Table S2), Which 23 156 We Used for Our Analysis

Originally published as:

Verkerk, P. J., Lindner, M., Pérez-Soba, M., Paterson, J. S., Helming, J., Verburg, P. H., Kuemmerle, T., Lotze-Campen, H., Moiseyev, A., Müller, D., Popp, A., Schulp, C. J. E., Stürck, J., Tabeau, A., Wolfslehner, B., Zanden, E. H. van der (2018): Identifying pathways to visions of future land use in Europe. - Regional Environmental Change, 18, 3, 817-830

10.1007/s10113-016-1055-7

[Please note: This is the submitted version of the paper before peer-review] Manuscript Click here to view linked References

1 Identifying pathways to visions of future land use in Europe 1 2 2 3 Pieter J. Verkerka*, Marcus Lindnera, Marta Pérez-Sobab, James S. Patersonc, John Helmingd, 3 e f g a 4 4 Peter H. Verburg , Tobias Kuemmerle , Hermann Lotze-Campen , Alexander Moiseyev , 5 5 Daniel Müllerh, Alexander Poppg, Catharina J. E. Schulpe, Julia Stürcke, Andrzej Tabeaud, 6 6 Bernhard Wolfslehneri, Emma H. van der Zandene 7 7 8 a 9 8 European Forest Institute, Sustainability and Climate change programme, Yliopistokatu 6, 10 9 80100 Joensuu, Finland 11 10 bALTERRA, Wageningen University & Research centre, P.O. Box 47, 6700 AA 12 13 11 Wageningen, the Netherlands 14 12 cLand Use Research Group, School of Geosciences, University of Edinburgh, UK 15 13 dLEI, Wageningen University and Research centre, PO Box 29703, 2502 LS The Hague, the 16 14 Netherlands 17 e 18 15 Department of Earth Sciences, VU University Amsterdam, De Boelelaan 1087, 1081 HV 19 16 Amsterdam, the Netherlands 20 17 fGeography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, 21 22 18 Germany 23 19 gPotsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473, Potsdam, 24 20 Germany 25 21 hLeibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor- 26 27 22 Lieser-Strasse 2, 06120 Halle (Saale), Germany 28 23 iEuropean Forest Institute, Central-East and South-East European Regional Office, c/o 29 24 University of Natural Resources and Life Sciences, Feistmantelstr. 4, A-1180 Vienna, Austria 30 31 25 32 26 *corresponding author; tel.: +34663625875; e-mail: [email protected] 33 27 34 28 E-mail addresses of co-authors 35 36 29 ML: [email protected] 37 30 MPS: [email protected] 38 31 JP: [email protected] 39 40 32 JH: [email protected] 41 33 PV: [email protected] 42 34 TK: [email protected] 43 35 HLC: [email protected] 44 45 36 AM: [email protected] 46 37 DM: [email protected] 47 38 AP: [email protected] 48 49 39 CJES: [email protected] 50 40 JS: [email protected] 51 41 AT: [email protected] 52 42 BW: [email protected] 53 54 43 EHZ: [email protected] 55 44 56 45 Word count (excl. references): 5879 words 57 58 46 Number of figures and tables: 6 59 47 60 61 62 63 64 65

48 Abstract 1 49 Plausible scenarios of future land use derived for model projections may differ substantially 2 3 50 from what is actually desired by society and identifying such mismatches is important for 4 51 identifying policies to resolve them. This paper presents an approach to link explorative 5 6 52 projections of future land use for the European Union (EU) to normative visions of desired 7 8 53 land use futures. We used the results of 24 scenario projections obtained from seven linked 9 54 simulation models to explore uncertainty in future land-use developments. Land use 10 11 55 projections are linked to statements made by stakeholders for three normative visions of 12 13 56 desired, future land use. The visions differed in the scale of multifunctionality of land use: at 14 57 European (Best Land in Europe), regional (Regional Connected) or local (Local 15 16 58 Multifunctional) level. To identify pathways to these visions, we analysed in which cases 17 18 59 projected land-use changes matched with the land use changes desired in the visions. We 19 identified five pathways to the vision Regional Connected, two pathways to the vision Best 20 60 21 61 Land in Europe, but no pathway to the vision Local Multifunctional. Our results suggest that 22 23 62 policies have the ability to change the development of land use such that it is more in line 24 25 63 with land-use futures desired by society. We believe our approach represents an interesting 26 64 avenue for foresight studies on land use, as it combines the credibility from explorative 27 28 65 scenarios with legitimacy and saliency of normative visions. 29 30 66 31 67 Keywords: explorative scenarios, land use, normative visions, pathways 32 33 68 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 2 63 64 65

69 1 Introduction 1 70 Land use provides multiple goods and services to society and is therefore of critical 2 3 71 importance to humans (Foley et al. 2005). However, the unsustainable use of the land 4 5 72 significantly contributes to climate change through greenhouse gas emissions (Smith et al. 6 73 2014), to biodiversity loss (Newbold et al. 2015), and to the degradation of ecosystem 7 8 74 services (Millennium Ecosystem Assessment 2005). Over the next decades, human population 9 10 75 is expected to increase strongly (United Nations 2015) and the demands to produce food, 11 12 76 feed, fibre and fuel from land are likely to continue to increase. Meeting simultaneously the 13 77 future needs of a rising population while conserving natural areas, halting biodiversity loss, 14 15 78 and switching to larger shares of renewable energy, will further exacerbate the competition 16 17 79 for land (Lambin and Meyfroidt 2011; Kraxner et al. 2013). To deal with such potential 18 80 conflicts, strategies for future land use are needed (e.g. Godfray and Garnett 2014; Fares et al. 19 20 81 2015). 21 22 82 23 83 Scenario analysis is considered an important foresight technique to support strategic decision 24 25 84 making while dealing with uncertainty (van de Heijden 2005; Pérez-Soba and Maas, 2015). 26 27 85 Scenario analysis helps to characterize the future in a structured way that allows imaginative 28 86 thinking (Rounsevell and Metzger 2010). Explorative scenarios are frequently used to 29 30 87 describe the uncertainty in developments and answer questions on what could happen. There 31 32 88 is however another group of scenarios that aims to answer the question what should happen 33 89 (Rounsevell and Metzger 2010; Vergragt and Quist 2011). This group of scenarios has a 34 35 90 normative focus and addresses a desirable endpoint or vision on what is wanted and where 36 37 91 one would like to be in the future. Combining these different scenario techniques has received 38 92 little attention (Seppelt et al., 2013; Castella et al., 2007). 39 40 93 41 94 To identify pathways on how one can reach a desirable future, a number of backcasting 42 43 95 approaches have been developed (Robinson 1982; Dreborg 1996). The starting point of this 44 45 96 foresight technique is a desirable future, from which the analysis goes backwards to the 46 97 present in order to determine the feasibility of that future, as well as to search for decisions 47 48 98 (e.g. policy measures) and conditions that would be required to reach the desired endpoint. 49 50 99 Recent backcasting efforts do not only rely on simulation modeling, but often include 51 100 participatory feedback by stakeholders to define the desired visions, to identify possible 52 53 101 obstacles to reach the vision, or to refine the proposed policy or management choices 54 55 102 necessary to reach the vision (Robinson et al. 2011; Kok et al. 2011). Backcasting has been 56 103 applied, for example, in studies on sustainable development (e.g. Robinson et al. 2011), water 57 58 104 management (van Vliet and Kok 2015), waste management (van der Pluijm et al. 2010) and 59 60 61 62 3 63 64 65

105 recently also land use planning (Haslauer 2015). Yet, backcasting has not been used in large- 1 106 scale land-use foresight studies, presumably because of the complexity of land-use dynamics. 2 3 107 4 5 108 Combining explorative scenarios with normative visions is an interesting approach for land 6 109 system science (Castella et al., 2007; Rounsevell et al. 2012; Seppelt et al. 2013) as the 7 8 110 credibility of explorative scenarios, as perceived by stakeholders, is combined with the 9 10 111 perceived legitimacy and saliency of normative visions (Rounsevell and Metzger 2010; Pérez- 11 12 112 Soba and Maas, 2015). Recently, Pérez-Soba et al. (2015) elicited normative visions of future 13 113 land use in the European Union (EU) for the year 2040 in a participatory stakeholder process. 14 15 114 This process resulted in three distinct land use visions, all having multifunctionality at their 16 17 115 core, but differing in the spatial scale at which multifunctionality should occur, i.e. European, 18 116 regional or local. Best Land in Europe is a vision in which optimal use of land is crucial to 19 20 117 ensure maximum production of food and other natural products. Regional Connected is a 21 22 118 vision in which societal needs are met regionally, in a coherent relationship between people 23 119 and their resources. Finally, in the vision Local Multifunctional, a diversity of land functions 24 25 120 co-occur in small areas, based on innovative approaches to living, working and recreation, 26 27 121 with a high diversity in goods and services, land uses and society. 28 122 29 30 123 The aim of our study was to identify pathways to the future land use desired by European 31 32 124 stakeholders by linking their normative visions to explorative scenarios simulated with a 33 125 hierarchical set of land use models. Specifically, our objectives were (i) to develop an 34 35 126 approach to link quantitative model projections to qualitative visions statements by 36 37 127 stakeholders, (ii) to apply the approach to the three visions of land use in Europe, and (iii) to 38 128 discuss the approach as a decision support tool in land use policy and planning. 39 40 129 41 42 130 2 Methods and data 43 131 2.1 Data on land use projections 44 45 132 To identify pathways to the three future land-use visions, we used simulations from Lotze- 46 47 133 Campen et al. (2013) and Verburg et al. (2013), derived from seven global and regional land- 48 134 use models for 27 European countries (i.e., the EU excluding Croatia). The global models 49 50 135 REMIND and MAgPIE provided trajectories on economic growth and population growth, 51 52 136 food and bioenergy demands, and land use change for major world regions. Selected outputs 53 137 from these two models were provided to the models MAGNET and EFI-GTM. MAGNET is a 54 55 138 global general equilibrium model, covering all economic sectors and projecting global 56 57 139 changes in land use, agricultural production and consumption patterns, and regional sub- 58 140 sector specific changes in bilateral trade flows. EFI-GTM is a global forest sector model 59 60 141 which uses changes in economic development and population as an input to derive future 61 62 4 63 64 65

142 trends in forest production. Simulation results from these global models were used as input to 1 143 the agricultural economic model CAPRI and the forest resource projection model EFISCEN. 2 3 144 CAPRI and EFISCEN provided spatially detailed insights into the agricultural and forest land 4 5 145 use sectors in Europe at the regional-level. To account for all changes in land cover and to 6 146 ensure consistency between the different types, the Dyna-CLUE model was included in the 7 8 147 model chain. The Dyna-CLUE model allocates demands for land use from different sectors on 9 10 148 a high-resolution spatial grid based on location factors, land-use history, spatial policies and 11 12 149 competition between land uses. For details on and references to the models, we refer to Table 13 150 S1 in the Supplementary Online Material. 14 15 151 16 17 152 Lotze-Campen et al. (2013) and Verburg et al. (2013) applied this modelling framework to 18 153 explore how land use would change according to four alternative global development 19 20 154 scenarios, as well as to assess how eleven policy options would alter two of the four global 21 22 155 development scenarios. In total, they developed 24 scenarios (Table 1 and Table S2), which 23 156 we used for our analysis. Projections results were used at the level of administrative regions 24 25 157 (Nomenclature des Unités Territoriales Statistiques - NUTS level 2). 26 27 158 28 159 <

> 29 30 160 31 32 161 While the process to define visions and the design of the exploratory scenarios to be 33 162 addressed by the modelling was loosely linked by focussing on similar land use types, the 34 35 163 detailed development of the modelling framework and the definition of the scenarios was 36 37 164 conducted mostly independent from the eliciting of visions. This was done to prevent that 38 165 stakeholders would be constrained in formulating their visions by the ability of the models to 39 40 166 project future land use. 41 42 167 43 168 2.2 Linking land use projections with stakeholder visions 44 45 169 To structure the linkage of scenario projections with visions, we identified land-use attributes 46 47 170 that were addressed in the visions and could be quantified by the models. To do this, we pre- 48 171 defined a set of land-use attributes to characterise various aspect of land use: land cover 49 50 172 extent (i.e. the area covered by a land cover type), land-use management (i.e. the intensity by 51 52 173 which land is managed), land-use pattern (i.e. the spatial configuration of different land uses), 53 174 land-use services (i.e. the benefits provided to society by land use), global land impacts (i.e. 54 55 175 indirect effects of land use in Europe on land use outside Europe), and lifestyle (i.e. behaviour 56 57 176 of people that affects land use). 58 177 59 60 61 62 5 63 64 65

178 In a next step, we compared a list of more than 450 model variables with statements made by 1 179 stakeholders in the definition of visions and identified 20 variables that adequately captured 2 3 180 stakeholder statements (Table S3). The selected model variables mainly covered the attributes 4 5 181 land-cover extent, land-use management and land-use patterns. We were unable to link the 6 182 attributes of global land impacts and lifestyle, due to unavailability of appropriate model 7 8 183 variables, or due to absence of detailed statements by stakeholders. Stakeholders provided 9 10 184 additional statements on land-use sectors (e.g., energy, water and transport), which could not 11 12 185 be addressed by the modelling framework and were therefore excluded from our analysis. 13 186 Similarly, many of the spatial details provided by the models were not addressed in the 14 15 187 stakeholder visions and could not be accounted for in detail. 16 17 188 18 189 To avoid redundancy, we checked for correlation between model variables. We first 19 20 190 calculated the change ratio between 2040 and the base year for each model variable (2010 for 21 22 191 CAPRI and EFISCEN variables, 2000 for Dyna-CLUE variables) for each administrative 23 192 region in our dataset. In a next step, we calculated Spearman rank correlations between all 24 25 193 model variables for the four global development scenarios separately. Correlations were 26 27 194 generally relatively low (<0.6), except for the variables extent of forest area and contribution 28 195 of abandoned agricultural land to wilderness (correlation = 0.66; Figure S1). However, these 29 30 196 variables relate to different attributes and therefore collinearity among them does not to 31 32 197 impair further analysis. All model variables were, therefore, used for subsequent analyses. 33 198 34 35 199 2.3 Matching desired and projected change 36 37 200 After selecting the model variables, we determined in which direction each of the selected 38 201 variables should change over time according to the three visions, and how they according to 39 40 202 the scenario simulations. From the documentation of the stakeholder visions, we recorded for 41 42 203 each model variable whether it was desired to increase (+1), remain constant (0) or decrease (- 43 204 1). In addition to the desired change, weights for the model variables were defined based on 44 45 205 statements made by stakeholders. Stakeholders only made statements for livestock in general 46 47 206 while the models provided three livestock variables separately. To address this we combined 48 207 the three model variables into one variable using an equal weight (w1) for each. We added a 49 50 208 second weight (w2) to take into account that stakeholders expressed repeatedly that a variable 51 52 209 should change strongly. As it was not clear what a strong change would entail according to 53 210 the stakeholders, we gave larger weight to model variables for which stakeholders indicated a 54 55 211 strong change. The desired changes for the three visions are shown in Table 2. 56 57 212 58 213 Stakeholder visions indicated distinct spatial distributions for the different land uses. As 59 60 214 regards agricultural production, in the vision Best Land in Europe the most productive areas 61 62 6 63 64 65

215 would be used for agriculture, i.e. agricultural land-use should increase in productive and 1 216 decrease in unproductive areas. To identify productive regions, we used the agricultural 2 3 217 productivity calculated by CAPRI as a proxy for each region in our dataset. We selected the 4 5 218 top third of all regions in terms of agricultural production assuming that these would be 6 219 productive regions where the agricultural area should increase (+1), and the lowest third of 7 8 220 regions would be the unproductive regions where the agricultural area should decrease (-1). 9 10 221 Medium productive areas would remain stable (0). As regards forestry production, Best Land 11 12 222 in Europe also implied that forest production would shift from the south of Europe to less 13 223 drought-prone areas in the north. This was implemented by assigning +1 to Northern Europe, 14 15 224 and a -1 for Southern Europe and 0 for Central Europe. We grouped countries in three main 16 17 225 geographical regions: north (i.e. Denmark, Estonia, Finland, Latvia, Lithuania and Sweden), 18 226 south (i.e. Bulgaria, Cyprus, Greece, Italy, Portugal, Slovenia and Spain), and central EU 19 20 227 (remaining EU countries). Local Multifunctional envisions creating local self-sufficiency by 21 22 228 optimising the use of land and the supply of goods and services on the spot. To address the 23 229 difference in scale of the vision as compared to the scale of our analysis, this was 24 25 230 implemented in a way that all land use forms should be present at the NUTS-2 level to a 26 27 231 certain extent. We assumed a minimal target of 20% for each land-cover type in a region: if a 28 232 certain land-cover type was below 20% of the total land area, then that land cover type should 29 30 233 increase (+1) and if the share exceeded 20% no change was desired (0). 31 32 234 33 235 To match the desired change of each variable to the model outcomes for this variable under 34 35 236 each scenario, the projected change in each model variable between 2010 and 2040 was also 36 37 237 reclassified into three classes. A projected increase corresponded to a value +1, no change 38 238 corresponded to 0 and a projected decreased corresponded to -1. We assumed a threshold of 39 40 239 5% to determine whether a variable was projected to change. 41 42 240 43 241 Finally, we compared the desired and projected changes for each model variable and 44 45 242 administrative region (Figure S2). We did this by calculating the absolute difference to 46 47 243 identify whether projected change was in full agreement (absolute difference = 0), 48 244 disagreement (absolute difference = 1), or strong disagreement (absolute difference = 2) with 49 50 245 the desired change. 51 52 246 53 247 2.4 Identifying pathways 54 55 248 To identify pathways towards the visions, we calculated the mean level of agreement for all 56 57 249 24 scenarios with regards to each vision. To do this, we used the absolute difference between 58 250 the reclassified desired and projected change for each model variable. We then calculated the 59 60 61 62 7 63 64 65

251 mean value over all 20 model variables using w1 and w2 as weights and we subtracted this 1 252 mean from 1. This can be written as: 2 3 253 4 n 5 ∑k=1 푤1k × 푤2j,k,l × 푚푣푎ri,k,l 6 254 푎푔푟푒푒푚푒푛푡i,j,l = 1 − n 2 × ∑ 푤1k × 푤2j,k,l 7 k=1 8 255 9 10 256 where w1 and w2 denote weights (see section 2.3), mvar denotes (reclassified) model 11 257 variables, and i denotes scenarios, j denotes visions, k denotes model variables, l denotes 12 13 258 administrative regions and n denotes the number of model variables (i.e. 20). This formula 14 15 259 results in values in the range [0,1]; a value of 1 means that a scenario projection is in full 16 260 agreement, a value of 0 implies full disagreement. 17 18 261 19 20 262 We assumed that for a scenario projection to be considered a pathway, a projection should 21 263 agree at least to 60% (i.e. agreement ≥0.6) with a vision for individual administrative regions 22 23 264 and that this should apply to at least a two-third majority of the land area and population in 24 25 265 the EU. By considering both land area and population, we prevented that small regions with 26 266 high population numbers (e.g. large cities) or large regions with low population (e.g. northern 27 28 267 Europe) would get a disproportionate weight in determining whether a scenario projection 29 30 268 would be a pathway. Population data for all regions in our analyses were obtained for the year 31 2010 from EUROSTAT (2014). 32 269 33 270 34 35 271 To analyse how different assumptions to identify pathways would affect our results, we 36 37 272 conducted a sensitivity analysis. We assessed the number of pathways that could be identified 38 273 if level of agreement would exceed (i) 70% between a scenario projection and a vision, (ii) 39 40 274 60% and apply to >50% majority of the population and land area; (iii) 60% and apply to a two 41 42 275 third majority of the population (not land area), (iv) 60% and apply to a two third majority of 43 276 the land area (not population), and (v) 60% and apply to a two third majority of the population 44 45 277 and land area and that the number of strong disagreements should not exceed three. We also 46 47 278 analysed the effect of applying alternative thresholds to determine whether a model variable 48 279 was projected to increase, to be stable, or to decrease by applying thresholds of 1% and 10%. 49 50 280 51 52 281 3 Results 53 282 3.1 Pathways to the visions 54 55 283 Matching desired and projected changes in future land use indicated that most scenario 56 57 284 projections agree with about 60% of the visions and that there were no scenario projections 58 285 that were in full agreement with any of the visions at the European level (Figure 1). When 59 60 286 adding the criteria on population and land area, we identified five pathways to Regional 61 62 8 63 64 65

287 Connected, two pathways to Best Land in Europe, but no pathways to Local Multifunctional. 1 288 Out of the 24 scenarios considered in the simulations, the B2 Nature Protection (B2NP) and 2 3 289 the B2 Payments for Carbon Sequestration (B2PC) policy scenarios were pathways to 4 5 290 Regional Connected and Best Land in Europe, while the B1, A2 Nature Protection (A2NP) 6 291 and B2 Payments for Recreational services (B2PR) scenarios were pathways to Regional 7 8 292 Connected. We did not identify any pathway in a world developing according to our A1 9 10 293 global development scenario. 11 12 294 13 295 <> 14 15 296 16 17 297 To analyse how different model variables contributed to the estimated level of agreement, we 18 298 derived heat maps indicating how frequent (based on the number of regions) the projected 19 20 299 change in a model variable is in agreement with the desired change for the four global 21 22 300 developments and all pathways in Figure 2. In Figures S4 and S5 results for all scenario 23 301 projections, as well as results for model variables in strong disagreement are shown. Our 24 25 302 results suggest that the extent of arable land and extent of semi-natural area explain why the 26 27 303 B1 scenario, A2 Nature Protection and B2 Nature Protection were pathways to the vision 28 304 Regional Connected, while for the B2 Payments for Carbon Sequestration the main variables 29 30 305 were carbon sequestration in forest biomass and dead wood. The connectivity index of (semi-) 31 32 306 natural area was also an important variable in the B2 Nature Protection to Regional 33 307 Connected. The same set of variables was also important for the two pathways to Best Land in 34 35 308 Europe. 36 37 309 38 310 <> 39 40 311 41 42 312 None of our identified pathways showed full agreement for all variables with any of the 43 313 visions. Always there were model variables that were in strong disagreement with the visions. 44 45 314 For all visions, the desired and projected change for the forest-related variables carbon 46 47 315 sequestration in forest biomass and dead wood were frequently in strong disagreement. 48 316 Another model variable that was frequently in strong disagreement was connectivity index of 49 50 317 (semi-) natural area. While these model variables were desired to increase in all three visions 51 52 318 (see Table 2), they were generally projected to decrease according to the models, and none of 53 319 the policy options brought them to the desired increase. Disagreements were less pronounced 54 55 320 for the Nature Protection and Payments for Carbon Sequestration scenarios. 56 57 321 58 59 60 61 62 9 63 64 65

322 3.2 Spatial patterns in pathways 1 323 To investigate regional patterns in the pathways, we mapped the agreement for pathways 2 3 324 (Figures 3 and 4) and developed heat maps for all scenarios for clustered regions (by country, 4 5 325 rurality class and environmental zone; Figure S6). Strikingly, the Baltic countries, Cyprus, 6 326 Denmark, Bulgaria, Czech Republic, Hungary, Poland, Slovakia, Slovenia, Spain, northern 7 8 327 Sweden, as well as several regions in the United Kingdom almost consistently agreed with 9 10 328 more than 60% with the desired land use changes according to Regional Connected. For Best 11 12 329 Land in Europe we detected different patterns; both the B2 Nature Protection and B2 13 330 Payments for Carbon Sequestration pathways were in agreement with the visions in the Baltic 14 15 331 countries, Denmark, Czech Republic, Poland, Romania, as well as western and central parts 16 17 332 of France and most regions in the United Kingdom. 18 333 19 20 334 <> 21 22 335 <> 23 336 24 25 337 For Regional Connected we found that many regions in Austria, Belgium, Germany, Greece 26 27 338 and Portugal, southern Sweden, and northwest and southeast France agreed with less than 28 339 60% with the vision, regardless of the pathway investigated. Interestingly, Germany, Greece 29 30 340 and Portugal were also generally in low agreement in pathways to Best Land in Europe. 31 32 341 However, this did not apply to Austria, Belgium and France where land use generally 33 342 developed in agreement with Best Land in Europe. Conversely, while land use generally 34 35 343 developed in agreement with Regional Connected in Cyprus, Italy, Slovakia and Slovenia, 36 37 344 there was low agreement for these countries with Best Land in Europe. 38 345 39 40 346 Our sensitivity analysis (Figure S7) showed that the identification of pathways strongly 41 42 347 depended on the assumed thresholds to define pathways. In case we would have applied more 43 348 strict thresholds, no pathways would be identified to any of the visions, while if we would 44 45 349 apply less strict criteria, we would detect a larger number of pathways to all visions. 46 47 350 Interestingly, when reducing the stringency of the criteria to identify pathways, we identified 48 351 nine pathways to the vision Local Multifunctional, but most of these pathways were not 49 50 352 pathways to the two other visions. 51 52 353 53 354 4 Discussion 54 55 355 4.1 Interpretation of results 56 57 356 Plausible scenarios of future land use derived for model projections may differ substantially 58 357 from what is actually desired by society and identifying such mismatches is important for 59 60 358 identifying policies to resolve them. We developed an analytical framework that links 61 62 10 63 64 65

359 stakeholder visions of future land use to model-based projections of possible land-use changes 1 360 according to four scenarios of possible future global developments and a range of policy 2 3 361 options towards 2040. While the visions represent normative views on desired developments, 4 5 362 the projections describe plausible developments of the near future, taking into account the 6 363 main driving factors of land-use change. These two fundamentally different approaches of 7 8 364 exploring the future of land use in Europe were brought together by comparing the endpoint 9 10 365 of the modelled projections with the target of the desired visions. We considered as pathways 11 12 366 those combinations of global developments and policy interventions that were leading to land 13 367 use futures that closely corresponded in multiple land use dimensions of the defined visions. 14 15 368 16 17 369 When applying the framework, we identified five pathways to the vision Regional Connected, 18 370 two pathways to the vision Best Land in Europe, but no pathways to the vision Local 19 20 371 Multifunctional. The B2 Nature Protection and B2 Payments for Carbon Sequestration policy 21 22 372 scenarios represented pathways to Regional Connected and Best Land in Europe. Both of 23 373 these policy scenarios pose restrictions on the expansion of agricultural land in favour of more 24 25 374 space for nature and which are also better connected with each other, and this fact explains 26 27 375 their comparable results. We identified three additional pathways leading to Regional 28 376 Connected. The policy options in these pathways impose restrictions on land use changes, 29 30 377 bringing these closer to the desired land use futures. 31 32 378 33 379 We observed interesting differences between Member States with regards to the pathways. 34 35 380 For example, the projected land-use changes in Austria, Belgium and France were in line with 36 37 381 the desired changes according to Best Land in Europe, but not to those in Regional 38 382 Connected. A possible explanation may be the intensity of the current land use in these 39 40 383 countries (Plutzar et al. 2015), where fertile lands (or ‘best lands’) are already used for 41 42 384 agricultural production, which is much in line with the future land use according to Best Land 43 385 in Europe. Interestingly, we found that projected land-use change in the Baltic countries, 44 45 386 Czech Republic, Poland and Romania were generally in line with both the visions Regional 46 47 387 Connected and Best Land in Europe. Land-use changes in these countries have already in the 48 388 recent past shown patterns of both intensification in areas suitable for farming, and dis- 49 50 389 intensification and cropland contraction in more marginal areas (Jepsen et al. 2015; Stoate et 51 52 390 al. 2009), in line with the changes desired by both visions. 53 391 54 55 392 While we identified pathways to Regional Connected and Best Land in Europe, there were no 56 57 393 pathways identified to Local Multifunctional. The main reason for the absence of pathways to 58 59 394 Local Multifunctional is the incapability of the models to project all aspects of multi- 60 395 functional land use at the local scale. All models, except Dyna-CLUE, operate at the level of 61 62 11 63 64 65

396 administrative regions or larger entities and do not capture well the local patterns as 1 397 envisioned in Local Multifunctional where more than one land use would be required locally 2 3 398 A second reason may be that Local Multifunctional requires a different set of policy 4 5 399 interventions than those analysed in our study. Recent land-use changes show patterns that are 6 400 more in line with the two other visions, with traditional farming landscapes, arguably the land 7 8 401 system most closely resembling Local Multifunctional, rapidly disappearing across Europe 9 10 402 (Fischer et al. 2012). We therefore speculate that the potential policies leading to Local 11 12 403 Multifunctional would need to be radically different from policy options we considered here, 13 404 and focus substantially more on maintaining and strengthening links between society and 14 15 405 nature at local scale (Fischer et al. 2012), in order to bring about the desired changes. Given 16 17 406 that land-use trajectories are highly path dependent (Jepsen et al. 2015), it may be difficult to 18 407 get closer to the vision Local Multifunctional without major shifts in land management 19 20 408 paradigms. 21 22 409 23 410 Our analysis provides insights into the individual factors that contribute to the degree of 24 25 411 agreement of the scenario projections with each of the visions. However, these results should 26 27 412 not be considered in isolation but rather in connection with each other, and cannot be used to 28 413 identify single factors that decision makers should address to reach a vision. For example, 29 30 414 abandonment of agricultural land positively contributed to increasing nature areas, as desired 31 32 415 according to the visions. However, stimulating abandonment all across Europe would mean a 33 416 decrease in self-sufficiency of the EU and could lead to displacement of land use and 34 35 417 feedbacks in the economic system. Such feedbacks are considered by the land use models 36 37 418 used here and explain why it may be difficult to reach the visions if this type of processes is 38 419 accounted for. 39 40 420 41 42 421 4.2 Reflections on the approach 43 422 Stakeholders are increasingly involved in land use modelling and scenarios construction (e.g. 44 45 423 van Berkel and Verburg 2012; Hewitt et al. 2014; Palacios-Agundez et al. 2015; Haatanen et 46 47 424 al. 2014). Yet, few attempts have been made to date to link desired land-use futures with 48 425 explorative scenarios. Our approach can be considered as a variant of backcasting, which is 49 50 426 recently also relying on forward-looking projections run by simulation models (van Vliet and 51 52 427 Kok 2015). As our approach is to our knowledge applied here for the first time, we also 53 428 reflect on the approach itself and identify avenues for improvement. 54 55 429 56 57 430 Firstly, we identified model variables based on statements made by stakeholders, but these 58 431 model variables were not used when eliciting the visions. Pérez-Soba et al. (2015) asked 59 60 432 stakeholders to imagine the future landscape they wished to live in and offered them elements 61 62 12 63 64 65

433 to help describe that future. These elements related to land use in Europe, but did not include 1 434 all variables available from the land-use models. From the perspective of the land-use models, 2 3 435 a larger set of variables could have been used to characterise the desired land-use futures, 4 5 436 although this comes at the cost of complexity which could be a barrier when eliciting visions. 6 437 In contrast, stakeholders also included elements in their visions for which the land-use models 7 8 438 did not provide variables (e.g., energy, water and transport). The pathways we identified 9 10 439 could therefore not address all elements included in the three visions. To account for the 11 12 440 multi-scale character and multiple dimensions of future land use it would be recommended to 13 441 better align the modelling and visioning processes such that both cover all relevant aspects to 14 15 442 ensure the pathways provide a balanced representation towards future land systems. 16 17 443 18 444 Secondly, scenario definition and elicitation of the visions were done in parallel and largely 19 20 445 independent. The fact that we identified only a few pathways to desired land-use futures 21 22 446 suggests that the scenarios that were analysed may be too conservative, or that the visions 23 447 defined by stakeholders are too radical and visionary. Moreover, the policy options addressed 24 25 448 may not have covered those interventions needed reach the visions. The first issue could be 26 27 449 overcome by developing more extreme scenarios that better cover the uncertainty in global 28 450 development including possible regime shifts. Likewise, considering a different set of policy 29 30 451 scenarios, or introducing iterations of scenarios with incremental policy changes until the 31 32 452 desired goals are reached (Robinson et al. 2011; Seppelt et al. 2013) could be used to better 33 453 align scenarios and visions. Regarding the second issue, a more strict linkage between the 34 35 454 process to formulate policy scenarios and the process to elicit visions would have permitted 36 37 455 the definition of more targeted policy scenarios that could be more appropriate to address 38 456 stakeholder wishes on future land use. 39 40 457 41 42 458 Thirdly, we had to make several strong assumptions to be able to decide when a scenario 43 459 would be a pathway to a vision. We checked the impact of our assumptions using sensitivity 44 45 460 analysis. The results suggest that the number of pathways was highly dependent on the 46 47 461 decision rules applied, which in turn depends on the trade-offs that the stakeholders are 48 462 willing to accept to reach a vision. Furthermore, we assumed that a model variable had 49 50 463 changed in time when a deviation of 5% was found compared to the present situation. 51 52 464 However, what is seen as a significant change could be different depending on the magnitude 53 465 of changes in the model variables as envisioned by the stakeholders. The analysis revealed 54 55 466 that the results were sensitive to the type of vision, i.e. no effect of this assumption on the 56 57 467 identification of pathways to Local Multifunctional, but on the contrary this assumption did 58 468 reveal medium to strong impacts on the identification of pathways to Best Land in Europe and 59 60 469 Regional Connected. In this paper, the decision rules needed in the various steps to identify 61 62 13 63 64 65

470 pathways to visions of future land use were defined in a simple and reproducible manner. In 1 471 future work, we suggest a stronger rationale behind these assumptions. 2 3 472 4 5 473 These three issues relate to the process on how to better link stakeholder visioning processes 6 474 and exploratory scenario modelling. Reflecting on our approach, we think it would be 7 8 475 beneficial to link more tightly the different modelling steps and the elicitation of the visions 9 10 476 of future land use, which would result in fewer assumptions needed when identifying 11 12 477 pathways. However, the linkage should not be too stringent either, because models represent a 13 478 simplification of reality and often allow for only a limited set of policy options to be analysed. 14 15 479 Such limitations should not impede the creativity and freedom of stakeholders to express their 16 17 480 desired future land use. 18 481 19 20 482 It is important to consider that the agreement between scenario projections and visions are 21 22 483 based on changes in land use rather than on the current or future state of land use. This 23 484 difference is important, because some regions may currently already be close to the desired 24 25 485 state, although our results may suggest a low level of agreement in 2040. A low level 26 27 486 agreement may still indicate a good fit of the situation with desired land use, while a high fit 28 487 does not necessary mean that land use in that region reaches the desired vision. Future 29 30 488 analysis may focus on both the state and change of land use to obtain a more complete 31 32 489 picture. 33 490 34 35 491 Our approach to identify pathways to desired land-use futures relied on model projections of 36 37 492 land use over several decades. Considering that our models do not include regime shifts in 38 493 land systems, for example to shock events such as economic crises, rapid institutional change, 39 40 494 or technological breakthrough, as well as changing boundary conditions, such as new value 41 42 495 systems, it is obvious that land use may develop differently than projected. Likewise, 43 496 stakeholders (and their successors) may change their perceptions and priorities over time, 44 45 497 leading to changing visions. This means that if society would like to move to any of the three 46 47 498 envisioned land-use futures, pathways should be evaluated repeatedly to verify whether the 48 499 changes in land use are in line with the desired changes and to adjust policies where needed in 49 50 500 an adaptive management process (Lindenmayer et al. 2009; Haasnoot et al. 2013). 51 52 501 53 502 5 Conclusions 54 55 503 This paper presents a novel approach to link stakeholder-based visions of future land use with 56 57 504 model-based projections of how land use may change in Europe. We analysed the projected 58 505 future land use for four global development scenarios and found that land-use changes in a 59 60 506 globalised world generally showed better agreement with one of the visions (Regional 61 62 14 63 64 65

507 Connected) as compared to a future world with a more regionally fragmented development. In 1 508 a regionally fragmented world, however, policies have the ability to change the development 2 3 509 of land use such that it is more in line with land-use futures desired by society. We also found 4 5 510 that none of our pathways were fully in line with the visions. This implies that the 6 511 identification of pathways to a desired future land use is subject to the trade-offs that 7 8 512 stakeholders (or society) should be willing to accept. Linking stakeholder-based visions to 9 10 513 quantitative, large-scale land use modelling remains challenging, for it is difficult to find the 11 12 514 right balance in connecting the two fundamentally different approaches. Nevertheless, we 13 515 believe our approach to combine explorative scenarios with normative visions represents a 14 15 516 promising avenue for foresight studies on land use. 16 17 517 18 518 Acknowledgements 19 20 519 This work has been funded through the EU 7th framework project VOLANTE (project 21 22 520 number 265104). The authors thank Sergey Zudin, Elena Zudina and Simo Varis from the 23 521 European Forest Institute for their help with data processing, Bep Schrammeijer from VU 24 25 522 University for scenario simulations, as well as all partners of the VOLANTE project for their 26 27 523 comments on the approach. Opinions expressed in this paper are those of the authors only. 28 524 29 30 525 References 31 Castella J-C, Pheng Kam S, Dinh Quang D, Verburg PH, Thai Hoanh C (2007) Combining top-down 32 526 33 527 and bottom-up modelling approaches of land use/cover change to support public policies: 34 35 528 Application to sustainable management of natural resources in northern Vietnam. Land Use 36 529 Policy 24 (3):531-545. doi:http://dx.doi.org/10.1016/j.landusepol.2005.09.009 37 38 530 Dreborg KH (1996) Essence of backcasting. Futures 28 (9):813-828. 39 531 doi:http://dx.doi.org/10.1016/S0016-3287(96)00044-4 40 41 532 EUROSTAT (2014) Population on 1 January by broad age group, sex and NUTS 3 region. 42 533 http://appsso.eurostat.ec.europa.eu/nui/show.do [accessed 2.6.2015] 43 44 534 Fares S, Scarascia Mugnozza G, Corona P, Palahí M (2015) Sustainability: Five steps for managing 45 535 Europe's forests. Nature 519 (7544):407-409. doi:10.1038/519407a 46 47 536 Fischer J, Hartel T, Kuemmerle T (2012) Conservation policy in traditional farming landscapes. 48 537 Conservation Letters 5 (3):167-175. doi:10.1111/j.1755-263X.2012.00227.x 49 50 538 Foley JA, DeFries R, Asner GP, Barford C, Bonan G et al. (2005) Global consequences of land use. 51 539 Science 309 (5734):570-574. doi:http://dx.doi.org/10.1126/science.1111772 52 53 540 Godfray HCJ, Garnett T (2014) Food security and sustainable intensification. Philosophical 54 541 Transactions of the Royal Society of London B: Biological Sciences 369 (1639) 55 56 542 Haasnoot M, Kwakkel JH, Walker WE, ter Maat J (2013) Dynamic adaptive policy pathways: A 57 543 method for crafting robust decisions for a deeply uncertain world. Global Environmental 58 59 544 Change 23 (2):485-498. doi:http://dx.doi.org/10.1016/j.gloenvcha.2012.12.006 60 61 62 15 63 64 65

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List of Figures

Figure 1: Result of matching desired and projected change in model variables (a value of 1 means full agreement). The boxplots are based on the 231 administrative regions as individual data points, without weighting them for area or population. The scenario names are explained in Table 1.

Figure 2: Heat maps indicating the frequency (based on the number of regions) a model variable is in full agreement with the visions for the global development scenarios and pathways (pathways are indicated in bold). Abbreviations are explained in Table 1.

Figure 3: Maps showing the level of agreement for the pathways to Best Land in Europe.

Figure 4: Maps showing the level of agreement for the pathways to Regional Connected. Figure 1 Best Land in Europe Regional Connected Local Multifunctional B2AE B2AE B2AE A2AE A2AE A2AE B2FP B2FP B2FP A2FP A2FP A2FP B2ZC B2ZC B2ZC A2ZC A2ZC A2ZC B2CR B2CR B2CR A2CR A2CR A2CR B2PR B2PR B2PR A2PR A2PR A2PR B2PC B2PC B2PC A2PC A2PC A2PC B2BE B2BE B2BE

Scenario A2BE A2BE A2BE B2AP B2AP B2AP A2AP A2AP A2AP B2NW B2NW B2NW A2NW A2NW A2NW B2NP B2NP B2NP A2NP A2NP A2NP B2 B2 B2 B1 B1 B1 A2 A2 A2 A1 A1 A1

0.3 0.4 0.5 0.6 0.7 0.8 0.3 0.4 0.5 0.6 0.7 0.8 0.3 0.4 0.5 0.6 0.7 0.8

Agreement [0,1] Agreement [0,1] Agreement [0,1] Figure 2

Scenario arab fore natu urba aryd rumi pigs poul wood resi conn peri shan wild rent self emis cseq ddwd trad

B2NP Best Land in Europe

B2PC

A2NP

B2NP Regional Connected

B2PC

B2PR

B2 Local Multifunctional

No region in agreement All regions in agreement 0% 20% 40% 60% 80% 100% Figure 3 Click here to download Figure Fig3.tif Figure 4 Click here to download Figure Fig4.tif Tables

Tables

Table 1: Brief description of the scenarios. References to more detailed descriptions are given in Table S2 of the Supplementary Online Material. Global # develop Code Scenario Brief description of policy alternative ment Globalized world with strong economic growth and weak 1 A1 A1 intervention. The Common Agricultural Policy (CAP) of the European Union (EU) is fully abolished. Fragmented world with modest economic growth and weak 2 A2 A2 Global development intervention. EU CAP remains unchanged. Globalised world with modest economic growth and strong 3 B1 B1 intervention. EU CAP is fully abolished. Fragmented world with modest economic growth and strong 4 B2 B2 intervention. EU CAP remains unchanged. A focus on nature protection, with expansion of protected zones 5 A2 A2NP beyond Natura2000, a robust ecological corridor network and Nature protection strengthened constraints on land cover conversions and restrictions 6 B2 B2NP on forest management. 7 A2 A2NW Nitrogen and water Strong reduction of the application of nitrates from animal manure 8 B2 B2NW quality to prevent further ground and/or surface water pollution. Faster achievement of higher yields, e.g. through additional 9 A2 A2AP Agricultural investments in R&D or improvements of labour/capital productivity. productivity The budget needed is taken from the direct farm payment budget in 10 B2 B2AP Pillar I of the Common Agricultural Policy (CAP) 11 A2 A2BE Demand for biomass is strongly increasing for material and energy Bio-based economy use. Constraints on removals of logging residues and stumps from 12 B2 B2BE and bioenergy forests are less strict. Incentives to (i) limit the conversion of grassland and Payment for 13 A2 A2PC Payment for carbon Ecosystem Services (PES) scheme to protect areas that are prone to sequestration carbon emissions due to their high soil organic carbon contents and 14 B2 B2PC (ii) to stimulate carbon sequestration in forest biomass. Direct payments to landowners (farmers and forest owners) in 15 A2 A2PR Payment for exchange for managing their land to provide recreational services. recreational services The budget needed is taken from the direct farm payment budget in 16 B2 B2PR Pillar I of the CAP. Additional agricultural employment is encouraged by extra EU 17 A2 A2CR CAP reform for rural subsidies. Additional rural employment may trigger production employment intensification and reduced pressure on land. In the agricultural 18 B2 B2CR sector, 20% of the EU CAP budget shifts to labour subsidy. 19 A2 A2ZC Zoning for compact Limitation of urban sprawl and creation and maintenance of 20 B2 B2ZC cities compact urban settlements and cities. 21 A2 A2FP Flood protection European-wide adoption of climate change adaptation measures. 22 B2 B2FP Climate change 23 A2 A2AE Agricultural sector has to contribute to overall emission reductions mitigation and by complying with climate policy frameworks based on emission agricultural emission 24 B2 B2AE pricing through emission trading or standards. taxes

Table 2: Desired change (d) and weight (w2) of the model variables according to the three visions. The desired changes +1, 0 and -1 indicate whether a model variable is desired to increase, not change or decrease, resp. In case multiple desired changes are shown, a regional pattern was assumed. The w2 indicates whether a model variable was desired to change strongly by the stakeholders. See text for details on assumed regional patterns and w2. Best Land in Europe Regional Connected Local Multifunctional Attribute Variable Abbreviation d w2 d w2 d w2 Extent of arable land arab -1/0/+1 1 -1 1 0/+1 1 Extent of forest area fore 0 1 +1 1 0/+1 2 Land cover extent Extent of (semi-) natu +1 1 +1 2 0/+1 2 natural area Extent of urban area urba 0 1 0 1 0 1 Crop yield aryd +1 2 +1 1 +1 1 Stocking density of rumi +1 2 +1 1 +1 1 ruminants Stocking density of pigs +1 2 +1 1 +1 1 Land use pigs management Stocking density of poul +1 2 +1 1 +1 1 poultry Roundwood removals wood -1/0/+1 1 +1 1 +1 2 Extracted logging resi -1/0/+1 2 +1 1 +1 1 residue and stumps Connectivity index of semi-natural area and conn +1 1 +1 2 +1 2 forest Growth of peri-urban peri -1 2 0 2 +1 2 area Land use pattern Shannon-index for shan +1 1 +1 1 +1 2 crop diversity Contribution of abandoned agricultural wild +1 2 0 1 -1 1 land to wilderness Shadow value of rent +1 1 +1 1 +1 2 agricultural land Production over self +1 2 +1 1 +1 1 domestic consumption Land use services Global warming emis 0 1 0 1 -1 1 potential in agriculture Deadwood in forest ddwd +1 2 +1 2 +1 2 Carbon sequestration cseq +1 1 +1 1 +1 2 in forest biomass Global land Net-trade of agri-food trad +1 2 0 1 0 1 impacts products

Electronic Supplementary MaterialClick here to download Electronic Supplementary Material Verkerk_pathways_SOM_submitted.docx

1 SUPPLEMENTARY MATERIAL 2 3 Identifying pathways to visions of future land use in Europe 4 5 Pieter J. Verkerk, Marcus Lindner, Marta Pérez-Soba, James S. Paterson, John 6 Helming, Peter H. Verburg, Tobias Kuemmerle, Hermann Lotze-Campen, Alexander 7 Moiseyev, Daniel Müller, Alexander Popp, Catharina J. E. Schulp, Julia Stürck, 8 Andrzej Tabeau, Bernhard Wolfslehner, Emma H. van der Zanden 9 10

11 Contents 12 Table S1: Web links to model descriptions and scientific references...... 2 13 Table S2: References to scenario descriptions...... 3 14 Table S3: Selected model variables...... 4 15 Table S4: Overview of rurality classes and environmental zones...... 6 16 Figure S1: Correlation plots...... 7 17 Figure S2: Schematic overview of matching projected and desired land use changes. 8 18 Figure S3: Pathways to the visions...... 9 19 Figure S4: Heat maps for full agreement for model variables...... 10 20 Figure S5: Heat maps for strong disagreement for model variables...... 11 21 Figure S6: Heat maps for the level of agreement of scenarios...... 14 22 Figure S7: Sensitivity analysis...... 15 23 References ...... 16 24 25

26 Table S1: Web links to model descriptions and scientific references. Model Reference Factsheet Lotze-Campen et al. http://www.volante- ReMIND/MAgPIE 2008; Leimbach et al. project.eu/images/Factsheets/A16_Model_MAgPIE_REM 2010 IND.pdf http://www.volante- MAGNET Woltjer et al. 2014 project.eu/images/Factsheets/A17_Model_LEITAP_MAG NET_2015.pdf http://www.volante- EFI-GTM Kallio et al. 2004 project.eu/images/Factsheets/A21_Model_EFI-GTM.pdf http://www.volante- CAPRI Britz and Witzke 2012 project.eu/images/Factsheets/A19_Model_CAPRI.pdf Sallnäs 1990; Schelhaas http://www.volante- EFISCEN et al. 2007 project.eu/images/Factsheets/A20_Model_EFISCEN.pdf Verburg and Overmars http://www.volante- Dyna-CLUE 2009 project.eu/images/Factsheets/A22_Model_DynaCLue.pdf 27

28 Table S2: References to scenario descriptions. # Code Scenario Reference

1 A1 http://www.volante- project.eu/images/Factsheets/A1_Fact_sheet_Marker_scenario_stor 2 A2 ylines.pdf Global development 3 B1 http://www.volante- project.eu/images/Factsheets/A2_Marker_scenario_model_impleme 4 B2 ntation.pdf 5 A2NP http://www.volante- Nature protection 6 B2NP project.eu/images/Factsheets/A5_VPA_Nature_Protection.pdf http://www.volante- 7 A2NW Nitrogen and water project.eu/images/Factsheets/A6_VPA_Nitrogen_and_water_qualit quality 8 B2NW y.pdf 9 A2AP Agricultural http://www.volante- 10 B2AP productivity increase project.eu/images/Factsheets/A7_VPA_Agricultural_increase.pdf 11 A2BE Bio-based economy http://www.volante- 12 B2BE and bioenergy project.eu/images/Factsheets/A8_VPA_Biobased_economy.pdf http://www.volante- 13 A2PC Payment for carbon project.eu/images/Factsheets/A9_VPA_Payment_for_carbon_seque sequestration 14 B2PC stration.pdf http://www.volante- 15 A2PR Payment for project.eu/images/Factsheets/A10_VPA_Payment_for_recreational_ recreational services 16 B2PR services.pdf 17 A2CR http://www.volante- CAP reform 18 B2CR project.eu/images/Factsheets/A11_VPA_CAP_reform.pdf 19 A2ZC Zoning for compact http://www.volante- 20 B2ZC cities project.eu/images/Factsheets/A12_VPA_zoning_compact_cities.pdf 21 A2FP http://www.volante- Flood protection 22 B2FP project.eu/images/Factsheets/A13_VPA_CC_Flood_protection_.pdf http://www.volante- 23 A2AE Climate change project.eu/images/Factsheets/A14_VPA_CC_mitigation_and_agricu mitigation 24 B2AE ltural_emission_taxes.pdf 29

30 Table S3: Selected model variables.

Attribute Variable Model Description Acreage of all arable, vegetable and horticultural crops in percent of Extent of CAPRI total land area. The variable also includes temporary grassland, arable land fallow land and set aside. The forest area in percent of total land area, containing production Extent of forest, protected forest, and forest not currently harvested for other Dyna-CLUE forest area reasons. It does not include other types of natural vegetation, nor does it contain agro-forestry land cover types. Land cover The area in percent of total land area of forests (see above) and all Extent of extent (semi-) natural vegetation types that are non-forest with the (semi-) Dyna-CLUE exception of small forest patches as occurring in agricultural natural area landscapes. This class includes natural grasslands and scrublands. All built-up area (and other human fabric) area in percent of total land area. It includes continuous urban fabric, discontinuous urban Extent of Dyna-CLUE fabric, industrial areas, commercial areas, road and rail networks, urban area (air)ports, mineral extraction sites, dump sites, construction sites, green urban areas, sports facilities, and leisure facilities. Average yield per ha of all arable crops, included in variable 'extent of arable land'. The individual crops are weighted by acreage per Crop yield CAPRI crop and corresponding revenue per crop per ha in constant euros of 2010. Stocking density of ruminants per fodder area (grassland plus Stocking fodder on arable land). Ruminants include dairy cows, suckler density of CAPRI cows, male and female beef cattle, all calves and heifers and sheep ruminants and goats. The individual animals are aggregated by livestock units with 1 cow is 1 livestock unit. Stocking Land use density of CAPRI Stocking density of pig fattening per ha of arable crop management pigs Stocking density of CAPRI Stocking density of poultry fattening per ha of arable crop poultry Roundwood The amount of roundwood removed from production forests for EFISCEN removals material and energy use per ha forest Extracted logging The amount of logging residues (stem tops, branches) and stumps EFISCEN residue and removed from production forests for energy production per ha forest stumps This indicator gives the approximation of the connectivity potential of the landscape for species and the viability of smaller habitats Connectivity within the landscape. It calculates the ease to reach larger sized index of areas of natural vegetation from smaller sized habitats, accounting Dyna-CLUE semi-natural for the land use types between the habitats. For example, an urban area area is very difficult to migrate through as a species (high resistance), while permanent grasslands are much easier (low resistance). Land use pattern Growth of Peri-urban growth, as opposed to urban sprawl/edge expansion of peri-urban Dyna-CLUE cities, is defined as outlying growth of built-up area (outside of area urban cores). Shannon- index for CAPRI Diversity index for agricultural crops, including grassland. crop diversity Contribution Formerly agricultural land, converted to nature (semi-natural or Dyna-CLUE of abandoned forest cover) which forms part of a wilderness patch. The definition 4

agricultural of wilderness follows "Wild Europe: A Working Definition of land to European Wilderness and Wild Areas” wilderness Shadow Shadow price of land represents its opportunity cost (the value of value of the land in its next best alternative use). The average shadow price CAPRI agricultural of land in a region, can be seen as an estimate of the economic value land of land in that region and an indicator of generating income Production over Production over domestic consumption. Soft wheat was used as an domestic CAPRI indicator for self-sufficiency in food consumption in the EU. consumption for softwheat Land use Global services Warming Emissions of greenhouse gases by agriculture expressed as a global CAPRI Potential in warming potential (in CO2 equivalents) agriculture Deadwood in The amount of standing and lying deadwood in production forests. EFISCEN forest Deadwood is an important indicator for forest biodiversity Carbon The annual amount of carbon removed from the atmosphere and sequestration EFISCEN stored in forest biomass. Carbon sequestration in forest biomass is in forest important for climate change mitigation. biomass Net-trade of Global land agri-food MAGNET The difference between export and import of agri-food products impacts products 31

32 Table S4: Overview of rurality classes and environmental zones. Cluster Proportion of Description regions in which dominant (%) Rurality High population density and high levels of Gross Domestic product Urban 3 (GDP). Low in agricultural land and very low in semi-natural vegetation. High population density and high levels of GDP. Regions include the tertiary sector, predominantly resulting in a relative small Peri-urban 22 agricultural share of the total GDP. Regions are still characterised by a large, but progressively declining, percentage of land in use for primary production, with wide geographical differences. Medium population density and average income with wide geographical differences. A large proportion of land is used for Rural 43 agricultural production with rural areas not always very distant from major urban centres. Deep rural 33 Low population density and low average income. Environmental zones North 7 Atlantic 33 Environmental stratification of Europe based on a selection of Continental 34 environmental variables (climatic variables, elevation data, Alpine 3 indicators for oceanicity and northing) Mediterranean 23 33 34 Source: van Eupen et al. 2012 35

A1 A2

B1 B2 36 Figure S1: Correlation plots. 37 The correlation plots the selected model variables the four global development scenarios. Spearmann 38 rank correlations were calculated based on the change ratio between 2040 and the base year for each 39 model variable. The size of the circle denotes the strength of the correlations and the colour indicates 40 whether the correlation is positive (blue) or negative (red). 41

42 43 Figure S2: Schematic overview of matching projected and desired land use changes. 44

45 Population Land area

Best Best Land in Europe

Connected

Regional

Local Multifunctional

Figure S3: Pathways to the visions. The pathways are shown in colour and non-pathways are shown in grey. The graphs show the cumulative proportion and land area (in columns) for different levels of agreement between model projections and the three consolidated stakeholder visions (in rows). The abbreviations of the scenarios are explained in Table 1. 46

Marker scenario or policy alternative arab fore natu urba aryd rumi pigs poul wood resi conn peri shan wild rent self emis cseq ddwd trad A1 A2 B1 B2 A2NP

B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR

Best Best Land in Europe B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE Marker scenario or policy alternative arab fore natu urba aryd rumi pigs poul wood resi conn peri shan wild rent self emis cseq ddwd trad A1 A2 B1 B2 A2NP

B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR Regional Connected B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE Marker scenario or policy alternative arab fore natu urba aryd rumi pigs poul wood resi conn peri shan wild rent self emis cseq ddwd trad A1 A2 B1 B2 A2NP B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR

Local Multifunctional B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE

0% 20% 40% 60% 80% 100% No region in agreement All regions in agreement Figure S4: Heat maps for full agreement for model variables. The heat maps indicate the frequency (based on the number of regions) the projected change of a model variable is in full agreement with the desired change. The abbreviations of the scenarios are explained in Table 1. 47

Marker scenario or policy alternative arab fore natu urba aryd rumi pigs poul wood resi conn peri shan wild rent self emis cseq ddwd trad A1 A2 B1 B2 A2NP

B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR

Local B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE

0% 20% 40% 60% 80% 100% No region in strong disagreement All regions in strong disagreement Figure S5: Heat maps for strong disagreement for model variables. The heat maps indicate the frequency (based on the number of regions) the projected change of a model variable is in strong disagreement with the desired change. The abbreviations of the scenarios are explained in Table 1. 48

Best Land in Europe Scenario Country / cluster A1 A2 B1 B2 A2NP B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary

Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal

Best Best Land in Europe Romania Slovakia Slovenia Spain Sweden United Kingdom Peri-Urban Rural Deep Rural North Atlantic Continental Alpine Mediterranean

Regional Connected Scenario Country / cluster A1 A2 B1 B2 A2NP B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Regional Connected Romania Slovakia Slovenia Spain Sweden United Kingdom Peri-Urban Rural Deep Rural North Atlantic Continental Alpine Mediterranean

Local Multifunctional Scenario Country / cluster A1 A2 B1 B2 A2NP B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary

Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands

Multifunctional Poland Portugal

Local Romania Slovakia Slovenia Spain Sweden United Kingdom Peri-Urban Rural Deep Rural North Atlantic Continental Alpine Mediterranean

<0.60 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Figure S6: Heat maps for the level of agreement of scenarios. The heat maps indicate the level of agreement of all scenarios with the visions for EU member states, rurality classes and main environmental zones. The abbreviations of the scenarios are explained in Table 1. 49 50

Scenario Sensivity test A1 A2 B1 B2 A2NP B2NP A2NW B2NW A2AP B2AP A2BE B2BE A2PC B2PC A2PR B2PR A2CR B2CR A2ZC B2ZC A2FP B2FP A2AE B2AE

Default

Sensivity tests related to decision criteria

Agreement >0.6 for >50% of population and area

Agreement >0.7 for >2/3 of population and area

Agreement >0.7 for >50% of population and area

Agreement >0.6 for >2/3 of population and area and <= 3 strong disagreements

Agreement >0.6 for >2/3 of population

Agreement >0.6 for >2/3 of area

Sensivity tests related to methods

Model variable is projected to increase when change > +1%

Best Best Land in Europe Model variable is projected to increase when change > +10%

Model variable is projected to decrease when change <-1%

Model variable is projected to decrease when change <-10% Equal weights for w2 Default

Sensivity tests related to decision criteria

Agreement >0.6 for >50% of population and area

Agreement >0.7 for >2/3 of population and area

Agreement >0.7 for >50% of population and area

Agreement >0.6 for >2/3 of population and area and <= 3 strong disagreements

Agreement >0.6 for >2/3 of population

Agreement >0.6 for >2/3 of area

Sensivity tests related to methods

Model variable is projected to increase when change > +1%

Model variable is projected to increase when change > +10%

Regional Connected Model variable is projected to decrease when change <-1%

Model variable is projected to decrease when change <-10% Equal weights for w2 Default

Sensivity tests related to decision criteria

Agreement >0.6 for >50% of population and area

Agreement >0.7 for >2/3 of population and area

Agreement >0.7 for >50% of population and area

Agreement >0.6 for >2/3 of population and area and <= 3 strong disagreements

Agreement >0.6 for >2/3 of population

Agreement >0.6 for >2/3 of area

Sensivity tests related to methods

Model variable is projected to increase when change > +1%

Model variable is projected to increase when change > +10%

Local Multifunctional Model variable is projected to decrease when change <-1%

Model variable is projected to decrease when change <-10% Equal weights for w2 Figure S7: Sensitivity analysis. Sensitivity analysis of how decision rules affect the identification of pathways to the visions. Pathways are indicated in blue. The abbreviations of the scenarios are explained in Table 1. 51 52

53 References 54 55 Britz W, Witzke P (2012) CAPRI model documentation 2012. University Bonn, Bonn 56 Kallio AMI, Moiseyev A, Solberg B (2004) The global forest sector model EFI-GTM – the 57 model structure. Technical report 15. European Forest Institute, Joensuu, Finland. 58 Leimbach M, Bauer N, Baumstark L, Edenhofer O (2010) Mitigation costs in a globalized 59 world: climate policy analysis with REMIND-R. Environmental Modeling and 60 Assessment 15, 155-173. 61 Lotze-Campen H, Müller C, Bondeau A, Rost S, Popp A, Lucht W (2008) Global food 62 demand, productivity growth and the scarcity of land and water resources: a spatially 63 explicit mathematical programming approach. Agricultural Economics 39(3): 325-338 64 Sallnäs O (1990) A matrix model of the Swedish forest. Studia Forestalia Suecica 183:23 65 Schelhaas M-J, Eggers J, Lindner M, Nabuurs GJ, Päivinen R, Schuck A, Verkerk PJ, Werf 66 DCvd, Zudin S (2007) Model documentation for the European Forest Information 67 Scenario model (EFISCEN 3.1.3). Alterra report 1559 and EFI technical report 26. 68 Alterra and European Forest Institute, Wageningen and Joensuu 69 van Eupen M, Metzger MJ, Pérez-Soba M, Verburg PH, van Doorn A, Bunce RGH (2012) A 70 rural typology for strategic European policies. Land Use Policy 29 (3):473-482. 71 Verburg PH, Overmars KP (2009) Combining top-down and bottom-up dynamics in land use 72 modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE 73 model. Landscape Ecology 24(9): 1167-118 74 Woltjer G, Kuiper M, Kavallari A, van Meijl H, Powell J, Rutten M, Shutes L, Tabeau A 75 (2014) The MAGNET model - Module description, LEI Report 14-057, The Hague