socio-economic scenario development for the assessment of climate change impacts on agricultural...

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


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 for
land 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


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


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


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
100
WM
52
RE
90
GS
74
LS
85
This 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 appropriate
subjects;

� 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


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


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


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


Table 6 – Overview of agricultural parameter judgements


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


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.


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.


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).

socio-economic scenario development for the assessment of climate change impacts on agricultural...

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