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NOTA DI
LAVORO
138.2010
V
aluation of Linkages
between Climate Change,
Biodiversity and Productivity
of European Agro-
Ecosystems
By Ruslana Rachel Palatnik, FEEM,
Italy, Department of Economics, the
Max Stern Academic College of Emek
Yezreel Israel, NRERC- Natural
Resource and Environmental Research
Center, University of Haifa
Paulo A.L.D. Nunes, FEEM, Italy and
Center for Environmental Economics
and Management, Department of
Economics, Ca’ Foscari University of
Venice, Italy
The opinions expressed in this paper do not necessarily reflect the position of
Fondazione Eni Enrico Mattei
Corso Magenta, 63, 20123 Milano (I), web site: www.feem.it, e-mail: working.papers@feem.it
SUSTAINABLE DEVELOPMENT Series
Editor: Carlo Carraro
Valuation of Linkages between Climate Change, Biodiversity
and Productivity of European Agro-Ecosystems
By Ruslana Rachel Palatnik, FEEM, Italy, Department of Economics, the
Max Stern Academic College of Emek Yezreel Israel, NRERC- Natural
Resource and Environmental Research Center, University of Haifa
Paulo A.L.D. Nunes, FEEM, Italy and Center for Environmental
Economics and Management, Department of Economics, Ca’ Foscari
University of Venice, Italy
Summary
It is clear that climate change involves changes in temperature and precipitation and
therefore directly affects land productivity. However, this is not the only channel for climatic
change to affect agro-systems. Biodiversity is subject to climatic fluctuations and in turn
may alter land productivity too. Firstly, biodiversity is an input into agro-ecosystems.
Secondly, biodiversity supports the functioning of these systems (e.g. the balancing of the
nutrient cycle). Thirdly, agro-systems also host important wildlife species which, though not
always, play a functional role in land productivity, nonetheless constitute important sources
of landscape amenities. The present paper illustrates a unique attempt to economically
assess this additional effect climate change may imply on agriculture. We first empirically
evaluate changes in land productivity due to climatic change effect on temperature,
precipitations and biodiversity. Then we estimate the economic cost of biodiversity impact
on agro-systems. Our key finding is that climate-change-induced biodiversity impact on
European agro-systems measured in terms of GDP change in year 2050 is sufficiently large
to deepen the direct climate-change effect in some regions and to reverse it in others.
Different economies show different resilience profiles to deal with this effect.
Keywords: Climate Change, Biodiversity, Agro-Ecosystems
JEL Classification: D58, Q54, Q57
The research leading to these results has received funding from the European Union Sixth Framework
Programme under the project 'Climate Change and Impact Research: the Mediterranean Environment
- CIRCE', contract n. 036961. In addition, the authors thank M. Bindi and R. Ferrise, Department of
Agronomy and Land Management, University of Florence, for crop productivity computations for Italy
under climate change. The authors also thank S. Silvestri and E. Lugato for their research assistance
and data management.
Address for correspondence:
Ruslana Rachel Palatnik
Fondazione Eni Enrico Mattei
Isola di San Giorgio Maggiore
30124 Venice
Italy
E-mail: ruslana.palatnik@feem.it
1
Valuation of Linkages between Climate Change, Biodiversity
and Productivity of European Agro-Ecosystems
Ruslana Rachel Palatnik 1, 2*, Paulo A.L.D. Nunes 1, 3
Abstract
It is clear that climate change involves changes in temperature and precipitation and
therefore directly affects land productivity. However, this is not the only channel for
climatic change to affect agro-systems. Biodiversity is subject to climatic fluctuations and
in its tern may alter land productivity too. Firstly, biodiversity is an input into agro-
ecosystems. Secondly, biodiversity supports the functioning of these systems (e.g. the
balancing of the nutrient cycle). Thirdly, agro-systems also host important wildlife species
which, though not always, play a functional role in land productivity, nonetheless
constitute important sources of landscape amenities. The present paper illustrates a unique
attempt to economically assess this additional effect climate change may imply on
agriculture. We first empirically evaluate changes in land productivity due to climatic
change effect on temperature, precipitations and biodiversity. Then we estimate the
economic cost of biodiversity impact on agro-systems. Our key finding is that climate-
change-induced biodiversity impact on European agro-systems measured in terms of GDP
change in year 2050 is sufficiently large to deepen the direct climate-change effect in some
regions and to reverse it in others. Different economies show different resilience profiles to
deal with this effect.
Aknowlegments:
The research leading to these results has received funding from the European Union Sixth
Framework Programme under the project 'Climate Change and Impact Research: the Medi-
terranean Environment - CIRCE', contract n. 036961. In addition, the authors thank M.
Bindi and R. Ferrise, Department of Agronomy and Land Management, University of Flo-
rence, for crop productivity computations for Italy under climate change. The authors also
thank S. Silvestri and E. Lugato for their research assistance and data management.
1 FEEM- Fondazione Eni Enrico Mattei, Italy.
2 Department of Economics, the Max Stern Academic College Of Emek Yezreel Israel; NRERC- Natural
Resource and Environmental Research Center, University of Haifa.
3 Center for Environmental Economics and Management, Department of Economics, Ca’ Foscari University
of Venice, Italy.
2
1. Introduction
In the 21st Century, the agricultural sector will be radically altered by both natural
disasters and anthropogenic factors, including climate change, changing world economies,
and potential changes in the Common Agricultural Policy (CAP) and the subsidies
currently paid to farmers and land managers. Both climate change and socio-economic
drivers will affect crop productivities and agricultural land use patterns. The work of
Rounsevell et al. (2005) shows that climatic impacts on agriculture vary across different
climate scenarios and land use changes will also influence future land management
scenarios.
Many studies have already coped with the difficulty of projecting variation in land
productivity caused by climatic change induced fluctuations in temperature and precipita-
tion. Brown and Rosenberg (1999), Rounsevell et al. (2005) and Kan et al. (2009) are just few
representative examples. However, this is not the only channel for climatic change to affect
agro-systems. Biodiversity is subject to climatic fluctuations and in its tern may alter land
productivity. This research aims at analyzing the potential effects of biodiversity variation
due to climatic changes on the agricultural sector in Europe in terms of the changes in land
productivity for various crops, agricultural output and ultimately GDP. Our analysis fo-
cuses on the depiction of different future scenarios of the agricultural sector in the next 40
years following four IPCC scenarios, i.e. A1FI, A2, B1 and B2. The proposed economic
valuation of consequences of climate-change-induced change in biodiversity is anchored in
a three step approach. The first step is the determination of the role of biodiversity in creat-
ing agro-ecosystems. The second step is empirical evaluation of the reduced quantity and
quality of agro-system services. Here, the magnitude of climate change impacts on agricul-
tural productivity is isolated and estimated by an econometric application where biodiver-
sity is tested as being a determinant of agricultural yield. The third step is the (monetary)
valuation of that loss employing Computable General Equilibrium (CGE) model. To the
best of our knowledge, this study represents an original attempt to uncover climate-change-
induced impact of biodiversity on agro-systems.
The paper is organized as follows. Section 2 discusses a roadmap to the monetization
of climate change impacts on agro-ecosystem services, exploring the role of the two agro-
systems of croplands and grasslands respectively. Section 3 focuses on the assessment of
climate change impacts on provisioning services, with particular attention paid to the role
of biodiversity. Section 4 provides an economic valuation of regional GDP loss due to
3
climate-change-induced impact of biodiversity on agro-systems. Section 5 concludes.
2. A roadmap to the monetization of climate change impacts on agro-ecosystems
Ecosystem goods and services provided by agro-ecosystems
Natural and modified ecosystems provide many services and goods that are essential for
humankind (Matson et al., 1997). Simultaneously, modern agriculture has both
substantially changed agro-ecosystems and severely impacted the environment; these
impacts include reductions in biodiversity and a degradation of soil quality (Solbrig, 1991).
The present study focuses on cultivated ecosystems (also known as agro-ecosystems), their
link to biodiversity, and how this is impacted by global climatic changes. Building upon
the Millennium Ecosystem Assessment (MEA 2005), we are able to identify the following
ecosystem services: food, feed, and fiber; soil erosion control; maintenance of the genetic
diversity essential for successful crop and animal breeding; nutrient cycles; biological
control of pests and diseases; erosion control and sediment retention; and water regulation.
These are the local benefits that agro-ecosystems can provide to local communities. In
addition, there are also global benefits to human wellbeing from agro-ecosystems in terms
of regulating services such as carbon sequestration (Swift et al., 2004; Allen & Vandever
2003; MEA, 2005). Moreover, we also distinguish between croplands and the grasslands
due to the very different types of ecosystem goods and services that these two distinct
agro-systems provide.
Croplands and grasslands
We discuss croplands and grasslands in detail for two main reasons. Firstly, croplands and
grasslands provide different goods for human consumption. Secondly, these two
agricultural systems are characterized by different profiles with respect to the supply of
regulating services. In terms of provisioning services, croplands provide three kinds of
natural products, including food, non-food, and bio-energy4 (see Table 1 for examples),
whereas grasslands are cultivated primarily for grazing. The distinction between croplands
and grasslands is therefore essential to the quantitative projections of ecosystem goods and
4 Food includes crops destined for human consumption, such as sugar crops, nuts, cereals, fruits, oils crops,
pulses, root and tubers, vegetables. “non-food” includes provisioning services non-destined for human
consumption, such as latex, pharmaceuticals and agro-chemicals product. On the other hand, bio-energy
includes crops for energy production, such as oilcrop for biodiesel and cereals for ethanol.
4
services under the climate change scenarios, and ultimately to the economic valuation
exercise.
Table 1 – Agricultural ecosystem goods and services
Cropland Grassland
Food, Non-Food, Bio-energy
Provisioning services
Food, fibre, latex, pharmaceuticals and
agro-chemicals. Different crop types for
food production, for animal feeding and
energy production
Grazing
Supporting services Genetic library Genetic library
Cultural services Agricultural landscape and agri-tourism Agricultural landscape and agri-tourism
Regulating services
Nutrient cycling, regulation of water
flow and storage, regulation of soil and
sediment movement, regulation of
biological population including diseases
and pests
Nutrient cycling, regulation of water
flow and storage, regulation of soil and
sediment movement, regulation of
biological population including diseases
and pests
Source: Swift et al. (2004), adapted
Biodiversity indicators in the agriculture system
Multiple dimensions of biodiversity in cultivated systems make it difficult to categorize
production systems into ‘‘high’’ or ‘‘low’’ biodiversity systems, especially at spatial and
temporal scales. In agro-ecosystems a distinction has been made between ‘planned’ and
‘associated’ diversity (Swift et al., 2004; Walker and Steffen, 1997). ‘Planned’ diversity
refers to plants and livestock deliberately, imported, stocked and managed by farmers. The
term ‘associated’ refers to the nature of the biota (plant, animal and microbial), associated
with the planned diversity and influenced by its composition and diversity. Farmers play a
dominant role in the context of agricultural biodiversity by the selection of the present
biodiversity stock, by the modification of the abiotic environment and by interventions
aimed at the regulation of specific populations (‘weeds’, ‘pests’, ‘diseases’ and their
vectors, alternate hosts and antagonists).
It is widely recognized that the relationship between cultivated systems and
biodiversity is complex (Macagno and Nunes, 2009). Firstly, biodiversity is an input into
agro-ecosystems (e.g. genetic resources for food and agriculture). Secondly, biodiversity
supports the functioning of agro-ecosystems (e.g. the balancing of the nutrient cycle).
Thirdly, agro-ecosystems also host important wildlife species which, though not always,
play a functional role in land productivity, nonetheless constitute important sources of
5
landscape amenities. Finally, agro-ecosystems can have an effect on biodiversity in the
surrounding areas outside the cultivated fields, for example habitat fragmentation impacts.
More recently, studies of intensive agro-ecosystems have pointed out that permanent
grasslands represent “hot spots” of biodiversity (Giardi et al., 2002; Anger et al., 2002;
Bignal and McCracken, 1996; de Miguel & de Miguel, 1999; Nagy, 2002; EEA, 2007).
Furthermore, the quality of soil is also higher in permanent grasslands with respect to
arable lands as confirmed by the many soil quality indicators (organic carbon, aggregate
stability). Against this background, the ratio between cropland and grassland can be
employed as a proxy indicator for the measurement of the levels biodiversity in agro-
ecosystems.
This, in turn, can be tested to determine if a significant role is played in the levels of
supply of provisioning services. In other words, we can investigate whether this indicator
affects the productivity of croplands. Furthermore, we propose to evaluate this link in the
context of global climate change through a methodological framework that is discussed in
the following section.
3. Assessing the impact of climate change on the provisioning services of agro-
ecosystems
A methodological framework
To understand the interface between climate change and the provisioning services of agro-
ecosystems, a graphical presentation is given in Figure 1 below. First of all, land
productivity for different crops is affected by physical climatic variables (CC) including
temperature and precipitation, and by the level of technology (T). In turn, both are
anchored in the specific IPCC scenario under consideration ranging from AIF1 to B2. In
addition, a biodiversity variable (Bio) is also assumed to impact land productivity.
Formally, we propose to estimate the
β
’s of the following equation:
(Equation 1): ]/[][][ 765
2
43
2
210 CLGRTrFPPTempTempCrP
ββββββββ
+++++++=
Where CrP is the land productivity of harvested product, measured in t/ha, 0
β
is the
intercept, Temp is the average annual temperature (°C), P denotes the annual precipitation
CC T Bio
6
(mm), F is the total fertilizer consumption per hectare (Mt), Tr refers to the total tractors
used per hectare, and GR/CL is the ratio of grassland to cropland. As expressed by the
equation, land productivity is a function of physical variables (Temp and P), technological
level (F and Tr) and a proxy of biodiversity (GR/CL)5.
Figure 1 –Methodological framework for the evaluation of IPCC story lines on agricul-
tural provisioning services
Biodiversity (Bio)
Climate
change
IPCC-
Scenarios
AIF1
A2
B1
B2
Climate (CC)
Ä T° 4.4 -2.1 °C
Ä Prec. -0.5 – 4.8 (%)
CO
2
518-779 ppm
Socio-economic
orientation (T)
Economic-
environmental
Global-local
Land use
Crop
Productivity
Provisioning
services
Food Bio-
energy
This section proceeds with presentation of the data used for estimating equation 1,
focusing first on cropland and grassland data and its projections across the different IPCC
story lines. We then discuss the results.
The grassland and cropland land-use data
Before entering into a specific discussion on the data, it is important to note that the
methodological framework in this study focuses on 33 European countries: Greece, Italy,
Portugal, Spain, Albania, Bosnia and Herzegovina, Bulgaria, Serbia and Montenegro,
Turkey, TFR of Yugoslav, Austria, Belgium, France, Germany, Ireland, Luxembourg,
Netherlands, Switzerland, Croatia, Czech Republic, Hungary, Poland, Romania, Slovakia,
Slovenia, Denmark, United Kingdom, Estonia, Latvia, Lithuania, Finland, Norway and
5 GR/CL is considered to be a good proxy for biodiversity at the European scale due to the fact that grass-
lands have been demonstrated to be biodiversity ‘hot spots’ within the intensive agro-ecosystems and are
therefore very important in the maintenance of associated biodiversity values (Baglioni et al 2009a, Baglioni
et al 2009b).
7
Sweden.
Quantitative data of present cropland and grassland areas and the respective crop
products in Europe are collected from the FAO 2005 database at national levels. In the
present study, we consider over 153 million hectares of croplands in Europe – see first
column in Table A1, in the Annex, and 92.5 million hectares of grassland – see first
column in Table A2, Annex. A large proportion is dedicated to cereal crops – see Table
A3, Annex. With respect to production, crop yields of each of the selected crop categories
are derived from the FAO database in terms of weighted average yield (i.e. t/ha, harvested
production per hectare) – see Table A4, Annex. By multiplying the weighted average yield
of a crop product by each country’s cropland area, we can calculate the total harvesting of
this specific type of crop for this country, see the first column of Tables A5 to A12, Annex.
If for example, the cereals area in Italy, for 2005, was 3.965 million ha and the average
yield of 5.4 t/ha, also measured in 2005, then total production of cereals produced by Italy
in that year was 3.965 Mha x 5.4 t/ha = 21 million tons, again as reported in the first
column of Table A5, Annex.
The calculation of the actual land devoted to bio-energy crops is based on the EEA
technical report No 12/2005, which shows that approximately 4.6 million hectares of
agricultural land in the EU-25 is directly devoted to biomass production for energy use, see
Table A13, Annex. As an illustration, in Italy, the total land area for bio-energy production
is estimated to be 355,000 ha in 2005, about 3.6% of total cropland area. The majority of
the land area for bio-energy production, about 83 per cent, is devoted to oil crops (used for
biodiesel), and the remaining 11 per cent is used to cultivate ethanol crops. Bearing in
mind the lack of data at the individual country level on the distribution between these two
land uses, we assume the same proportions to calculate the oil crops and cereals used for
biodiesel and ethanol production at country level, respectively. With respect to the
remaining non-EU countries, the distribution is based on the average estimate of relative
area devoted to bio-energy of the EU member states located at the same latitude.
Moreover, we assume that the quantity of oil crops and cereals used for bio-energy
production equals that of food crops – see last column in Table A4, Annex. This
assumption enables us to calculate the total production of bio-energy – see Tables A14 and
A15, Annex. Again, taking Italy as an example, our calculation shows that about 1 million
tons of oil crops and more than 167,000 tons of cereals are used for bio-energy production.
Next, we estimate the agricultural areas assigned for cropland, grassland and bio-energy
production in each country in 2050. Here we adopt two approaches. The primary approach
8
is to base our calculation on the land use change results of ATEAM model (Schröter et al.
2004, Schröter et al. 2005), which provides downscaled projections of soil used for the
European Agro-ecosystems at country level using IPCC SERS circulation model. The
results obtained are consistent with that of the IPCC report. Once again, taking Italy as an
example, our estimation shows that the country’s cropland area in 2050 will range between
5.9 and 8 Mha depending on the scenario – see last columns of Table A1, Annex. These
figures indicate a general contraction of cultivated areas. However, the limitation of the
ATEAM model is that it covers only 17 developed European countries. For this reason we
referred to an IMAGE 2.2 Integrated Assessment Model (IMAGE team, 2001) to calculate
the required information on agro-ecosystem land use patterns for the 16 remaining
countries of interest. This is done based on a global projection of land use changes. Final
results are presented in Tables 1A and 2A respectively for croplands and grasslands.
Projections of land productivities for all four IPCC scenarios are the focus of the next
section.
Land productivities under different IPCC scenarios: results
As seen in Figure 1, the estimation of the future crop yield takes into account the impacts
of advancements in technology (T), direct climate effects (CC) and biodiversity
contributions (Bio). With respect to the technology factor (T), the parameter value was
derived from Ewert et al. (2005) who provide a mean coefficient for Europe - see Table 2.
For instance, in the global economic scenarios (A1 and A2) show higher technological
impacts on crop productivity when compared to the B’s scenarios. As an illustration, the
actual cereals yield in Italy may increase from present 5.4 t/ha to 6.8 t/ha in 2050 in the
scenario B2, using the parameters of relative change in crop productivity presented in
Table 2.
Table 2 – Estimated relative change in crop productivity due to technology factor on 2050
A1FI A2 B1 B2
1.87 1.81 1.63 1.28
Source: Ewert et al., (2005)
9
In addition, with respect to climate change impacts, the coefficient (CC) was calculated on
the basis of a study developed by Tor (2007), which estimates the relative wheat yield
changes in 2050 for the European Environmental Zones under different IPCC scenarios.
The information regarding the percentage of each environmental zone within the EU
countries is used to calculate a weighted average for an estimation of the relative wheat
yield changes for all 33 European countries of interest. Moreover, since wheat is the most
cultivated crop in Europe, it is considered the most representative of net primary
production (NPP) variation and can therefore be an important crop to be studied in terms of
the consequences of changing climatic parameters (such as temperature, precipitation and
CO2). All of the calculated CC coefficients are reported in Table 3.
Table 3 – Estimated relative changes in land productivity (2050) as affected by changes in
climatic conditions (CC) and biodiversity (Bio) for different IPCC scenarios
CC Bio
Country A1FI A2 B1 B2 A1FI A2 B1 B2
Greece 0.91 0.93 0.98 0.96 1.14 0.98 1.20 1.00
Italy 0.94 0.95 0.98 0.97 1.00 0.99 0.99 0.99
Portugal 0.91 0.92 0.98 0.96 0.94 0.87 0.90 0.86
Spain 0.92 0.93 0.98 0.96 1.05 0.97 1.09 1.00
Albania 0.95 0.96 0.99 0.98 0.92 0.94 0.92 0.94
Bosnia and Herz. 1.05 1.04 1.01 1.03 0.91 0.93 0.91 0.93
Bulgaria 1.01 1.01 1.00 1.01 0.94 0.96 0.94 0.96
Serbia and Mont. 1.03 1.03 1.01 1.02 0.94 0.96 0.94 0.95
Turkey 1.03 1.03 1.01 1.02 0.91 0.94 0.92 0.94
TFR of Yugoslavia 1.02 1.02 1.01 1.01 0.88 0.91 0.88 0.90
Austria 1.07 1.06 1.02 1.04 0.94 0.92 0.98 0.93
Belgium 0.98 0.98 0.99 0.99 1.00 0.99 1.00 0.99
France 0.95 0.96 0.99 0.98 0.99 0.99 1.00 0.99
Germany 1.01 1.01 1.00 1.01 0.99 0.99 1.00 0.99
Ireland 0.95 0.96 0.99 0.98 0.98 0.99 1.01 0.99
Luxembourg 0.98 0.99 1.00 0.99 1.00 0.99 1.00 0.99
Netherlands 0.96 0.97 0.99 0.98 1.01 0.98 1.00 0.98
Switzerland 1.08 1.07 1.02 1.04 0.92 0.90 0.96 0.92
Croatia 0.99 0.99 1.00 0.99 0.91 0.94 0.92 0.93
Czech Republic 1.05 1.04 1.01 1.02 0.97 0.98 0.98 0.98
Hungary 1.01 1.01 1.00 1.01 0.98 0.98 0.98 0.98
Poland 1.03 1.03 1.01 1.02 0.97 0.97 0.97 0.97
Romania 1.04 1.03 1.01 1.02 0.92 0.94 0.92 0.94
Slovakia 1.03 1.03 1.01 1.02 0.92 0.93 0.92 0.93
Slovenia 1.08 1.07 1.02 1.04 0.93 0.95 0.93 0.94
Denmark 1.00 1.00 1.00 1.00 0.99 0.99 1.00 0.99
United Kingdom 0.97 0.97 0.99 0.98 0.98 0.98 1.01 0.98
Estonia 1.06 1.05 1.02 1.03 0.98 1.00 0.98 0.99
Latvia 1.04 1.04 1.01 1.02 0.92 0.94 0.92 0.94
Lithuania 1.02 1.01 1.00 1.01 0.99 1.00 0.99 1.00
Finland 1.12 1.10 1.03 1.06 1.03 1.02 1.05 1.02
Norway 1.20 1.17 1.05 1.10 1.00 1.00 1.02 1.00
Sweden 1.12 1.10 1.03 1.06 1.00 1.00 1.01 1.00
10
Again as an example, considering the present Italian cereal productivity (5.4 t/ha) and a
CC coefficient value of 0.94 for the scenario A1FI, this country’s cereal yield in 2050 will
be 5.4 t/ha×0.94 = 5.08 t/ha as a result of the future climatic variation.
Finally, with respect to biodiversity impacts, the coefficient (Bio) was calculated on
the basis of an econometric exercise that isolated the marginal impact of biodiversity as
modeled by equation 1. We created an ad hoc database for the analysis on wheat yields,
covering 19 countries over the period 1974 and 2000, see a sample in Table A16, Annex.
Moreover, information regarding wheat yield, grassland and cropland areas, total fertilizers
used and total tractors is derived from FAO statistics whereas information about tempera-
ture and precipitation is derived from the Tyndall database. The regression model results
are summarized in Table 4. We can see that the model is statistically significant (P<0.01),
as are other variables selected. In particular, the GR/CL parameter is significant (P<0.01)
with a coefficient g of 0.549. This implies that, if the actual ratio GR/CL is 0.44 for Italy
(from Table A1 and A4, Annex), the contribution of biodiversity to the wheat yield is
0.44×0.549 = 0.24 t/ha.
Table 4 – Crop productivity function for the estimation of the effects of biodiversity on
wheat yield
B Std. Err. of B p-level
Intercept -0.480 0.518 0.354
Bio(grass/crop) 0.549 0.075 0.000
Avg_T 0.469 0.058 0.000
Avg_T2 -0.033 0.003 0.000
Prec 0.004 0.001 0.001
Prec2 0.000 0.000 0.006
Fert. (t/ha) 10.002 1.075 0.000
tractor (n/ha) 1.002 2.334 0.668
R= .74 R²= .55 Adjusted F(7,505)=89.247 p<0.0000 Std.Error of estimate: 1.1959
At this point, it was possible to calculate changes in land productivity due to changes in
biodiversity based on the estimated variation (D) of the ratio GR/CL for the IPCC
scenarios in 2050 (using data from Table 1 and 4, Annex), as follows:
11
(Equation 2): (GR/CL) scenario * β7 – (GR/CL) 2005 * β7= Yield_variation
[(GR/CL) scenario – (GR/CL) 2005] * β7 = Yield_variation
∆[GR/CL] * β7 = Yield_variation
where ‘scenario’ refers to the A1FI, A2, B1 and B2 scenarios reported by the IPCC. To
standardize the wheat yield variation due to biodiversity, we performed the following
correction:
(Equation 3): (Yield_variation/Yield_2005)*100 = Relative_variation
For example, assuming that the actual wheat yield is 3.2 t/ha, the GR/CL is 0.39 and
0.33 at present, and we operate in the A2 scenario (2050), then the final coefficient will be:
[(0.33-0.39)* 0.55]/3.2*100 = -0.9% or 0.99 if expressed as projected final yield values
(3.2 t/ha * 0.99 = 3.18 t/ha). The full ranges of the Bio coefficients calculated for each
country are reported in Table 3. At this stage, we are finally able to obtain disaggregated
total crop productions (tons) for the different IPCC storylines. The calculation is conducted
using the formula below, and the results are reported in Tables A5 -A12, Annex.
(Equation 4):
()
(
)
()
∑×
i
i
ihakgyieldfuthaareacroplandestimated /.__
As an example, assuming that present cereals yield in Italy is 5.4 t/ha, its predicted
value for the B2 scenario will therefore be 6.7 t/ha (5.4 t/ha
×
1.24 according to Table A16,
Annex). Taking into account the estimated cropland area, the total cereals production in
2050 is estimated to be more than 21 Mt for the B2 scenario – see Table A5, Annex. The
future trends of the selected indicators are projected individually for the period of 2005 to
2050 based on global circulation models, where greenhouse gas concentration and climatic
and socioeconomic factors are the drivers of land use changes (Nakicenovic and Swart
2000; Schöter et al. 2004; Schöter et al. 2005, Ewert 2007). These results are validated by
the recent study by carried out by Ferrise in which the authors explore the use of crop
simulation model (SIRIUS) and applied to the durum wheat using data from open-field
experimental in Florence in 2003-2005, see Ferrise et al., (in press). As a consequence, we
are able to present four different development dimensions of agricultural ecosystem goods
and services in Europe that are consistent with the four IPCC storylines: A1FI, A2, B1 and
B2, as shown in Table 3.
12
4. Economic valuation of the linkages between Climate change, biodiversity and
the productivity of European agro-ecosystems
Most of the economic studies of biodiversity end up with sectoral, partial-equilibrium
analysis. However, agricultural products are important market commodities for human
consumption. The projection of the agricultural output and respective market prices are
therefore subject to standard macro-economic theory, determined by the future supply and
demands of these commodities under climate change scenarios. For this reason, the eco-
nomic valuation of crops in the scenario of climate change shall not be tackled in a partial
equilibrium analysis. Instead, we apply the quantitative information obtained from the
physical projections in Section 3 to a general equilibrium model. This way we are able to
evaluate, in economic terms, the impact of climate-change-induced variation in biodiver-
sity on the productivity of agro-systems.
The Methodological Framework
We employ a static multi-regional CGE model of the world economy called GTAP-EF
(Roson, 2003; Bigano et al., 2006). The latter is a modified version of the GTAP-E model
(Burniaux and Troung, 2002), which in turn is an extension of the basic GTAP model
(Hertel, 1997). It is calibrated to replicate regional GDP growth paths consistent with the
A2 IPCC scenario and is then used to assess climate change economic impacts in 2050
with respect to 2000.
Although regional and industrial disaggregation in the model may vary, the results
presented here refer to 19 macro-regions in which several European countries appear dis-
aggregated, as distinct economic entities, whereas the rest of the world is aggregated in
four major trading blocks. Regional economies are represented by 19 sectors which can be
classified in three major industries, where land using industries are presented in broadest
disaggregation possible in GTAP database. Table 5 depicts the regional and sectoral disag-
gregation.
As in all CGE frameworks, the standard GTAP model makes use of the Walrasian
perfect competition paradigm to simulate adjustment processes (Ronneberger et al., 2009).
Industries are modelled through a representative firm, which maximizes profits in perfectly
competitive markets. The production functions are specified via a series of nested Constant
Elasticity of Substitution (CES) functions. Domestic and foreign inputs are not perfect
substitutes, according to the so-called Armington assumption, which accounts for product
heterogeneity. A representative consumer in each region receives income, defined as the
13
service value of national primary factors (natural resources, land, labour and capital). Capi-
tal and labour are perfectly mobile domestically, but immobile internationally. Land (im-
perfectly mobile) and natural resources are industry-specific. The national income is allo-
cated between aggregate household consumption, public consumption and savings. The top
level utility function has a Cobb-Douglas specification. Private consumption is split in a
series of alternative composite Armington aggregates. The functional specification used at
this level is the Constant Difference in Elasticities (CDE) form: a non-homothetic function,
which is used to account for possible differences in income elasticities for the various con-
sumption goods.
Table 5: GTAP-EF Sectoral and Regional Disaggregation
Regions Sectors
N Code Description Description
1 Italy Italy Rice
2 Spain Spain Wheat
3 France France Cereal Crops
4 Greece Greece Vegetable Fruits
5 Malta Malta Oil Seeds
6 Cyprus Cyprus Sugar Cane
7 Slovenia Slovenia Plant-Based Fibers
8 Croatia Croatia Other Crops
9 FYug Bosnia, Montenegro, Serbia Animals
10 Albania Albania Forestry
11 Turkey Turkey Fishing
12 Tunisia Tunisia Coal
13 Morocco Morocco Oil
14 RoNAfrica Rest of North Africa Gas
15 RoMdEast Rest of Middle East Oil Products
16 RoNME non-Mediterranean Europe Electricity
17 RoA1 Other Annex 1 countries Other industries
18 ChInd China and India Market Services
19 ROW Rest of the World Non-Market Services
Proposed here economic valuation of consequences of climate-change-induced
change in biodiversity is fastened in a two step approach. The first step is creating bench-
mark data-sets for the world economy “without climate change” at year 2050, using the
methodology described in Bosello and Zhang (2005). This entails inserting, in the GTAP-
EF model calibration data, forecasted values for some key economic variables, to identify a
hypothetical general equilibrium state in the future. Since we are working on the medium-
long term, we focused primarily on the supply side: forecasted changes in the national en-
14
dowments of labour, capital, land, natural resources, as well as variations in factor-specific
and multi-factor productivity. We obtained estimates of the regional labour and capital
stocks by running the G-Cubed model (McKibbin and Wilcoxen, 1998) and of land en-
dowments and agricultural land productivity from the IMAGE model version 2.2 (IMAGE
Team, 2001). By changing the calibration values for these variables, the CGE model has
been used to simulate a general equilibrium state for the future world economy.
The second step is imposing over this benchmark equilibrium climate-change-
induced temperature and precipitaions (CC), as well as biodiversity (Bio) impacts on land
productivity for crops in different regions employing estimations presented in Table 3. For
GTAP-EF regions, which absent from analysis in Section 3, we used values from available
countries in same geo-climatic category, including latitude groups 35°-45°, 45°-55°, 55°-
65° and 65° to 71° as we used before. We run this model for four scenarios about the cli-
mate (A1F1, A2, B1, B2). In this way, GTAP-EF generates three sets of re-
sults: a baseline growth for the world economy, in which climate change
impacts are ignored, and counterfactual scenarios in which temperature
and precipitaions, and biodiversity impacts are imposed.
Results
Table 6 presents changes in output of a representative crop, wheat, due to climate-change-
induced variations in temperature and precipitations (CC), and biodiversity (Bio) in year
2050 versus baseline projection. Here already evidences for significant effect of biodiver-
sity above direct climatic impact can be observed. For instance, examining percent change
in wheat output in Italy under A1F1, A2 and B2 scenarios, it becomes clear that biodiver-
sity added effect reverses direct climatic change impact, so that wheat production is pro-
jected to increase with Bio when compared to benchmark dynamics. The output change is
negative when only direct CC shock is evaluated.
15
Table 6 – Percentage change in wheat output versus no climate change baseline in 2050
CC Bio
Region A1F1 A2 B1 B2 A1F1 A2 B1 B2
Italy -0.067 -0.123 0.150 -0.064 0.333 0.202 0.061 0.108
Spain -1.683 -1.511 -0.245 -0.821 1.551 -0.522 2.288 0.215
France -0.436 -0.469 0.478 -0.128 0.609 0.352 0.647 0.173
Greece -3.331 -2.574 -0.540 -1.432 5.420 -0.536 7.258 0.204
Malta -1.482 -1.535 0.330 -0.775 2.474 -0.279 3.342 0.468
Cyprus 0.731 0.408 0.775 0.293 1.453 0.577 1.355 0.449
Slovenia 0.419 0.322 0.212 0.198 0.144 0.108 0.026 0.050
Croatia 0.439 0.236 0.432 0.103 -0.595 -0.387 -0.596 -0.615
FYug 0.311 0.255 0.189 0.154 -0.250 -0.193 -0.328 -0.253
Albania -0.547 -0.443 -0.042 -0.202 -0.703 -0.597 -0.762 -0.594
Turkey 0.317 0.226 0.198 0.146 0.081 0.057 0.024 0.016
Tunisia 0.323 0.235 0.209 0.152 0.101 0.074 0.039 0.035
Morocco 0.322 0.246 0.197 0.156 -0.046 -0.026 -0.072 -0.059
RoNAfrica 0.194 0.145 0.129 0.094 -0.052 -0.030 -0.055 -0.053
RoMdEast 0.984 0.606 0.757 0.396 0.915 0.558 0.708 0.374
RoNME 0.269 0.139 0.209 0.081 0.234 0.145 0.250 0.081
RoA1 0.372 0.250 -0.012 0.159 0.346 0.244 -0.019 0.183
ChInd -0.612 -0.365 0.184 -0.243 -0.613 -0.366 0.183 -0.243
RoW -0.630 -0.372 -0.669 -0.251 -0.633 -0.377 -0.666 -0.246
The comparison between climate induced temperature (CC) impact with the com-
bined effect of temperature and biodiversity (Bio) on agricultural output and regional GDP
allows us to detect the marginal effect of biodiversity on these economic variables. As
illustrates Figure 2, for some regions, the added effect of biodiversity operates in the same
direction as temperature change. However, there are regions where this effect is reversed
and in some cases it is even larger than temperature and precipitations impact so that the
overall effect operates in the opposite direction.
16
Figure2- Percent change in regional GDP in 2050 due to temperature and biodiversity
variation under B1 storyline versus baseline.
Table 7 reflects that this GDP pattern presents in all storylines. Here, "+" stands for cases
where the marginal impact of biodiversity is non-negative, and "-" otherwise. Lighter col-
ors of the cells signal when biodiversity impact on agro-ecosystems reverses direct cli-
matic, CC, effect. Close examination of the outcome illustrated in Table 7 brings to the
following conclusions: a) for the European Mediterranean countries, the climate-change-
induced effects of biodiversity on agricultural productivity, when measured in terms of
changes in GDP, are non-negative; b) in particular, for the majority part of the European
Mediterranean countries B1 type of climate change scenario, the inclusion of this valuation
transmission mechanism is able to reverse the marginal loss of GDP obtained under cli-
mate-change-alone impact evaluation (with the exception of Italy and France); c) for all the
rest of the Mediterranean countries as well as for Rest of Middle East region, the climate-
change-induced effects of biodiversity on agricultural productivity, when measured in
terms of changes in GDP, is negative; i.e. the observed biodiversity impacts will further
decrease the level of human welfare of these populations as originally measured by the
CGE model; d) for Albania, the Rest of Middle East countries and Turkey (when analyzed
at the B1 scenario) the magnitude of the negative impact marginal economic impact of
biodiversity above temperature effect on land productivity is such that reverses the original
CGE welfare impact; and, finally, e) for all non European countries, including China and
17
India and the rest of the World, the marginal impact of biodiversity is non-negative, how-
ever of low magnitudes.
Table 7- Marginal economic impact of biodiversity above temperature effect on land pro-
ductivity
Region A1 A2 B1 B2
Italy ++++
Spain ++++
France ++++
Greece ++++
Malta ++++
Cyprus ++++
Slovenia ----
Croatia ----
FYug ----
Albania ----
Turkey ----
Tunisia ----
Morocco ----
RoNAfrica ----
RoMdEast ----
RoNME ++++
RoA1 + + - +
ChInd ++++
RoW ++++
To summarize, despite the fact that in general we are assisting to a worldwide de-
crease in the levels of biological diversity, from an economic perspective, which is here
approached from the productivity of the agro-ecosystems, this stylized fact is not always
corresponding to a similar welfare or GDP change pattern to all. In fact not only European
countries will experience diverse impacts. Some countries will more impacted than others,
more countries will lose more than others, and some countries will gain, depending on the
geographical location, existing markets and profile with respect to biodiversity indicators
and land use patterns.
18
5. Conclusions
We propose to contribute to the ongoing study of the relationship between biodiversity,
ecosystem services and human well-being. In particular, this study reports an economic
valuation of the economy-wide consequences of climate-change-induced change in biodi-
versity. This approach depicts the world economy as a system of markets interacting
through exchanges of inputs, goods and services responding to changes in relative prices
induced by climate shocks. In other words, market-driven or autonomous social-economic
adaptation is explicitly described, the mechanisms through which it is likely to operate are
highlighted, and the interaction of impacts is stressed. To our knowledge, this exercise
constitutes an original procedure, at a global level of analysis, in the economic welfare
assessment of biodiversity impacts induced by climate change. First, there is an explicit effort
to measure, model and estimate empirically the impact of biodiversity on agriculture. Econometric
estimates confirm the presence of a positive and statistically significant magnitude, i.e. biodiversity
contributes to explain the land productivity in the agro-ecosystem sector. Second, economic valua-
tion results of the climate-change-caused impacts on biodiversity, agricultural provisioning services
and the productivity of European agro-ecosystems are multifaceted. These, in turn, are
anchored on the underlying IPCC storyline, that includes both climatic and socio-economic
changes, as well as the type of ecosystem services under consideration. All in all, from an
aggregate perspective, they do not reveal significant welfare losses. However, estimation
results show that respective dimension and its distribution across the different European
countries varies significantly. These results reiterate the importance of a welfare analysis
of climate-change-caused impacts on biodiversity and ecosystem services that focus on the
redistributive aspects involved with these changes: impacts are not distributed in a uniform
way across the European countries under consideration; some countries, and respective
economies, show to be less resilient than others; and most of the times the welfare changes
involved clearly signal the presence of winners and losers. In particular, while developed
regions lose slightly, or even gain as in the case of Central and Northern Europe, develop-
ing regions can lose considerably more. This highlights their greater vulnerability to cli-
matic change with respect to developed economies, a vulnerability that results from a com-
bination of higher degrees of exposure and sensitivity. Particularly enlightening is the case
of Mediterranean Europe where initial negative impacts are eventually turned into gains.
There, negative direct impacts are in fact counterbalanced by terms of trade improvements.
Even in terms of final impacts on economic activity, the developing world is more severely
affected than the developed one. Lastly, we found that studies that don't count for the indi-
19
rect effect of climate change on agriculture are in danger of providing incorrect results as
while counting for biodiversity, the climate change impact is stronger and may even re-
verse direction comparing to the case when biodiversity impact is ignored.
It is true that in this analysis we are looking at the tip of the iceberg, since welfare
impacts of biodiversity are not restricted to market/productivity anchored transmission
mechanism, and surely the link of biodiversity and human wellbeing is not limited to the
agro-ecosystem sector and finally that the most efficient way to measure biodiversity may
not be to proxy it as the ration between grassland and cropland. Having said that, and since
we are not embracing a reductionist approach, we do have the ambition to provide a clear,
unique and indisputable reply to the quantification of the biodiversity loss effects on GDP,
and therefore on human wellbeing. The crucial point that we raise here is that the econo-
mies, which also reflect complex social systems, show different resilience profiles to deal
with this type of effects; some economies, and respective social systems, are able to buffer
the impacts, others not. Naturally further research is needed to better understand the eco-
logical-social systems interactions and the role of biodiversity as a determinant.
20
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23
ANNEX
The crops market by “+” represent aggregated groups. Cereals + includes: barley, buck-
wheat, canary seed, cereals nes, maize, millet, mixed grain, oats, rice paddy, rye, sorghum,
triticale, wheat. Fruits + includes: apples, apricots, avocados, bananas, carobs, cherries,
citrus fruit nes, currants, dates, figs, gooseberries, grapefruit (inc. pomelos), grapes, kiwi
fruit, lemons and limes, oranges, peaches and nectarines, pears, persimmons, pineapples,
plums and sloes, quinces, sour cherries, stone fruit nes strawberries, tangerines, mandarins,
clem. Oils crops + includes: castor oil seed, groundnuts, with shell, linseed, melon seed,
mustard seed, olives, poppy seed, rape seed, safflower seed, seed cotton, sesame seed, soy-
beans, sunflower seed. Pulses + includes: beans dry, broad beans dry, horse beans dry,
chick peas, cow peas dry, lentils, lupins, peas dry, pulses nes, vetches. Root and tubers +
includes: potatoes, roots and tubersnes, sweet potatoes, yams. Vegetables + includes: arti-
chokes, asparagus, beans green, cabbages and other brassicas, carrots and turnips, cauli-
flowers and broccoli, chillies and peppers green, cucumbers and gherkins, eggplants (au-
bergines), garlic, leguminous vegetables nes, lettuce and chicory, maize green, mushrooms
and truffles, okra, onions (inc. shallots) green, onions dry, other melons (inc. cantaloupes),
peas green, pumpkins squash and gourds, spinach, string beans, tomatoes, vegetables fresh
nes, watermelons.
24
Table A1 - Cropland area (1,000 ha) on 2005 and 2050 (Source: cropland area on 2005 - FAO
dataset; cropland area on 2050 - our projections based on ATEAM and IMAGE 2.2 model).
Latitude Country 2005
2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 3,401 1,054 1,740 1,799 2,230
Italy 9,928 5,920 6,138 7,520 8,002
Portugal 1,821 662 1,301 1,143 1,577
Spain 17,863 4,383 8,756 8,601 11,826
Albania 692 660 585 602 478
Bosnia and Herzegovina 1,093 1,038 921 948 753
Bulgaria 3,208 3,565 3,163 3,256 2,585
Serbia and Montenegro 3,731 3,526 3,128 3,220 2,556
Turkey 25,952 24,894 22,086 22,737 18,047
35 to 45
TFR of Yugoslav 608 575 510 525 417
Austria 1,421 1,187 1,272 1,679 1,747
Belgium 859 1,111 729 1,225 978
France 19,100 14,688 13,593 18,104 17,889
Germany 11,730 8,926 9,289 12,567 12,745
Ireland 1,214 89 115 105 134
Luxembourg 61 5 11 9 14
Netherlands 938 931 612 1,014 862
Switzerland 427 525 476 704 681
Croatia 1,191 1,497 1,328 1,367 1,085
Czech Republic 3,183 3,131 2,778 2,860 2,270
Hungary 4,626 4,533 4,021 4,140 3,286
Poland 12,325 13,523 11,998 12,352 9,804
Romania 9,516 9,350 8,296 8,540 6,778
Slovakia 1,362 1,486 1,319 1,358 1,078
45 to 55
Slovenia 202 193 171 176 140
Denmark 2,206 2,092 1,328 2,311 1,799
United Kingdom 5,608 4,778 3,316 5,426 4,557
Estonia 590 807 716 737 585
Latvia 1,085 927 822 846 672
55 to 65
Lithuania 1,913 2,757 2,446 2,518 1,998
Finland 2,213 262 423 329 530
Norway 863 332 289 344 368
65 to 71
Sweden 2,677 1,736 1,933 2,203 2,482
Total 153,615 121,145 115,611 131,270 120,957
25
Table A2 - Grassland area (1,000 ha) on 2005 and 2050 (Source: cropland area on 2005 - FAO
dataset; cropland area on 2050 – our projections based on ATEAM and IMAGE 2.2 model).
Latitude Country 2005
2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 4,600 2,017 1,937 3,977 2,665
Italy 4,411 2207 2,026 2,603 2,768
Portugal 1,769 315 330 409 374
Spain 10,400 3,963 3,981 9,679 7,707
Albania 423 129 179 121 141
Bosnia and Herzegovina 1,050 319 443 300 351
Bulgaria 1,891 575 798 540 632
Serbia and Montenegro 1,768 538 746 505 591
Turkey 14,617 4,447 6,170 4,176 4,886
35 to 45
TFR of Yugoslav 630 192 266 180 211
Austria 1,810 944 831 1924 1,277
Belgium 519 653 355 759 456
France 9,934 6,539 4,675 9,087 5,900
Germany 4,929 2,955 2,480 4,570 3,309
Ireland 3,010 2,000 1,683 4,384 1,893
Luxembourg 67 24 26 59 34
Netherlands 980 1,083 441 1,014 708
Switzerland 1,091 844 631 1,420 1,125
Croatia 1,469 447 620 420 491
Czech Republic 974 296 411 278 326
Hungary 1,057 322 446 302 353
Poland 3,387 1,030 1,430 968 1,132
Romania 4,685 1,425 1,978 1,338 1,566
Slovakia 524 159 221 150 175
45 to 55
Slovenia 305 93 129 87 102
Denmark 345 181 78 217 102
United Kingdom 11,180 7,320 5,330 10,897 7,383
Estonia 231 70 98 66 77
Latvia 629 191 266 180 210
55 to 65
Lithuania 891 271 376 255 298
Finland 26 52 48 122 76
Norway 169 47 42 111 66
65 to 71
Sweden 513 242 249 568 410
Total 92,558 69,704 63,130 102,301 76,167
26
Table A3 - Cropland area (1,000 ha) on 2005 for the eight selected crop categories (Source: FAO
dataset).
Latitude Country
Cereals +
Fruits
(exc melons) +
Nuts +
Pulses +
Roots and Tubers +
Sugarcrops +
Vegetables
(inc melons) +
Oil crops +
Greece 1,243 240 41 24 45 41 135 1,191
Italy 3,965 1,219 184 85 71 253 593 1,106
Portugal 387 412 90 34 45 9 83 297
Spain 6,516 1,806 660 565 97 103 395 2,448
Albania 147 28 2 24 10 0 33 24
Bosnia and Herz. 315 43 3 14 41 0 142 5
Bulgaria 1,710 178 11 13 24 1 44 520
Serbia and Mont. 1,931 352 13 52 95 64 136 258
Turkey 13,854 1,074 557 1,277 154 336 1,060 1,862
35 to 45
TFR of Yugoslav 200 46 3 12 13 2 46 8
Austria 792 55 6 43 22 45 13 87
Belgium 320 18 0 1 65 86 74 19
France 9,145 990 28 439 164 378 270 1,584
Germany 6,786 178 4 169 277 420 106 1,085
Ireland 274 2 0 3 12 31 5 3
Luxembourg 28 3 0 0 1 0 0 3
Netherlands 213 21 0 4 156 91 93 6
Switzerland 166 23 2 5 13 18 14 19
Croatia 535 72 3 3 19 29 21 105
Czech Republic 1,604 46 1 35 36 66 15 306
Hungary 2,911 192 5 22 25 62 85 541
Poland 8,264 387 5 119 588 286 227 440
Romania 5,758 380 2 70 285 25 286 912
Slovakia 788 19 4 17 19 33 29 165
45 to 55
Slovenia 95 21 0 2 6 5 4 3
Denmark 1,497 7 0 16 40 47 9 89
United Kingdom 2,895 21 0 219 137 148 132 496
Estonia 280 12 0 4 14 0 3 36
Latvia 468 13 0 2 45 14 14 57
55 to 65
Lithuania 949 33 0 36 74 21 21 86
Finland 1,177 7 0 4 29 31 9 59
Norway 323 5 0 0 14 0 7 5
65 to 71
Sweden 1,016 5 0 25 30 49 23 71
27
Table A4 - Yield (t/ha) on 2005 for the eight selected crop categories (Source: FAO dataset).
Latitude Country
Cereals +
Fruits
(exc melons) +
Nuts +
Pulses +
Roots and Tubers +
Sugarcrops +
Vegetables
(inc melons) +
Oil crops +
Greece 4.1 15.1 2.4 1.8 20.1 63.6 29.1 3.0
Italy 5.4 14.9 1.5 1.9 24.9 55.9 27.0 3.3
Portugal 2.0 4.5 0.7 0.6 13.4 70.2 29.0 0.6
Spain 2.2 8.6 0.4 0.5 26.8 71.4 33.8 1.6
Albania 3.5 7.9 1.2 1.1 16.7 165.8 20.5 1.1
Bosnia and Herz. 4.3 5.3 1.2 1.7 11.1 21.0 5.6 2.2
Bulgaria 3.4 2.1 0.4 1.2 15.6 19.1 12.0 1.5
Serbia and Mont. 4.9 3.3 1.6 2.7 11.6 48.2 9.2 2.2
Turkey 2.6 12.1 1.5 1.2 26.5 45.2 24.8 2.3
35 to 45
TFR of Yugoslav 3.2 9.1 1.7 1.6 14.5 36.4 11.7 1.8
Austria 6.1 18.5 2.8 2.5 34.4 69.0 39.7 2.3
Belgium 8.6 33.3 2.3 3.4 42.8 70.0 32.9 1.4
France 7.0 10.0 1.7 4.0 41.6 82.3 22.4 3.1
Germany 6.7 14.5 4.0 2.4 42.0 60.2 29.7 3.7
Ireland 7.0 14.4 0.0 5.2 34.7 45.0 41.0 3.8
Luxembourg 5.6 7.2 1.8 3.2 31.8 0.0 41.0 3.6
Netherlands 8.3 29.1 0.0 3.8 43.4 65.2 44.5 1.7
Switzerland 6.3 19.1 0.8 3.7 38.8 77.2 22.2 3.2
Croatia 5.6 4.5 3.9 3.1 14.5 45.5 13.4 2.0
Czech Republic 4.7 8.4 4.9 2.5 28.1 53.3 19.6 2.4
Hungary 5.5 6.6 0.8 2.4 25.9 57.0 18.3 2.1
Poland 3.2 7.6 1.8 2.1 17.6 41.6 24.7 2.6
Romania 3.3 5.7 22.9 1.2 13.1 29.2 13.4 1.5
Slovakia 4.5 5.1 0.3 2.1 15.8 52.2 11.8 2.1
45 to 55
Slovenia 6.0 12.5 15.2 2.8 22.9 51.4 24.6 2.6
Denmark 6.2 10.7 1.2 3.3 39.4 58.8 27.7 3.0
United Kingdom 7.2 16.9 0.0 3.6 43.4 58.7 21.1 3.1
Estonia 2.7 1.4 0.0 1.3 15.1 0.0 18.5 1.8
Latvia 2.8 4.1 0.0 1.6 14.6 38.5 12.6 2.0
55 to 65
Lithuania 2.9 3.7 0.0 1.6 12.1 38.1 16.2 1.8
Finland 3.4 2.3 0.0 2.2 25.7 37.9 28.0 1.4
Norway 4.0 5.4 0.0 0.0 23.1 0.0 26.5 1.7
65 to 71
Sweden 4.9 6.3 0.0 2.7 31.1 48.4 14.1 2.3
28
Table A5 – Cereal + production (t) on 2005 and 2050 (Source: 2005 data - FAO dataset; 2050
data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 5,044,447 3,002,842 4,439,562 4,831,054 4,101,528
Italy 21,255,971 22,942,046 22,996,953 25,760,869 21,245,161
Portugal 779,322 487,601 890,875 738,476 742,122
Spain 14,251,846 6,434,496 11,946,148 11,666,387 11,700,083
Albania 507,211 841,525 733,718 680,257 420,722
Bosnia and Herzegovina 1,339,237 2,327,477 2,008,488 1,800,531 1,143,279
Bulgaria 5,793,514 11,717,549 10,167,172 9,232,081 5,834,069
Serbia and Montenegro 9,459,464 16,445,808 14,273,310 12,898,199 8,099,227
Turkey 36,102,256 62,681,762 54,688,677 49,342,575 31,130,096
35 to 45
TFR of Yugoslav 639,607 1,069,689 932,928 839,001 521,347
Austria 4,864,818 7,638,847 7,793,751 9,366,151 7,475,584
Belgium 2,764,826 6,617,013 4,177,512 6,388,125 3,967,729
France 63,730,919 88,706,630 79,827,340 97,860,886 74,615,112
Germany 45,621,294 64,916,294 65,387,024 79,669,794 63,448,351
Ireland 1,925,117 253,699 321,133 272,481 266,318
Luxembourg 159,316 25,939 51,414 40,050 46,653
Netherlands 1,761,320 3,217,507 2,021,857 3,086,582 2,008,815
Switzerland 1,048,253 2,408,068 2,080,775 2,779,618 2,071,451
Croatia 3,003,263 6,680,578 5,826,461 5,343,270 3,283,358
Czech Republic 7,608,004 14,146,527 12,152,172 11,074,844 6,945,350
Hungary 16,085,918 29,317,246 25,170,829 23,177,719 14,511,427
Poland 26,717,757 54,819,895 47,074,960 43,107,919 26,989,602
Romania 19,199,423 34,523,638 29,792,041 26,879,715 16,958,339
Slovakia 3,557,271 7,062,926 6,093,984 5,529,329 3,460,306
45 to 55
Slovenia 571,789 1,026,958 886,871 788,291 498,955
Denmark 9,210,550 16,249,014 9,976,598 15,725,737 9,536,770
United Kingdom 20,833,615 32,300,961 21,683,385 32,854,422 20,993,243
Estonia 754,142 1,969,217 1,701,328 1,534,908 971,627
Latvia 1,312,874 2,052,742 1,781,357 1,598,241 1,008,326
55 to 65
Lithuania 2,789,156 7,554,512 6,488,357 5,945,634 3,757,807
Finland 4,027,219 962,763 1,486,763 1,023,504 1,311,468
Norway 1,288,314 1,025,030 854,532 872,704 758,585
65 to 71
Sweden 5,011,178 6,464,826 6,909,040 6,887,382 6,226,207
Total 339,019,210 517,891,625 462,617,315 499,596,739 356,049,017
29
Table A6 – Fruits + production (t) on 2005 and 2050 (Source: 2005 data - FAO dataset; 2050
data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 3,614,623 2,151,701 3,181,190 3,461,715 2,938,970
Italy 18,133,975 19,572,406 19,619,248 21,977,211 18,124,753
Portugal 1,844,808 1,154,247 2,108,878 1,748,120 1,756,749
Spain 15,536,631 7,014,557 13,023,078 12,718,097 12,754,830
Albania 218,490 362,502 316,062 293,033 181,233
Bosnia and Herzegovina 227,443 395,276 341,102 305,785 194,164
Bulgaria 369,124 746,565 647,785 588,207 371,708
Serbia and Montenegro 1,162,487 2,021,050 1,754,068 1,585,079 995,326
Turkey 12,997,760 22,567,079 19,689,359 17,764,622 11,207,652
35 to 45
TFR of Yugoslav 415,394 694,711 605,892 544,891 338,590
Austria 1,024,542 1,608,759 1,641,382 1,972,533 1,574,376
Belgium 589,623 1,411,135 890,891 1,362,323 846,153
France 9,906,640 13,788,985 12,408,745 15,211,966 11,598,532
Germany 2,577,952 3,668,268 3,694,867 4,501,953 3,585,318
Ireland 22,781 3,002 3,800 3,224 3,151
Luxembourg 24,274 3,952 7,834 6,102 7,108
Netherlands 605,541 1,106,177 695,113 1,061,165 690,630
Switzerland 431,847 992,048 857,213 1,145,114 853,372
Croatia 326,522 726,328 633,467 580,933 356,974
Czech Republic 389,475 724,201 622,104 566,953 355,552
Hungary 1,268,110 2,311,183 1,984,306 1,827,182 1,143,987
Poland 2,920,439 5,992,201 5,145,625 4,712,000 2,950,154
Romania 2,156,667 3,878,033 3,346,534 3,019,393 1,904,927
Slovakia 99,270 197,100 170,060 154,303 96,564
45 to 55
Slovenia 259,975 466,926 403,233 358,412 226,860
Denmark 72,988 128,764 79,058 124,617 75,573
United Kingdom 354,916 550,271 369,393 559,699 357,636
Estonia 16,798 43,863 37,896 34,189 21,642
Latvia 55,039 86,056 74,679 67,002 42,272
55 to 65
Lithuania 123,961 335,752 288,368 264,247 167,012
Finland 16,577 3,963 6,120 4,213 5,398
Norway 26,403 21,007 17,513 17,885 15,547
65 to 71
Sweden 32,573 42,022 44,909 44,768 40,471
Total 77,823,651 94,770,090 94,709,772 98,586,938 75,783,181
30
Table A7 – Nuts production (t) on 2005 and 2050 (Source: 2005 data - FAO dataset; 2050 data –
our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 100,803 60,006 88,716 96,539 81,961
Italy 279,442 301,608 302,330 338,666 279,300
Portugal 61,699 38,603 70,531 58,465 58,754
Spain 263,526 118,978 220,892 215,719 216,342
Albania 2,883 4,783 4,170 3,867 2,391
Bosnia and Herzegovina 3,024 5,255 4,535 4,066 2,582
Bulgaria 4,572 9,247 8,024 7,286 4,604
Serbia and Montenegro 21,766 37,841 32,843 29,678 18,636
Turkey 837,000 1,453,223 1,267,910 1,143,966 721,725
35 to 45
TFR of Yugoslav 5,447 9,110 7,945 7,145 4,440
Austria 17,031 26,742 27,285 32,789 26,171
Belgium 500 1,197 755 1,155 718
France 47,456 66,054 59,442 72,870 55,561
Germany 17,661 25,131 25,313 30,842 24,562
Ireland 0 0 0 0 0
Luxembourg 140 23 45 35 41
Netherlands 0 0 0 0 0
Switzerland 1,483 3,407 2,944 3,932 2,931
Croatia 10,079 22,420 19,554 17,932 11,019
Czech Republic 4,903 9,117 7,832 7,137 4,476
Hungary 4,133 7,533 6,467 5,955 3,728
Poland 9,005 18,477 15,866 14,529 9,097
Romania 47,889 86,112 74,310 67,046 42,299
Slovakia 1,197 2,377 2,051 1,861 1,164
45 to 55
Slovenia 3,109 5,584 4,822 4,286 2,713
Denmark 7 12 8 12 7
United Kingdom 0 0 0 0 0
Estonia 0 0 0 0 0
Latvia 0 0 0 0 0
55 to 65
Lithuania 0 0 0 0 0
Finland 0 0 0 0 0
Norway 0 0 0 0 0
65 to 71
Sweden 0 0 0 0 0
Total 1,744,755 2,312,839 2,254,589 2,165,779 1,575,221
31
Table A8 – Pulses + production (t) on 2005 and 2050 (Source: 2005 data - FAO dataset; 2050
data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 44,224 26,326 38,921 42,353 35,958
Italy 160,639 173,382 173,796 194,684 160,558
Portugal 20,071 12,558 22,944 19,019 19,113
Spain 288,495 130,251 241,822 236,159 236,841
Albania 25,959 43,069 37,552 34,816 21,533
Bosnia and Herzegovina 24,330 42,284 36,488 32,710 20,770
Bulgaria 16,183 32,731 28,400 25,788 16,296
Serbia and Montenegro 140,788 244,768 212,434 191,968 120,543
Turkey 1,565,367 2,717,835 2,371,260 2,139,457 1,349,778
35 to 45
TFR of Yugoslav 19,285 32,253 28,129 25,297 15,719
Austria 107,479 168,766 172,188 206,927 165,159
Belgium 5,078 12,153 7,673 11,733 7,287
France 1,754,077 2,441,488 2,197,102 2,693,442 2,053,645
Germany 405,900 577,571 581,759 708,835 564,510
Ireland 14,000 1,845 2,335 1,982 1,937
Luxembourg 1,489 242 481 374 436
Netherlands 14,703 26,859 16,878 25,766 16,769
Switzerland 17,888 41,093 35,508 47,433 35,348
Croatia 9,753 21,695 18,921 17,352 10,663
Czech Republic 86,031 159,968 137,416 125,234 78,538
Hungary 54,519 99,363 85,310 78,555 49,183
Poland 254,601 522,394 448,590 410,787 257,191
Romania 80,913 145,495 125,555 113,281 71,469
Slovakia 35,045 69,581 60,036 54,473 34,090
45 to 55
Slovenia 5,540 9,950 8,593 7,638 4,834
Denmark 53,000 93,501 57,408 90,490 54,877
United Kingdom 791,403 1,227,010 823,683 1,248,035 797,466
Estonia 5,690 14,858 12,837 11,581 7,331
Latvia 3,540 5,535 4,803 4,309 2,719
55 to 65
Lithuania 58,900 159,532 137,018 125,557 79,355
Finland 8,100 1,936 2,990 2,059 2,638
Norway 0 0 0 0 0
65 to 71
Sweden 66,280 85,506 91,382 91,095 82,350
Total 6,139,271 9,341,798 8,220,211 9,019,189 6,374,904
32
Table A9 – Roots and tubers + production (t) on 2005 and 2050 (Source: 2005 data - FAO data-
set; 2050 data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 895,936 533,330 788,504 858,036 728,466
Italy 1,773,777 1,914,477 1,919,059 2,149,703 1,772,875
Portugal 600,580 375,767 686,548 569,103 571,912
Spain 2,595,018 1,171,612 2,175,190 2,124,250 2,130,386
Albania 169,300 280,890 244,905 227,061 140,431
Bosnia and Herzegovina 458,615 797,033 687,797 616,583 391,510
Bulgaria 375,459 759,377 658,902 598,302 378,087
Serbia and Montenegro 1,102,392 1,916,571 1,663,391 1,503,138 943,872
Turkey 4,090,200 7,101,521 6,195,946 5,590,260 3,526,880
35 to 45
TFR of Yugoslav 186,653 312,161 272,251 244,841 152,142
Austria 763,165 1,198,339 1,222,640 1,469,309 1,172,727
Belgium 2,780,865 6,655,401 4,201,748 6,425,185 3,990,747
France 6,838,112 9,517,921 8,565,203 10,500,141 8,005,949
Germany 11,624,201 16,540,523 16,660,464 20,299,681 16,166,494
Ireland 409,200 53,926 68,260 57,918 56,608
Luxembourg 19,329 3,147 6,238 4,859 5,660
Netherlands 6,777,000 12,379,946 7,779,462 11,876,186 7,729,283
Switzerland 485,000 1,114,152 962,721 1,286,058 958,407
Croatia 273,409 608,182 530,425 486,437 298,908
Czech Republic 1,013,000 1,883,599 1,618,052 1,474,607 924,768
Hungary 656,721 1,196,901 1,027,620 946,250 592,441
Poland 10,377,542 21,292,797 18,284,558 16,743,705 10,483,130
Romania 3,738,594 6,722,592 5,801,235 5,234,134 3,302,201
Slovakia 301,169 597,968 515,935 468,129 292,960
45 to 55
Slovenia 144,714 259,913 224,458 199,508 126,280
Denmark 1,576,400 2,781,044 1,707,510 2,691,484 1,632,233
United Kingdom 5,961,000 9,242,085 6,204,140 9,400,443 6,006,674
Estonia 212,902 555,930 480,302 433,320 274,300
Latvia 658,200 1,029,127 893,071 801,267 505,517
55 to 65
Lithuania 894,700 2,423,323 2,081,323 1,907,230 1,205,422
Finland 742,700 177,553 274,189 188,755 241,861
Norway 316,617 251,912 210,011 214,476 186,430
65 to 71
Sweden 947,300 1,222,094 1,306,067 1,301,973 1,176,986
Total 69,773,572 112,871,112 95,918,124 108,892,333 76,072,549
33
Table A10 – Sugar-crop production (t) on 2005 and 2050 (Source: 2005 data - FAO dataset;
2050 data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 2,596,312 1,545,524 2,284,985 2,486,481 2,111,004
Italy 14,155,683 15,278,547 15,315,112 17,155,777 14,148,484
Portugal 609,129 381,116 696,321 577,204 580,053
Spain 7,334,497 3,311,416 6,147,904 6,003,930 6,021,271
Albania 21,223 35,212 30,701 28,464 17,604
Bosnia and Herzegovina 21 36 31 28 18
Bulgaria 24,731 50,019 43,401 39,409 24,904
Serbia and Montenegro 3,101,176 5,391,568 4,679,339 4,228,525 2,655,238
Turkey 15,181,248 26,358,114 22,996,966 20,748,893 13,090,420
35 to 45
TFR of Yugoslav 57,836 96,726 84,359 75,866 47,142
Austria 3,083,792 4,842,239 4,940,433 5,937,172 4,738,748
Belgium 5,983,173 14,319,433 9,040,273 13,824,112 8,586,295
France 31,149,554 43,356,851 39,016,949 47,831,147 36,469,385
Germany 25,284,702 35,978,574 36,239,467 44,155,410 35,164,996
Ireland 1,395,000 183,838 232,703 197,448 192,983
Luxembourg 0 0 0 0 0
Netherlands 5,931,000 10,834,506 6,808,320 10,393,633 6,764,404
Switzerland 1,409,357 3,237,603 2,797,563 3,737,144 2,785,027
Croatia 1,337,750 2,975,744 2,595,293 2,380,064 1,462,513
Czech Republic 3,495,611 6,499,832 5,583,496 5,088,502 3,191,144
Hungary 3,515,865 6,407,809 5,501,535 5,065,905 3,171,732
Poland 11,912,444 24,442,131 20,988,955 19,220,201 12,033,649
Romania 729,658 1,312,042 1,132,221 1,021,541 644,487
Slovakia 1,732,612 3,440,084 2,968,149 2,693,127 1,685,384
45 to 55
Slovenia 260,095 467,142 403,420 358,577 226,964
Denmark 2,762,600 4,873,707 2,992,367 4,716,756 2,860,446
United Kingdom 8,687,001 13,468,545 9,041,330 13,699,321 8,753,561
Estonia 0 0 0 0 0
Latvia 519,900 812,888 705,420 632,906 399,299
55 to 65
Lithuania 798,500 2,162,761 1,857,534 1,702,160 1,075,812
Finland 1,181,300 282,406 436,111 300,223 384,692
Norway 0 0 0 0 0
65 to 71
Sweden 2,381,200 3,071,941 3,283,022 3,272,731 2,958,555
Total 156,632,968 235,418,354 208,843,683 237,572,659 172,246,215
34
Table A11 – Vegetables + production (t) on 2005 and 2050 (Source: 2005 data - FAO dataset;
2050 data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 3,938,829 2,344,693 3,466,520 3,772,207 3,202,575
Italy 15,994,285 17,262,990 17,304,306 19,384,044 15,986,151
Portugal 2,419,883 1,514,056 2,766,269 2,293,054 2,304,373
Spain 13,355,750 6,029,922 11,195,025 10,932,855 10,964,431
Albania 685,991 1,138,144 992,336 920,032 569,017
Bosnia and Herzegovina 798,455 1,387,645 1,197,463 1,073,479 681,624
Bulgaria 522,125 1,056,013 916,290 832,017 525,780
Serbia and Montenegro 1,251,848 2,176,408 1,888,904 1,706,924 1,071,837
Turkey 26,290,250 45,645,878 39,825,183 35,932,065 22,669,442
35 to 45
TFR of Yugoslav 541,992 906,436 790,547 710,955 441,781
Austria 511,614 803,348 819,639 985,002 786,178
Belgium 2,419,267 5,789,993 3,655,391 5,589,713 3,471,827
France 6,037,846 8,404,037 7,562,816 9,271,308 7,069,011
Germany 3,157,823 4,493,388 4,525,971 5,514,599 4,391,780
Ireland 209,974 27,671 35,026 29,720 29,048
Luxembourg 983 160 317 247 288
Netherlands 4,149,347 7,579,857 4,763,124 7,271,421 4,732,400
Switzerland 312,702 718,345 620,711 829,181 617,930
Croatia 286,753 637,865 556,313 510,178 313,497
Czech Republic 295,227 548,953 471,563 429,757 269,513
Hungary 1,547,425 2,820,245 2,421,370 2,229,638 1,395,963
Poland 5,620,855 11,532,955 9,903,583 9,069,001 5,678,046
Romania 3,826,612 6,880,862 5,937,814 5,357,362 3,379,945
Slovakia 338,906 672,895 580,582 526,787 329,668
45 to 55
Slovenia 89,076 159,984 138,161 122,804 77,730
Denmark 252,701 445,809 273,718 431,452 261,651
United Kingdom 2,772,139 4,297,995 2,885,211 4,371,639 2,793,380
Estonia 63,521 165,866 143,302 129,285 81,840
Latvia 172,706 270,034 234,334 210,246 132,643
55 to 65
Lithuania 338,042 915,597 786,380 720,603 455,441
Finland 250,532 59,893 92,491 63,672 81,586
Norway 184,121 146,493 122,127 124,724 108,414
65 to 71
Sweden 327,131 422,026 451,024 449,610 406,449
Total 98,968,536 137,256,457 127,323,811 131,795,578 95,281,236
35
Table A12 – Oil crops production (t)(bio-energy excluded) on 2005 and 2050 (Source: 2005 data
- FAO dataset; 2050 data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 3,565,820 2,122,649 3,138,238 3,414,976 2,899,289
Italy 3,653,632 3,943,447 3,952,885 4,427,967 3,651,774
Portugal 184,356 115,347 210,745 174,694 175,556
Spain 3,799,369 1,715,358 3,184,698 3,110,117 3,119,100
Albania 26,220 43,502 37,929 35,165 21,749
Bosnia and Herzegovina 10,537 18,312 15,803 14,166 8,995
Bulgaria 755,987 1,529,005 1,326,698 1,204,680 761,279
Serbia and Montenegro 559,942 973,491 844,892 763,494 479,425
Turkey 4,276,058 7,424,214 6,477,489 5,844,281 3,687,141
35 to 45
TFR of Yugoslav 14,336 23,977 20,911 18,806 11,686
Austria 203,333 319,278 325,753 391,474 312,455
Belgium 26,513 63,453 40,060 61,258 38,048
France 4,855,802 6,758,758 6,082,224 7,456,241 5,685,093
Germany 3,987,661 5,674,196 5,715,341 6,963,768 5,545,886
Ireland 10,986 1,448 1,833 1,555 1,520
Luxembourg 11,376 1,852 3,671 2,860 3,331
Netherlands 9,808 17,916 11,258 17,187 11,186
Switzerland 60,267 138,447 119,630 159,808 119,094
Croatia 213,163 474,168 413,546 379,250 233,043
Czech Republic 739,110 1,374,320 1,180,571 1,075,910 674,734
Hungary 1,153,081 2,101,538 1,804,312 1,661,440 1,040,218
Poland 1,140,066 2,339,204 2,008,722 1,839,445 1,151,665
Romania 1,396,026 2,510,279 2,166,235 1,954,475 1,233,073
Slovakia 350,808 696,527 600,972 545,287 341,246
45 to 55
Slovenia 6,546 11,757 10,153 9,024 5,712
Denmark 266,133 469,505 288,267 454,385 275,559
United Kingdom 1,540,409 2,388,288 1,603,240 2,429,211 1,552,211
Estonia 64,426 168,230 145,344 131,127 83,006
Latvia 113,690 177,759 154,258 138,401 87,317
55 to 65
Lithuania 157,785 427,365 367,052 336,349 212,582
Finland 81,697 19,531 30,161 20,763 26,605
Norway 8,686 6,911 5,761 5,884 5,114
65 to 71
Sweden 165,483 213,486 228,155 227,440 205,606
Total 33,409,109 44,263,515 42,516,806 45,270,888 33,660,295
36
Table A13 - Cropland area for bio-energy (1,000 ha) on 2005 and 2050 (Source: 2005 data - EEA
Technical - report No 12/2007; cropland area on 2050 - our projections based on ATEAM and
IMAGE 2.2 model).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 358 526 477 249 435
Italy 355 2,569 2,520 1,520 1,209
Portugal 90 758 603 589 277
Spain 767 1,686 1,247 911 962
Albania 8 168 146 105 85
Bosnia and Herzegovina 4 313 273 197 159
Bulgaria 166 665 580 417 338
Serbia and Montenegro 91 624 544 392 317
Turkey 654 4,710 4,108 2,957 2,392
35 to 45
TFR of Yugoslav 4 155 136 98 79
Austria 32 457 444 217 189
Belgium 8 289 429 66 309
France 535 6,101 6,541 3,488 4,273
Germany 371 4,309 4,057 1,908 1,919
Ireland 3 888 921 30 1,562
Luxembourg 1 39 33 4 45
Netherlands 3 217 403 43 353
Switzerland 7 143 180 19 100
Croatia 35 534 599 141 550
Czech Republic 102 738 828 195 760
Hungary 181 856 960 226 881
Poland 194 2,926 3,282 774 3,013
Romania 312 2,197 2,464 581 2,262
Slovakia 55 459 515 122 473
45 to 55
Slovenia 1 192 215 51 198
Denmark 38 392 533 86 177
United Kingdom 168 1,775 2,426 234 1,982
Estonia 13 351 479 64 262
Latvia 20 516 704 93 385
55 to 65
Lithuania 33 520 708 94 388
Finland 27 1,210 923 151 1,279
Norway 4 1,703 1,717 51 2,822
65 to 71
Sweden 29 2,003 1,642 505 1,343
Total 4,668 41,474 42,061 16,641 32,339
37
Table A14 – Oils crop for biodiesel production (t) on 2005 and 2050 (Source: 2005 data - EEA
Technical - report No 12/2007; 2050 data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 1,043,292 2,663,088 2,161,313 1,189,159 1,419,990
Italy 1,068,984 13,512,377 12,816,176 7,068,330 4,357,778
Portugal 53,939 712,621 527,009 486,488 166,331
Spain 1,111,624 4,238,045 2,912,572 2,115,436 1,630,110
Albania 7,671 280,092 240,089 155,641 98,118
Bosnia and Herzegovina 3,083 1,090,007 924,747 579,645 375,161
Bulgaria 221,187 1,546,916 1,319,593 837,813 539,663
Serbia and Montenegro 163,829 2,197,404 1,874,947 1,184,680 758,262
Turkey 1,251,094 17,223,540 14,773,670 9,320,096 5,993,530
35 to 45
TFR of Yugoslav 4,195 444,752 381,346 239,795 151,883
Austria 59,491 1,772,204 1,638,327 728,608 487,496
Belgium 7,757 646,340 924,663 129,050 470,690
France 1,420,717 29,793,704 31,057,732 15,243,317 14,411,011
Germany 1,166,715 26,054,602 23,746,706 10,056,465 7,941,856
Ireland 3,214 5,400,686 5,474,385 162,717 6,594,311
Luxembourg 3,328 229,891 190,300 19,134 180,927
Netherlands 2,870 597,447 1,063,922 104,660 654,923
Switzerland 17,633 757,779 903,850 87,196 350,926
Croatia 62,368 1,682,511 1,855,159 389,748 1,174,630
Czech Republic 216,250 2,962,588 3,217,419 671,726 2,066,116
Hungary 337,370 2,986,249 3,241,401 683,765 2,099,675
Poland 333,562 12,464,383 13,531,781 2,838,723 8,717,022
Romania 408,451 5,416,937 5,909,761 1,221,505 3,779,720
Slovakia 102,640 1,562,806 1,704,724 354,345 1,087,612
45 to 55
Slovenia 1,915 826,167 902,004 183,669 570,185
Denmark 77,865 1,911,410 2,515,438 368,106 590,417
United Kingdom 450,695 8,835,003 11,678,074 1,042,730 6,720,000
Estonia 18,850 1,051,059 1,395,057 162,192 533,860
Latvia 33,263 1,661,166 2,214,627 256,055 839,990
55 to 65
Lithuania 46,165 1,577,179 2,081,037 245,745 807,613
Finland 23,903 2,968,012 2,163,072 312,753 2,112,998
Norway 2,541 5,202,472 5,016,294 129,032 5,747,883
65 to 71
Sweden 48,417 8,156,854 6,414,825 1,724,267 3,681,575
Total 9,774,878 168,426,293 166,772,021 60,292,590 87,112,262
38
Table A15 – Cereals for ethanol production (t) on 2005 and 2050 (Source: 2005 data - EEA
Technical - report No 12/2007; 2050 data – our projection).
Latitude Country 2005 2050
A1FI
2050
A2
2050
B1
2050
B2
Greece 39,682 492,262 399,510 219,811 262,480
Italy 167,208 2,991,548 2,837,414 1,564,880 964,782
Portugal 6,130 315,193 233,097 215,174 73,568
Spain 112,111 814,325 559,640 406,474 313,220
Albania 3,990 121,215 103,903 67,357 42,463
Bosnia and Herzegovina 10,535 292,656 248,286 155,629 100,727
Bulgaria 45,574 491,626 419,381 266,266 171,511
Serbia and Montenegro 74,412 675,306 576,209 364,076 233,029
Turkey 283,995 2,665,690 2,286,523 1,442,473 927,620
35 to 45
TFR of Yugoslav 5,031 105,816 90,730 57,053 36,136
Austria 38,269 633,068 585,245 260,274 174,144
Belgium 21,749 554,440 793,189 110,701 403,764
France 501,333 9,234,565 9,626,351 4,724,670 4,466,696
Germany 358,876 6,500,545 5,924,731 2,509,058 1,981,469
Ireland 15,144 1,346,171 1,364,541 40,559 1,643,693
Luxembourg 1,253 48,786 40,384 4,061 38,395
Netherlands 13,855 396,281 705,689 69,420 434,404
Switzerland 8,246 202,682 241,751 23,322 93,862
Croatia 23,625 636,787 702,130 147,510 444,567
Czech Republic 59,848 794,381 862,710 180,115 554,003
Hungary 126,538 1,055,732 1,145,936 241,732 742,301
Poland 210,173 2,122,654 2,304,430 483,428 1,484,488
Romania 151,030 1,608,593 1,754,940 362,733 1,122,411
Slovakia 27,983 452,736 493,849 102,652 315,075
45 to 55
Slovenia 4,498 261,247 285,228 58,079 180,301
Denmark 72,454 538,196 708,272 103,648 166,244
United Kingdom 163,886 2,789,738 3,687,465 329,252 2,121,905
Estonia 5,932 217,069 288,113 33,496 110,255
Latvia 10,328 317,883 423,794 48,999 160,742
55 to 65
Lithuania 21,941 344,773 454,917 53,720 176,545
Finland 31,680 1,003,597 731,416 105,753 714,484
Norway 10,134 1,688,843 1,628,405 41,887 1,865,896
65 to 71
Sweden 39,420 2,359,802 1,855,828 498,835 1,065,091
Total 2,666,862 44,074,205 44,364,007 15,293,094 23,586,269
39
Table A16 – Example of the database built for the biodiversity estimation approach
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SD 122.2010 Emiliya Lazarova and Dinko Dimitrov (lxxxix): Status-Seeking in Hedonic Games with Heterogeneous
Players
SD 123.2010 Maria Montero (lxxxix): The Paradox of New Members in the EU Council of Ministers: A Non-cooperative
Bargaining Analysis
SD 124.2010 Leonardo Boncinelli and Paolo Pin (lxxxix): Stochastic Stability in the Best Shot Game
SD 125.2010 Nicolas Quérou (lxxxix): Group Bargaining and Conflict
SD 126.2010 Emily Tanimura (lxxxix): Diffusion of Innovations on Community Based Small Worlds: the Role of
Correlation between Social Spheres
SD 127.2010 Alessandro Tavoni, Maja Schlüter and Simon Levin (lxxxix): The Survival of the Conformist: Social Pressure
and Renewable Resource Management
SD 128.2010 Norma Olaizola and Federico Valenciano (lxxxix): Information, Stability and Dynamics in Networks under
Institutional Constraints
GC 129.2010 Darwin Cortés, Guido Friebel and Darío Maldonado (lxxxvii): Crime and Education in a Model of
Information Transmission
IM 130.2010 Rosella Levaggi, Michele Moretto and Paolo Pertile: Static and Dynamic Efficiency of Irreversible Health
Care Investments under Alternative Payment Rules
SD 131.2010 Robert N. Stavins: The Problem of the Commons: Still Unsettled after 100 Years
SD 132.2010 Louis-Gaëtan Giraudet and Dominique Finon: On the Road to a Unified Market for Energy Efficiency: The
Contribution of White Certificates Schemes
SD 133.2010 Melina Barrio and Maria Loureiro: The Impact of Protest Responses in Choice Experiments
IM 134.2010 Vincenzo Denicolò and Christine Halmenschlager: Optimal Patentability Requirements with Fragmented
Property Rights
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Opportunity or a Risk?
SD 136.2010 Carlo Carraro and Emanuele Massetti: Beyond Copenhagen: A Realistic Climate Policy in a Fragmented
World
SD 137.2010 Valentin Przyluski and Stéphane Hallegatte: Climate Change Adaptation, Development, and International
Financial Support: Lessons from EU Pre-Accession and Solidarity Funds
SD 138.2010 Ruslana Rachel Palatnik and Paulo A.L.D. Nunes:
V
aluation of Linkages between Climate Change,
Biodiversity and Productivity of European Agro-Ecosystems
(lxxxvi) This paper was presented at the Conference on "Urban and Regional Economics" organised by the
Centre for Economic Policy Research (CEPR) and FEEM, held in Milan on 12-13 October 2009.
(lxxxvii) This paper was presented at the Conference on “Economics of Culture, Institutions and Crime”
organised by SUS.DIV, FEEM, University of Padua and CEPR, held in Milan on 20-22 January 2010.
(lxxxviii) This paper was presented at the International Workshop on “The Social Dimension of Adaptation to
Climate Change”, jointly organized by the International Center for Climate Governance, Centro Euro-
Mediterraneo per i Cambiamenti Climatici and Fondazione Eni Enrico Mattei, held in Venice, 18-19 February
2010.
(lxxxix) This paper was presented at the 15th Coalition Theory Network Workshop organised by the
Groupement de Recherche en Economie Quantitative d’Aix-Marseille, (GREQAM), held in Marseille, France, on
June 17-18, 2010.
