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Project no.: 022793 FORESCENE Development of a Forecasting Framework and Scenarios to Support the EU Sustainable Development Strategy

Instrument: STREP Thematic Priority 8.1: Policy-oriented research, scientific support to policies, integrating and strengthening the European Research Area

D.6.2 – Final report

Submission date: 4 February 2009

Start date of project: 1/12/2005

Duration: 36 months

Organisation name of lead contractor for this deliverable: Wuppertal Institute for Climate, Environment and Energy Revision: final

Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006) Dissemination Level PU PP RE CO

Public Restricted to other programme participants (including the Commission Services) Restricted to a group specified by the consortium (including the Commission Services) Confidential, only for members of the consortium (including the Commission Services)

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FORESCENE

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Final report

Stefan Bringezu, Mathieu Saurat Roy Haines-Young, Alison Rollett Mats Svensson Wuppertal Institute for Climate, Environment and Energy University of Nottingham, Centre for Environmental Management Lund University, Centre for Sustainability Studies

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Table of Content

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INTRODUCTION

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FRAMEWORK

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3

4

2.1

Question 1: What is the problem?

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2.2

Question 2: Where shall we go to?

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2.3

Question 3: How do we get there?

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2.4

Question 4: How to measure and model this?

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2.5

Question 5: What is likely to happen under business-as-usual conditions?

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2.6

Question 6: Which alternative scenarios are possible?

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MODEL PROTOTYPE

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3.1

Bayesian modelling

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3.2

Dealing with time

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3.3

Dealing with uncertainties

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3.4

General structure and system boundaries

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3.5

Integration of the model components, linkages between the environmental topics

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3.6

Data base

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3.7

Methodology for forecasting

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3.8

Methodology for backcasting

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MODELLING RESULTS

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4.1

Baseline scenario

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4.2

Alternative scenarios

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POSSIBLE APPLICATIONS OF FORESCENE IN POLICY DEVELOPMENT

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POSSIBLE EXTENSIONS / FOLLOW-UP

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CONCLUSIONS

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REFERENCES

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APPENDIX

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9.1

Conditional Probability Tables (CPTs)

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9.2

Alternative scenarios

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List of Figures Figure 1: Socio-industrial metabolism and Driving Response (DPSIR) framework in FORESCENE

Forces-Pressures-State-Impacts10

Figure 2: The six questions of the FORESCENE framework

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Figure 3: How FORESCENE addressed Question 1 "What is the problem?"

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Figure 4: How FORESCENE addressed Question 2 "Where shall we go to?"

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Figure 5: How FORESCENE addressed Question 3 "How do we get there?"

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Figure 6: How FORESCENE addressed Question 4 "How to measure and model this?"

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Figure 7: First option for dealing with time in Bayesian networks

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Figure 8: Second option for dealing with time in Bayesian networks.

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Figure 9: General overview of the structure of the FORESCENE model prototype

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Figure 10: Graphical representation of the Bayesian network for the FORESCENE meta model 27 Figure 11: Forecasting method in the FORESCENE Bayesian network model prototype

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Figure 12: Backcasting method in the FORESCENE Bayesian network model prototype

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Figure 13: Overview of the modelling results from the baseline scenario.

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Figure 14: Framework for developing alternative sustainability scenarios

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Figure 15: Overview of the modelling procedure and results examplified with the first alternative scenario ‘commitment to change’. 42

List of Tables Table 1: Data base of the FORESCENE model prototype

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Table 2: Alternative Sustainability Scenarios

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1 Introduction Since the Gothenburg summit in 2001, the implementation of the concept of sustainable development has been a core challenge for policy making in the European Union. To improve the basis for policy design, and also comply with the specific needs of ex-ante impact assessments, there is a need for a forecasting framework to develop harmonised middle and long-term baseline and alternative policy scenarios. In the context of the Sustainable Development Strategy (European Commission 2001; Council of European Union 2006) the forecasting framework should allow to develop scenarios that can be used for strategic policy preparation to better specify and disentangle the mutual relationships between environmental, economic and social trends. Against this backdrop, FORESCENE developed a generic framework for creating and simulating sustainability scenarios (forecasting and backcasting). The framework has been applied on the EU level but could potentially be used at different scales and in various contexts. Environmental topics such as water, soil, biodiversity, waste and natural resources, and priority policy fields such as agriculture, infrastructures and land use, industry and economy have been selected in close contact with the EU Commission. The objectives of FORESCENE were to (1) describe the selected environmental problems, review policy objectives and indicators, and determine the cross-cutting driving forces; (2) develop core elements of integrated sustainability scenarios (goal definition); (3) determine measures and processes to be considered for change (pre-backcasting); (4) address quantitative and qualitative parameters for measurement (parameterization); (5) check the options for modelling and develop its own model prototype; (6) develop a Business-As-Usual (BAU) scenario framework and example projections (forecasting); (7) develop alternative scenarios (incl. backcasting), and (8) work out conclusions and perspectives for the use and extension of the FORESCENE framework and model prototype. To address these objectives, the project was structured around a sequence of questions, referred to as “FORESCENE framework�, that was explored by combined analytical and participatory approaches. A series of workshops involving DGs, stakeholders and experts was organised in order to integrate knowledge on various environmental problems and priority policy fields, and define essentials for integrated sustainability scenarios in terms of goals and cross-cutting policy measures. Experts have also been involved at the stage of model prototype development through a workshop and commissioned reviews. In the following, chapter 2 unfolds the FORESCENE framework and details its six questions, giving a complete overview of the project. Then, chapter 3 introduces the structure and methodology behind the model prototype, providing also insight into its potential for forecasting and backcasting. Chapter 4 presents assumptions and narratives for a businnesas-usual scenario and three alternative scenarios. It also compiles the first quantitative results from the model prototype. Chapter 5 lists the low hanging fruits offered by the FORESCENE project which could be harvested for policy development. Finally, chapter 6 suggests possible future developments and follow-ups for the FORESCENE framework and model prototype. Finally, chapter 7 concludes.

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2 Framework To be effective, policy development and appraisal needs to understand the key driving forces and their cross-cutting linkages, which lead to increased pressure on different aspects of the environment. Measures which are designed to solve single problems often risk shifting the burden to other sectors, and they may be ineffective due to the complex interaction of environmental effects. The need for policies to be based on a cross-cutting approach has been highlighted in “The 2005 Review of the EU Sustainable Development Strategy: Initial Stocktaking and future orientations” (COM(2005)37 final), as well as in the Commission’s Communication on its Strategic Objectives 2005-2009 (COM (2005) 12). However, crosscutting driving forces which are relevant for various environmental and sustainability related problems have not yet been analyzed in a systematic policy-oriented manner. The FORESCENE project is an attempt to identify cross-cutting measures that are effective and efficient, and which have the potential to mitigate several environmental problems at the same time. As shown in Figure 1, three broad environmental themes are covered: ‘resource use and waste generation’, ‘water’, and ‘biodiversity, soils and landscape’. The aim of the project has been to capture the anthropogenic cross-cutting drivers that generate environmental pressures and the inter-linkages between them, so that an integrated forecasting framework could be constructed. It should, in turn, offer the possibility to test alternative policy strategies against some baseline. To do so, the framework used in FORESCENE was inspired and adapted from the backcasting methodology. As shown in Figure 2, the project’s framework consists of six questions that were addressed during a series of workshops. By applying the framework the project aimed to:  determine cross-cutting (cross-thematic and cross-sectoral) driving forces for

environmental problems in the three targeted fields (relates to Question 1);  define essential elements of sustainable development in the different topic areas, particularly in the form of desired sustainability goals (relates to Question 2);  describe cross-sectoral measures expected to exert a multi-beneficial impact over the environmental fields considered, so that the sustainability goals identified can be achieved (relates to Question 3); and,  combine the scenario elements (driving forces, strategies, goals etc) into baseline and alternative scenarios (relates to Questions 4 to 6). The following sections of this chapter describe each step of the FORESCENE framework presented in Figure 1 and Figure 2.

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Figure 1: Socio-industrial metabolism and Driving Forces-Pressures-State-ImpactsResponse (DPSIR) framework in FORESCENE

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Figure 2: The six questions of the FORESCENE framework

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2.1 Question 1: What is the problem? At first, a plausibility check and relevance analysis for the three environmental fields (‘resource use and waste’, ‘water’ and ‘biodiversity, soils and landscape’) was performed regarding the major economic activites and underlying factors in order to filter out crosscutting drivers. The result was provided as background for the stakeholder workshop (first integration workshop, in Brussels) which then lead to a prioritization of the cross-cutting drivers. The purpose of the meeting was so as to define more precisely the major economic activities and underlying factors that needed to be considered in order to pinpoint crosscutting drivers related to the sustainability issues, and how these simultaneously influence the three environmental problem areas. The views of the participants at the workshop were collected in the form of mind-maps (see upper left part of Figure 3). These have then been systematically analysed and organised in a matrix showing the correspondence between the three environmental fields considered, economic activities and “underlying factors” (see upper right part of Figure 3). The latter two elements constitute drivers that have been cited by the stakeholders, who also gave them weights. The matrix representation was then turned into a scorecard which helped stakeholders pinpoint the economic activities and underlying factors that they thought were relevant (see lower left part of Figure 3). Energy supply, agriculture, water supply and construction appeared to be the activities most likely to cause pressures and impacts on the three environmental themes. Transport, forestry, chemicals, basic metals, and food products were also activities or product groups that stakeholders thought as potentially important to consider. The underlying factors were sorted into five categories: economic development, production patterns, consumption patterns, demography and natural system. The categories ‘production patterns’ and ‘economic development’ were the groupings of underlying factors that achieved the highest scores in the evaluation. The factors within the ‘production patterns’ group (‘material intensity’, ‘composition of material input’, ‘innovation’ and ‘recycling’) seem to be among the most powerful underlying factors. They were all expected to have a strong, direct and cross-cutting influence on the most important activities in relation to the three topic areas. ‘Globalisation’, ‘economic growth’ and ‘investment patterns’ can also have considerable influence, but only cross-cutting environmental effects within a more limited number of activities. The underlying factor categories for the ‘natural system’ and ‘consumption patterns’ groups, follow ‘production patterns’ and ‘economic development’ in the ranking. For 'natural system', ‘depletion of resources’ and ‘climate change’ were the most relevant underlying factors, and ‘food and drink’ and ‘transport and communication’ were the most important under 'consumption patterns'. ‘Natural system’ and ‘consumption patterns’ are more indirect drivers in nature, but according to the relevance analysis they are important and should not be neglected, particularly in connection with agriculture, construction, energy and water supply, and transport. In relation to these activities, these underlying factors have a considerable cross-cutting influence. The results from this participatory screening of cross-cutting drivers made it quite clear that, in the rest of the project indicators of ‘production patterns’ (such as resource intensity of 12


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manufacturing, or green vs. brown agricultural practices) and ‘consumption patterns’ (such as consumption of manufactured goods vs. that of services, or people’s diet) need to be considered within a frame set by factors of economic development (such as economic growth, R&D investments, trade). Energy generation and use also needed to be integrated in the scenarios (especially the share of renewable energy, with a particular focus on the use of biomass for energy purposes).

Figure 3: How FORESCENE addressed Question 1 "What is the problem?"

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2.2 Question 2: Where shall we go to? The next task was to develop visions for sustainability in terms of a positively defined future. Three expert workshops were organised for the broad activity and policy fields: ‘agriculture’, ‘infrastructure / built environment’ and ‘industry / economy’. Experts were invited to share their long-term vision of sustainability based on their field of expertise, considering essentials of environmental, economic and social development. The experts took part in an interactive exercise in which they were asked to mark on several two-dimensional graphs the desired direction of future development. The aim was to specify the overall sustainability visions in the most concrete manner. It was necessary to find out which essential elements of sustainability were considered to be an integral part of the vision so that they could be used to help develop scenarios. Experts were also asked to reflect on whether current policy objectives match or could deliver their sustainability visions. Results from the three expert workshops were then further discussed in a stakeholder-expert workshop (second integration workshop, in Brussels) to establish a list of sustainability goals that could serve as references for the subsequent scenario and modelling work. Figure 4 presents a condensed overview of the sustainability goal references. In this general formulation, the goals listed are relevant to a large span of economic activities and environmental problem fields. Clearly when the question of how to measure and model these goals arose later in the project, quantitative parameters needed to be introduced. For instance, happiness and well-being will, for the time being, remain at the qualitative level in the scenario narratives, but clearly for any modelling exercise some suitable surrogates or proxies may have to be used to try to express changes over time. The same applies in relation to the goal of ‘competitiveness for the EU’ as the required econometric modelling would have exceeded the scope of the FORESCENE project. For some of the sustainability goals in Figure 4 quantitative targets have already been formulated elsewhere. For instance, according to IPCC studies, emissions of greenhouse gases should be cut by 80% in 2050 in industrial countries. Some other target values have also been suggested which could alternatively be used for assessing the progress of the EU towards priority sustainability goals in concrete terms. For example, the EU is committed to reducing its overall emissions to at least 20% below 1990 levels by 2020, and is ready to scale up this reduction to as much as 30% under a new global climate change agreement when other developed countries make comparable efforts. It has also set itself the target of increasing the share of renewables in energy use to 20% by 2020. For the environmental component, targets such as those arising from the Habitats and Water Framework Directives could be used. Some goals related to resource use and waste generation can be translated in quantitative terms using existing material flow indicators, such as total material requirement (TMR)1. An ambitious target for TMR could be an 80% reduction by 2050. Because TMR is a comprehensive indicator for the material basis of a society, such a change would certify that the EU has adopted a resource extensive pattern of production and consumption. The different components of the TMR indicator can also be considered separately and be 1

The indicator 'Total material consumption' is currently being developed within the European Statistical System (CEC 2007). When considering also the resource requirements for the production of exports, the Total Material Requirement (TMR) seems adequate.

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assigned quantitative or qualitative targets which would translate in more concrete terms the general goal reference formulation in Figure 4. For example, the reduction by 80% could be targeted only to the non-renewable part of TMR, while the use of biomass as such should not increase significantly (in order to avoid problem shifting). The ratio of foreign to domestic TMR should not increase if the issue of problem shifting across regions is to be addressed.

Figure 4: How FORESCENE addressed Question 2 "Where shall we go to?"

2.3 Question 3: How do we get there? In order to accomplish Step 3, a backcasting approach was adopted, which explored how the previously defined sustainability goals could be reached. Therefore, during the course of the workshops described in section 2.2, experts were invited to identify a list of supposedly most promising strategies, policies and instruments to pursue those goals, which could become part of the instrumental mix of the European Union towards sustainability. Those lists were compiled separately for the various activity fields of ‘agriculture’, ‘infrastructure / built environment’ and ‘industry / economy’, and the experts were then also asked to weigh the relative importance of the listed strategies/policies. 15


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The results from the three expert workshops were then considered together at the second integration workshop in Brussels. The aim was to highlight key strategies which could potentially show cross-sectoral and multi-beneficial effects, and at the same time minimize the risk of problem shifting. As shown in Figure 5, the workshop participants agreed on twenty-five key strategies which could be further sorted in seven broad categories: ‘Improving orientation and target setting', 'Improving information and decision processes', 'Improved planning', 'Changing use of capital', 'Changing environmental performance of production and consumption', 'Improvement of product management and procurement' and 'Improving state finance and social security systems'. The key strategies have been organised in Figure 5 along two axes representing the level of consensus among the workshop participants and/or society as a whole, and the level of specification of the measure. Most of the strategies from the set 'Changing environmental performance of production and consumption', which is closely related to the mainly environmental goals in Figure 4, are found in the top right-hand corner, which translates to high levels of both consensus and specification. For instance, there was a wide agreement on the need to foster energy/resource efficiency (which can be specified in concrete terms) through innovation, which could contribute to reduced use of non-renewable resources and the mitigation of greenhouse gas emissions. Such a strategy could be implemented by the design of appropriate economic instruments and be aligned with incentives to use a higher share of renewable energy. Strategies in the lower parts of Figure 5 have been more controversial and address sensitive socio-economic aspects. For instance, regional food supply within the EU may reduce transport and contribute to security of food supply, but may also deprive less developed countries of an opportunity to develop through international trade. A similar case may arise in connection with bio-energy. For the next steps within the framework, strategies characterized by a high level of consensus have been considered first. Currently, for instance, the need for increasing resource productivity and the use of renewable energy is widely acknowledged as an essential step in relation to the goals of sustainability. However, there may be important disagreements concerning appropriate and effective measures under these strategies; for example, concerning the relation between intra- vs. inter-sectoral change, and the potential contribution of biomass to renewable energies. ‘Risky trade-offs’ and ‘problem shifting’ were considered within the project when assessing the sustainability of alternative pathways compared to a ‘business-as-usual’ development.

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Figure 5: How FORESCENE addressed Question 3 "How do we get there?"

2.4 Question 4: How to measure and model this? Following the stakeholder and expert involvement in steps 1 to 3, and the collection of sustainability elements (cross-cutting drivers, reference goals, and key strategies) deemed essential for scenario development, a review of established scenario modelling studies was conducted. Scenarios and models were selected with regard to their potential relevance and 17


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applicability for scenario construction in FORESCENE. The review primarily assessed how scenario studies and models dealt with the three environmental topics considered in the project, and what problem areas, cross-cutting drivers, scope and time horizon have been addressed in these studies. Most existing models have been developed to deal in-depth with rather specific aspects of sustainability, whereas FORESCENE adopts a much broader perspective. Here the aim has been to integrate environmental problems and activities which have generally been treated separately with an extended spatial and temporal perspective. Furthermore, when considering the sum of elements assembled via steps 1 to 3 in relation to the task of scenario building and modelling, it became clear that different components were relevant at different scales, that the causal relationships could not always be expressed deterministically and that many parameters had considerable uncertainties associated with them. Data for quantifying the parameters included in the modelling and scenario exercises were also found to be very heterogeneous with regard to type of observation and accuracy. As shown by the review of scenario studies, although many of the subjects dealt with in the FORESCENE project have been studied elsewhere, they have rarely been looked at in association with each other. Thus, it was apparent that a new modelling approach was needed, that allowed the integration of the different sustainability elements generated in the preceding steps. Therefore, the project has sought to develop a tool that:  allowed the expression of non-deterministic relationships between parameters;  allowed different geographical scales to be considered;  included parameter uncertainties;  could be expanded or simplified depending on the purpose, data base and status of knowledge;  was oriented towards decision support;  gave an endogenous description of the links between the cross-cutting drivers and the targeted environmental issues;  could describe the influence of strategic policy decisions on multiple environmental problems considered; and,  could ‘back-cast’ from targeted environmental goals and identify drivers that might achive desired outcomes that are amenable to human influence. The tool developed in the FORESCENE project was based on the Bayesian Network concept, which is an approach that allowed most of these criteria to be met. The method has shortcomings, however, such as the inability to model feedback loops and the difficulty to handle time dynamics within a single network. Nevertheless, positive aspects such as the intrinsic graphical representation of the model, the possibility if using non-deterministic relationships (derived from expert judgement) or functional equations parameterised with probability distributions rather than point value coefficients, seemed to outweigh the limitations of the method. It is important to note that the Bayesian Network approach is not seen here as a replacement for other models in current use but rather as a means of integrating different forms of knowledge, whether from existing models, reported data or expert judgement. In other words, it can be used to construct a ‘meta-model’. The general structure of the meta-model prototype developed in the FORESCENE project is shown in Figure 6 (upper left diagram). The geographical system boundaries for the driving 18


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forces are those of the EU-25, while the impacts on most of the environmental issues are considered at the global level, distinguishing between intra- and extra-EU impacts. The component parts of the model are referred to as ‘modules’. The following modules were developed: ‘Economy’, ‘Mineral materials’, ‘Fossil fuels’, ‘Biofuels’, ‘GHG emissions’, ‘Agricultural land use’, ‘Water’, ‘Biodiversity’ and ‘Soils’. Causal relationships between the nodes can be characterized either with functional equations or conditional probability tables (CPTs, see Appendix 9.1). The first option is, however, often preferred because the specification of functional equations between variables reduces the variability of the results. Probabilities are introduced into the functional equations by assigning probability distributions to the equation parameters, which represent knowledge uncertainty about the parameter value from a Bayesian perspective. It is important to note that the time dimension is not explicitly present in a Bayesian network model; rather a collection of networks can be necessary to represent an evolution over time. Figure 6 (central diagram) shows how the time dimension was dealt with for parts of the model prototype developed by FORESCENE. The networks of the time series differ from one another by the input values assigned to their marginal nodes, which correspond to the initial input values for given years. These inputs are calculated separately and for different scenarios. The target nodes from each network can then deliver their output in the form of probability distributions for each year considered, which can, in turn, be worked out into time series outside the networks (see lower part of Figure 6).

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Figure 6: How FORESCENE addressed Question 4 "How to measure and model this?"

2.5 Question 5: What is likely to happen under business-as-usual conditions? Having established the model structure of interconnected modules, the next task was to observe the parameters’ behaviour under business-as-usual conditions. The first step consists in building a consistent set of baseline assumptions for the input nodes 20


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(representing, for example, the driving forces). In a second step modelling results for key output nodes are forecasted using the possibilities offered by the Bayesian network model. The assumptions for the input nodes in the baseline scenario are derived from existing business-as-usual forecasts. The model integrates into one consistent framework assumptions and modelling results from different sources relevant for the modules’ objects. If a quantification of the uncertainty is available for the data chosen, it is implemented in the model (using, for example, normal distributions). If such quantification does not exist, the chosen data are used as point values. Time series of input parameters are then fed into the Bayesian networks modules for consecutive modelling. The target nodes then deliver the modelling results that make up the ‘predictions’ for the baseline scenario. Results are obtained as full probability distributions in order to account for predictive uncertainty and thus both enhance theoretical understandings and inform practical decision making. A more technical description of the forecasting method in FORESCENE using Bayesian networks is developed in section 3.7. Quantitative baseline assumptions for major input nodes and modelling results for key output nodes are presented in section 4.1.

2.6 Question 6: Which alternative scenarios are possible? As a result of Steps 3 and 4 we can define a broad set of sustainability goals and their relationships to the various strategies that might achieve them. Using this information, scenario narratives defining these alternative potential pathways to the future can be developed. The aim is then to use the Bayesian network approach to model the quantifiable parts of the narratives and compare these alternative modelling results to the baseline trends modelled in step 5. The modelling exercise, aiming at the quantification of the narratives, can be conducted using either the forecasting or the backcasting method, though the purpose differs between the two methods. With forecasting, the procedure is similar to that described in step 5 except that input assumptions are no longer “baseline” but correspond to the alternative development imagined in the scenario narratives. Forecasts can be iterated with modified input parameters until the modeller is satisfied with the quantitative representation obtained for target parameters with regard to the narratives. The question underlying this approach is: what happens to the output if the input parameters are tweaked this or that way compared to the baseline? With backcasting, the approach is somewhat reversed. Translating the narratives, desired output values for key target nodes are entered into the model while input nodes of interest are let free to vary. Running the model in backcasting mode then means answering the following question: what happens to the input if the target nodes fulfil the desired goals? Because uncertainties are not ignored but built into the model using probability distributions, the backcasting approach can include that the target nodes fulfil the desired goals with a certain level of confidence, for example asked for by the decision makers. A more technical description of the backcasting method in FORESCENE using Bayesian networks is developed in section 3.8. Narratives of alternative scenarios and quantitative modelling results compared to the baseline are presented in section 4.2. 21


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3 Model prototype 3.1 Bayesian modelling Bayesian networks (BNs) are an increasingly popular method applied to uncertain and complex domains such as environmental modelling and management. They can easily be used as or in combination with decision tools. BN models offer the possibility to incorporate knowledge of different accuracies (e.g. absence/presence of an observation vs. quantitative data from measurements) and from different sources (Marcot et al. 2001). Carefully elicited, expert knowledge can be combined with empirical data, in a mathematically coherent manner. Parameter values that come with uncertainties can be expressed as probability distributions rather than average values. The higher the uncertainty, the wider is the probability distribution. The end-point modelling results are also represented as probability distributions which prevents overconfidence in single estimated values and allows for an estimation of risks and uncertainties (Uusitalo 2007). The Bayesian network approach to modelling presents, however, three main shortcomings (Borsuk et al. 2004, Uusitalo 2007). First, BNs are acyclic graphs and therefore do not support feedback loops (Jensen 2001). Second, and closely related shortcoming, is the difficulty of modelling temporal dynamics in BNs. A possible workaround consists in using a separate network for each time slice (Jensen 2001, Uusitalo 2007). This is however often very tedious. The third difficulty associated with BN models is their limited ability to deal with continuous data when used in compiled form. In such a form, the continuous variables and parametric equations between variables need to be discretized over discrete domains chosen by the user. This implies a trade-off as the discretization can only account for rough characteristics of the original continuous distributions and relationships. The problems inherent to the discretization of continuous variables can be avoided by using the other major approach in Bayesian modelling beside Bayesian networks, namely hierarchical simulationbased modelling (Uusitalo 2007). In the FORESCENE model prototype both branches of Bayesian modelling are implemented. The Bayesian network approach is used, for example, with the 'biodiversity, soils and landscape' submodel that mainly consists of discrete variables. Once the submodel is compiled the BN can provide instant responses to queries such as the modification of an input parameter. The result is also directly visible in the graphical representation of the network. The simulation-based approach is used for other modules of the model prototype that contain mainly or exclusively continuous variables. The probability distributions of target parameters are estimated by generating samples from these distributions by simulation (Borsuk et al. 2003). The results, however, require some processing before they can be shown. But, on the other hand, the least possible information is lost.

3.2 Dealing with time The time dimension is not directly accessible in a Bayesian network model. Using a collection of networks is an option to represent an evolution over time. Figure 7 shows how time dynamics is dealt with in FORESCENE, for some parts of the model prototype (e.g. 22


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biodiversity and soils submodel). The networks of the time series differ from one another by the input values of their marginal nodes, which correspond to initial input values for given years. These inputs are calculated separately and for different scenarios. The target nodes from each network then deliver output values for each year considered, which can, in turn, be worked out into time series outside the networks. Another option consists in having the different years in one overall network (see Figure 8). This option is adapted for networks used with simulation-based modelling (see previous section). It also facilitates the updating of input node values, for example for different scenarios, as there is only one file to update. However, this option is to be reserved to networks that are not too large and ramified.

Figure 7: First option for dealing with time in Bayesian networks

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Figure 8: Second option for dealing with time in Bayesian networks.

Note: Node types are as follows: input and control nodes (turquoise and orange), intermediary nodes (pink), output/target nodes (yellow)

3.3 Dealing with uncertainties Building a model and operating it is confronted to two main types of uncertainties: uncertainty in the causal structure and parameter uncertainty. In the present model prototype, as in most models, the first type of uncertainty is not quantified. Therefore, the real uncertainty in model predictions will be greater than that suggested by the model. In the case of Bayesian networks, the following options have been suggested for addressing uncertainty in model structure: Bayesian model averaging, learning from additional data and thorough model testing (Borsuk et al. 2004). Considering the third option, we have compared the modelling results from the model prototype with existing data. This comparison, however, does not provide a true validation, as much of the model building was based on the same or related research that generated the data. However, in contrast to most modelling approaches, parameter uncertainty can be accounted for at a very early stage in the modelling process, rather than at the end using sensitivity analysis. When setting up input and control nodes, parameter uncertainty can be captured by probability distributions. Modelling results from the output nodes can therefore also be expressed as probability distributions that reflect the combined uncertainties from all input and control parameters, and hence uncertainty in model predictions. This probabilistic approach implies that even with a fixed criterion regarding the desired value of a given output parameter, the choice of action on the control parameters depends on the degree of confidence required by decision makers. That degree of confidence can be translated directly in terms of percentiles of the probability distributions of target parameters. In most modelling approaches, such a margin of safety in obtaining the desired outcome would be sought through conservative model assumptions. Though such assumptions can be justified, the modeller implicitly chooses a particular level of confidence in making them. Such a task, however, is a risk management decision that should better be made by decision makers. Therefore a model approach that accounts for prediction uncertainties provides an explicit basis for choosing a decision criterion that includes a margin of safety. The size of the margin, first decided by decision makers, might later be reduced by the decision makers themselves if they settle for a lower degree of confidence, or by reducing prediction 24


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uncertainty. The latter requires further data collection to reduce the uncertainty of input parameters and of relationships between parameters. This, in turn, requires further communication with stakeholders, experts and, eventually, decision makers. In that respect, the explicit dealing with uncertainty of the Bayesian approach fits well in a heuristic approach.

3.4 General structure and system boundaries The general structure of the model prototype developed in the FORESCENE project is shown in Figure 9. The geographical system boundaries are that of the EU-25 with regard to the driving forces but the impacts on most of the environmental issues are considered at the global level.

Figure 9: General overview of the structure of the FORESCENE model prototype

Note: The components of the model considered beyond the EU-25 system borders are represented with shadows.

More detailed, Figure 10 outlines the general structure of the model prototype, showing variables and dependencies relevant for the selected environmental fields: ‘resource use and waste generation’, ‘water’ and ‘biodiversity, landscape and soils’. The central part of the figure represents the organising structure of the model, consisting of separately developed modules. In the graphical representation of a Bayesian network, variables are depicted by nodes, and a dependence between one variable and another is represented by an arrow. The absence of an arrow between any two nodes implies the conditional independence. Nodes can be either discrete (i.e. with a defined finite set of possible values called states) or continuous (i.e. can take in a value between any other two values). The ‘sub-models’ are the component parts of the model prototype, covering one of the three environmental topics, and ‘modules’ are component parts within a sub-model. The following sub-models and modules are shown in Figure 10: 

Economy



Mineral materials 25


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

Fossil fuels



Biofuels



Greenhouse gases emissions



Agricultural land use



Water



Biodiversity and soils

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Some modules have been or could be developed in more complex versions, in order, for example, to further differentiate and parameterize mineral, fossil fuel, greenhouse gas and crop types. This potential for refinement illustrates the strength of the Bayesian approach which allows for gradual elaboration of individual elements. The biofuel module actually also includes a simplified transport module. The biomass and agricultural land use is not fully developed at this stage of the model prototype. Solely land use for biofuel crop production within and outside the EU is calculated from within the BN module. Results from existing land use models are used in an aggregated form outside the BNs to ensure consistency of the input data sets and results stemming from the functioning part of the module. Bayesian networks represented graphically can usually be best explained by starting with their end-points and proceeding in the “up-arrow” direction. When building the diagram, intermediate variables and relationships are only included if they contribute to the model’s ability to predict values of the end-point indicators, that is if they are controllable (e.g. human dependent variables), predictable or observable at the scale of the modelling exercise. If a variable does not have one of these characteristics, then it is not explicitly included, and the resulting variability becomes part of the uncertainty associated with the model (Borsuk 2004). For some modules (e.g. economy) the end-point or intermediary nodes deliver interim results that are used in the other modules. The end-point indicators of most modules (e.g. TMR minerals for the minerals module) are proxies for the assessment of the progress made towards the sustainability goals. In developing the model structure, one logically starts with the end-point nodes, which are then related to their immediate causal variables, which are then related back to their own causes, and so on, back to the drivers. The parentless nodes at the top of the diagram can either be considered marginal variables representing natural variability, or those that will be influenced by sustainability strategies.

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Figure 10: Graphical representation of the Bayesian network for the FORESCENE meta model Central figure: submodels are indicated with rectangular blocks, model parameters with round nodes, and causal relationships are indicated with arrows. Surrounding insets: networks representing submodels of the main network. (Note: network images used in this figure do not correspond to their latest versions).

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3.5 Integration of the model components, linkages between the environmental topics FORESCENE aimed to focus on the interdependencies between the three environmental topics ‘resource use and waste’, ‘water’, and ‘biodiversity and soils’. At the present stage of development not all interdependencies considered are explicitly modelled in the Bayesian networks. Some linkages, such as those between agricultural land use and biodiversity, are accounted for but are partly exogenously determined. For example, because the biomass and land use module has been limited to biofuels, the inputs ‘agricultural land use in EU’ or ‘intensity of agriculture in EU’ are determined exogenously based on results from existing models and studies and injected into the model, after ensuring consistency with results from the other BN modules. The in-built modular structure of the model prototype implies that attention needs to be paid to the linkages between the modules, also within a sub-model (corresponding to one environmental topic). The input nodes of one given module consist of its own marginal nodes and of output or intermediary nodes from another module. Often, the purpose of a marginal node in one module is to operationalize a linkage between this module and a ‘parent’ module that influences it. For example, water intensity coefficients (marginal/input nodes of the water module) operationalize the linkages with the mineral materials and fossil energy modules by associating water abstraction to material use. Therefore, input data sets generated exogenously (such as water coefficients) and fed into the marginal nodes need to be consistent across all modules as well as with regard to modelling results coming as input data from other modules.

3.6 Data base Data mining and literature assessment, conducted in order to ‘populate’ the model prototype, served the following purposes:  provide a basis of empirical data and expert statements from which relationships

between nodes could be characterised and quantified (whether parameters in functional equations, or probabilities in CPTs);  account for parameter uncertainty with probability distributions when different studies

publish diverging data;  compile time series of input data to be fed into the marginal nodes of the model

prototype. Assumptions and modelling results from existing business-as-usual forecasts were considered in FORESCENE to build input data sets for the baseline scenario. Knowledge from different sources could be integrated into one data set while acknowledging their diversity through the use of probability distributions rather than point average values. To each module in the model corresponds a spreadsheet containing time series (in five year time slices) for the input nodes. Spreadsheets are linked in a way to ensure consistency of input data across the modules (see also section 3.7 ‘Methodology for forecasting’ below). An overview of the data base of the FORESCENE model prototype is shown in Table 1.

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Table 1: Data base of the FORESCENE model prototype Module

Data sources

Economy

Eurostat, GINFORS model, EEA

Mineral materials

WI data base, ETC/RWM NAMEA, MOSUS

Fossil fuels

WI data base, Eurostat, EEA, DG TREN

Biofuels

WI data base, Eurostat, EEA, DG-ENER

GHG emissions

ETC/AAC, IPCC, UNEP/IPSRM, DG TREN

Agricultural land use

FAO, FAPRI, EEA, UNEP/IPSRM, Eurostat

Water

Aquastat, Eurostat

Biodiversity and soils

EEA, Eurostat, FAO, DG AGRI, and diverse specialised literature sources

3.7 Methodology for forecasting This section briefly explains the forecasting technique used in FORESCENE. Figure 11 gives an overview of the different steps involved. The upper, middle and lower parts of the figure summarize the process into three phases:  Construction of the network, parameterization and characterization of the linkages  Construction of input data sets for each module  Model runs, representation and analysis of the results.

The upper and lower parts of the figure are further divided into two boxes. On the left-hand side the technical description concerns network modelling, while on the right-hand side simulation-based modelling is described. The former option is reserved to BNs with discrete variables (e.g. ‘Agricultural management’ is ‘green’ or ‘brown’) or continuous variables that can be discretized without increasing the variability of the results (e.g. ‘pollutant load’ with known thresholds). The latter method regards BNs with continuous variables linked by functional equations, which would be much less accurate if discretized. Both approaches start with a graphical representation of the model structure (upper part of Figure 11). In FORESCENE, a commercial software called Netica2 was used. Parameters deemed necessary to be considered can stem from participatory processes (see also the description of the FORESCENE framework in chapter 2). The quantitative relationships characterizing the conditional distributions between the model components need then to be established. The degree of belief and uncertainty underlying the functional relationships between the variables is acknowledged through probabilities in CPTs or equations with parameters expressed as probability distributions. Once the linkages are characterized, the marginal nodes (without parent nodes) are updated with their probability distributions (discrete or continuous). Necessary information are read for 2

www.norsys.com

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each time slice, for each marginal node of each module by a vba-macro from a common spreadsheet and written into the text-file (i.e. non graphic) version of the BN. When the model is “populated”, one can re-open its graphical version and run the forecast. With discrete nodes and a compiled network (lower part, left-hand side of Figure 11) the modelled probability distributions of target nodes over their discrete states (e.g. ‘Biodiversity status’ is declining, stable or improving) can be read directly in Netica. Each time slice is modelled in a separate BN. If an input node is modified, the influence of the change immediately appears. For simulation-based modelling, sample cases should be generated (Netica has a Monte Carlo-like function for it) in a sufficient number to cover the extent of the probability distributions of the target nodes. Then, using the open source softwares for numeric calculation Scilab3 or for statistics R4, these distributions can be visualised and their characteristics calculated. With the simulation-based Bayesian modelling, results are more than single point values: the predictive precision, given model uncertainties, of policy relevant variables is quantified.

3

www.scilab.org

4

www.r-project.org

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Figure 11: Forecasting method in the FORESCENE Bayesian network model prototype

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3.8 Methodology for backcasting This section briefly presents the backcasting technique used in FORESCENE. Figure 12 gives an overview, summarized in three steps (upper, middle and lower parts of the figure), as follows:  Construction of the network, parameterization and characterization of the linkages

(same as in the forecasting case)  Defining quantitative targets for each time step  Model runs, representation and exploitation of the results.

As in the previous section, the lower part of the figure is divided into two boxes: Bayesian network modelling on the left-hand side, and simulation-based modelling on the right-hand side. The first step (establishing the BN) is identical to the forecasting process, and the same BN can actually be used for both. Then, quantitative desired goals and milestones (i.e. intermediate goals for some time steps up to the horizon considered) used in the scenario narratives are assigned to the corresponding target nodes at the corresponding time slices (middle part of Figure 12, left). A path (linear, S-shaped, other) is defined from today’s values for the target nodes being considered to the values set as ‘desirable goals’, via the intermediate milestones (middle part of Figure 12, right). This way, each target node of interest has a goal value set for it at each time step. Backcasting with Bayesian network modelling (i.e. for compiled BNs of discrete or discretized variables) consists in switching target nodes onto the state corresponding to the desired goal (e.g. ‘Biodiversity status’ set to ‘favourable’). The program then updates the whole BN using Bayesian inference, which concretely consists in re-calculating the probability distributions of all nodes (backwards up to the input nodes), after a particular desired state was imposed to a target node. One can then see on the network the new “backcasted” configuration of input nodes. In the illustration of Figure 12 (lower part, left), the discrete probability distribution of the input node “Agri-environmental support” changed when the status of the output node “Status of terrestrial biodiversity” was set to ‘favourable’. The probability that more than half of the rural development budget has to be spent on agri-environmental support increased by about 15% up to almost 80%, strongly suggesting the necessity to increase the budgetary effort in that area. So as to know exactly which share of the rural development budget should be spent on agri-environmental support, it is a question that is not accessible to the level of detail of the model prototype in its current form. In the case of simulation-based modelling, sample cases (see previous section) of the target nodes are generated at all time steps with different sets of input values for the input nodes representing key strategic drivers that can be influenced in some way. Then, using a graphical or analytical method, suitable sets of input nodes’ values are determined that drive the values of the target nodes along the chosen path towards the desired goals. The graphical backcasting method in simulation-based modelling is shown in Figure 12 (lower part, right). Drawing, firstly, a horizontal line at the desired goal value for the target parameter (represented on the y axis) and observing where it intersects the curve of predictions, and, 32


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secondly, drawing a vertical line from this intersection to the horizontal axis, suggests the suitable value for the input (driving) parameters (represented on the x axis). However, there are in fact several curves of predictions (percentile curves) and the choice of the intersection of the line drawn horizontaly with one or the other percentile curve depends on the degree of confidence one wishes for the desired goal to be met, given the uncertainty in model predictions. If a higher degree of confidence is required, then the intersection with a curve of higher percentile must be used to determine the necessary value for the driving input parameter (Borsuk et al. 2003). In a traditional model using only median predictions (or, equivalently, model predictions that do not account for uncertainty), modelling results would appear as point values, not probability distributions. In such a case the modelled curve would in fact be the 50% percentile curve and there would de facto be 50% confidence that the desired goal will be met if the input parameter has the value shown by the graphical backcasting method. A positive aspect in the simulation-based Bayesian modelling approach is, therefore, that the model assesses the uncertainty associated with the predictions. It provides an explicit basis for choosing sustainability strategies (i.e. modified input parameters compared to baseline) which include a margin of safety. The acceptable size of the margin depends on the risk tolerance of decision makers who have to make a choice based on the predictive uncertainty in the Bayesian model (Borsuk et al. 2003). Finally, this backcasting method is probably well suited for a heuristic approach, involving consultations with decision makers and stakeholders. The “backcasted� sets of inputs will certainly not be the panacea that shows the way towards sustainability. Nevertheless, based on the model results it can be discussed whether such a development is feasible. Considering the whole model, the method should also highlight the potentially negative sideeffects of the modelled sustainability path on other parts of the model, indirectly connected to the drivers considered. In case of disagreement, the process can be iterated, alternative targets or pathways can be considered. Some controversial scenario elements set aside in the previous steps of the Framework (e.g. in Questions 2 and 3) could surface again. It is also necessary to include this reflection in the decision process as a whole by considering what modelling or consulting activities are required next to determine who should do what and when.

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Figure 12: Backcasting method in the FORESCENE Bayesian network model prototype

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4 Modelling results 4.1 Baseline scenario This section presents baseline assumptions for major input nodes (representing, for example, the driving forces) and modelling results for key output nodes. Figure 13 presents the baseline trends for the main output parameters of the model. In the economy module, for example, future growth of GDP per capita is modelled using a normal distribution centered in 2% until 2030 and in 1.5% afterwards (0.3% standard deviation in each case). The use of probability distributions instead of point values makes it possible to account for a degree of parameter uncertainty. The range defined by the normal distribution for GDP per capita was chosen to reflect differences in assumptions observed in existing European scenario studies. Other controlling or driving parameters were also assigned a normal distribution, such as material intensity coefficients used to calculate total material requirement, or self-suffiency of Europe (i.e. share of imports in final demand) regarding mineral materials, fossil fuels and biomass. As a consequence, modelling results for target indicators are shown in Figure 13 as “corridors” that represent the 90% confidence intervals of the corresponding modelling results, given the input parameter uncertainties. The width of the results’ confidence intervals tend to increase the further we look into the future and the more the values of the target indicators increase. The 90% confidence interval for TMR minerals, for example, increases from 7.8 – 9.2Gt in 2005 to 12.4 – 18Gt in 2050. Though the direct material input levels off over time, due to increasing material productivity and the assumption of a slower GDP growth after 2030, TMR keeps increasing. This can be explained by the fact that the indirect flows, especially unused extraction such as mining waste associated with direct material input, keep increasing as a result of resource depletion. Furthermore, increasingly this waste production occurs in other parts of the world that export to Europe. This worsening of the problem shifting is illustrated by the ratio foreign TMR over domestic TMR in Figure 13. Some results have a broader distribution, such as land conversion outside Europe for its biofuel supply and greenhouse gases emission savings associated with the use of biofuel. It reflects the uncertainty on parameters such as biofuel crop yields or the actual amount of greenhouse gases emitted per unit area of converted land. Due to the limited time dynamic capabilities of the Bayesian Network approach, the “carbon debt” (Fargione et al. 2008) associated with land use change (LUC) outside EU is accounted for all at once, in the year when land conversion occurs. It explains the large “negative savings” from biofuel use in EU in 2010 (-1.1Gt) when demand is growing, supported by political decisions. Towards the end of the modelled time period, when demand for biofuels has stabilised, and production yields and fuel efficiency have increased, the median of “GHG savings from biofuels with land use change” is close to the x-axis, meaning that the land use change induced GHG emissions are about the same order of magnitude as the savings by biofuels without LUC. The savings are, however, still negative. The water usage efficiences within the industrial, power generation and household sectors are assumed to increase linearly but modestly in a business-as-usual scenario, while industrial recycling and desalinization are assumed to increase exponentially, starting from a 35


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low level. The overall usage is expected to increase during the period, with increasing material and energy use, which is outside the water submodel boundaries. It is also assumed that the number of households will continue to increase, even if the EU-25 population level remains stable, which will lead to increase in overall household water consumption. The water balance (difference between withdrawals and supply) is not expected to change significantly, as increasing precipitations (as a consequence of climate change, especially in the north of Europe), recycling and desalinization (especially in the south of Europe) tend to offset increased consumption. Finally, some more qualitative indicators are shown as discrete probability distributions (e.g. biodiversity status). They are linked probabilistically through the result of expert judgement elicitation to a number of drivers, either specific to that module, such as cross-compliance or agricultural management, or stemming from other modules, such as water withdrawal. Thus the framework allows the inclusion of both physically measurable data and more qualitative “ecological variables” that otherwise could not be related mechanistically to the drivers. Figure 13 shows how the probabilities associated with the discrete states used to describe European biodiversity status (Declining, Stable, Improving) and soil quality (Low, Medium, High) evolve over the modelled period. The biodiversity status within the EU (not outside) seems to show a sort of polarization between its ‘declining’ and ‘improving’ states. It might indicate that under current policy development (e.g. with the implementation of the Water Framework Directive), the status of biodiversity in the EU may improve in the long run (although with time needed for current policies to be applied and take effect). But results also shows that it may as well decline, suggesting that more action is needed to reduce the uncertainty of the outcome. Those results are to be taken with care, however. More testing is needed to check the robustness of the model prototype, especially of the ‘biodiversity and soils’ sub-network whose long ramifications of discrete and discretized nodes tend to buffer the effect of influencing parameters situated upstream. The biodiversity outside the EU has not yet been included in the model explicitely. Nevertheless, due to the increased land use change for biofuel production – which leads to enhanced GHG emissions – one may conclude that biodiversity in other regions is negatively affected.

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Figure 13: Overview of the modelling results from the baseline scenario.

Note: Solid lines represent median values of ouput parameters for the period 2005-2050. Uncertainty is summarized by 90% confidence intervals (dashed lines) of the resulting conditional probability distributions. The numbers given are the maximum and minimum values reached by the median over the period.

4.2 Alternative scenarios As a result of questions 2 to 4 of the framework, we can define a broad set of sustainability goals and their relationships to the various strategies that might achieve them. Using this information a scenario framework (see Figure 14) defining these alternative potential pathways to the future can be developed and compared using the FORESCENE model to the trends resulting from the ‘baseline’ assumptions defined above. Figure 14 represents the ‘alternative scenario’ framework in terms of a set of nested relationships and strategies. The elements in the diagram have been arranged so that they become increasingly more specific moving downwards (i.e. potentially measurable). The arrangement from left to right reflects the degree of consensus expressed within the groups consulted; those elements to the right of the diagram covered potential developments that were more controversial such encouraging new lifestyles that gave less emphasis to consumption as an element of well-being through, say, as finding satisfaction in alternatives to paid employment. 37


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Figure 14: Framework for developing alternative sustainability scenarios

There was broad agreement amongst those consulted that most strategies for achieving sustainable development had to include an improvement in the environmental performance of consumption and production systems. All of the elements in Figure 14 below this high level strategy are consistent with this broad aim. Some, such as reduction in GHG and waste emissions are more obviously so, while others are linked more indirectly, such as the localisation of markets. The latter would reflect, for example, changing lifestyles and the development of local food markets and employment systems that reduce the need for transport of people and goods. Using the combinations of the lower, more specific elements of the framework shown in Figure 14, a series of alternative scenarios can be ‘back-cast’ from the strategies identified. Although those consulted agreed on a similar, general desired future direction, a range of potential ‘futures’ might be imagined on the basis of different combinations of the elements being emphasised or down-played. Moreover individual, sectoral strategies might be implemented at different speeds, or be achieved with different levels of success by the end of the scenario period. Therefore, in an attempt to cover a range of the possible outcomes, three contrasting, alternative trajectories have been developed (see Appendix 9.2). Two of them (‘Commitment to Change’, and ’Muddling Through’) reflect the same broad set of ambitions derived from the workshops, and differ only in terms of the degree to which the more specific desired sustainable outcomes identified in the lower parts of Figure 14 have been attained. The third is a more pessimistic scenario in which most of the objectives have not been realised and performance across most of the areas identified in Figure 14 does not even match the baseline. Table 2 (see Appendix) expands each of the three scenarios in terms of the specific elements identified in the lower part of Figure 14. In developing the scenarios the aim has been to move from the general trends identified through to the specification of the states which particular nodes in the FORESCENE model would require for particular time periods. Where possible we have attempted to build the alternative scenarios around the same set of 38


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nodes used for the baseline analysis. However, since some of the alternatives include the implementation of new measures (such as a Soil Framework Directive) there are also some structural differences between baseline and alternative scenarios. The first scenario describes what might be termed an ideal outcome. Under the ‘commitment to change’ the foundations needed to achieve more sustainable outcomes are put in place early and to greater effect. It is imagined that there are marked improvements in the efficiencies of using energy, materials and water, and an expansion in renewable energy production. The rate of change is moderate to high and significant reductions of pressures have been achieved by 2030, with the rate of investment in R & D being mostly over 3%. The level of greenhouse gas emissions has fallen by 20% by 2020, and the 80% cut by 2050 has been attained. There is reduced pressure from the expansion of urban land and intensive agriculture from around the same time. Improvements in production of second generation fuel crops and other renewable energy technologies means that pressure from fuel crop expansion is minimised. The area for food crops is adequate to meet needs, and so EU consumption does not lead to pressure at global scales. Regulation measures, including strong cross-compliance have been implemented, and farming and forestry management practices are predominantly green as early as 2020. The second scenario is less promising than the first, in terms of achieving concrete sustainably outcomes. In this case while there are achievements but they are patchy. This scenario therefore represents a future that is characterised essentially as ‘muddling through’. As with the first scenario, improvements in the efficiencies of using energy materials and water are made, and there is expansion in renewable energy production. However, the rate of change is only moderate compared to the first scenario. The rate of investment in R & D is greater than in the baseline scenario, at 2-3%. However, the patchy nature of the outcome is indicated by greenhouse gas emissions continuing to reduce only slowly. The 2020 target is not met until 2035, and only a 65% reduction is achieved by 2050. There is on-going pressure from the expansion of urban land and intensive agriculture. As with the first scenario strong regulation measures and better management practices are in place. The final scenario is the one which sets out a pessimistic future, characterised as ‘failing to deliver’. Thus even by 2050 the transition to sustainability has not really been achieved. Increased consumption of resource intensive goods has meant that there has been less progress towards higher energy and resource efficiency. There is some expansion in renewable energy production, but the rate of change has been slow. The rate of investment in R & D has been no more than 2%. Greenhouse gas emissions have increased and there is still pressure from the expansion of urban land and intensive agriculture. Lack of improvements in efficiency of production of fuel crops means that area for food crops is insufficient and that EU consumption exerts pressure on land at global scales. Nevertheless on the positive side regulation measures, including strong cross-compliance have been implemented, and farming and forestry management practices are mostly green within the EU, in contrast to rising pressures induced outside. The scenario narratives presented above are plausible stories about a vision of the future and the pathway to reach it. Now, the next step consists in using the model prototype to quantify parts of the narratives. A complete integrated scenario can usually not be modelled entirely. Nevertheless, some elements of the pathways described above can be quantified and analysed with the Bayesian model prototype. 39


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Figure 15 shows some results of the FORESCENE model applied to the first scenario narrative (‘commitment to change’) presented above. The goals set by this narrative are the highest among the three alternative scenarios and, as such, a strong development of the input parameters would be needed to reach them. Both forecasting and backcasting modelling techniques described in sections 3.7 and 3.8 were used to quantify the parameters shown in Figure 15. Forecasting was used as ‘trial and error’ in simulation-based modelling, in order to find values of input parameters that result in the targeted values of the output nodes. Backcasting was used with the discrete sub-model ‘biodiversity and soils’ following the network modelling approach (see Figure 12). It was also used to some extent in simulation-based modelling. The selected targets presented in Figure 15 are far-reaching ones. The ambitious goals set for the reduction of Europe’s total material requirement (TMR) translates not only into reduced use of primary natural resources but also into reduced waste generation within and outside the EU (a consequence of reducing the material throughput of the socio-industrial system). This also reduces landscape disturbances due to mining and associated and subsequent material flows down to final disposal of consumer products. Reducing TMR also implies the mitigation of shifting environmental problems to other regions of the world through imports of raw materials or material intensive products. The targets set for the reduction of greenhouse gas emissions are also expected to contribute to the mitigation of climate change as well as related problems. Issues such as water supply in Southern Europe, or biodiversity will be influenced in a sustainable manner if those ambitious goals are met with strategies that do not induce a shifting of the problem onto other resources or world regions. Therefore, in this case, the targets for greenhouse gas emissions are met under the condition to halt the conversion of natural land into cropland for the purpose of cultivating biofuel crops (as the conversion adversely affects biodiversity and the carbon balance of biofuels). The main input variables considered for quantification in this modelling exercise are material productivity and the share of services in final demand for their direct influence on mineral materials consumption, and indirectly on energy demand and water use. The model suggests that the target could be met if the yearly material productivity increase of manufacturing were to double (Figure 15, middle part). A rise of the service economy would also lead the way towards dematerialisation, with as much as 80% of the EU’s final demand consisting of services in 2050. The material efficiency of service production shall also increase but no big jump is expected (contrary to the industrial sector) because the service sector will indirectly require resources (e.g. real estate services relying on maintenance of constructions). Energy productivity (from 1.6% to 2.7% yearly increase), the share of renewables (up to at least 50%), and specifically the share of biofuels (no more than 5% in the transport fuel mix) are considered for their importance in fossil fuel consumption and land use change, particularly outside the EU. Drivers for the biodiversity and soils module, such as cross-compliance and farming management, are also part of the exercise. These input variables see their discrete states set to the most possible “green” configurations. Some of the changes assumed to be a precondition for a more sustainable development, such as the strong shift towards a service oriented economy, need to be discussed with regard to their relevance, plausibility and feasibility. An assessment according to those criteria will probably depend on the general frame that one is willing to adopt. If the underlying assumption is that of an economic growth which continues to rely on 40


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manufacturing as today and a growing production of resource intensive products for export, then the results would have little chance of looking acceptable. The crisis of the global automotive industry, however, indicates that such assumptions need to be reconsidered. At that point one could also look back at the diverse elements collected along the different steps of the FORESCENE Framework and remember that the somewhat fuzzy ideas of ‘changing paradigm’ or ‘redefining wealth and happiness away from material possessions’ came up more than once in the different workshops. One could then imagine a system coming into sight where economic growth is sustained by growing production of education, music, art, cultural services, in all their forms and at all ages, as well as growing provision of elder care, rather than by increased goods production or increased transport. With a long-term perspective, this set of backcasted results could eventually make sense.

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Figure 15: Overview of the modelling procedure and results examplified with the first alternative scenario ‘commitment to change’.

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5 Possible applications of FORESCENE in policy development The FORESCENE model in its present, or slightly expanded version, can be used as a “warning tool” informing decision makers at an overview level on risks of interdependencies, problem shifting, and critical thresholds that may constrain the effectiveness of policies. Thus it may be used support strategic policy design, and the “experimental” or “scoping and screening” stage of an Integrated Sustainability Assessment. The model can also enrich policy debates about sustainability by virtue of its holistic character, showing the linkages and potential trade-offs between sectors. The framework and the model may be applied and further developed as a heuristic device and learning tool. Using the model in stakeholder interactions would allow to show them how their assessment of specific processes or targets would influence the overall development. An iterative application of the tool with experts and stakeholders could be used for stepwise refining and extension of the model. Ideally the framework and the model are applied for planning purposes, and could support consultation processes, to identify the relevant parameters to be considered and their potential variations that need to be modelled. As a result of an expert review of the FORESCENE Project the following general observations emerged about the approach:  That the Bayesian learning tool could be used interactively in policy exercises to support

exploration and learning about the feasibility of achieving sets of policy goals, the robustness of policy outcomes to management options, performing sensitivity analysis, etc. and for helping change the culture of policy making toward a search for robust policies  That it was useful in facilitating communication, interaction and joint working among

different policy departments  That it was useful in stressing the need for policy packages rather than individual

instruments and in helping search for synergistic sets of policy options that might support the achievement of several policy objectives.  That it was useful for long-term exploratory analyses aimed at supporting creative

thinking about alternative futures, the desirability and feasibility of achieving these and ways of achieving these  That it was useful for pinpointing critical issues that should be explored in more detail

using other models. The FORESCENE model could also be used to test individual policy proposals. The approach used in the framework and the modelling methodology can be applied as a way of developing in-depth sub-models restricted to specific topics (e.g. soil quality, water quality) and/or specific geographical scales (e.g. a region). These could still be integrated in the broader picture because sub-nets showing different levels of detail or regarding different geographical scales can still be connected together. These, more detailed sub-models can also be developed following the same framework for more ad-hoc purposes, like contributing to the impact assessment of a specific policy. The probabilistic assessment and the consideration of the intra- and extra-EU dimension would 43


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add value to the IA process. This tool would complement rather than replacement for other tools used in IA.

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6 Possible extensions / follow-up A number of possible extensions to the model could also be anticipated, these include: 1. Deepening the level of detail for the economic module, i.e. differentiating between the economic sectors. This can be done in partnership with economic modellers. In its current version, the model can give insight (via forecasting and backcasting) into how much (and when) the driving parameters should be tweaked in order to reach sustainability goals. A deeper economic modelling would then be useful and necessary to address further questions such as “who should do how much?”, “which measures could make this happen?” etc. 2. Considering more social aspects. Additional modules oriented towards modelling social issues could be developed and integrated to the model. It could use both qualitative and quantitative modelling approaches. 3. Enhance public, stakeholder and expert participation. The participative approach taken in FORESCENE was adapted to building a prototype model but to expand the model or redesign it for a specific purpose (e.g. sustainability impact assessment) the level of participation for both the framing phase as well as for the data collection and generation phase could be raized. Different groups of stakeholders and/or experts could be distinguished according to their beliefs and assumptions and represented in the model. Using the model for online consultation and Delphi processes could potentially help to reduce the need for traditional meetings, related workload and costs. 4. Opening the access to the tool to other users. For that purpose should the interface be improved and made accessible. A user manual and demonstration cases should then also be developed.

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7 Conclusions FORESCENE has developed a framework for scenario development based on six guiding questions, and a targeted involvement of stakeholders and experts. The approach allows to address cross-cutting drivers for different environmental problems, in particular resource use and waste generation, biodiversity, soil, and water. Sustainability goals have been defined and key strategies have been described with a cross-sectoral multi-beneficial effect. For instance, an increase of resource productivity may reduce the environmental burden of resource extraction and the generation of waste (within and outside of the EU), contribute to the mitigation of greenhouse gas emissions and an improved water balance due to lower demand for cooling water for power supply and material efficiency in manufacturing. It could also reduce the demand for non-food biomass such as biofuels and mitigate the pressure to land use change with impacts on biodiversity (also outside the EU). Resource productivity increase could be complemented with measures to improve the environmental quality of land management by agriculture, also to improve water quality within the EU. A prototype of a meta model has been developed which can be further used and refined to support policy design and appraisal. The Bayesian network methodology allows to combine "hard" evidence from analytical models with "soft" estimations from expert judgement. Providing results in terms of probabilities conveys a more reliable support for decision making. The FORESCENE meta model allows to find effective mix of strategies to reach invisaged sustainability targets. Emphasis has been given to consider the intra- and extra-EU dimension in order to detect possible shifts of environmental pressure to other regions. For instance, the rate of resource productivity increase and structural economic change can be determined which would be required to reach a certain level of total resource extraction or potential waste generation (within and outside the EU for domestic consumption), and the fuel efficiency of the car fleet which can avoid expansion of crop land at the expense of natural ecoystems in biofuel producing countries which supply the EU. The meta model can be further developed through combination with more detailed sectoral models, and it can be further developed towards a heuristic learning tool to support stakeholder and expert involvement in the policy preparation phase. The FORESCENE approach can be applied in different contexts where certain interlinked problems shall be overcome, and a desirable future shall be reached in a systematic and effective way. Its primary strength lies in the support for strategic planning and it could also be applied in specific sustainability impact assessments.

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8 References Borsuk, M. E., C. A. Stow and K. H. Reckhow. 2003. Integrated approach to total maximum daily load development for Neuse river estuary using Bayesian probability network model (Neu-BERN). Journal of Water Resources Planning and Management 129(4): 271-282. Borsuk, M. E. 2004. Predictive assessment of fish health and fish kills in the Neuse river estuary using elicited expert judgment. Human and Ecological Risk Assessment 10: 415-434. Borsuk, M. E., C. A. Stow and K. H. Reckhow. 2004. A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling 173: 219239. Cain, J. 2001. Planning improvements in natural resources management – Guidelines for using Bayesian networks to support the planning and management of development programmes in the water sector and beyond. Centre for Ecology & Hydrology: UK. Commission of the European Communities. 2001. A Sustainable Europe for a Better World: A European Union Strategy for Sustainable Development. Brussels, 15.5.2001, COM(2001)264 final. Commission of the European Communities. 2005a. Commission’s Communication on its Strategic Objectives 2005-2009 (COM (2005) 12). Commission of the European Communities. 2005b. The 2005 Review of the EU Sustainable Development Strategy (COM(2005)37 final). Council of the European Union. 2006. Review of the EU Sustainable Development Strategy (EU SDS) − Renewed Strategy. 10907/06 Fargione,J., J. Hill, D. Tilman, S. Polasky and P. Hawthorne. 2008. Land clearing and the biofuel carbon debt. Science 319(5867): 1235-1238. Jensen, F. V. 2001. Bayesian networks and decision graphs. Springer-Verlag: New-York. Marcot, B. G., R. S. Holthausen, M. G. Raphael, M. Rowland and M. Wisdom. 2001. Using Bayesian Belief Networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact assessment. Forest Ecol. Manage. 153, 29-42. Uusitalo, L. 2007. Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203: 312-318.

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9 Appendix 9.1 Conditional Probability Tables (CPTs) Cain (2001) defines a Bayesian Network as a ‘graphical tool for building decision support systems to help make decisions under uncertain conditions’. The key phrase to focus on in this definition is ‘uncertain conditions’. As Cain points out, BNs were originally developed to allow the impact of uncertainty in management to be accounted for. Using the tools decision makers could balance the desirability of an outcome against the chance that the management option selected might fail. The representation of a system in terms of a set of relationships that have probabilities associated with them is at the heart of the Bayesian approach. An example of a simple BN is shown in Figure 1. Figure 1: A simple BN (after Cain, 2001) If we think about the sorts of things that might influence agricultural yield, for example, then these might include water supply and fertilizer applications. The amount of water applied to the crop might, in turn, be influenced by such factors as soil type and the level of irrigation. Figure 1 shows this diagrammatically. The key variables (that is the things that can change, such as yield, fertilizer applications and soil type etc.) are shown as a set of boxes or nodes. The relationships between the variables are shown as a set of arrows. These arrows simply set out the linkages between the variables; they show what influences what. The arrows describe what we think the causal relationships are within the system; notice that the arrows have a direction to express this idea of causality. In Figure 1 each of the nodes are shown as being able to take various states. Thus yield can be ‘good’ or ‘poor’, or soil type can be ‘sandy’ or ‘clay’. In the diagram, the nodes are represented by a special type of box, called a ‘belief bar’, which we can use to express the probability that the variable (node) is in a particular state, and how this might influence the other nodes to which it is linked. At the moment no real probabilities have been assigned, and all the nodes show a 50:50 chance of being in a particular state. What the BN allows us to do is to assign probabilities to the states for the different nodes to describe how we think the world actually works. The way we do this is to build Conditional Probability Tables (CPT) for each node.

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Table 1: CPT for the Crop water Application Node

Table 1 shows the CPT for the node ‘Crop Water Application’ that is part of the network described in Figure 1. Technically CPT is the contingency table of conditional probabilities5 stored at each node, containing the probabilities of the node given each configuration of parent values. Clearly when building a BN we do not have to confine ourselves to variables that have only two states. The software can handle a number of discrete states or even continuous values.

5

The conditional probability of an event is the probability of the event occurring under certain given conditions.

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9.2 Alternative scenarios

GHS reduction targets for 2020 and 2050 met.

Habitat and WFD goals only achieved by 2025.

Green management practices in forestry and agriculture widespread.

Diversity of agricultural production is high and from 2030, and there is the EU approache s selfsufficiency in most key food and energy products.

Shift from paid employment to alternatives

Strong soil framework directive implementted by 2025.

Localisation of markets

Rate of urban expansion and growth in area of intensive agriculture slackens by 2030. Improvements in production of fuel crops means that area for food crops is adequate and that EU consumption does not lead to pressure at Global scales.

Shift to precaution and prevention, and internalisation of damage costs

No marked expansion of renewable energy sources and reduced pressure on area of land devoted to food cops from biofuels.

Improved planning and decision making

High rates of investment in R&D (>3%) means that increase in nutrient transfer efficiencies are good so that there has been improvement in water quality.

Improving status of terrestrial and aquatic semi-natural habitats

Reduced pressure from urban sprawl, intensive agriculture and expansion of transport infrastructure

Significant improvements in the efficiency of material use and waste minimisation. Water abstraction rates are stable or declining.

Expansion of renewable energy sources

AS1, Commitment to change: Under this scenario there is clear commitment to the goals of sustainability. There are marked improvements in the efficiencies of using energy, materials and water, and there is expansion in renewable energy production. The rate of change is moderate to high and significant improvements have mostly been achieved by 2030. The rate of investment in R& D has been over 3%. Greenhouse gas emissions have reduced significantly by 2030 and there is reducing pressure from the expansion of urban land and intensive agriculture from 2030. Regulation measures, including strong cross-compliance have been implemented, and farming and forestry management practices are predominantly green as early as 2020.

Reduced pollution loads

Alternative scenario

Improving energy, material and water efficiencies

Table 2: Alternative Sustainability Scenarios

Demand for consumer goods has increased at 2000 rates until 2025, but returns to 2000 levels by 2050.

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GHS reduction targets for 2020 and 2050 not fully met.

Habitat and WFD goals s only achieved by 2025.

Green management practices in forestry and agriculture widespread.

Intensive agriculture and specialisation is still widespread, but diversity of agriculture increases after 2030, and there is greater EU self sufficiency in food and energy production.

Shift from paid employment to alternatives

Strong soil framework directive implementted by 2025.

Localisation of markets

Rate of urban expansion and growth in area of intensive agriculture slackens by 2030. Expansion of non-food crop area means that EU need for food cops leads to some pressure at Global scales.

Shift to precaution and prevention, and internalisation of damage costs

Marked expansion in area of land devoted to bio-fuels.

Improved planning and decision making

Moderate rates of investment in R&D means that increase in nutrient transfer efficiencies are good so that there has been some improvement in water quality.

Improving status of terrestrial and aquatic semi-natural habitats

Reduced pressure from urban sprawl, intensive agriculture and expansion of transport infrastructure

Moderate improvement in the efficiency of material use and waste minimisation. Water abstraction rates are stable.

Expansion of renewable energy sources

AS2, Muddling through: Under this scenario some of the sustainability goals identified are achieved in the medium term, but success is patchy and modest. In contrast to AS1, there have been improvements in the efficiencies of using energy materials and water are made, and there is expansion in renewable energy production. The rate of change is moderate and significant improvements have been achieved by 2030. The rate of investment in R& D has been between 2-3%. However, greenhouse gas emissions have continued to increase slowly and there is still pressure from the expansion of urban land and intensive agriculture. On the positive side regulation measures, including strong cross-compliance have been implemented, and farming and forestry management practices are mostly green.

D.6.2 – Final report

Reduced pollution loads

Alternative scenario

Improving energy, material and water efficiencies

FORESCENE

Demand for consumer goods has continued to grow at 2000 rates, but slackens by 2040.


Sgnificant reductions in GHG emissions not achieved.

Habitat and WFD goals s only achieved by 2050.

Strong soil framework directive only implementted by 2050.

Green management practices in forestry and agriculture widespread.

Intensive agriculture and specialisation is still widespread and there is some tendency to shift problems outside EU in order to remedy detrimental environme ntal effects ‘at home’.

Shift from paid employment to alternatives

Rate of urban expansion and growth in area of intensive agriculture does not slacken. Lack of improvements in efficiency of production of fuel crops means that area for food crops is insufficient and that EU consumption exerts pressure on land at Global scales.

Localisation of markets

Marked expansion in area of land devoted to bio-fuels.

Shift to precaution and prevention, and internalisation of damage costs

Low rates of investment in R&D means that increase in nutrient transfer efficiencies are modest, meaning that improvements in water quality are slow.

Improved planning and decision making

Reduced pressure from urban sprawl, intensive agriculture and expansion of transport infrastructure

Modest improvement in the efficiency of material use and waste minimisation. Water abstraction is increasing or stable at best compared to 2000 levels.

Improving status of terrestrial and aquatic semi-natural habitats

Expansion of renewable energy sources

AS3, Failing to deliver: Under this scenario the transition to sustainability has been unsuccessful or weak. Increased consumption of resource intensive goods has meant that there has been less progress towards higher energy and resource efficiency. There is some expansion in renewable energy production, but the rate of change is slow and significant improvements are not achieved until 2050. The rate of investment in R& D has been no more than 2%. Greenhouse gas emissions have increased and there is still pressure from the expansion of urban land and intensive agriculture. On the positive side regulation measures, including strong cross-compliance have been implemented, and farming and forestry management practices are mostly green, but these are token gestures.

Reduced pollution loads

Alternative scenario

D.6.2 – Final report

Improving energy, material and water efficiencies

FORESCENE

Demand for consumer goods has continued to grow at 2000 rates.

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FORESCENE final report  

Final report of the EU project FORESCENE. Project summary, conclusions and perspectives.

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