socio-economic scenario development for the assessment of climate change impacts on agricultural...
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e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5
Socio-economic scenario development for the assessmentof climate change impacts on agricultural land use:a pairwise comparison approach
J. Abildtrup a,*, E. Audsley b, M. Fekete-Farkas c, C. Giupponi d,M. Gylling a, P. Rosato e, M. Rounsevell f
aDanish Research Institute of Food Economics, The Royal Veterinary and Agricultural University,
Rolighedsvej 25, DK-1958 Frederiksberg C, DenmarkbMathematics and Decision Systems Group, Silsoe Research Institute, Silsoe, Bedford MK45 4HS, United KingdomcDepartment of Economics, Szent Istvan University, Pater K.u. 1, 2103 Godollo, HungarydDepartment of Crop Production, University of Milan, Via Celoria, 2, 20133 Milano, ItalyeDipartimento di Ingegneria Civile, Universita degli Studi di Trieste, Piazzale Europa 1, 34127 Trieste, ItalyfDepartment of Geography, Universite catholique de Louvain, Place Pasteur, 3, Louvain-la-Neuve, Belgium
a r t i c l e i n f o
Published on line 19 January 2006
Keywords:
Scenario development
SRES scenarios
Climate change
Agricultural land use
Pairwise comparison
a b s t r a c t
Assessment of the vulnerability of agriculture to climate change is strongly dependent on
concurrent changes in socio-economic development pathways. This paper presents an
integrated approach to the construction of socio-economic scenarios required for the
analysis of climate change impacts on European agricultural land use. The scenarios are
interpreted from the storylines described in the intergovernmental panel on climate change
(IPCC) special report on emission scenarios (SRES), which ensures internal consistency
between the evolution of socio-economics and climate change. A stepwise downscaling
procedure based on expert-judgement and pairwise comparison is presented to obtain
quantitative socio-economic parameters, e.g. prices and productivity estimates that are
input to the ACCELERATES integrated land use model. In the first step, the global driving
forces are identified and quantified for each of the four SRES scenario families. In the second
step, European agricultural driving forces are derived for each scenario from global driving
forces. Finally, parameters for the agricultural land use model are quantified. The stepwise
procedure is appropriate when developing socio-economic scenarios that are consistent
with climate change scenarios used in climate impact studies. Furthermore, the pairwise
comparison approach developed by Saaty [Saaty, T.L., 1980. The Analytic Hierarchy Process.
McGraw Hill, New York] provides a useful tool for the quantification from narrative story-
lines of scenario drivers and model parameters. Descriptions of the narratives are, however,
helpful at each step to facilitate the discussion and communication of the resulting
scenarios.
# 2005 Elsevier Ltd. All rights reserved.
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* Corresponding author. Tel.: +45 3528 6876; fax: +45 3528 6802.E-mail address: [email protected] (J. Abildtrup).
1462-9011/$ – see front matter # 2005 Elsevier Ltd. All rights reserved.doi:10.1016/j.envsci.2005.11.002
e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5102
1. Introduction
The assessment of climate change impacts cannot ignore
concurrent changes in socio-economics – the context of
climate change – because these changes may amplify or
reduce the impacts of climate change (Carter et al., 2001; UK
Climate Impacts Programme, 2001). It is essential, therefore, to
develop climate change and socio-economic scenarios that are
coherent and internally consistent. The long time horizon of
climate change and the difficulties of predicting even short-
term socio-economic changes, imply that the development of
consistent long-term socio-economic scenarios is not easy
(Berkhout et al., 2002). In this paper, we describe an approach
to the development of socio-economic scenarios which was
applied in the ACCELERATES1 project. One of the aims of this
project was to analyse the medium and long-term (2020, 2050,
and 2080) climate impact on agricultural land use in Europe by
linking agricultural land use with environmental and socio-
economic variables in an integrated assessment model. The
basic assumption of the model is that future patterns of land
use will depend on the decisions of profit maximizing
landowners and land users. The land use decisions are
modelled in a linear programming model including environ-
mental and socio-economic constraints across Europe. The
socio-economic constraints, e.g. prices on agricultural inputs
and outputs, technology, and environmental regulations, are
defined within socio-economic scenarios.
The scenario approach is widely used in many sciences
(physical, economic, and social) in varied circumstances and
for different purposes. Scenarios represent one of the main
tools in climate change analyses, which are characterised by
the assessment of future developments in complex systems
that often are inherently unpredictable, are insufficiently
understood, and have high scientific uncertainties (Carter
et al., 2001). Different definitions exist for the term ‘scenario’.
For the intergovernmental panel on climate change (IPCC) a
scenario is defined as ‘‘a coherent internally consistent and
plausible description of a possible future state of the world’’
(IPCC, 1994, p. 3). Scenarios are not predictions; rather they are
pictures of possible futures. Scenarios can be used to explore
alternative, plausible outcomes if basic assumptions about
future developments are changed, for example regarding
policy intervention.
Socio-economic scenarios can be constructed in various
ways and a variety of approaches may be combined in a single
exercise. Often the underlying drivers of socio-economic
development pathways, i.e. social values and governance
institutions, are defined through expert judgement whereas
quantitative assessments of population growth and economic
activity are often based on modelling. Even if formal models
are applied in the development of scenarios, experts will
normally have an important role in filling in gaps that cannot
otherwise be filled and to blend the pieces into coherent and
plausible scenarios (Tol, 1998). Socio-economic scenarios may
be characterised by qualitative descriptions (e.g. the Acacia
project: Parry, 2000; the Visions project: Rotmans et al., 2000;
1 ACCELERATES: Assessing Climate Change Effects on Land useand Ecosystems from Regional Analysis to The European Scale.Funded by the EU 5th Framework Programme.
The WBCSD scenarios: WBCSD, 1999) or by quantitative
estimates (e.g. UK Climate Impacts Programme, 2001: Berkh-
out et al., 2002). Most socio-economic scenarios have been
constructed for short time-horizons (e.g. SeEOR: Alexandratos,
1995) or for small, well-characterised study regions (e.g. RegIS:
Holman and Loveland, 2001).
In the construction of regional scale scenarios for use in
climate impact assessments it is important to ensure that they
are consistent with the global scenarios as well as taking into
account different local trends and developments (Carter et al.,
2001; Lorenzoni et al., 2000a; Rounsevell, 2000). It is the
aggregate global activities, which generate the greenhouse gas
emissions that drive changes in climate. Besides having an
impact on emissions, the global socio-economic drivers will
impact on regional socio-economics through, e.g., increased
demand for food by a changing world population. The impact
of climate change and global socio-economic change will
depend on environmental conditions as well as the adaptive
capacity of the region. The publication of the special report on
emission scenarios, the so-called SRES scenarios, facilitated
the development of socio-economic scenarios that are con-
sistent with the emission scenarios used as input in the
climate models (Nakicenovic et al., 2000). The starting point for
the development of the SRES scenarios was four narrative
storylines, describing different ways in which the world
population, economics, technology, and (non-climate) policies
may evolve over the coming decades. These storylines provide
a framework within which to develop socio-economic
scenarios as a basis for impact, adaptation and vulnerability
assessments. However, the narrative storylines and their
generalised quantitative indicators are global in extent and
need to be downscaled to spatial and temporal scales that are
appropriate for assessment studies (Arnell et al., 2004). Until
now, there have only been a few examples of assessment
studies that have applied socio-economic scenarios derived
from SRES. One such study was the ATEAM project (Ewert
et al., 2005; Rounsevell et al., 2005). The ATEAM scenarios were
based on an interpretation of the four SRES storylines, using a
simple supply-demand model of agricultural area quantities
at the European scale and the disaggregation of these
quantities using scenario-specific, spatial allocation rules.
These scenarios also demonstrated the importance of
assumptions about technological development for future
agricultural land use in Europe. Other examples include the
socio-economic scenarios developed for UK regions (Berkhout
et al., 2002; UK Climate Impacts Programme, 2001). As a
starting point for the development of scenarios, socio-
economic indicators were projected from present-day figures
into future estimates given a business-as-usual assumption.
Then, by applying expert judgement the scenario indicators
were quantified by modifying the projected estimates accord-
ing to the values-governance conditions prevalent in a given
scenario. The same approach was applied in the RegIS project
where agricultural price and cost variables were estimated
using the same storylines as in the UKCIP project (Holman
et al., 2005a,b; Shackley and Wood, 2001). The estimated
variables were used as parameters in agricultural land use
models applied to two regions in the UK. Building on the SRES
scenarios Gaffin et al. (2004) produced a database containing
downscaled socio-economic scenario indicators of future
e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5 103
population and GDP at a country level and on a geo-referenced
gridscale relevant for regional assessments.
This paper presents a systematic and integrated approach to
the development of socio-economic scenarios required for the
analysis of climate change impacts on European agricultural
land use, adaptation and vulnerability. The approach presented
here is based on stepwise expert-judgements, which facilitates
the estimation of regional socio-economic scenario variables
from storylines formulated at the global scale, i.e. the SRES
storylines. The proposed methodology is demonstrated by
quantifying price and cost variables, which are used as input to
programming models that estimate the impact of climate
change on agricultural land use (Audsley et al., this volume). By
usingtheSRESframework, thescenarios areconsistentwith the
climate change scenarios (Mitchell et al., 2004) that were also
applied to the integrated land use model.
2. Methodology
2.1. The scenario development approach
Developing internally consistent, socio-economic scenarios
implies the need to handle a large number of interdependent
factors and may involve a group of people with different
disciplinary backgrounds. A procedure for the development of
scenarios should therefore support the management and
exchange of available information for carrying out evaluations
and judgements and provide a common approach to these
judgements. Applying a systematic approach to scenario
development provides discipline, in particular, to formally
Fig. 1 – The hierarchical scenario development approach (adapt
forces; EASDF: European agricultural driving forces.
justify what has been included or excluded in the assessments
and to record the various decisions made. A hierarchical
organisation of the scenario presentation can also help to
achieve the balance between completeness, i.e. itemisation of
all possible scenarios, and traceability, i.e. the documentation
of the line of reasoning, on one side and transparency and
simplicity on the other side (OECD, 2001).
In the study presented here, a systematic and hierarchical
procedure for expert-judgement proposed by Rosato and
Giupponi (2003) was adapted for the development of regionally
consistent socio-economic scenarios. The approach recog-
nises three main steps in the derivation of the regional
quantitative scenarios from global storylines, corresponding
to three spatial and socio-economic levels (see Fig. 1):
� le
ed
vel 1: global driving forces (GDF);
� le
vel 2: European agricultural sector driving forces (EASDF);� le
vel 3: regionally specific values of input parameters forland use models.
At each level, the available information is synthesised in a
matrix describing the importance of each socio-economic
driver or parameter for a given scenario. Matrices 2 and 3 are
derived applying the pairwise comparison approach described
in Section 2.2.
The first step in the quantification of the scenarios is the
identification of global socio-economic drivers, which repre-
sent the principal factors that influence the evolution of the
world in general (see Section 3.1). In the ACCELERATES project
the four SRES storylines (Nakicenovic et al., 2000) and
associated indicators were used as a starting point.
from Rosato and Giupponi, 2003). GDF: global driving
e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5104
The four SRES storylines represent different world futures,
which describe the ways in which global population, econo-
mies and non-climate policies may evolve over the coming
decades. Even though, the storylines occupy a multidimen-
sional space and no simple metric can be used to classify them
it has been useful to describe the scenarios using two-
dimensional space. The first dimension designates a more
economic (A) or a more environmental (B) orientation and the
second dimension a more global (1) or a more regional (2)
orientation. Accordingly, the storylines are termed A1, A2, B1,
and B2. The A1 storyline was further sub-divided into three,
which differ with respect to their dependence on fossil energy.
In this study, the fossil intensive energy storyline (A1FI) was
selected as this was expected to provide the most distinctive
climate change patterns compared with the A2, B1, and B2
storylines. However, we will not distinguish between the
different A1 families in the derivation of the socio-economic
scenarios. No storyline is more likely to occur than any of the
others, but they are formulated to cover a range of possible
global development paths.
In a review of the literature on global futures Berkhout et al.
(1999) identified five dimensions of change: demography and
settlement patterns, the composition and rate of economic
growth, the rate and direction of technological change, the
nature of governance, and social and political values. These
five dimensions were also covered by the drivers and
indicators specified in SRES and accordingly in the ACCEL-
ERATES project. In Table 1 the main global driving forces of the
SRES storylines are summarized.
Based on the SRES storylines the ACCELERATES scenario
team derived a list of global driving factors (GDF) which were
described or quantified for each scenario in matrix 1 (Section
3.1). The purpose of matrix 1 is to facilitate a common
understanding of the four SRES storylines by the experts
involved in identifying and evaluating the European agricul-
tural sector driving forces (EASDF). Therefore, matrix 1
provides a more finely grained portrayal of the futures than
Table 1 – Summary of the SRES socio-economic futures
Dimension
A1FI/VM A2/RE
Demography Low population growth High population
Composition
and rate
of economic
growth
Very high levels of
economic growth
and global convergence
Economic growt
Rate and direction of
technological change
High rates of innovation.
Transition to service
economy
Technological ch
fragmented and
diffusion of new
Nature of
governance
Market-oriented
solutions and free
trade
Decision-making
is devolved dow
Social and
political values
Public awareness and
willingness to pay for
better quality of life
increases
Attention to loca
environmental is
but global enviro
concern is weak
the SRES storylines. In Section 3.1, the resulting matrix of
global driving factors is presented.
In step two, the global driving forces are downscaled to the
European agricultural sector, and matrix 2, which describes
the role and importance of the European agricultural driving
forces was developed (Section 3.2). Through interviews,
experts were asked to propose a list of drivers that are
potentially of importance for the future development of
European agriculture. The same experts were then asked to
evaluate the relative importance of each of these drivers. The
evaluation process was undertaken by applying the pairwise
comparison procedure described in the following section. The
selection of the experts is discussed in Section 2.3. The experts
use their experiences, the literature and conceptual models
within their disciplines to make judgements. Background
sources of information are disparate, but included, for
example, results reported in the literature based on general
equilibrium models (e.g. the GTAP model: Hertel, 1997) and
explicit economic analysis of climate change impacts on the
agricultural sector (e.g. IMAGE Team, 2001; Kaiser et al., 1993;
Parry et al., 1999; Rosenzweig and Parry, 1994; Tsigas et al.,
1997), historical changes in agricultural productivity (Ewert
et al., 2005) and an analysis of regional labours costs within
Europe (Abildtrup, 2002).
The evaluation procedure for step two was repeated when
compiling matrix 3 (Section 3.3). In the evaluation at step 3 the
experts used matrices 1 and 2 as common reference points for
their judgments. Matrix 3 describes the parameter values that
were used as input to the land use model runs for each
scenario, e.g. prices of agricultural outputs and inputs,
technology, and policy variables. At level three the scenarios
were downscaled from the European to the regional level, to
take account of regional differences within Europe with
respect to the parameters of the land use model, e.g. the
opportunity costs of labour.
The compiled matrices summarize the expert judgements
and were found to be useful when providing an overview of the
Storylines
B1/GS B2/LS
growth Low population growth Moderate population
growth
h is uneven High levels of economic
growth and global
convergence
Intermediate levels of
economic growth and
slow global growth
ange is
slow
technology
New technology
facilitates conversion
to a service and
information economy
Less rapid and more
diverse technological
change then in the
WM and GS worlds
system
nwards
Governance is globalised
and strong partnerships
between governments
NGO-s and business
organisations
Focus on local solutions
and emphasis on food
self-reliance
l
sues
nmental
High level of
environmental
an social consciousness
Increased public
awareness and
focus on education
e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5 105
Fig. 2 – The pairwise comparison procedure. I: inconsistency index; k: threshold (after Rosato and Giupponi, 2003).
Table 2 – Different pairwise comparison scales
Comparativejudgment
Numericalequivalent
1st
Numericalequivalent
2nd
Numericalequivalent
3rd
Absolutely more
important
9 5 3
Much more
important
7 4 2.5
More important 5 3 2
Slightly more
important
3 2 1.5
Equal importance 1 1 1
Slightly less
important
1/3 1/2 2/3
Less important 1/5 1/3 1/2
Much less
important
1/7 1/4 2/5
Absolutely minor
importance
1/9 1/5 1/3
scenarios to stakeholders, e.g. other experts, policymakers, or
end-users. The stakeholders are not directly involved in the
pairwise comparison of drivers, but evaluated the matrix in
order to identify implausible relationships. The matrices were
evaluated at ad hoc focus group meetings and by the project
participants. In order to facilitate the discussion of the
scenarios, the matrices were supplemented with narrative
descriptions that explained the reasoning behind the experts’
judgements. These explanations were derived from inter-
views with the experts (matrix 2) or by synthesising the
discussion at an expert workshop (matrix 3).
It was found valuable to be able to test the effect of climate
change alone, as well as the effect of climate change coupled
with socio-economic change for the SRES futures (see Audsley
et al., this volume). In order to be able to distinguish between
the climate and socio-economic change scenarios a specific
nomenclature is used in this paper. The SRES labels (A1FI, A2,
B1, B2) are used exclusively to indicate the effects of climate
change only. The socio-economic change scenarios are
identified by the symbols WM (world markets), RE (regional
enterprise), GS (global sustainability) and LS (local steward-
ship), although in practice these correspond to the SRES labels
A1FI, A2, B1, and B2, respectively (as given above).
2.2. The pairwise comparison approach
The construction of matrices 2 and 3 was based on expert-
judgements. There are a number of different techniques
available for facilitating the elicitation and analysis of expert-
judgements, e.g. interactive groups, the delphi method,
ranking methods, Monte Carlo analysis (Meyer and Booker,
1991). If an objective of the scenario exercise is to commu-
nicate with stakeholders then social learning participatory
approaches are preferred that bring experts and stakeholders
together (Lorenzoni et al., 2000b; Berkhout et al., 2002). In the
present study the task was to derive consistent quantitative
scenarios from given storylines and global driving forces.
Consequently, there was less focus on involving stakeholders
in designing the images of the future. We applied a pairwise
comparison approach derived from the Analytic Hierarchy
Process of Saaty (1980). This approach has been applied in
various disciplines, e.g. decision making, planning, conflict
resolution and forecasting (Saaty, 1987; Saaty and Vargas,
2001). Pairwise comparison has also, however, been applied in
the evaluation of scenarios (e.g. Saaty and Rogers, 1976).
Saaty’s approach enables the conversion of verbal compara-
tive judgements of experts into numerical scales. This is
appropriate in the present study where the starting point is a
set of narrative storylines and the end results are quantitative
model input parameters. The comparison procedure follows a
series of phases represented in Fig. 2 (Rosato and Giupponi,
2003), i.e. for each driving force or parameter the procedure
includes: (1) pairwise comparison of storylines, (2) translating
the qualitative evaluations into numerical values, and (3)
evaluating the consistency of the evaluations by each expert
and, if consistent, a normalised eigenvector is calculated.
The evaluation was undertaken by comparing two story-
lines at a time with respect to one driving force (e.g. the role of
European Union Agricultural Policy now and in 2020) and by
formulating judgements of relative importance based on the
ranking system proposed by Saaty (1980) (Table 2). This system
allows the transformation of qualitative judgements into
quantitative figures. Given the aims of this work, the original
scale proposed by Saaty, which ranges from 9 to 1/9 was used
together with two other more compact scales ranging from 5
to 1/5, and 3 to 1/3, thus obtaining eigenvectors in the matrix of
pairwise comparison with smaller ranges. These scales (with
less disperse judgements) provided better performance in
e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5106
Table 3 – An example of a comparison matrix for the driver ‘‘EU Agricultural Policy’’
WM RE GS LS
PS More important Slightly more to more important Equally to slightly more important Equally important
WM Equally to slightly less important Equally to slightly less important Slightly less important
RE Equally to slightly more important Equally to slightly more important
GS Equally to slightly less important
cases where, rather than ranking alternative options, the
identification of trends was required.
An example of a pairwise comparison matrix is given in
Table 3 for an evaluation of the driving force ‘‘EU Agricultural
Policy’’ in 2020 compared with the present situation, for the four
alternative storylines. Judgements were formulated by exam-
ining the present situation (PS) and the situation in 2020 given
the four scenarios. In practice, the contributions of experts were
collected by asking them questions and then using the Saaty
scale as a reference for the answers. For example:
Question: What is the role of the EU Agricultural Policy in the
present situation compared to the situation in the WM world
in 2020?
Answer: More important.
After having compiled the matrix it was necessary to
choose, amongst the three scales proposed above (Table 2), the
one to be adopted when transforming verbal judgements into
numeric values. This choice should be based on the maximum
range that is considered to be reasonable for the eigenvector to
be produced from the pairwise comparison matrix in question.
In other words, it is expressed in terms of the expected
intensity with which the parameter under evaluation may
affect the future scenarios: the smaller the expected effect, the
smaller the range of the comparison scale. In this study the
choice of scale was determined by the scenario team based on
the given parameter to be evaluated. If this was a parameter
describing the annual rate of change (trend variable), e.g.
changes in productivity, a narrow scale was applied. On the
other hand, a wider range was used when it was the level of
subsidy in 2020 which was addressed.
After the conversion of verbal judgements into numeric
values, eigenvectors were calculated to obtain a synthesis of
the judgements expressed during the pairwise comparison.
The consistency of the expert’s pairwise comparison of the
importance of different drivers in each scenario was evaluated
by calculating a consistency index (I) (Saaty, 1980). The index
(I) has a value of 0 if the experts are perfectly consistent and 1 if
they are perfectly inconsistent. The experts repeated their
judgements if the consistency index was above a threshold
value, k. Following recommendations in the literature, the k
value was set to 0.10 for the original Saaty scale.
Following the example given above for the driving force, EU
Common Agricultural Policy (CAP), the resulting eigenvector
calculated from the pairwise comparison matrix expressed on
the scale 3–(1/3) was:
PS
100WM
52RE
90GS
74LS
85This means that the importance of the CAP in the future is
expected to decline for all scenarios, but especially for WM and
GS, and the results were considered to be internally consistent
(I = 0.01).
The main advantages of Saaty’s technique for pairwise
data analysis are that quantitative estimates are derived from
qualitative judgements and that the experts easily understand
the pairwise comparisons (Meyer and Booker, 1991). Further-
more, the method enables the consistency of the experts’
valuations to be tested. However, although the pairwise
comparison is appealing for experts it is rather time consum-
ing when finding all possible combinations. Furthermore, the
relative data relationships provided by the pairwise compar-
ison depend on the scale applied. The scale has to be chosen
beforehand by the scenario builders or as a part of the expert
judgement. The calculation of numeric results from the
pairwise comparisons can easily be calculated using standard
software programmes for decision analysis, e.g. Expert Choice
Pro version 9.5, which was applied in the present study.
2.3. The expert involvement
A major issue when setting up expert panels is the identifica-
tion of the group of experts to be involved. It is generally
advisable to obtain diverse experts so that the problem is
thoroughly considered from many viewpoints (Meyer and
Booker, 1991). To avoid individual selection bias and provide a
better representation of expert positions, it is recommended to
form an ad hoc steering group including a few experts, a
representative from the scenario project consortium, and a
facilitator with accurate knowledge about the elicitation
methodology applied, e.g. the pairwise comparison approach.
An expert is anyone who is knowledgeable in the field and
at the level of detail being elicited. In this project, the experts
represented different academic fields within the social
sciences. Academic and professional experts were identified
using the following criteria:
� n
umber of papers on the subject under consideration,published on scientific journals in the last 5 years;
� p
articipation in funded research projects, with appropriatesubjects;
� c
onsultancy work on the subject under consideration;� n
umber of contracts awarded in the field of interest.Another important issue in expert judgment is the weight-
ing of the judgements by the different experts (Meyer and
Booker, 1991; Zio, 1996). In the present study, simple averages
were used to calculate the matrices, i.e. the experts were
weighted equally. A disadvantage of simple averaging is that if
only a few experts provide answers and one expert gives an
answer that is quite different from the others, then that ex-
treme value has a disproportionate effect on the mean. For
some model parameters (matrix 3) there was considerable
divergence between the expert judgments. Rather than using
e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5 107
weighting schemes to overcome this problem, e.g. the median
or geometric mean, the experts were brought together in a
focus group meeting where they discussed differences of
opinion and resolved apparent disagreements.
The ACCELERATES team responsible for developing the
socio-economic scenarios included researchers from five
different institutions in four European countries and repre-
sented the disciplines geography, agronomy, and agricultural
economics. A parallel group was established in the Central and
Eastern European Countries (CEEC) using the same global
driving forces (matrix 1). However, the focus of this paper is on
the development of scenarios for the EU-15 countries.
The external experts involved in developing the EASDF
matrices were a group of four agricultural economists at the
University of Padova who were interviewed by the members of
the ACCELERATES scenario team. They were asked to
comment on the proposed lists of drivers and to evaluate
these drivers using the pairwise evaluation approach. Matrix 2
was estimated from these expert judgements. The scenario
team formulated narrative descriptions of the four different
futures of the European agriculture sector using the interviews
and matrix 2.
The experts involved in the evaluation of the model
parameters (matrix 3) comprised the researchers in the
ACCELERATES the project. Matrix 3 was synthesised at a
Table 4 – Matrix 1: the global driving forces for the present an
Global drivingforce
Presentsituation
WM
Population 100 109.0
Annual GDPa
increase
100 150
Green GDP
variation (ISEW)b100 75
GNP/GDPa 100 151.2
Social discount ratec High High
Global governance Weak Weak
Local governance Mixed Weak
Global market power Mixed Strong
Local market power Strong Weak
Environmental
policy impact
Low Low
Rural areas
development
Low Low
Climate convention No (weak) regime Emission trading
Equity Stable-declining Decline
Growing sector Health care, Leisure,
Distribution,
Financial services
Health care, Leisure,
Distribution,
Financial services
Declining sector Manufacturing,
Agriculture
Manufacturing,
Agriculture
a GDP: gross domestic product; GNP: gross national product. The GNP can
of final goods and services and the GDP is the GNP minus the net incob ISEW: Index of Sustainable Economic Welfare. ISEW makes adjustmen
land loss, and pollution costs (Daly and Cobb, 1989).c The social discount rate is used to evaluate public decisions. A low di
higher weight in the evaluation of the policies or projects.
concluding workshop in which the experts met to compare
and evaluate the results and acquire a consensus on the final
set of matrices. The scenario team had identified previously
those parameters where the panel disagreed significantly, e.g.
disagreeing about whether a parameter will increase or
decrease compared to the present situation. At the workshop
the experts had the opportunity to discuss their evaluations
and disagreements. The scenario team synthesised this
discussion by formulating storylines for each scenario
describing some commonly agreed factors of importance
when undertaking the evaluation. The developed storylines
were sent to the experts who were asked to repeat the pairwise
comparison. After this iteration the evaluations converged
and a final parameter matrix was produced by averaging the
matrixes provided by the experts.
3. Analysis and results
3.1. Global driving forces: matrix 1
Based on the SRES storylines and the scenario literature (e.g.
Berkhout et al., 1999; Parry, 2000) a more detailed list of socio-
economic drivers was proposed by the ACCELERATES scenario
team (matrix 1, see Table 4). The matrix provides common
d in 2020
2020: scenarios
RE GS LS
111.2 109.8 107.2
75 100 50
50 200 150
138.8 159.9 143.6
High Low Low
Weak Strong Weak
Strong Mixed Strong
Weak Strong Weak
Strong Weak Strong
Low High High
Low Mixed High
Regime fails Strong regime Weak regime
Decline Improvement Improvement
Private health care,
Maintenance services,
Defence
Renewable energy,
Household services,
Information services,
Nuclear power
Small scale
manufacturing,
Small scale
agriculture
High-tech services,
Financial services
Fossil-fuel based
activity, Intensive
agriculture and
manufacturing
Retailing,
Leisure,
Tourism
be defined as the total monetary value of a country’s annual output
me from abroad.
ts to GDP, correcting for distributional inequity, resource depletion,
scount rate implies that future generations’ preferences are given a
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information for the experts involved in the subsequent
evaluation steps. The choice of indicators assumed that the
experts were familiar with socio-economic terminology, e.g.
various indicators of economic activity.
3.2. EU agricultural sector driving forces
The starting point for the second level was the identification of
the drivers influencing European agriculture (see Table 5).
Matrix 1 was used to guide the evaluation of the importance of
each driver for each of the four storylines. In the ACACIA
project (Parry, 2000), scenarios for Europe were also developed
using the SRES scenarios as a starting point. There are,
therefore, similarities between the ACACIA scenarios and
those reported here, e.g. the expected role of the Common
Agricultural Policy (CAP). However, some of the results were
different. For example in ACACIA, the CAP is not expected to
have a role in rural development in Europe in the LS future.
It was found useful for subsequent evaluation of the
scenarios and for the communication and discussion of
driving factors to formulate narrative storylines to supple-
ment matrix 2. One of the important contributions of the
storylines is in the explanation of causality, e.g. how the
drivers are linked. Matrix 2 was used as a guideline in the
formulation of the narrative storylines, which ensured
consistency between each of the drivers within the descrip-
tions. A synthesis of the European Agricultural Sector
narratives is presented below.
3.2.1. WM European agricultureThe CAP is realigned and consequently rural development
policy and subsidies to farmers diminish rapidly in impor-
tance. This leads to a decline (and abandonment) of farming in
the more marginal areas. Lower food prices encourage farmers
to search for improved productivity. Rapid enlargement of the
EU brings about significant changes in EU agriculture because
of the large agricultural potential of the CEECs and the
differences between their development and economic struc-
ture. The pressure exerted by other economic sectors on the
use of production resources (land, water, capital, labour, etc.)
increases with the liberalisation of economic activities and
commerce in a consumerist world. Agricultural supply and
demand trends are closely linked to the trade liberalisation
process and have, therefore, a large effect. Agricultural
production in Europe increases and becomes increasingly
concentrated, industrialised and global in scope. Farm sizes
Table 5 – Matrix 2: agricultural sector driving forces (the prese
Agricultural sector driving forces Present situation
CAP ‘‘market’’ 100
CAP ‘‘rural development’’ 100
Environmental policy pressure 100
EU enlargement 100
Resource competition 100
World demand/supply 100
WTO Role 100
increase because of economies of scale. Population growth
and increases in meat demand influence grassland and
pasture areas. There is some increase in biofuels, but a range
of other energy sources is also available.
3.2.2. RE European agricultureThe CAP remains as it was before the implementation of the
EU mid-term reform in 2005. There is little concern about rural
areas resulting in a decline in the most marginal areas
(economically and environmentally). Agricultural policy aims
to protect national agriculture and the food industry, so that
self-sufficiency in food supply increases slightly. EU enlarge-
ment is not important because of opposition to enlargement
and adequate protection, creating a barrier against increased
competition for agriculture in the countries of the EU15. The
pressure exerted by other economic sectors on the use of
production resources (land, water, capital, labour, etc.)
increases with the liberalisation of economic activities and
commerce in a consumerist world. Agricultural supply and
demand trends are closely linked to trade liberalisation and
have, therefore, reduced importance. The role of the WTO
declines.
The demand for meat remains high, which influences the
demand for agricultural land. Agriculture intensifies with high
inputs of pesticides and fertiliser and increases in productiv-
ity. However, productivity growth slows gradually because
technological innovation is reduced in a regionally orientated
world and there is only a moderate trend towards larger farms.
There are some increases in biofuels, but the emphasis is more
on high-value food than energy production.
3.2.3. GS European agricultureThe aim of agricultural policy is to balance high agricultural
yield and low environmental impacts. The CAP becomes a
rural policy, directed not at maximising agricultural produc-
tion, but at reducing social problems and protecting the
environment across Europe. Whilst the pressure from envir-
onmental policies is high, e.g. constraints imposed on the
emissions of CO2, more intensive agricultural practices
develop. Technologically advanced farming practices result
in lower pesticide use. Large scale livestock farming decreases.
Increases in agricultural productivity results in land being
taken out of production. This area is used to support nature
conservation rather than recreation. New areas are designated
as protected areas that were previously less favoured areas
(LFA).
nt and 2020)
2020: scenarios
WM RE GS LS
52 90 74 85
58 106 100 163
85 97 183 173
108 67 92 53
161 123 92 52
172 106 121 79
188 70 124 61
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The effects of EU enlargement on agriculture is less
important than for WM because of the slower rate of
enlargement resulting from the need for new entrants to
achieve higher environmental standards. Competition for
production resources (land, water, capital, labour, etc.)
reduces as a result of the introduction of economic disin-
centives for high consuming natural resource activities.
Agricultural supply and demand trends are closely linked to
the trade liberalisation process. The role of the WTO becomes
more important. There are increases in biofuel crops as an
environmental objective.
3.2.4. LS European agricultureThe CAP is replaced by a heterogeneous pattern of regionally
orientated, environmental policies. The goal of agricultural
policy is to support a broader social desire for self-sufficiency
and traditional farming methods. Rural development policy,
therefore, increases in importance. The pressure from
environmental policies is high because of the protection of
rural landscapes and other local environmental resources, and
a move toward multifunctional farming. Research and
technological development increase the productivity of low-
input farming systems. Food is increasingly produced locally
and large-scale farming is not encouraged. To achieve this,
agriculture is heavily subsidised. The demand for meat
declines and, consequently, livestock production decreases,
but the total area of agricultural land remains relatively stable.
Opposition to EU enlargement means it is no longer important
and trade barriers protect agriculture in the countries of the
EU15. Accordingly, the role of the WTO and trade liberalisation
declines. Competition for production resources (land, water,
capital, labour, etc.) reduces as a result of the renovated role of
agriculture in environmental protection. There is a large
increase in biofuel crops for the local production of renewable
energy.
3.2.5. Matrix 2: agricultural sector driving forcesThe results of the evaluation (see Table 5) highlight a revision
of the CAP for all scenarios, which underlines the progressive
reduction in EU intervention in the agricultural commodity
markets. This reduction is more evident in the global scenarios
(WM and GS), but is also considered necessary (to a lesser
extent) in the scenarios dominated by regionalism (RE and LS),
and results from an increasing attention to public spending
and the declining role of agriculture in the European economy.
The role of rural development policy is more differentiated
across the scenarios with, for example, a strong reduction in
the WM scenario, but a strong rise in the LS scenario. The
pressure of environmental policies is particularly high in the
GS and LS scenarios. Enlargement of the EU only brings
significant changes in EU agriculture in the WM scenario due
to increased competition from Central and Eastern European
Countries (CEECs). In the other scenarios and, in particular, the
regional scenarios, opposition to enlargement or increased
protectionism supports EU agriculture. The pressure from
other economic sectors for the use of production resources
(land, water, capital, labour, etc.) increases with the liberal-
ization of commerce in the WM and RE scenarios. Conversely,
in the environmental scenarios, competition is reduced as a
result of the introduction of limits on high consuming natural
resource activities (GS scenario) and the rejuvenated role of
agriculture in environmental protection (LS scenario). World
supply and demand trends and the WTO play a much more
important role in the WM and GS scenarios than at present,
but a much less important role in the RE and LS scenarios.
3.3. EU agricultural model parameters
The level 3 matrix provides estimates of the ACCELERATES
agricultural model parameters (see Audsley et al., this volume)
and is based on the pairwise comparison method. Initially, a
matrix was constructed for 2020 for the four scenarios, and the
2020 parameters were then extrapolated linearly to 2050 and
2080. Following a workshop where the expert evaluations of
the model parameters were discussed (see Section 2.3), the
scenario team summarized the commonly agreed factors of
importance for doing the evaluations and these were
forwarded to the experts. These evaluation factors are
presented for each scenario in Table 6 and the model
parameters for the four scenarios are given in Table 7.
The scenario development framework was also applied to
define agricultural model parameters for the CEECs. These
parameter values were different from the EU15 countries,
reflecting the economic transition period post-communism
and the process of accession to the European Union (EU25)
(Fekete-Farkas et al., 2003). Thus, for the CEECs it was
necessary to separate the impact of the transition from
longer-term drivers. The year 2020 was considered to be a
transition year, i.e. in 2050, the CEECs were assumed to
become close to the EU15 member states and, by the year 2080,
they were assumed to have converged with the EU15.
Therefore, the level 3 socio-economic input parameters were
the same in 2080 for all European countries. The final scenario
values are given in Table 8 for the years 2020 and 2050.
4. Discussion
The scenario development procedure applied in this study was
designed to provide quantitative socio-economic drivers that
could be used as input in an integrated assessment model of
climate change impact on agricultural land use in Europe. The
SRES scenario framework was applied, providing consistency
between global socio-economic drivers and the emissions
driving the climate changes scenario applied to the land use
model. The challenge was, therefore, to derive quantitative
regional socio-economic variables that were consistent with
the global drivers described in the SRES storylines. The
scenarios were developed by applying expert-judgement
techniques based on Saaty’s pairwise comparison approach.
A limitation of the Saaty approach is that the evaluation
scale must be chosen by the scenario builder. When estimat-
ing, for example, the rate of productivity changes in each
scenario, a narrow scale will be preferred whereas estimating
the absolute values of, for example, prices and/or subsidies a
wider scale may be more relevant. There is no predetermined
rule about choosing the scale, which has to be based on the
scenario builders’ judgement.
An alternative way of estimating the model parameters in
matrix 3 would be to use formal mathematical or econometric
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Table 6 – Overview of agricultural parameter judgements
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Table 7 – Matrix 3: agricultural model parameters for 2020, 2050 and 2080 in the EU15
Parameters Present 2020 2050 2080
WM RE GS LS WM RE GS LS WM RE GS LS
Divergence/convergence
parameter
100 172 101 148 91 279 103 220 78 386 105 293 65
Costs of fertiliser (s) 100 94 107 136 164 85 118 189 259 76 129 243 355
Costs of seed (s) 100 120 108 104 94 149 121 111 85 179 133 118 77
Costs of pesticides (s) 100 88 102 141 147 69 104 203 218 51 106 266 289
Costs of machinery (s) 100 88 106 121 132 70 116 153 179 52 125 185 227
Costs of fuel (s) 100 90 110 143 157 76 125 209 241 62 140 274 326
Cost of labour (s/person) 100 151 114 133 110 228 135 184 125 305 157 234 140
Cost of contractors (s) = 40%
labour + 60% machinery
100 105 112 123 133 112 131 156 183 119 149 190 234
Area subsidy 100 0 0 91 107 0 0 77 117 0 0 63 127
Cereal prices (s/t) 100 83 92 102 112 59 80 106 131 34 67 109 149
Cereal area subsidy (s/ha) 100 0 99 60 101 0 99 0 103 0 98 0 104
Maize prices 100 91 99 105 106 78 99 112 116 65 98 119 125
Maize area subsidy 100 0 94 60 89 0 85 0 72 0 76 0 56
Sugar beet price (s/t) 100 82 95 90 102 54 88 74 104 27 81 58 107
Oilseeds price (s) 100 82 90 94 106 54 74 86 114 27 58 78 123
Oilseed area subsidy (s/ha) 100 0 101 60 115 0 104 0 138 0 106 0 160
Olive oil price (s/t) 100 82 94 90 106 54 85 74 114 27 76 59 123
Olive area subsidy (s/t) 100 0 92 60 107 0 79 0 116 0 67 0 126
Roots and tubers price (s/t) 100 88 98 113 124 69 94 134 160 51 91 154 196
Protein crop price (s/t) 100 81 93 93 108 54 82 83 121 26 70 73 133
Protein area subsidy 100 0 89 60 85 0 74 0 62 0 58 0 39
Cotton prices 100 86 91 93 112 65 78 82 129 44 65 72 146
Cotton subsidy (s/t) 100 0 89 83 106 0 73 58 116 0 57 32 126
Tobacco price (s/t) 100 90 93 92 111 75 83 80 128 60 73 68 144
Set-aside subsidy (s/ha) 100 79 99 104 112 47 98 110 130 15 97 116 148
Set-aside quota (%) 100 0 100 95 105 0 100 88 113 0 100 81 121
Meat prices (s/t) 100 96 98 101 110 89 95 103 124 83 92 104 139
Milk prices (s/l) 100 88 96 104 119 70 90 109 147 52 84 114 175
Less favoured area
payments (s/ha)
100 81 105 92 121 52 112 79 153 24 119 67 184
Crop yield changes due to
technological advances
100 167 131 130 104 268 177 176 109 368 223 221 115
Irrigation infrastructures 100 133 138 124 116 183 195 161 139 232 252 198 162
Water cost (s/l) 100 134 118 159 149 184 145 246 222 234 172 334 296
Irrigation efficiency (%)
(water to achieve same)
100 135 136 153 142 188 190 232 205 241 244 311 268
Chemical restriction 100 92 94 132 132 79 84 180 181 67 74 228 230
Farm sizes (mean ha) 100 164 126 132 105 260 165 179 112 356 205 227 120
Rotational penalties 100 80 87 94 101 51 67 84 102 22 47 75 103
models, e.g. general equilibrium models, which would ensure
consistency in price estimates arising from changes in supply
and demand. However, formal economic models were not
applied in the present study because of the long time horizons
of the scenarios. General equilibrium models are typically
developed and calibrated against historical data. Therefore,
projecting changes in the long-term, e.g. the 20–80 years
required of the ACCELERATES analysis, should take into
account potential structural changes in supply and demand
patterns. It was concluded that trying to encompass the
scenario storylines in economic models would be more
speculative than estimating the model parameters directly
by expert-judgment. Furthermore, relevant general equili-
brium models only address aggregated groups of agricultural
products (e.g. Tsigas et al., 1997) whereas the ACCELERATES
project modelled farmer choices of specific crops. Economic
analyses using formal models were not completely ignored
since the experts were able to draw on the results of existing
analyses undertaken elsewhere when making their judg-
ments. We believe also that by applying expert-judgement as
an alternative to formal models, the users of the scenarios will
be less tempted to believe that the scenarios are facts. They
will know that the scenarios are based on judgements, which
can be discussed.
The study found that prices were rather difficult to
estimate consistently by expert-judgment. An alternative
approach would be for the experts to estimate changes in
regional crop productivity and consumer preferences and to
use these estimates as input to a market equilibrium model to
estimate prices. For example, if the production estimates
provided by the land use model differed significantly from the
scenario estimates then the equilibrium model would be run
again to provide new equilibrium prices.
The stakeholders in this study only had the role of advisors,
i.e. they were invited to comment on the scenarios proposed
by the experts. The stakeholders could have been involved
more directly in the pairwise comparison of drivers in each
scenario, but the objective of ACCELERATES was not to
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Table 8 – Matrix 3: agricultural model parameters for 2020 and 2050 in the CEECs
Parameters Present 2020 2050
WM RE GS LS WM RE GS LS
Divergence/convergence
parameter
100 120 110 120 70 300 110 340 60
Costs of fertiliser (s) 100 115 110 130 120 90 105 160 125
Costs of seed (s) 100 120 110 120 100 155 135 121 100
Costs of pesticides (s) 100 115 105 130 105 85 115 150 115
Costs of machinery (s) 100 100 110 130 105 85 110 136 105
Costs of fuel (s) 100 90 110 140 110 90 110 140 110
Cost of labour (s/person) 100 220 180 180 150 340 260 270 140
Cost of contractors (s) = 40%
labour + 60% machinery
100 148 138 156 120 170 160 150 130
Area subsidy 100 0 300 250 100 0 150 140 110
Cereal prices (s/t) 100 105 110 110 100 80 100 90 100
Cereal area subsidy (s/ha) 100 0 300 200 120 0 150 140 110
Maize prices 100 110 105 115 103 90 101 105 103
Maize area subsidy 100 0 300 200 120 0 150 140 110
Sugar beet price (s/t) 100 95 100 95 102 95 100 95 102
Oilseeds price (s) 100 98 110 102 105 98 110 102 105
Oilseed area subsidy (s/ha) 100 0 300 200 120 0 150 120 120
Roots and tubers price (s/t) 100 95 100 120 130 90 106 125 130
Protein crop price (s/t) 100 100 105 110 100 75 92 100 100
Protein area subsidy 100 0 300 200 120 0 150 200 120
Cotton prices 100 95 95 100 90 80
Cotton subsidy (s/t) 0 150 140 110
Tobacco price (s/t) 75 85 58 60
Set-aside subsidy (s/ha) 100 0 300 200 120 0 300 0 0
Set-aside quota (%) 100 0 110 105 100 0 110 0 0
Meat prices (s/t) 100 130 120 110 100 100 105 110 105
Milk prices (s/l) 100 130 125 120 110 105 115 130 132
Less favoured area
payments (s/ha)
100 0 300 300 150 0 150 150 130
Crop yield changes due to
technological advances
100 180 140 150 110 210 160 150 140
Irrigation infrastructures 100 155 140 150 120 170 200 160 125
Water cost (s/l) 100 150 140 200 130 200 160 260 150
Irrigation efficiency (%)
(water to achieve same)
100 200 150 300 130 250 200 350 150
Chemical restriction 100 120 120 160 130 130 140 200 140
Farm sizes (mean ha) CZ, SL, 100 80 80 70 60 75 80 60 50
Farm size (mean ha) HU, RO, BLG, PL 100 250 160 200 150 400 300 250 150
Biofuel areas (% of UAS) from SRES-IMAGE 100 200 250 150 180 500 550 400 300
develop a participatory approach. Instead, the aim was to
develop consistent socio-economic parameter values for land
use modelling.
The hierarchical approach for scenario development
applied here does not ensure reproducibility, i.e. the results
for the model parameters were not compared with those
estimated by another, independent group of experts and the
model parameters depend entirely on the subjective judg-
ments of the experts. However, we believe that the systematic
approach applied here makes it much easier to verify the
estimated parameters by inviting other experts to participate
in the scenario process, since there is a systematic processing
of the information that links the storylines to the formulation
of the model parameters.
The pairwise comparison matrices were compiled by the
experts and the results were afterwards either discussed
with the experts individually (matrix 2) or in a focus group
(matrix 3). Based on this experience we believe that a better
approach would have been to bring the experts together at a
workshop when compiling their matrices. This would have
ensured a continuous dialogue about the scenario reasoning
and less divergence in the judgements of the experts.
Experts, however, often have limited time for participation
in such workshops and so, the planning of expert involve-
ment should include resources to compensate experts for
their time.
The stepwise procedure was found to be appropriate when
developing socio-economic scenarios that are consistent with
climate change scenarios used in climate impact studies. The
pairwise comparison approach developed by Saaty (1980)
provided a useful tool for the quantification from narrative
storylines of scenario drivers and model parameters. How-
ever, narrative descriptions of the scenarios were helpful at
each step to facilitate discussion and to communicate the
scenarios to stakeholders. There is no short cut in the
development of socio-economic scenarios that will always
require creative and consistent thinking. A scenario develop-
ment exercise should be adapted to each situation, i.e. the
objectives of the scenarios, the resource constraints, and the
stakeholders and experts involved.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 9 ( 2 0 0 6 ) 1 0 1 – 1 1 5 113
For policy makers to be able to rely on the results from
analyses using the scenarios, it is important that a systematic
procedure is used to select viable, self-consistent possibilities
which properly span the future space. Scenario analyses
typically use four scenarios but in reality these are composed
of many factors which can adopt many levels and are thus a
minimal selection from a very large possibility set. The more
detail one attempts to achieve, as in this project, the larger the
possibility set. This use of a hierarchical procedure, plus
combining experts with an analytical analysis, provides a
sound method so that policy-makers can be confident that the
subsequent analyses using them, deliver valuable information
about which regions are robust and which are vulnerable to
changes.
Acknowledgement
The authors would like to thank the two referees of an earlier
draft, who provided much constructive criticism.
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Jens Abildtrup is Assistant Professor of environmental andresource economics at The Food and Resource Economics Insti-tute, The Royal Veterinary and Agricultural University, Copenha-gen. He holds a PhD degree in forest economics and management.His research and publication focus mainly on applied environ-mental economics, regulation of agricultural land use, and climatechange impact analysis. He is also been a member of severalgovernmental committees on agri-environmental policies.
Eric Audsley has over 30 years experience of applying mathema-tical, operational research and systems modelling techniques tothe analysis and optimization of decisions concerning agriculturalsystems. One of his main areas is the application of linear pro-gramming to whole farm modelling to allow complete flexibility ofoptimal choice of cropping and machinery, constrained only byagronomic and physical factors. Models have been developed forarable, horticultural and grass farm systems, for decision makingwith uncertainty, and for calculating environmental emissions asa function of the type and timing of operations, with multipleobjective optimization.
Maria Fekete-Farkas is an Associate Professor, Head of Depart-ment of Microeconomics, Institute of Economics, Szent IstvanUniversity, Hungary. Her research work includes the area of agri-cultural economics focusing on the relationship between land usechange, competitiveness, productivity and state of environmentin the Central and Eastern European countries during the transi-tion period.
Carlo Giupponi is Associated Professor of Agri-EnvironmentalModelling, Agricultural Ecology and Agronomy at the Departmentof Crop Production of the University of Milan (Italy). Since 2000 heis also coordinator of the ‘‘Natural Resource Management’’Research Programme of Fondazione Eni Enrico Mattei. Hisresearch activity focuses on the management of natural resourcesand in particular on the relationships between agriculture andenvironment. Since 1994 he teaches classes at graduate andundergraduate level, among them ‘‘GIS and Land use’’, ‘‘Multi-criteria Valuation Laboratory’’, ‘‘Agricultural Ecology and Agron-omy’’ and ‘‘Agri-Environmental Analysis and Modelling’’. He hasauthored or co-authored over 150 scientific and professionalpapers.
Morten Gylling has broad and extensive knowledge of agriculturalproduction and production systems and has many years experi-ence in technological-economic analysis and assessment of agri-cultural productions systems within a commercial as well asenvironmental framework. The main focus of the work has beenon farmers’ possible exploitation of new markets and productionsand the farmers’ position in the production/value chain.
Paolo Rosato, PhD, is Professor of Economics and Real EstateAppraisal at the Faculty of Engineering, University of Triesteand Faculty of Architecture, IUAV – Venice. His research interestsare agricultural and natural resources economics, policy evalua-tion and real estate appraisal. He is author of papers concerningenvironmental economics and multiple criteria analysis, with aspecial focus on externalities’ management and economic assess-ment of environmental protection.
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Mark Rounsevell is Professor of Geography at Universite catholi-que de Louvain and head of the Laboratory of GIS and Environ-mental Change. He has research interests in the effects ofenvironmental and policy change on land use systems, particu-larly in rural and periurban areas. He has participated in severalprojects for the European Commission and the European Environ-ment Agency, such as ACCELERATES (as coordinator), ATEAM,
VISTA, FRAGILE and PRELUDE, that have developed spatial mod-elling approaches for the assessment of land use change and/orhave derived future socio-economic and land use change scenar-ios, employing participatory approaches. He has contributed as aLead Author to the Intergovernmental Panel on Climate Change(IPCC) second, third and fourth assessment reports and the Inter-national LUCC project (Land Use and land Cover Change).