Volante-Project - Deliverable No: 12.3 Value-based …...ecosystem service provision, and for the further design of the Volante Roadmap. D12.3Trade-off evaluation of future ESS 4 1

EC Contract Ref: FP7-ENV-2010-265104

Deliverable No: 12.3

Value-based trade-off evaluation of future ecosystem service supply under selected land

use scenario

Due date of deliverable: 31 July 2014 Actual submission date: 20 March 2015 Version: Final Main Authors: Bernhard Wolfslehner, Patrick Huber Contributing authors Hans Verkerk Reviewers: Sandra Lavorel Dissemination: PU Keywords: trade-off, multi-criteria analysis, decision support

D12.3Trade-off evaluation of future ESS 2

Table of Contents Abstract ......................................................................................................................... 3 1. Introduction .......................................................................................................... 4 2 Methods and data ................................................................................................. 5

2.1 General description ....................................................................................... 5

2.2 Data ................................................................................................................. 5

2.2.1 Policy alternatives ...................................................................................... 6

2.2.2 Model results .............................................................................................. 7

2.3 Multi-criteria analysis (MCA) ...................................................................... 8

2.3.1 SMART (Simple Multi-Attribute Rating Technique) ............................ 9

2.3.2 Linking consolidated stakeholder visions to weighting scenarios ....... 10

3 Results .................................................................................................................. 14 3.1 Regional patterns within Europe ............................................................... 14

3.1.1 Land cover extent .................................................................................... 14

3.1.2 Land use management intensity ............................................................. 17

3.1.3 Land use pattern ...................................................................................... 20

3.1.4 Land use services ..................................................................................... 22

3.1.5 Global land impacts ................................................................................. 25

3.2 World regions .............................................................................................. 27

3.3 MCA of NUTSII regions in Europe ........................................................... 30

4 Discussion ............................................................................................................ 35 4.1 Trade-off interpretation ............................................................................. 35

4.1.1 Europe ....................................................................................................... 35

4.1.2 Global ........................................................................................................ 36

4.1.3 Spatial MCA ............................................................................................. 36

5 Outlook ................................................................................................................ 37 References ................................................................................................................... 38 Annex I: World regions by MAgPIE ....................................................................... 40 Annex II: Database outputs ...................................................................................... 42 Annex III: Outputs Trade-off analysis (national) ................................................... 47


Abstract This deliverable addresses the trade-offs that can be related to pathways leading to the three Volante visions, regional (CSVa), European (CSVb), or local (CSVc). It builds on Volante deliverable 11.3 where feasible pathways have been identified. The trade-offs are dealt with on NUTS II level, and on their global impacts respectively. They are represented in absolute terms, indicating the expected changes in indicators for sustainable land-use, and in synthesized relative terms by means of a spatial multi-criteria analysis MCA) to analyse the regional patterns of trade-offs. This includes generic weighting of building block components as translated from the implicit statements given in the vision development. The results of the value-based trade-off analysis will be further used in Volante deliverable 12.4 to identify hotspots of trade-offs across Europe including the ecosystem service provision, and for the further design of the Volante Roadmap.


1. Introduction The world we live in is affected by an ever increasing rate of change with a predominant driving force that shapes the environment on a global scale – human activity. Related consequences range from anthropogenic impacts on climate change (e.g. Myhre et al, 2013; Mertz, 2010) to the exploitation of natural resources (e.g. Ascher, 1999; Hardin, 1968) as well as to various socio-economic pressures (e.g. Meadows et al, 2004). However, awareness about risks and uncertainties that relate to numerous effects of human behavior is continuously rising and influencing the political agenda around the globe. Science has a prominent role in tackling the challenges of global change and attempts to provide decision makers with arguments that intend to pinpoint possible courses of action. Trade-off analysis tries to capture and synthesize both available scientific-empirical and value information and make explicit sources and mechanisms of potential conflicts at different scales. There are various approaches to trade-off analysis of ecosystem services (Mouchet et al. 2014). Classical approaches have often been implemented through linear programming e.g., in biophysical and economic land-use analysis (Bouman et al. 1999). To integrate stakeholder perceptions and interests in a participatory process, multi-criteria analysis (MCA) methods have been successfully used (e.g. Brown et al. 2001; Janssen et al., 2007; Strager and Rosenberger, 2006). The integration of spatial aspects into MCA has been demonstrated inter alia in vulnerability assessment (Tran et al. 2004) and land conservation (Strager & Rosenberger 2006). Spatial MCA provides a broader portfolio of analyses that view trade-offs not only amongst land-use patterns at different geographical scales and different time horizons from scenarios, but also amongst stakeholder interests and objectives (Polasky et al. 2011). In this respect, it is important that both a science-based quantification and a value-based evaluation are made identifying, consolidating and considering also the subjective choices and tacit knowledge of stakeholders (Rounsevell et al., 2012). With respect to future ecosystem service (ES) supply VOLANTE aims to identify implications of a set of land use scenarios derived from prior project activities (i.a. stakeholder workshops, scenario development, modelling results, pathway analysis). To evaluate impacts of those scenarios for Europe and highlight relationships amongst ES the concept of trade-offs is applied to shed light on the complex connection of environmental effects triggered by human decisions. A trade-off occurs in case of an exchange where one thing is given up in order to get something else. It thus refers to a certain situation that involves losing one quality or aspect of something in return for gaining another quality or aspect. In contrast, synergies refer to situations where both aspects either increase or decrease (Briner et al, 2013). The value-based trade-off analysis targets the designation of possible winners and losers across Europe in the future (i.e. the year 2040) and refers to specific rationales of policy alternatives applied (Paterson et al, 2012).


This deliverable thus explains the analytical set up built upon recent project achievements and describes trade-off results on a defined spatial scale (e.g. NUTS II regions). As an asset for the support of decision-making processes, a multi-criteria analysis (MCA) is applied to estimate the performance of VOLANTE pathways with respect to individual belief systems that underlie both, the VOLANTE scenarios (cf. Paterson et al, 2012) and consolidated stakeholder visions (CSV) (cf. Verkerk et al, 2014).

2 Methods and data 2.1 General description The main objective of the VOLANTE trade-off analysis is the identification of potential hot or cold spots (or winners and losers) in Europe by the application of a set of selected VOLANTE policy scenarios (VPS) at a defined spatial scale. For this purpose the demonstration of changes for a set of indicators from the current state to a specified point in time (i.e. the year 2040) is core to the evaluation that is used as an approach to deliberate the “cost” and “benefit” of a certain choice. 2.2 Data This analysis builds on recent project achievements and utilizes an array of selected upproject results to grasp the dimensions of future land use change (LUC) for a defined set of EU regions (i.e. NUTS II) on the one hand, and for a subset of indicators for clustered world regions on the other (as defined by REMIND/MagPie, see Annex I). It particularly builds on the outcomes of D 11.3, where a selection of feasible pathways to the visions regional (CSVa), European (CSVb), or local (CSVc) has been identified (Verkerk et al, 2014): CSVa relates to to the stakeholder vision “Regional Connected”, which aims at a regional supply of goods and services based on green and blue connectivity, the results of the trade-off analysis pinpoint at potential hot and cold spots of ecosystem service supply in Europe and highlight effects of distinct policy alternatives. CSVb relates to the stakeholder vision “Best Land in Europe”, that targets at maximising the value of existing land by using the most suitable locations in the EU for the production of goods and services, the results of the trade-off analysis pinpoint at potential hot and cold spots of ecosystem service supply in Europe and highlight effects of distinct policy alternatives. CSVc relates to stakeholder vision “Local Multifunctional”, that intends to foster self-sufficiency by securing a high degree of local supply of goods and services, the results of the trade-off analysis pinpoint at potential hot and cold spots of ecosystem service supply in Europe and highlight effects of distinct policy alternatives. In this set-up, we identified five pathways to CSVa (the B1 marker scenario and the policy alternatives A2 Nature Protection, B2 Nature Protection, B2 Payments for Carbon Sequestration and B2 Payments for Recreational services), two pathways to CSVb (policy alternatives B2 Nature Protection and B2 Payments for Carbon Sequestration), but no pathway to CSVc (Verkerk et al., 2014)


2.2.1 Policy alternatives Within VOLANTE a set of 4 marker scenarios and 11 policy alternatives, the VPS, was applied to estimate potential impacts on the future land use in the EU (Paterson et al, 2012). Both vision and model experts spent huge efforts to trace pathways of model projections that correlate to consolidated stakeholder visions (e.g. Verburg et al, 2013; Verkerk et al, 2014). Hence the value-based trade-off analysis puts emphasis on the 5 identified pathways at the European level and concentrates on the 4 marker scenarios for worldwide effects (see Table 1).

Table 1: Short description of policy alternatives that have been applied for the trade-off analysis (highlighting VOLANTE pathways in green)

Code Policy alternative Description of policy alternative

A1 Marker scenarios Globalised world with strong economic growth and weak intervention

A2 Fragmented world with modest economic growth and weak intervention

B1 Globalised world with modest economic growth and strong intervention

B2 Fragmented world with modest economic growth and strong intervention

A2NP Nature protection A focus on nature protection, with expansion of protected zones beyond Natura2000, a robust ecological corridor network and strengthened constraints on land cover conversions and restrictions on forest management.

B2NP

B2PC Payment for carbon sequestration

Incentives to (i) limit the conversion of grassland and Payment for Ecosystem Services (PES) scheme to protect areas that are prone to carbon emissions due to their high soil organic carbon contents and (ii) to stimulate carbon sequestration in forest biomass.

B2PR Payment for recreational services

Direct payments to landowners (farmers and forest owners) in exchange for managing their land to provide recreational services.


2.2.2 Model results

Based on model variables that could be linked to CSVs and respective data availability, results for 17 out of 20 building block components (see Annex II) could be used to feed into the trade-off analysis for the EU.

The following components are subject to the trade-off analysis:

• Land cover extent (i.e. the area covered by a land cover type) o Extent of arable land o Extent of forest area o Extent of (semi-)natural area and forest o Extent of urban area

• Land use management intensity (i.e. the intensity by which land is managed) o Arable crop yield o Stocking density of ruminants o Stocking density of pigs o Stocking density of poultry o Roundwood removals

• Land use pattern (i.e. the spatial configuration of different land cover and land use types)

o Connectivity index of semi-natural area and forest o Shannon index for crop diversity

• Land use services (i.e. benefits provided to society by land use) o Shadow value of agricultural land (rural viability) o Self-sufficiency (production over consumption for softwheat) o Global Warming Potential in agriculture o Deadwood in forest o Carbon sequestration in forest biomass

• Global land impacts (i.e. indirect effects of land use in Europe on land use outside Europe)

o Net-trade of agri-food products

Due to data availability the following three indicators are missing (cf. Verkerk et al, 2014):

• Forestry yield (i.e. extracted logging residue and stumps) • Urban development (i.e. growth of peri-urban area) • Wilderness (i.e. contribution of abandoned agricultural land to wilderness)

Data was provided by modelling experts who queried the VOLANTE database for the relevant variables (i.e. building block components, national and global indicators) and delivered the results in form of a csv.file incorporating respective model projections (i.e. values) for different points in time (see Annex II). To mirror the “current state” for a single building block component a distinct reference year had to be defined. For the following variables it is the year 2000 : i) extent of forest area, ii) extent of semi-


natural area, iii) extent of urban area, and iv) connectivity index for semi-natural area and forest, whereas for all others it is the year 2010. As “future projection” data for the year 2040 could be used for all components.

For the global analysis modellers delivered a dataset from the models REMIND and MAgPIE (see Annex II). With respect to data availability the trade-off analysis for world regions is based upon database values for the 4 VOLANTE marker scenarios, as highlighted in Table 1 (chapter 2.2.1). 2.3 Multi-criteria analysis (MCA) Multi-criteria analysis (MCA) techniques have been applied in various fields of indicator based assessments and are used to: i) represent a higher degree of complexity underlying an indicator framework, ii) enhance analytical features to facilitate understanding of the indicator system, iii) create aggregated priority or utility values for management alternatives, and iv) analyse the trade-offs between indicators (Wolfslehner, 2007). They can be applied for multi-indicator evaluation to support justifiable and explainable decisions in order to foster rational and transparent decision-making processes (cf. Wolfslehner et al, 2012). Methods for multi-criteria decision making can be distinguished in multi-objective decision making (MODM) and multi-attribute decision making (MADM). While MODM works with decision variables that are functionally related to objectives and constraints to find an optimal solution among a potentially infinite number of solutions, MADM techniques are proposed for the selection and evaluation of a finite number of alternatives (Wolfslehner, 2007). According to Triantaphyllou et al (1998) the following features are common to all multi-attribute approaches:

• a finite set of alternatives is to be defined, compared and ranked • each decision problem is associated with multiple attributes which refer to the

objectives of the problem • different attributes may be conflicting with regard to the objective • different attributes are defined at incommensurable scale of units • a multi-attributive decision problem can be expressed in a decision matrix in

the format “alternatives x attributes”

In VOLANTE the Simple Multi-Attribute Rating Technique is applied to analyse the trade-off results for the VOLANTE pathways.


2.3.1 SMART (Simple Multi-Attribute Rating Technique) Based on a linear additive model the Simple Multi-Attribute Rating Technique (SMART) calculates an overall value of a given alternative as the total sum of the score (i.e. value) of each criterion (i.e. attribute) multiplied with the weight of that criterion. Adopted from Olson (1996) the main stages of the analysis cover:

1. identify the decision-maker 2. identify the issue of the evaluation 3. identify the alternatives 4. identify the criteria 5. rank the criteria in order of importance 6. determine the alternative ratings under each criterion 7. determine the weight of each of the criteria 8. calculate utility values 9. decide

Tasks 1 to 4 have already been accomplished within the VOLANTE project prior to the evaluation. A decision-maker can be interpreted as a (group of) person or organization whose utilities are to be maximized. The issue of the evaluation is defined by the VOLANTE project itself and refers to the determination of a potentially best policy alternative for land use in Europe. Stakeholders involved in the workshops supported the identification of the alternatives (i.e. VPS) as well as the criteria (i.e. building block components that could be linked to stakeholder arguments). Ranking is achieved by weighting and is based upon the consolidated stakeholder visions (cf. Verkerk et al, 2014) as explained in chapter 2.3.2.

To determine alternative-dependent ratings for each criterion it is necessary to calculate ratios amongst alternatives. The maximum or minimum alternative (i.e. value) for each criterion is assigned a rating of 1, according to stakeholders´ preferences derived from the desired change of the respective variable (cf. Verkerk et al, 2014). All other ratings are derived by a simple mathematical division:

1. if a variable is desired to increase, i.e. r = xi(n)/xi(max) 2. if a variable is desired to decrease, i.e. r = xi(min)/xi(n)

x…represents the value for a ceratin criterion

i…indicates the alternative (i.e. VPS)

As already mentioned, the weight for each criterion is assigned by interpretation of the CSVs (see chapter 2.3.2). Once the weights are normalized (i.e. each weight has to be divided by the sum of all weights), the utility value (U) of each alternative i (i.e. VPS) for a single NUTS II region can then be calculated as:

Ui = ∑xi wxi rxi wxi…weight for a single criterion (xi)

rxi…alternative-rating for a single criterion (xi)

Finally, it is up to the decision maker to select a single alternative according to the maximum utility value.


2.3.2 Linking consolidated stakeholder visions to weighting scenarios

Since MCA tools heavily build on stakeholder engagement, one of the aims of this exercise was to integrate the three Consolidated Stakeholder Visions (CSVs) into the application that provides users with the opportunity to analyse a predefined set of policy alternatives for the entire set of NUTS II regions in Europe covered within VOLANTE. With regard to individual beliefs this can be achieved by assigning a certain weight to each building block component and thus shaping the influence of each indicator on the overall result. Based upon the statements provided by stakeholders in the workshops, vision experts determined both the desired direction of change and whether a strong change was desired for each of the building block components (Verkerk et al, 2014). This approach is used as a basis for the development of three different weighting scenarios applied to the MCA (see Table 2).

Table 2: Interpreting the CSVs for the definition of MCA weighting scenarios according to Verkerk et al (2014)

Desired change Degree of change Product Weight interpretation

-1 1 -1 0,5 -1 2 -2 1 0 1 0 0,25 0 2 0 0,25

+1 1 1 0,5 +1 2 2 1

As desired change (d) indicates whether a component should increase (+1), remain unchanged (0) or decrease (-1) over time, and the intensity of change (w) reports whether stakeholders desire a strong change (2) or not (1), the interpretation of both variables for the final weights is achieved via multiplication of both parameters, resulting in:

• 0,25 if d*w = 0

• 0,5 if d*w = -1 or 1

• 1 if d*w = -2 or 2

If stakeholders desired a component to remain unchanged in the future it can be assumed that there is no influence of the assigned intensity of change, thus both cases result in equal weights. This approach generates the following weighting schemes for the visions (Tables 3-5).


Table 3: Building blocks, their components and relative weights for the spatial MCA, weights derived from interpretation of pathway analysis parameters for vision CSVa

Building block Building block component Weight La

nd c

over

ext

ent Extent of agricultural land

5%

Extent of forests 5%

Extent of natural land 10%

Extent of urban land 2,5%

Land

use

man

agem

ent i

nten

sity

Arable crop yield 5%

Livestock yield(ruminants) 5%

Livestock yield (pigs) 5%

Livestock yield (poultry) 5%

Forestry yield(wood removals) 5%

Forestry yield (residues) 5%

Land

use

pat

tern

Connectivity of natural area 10%

Urban development 2,5%

Crop diversity 5%

Wilderness 2,5%

Land

use

serv

ices

Rural viability/income possibilities from agriculture 5%

Self sufficiency 5%

Emissions to the environment 2,5%

Biodiversity 10%

Carbon sequestration 5%

Glo

bal l

and

impa

cts

Trade of food products 2,5%


Table 4: Building blocks, their components and relative weights for the spatial MCA, weights derived from interpretation of pathway analysis parameters or vision CSVb

Building block Building block component Weight

Land

cov

er e

xten

t Extent of agricultural land 2%



Extent of urban land 2%

Land

use

man

agem

ent i

nten

sity

Arable crop yield 8%

Livestock yield(ruminants) 8%

Livestock yield (pigs) 8%

Livestock yield (poultry) 8%


Forestry yield (residues) 2%

Land

use

pat

tern


Urban development 8%

Crop diversity 4%

Wilderness 8%

Land

use

serv

ices


Self sufficiency 8%

Emissions to the environment 2%

Biodiversity 8%


Glo

bal l

and

impa

cts

Trade of food products 8%


Table 1: Building blocks, their components and relative weights for the spatial MCA, weights derived from interpretation of pathway analysis parameters or vision CSVc

Building block Building block component Weight

Land

cov

er e

xten

t Extent of agricultural land 3,5%



Extent of urban land 2%

Land

use

man

agem

ent i

nten

sity

Arable crop yield 3,5%

Livestock yield(ruminants) 3,5%

Livestock yield (pigs) 3,5%

Livestock yield (poultry) 3,5%


Forestry yield (residues) 3,5%

Land

use

pat

tern


Urban development 7%

Crop diversity 7%

Wilderness 3,5%

Land

use

serv

ices


Self sufficiency 3,5%

Emissions to the environment 3,5%

Biodiversity 7%


Glo

bal l

and

impa

cts

Trade of food products 2%


3 Results The outcomes of the evaluation at the EU level are structured following Verkerk et al (2014) and thus provide insights into the five scenarios that have been identified as VOLANTE pathways to CSVs, highlighting results for building blocks and their respective components. Further indicators, where data is available on a national level only due to model restrictions, are attached to this deliverable (see Annex III) and aim to complement the European picture. In addition, a set of indicators depict global impacts for the four VOLANTE marker scenarios. Finally, the MCA of the VOLANTE pathways provides an assessment of potentially best alternatives for NUTS II regions in Europe. 3.1 Regional patterns within Europe The following subchapters provide an overview of trade-off analysis results, based upon the modelling outputs for representable building block components with regard to all European NUTS II regions covered in the model outputs. For the sake of clarity, the outputs are displayed in form of maps that were built in Esri´s ArcGIS 10. For each map a harmonised color scheme applied to all NUTS II outputs shows the direction of change (i.e. the deviation from the current state in the year 2040) in percent, categorized in 12 classes (see Figure 1).

Figure 1: Harmonised colour scheme for GIS maps of NUTS II regional results, indicating the change in percent.

For those regions where no data is available, the colour is left aside and appears white. 3.1.1 Land cover extent This building block consists of four building block components that mirror the area covered by a certain land cover type and includes:

• the extent of arable land (arable land) • the extent of forest area (Forest area) • the extent of semi-natural area (semi-natural area) and • the extent of urban area (urban area)

Figure 2 highlights the results for all pathways in a spatially explicit way.



Figure 2: Trade-offs for NUTSII regions according to the VOLANTE building block "Land cover extent"

At first glance it can be denoted that none of the building block components is very sensitive to the VPS applied. Although the degree of change differs amongst the scenarios within each indicator, the overall regional patterns that are provoked by the policy targets that underlie the respective alternative remain nearly unchanged.

While the extent of arable land remains pretty stable throughout Europe, apart from a few regions where it decreases (particularly in Northern and South-Eastern Europe), the forest area unexpectedly tends to increase in general, showing highest increment ratios in the Mediterranean regions. The most diverging trends on a continental scale can be observed with regard to the change in (semi-)natural area. Severe losses in Scandinavian countries as well as a substantial decline across the Mediterranean’s are accompanied by positive trends in Central- and Eastern Europe. Additionally, this indicator shows most significant results in differing responses to the underlying VPS. With respect to all built-up area it can be recognized that the extent of urban area will steadily increase, depicting several hot spots of agglomeration, i.e. within the Iberian Peninsula and Ireland. 3.1.2 Land use management intensity Targeting at both agricultural and forestry yields the following building block components are used to mirror changes of land use management intensity across Europe:

• Crop yield (arable crop yield) • Stocking density of ruminants (ruminants) • Stocking density of pigs (pigs) • Stocking density of poultry (poultry) and • Roundwood removals (wood removals)

The outputs of the analysis are shown in Figure 3.



Figure 3: Trade-offs for NUTSII regions according to the VOLANTE building block "Land use management intensity"

Again it can be stated that the results show only limited sensitivity to the VPS applied, except for wood removals. Detected regional trends remain nearly unchanged with regard to policy alternatives pinpointing at continuous trends of agricultural intensification. Average yields per ha of arable crops generally increase across the EU, depicting only a few areas where trends are opposite. Livestock yields, expressed as stocking densities for selected livestock species, show contrasting results that indicate a shift in the production (i.e. both for sites as well as for products). For instance, stocking densities of pigs are most likely decreasing on the Iberian Peninsula while at the same time stocking densities for ruminants and particularly for poultry tend to increase. Similar results can be observed for several regions in other Mediterranean, Eastern and Northern European countries. With regard to wood mobilization it becomes obvious that policy targets shape the amount of wood extracted from production forests. Nevertheless there are certain hot spots like Ireland, UK or Italy where, regardless to the VPS, roundwood removals tend to increase in general. In contrast there are some regions in Spain, Portugal, Scandinavia and Central Europe particularly, where wood removals will decrease for all scenarios. 3.1.3 Land use pattern Due to a lack of data, the analysis of land use patterns highlights results for only two out of four indicators that constitute this building block (see Figure 4), i.e.:

• Connectivity index of semi-natural area and forest (connectivity) and • Shannon index for crop diversity (crop diversity)


Figure 4: Trade-offs for NUTSII regions according to the VOLANTE building block "Land use pattern"

Obviously there is again a lack of sensitivity to scenarios for those indicators, as regional responses are quite homogeneous across all VPS. While the connectivity potential of the landscape shows diverging responses, the trends with regard to the diversity index for agricultural crops are quite homogeneous throughout the EU. This gives clear indication that crop diversity is about to decline in the future. For the approximation of the connectivity potential of the landscape, i.e. the ease to migrate from smaller sized habitats to larger areas of natural vegetation, a general trend of aggravation has to be recognized with only a limited number of regions where the potential may increase substantially. 3.1.4 Land use services Land use services refer to good, services, and values created by land use. The building block concept refers to a set of indicators that relate to agricultural and forest uses and includes:

• Shadow value of agricultural land (rural viability) • Production over consumption for soft wheat (self-sufficiency) • Global Warming Potential in agriculture (GHG emissions) • Deadwood in forest (Biodiversity) • Carbon sequestration in forest biomass (C sequestration)

Corresponding outputs of the trade-off analysis are shown in Figure 5.



Figure 5: Trade-offs for NUTSII regions according to the VOLANTE building block " Land use services"

The shadow value of agricultural land represents its opportunity cost, i.e. the value of the land in its next best alternative use, and assesses the potential to create agricultural income in the respective region (Verkerk et al., 2014). The results show high variability across VOLANTE pathways with most positive effects for VPS B1, while both alternatives that aim at nature protection tend to negatively affect rural viability throughout the EU. The remaining indicators are pretty stable to the VPS applied. While self-sufficiency and C sequestration show clear regional patterns, responses for GHG emissions and biodiversity are fairly homogenous. Central and Northern European countries score with higher soft wheat production capacities that exceed domestic consumption, trends that are reverse in the Mediterranean region. Positive signals can be observed for GHG emissions by agriculture. Although this must not be understood as a positive carbon balance (i.e. C emissions < C sequestration) it refers to declining emissions (in CO2 equivalents) in the future compared to current emissions. Deadwood, within VOLANTE representing the amount of standing and lying deadwood in production forests, is considered as an important indicator of forest biodiversity. Future trends of deadwood stocks tend to level off or even slightly decrease for the EU as a whole. As regards climate change mitigation via carbon sequestration in forest biomass there are clear identifiable hot and cold spots, indicating changes in forest stocks. 3.1.5 Global land impacts Net-trade of agri-food products, i.e. the difference between exports and imports of agri-food products serves as an indicator to mirror global effects of LUC in VOLANTE. Trade-off analysis results are highlighted in Figure 6.


Figure 6: Trade-offs for NUTSII regions according to the VOLANTE building block "Global land impacts”

Apart from a few Eastern-European countries the trade balance of agri-food products declines substantially across EU member states (i.e. exports < imports) for most VPS. Only A2NP shows contrasting results and outlines a potential reduction of the import dependence for some parts of the EU. 3.2 World regions

In addition to the trade-off analysis for NUTS II regions in the EU, the investigation for world regions (as classified by REMIND/MagPie, see Annex I) evaluates a set of selected global indicators (see Table 3) for the four VOLANTE marker scenarios: i) A1 reference, ii) A2 reference, iii) B1 reference, and iv) B2 reference.

Table 3: Brief description of indicators that are subject to the global trade-off analysis

Indicator Description Population Accounts for the population number in million GDP Gross domestic product and its components in 2001 $ Cropland Cropland area in million ha LUC-related C loss Carbon emissions due to land use change in million tons C

per year non-CO2 agricultural emissions

CH4 and N2O emissions from agricultural production in CO2 equivalents

Gross domestic product is defined as the value of all goods and services produced minus the value of any goods or services used in their creation. Data are calculated as chain-linked volumes in accordance to the year 2001 (in $).

The following Figures 7 to 11 highlight the final outputs of the analysis.


Figure 7: Results of trade- off analysis for the indicator "Population" with reference to the four VOLANTE marker scenarios

Figure 8: Results of trade- off analysis for the indicator "GDP" with reference to the four VOLANTE marker scenarios

-20

0

20

40

60

80

100

AFR CPA EUR FSU LAM MEA NAM PAO PAS SAS

chan

ge in

per

cent

Population

A1 reference

A2 reference

B1 reference

B2 reference

0

100

200

300

400

500

600

700

800


chan

ge in

per

cent

GDP

A1 reference

A2 reference

B1 reference

B2 reference


Figure 9: Results of trade- off analysis for the indicator "Cropland" with reference to the four VOLANTE marker scenarios

Figure 10: Results of trade- off analysis for the indicator "Land Use Change related carbon loss" with reference to the four VOLANTE marker scenarios

0

20

40

60

80

100

120

140


chan

ge in

per

cent

Cropland

A1 reference

A2 reference

B1 reference

B2 reference

2,07

9

184

152

100

384

4,24

2

1,27

3

224

67,3

15

-150

-125

-100

-75

-50

-25

0

25

50

75

100

125

150


chan

ge in

per

cent

LUC-related C loss

A1 reference

A2 reference

B1 reference

B2 reference


Figure 11: Results of trade-off analysis for the indicator "non-CO2 agricultural emissions" with reference to the four VOLANTE marker scenarios

Population growth shows significant increments for developing countries, as expected, with only little subjection to underlying rationales (i.e. VPS). Trends in GDP are positive across all scenarios but indicate potential further discrepancies in terms of global justice (e.g. north-south-divide), particularly in relation to population growth. Responses in GDP are heavily diverging between scenarios. Trends for cropland area are homogenous for all cases, indicating agricultural intensification particularly for African, Latin-American and Pacific OECD regions. Huge increments in agricultural non-CO2 emissions reflect the change in cropland area only to a certain extent, as they increase in general apart from EU countries as well as North- and Latin-America. This indicator is sensitive to the VPS applied. LUC related C loss shows significant regional patterns, although the calculated trade-offs nearly compensate on a global scale. This is true for all scenarios even though they perform different (i.e. they are case-sensitive). Nevertheless, the overall carbon balance remains severely negative in the year 2040 revealing increasing carbon emissions due to land use change in the future. 3.3 MCA of NUTSII regions in Europe

As introduced in chapter 2.3 the trade-off analysis additionally aims at the provision of a decision-support tool to identify a potentially best VPS on the level of NUTS II regions in the EU. Multi-criteria analysis (MCA) results for the VOLANTE pathways according to the three scenarios derived from the consolidated stakeholder visions (CSVs) are shown in Figures 12-14, expressed as utility values for a single region.

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A1 reference

A2 reference

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Figure 12: MCA-Results for VOLANTE Pathways on NUTS II level for weighting scenario” CSVa” – the colour code indicates utility value categories for MCA outputs

Figure 13: MCA-Results for VOLANTE Pathways on NUTS II level for weighting scenario” CSVb” – the colour code indicates utility value categories for MCA outputs


Figure 14: MCA-Results for VOLANTE Pathways on NUTS II level for weighting scenario” CSVc” – the colour code indicates utility value categories for MCA outputs

It becomes obvious that the rationale behind a certain VPS severely affects the regional responses to the building block components as the performances across scenarios (but within a CSV) differ to a great extent. This is due to distinct policy targets that are key to the VPS applied and depicts the most favorable strategy in terms of policy interventions for a particular land use vision in Europe. For instance, the most suitable pathway for CSV b is the A2NP scenario, as the majority of the regions seem to benefit from related impacts with only a few areas in Central, Northern and Eastern Europe who can be expected to be exposed to certain disadvantages. In contrast, the B2PC scenario reveals to be least beneficial in accomplishing the objectives inherent to the vision. Additionally, it depends on policy makers´ perception with regard to future land use how well a specific pathway may perform, as the application of different weighting scenarios supports further embedment of individual belief systems and shows the variability within a VPS if the main focus of the analysis is adapted. As an example, the B1 scenario generally performs well across all CSVs. Nevertheless, regional responses vary to a certain extent. The reasons therefor are twofold: i) the policy interventions of the scenario effectively shape future land use, and ii) the weightings applied favor building block components that positively affect the output of the analysis. Utility values indicate how valuable a chosen policy intervention would be for a single region (i.e. the higher the utility value the more preferable the alternative). Although they are quite low in general, mostly performing around or below 0,5 with a particular exception being the A2NP scenario in CSV b (i.e. 0,86 at maximum), they provide a sophisticated overview on a possible best policy alternative. According to CSVa either VPS B1 or VPS A2NP would lead to the most positive effects for the EU in general. With respect to CSV b, the VPS in favor would be A2NP, while CSV c reveals VPS B1 as being the most appropriate. However, the results show clear regional patterns and pinpoint potential winners and losers of a certain choice. Figure 15 gives an overview about the specific preferences as regards pathways in different NUTS II regions. It gives evidence about the ranking of preferred pathways for the three visions. For CSVa, A2NP is the pathway that fits best for most of the NUTS II regions (i.e. 121 NUTS cells). This outcome is even stronger for CSVb, where A2NP is favorable for 197 NUTS II regions, out of 262 analysed. For CSVc, the local multifunctional vision, the marker scenario B1 shows the highest preference, with A2NP the highest among the policy scenarios.


Figure 15: Ranking of pathways fitting for NUTS II levels (ranks 1-5)


4 Discussion 4.1 Trade-off interpretation Within VOLANTE trade-offs are calculated as the future change of selected indicators in contrast to the current state, which reflects a measured quantity of something expressed in a specified unit. To gain insights into the spatial distribution of potential winners and losers if certain policy targets are applied it is necessary to provide percentage results for each region. Thus, it is of utmost importance to bear in mind that the outputs indicate the future deviation as compared to the current state and do not show whether this future scenario can be evaluated as positive or negative in general for a particular region (e.g. GHG emissions may decline in the future compared to 2010, nevertheless GHG emissions show no sign of decline. 4.1.1 Europe The following findings can be concluded for the European scale: Pathway 1 (B1 marker) Apart from a few Eastern-European countries the trade balance of agri-food products declines substantially across EU member states (i.e. exports < imports). This is to a certain extent reflected in the degree of self-sufficiency in food consumption as well as in decreasing stocking densities for several livestock species. While the extent of arable land remains stable except for Northern Europe, average yields per ha cropland generally increase. Agricultural GHG emissions tend to lessen. The forest area increases in most European countries, particularly in the Mediterranean. Regional patterns for wood removals well coincide with trends for carbon sequestration (i.e. less wood removals lead to more C sequestration in forest biomass, depicting Northern Europe and the Iberian Peninsula as emerging carbon sinks (both increase of forest area and less removals) as compared to the rest of the EU. Pathway 3 + 4 (A2NP + B2NP) Surprisingly both policy scenarios with a focus on nature protection generally show less positive effects on the extent of forest and (semi-)natural area than B1 (mainly due to agricultural intensification). Hence the connectivity potential of landscapes for species declines in both cases. On a European scale even the amount of deadwood is lower compared to the marker scenario. In contrast, carbon stored in forest biomass increases to a great extent due to cuts of wood removals. Related CC mitigation effects are lowered as GHG emissions by agriculture rise for both scenarios. A huge negative impact on the shadow value of agricultural land has to be recognized. Arable crop as well as livestock yields show diverging trends and have to be analyzed on a regional level in more detail. Again, agri-food imports heavily exceed exports.


Pathway 4 + 5 (B2PC + B2PR) The scenario on PES, which aims to foster C sequestration, counts with more balanced carbon stocks in forest biomass, as wood removals are far below their maximum yields across Europe. For both scenarios the increase in forest area is accompanied with losses in (semi-)natural areas. Effects on the connectivity potential are negligible in case of B2PC but positive for B2PR. Both trends are mirrored in terms of deadwood, so B2PR performs best in terms of biodiversity. Agricultural production intensifies in both cases, similar to all alternatives, resulting in high GHG emissions out of the sector. Trade balances for agri-food are negative (as for all other Pathways). 4.1.2 Global The trade-off analysis reveals that there is a general trend in outsourcing land use and land use change effects from Europe, mostly notwithstanding which scenario and pathway are considered. While population growth seems to be a general, non-halting trend, we can see a particular trend for increase of agricultural area and agricultural emissions in almost all regions of the world. This effect is the stronger the more explicit land use extensification efforts are assumed for Europe (e.g. nature protection, carbon sequestration). On the other hand, a veritable increase in GDP in developing countries can be expected. The results show that the world’s development in land use is only partly sensitive to the pathways we choose, but largely subject to global trends in globalisation, agricultural production and land use. 4.1.3 Spatial MCA The MCA approach was applied to understand the overall impact of pathways with regards to the three visions. By using SMART, a compensatory approach has been used that may allow for bad performances being compensated by good ones. This is perceived as a valid approach since knock-out criteria had been already applied in the pathway analysis, with installing a 60% rule of reaching a target to render a building block component a scenario a pathway (Verkerk et al, 2014).. The MCA also tells about the regional patterns of preferability of a pathway and which regions are affine towards which policy scenarios. The MCA results imply that – out of the policy scenarios that are proposed as pathways – A2NP, the nature protection pathway, may look as a good compromise pathway, while marker scenario B1 shows fairly good acceptance as regards the provision of the three stakeholder visions. Although each pathway has been identified as suitable course of action to reach a certain land use vision in the future, there are various regions that benefit from distinct policy interventions and others that do not. In general, the analysis depicts most disadvantages for Mediterranean regions as well as for some areas in Northern, Central and Eastern Europe. Even though the weighting scenarios affect the MCA


output by emphasizing distinct indicators that may trigger impacts on the overall performance of a VPS (e.g. an indicator may cause severe trade-offs and thus is more sensitive to the weight applied), each CSV shows similar clusters of “winner” or “looser” regions per VPS. In contrast to the trade-off analysis MCA results are more sensitive to the VPS applied, mainly due to indicator sensitivity as regards the weighting. More in-depth analysis on this issue will be included in D12.4.

5 Outlook Results of D 11.3, D12.2, and D12.3 will be synthesised in D 12.4 to identify trade-off hotspots across Europe. There will be focus given on specific effects on eco-regions and we will investigate how European regions differ with regard to land use and land use change implications for the future. This approach shall enable the identification of more tailored policy-formulation and implementation in a Europe of the regions.


References Bouman BAM, Jansen HGP, Schipper RA, Nieuwenhuyse A, Hengsdijk H, Bouma J (1999). A framework for integrated biophysical and economic land use analysis at different scales. Briner, S., Huber, R., Bebi, P., Elkin, C., Schmatz, D.R. and Gret-Regamey, A. (2013): Trade-Offs between Ecosystem Services in a Mountain Region. Ecology and Society 18(3): 35. doi:10.5751/ES-05576-180335 Brown, K., Adger, W.N., Tompkins, E., Bacon, P., Shim, D., and Young, K. (2001). Trade-off analysis for marine protected area management. Ecological Economics 37, 417-434. Hardin, G. (1968): The tragedy of the commons. Science 162 (3859), pp. 1243-1248 DOI: 10.1126/science.162.3859.1243 Janssen, R., Herwijnen, M. van, Stewart, T.J., Aerts, J.C.J.H. (2007). Multiobjective decision support for land use planning. Environment and Planning B, 34, Meadows, D.H., Randers, J. and Meadows, D. (2004): Limits to growth: the 30-year update. Chelsea Green Publishing Company, United States. Mertz, B. (2010): Controlling Climate Change. Cambridge University Press, New York. Mouchet, M., Lamarque, P., Martin Lopez, B., Crouzat, E., Gos, P., Byczek, C. & Lavorel, S. (2014) An interdisciplinary methodological guide for quantifying associations between ecosystem services. Global Environmental Change, 28, 298-308. Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang (2013): Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Olson, D.L. (1996): Decision aids for Selection Problems. Springer, New York. Paterson, J., Metzger, M. and Walz, A. (2012): The VOLANTE scenarios: framework, storylines and drivers. VOLANTE Deliverable 9.1


Pérez-Soba M., Houtkamp J., Cormont A., Maes M., Gramberger M., Chiamparino T., Metzger, M., Murray-Rust D., Paterson J.S., Jensen, A. (2013): Synthesis report on visions workshops. VOLANTE Deliverable 10.2. Polasky, S., Nelson, E., Pennington, D., Johnson, K.A. (2011): The Impact of Land-Use Change on Ecosystem Services, Biodiversity and Returns to Landowners: A Case Study in the State of Minnesota. Environmental and Resource Economics 48(2), 219-242, DOI: 10.1007/s10640-010-9407-0 Rounsevell, M.D.A., Pedroli, B., Erb, K.-H., (...), Verburg, P.H., Wolfslehner, B. (2012): Challenges for land system science. Land Use Policy 29 (4), 899-910. Strager, M. P., and Rosenberger, R. S. (2006). Incorporating stakeholder preferences for land conservation: Weights and measures in spatial MCA. Ecological Economics 58, 79-92. Tran, L. T., Knight, C. G., O'Neill, R. V., and Smith, E. R. (2004). Integrated environmental assessment of the Mid-Atlantic region with analytical network process. Environmental Monitoring and Assessment 94, 263-277. Verburg, P., Lotze-Campen, H., Popp, A., Lindner, M., Verkerk, H., Kakkonen, E., Schrammeijer, E., Helming, J., Tabeau, A., Schulp, N., Van der Zanden, E., Lavalle, C., Batista e Silva, F. and Eitelberg, D. (2013): Report documenting the assessment results for the scenarios stored in the database. VOLANTE Deliverable 11.1 Verkerk, H., Lindner, M., Helming, J., Kuemmerle, T., Lotze-Campen, H., Müller, D., Paterson, J., Perez-Soba, M., Verburg, P. and Wolfslehner, B. (2014): Identification of pathways to consolidated visions of future land use in Europe. VOLANTE Deliverable 11.3 Wolfslehner, B. (2007): The use of indicator models for the evaluation of sustainable forest management in a multi-criteria analysis framework. PhD thesis, University of Natural Resources, Vienna. Wolfslehner, B., Brüchert, F., Fischbach, J., Rammer, W., Becker, G., Lindner, M. and Lexer, M.J. (2012): Exlporatory multi-criteria analysis in sustainability impact assessment of forest wood-chains: the example of a regional case study in Baden-Württemberg. Eur J Forest Res (2012) 131: 47-56. Doi:10.1007/s10342-011-0499-z


Annex I: World regions by MAgPIE For the analysis of global impacts data could only be provided for the four VOLANTE marker scenarios (A1, A2, B1 and B2). Originating from the global land use allocation Model of Agricultural Production and its Impact on the Environment (MAgPIE) and the global multi-regional model (REMIND), both developed by the Potsdam Institute for Climate Impact Research, the dataset included values for a set of indicators clustered in 11 world regions. In Table 6 definitions of the world regions are listed. An excerpt of data provided by the modelling group is shown in Annex II.


Table 6: World regions and corresponding countries as defined by MagPie

AFR CPA EUR FSU LAM MEA NAM PAO PAS SASSub-Saharan Africa Centrally-Planned Asia Europe Former Soviet Union Latin America Middle East/North Africa North America Pacific OECD Pacific Asia South AsiaAngola Cambodia Albania Azerbaijan, Republic of Argentina Algeria Canada Australia Indonesia AfghanistanBenin China Austria Belarus Belize Egypt United States of America Japan Korea, Dem People's Rep BangladeshBotswana Laos Belgium-Luxembourg Georgia Bolivia Iran, Islamic Rep of New Zealand Korea, Republic of BhutanBurkina Faso Mongolia Bosnia and Herzegovina Kazakhstan Brazil Iraq Malaysia IndiaBurundi Viet Nam Bulgaria Kyrgyzstan Chile Israel Papua New Guinea MyanmarCameroon Croatia Moldova, Republic of Colombia Jordan Philippines NepalCentral African Republic Czech Republic Russian Federation Costa Rica Kuwait Solomon Islands PakistanChad Denmark Tajikistan Cuba Libyan Arab Jamahiriya Thailand Sri LankaCongo, Dem Republic of Estonia Turkmenistan Dominican Republic MoroccoCongo, Republic of Finland Ukraine Ecuador OmanCôte d'Ivoire France Uzbekistan El Salvador Saudi ArabiaDjibouti Germany French Guiana Syrian Arab RepublicEquatorial Guinea Greece Guatemala TunisiaEritrea Hungary Guyana United Arab EmiratesEthiopia Iceland Haiti YemenGabon Ireland HondurasGhana Italy MexicoGuinea Latvia NicaraguaGuinea-Bissau Lithuania PanamaKenya Macedonia,The Fmr Yug Rp ParaguayLesotho Netherlands PeruLiberia Norway SurinameMadagascar Poland UruguayMalawi Portugal VenezuelaMali RomaniaMauritania SlovakiaMozambique SloveniaNamibia SpainNiger SwedenNigeria SwitzerlandRwanda TurkeySenegal United KingdomSierra Leone Yugoslavia, Fed Rep ofSomaliaSouth AfricaSudanSwazilandTanzania, United Rep ofTogoUgandaWestern SaharaZambiaZimbabwe

Annex II: Database outputs

Focusing on recent project achievements, the trade-off analysis mirrors an array of VOLANTE results and aims to provide a comprehensive synthesis for Europe. To come to grips with the huge amount of data, both for different spatial and temporal scales as well as for varying VOLANTE policy scenarios (VPS), it was necessary to select i) a subset of VPS as a framework, ii) indicators for regional, national and global impacts, iii) the starting point and iv) the end point of the analysis.

With respect to data availability it was decided to concentrate on the VPS that have been identified as pathways within VOLANTE for the regional analysis. The NUTS II regions that could be investigated are listed in Table 7.

Table 7: List of NUTS II regions in Europe that are subject to the regional trade-off analysis

AT11 DE26 ES43 GR25 NL23 SI00 AT12 DE27 ES51 GR30 NL31 SK01 AT13 DE30 ES52 GR41 NL32 SK02 AT21 DE41 ES53 GR42 NL33 SK03 AT22 DE42 ES61 GR43 NL34 SK04 AT31 DE50 ES62 HU10 NL41 UKC1 AT32 DE60 ES63 HU21 NL42 UKC2 AT33 DE71 ES64 HU22 PL11 UKD1 AT34 DE72 ES70 HU23 PL12 UKD2 BE10 DE73 FI13 HU31 PL21 UKD3 BE21 DE80 FI18 HU32 PL22 UKD4 BE22 DE91 FI19 HU33 PL31 UKD5 BE23 DE92 FI1A IE01 PL32 UKE1 BE24 DE93 FI20 IE02 PL33 UKE2 BE25 DE94 FR10 ITC1 PL34 UKE3 BE31 DEA1 FR21 ITC2 PL41 UKE4 BE32 DEA2 FR22 ITC3 PL42 UKF1 BE33 DEA3 FR23 ITC4 PL43 UKF2 BE34 DEA4 FR24 ITD1 PL51 UKF3 BE35 DEA5 FR25 ITD2 PL52 UKG1 BG11 DEB1 FR26 ITD3 PL61 UKG2 BG12 DEB2 FR30 ITD4 PL62 UKG3 BG13 DEB3 FR41 ITD5 PL63 UKH1 BG21 DEC0 FR42 ITE1 PT11 UKH2 BG22 DED1 FR43 ITE2 PT15 UKH3 BG23 DED2 FR51 ITE3 PT16 UKI1 CY00 DED3 FR52 ITE4 PT17 UKI2 CZ01 DEE1 FR53 ITF1 PT18 UKJ1 CZ02 DEE2 FR61 ITF2 RO01 UKJ2 CZ03 DEE3 FR62 ITF3 RO02 UKJ3


CZ04 DEF0 FR63 ITF4 RO03 UKJ4 CZ05 DEG0 FR71 ITF5 RO04 UKK1 CZ06 DK00 FR72 ITF6 RO05 UKK2 CZ07 EE00 FR81 ITG1 RO06 UKK3 CZ08 ES11 FR82 ITG2 RO07 UKK4 DE11 ES12 FR83 LT00 RO08 UKL1 DE12 ES13 GR11 LU00 SE01 UKL2 DE13 ES21 GR12 LV00 SE02 UKM1 DE14 ES22 GR13 MT00 SE04 UKM2 DE21 ES23 GR14 NL11 SE06 UKM3 DE22 ES24 GR21 NL12 SE07 UKM4 DE23 ES30 GR22 NL13 SE08 UKN0 DE24 ES41 GR23 NL21 SE09

DE25 ES42 GR24 NL22 SE0A

The subset of indicators was also defined by the pathway analysis (i.e. building lock components, see Figure 16) and additionally affected by data availability for NUTS II regions in Europe, as shown in Figure 17.


Figure 16: Building blocks and respective compontents incorporated in the trade-off analysis for NUTSII regions

The data provided by modellers as a .csv file was transferred to an MS Access database to avoid potential data loss, merged (i.e. parameters had to be tied together) and exported to MS Excel in the following format.

Figure 17: Exzerpt of database outputs for NUTS II regions in Europe, highlighting data availability for Austria

Two aspects are visible with regard to the amount of data available:

1. heterogeneity across NUTS II regions 2. lack of data for some indicators in general

Both do not affect the trade-off results apart from the fact that some NUTS II regions reflect “no data available” on the maps (i.e. white colour). For the MCA the number of indicators covered is essential for the normalization of the weights and has to be taken into account for each NUTS II region by adaptation of the divisor (e.g. for AT 11 all 17 building block components are filled hence the divisor for the normalization task is “17”). Data for the global analysis was delivered by REMIND/MAgPIE experts in MS Excel and had the following format (Figure 18).

Global lan Building block component arab fore natu urba aryd rumi pigs poul wood conn shan rent self emis ddwd cseq tradeBuilding block Land use servicesLand use patternLand use management intensityLand cover extent

region id country arab fore natu urba aryd rumi pigs poul wood resi conn peri shan wild rent self emis ddwd cseq tradearab_ext_201for_ext_2000semi_ext_20urb_arext_20arab_yld_201rude_yld_201pig_prod_201pou_prod_20wood_rems_res_extr_201conn_2000 NA shann_2010 rewild_2000 landrent_201selfsuf_agr_2emissions_agdwood_2010cbiom_2010 agri_trade_20

AT11 AT 190,726855 1222 37 214 1375,86015 0,77962768 111,191965 3,29320066 433,81439 NA 4,6357 NA 2,73172243 NA -568,871201 117,568072 248,287311 1455,74619 235,585736 -2388AT12 AT 722,067664 7607 191 1039 1238,75029 1,17766631 1308,72456 16,6568967 3959,70544 NA 6,65354 NA 2,72840808 NA -566,105012 117,568072 1793,05682 11798,3386 549,874566 -2388AT13 AT NA 77 3 255 NA NA NA NA NA NA 5,28375 NA NA NA NA 117,568072 NA NA NA -2388AT21 AT 70,2713411 5400 794 294 658,537169 0,64013514 244,320207 6,18541374 3470,79873 NA 1,13757 NA 1,59132425 NA -359,336645 117,568072 570,921601 9426,746 95,7987316 -2388AT22 AT 153,324357 9307 948 613 1748,63642 0,77839586 1290,84843 9,41692726 6181,36188 NA 2,20827 NA 1,87519083 NA -447,306274 117,568072 1057,08248 17898,8091 89,2461426 -2388AT31 AT 299,105485 4788 68 532 814,316232 1,2924718 1756,18766 9,90526306 3181,9595 NA 6,17176 NA 2,37688191 NA -559,012136 117,568072 1686,18935 8274,96694 179,990011 -2388AT32 AT 7,84240686 3253 1126 190 729,369177 0,55600389 21,2807015 4,60383751 1955,99773 NA 1,42968 NA 0,8529858 NA -314,107389 117,568072 524,381794 7291,36969 -162,282709 -2388AT33 AT 15,5677848 4953 2142 228 309,800684 0,41883067 35,9654585 7,4280302 2229,34509 NA 0,856016 NA 0,88606893 NA -270,971127 117,568072 589,31416 9239,18244 -403,919321 -2388AT34 AT 3,82223114 971 573 140 526,994957 0,49801788 22,7575099 1,93553969 475,009446 NA 1,56512 NA 0,87350614 NA -299,203629 117,568072 207,648819 1711,7267 -104,556757 -2388

bbcomp.regional.complet



Figure 18: Excerpt of data outputs for world regions (as defined by MAgPie)

Scenario VPS Year AFR CPA EUR FSU LAM MEA NAM PAO PAS SASA1 reference 0 2005 776 1440 598 289 557 368 319 154 460 1515A1 reference 0 2010 878 1473 596 293 593 412 332 157 487 1631A1 reference 0 2015 983 1504 603 297 628 458 345 158 513 1741A1 reference 0 2020 1087 1527 608 299 659 503 359 159 538 1843A1 reference 0 2025 1184 1538 613 301 687 547 372 160 559 1932A1 reference 0 2030 1268 1536 616 303 710 588 385 160 578 2006A1 reference 0 2035 1333 1524 619 304 728 624 397 160 593 2061A1 reference 0 2040 1398 1502 618 304 742 658 407 160 605 2107A2 reference 0 2005 787 1506 605 295 576 375 323 156 476 1538A2 reference 0 2010 897 1586 607 303 627 428 338 159 514 1672A2 reference 0 2015 1011 1668 615 311 679 485 354 161 552 1805A2 reference 0 2020 1128 1750 622 318 733 547 370 161 590 1937A2 reference 0 2025 1245 1835 629 325 788 615 385 162 628 2066A2 reference 0 2030 1357 1925 635 335 846 689 401 163 667 2193A2 reference 0 2035 1461 2020 641 347 905 769 416 164 706 2314A2 reference 0 2040 1565 2109 645 359 964 853 430 164 744 2431B1 reference 0 2005 776 1439 598 289 557 368 319 154 460 1515B1 reference 0 2010 878 1473 596 293 593 412 332 157 487 1631B1 reference 0 2015 983 1504 603 297 628 458 345 158 513 1741B1 reference 0 2020 1087 1527 608 299 659 503 359 159 538 1843B1 reference 0 2025 1184 1538 613 301 687 547 372 160 559 1932B1 reference 0 2030 1268 1536 616 303 710 588 385 160 578 2006B1 reference 0 2035 1333 1524 619 304 728 624 397 160 593 2061B1 reference 0 2040 1398 1502 618 304 742 658 407 160 605 2107B2 reference 0 2005 790 1417 590 285 548 370 319 152 481 1548B2 reference 0 2010 899 1469 585 287 585 412 331 153 507 1666B2 reference 0 2015 1017 1522 587 289 621 454 345 153 532 1772B2 reference 0 2020 1142 1571 587 289 654 494 358 152 555 1876B2 reference 0 2025 1268 1611 587 289 685 533 369 151 578 1976B2 reference 0 2030 1391 1638 584 289 715 571 375 149 601 2068B2 reference 0 2035 1509 1656 579 288 742 607 379 146 621 2152B2 reference 0 2040 1614 1670 573 287 766 640 381 145 637 2226

Annex III: Outputs Trade-off analysis (national)

Apart from trade-offs for building block components in accordance to the VOLANTE pathways on the level of NUTS II regions in Europe, various indicators were assessed and calculated within VOLANTE on a national level (i.e. NUTS 0) and were applied to the trade-off analysis in addition. In the following, results for the marker scenarios and selected NUTS 0 regions are shown for:

• land demand by sector in km² (i.e. cattle, grain, horticultural products, wheat)

• private consumption volume in 2001 $ (i.e. cattle, forestry, grain, horticulture, services, wheat)

• production volume in 2001 $ (i.e. cattle, forestry, grain, horticulture, services, wheat)

206

-100

-80

-60

-40

-20

0

20

40

60

80

100

AT CZ DE DK ES FI FR GR HU IE IT NL PL PT SE SI SK UK

chan

ge in

per

cent

A1 marker

cattle

grain

horticulture

wheat


-100

-80

-60

-40

-20

0

20

40

60

80

100


chan

ge in

per

cent

A2 marker

cattle

grain

horticulture

wheat

257

-100

-80

-60

-40

-20

0

20

40

60

80

100


chan

ge in

per

cent

B1 marker

cattle

grain

horticulture

wheat


-100

-80

-60

-40

-20

0

20

40

60

80

100


chan

ge in

per

cent

B2 marker

cattle

grain

horticulture

wheat


Table 9: Results of trade-off analysis for consumption and production of selected products - VOLANTE marker scenario A1




Table 10: Results of trade-off analysis for consumption and production of selected products - VOLANTE marker scenario A2




Table 11: Results of trade-off analysis for consumption and production of selected products - VOLANTE marker scenario B1




Table 12: Results of trade-off analysis for consumption and production of selected products - VOLANTE marker scenario B2



Documents

Volante-Project - Deliverable No: 12.3 Value-based …...ecosystem service provision, and for the further design of the Volante Roadmap. D12.3Trade-off evaluation of future ESS 4 1