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

www.elsevier.com/locate/envsci

Environmental Science & Policy 8 (2005) 378–391

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, UKb 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 manage-

ment (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 naıve 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

S. French, J. Geldermann / Environmental Science & Policy 8 (2005) 378–391 379

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 manage-

ment 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 inte-

grated pollution prevention and obviously ERS play a major

role. The necessary emission reductions for achieving

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).

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 life-

cycle 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-Prin-

ciples and Framework (see Fig. 4):

(1) d
etermination of objectives, scope, system boundaries
and functional unit;

(2) in
ventory analysis;
(3) im
pact assessment (classification, characterisation and
valuation of the emissions and consumptions);

(4) in
terpretation (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 (Guinee 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; Seppala et al., 2002).

2.3. Emergency management

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 unantici-

pated 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

S. French, J. Geldermann / Environmental Science & Policy 8 (2005) 378–391380

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 decision-

making, but in the applications literature, it is far from

universal. When discussing preferences or value judge-

ments, 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 non-

programmed (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 (Guinee

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 social-

political issues: yet much work on environmental decision-

making 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


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)

Structured Structured More unstructured

Repetitive context vs. one-off Repetitive Repetitive One-off One-off

Urgency of decision Not urgent, lots of time Not urgent, lots of time Urgent,

very little time

Less urgent,

but more time

Uncertainty and imprecision

in data and forecasts

Very high Very high Extremely high Moderate

Time-span of environmental impacts Long (mass pollutants) Mixed (cf. impact

assessment factors)

Very long Very long

Number of harmful substances Few (but also sum parameters) Many Few Few

Number of alternatives Many (depending on the concerned

industrial sectors)

Few Few Very many (many

combinations)

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 opera-

tional 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 stake-

holders, 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 accountabil-

ities; 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. Environ-

mental 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, atti-

tudes, 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 indus-

trialists (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


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

Balance between short

and long-term

Proper policy can avoid

many problems

Inclusion based on consensus

Individualists and Entrepreneurs see risks as

opportunities, save those that threaten

freedom of choice and action

within free markets

Short time Technology can avoid

many problems

Only proven effects

Egalitarians fear risks to the environment,

the collective good and future

generations

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 assess-

ment 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; Hansjurgens,

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:

� D
ramatic, easily recalled or imagined events tend to be
judged as more likely than they actually are (availability

bias).

� J
udgements tend to become fixed early in discussion
clustering around the values first suggested (anchoring

and adjustment biases).

� R
ecent evidence overrides general knowledge of base
rates of occurrence, whatever the relative reliabilities

(insensitivity to base rates).

� D
Ms’ risk attitudes can be changed simply by re-
expressing the risks in either positive or negative terms

(framing bias).

� D
Ms 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


Fig. 3. Overview of the decision process.

environmental decision-analysis. Similarly, decision analy-

tic 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 dis-

seminated 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 (Kar-

aoglou 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

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. 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 discus-

sion 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 ill-

defined 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

Fig. 5. The analysis underpinning t

first with the DMs and later the experts and stakeholders can

use to identify objectives, key stakeholders, potential

consequences, key uncertainties, contingencies and depen-

dencies, 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), Rosen-

head 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

he stage ‘‘evaluate options’’.


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 optimisa-

tion 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 judg-

ments. 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 –

Fig. 6. Attribute tree built for the environmental assessment of recycling me

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 conse-

quences 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 pro-

blems 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

asures in the iron and steel making industry with the weights w1–w8.


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; . . . ; cqÞ ¼Xq

i¼1

wi � viðciÞ (1)

where the functions vi (�) model the valuation of the ith

impact and the wi are weights reflecting the relative impor-

tance 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; . . . ; cqÞ ¼ 1 � e�vðc1;c2;...;cqÞ=r

¼ 1 � e�Pq

i¼1wi�viðciÞ=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 (Klosgen 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, none-

theless, 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). Dzeroski (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 out-

ranking approaches (for definitions, see Bouyssou et al.,

2000; French and Rios Insua, 2000; Belton and Stewart,

2002). Decision analysis provides a family of techniques,


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 combi-

natorial 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 (Seppala et al., 2002;

Hamalainen, 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

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 under-

lying 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; Gelder-

mann and Rentz, 2004a). Although non-linear optimisation

might be more appropriate for depicting the real depen-

dencies 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


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 inappro-

priately against a public acceptability attribute, but

sensitivity analysis showed that taking an extreme alter-

native position on this attribute would not change the overall

ranking; thus reassured, the group withdrew their objections.

4.7. Artificial intelligence (AI) and expert systems

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 perfor-

mance 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

Fig. 7. Categorisation of a variety of methods according to the degree of struc

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.

5. Discussion

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

ture assumed in the problem and the level of decision support provided.


tackle environmental problems one will almost certainly

need a multi-disciplinary approach. At this stage, there is a

real danger of over simplification that naıve multi-

disciplinarity 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 inter-

face (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 under-

represented 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 perspec-

tives 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.

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.

References

Alcamo, J., Shaw, R., Hordijk, L., 1990. The RAINS Model of Acidifica-

tion: Science and Strategies in Europe. Kluwer, Dordrecht.

Atherton, E., French, S., 1997. Issues in supporting intertemporal choice. In:

Karwan, M.H., Spronk, J., Wallenius, J. (Eds.), Essays in Decision-

Making. Springer, Berlin.

Atherton, E., French, S., 1998. Valuing the future: a MADA example

involving nuclear waste storage. J. Multi Crit. Decis. Anal. 7, 304–321.

Atherton, E., French, S., 1999. Structuring long-term decisions. J. Environ.

Assess. Policy Manage. 1, 213–338.

Bazerman, M.H., 2002. Managerial Decision-Making. John Wiley and

Sons, Chichester, p. 5.

Bechmann, G., 1996. Praxisfelder der Technikfolgenforschung: Konzepte.

Methoden, Optionen, Campus, Frankfurt/Main.

Beck, U., 1998. Risikogesellschaft: Auf dem Weg in Eine Andere Moderne,

vol. 14. Suhrkamp, Frankfurt am Main.

Belton, V., Stewart, T., 2002. Multiple Criteria Decision analysis: An

Integrated Approach. Kluwer Academic Press, Boston.

Bennett, P.G., Calman, K.C., 1999. Risk Communication and Public Health:

Policy, Science and Participation. Oxford University Press, Oxford.

Bouyssou, D., Marchant, T., Pirlot, M., Perny, P., Tsoukias, A., Vincke, P.,

2000. Evaluation and Decision Models: A Critical Perspective. Kluwer,

Dordrecht.

Covello, V.T., 1993. The perception of technological risks: a literature

review. Technol. Forecast. Soc. Change.

Cox, D., Darby, S. (Eds.), 2003. The communication of risk. J. R. Stat. Soc.

A166 (Part 2), 203–270 (special issue).

Denardo, E.V., 2002. The Science of Decision-Making. John Wiley and

Sons, New York.

DeTombe, D.J., 2001. Compram: a method for handling complex societal

problems. Eur. J. Oper. Res. 128 (2), 266–282.

Douglas, M., 1992. Risk and Blame: Essays in Cultural Theory. Routledge,

London.

Dzeroski, S., 2002. Data-mining in the environmental sciences. In: Klosgen,

W., Zytkow, J.M. (Eds.), Handbook of Data-Mining and Knowledge

Discovery. Oxford University Press, Oxford, pp. 817–830.

Fichtner, W., Fleury, A., Rentz, O., 2003. Effects of CO2 emission reduction

strategies on air pollution. Int. J. Glob. Environ. Issues 3 (3), 245–265.

Fischhoff, B., 1977. Cost benefit analysis and the art of motorcycle

maintenance. Policy Sci. 8, 177–202.

Fischhoff, B., 1995. Risk perception and communication unplugged: twenty

years of process. Risk Anal. 15, 137–145.

French, S., 1986. Decision Theory: An Introduction to the Mathematics of

Rationality. Ellis Horwood Ltd..

French, S., 1995. Uncertainty and imprecision: modelling and analysis. J.

Oper. Res. Soc. 46, 70–79.

French, S., 1996. Multi-attribute decision support in the event of a nuclear

accident. J. Multi Crit. Decis. Anal. 5, 39–57.

French, S., 2000. Decision support, data assimilation and uncertainty

handling within RODOS. In: Ehrhardt, J., Weiss, A. (Eds.), RODOS:

Decision Support System for Off-Site Nuclear Emergency Management


in Europe. European Commission, Office for Official Publications of the

European Communities, Luxemburg, EUR 19144 en (Chapter 3.6).

French, S., 2002. Utility theory. In: El-Shaarawi, A.H., Piegorsch, W.W.

(Eds.), Encyclopedia of Environmetrics. John Wiley and Sons, Chiche-

ster, pp. 2302–2309.

French, S., 2003. Modelling making inferences and making decisions: the

role of sensitivity analysis. TOP 11 (2), 229–252.

French, S., Niculae, C., 2005. Believe in the model: mishandle the emer-

gency. J. Homeland Secur. Emerg. Manage. 2 (1), http://www.bepress.

com/jhsem/vol2/iss1/2.

French, S., Papamichail, N., 2003. Decision Behaviour, Analysis and

Support-Decision-Making and How Computers and Analysis Bay Sup-

port This. Manchester Business School (MBS), Manchester.

French, S., Rios Insua, D., 2000. Statistical Decision Theory. Edward

Arnold, London.

French, S., Smith, J.Q., 1997. The Practice of Bayesian Analysis. Arnold,

London.

French, S., Harrison, M.T., Ranyard, D.C., 1997. Event conditional attribute

modelling in decision-making when there is a threat of a nuclear

accident. In: French, S., Smith, J.Q. (Eds.). The Practice of Bayesian

Analysis. Arnold, London, pp. 131–150.

French, S., Bartzis, J., Ehrhardt, J., Lochard, J., Morrey, M., Papamichail,

N., Sinkko, K., Sohier, A., 2000. RODOS: decision support for nuclear

emergencies. In: Zanakis, S.H., Doukidis, G., Zopounidis, G. (Eds.),

Recent Developments and Applications in Decision-Making. Kluwer

Academic Publishers, Dordrecht, pp. 379–394.

French, S., Bedford, T., Atherton, E., 2004. Supporting ALARP decision-

making by cost benefit analysis and multi-attribute utility theory. J. Risk

Res..

French, S., Maule, J., Mythen, G., 2005. Soft modelling in risk commu-

nication and management: examples in handling food risk. J. Oper. Res.

Soc., in press.

Geldermann, J., 1999. Entwicklung eines multikriteriellen Entscheidung-

sunterstutzungssystems zur integrierten Technikbewertung, vol. 16

(Technik und Wirtschaft). VDI Verlag, VDI Fortschrittsberichte, Dus-

seldorf.

Geldermann, J., Rentz, O., 2001. Integrated technique assessment with

imprecise information as a support for the identification of best available

techniques (BAT). OR Spectr. 23, 137–157.

Geldermann, J., Rentz, O., 2004a. Decision support for the development of

national emission reduction strategies according to the NEC-directive

with the dynamic mass flow optimisation model ARGUS. Ecol. Econ..

Geldermann, J., Rentz, O., 2004b. Decision support through mass and

energy flow management in the vehicle-refinishing sector. J. Ind. Ecol. 8

(4), 173–188.

Geldermann, J., Rentz, O., 2004c. Multi-criteria analysis for technique

assessment: case study from industrial coating. J. Ind. Ecol., in

press.

Geldermann, J., Jahn, C., Spengler, T., Rentz, O., 1999. Proposal for an

integrated approach for the assessment of cross-media aspects relevant

for the determination of ‘‘best available techniques’’ BAT in the

European Union. Int. J. Life Cycle Assess. 4 (2), 94–106.

Geldermann, J., Avci, N., Wenzel, S., Rentz, O., 2003. Decision support for

the national implementation of emission reduction measures by the

dynamic mass flow optimisation model ARGUS. In: Leopold-Wild-

burger, U., Rendl, F., Wascher, G. (Eds.), Operations Research Proceed-

ings 2002. Springer Verlagapp, Heidelberg, pp. 296–301.

Geldermann, J., Zhang, K., Rentz, O., 2003b. Development of an integrated

multicriteria group decision-support system (MGDSS). J. Group Decis.

Negotiat..

Geldermann, J., Zhang, K., Rentz, O., 2003c. Sensitivitatsanalysen fur

das Outranking-Verfahren PROMETHEE. In: Habenicht, W., Scheu-

brein, B., Scheubrein, R. (Eds.), Multicriteria-und Fuzzy-Systeme in

Theorie und Praxis. Deutscher Universitats-Verlag, Stuttgart, pp. 127–

152.

Gigerenzer, G., 2002. Reckoning With Risk: Learning to Live With

Uncertainty. Penguin Books, Harmondsworth.

Goicoechea, A., Hansen, D.R., Duckstein, L., 1982. Multi-Objective Deci-

sion analysis With Engineering and Business Applications. John Wiley

and Sons, New York.

Goossens, L.H.J., Kelly, G.N., 2000. Expert judgement and accident con-

sequence uncertainty analysis. Radiat. Protect. Dosimetry 90, 3 (special

issue).

Graedel, T., Allenby, B.R., Industrial Ecology, AT&T, 2003.

Grin, J., Grunwald, A., 2000. Vision Assessment: Shaping Technology in

21st Century Society: Towards a Repertoire for Technology Assess-

ment. Springer, Heidelberg.

Guinee, J.B., Gorree, M., Heijungs, R., Huppes, G., Kleijn, R., de Koning,

A., van Oers, L., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H.A., de

Bruijn, H., Van Duin, R., Huijbregts, M.A.J., 2002. Handbook on Life

Cycle Assessment: Operational Guide to the ISO Standards. Kluwer

Academic Publishers, Dordrecht.

Hamalainen, R.P., 2003. Reversing the perspective on the applications of

decision analysis. Decis. Anal..

Hansjurgens, B., 1999. Umweltrisikopolitik. Zeitschrift fur angewandte

Umweltforschung (Sonderheft), vol. 10.

Hatfield, A.J., Hippel, K.W., 2002. Risk and systems theory. Risk Anal. 22,

1043–1057.

Hobbs, B.F., Meier, P., 2000. Energy Decisions and the Environment. A

Guide to the Use of Multicriteria Methods. Kluwer Academic Publish-

ers, Boston/Dordrecht/London.

Hofstede, G.H., 1980. Culture’s Consequences: International Differences in

Work-Related Values. Sage Publications, Beverly Hills.

Hofstetter, P., 1998. Perspectives in Life Cycle Impact Assessment: A

Structured Approach to Combine Models of the Technosphere, Eco-

sphere and Valuesphere. Kluwer Academic Publishers, Dordrecht.

Hordijk, L., Kroeze, C., 1997. Integrated assessment models for acid rain.

Eur. J. Oper. Res. 102, 405–417.

HSE, 1998. Reducing risks, protecting people. Health and Safety Executive.

HMSO, London, Rose Court, 2 Southwark Bridge Road, London SE1

9HS.

Huijbregts, M.A.J., 2001. Uncertainty and Variability in Environmental

Life-Cycle Assessment. Amsterdam.

ILGRA, 1998. Risk Communication: A Guide to Regulatory Practice.

Health and Safety Executive Report.

Kahneman, D., Slovic, P., Tversky, A., 1982. Judgment Under Uncertainty:

Heuristics and Biases. Cambridge University Press, Cambridge.

Karaoglou, A., Desmet, G., Kelly, G.N., Menzel, H.G. (Eds.), 1996. The

Radiological Consequences of the Chernobyl Accident. CEC, Luxem-

bourg, eur 16544 en.

Keefer, D.L., Kirkwood, C.W., Corner, J.L., 2004. Perspective on

decision analysis applications, 1990–2001. Decision Analysis 1,

4–22.

Keeney, R.L., 1996. Value-Focused Thinking: A Path to Creative Decision-

Making. Harvard University Press.

Keeney, R.L., Raiffa, H., 1976. Decisions with Multiple Objectives: Pre-

ferences and Value Tradeoffs. John Wiley, New York.

Kelly, G.N., Jones, R., Crick, M.J., Weiss, W., Morrey, M., Lochard, J.,

French, S., 2004. Summary and conclusions: capabilities and chal-

lenges. Radiat. Protect. Dosimetry 109, 155–164.

Kleindorfer, P.R., Kunreuther, H.C., Schoemaker, P.J., 1993. Decision

Sciences. Cambridge University Press, Cambridge.

Klosgen, W., Zytkow, J.M., 2002. Handbook of Data Mining and Knowl-

edge Discovery. Oxford University Press, Oxford.

Korb, K.B., Nicholson, A.E., 2004. Bayesian Artificial Intelligence. Chap-

man and Hall/CRC, Boca Raton.

Landrieu, G., Mudgal, S., 2000. Simple is beautiful and efficient. Com-

mentaries on the results of studies requested from IIASA by the French

Ministry for Land-Use Planning and the Environment. Task Force on

Integrated Assessment Modelling, 25th Session.

Lave, L.B., Dowlatabadi, H., 1993. Climate change policy: the effects of

personal beliefs and scientific uncertainty. Environ. Sci. Technol. 27

(10), 1962–1972.

Levy, P., 1995. L’intelligence Collective. Dunod, Paris.

http://www.bepress.com/jhsem/vol2/iss1/2

http://www.bepress.com/jhsem/vol2/iss1/2


Lochard, J., Schneider, T., French, S., International Chernobyl Project. EC

Report EUR 14543 EN (ISBN 92-826-4631-9). Summary Report of

Decision Conferences held in the USSR, October–November 1990,

1992.

Mai, M., 2001. Technikbewertung in Politik und Wirtschaft: Beitrag zum

Problem ihrer Institutionalisierung. Baden–Baden.

Makowski, M., 2000. Modeling paradigms applied to the analysis of

European air quality. Eur. J. Oper. Res. 122, 219–241.

Miller, G.A., 1956. The magic number seven plus or minus two: some limits

on our capacity for communicating information. Psychol. Rev. 63, 81–

97.

Munda, G., 1995. Multicriteria Evaluation in a Fuzzy Environment: Theory

and Applications in Ecological Economics. Physica-Verlag, Heidelberg.

Niculae, C., Carter, E., French, S., 2004. Emergency management: does it

have a sufficiently comprehensive understanding of decision-making,

process and context? Radiat. Protect. Dosimetry 109, 97–100.

O’Riordan, T., 1995. Frameworks for choice: core beliefs and the environ-

ment. Environment 37 (8), 4–9.

Owen, J.W., 1996. LCA impact assessment categories: technical feasibility

and accuracy. Int. J. Life Cycle Assess. I (1), 151–159.

Pateau, J., 1998. Une etrange alchimie: la dimension interculturelle dans la

cooperation franco-allemande; Travaux et documents du CIRAC, Leval-

lois-Perret.

Paton, D., Flin, R., 1999. Disaster stress: an emergency management

perspective. Disaster Prev. Manage. 8, 261–267.

Payne, J.W., Bettman, J.R., Johnson, E.J., 1993. The Adaptive Decision

Maker. Cambridge University Press, Cambridge.

Phillips, L.D., 1987. On the adequacy of human judgements. In: Wright,

G., Ayton, P. (Eds.), Judgemental Forecasting. John Wiley and Sons,

Chichester, pp. 11–30.

Phillips (Lord), 2000. The Phillips Report on the BSE Crisis. HMSO,

London.

Politis, K., Roberson, L., 2004. Bayesian updating of atmospheric disper-

sion after a nuclear accident. J. R. Stat. Soc. C53 (4), 583–600.

Ragsdale, C.T., 2001. Spreadsheet Modelling and Decision analysis. South

Western College Publishing, Cincinnati.

Renn, O., 1998. The role of risk communication and public dialogue for

improving risk management. Risk Decis. Policy 3, 5–30.

Renn, O., Webler, T., Wiedermann, P., 1995. Fairness and Competence in

Citizen Participation: Evaluating Models and Environmental Discourse.

Kluwer, Dordrecht.

Rentz, O., 2004. Operations research and environmental policy. In: Haasis,

H.-D., Spengler, T. (Eds.), Produktion und Umwelt. Springer,

Heidelberg, pp. 1–26.

Rosenhead, J., Mingers, J., 2001. Rational Analysis for a Problematic World

Revisited. John Wiley and Sons, Chichester.

Rotmans, J., van Asselt, M.B.A., de Bruin, A.J., den Elzen, M.G.J., de

Greef, J., Hilderink, H., Hoekstra, A.Y., Janssen, M.A., Koster, H.W.,

Martens, W.J.M., Niessen, L.W., de Vries, H.J.M., 1994. Global change

and sustainable development: a modeling perspective for the next

decade. RIVM-Report No. 46150200. National Institute of Public

Health and Environmental Protection (RIVM), Bilthoven (NL).

Roy, B., 1996. Multi-Criteria Modelling for Decision Aiding. Kluwer,

Dordrecht.

Saltelli, A., Chan, K., Scott, E.M., 2000. Sensitivity Analysis. John Wiley

and Sons, Chichester.

Schleisner, L., 2000. Comparison of methodologies for externality assess-

ment. Energy Policy 28, 1127–1136.

Seppala, J., Basson, L., Norris, G.A., 2002. Decision analysis frameworks

for life-cycle impact assessment. J. Ind. Ecol. 5 (4), 45–68.

Simon, H., 1960. The New Science of Management Decision. Harper, New

York.

Slovic, P., 2001. Perceptions of Risk. Earthscan Library, London.

Smith, J.Q., French, S., 1993. Bayesian updating of atmospheric dispersion

models for use after an accidental release of radiation. Statistician 42,

501–511.

Snowdon, D., 2002. Complex acts of knowing-paradox and descriptive self-

awareness. J. Knowledge Manage. 6, 100–111.

Spengler, T., Geldermann, J., Hahre, S., Sieverdingbeck, A., Rentz, O.,

1998. Development of a multiple criteria based decision-support system

for environmental assessment of recycling measures in the iron and steel

making industry. J. Cleaner Prod. 6 (1), 37–52.

Teale, M., Dispenza, J., Flynn, J., Currie, D., 2003. Management Decision-

Making: Towards an Integrative Approach. FT Prentice Hall, Harlow.

TED, 2003. European Science Foundation Programme Towards Electronic

Democracy–Internet-based Complex Decision Support.

Thompson, M., Ellis, R., Wildavsky, A., 1990. Cultural Theory. Westview

Print, Boulder USA.

Turban, E., Aronson, J., 2001. Decision-Support Systems and Intelligent

Systems. Prentice-Hall, New Jersey.

Wrisberg, N., Udo de Haes, H.A., Triebswetter, U., Eder, P., Clift, R., 2002.

Analytical Tools for Environmental Design and Management in a

Systems Perspective. Kluwer Academic Publishers, Dordrecht.

Simon French is professor of information and decision sciences at Man-

chester Business School. Trained as a mathematician, he now works in

decision-analysis and decision-support systems with interests in Bayesian

statistics, risk analysis, management and communication, and knowledge

management. He has extensive experience in the nuclear, energy, and food

industries. Currently, he is looking at decision analytic approaches to e-

democracy.

Dr. Jutta Geldermann is head of the interdisciplinary research team on

technique assessment and risk management at the French–German Institute

for Environmental Research (DFIU/IFARE), University of Karlsruhe (TH).

She holds a diploma in industrial engineering and a PhD in business

administration. Her major research areas are multi-criteria decision-support

systems and the development of methodologies for the assessment and

optimisation of the economic performance of VOC emission reduction

strategies on a regional, national and supranational level.

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

Documents

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