Spengler et al., 1998; Geldermann and Rentz, 2001). The first step in a decision analysis is one in which the DM and analyst structure the representation of the consequences. An attribute tree is developed, which summarises and organises the key values to be taken into account (Keeney, 1996). Fig. 6 shows an attribute tree (or criteria hierarchy) that was developed for obtaining the value judgements involved in an environmental assessment of recycling measures in the iron and steel making industry (Spengler et al., 1998). It should be noted that in LCA, the emissions of zinc and lead would be transformed into the

Fig. 6. Attribute tree built for the environmental assessment of recycling measures in the iron and steel making industry with the weights w1 –w8 .

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impact category human toxicity (Geldermann et al., 1999). Then, a weighting might be achieved by considering the quantitative and ecological relevance of these environmental impact potentials. The choice of the weighting factors indicates the importance of each criterion within the overall decision. Once an attribute tree (Fig. 6) has been defined, the consequences are represented as a vector of scores against the different attributes, c = c1, c2, . . ., cq. The scores ci may be calculated by use of consequence models (left hand side of Fig. 5) or they may be elicited subjectively from DMs, experts or stakeholders. The overall valuation of a consequence is then synthesised via a multi-attribute value or utility function. The distinction between value and utility functions being that the former incorporate no notion of risk attitude, and thus, apply in conditions in which there is no or, more likely, negligible uncertainty. The latter explicitly acknowledge risk and are suited to decision-making under uncertainty. For instance, a typical form of a multi-attribute value function is: v ðc1 ; c2 ; . . . ; cq Þ ¼

q X wi  vi ðci Þ

(1)

i¼1

where the functions vi () model the valuation of the ith impact and the wi are weights reflecting the relative importance of the different impacts. Such an apparently simple structure can be surprisingly effective even in complex problems; see e.g. the analysis of the Chernobyl accident consequences (French, 1996). A typical multi-attribute utility function is: uðc1 ; c2 ; . . . ; cq Þ ¼ 1  evðc1 ;c2 ;...;cq Þ=r Pq ¼ 1  e i¼1 wi vi ðci Þ=r

(2)

in which an exponential transformation is made of (1) and r provides a measure of risk attitude. This form of utility is useful in separating the elicitation of trade-offs from that of risk attitude. However, it should be emphasised that both (1) and (2) are just examples and their suitability should be assessed against the actual context. Keeney and Raiffa (1976) discuss procedures whereby functional forms for the DMs’ value judgements may be identified for more complex situations. 4.3. Consequence modelling, statistical analysis and data mining Returning to the left hand side of Fig. 5, once the problem has been formulated the experts and analysts need to model the impacts quantitatively, e.g. for impact assessment within ERS, energy and mass flow models have been developed. However, a limiting factor is the availability and quality of input data, e.g. statistics on activities, information on plants and applied processes (Geldermann and Rentz, 2004a).

The need to incorporate more data and improve the quality of consequence modelling is recognised in Fig. 5 through the inclusion of statistical analysis and forecasting. Even within the urgency of EM, there may be time to assimilate the latest data and update predictions accordingly. For instance, in the development of RODOS, a DSS for supporting the management of off-site nuclear emergencies (French, 2000; French et al., 2000), methods for updating forecasts of atmospheric dispersion with the latest radiation monitoring data have been developed (Smith and French, 1993; Politis and Roberson, 2004). Similarly, later on in the EM process when the contamination reaches the ground, methods have been developed to update the inputs to the food chain model with the latest ground monitoring data. Such methods rely on statistical methodologies, often Bayesian statistical methodologies because of their ‘fit’ with decision analytic methods (French and Rios Insua, 2000). One development in statistical techniques that we should note is data mining (Klo¨ sgen and Zytkow, 2002). Today, DMs and experts have vast quantities of data available that can help them understand past environmental impacts and support their decision-making on for instance regulation. Many data mining techniques have been developed to support the exploration of large databases. Firstly, there are the long established methods of statistical analysis, such as multi-variate analysis, regression analysis, and time series analysis. These are best suited to finding global or near global patterns across data sets. While they may draw on established statistical models, such methods might, nonetheless, use modern quick AI algorithms to fit the models and extract the patterns. Secondly, there are series of new methods, which find local patterns that only hold true for small subsets of the data in statistical terms, local or conditional correlations. A promising research area in this respect is the automatic construction of Bayesian belief nets by exploiting the empirical correlations in very large databases (Korb and Nicholson, 2004). Dzˇ eroski (2002) provides a survey of data mining methods in the environmental sciences. In terms of Fig. 5, data mining may support the statistical analysis stage, but because of its exploratory nature and search for patterns it may also be useful earlier on during problem formulation. 4.4. Decision analysis Decision analysis has a range of meanings; from a generic term describing the analyses that bring together and balance models of preferences and uncertainties in order to support DMs to rather specific sets of techniques based upon decision trees, influence diagrams and multi-attribute utility. We use it here generically, including in our definition many multi-criteria decision tools, such as multi-attribute value analysis, the analytic hierarchy process (AHP) and outranking approaches (for definitions, see Bouyssou et al., 2000; French and Rios Insua, 2000; Belton and Stewart, 2002). Decision analysis provides a family of techniques,

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which fit into the lower box in Fig. 5. All tend to apply to strategic and tactical decisions, which began from a fairly unstructured set of issues; the methods may become too complex to apply in more detailed operational contexts. In addition, they are best applied to enable evaluation and propose a ranking of a fairly small number of discrete strategies. If there are many strategies to be evaluated, the methods, although straightforward, may encounter combinatorial problems in both the number of judgements that need to be elicited from the DMs and in the computations subsequently required. Decision trees, influence diagrams and similar methods seek to rank the strategies by evaluating the SEU model, i.e. forming expectations of Eq. (2) against the uncertainties modelled via probabilities (French and Rios Insua, 2000; French, 2002). Thus, they apply in circumstances in which the uncertainty is non-negligible and must be taken into account, such as in the early phase of nuclear emergency management (French et al., 1997). Multi-attribute value analysis, AHP and outranking all apply to circumstances in which uncertainty is much less of an issue, and focus attention on balancing conflicting objectives, as in later phases of nuclear emergency management (French, 1996). Multi-criteria decision methods are also needed for the last step of an LCA, comprising the interpretation of the results of the ecological impact assessment and the weighting of the environmental importance (Geldermann and Rentz, 2001). The integration of such methods into the framework of LCA is at the present well accepted; but no single method has been identified as universally valid; there is still debate about the relative merits of multi-attribute value analysis, AHP and outranking (Seppa¨ la¨ et al., 2002; Ha¨ ma¨ la¨ inen, 2003). In applications, the nuances in preference modelling and aggregation procedures might not matter, as long as sensitivity analyses illustrate the consequences of subjective choices during the decision process. Inclusion of all the methods as modules in a DSS might also help to concentrate the discussion on the decision problem and not on the choice of the ‘‘correct’’ or most suitable approach (Geldermann et al., 2003b). 4.5. Operational research and mathematical programming Mathematical programming and other operational research techniques, provide a means of bringing together preference and uncertainty models to identify an ‘optimal’ strategy when the set of potential strategies is essentially infinite. We have in mind here circumstances in which the strategies are defined implicitly by setting parameters within some allowable range, defining a feasible region from which to choose the strategy. In the case that there is a single objective function (defined perhaps using multi-attribute value or utility formulations), linear, quadratic, integer, stochastic, dynamic and other mathematical programming techniques may be used to find an optimal strategy subject to

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the constraint that the strategy lies within the feasible region. In the case that multiple criteria have been defined, but with no overall synthesising objective function, goal, vector or interactive mathematical programming methods may be used, e.g. (Goicoechea et al., 1982; Ragsdale, 2001; Denardo, 2002). Obviously, mathematical programming models are simplifications of reality, their ‘optimal’ solution providing no more than a guide to a direction for change that may lead to an improvement in the real system. Because these methods require considerable structure in the underlying problem, they tend to apply to tactical and operational rather than strategic decision contexts. In ERS applications, which aim to set broad parameters to achieve targets, the objective might be to minimise the sum of the discounted costs over the planning horizon. Consequence modelling predicts the emissions for a base year and their evolution until a target year, as well as the elaboration of cost functions at a sector and national level. A model based on linear programming thus seeks to account for all relevant technical and structural emission reduction options (Geldermann et al., 2003a; Rentz, 2004; Geldermann and Rentz, 2004a). Although non-linear optimisation might be more appropriate for depicting the real dependencies between the relevant factors, the model’s size (comprised of approximately 1600 technical processes in some 40 industrial sectors) urges restriction of the problem to a linear one. 4.6. Sensitivity analysis Finally, in applying all of these techniques, we should assess their sensitivity to the accuracy of the data and judgemental inputs. Sensitivity analysis simply seeks to learn how the output of a model changes with variations in the input (French and Papamichail, 2003; Geldermann et al., 2003c). The output must be interpreted with great care whenever it varies significantly for input fluctuations that are within the realm of error or – perhaps more appropriate – within the realm of confidence in their values. Saltelli et al. (2000) provide an excellent introduction and survey of sensitivity techniques. Sensitivity analysis can have many purposes (French, 2003); here, we note two general objectives. Firstly, it helps DMs and analysts assess the importance of broad uncertainties in their data and models, draw out the general import of the analysis and understand what it is saying. On the basis of this understanding, they can judge whether the analysis is necessary, i.e. there is a sufficient basis to enable a decision, or whether they need to gather more data and develop the models used to allow a more sophisticated analysis (French and Rios Insua, 2000; Geldermann and Rentz, 2001). Secondly, it can help build consensus between among the DMs and between the DMs and the stakeholders. Sensitivity analysis can be a very powerful medium of communication in the service of building consensual understanding (Renn

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et al., 1995). In the International Chernobyl Project (Lochard et al., 1992; French, 1995), one group of DMs was very concerned that the alternatives had been ranked inappropriately against a public acceptability attribute, but sensitivity analysis showed that taking an extreme alternative position on this attribute would not change the overall ranking; thus reassured, the group withdrew their objections.

thought of as a distinct decision methodology. Rather, they replace the older simplex and similar algorithms of operational research with computationally more efficient ones, such that applications might become feasible.

4.7. Artificial intelligence (AI) and expert systems

We began this paper by suggesting that the literature to date has not sufficiently differentiated between the environmental decision contexts to which different support techniques should be applied. Here we categorise such methods according to the structure they assume in the problem context and the level of support, which may range from the simple presentation and organisation of the relevant data through forecasting of future environmental patterns and potential impacts of different strategies to methods that help the DMs evolve and balance their judgements. Fig. 7 summarises the methods and tools that we have described in terms of their different levels of support and the kind of structure assumed in the problem context. Taking a step back from the details of our survey, we can now suggest how one should identify appropriate decision support methods for a given set of environmental issues. Firstly, one should address the questions in Fig. 2. Recognising where the context lies on the strategic (unstructured) versus operational (structured) dimension along with the level of decision support that one needs (or can afford in terms of the time and resources available for the analysis), allows one to identify appropriate types and methods of decision support (Fig. 7). Next, identifying the players and being aware of their cognitive needs is important in shaping the process of decision support. First one needs to identify the DMs, experts, stakeholders and analysts. With these in mind, one can build the team for analysing the decision. In order to

Artificial intelligence is a broad term encompassing many definitions. Its broader goal is to develop machines that can mimic human intelligence. Over the recent years, there have been many successes with AI, with the development of a range of techniques, such as expert systems and neural nets, which can emulate human decision-making (see e.g. Turban and Aronson, 2001). However, although such methods undoubtedly have applications in the environmental arena, they are limited by their need to be trained, and are thus more suitable for operational decisions than for strategic ones. In the case of knowledge or rule based expert systems, the training is accomplished by eliciting rules and other knowledge from experts and testing the system’s performance against the experts. Neural nets are trained by allowing them to learn from observation of experts’ decision-making in a series of similar contexts. In both cases, it is implicit that the context of decision-making is highly structured and repeatable. Thus, the methods are still a long way from being suitable for EM, since emergencies are hoped to be rather infrequent. Likewise, they are unsuitable for the relatively unstructured context of LCA. One other set of AI techniques we should mention here are the search optimisation methods, such as genetic algorithms, tabu search and simulated annealing. These methods are being used increasingly to solve complex mathematical programmes. However, they should not be

5. Discussion

Fig. 7. Categorisation of a variety of methods according to the degree of structure assumed in the problem and the level of decision support provided.

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tackle environmental problems one will almost certainly need a multi-disciplinary approach. At this stage, there is a real danger of over simplification that naı¨ve multidisciplinarity may bring. True multi-disciplinarity means the bringing together of experts from different disciplines who draw on their shared expertise to build a common understanding of the issues. However, there is a risk that some disciplines may be underrepresented. For instance, there are cases of environmental decision-support systems being built by environmental scientists without adequate input from software engineers and human–computer interface (HCI) experts (for discussion of this in the context of EM, e.g. see French et al., 2000; French and Niculae, 2005). Likewise, economists and sociologists are often underrepresented on teams tackling LCA issues. In EM, there are calls for more social scientists to become part of the teams (Kelly et al., 2004). Turning to the stakeholders, most environmental decisions have an impact on the public to some extent, and therefore, it is necessary to communicate the issues and risks to them. French et al. (2005) argue that planning the risk communication strategy and dialogue with the public and stakeholder groups should permeate the entire decision process rather than just be a simple add-on. Indeed, if there is an intention to truly involve the public or at least some of the stakeholders in the decision, one will need to consider the use of stakeholder workshops, citizens’ juries or e-democratic methods. Planning such interactions will also shape the choice of decision analytic tools. The analysis must be explicable to all those involved; the more public and stakeholder involvement, the greater the need for transparency in the methods used. It is much easier to explain the use of, say, a multi-attribute value analysis than a stochastic dynamic programme to a stakeholder workshop. Yet, numerous environmental decisions are not seen by a large portion of the public, such as a LCA done for an investment decision of an iron and steel works, or the calculation of cost curves in the context of ERS. It may not be advisable to leave decisions to decision panels or stakeholder workshops (even if this seems to be a current trend), replacing experts’ judgements by majority votes of interested laymen. Given the difficulties caused by cognitive factors, there is no guarantee that a large number of DMs will come to better decisions than a few experts following sound consultations and assisted by appropriate decision support, but equally, we must emphasise that those sound consultations, which introduce the perspectives of the different stakeholders into the analysis, are essential. These final remarks should make clear that there is no common recipe for any environmental decision, but that there is a need for thorough interplay between environmental modelling and decision support case by case. Nonetheless, this paper should provide some advice on how to structure environmental decision problems.

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Acknowledgements This work was supported by grants from the British Council and the German Academic Exchange Service (DAAD), for which we are very grateful. We also wish to acknowledge many helpful interactions and discussions with Roger Cooke, John Maule, Carmen Niculae, Nadia Papamichail, Kejing Zhang, Martin Treitz, and Otto Rentz. We are grateful to referees of an earlier draft who provided much constructive criticism.

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