European Commission DG ENVec.europa.eu/environment/enveco/others/pdf/landuse... · 2015-11-11 ·...

in association with

European Commission DG ENV

MODELLING OF EU LAND‐USE CHOICES AND ENVIRONMENTAL IMPACTS –

SCOPING STUDY

Contract N° 070307/2007/485312/ETU/G1

Final Report ‐Appendices

August 2008

in collaboration with

Contact BIO Intelligence Service Shailendra Mudgal – Patricia Benito

℡ + 33 (0) 1 56 20 28 98 [email protected] [email protected]

2 European Commission, DG ENV Scoping study on modelling of EU land‐use choices August 2008

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August 2008 European Commission, DG ENV Scoping study on modelling of EU land‐use choices

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Contents

Appendices ..................................................................................................................................... 5

Appendix 1 – Presentations of the workshop on “Modelling of EU land‐use choices and environmental impacts” ............. 5 Appendix 2 – List of participants of the workshop............................................................................................................... 29 Appendix 3 ‐ Minutes workshop ‘Modelling of EU land‐use choices” ................................................................................. 31 Appendix 4 – Land‐use/land‐cover classes in models and databases .................................................................................. 43 Appendix 5 – Driving forces considered in existing models ................................................................................................. 49 Appendix 6 – Non‐exhaustive list of projects and programmes related to Land‐use change .............................................. 51 Appendix 7 – Pre‐processing phase in selected existing land‐use modelling frameworks .................................................. 61 Appendix 8 – Non‐exhaustive inventory of existing sector‐specific and global models related to land‐use ....................... 65 Appendix 9 – Non‐exhaustive list of existing Land‐use models ........................................................................................... 71 Appendix 10 – Set of criteria for assessing land‐use models ............................................................................................... 75 Appendix 11 – EU policy analytical needs ............................................................................................................................ 77

August 2008 European Commission, DG ENVScoping study on modelling of EU land‐use choices

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APPENDICES

APPENDIX 1 – PRESENTATIONS OF THE WORKSHOP ON “MODELLING OF EU LAND‐USE CHOICES AND ENVIRONMENTAL IMPACTS”


1‐ Introduction and objective of the workshop by Jacques Delsalle (EC DG ENV, G.1)


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2‐ Requirements for a EU land‐use model by Shailendra Mudgal (BIO Intelligence Service)


3‐ Available modelling frameworks by Patricia Benito (BIO Intelligence Service)


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4‐ Potential options for an EU modelling tool by Eric Koomen (SPINlab)


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5‐ Application of land use models for policy analysis and spatial planning by Judith Borsboom (Netherlands Environmental Assessment Agency)


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6‐ Development of policy support tools by Hedwig van Delden (RIKS)


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7‐ Integrating models and indicator rules for land use impact assessment and policy analysis by

Eric Koomen (SPINlab)


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8‐ Scenario development by Peter Verburg, Wageningen University


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9‐ What is being done in other areas and the link between land‐use modelling for environmental impacts and land‐use modelling in other contexts? by Barry Zondag, (Significance/Delft University of Technology)


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10‐ Defining the requirements for an EU modelling framework by BIO/SPINlab



.


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APPENDIX 2 – LIST OF PARTICIPANTS OF THE WORKSHOP

First Name Family Name Organisation

Hedwig van Delden RIKS

Jasper van Vliet RIKS

Maarten Hilferink Object Vision BV p/a CIMO ‐ Vrije Universiteit

Martin van der Beek Object Vision BV p/a CIMO ‐ Vrije Universiteit

Martha Bakker Wageningen University Peter Verburg Wageningen University

Erez Hatna Wageningen University

Louise Willemen Wageningen University

Eric Koomen Faculty of Economics and Business Administration, Vrije Universiteit (FEWEB‐VU)

Shailendra Mudgal Bio Intelligence Service

Patricia Benito Bio Intelligence Service

Judith Borsboom Dutch Environmetal Assessment agency

Barry Zondag Delft University of Technology Wolfgang Loibl Austrian Research Centers

GmbH ‐ ARC Tom Kuhlman Wageningen University

Jan Erik Wien Centre for Geo‐Information Wageningen UR, Alterra

Katharina Olsacher State Government of Lower Austria

Ernesto Ruiz ATECMA SL Sylvain Doublet Solagro

Eduardo Carqueijeiro European Commission DG Research, I.4

Jacques Delsalle European Commission DG ENV, G.1

Agnieszka Romanowicz EEA

Peter De Smedt European Commission ‐ DG Research, I.2


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APPENDIX 3 ‐ MINUTES WORKSHOP ‘MODELLING OF EU LAND‐USE CHOICES”

Title Modelling of EU land‐use choices and environmental impacts

Location Rue Van Maerlant 2, 1040 Brussels, room VM 1

Moderators Mr. Jacques Delsalle (European Commission DG ENV) Mr. Shailendra Mudgal (BIO Intelligence Service) Mr. Eric Koomen (SPINlab, Vrije Universiteit)

Contact BIO Shailendra Mudgal Patricia Benito Tel. : +33 (0)1 56 20 28 98 Email : [email protected]

[email protected]

Contact SPINlab Mr. Eric Koomen E‐mail: [email protected]

Content

1. Opening and welcome remarks 2. Introduction to the study and objective of the workshop 3. Requirements for a EU land‐use model 4. Available modelling frameworks 5. Potential options for an EU modelling tool 6. Application of land use models for policy analysis and spatial planning 7. Development OF POLICY support tools 8. Integrating models and indicator rules for land use impact assessment and policy analysis 9. Scenario development 10. What is being done in other areas 11. Discussion on the requirements for an EU modelling framework

Workshop “Modelling of EU land‐use choices and environmental impacts”

‐ Minutes ‐

Brussels, 26 June 2008


1. OPENING AND WELCOME REMARKS

Mr. Delsalle (EC) welcomed the attendees to the workshop on modelling of EU land‐use choices and environmental impacts. He briefly described the context of the current scoping study. He explained that today’s society faces the challenge of reducing the negative environmental impacts of land‐use practices while maintaining socio‐economic benefits. Land use modelling tools can be used to assess to what extent full implementation of different policies may achieve adequate protection or strengthening of land services. Tools exist, but the operational link with DG Environment is not build yet. In this context, the current study aims at identifying and comparing on‐going and forthcoming relevant land‐use modelling research programs and evaluating their usefulness for policy analysis purposes. On this basis, the study performs an analysis of the options for a future land modelling framework useful for the European Commission and defines a roadmap for the development of such modelling framework.

Mr. Delsalle also highlighted that a call for tender was being launched (closed in 25/7/08) for developing such modelling tool and the definition, implementation and elaboration of a number of different scenarios and policy options. In the medium term, the recommendations of the scoping study might be implemented through different research projects, complemented by service contracts.

2. INTRODUCTION TO THE STUDY AND OBJECTIVE OF THE WORKSHOP

Mr. Mudgal (BIO) presented a more detailed introduction to the study. He further explained the approach followed in the study, the methodology used for data gathering (literature review and telephonic interviews with experts) and also the main objectives of the workshop, including:

• The validation of the information contained in the report on existing relevant modelling tools.

• The discussion of the identified options for this potential EU land‐use modelling framework (pros and cons).

• The discussion of the proposed roadmap for further development of a EU land‐use modelling framework included in the background document (draft final report).

• The definition of the main requirements for an integrated assessment modelling framework.

He invited the attendees to participate in the discussion and contact BIO, during or after the workshop, if there were any questions or issues to be discussed or to provide any additional information.


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3. REQUIREMENTS FOR A EU LAND‐USE MODEL

Mr. Mudgal (BIO) gave a presentation on the requirements for an EU land‐use model, addressing in particular the needs for quantitative modelling, how the land‐use modelling can be applied in policy making and what would be the requirements for an EU land‐use model meeting the analytical requirements of the Commission.

He explained that when developing a modelling framework, different aspects have to be taking into consideration, in particular the policy question that is to be addressed, which in turn will define other elements of the modelling framework such as the main drivers and pressures to be considered, the impacts to be assessed, the sector‐specific models that will be necessary, the type of application of the results of the model, or the time horizon.

Mr. Mudgal indicated that from the analytical point of view, a modelling framework meeting the European Commission needs should allow simulating different types of land‐use changes simultaneously. Regarding the use of ex‐ante assessment tools, a key issue is whether the tool answers a question that is relevant for developing/assessing the policy and making it acceptable. Overall, a relevant land‐use modelling tool has to support the policy needs of different DGs of the European Commission such as ex‐ante assessment and impact assessments. Such framework should be able to estimate the economic, environmental and social impacts of land‐use across a range of scales (from EU‐27) to MS and regional level) while taking into account global sources driving forces such as demography, economic growth or climate change. The modelling framework should focus on a broad understanding of cross‐cutting trade‐offs of sector impacts and be flexible to allow taking into consideration new policy developments. Furthermore, the modelling tool shall generate projections that are reliable and plausible to a degree that is useful to the Commission, be transparent regarding the data, scenarios, baselines, etc. and that can be applied to locations other than the one(s) for which it was originally developed and results can be replicable by others. He also highlighted that, from an operational point of view, the modelling tool shall allow linkage to other models currently, in use by, or of interest to, the Commission and its input should be easy to interpret and usable by policy makers.

4. AVAILABLE MODELLING FRAMEWORKS

Ms. Benito (BIO) presented different existing relevant research projects and summarised main differences and similarities among existing modelling tools.

She first presented the general structure of a modelling framework and the different potential components (pre‐processing phase, the core modelling system and the post‐processing phase). Using the mentioned structure, she presented the main characteristics of different existing modelling tools in terms of the policy questions addressed, the drivers, the policy scenarios considered, modelling structure, and the indicators that are used. She proceeded comparing the different modelling tools in terms of Relevancy, operation and technical requirements. Finally, she concluded with some general remarks made on the basis of such comparison of existing models. For example, she highlighted that current versions of existing modelling frameworks usually focus on specific policy


questions, e.g. the CAP reform. In general environmental related issues are addressed to a limited extent and social aspects and drivers are generally not well represented in most modelling tools. Two of the main observed limitations in existing modelling tools are their limited re‐utilisation in different policy questions than the ones addressed and the difficulties experienced in linking the different components or models.

5. POTENTIAL OPTIONS FOR AN EU MODELLING TOOL

Mr. Koomen (SPINlab) made a presentation on the possible options for a land‐use policy model to be used by the Commission and a roadmap for further development of such modelling tool.

Mr. Koomen started explaining that different options are possible to develop a land‐use modelling framework for assessing environmental impacts of land‐use changes, and that the better suitability of one or the other will depend on the foreseen applications, the preferred modelling structure and other related operational issues. For example, from the ‘application’ perspective, a model can be sector specific (e.g. agriculture, urban sprawl, transport) or integrated (e.g. addressing cross‐cutting issue such as climate change affecting all types of land use and different sectors), and its results can be used during different phases of the policy‐making process (i.e. preparation, development and evaluation of large‐scale spatial plans and strategies). From an architectural point of view, models can be developed as stand‐alone entities that replace existing components (such as SEAMLESS) or as integration framework that use existing components (such as Eururalis). Depending on the architecture, the modelling tool will be more or less user‐friendly or a specialist's tool (demanding, but flexible). From the operational perspective, land‐use model output is typically delivered in the form of tables and maps, but in order to provide sensible results for policymaker, additional impact assessment tools may be applied.

Mr. Koomen explained also the roadmap that had been proposed in the background paper for an EC land‐use modelling framework, discussing the various options presented. He explained that an integrated application will be most suitable for the current and foreseen Commission’s analytical needs. This approach requires, however, complex linkages between (sub‐) domains and using sector‐specific models (likely: hydrology, climate, tourism, agriculture, forestry, economics, and transport) that can simulate different types of land use. The main application type of the modelling tool to be developed will be ex‐ante policy assessment and planning during the development phase, and it will include many different types of scenarios and spatially explicit policy options. He also explained that for the basic modelling architecture, the use of a component‐based model seemed to be most appropriate; with different sector‐specific models (representing different processes at different hierarchical levels) constituting discrete and reusable components that could be integrated in the modelling framework depending of the policy questions to be addressed. Mr. Koomen indicated that such model will have to be run by trained modellers given the complexity of the expertise required to integrate and calibrate the different components of the model. Therefore, it would consist of a specialist’s tools, but that the participation of the policy‐makers would be necessary in the pre‐modelling and post‐modelling phase.


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As an initial guideline for a generic land‐use modelling framework, a number of model characteristics were proposed, including, for example, a spatial resolution of 500m grid cells, 2040/2050 for the time horizon of the projections with 10 year time steps, covering the 27‐EU territory and with a regional division for aggregation of Nuts 1/Nuts 3.

A summary of discussions in light of this presentation is presented below:

Some participants discussed the issue of the specific structure of the potential modelling framework. For example, it was highlighted that while macro‐economic models are specific at the national level, for sectoral models there is not much information available on the market sectors within each regions. Therefore, it would be interesting to have a sectoral model that produces itself regional output. Mr. Zondag (Significance/Delft University of Technology) indicated that when modeling, two approaches are possible: an integrated, in depth model or a broad, light model. An integrated framework could be the most pragmatic solution but it might pose some difficulties, especially regarding the consistency and feedback between components. He highlighted that modelling should be tailored to the policy question that is to be addressed and the output indicators/information it needs to produce for the evaluation, in particular regarding the component to be added. For those case where feedbacks between components might be important, ‘lighter’ models might be more suitable.

Mr. Koomen (SPINlab) agreed that the specific components to be added in the modelling framework and the potential linkage issues will be conditioned by the policy questions. He added that macro‐economic and demographic models would most likely be required.

Regarding the variables to be evaluated by the modelling tool, Mr. Loibl (Austrian Research Centers), that besides land‐use classes, another important aspect to take into account is land‐use density does, which is rarely considered in existing models.

Another issue that was briefly discussed was the time horizon of the analysis to be performed by the modelling framework. In this regard, Agnieszka Romanowicz (EEA) indicated that if the results of the models were to be relevant for policy making, the modelling tool must take into account policies’ lifespan, which is usually shorter than 10 years (proposed for the modelling tool in Mr. Koomen’s presentation). She recommended simulating for each of the budgetary cycles, which is usually between 5‐7 years.

6. APPLICATION OF LAND USE MODELS FOR POLICY ANALYSIS AND SPATIAL PLANNING

Ms. Borsboom (Netherlands Environmental Assessment Agency) made a presentation on the application of land use models for policy analysis and spatial planning in the Netherlands. She started by referring to the main reasons that have motivated to the use of this type of tools in policy making, the most important being the fact that many environmental and ecological problems are related to developments in land use, which motivated an interest in gaining further insight into possible spatial changes in the future.


In this context, the Netherlands Environmental Assessment Agency (MNP) participated in the development of the model Land Use Scanner (1997) and the Environment Explorer (1998) modelling tool (property of RIVM/MNP and developed by RIKS), and the application of UrbanSIM (2005) and in the Eururalis project (2003). These modelling tools have been mainly applied in the context of the Environmental Outlooks, ex‐ante evaluation of the National Spatial Policy Plan and the Sustainability Outlooks 1 and 2. Furthermore, the tool EURURALIS 2.00 has been applied under the motion in Parliament for assessing the future of the Dutch agriculture (2002), and for investigating the potential impacts of biofuels (2006).

Ms. Borsboom described more in detail the main output of the application of land‐use modelling tools for the Sustainability Outlooks and the application of the Eururalis results to support policy discussions.

Overall, she concluded that in general, land use models can be a very useful tool support policy preparation and policy making for a number of issues, nevertheless, it is important to consider that, to date, not all environmental problems can be properly addressed with current models (e.g. sometimes very detailed level needed that cannot be achieved with current models, e.g. air and noise pollution). She highlighted that, currently, there is a gap between existing ecological models and land‐use models in terms of detail in the analysis. She also indicated that it was important to differentiate and recognise the importance of societal developments and people’s behaviour and to consider other types of measures other than spatial planning and zoning, such as source measures, fiscal/price measures, or subsidies. Finally, she pointed out the importance of considering that there are different levels of decision making (municipalities, provinces, federal states, multinationals, etc.) that have an important influence on land‐use configuration and changes and that are difficult to model.

7. DEVELOPMENT OF POLICY SUPPORT TOOLS

Ms. van Delden (RIKS) gave a presentation on the development of policy support tools for modelling the impact of interventions on land use. She first introduced the link between planning and decision support and explained the concept of Policy Support System (PSS). She also described briefly the LUMOCAP PSS as an example that uses the software environment GEONAMICA that allows the plugging of the different models.

She argued that three groups are important during the development of PSS: the end‐users, which in this case are usually policy‐makers, scientist, and the IT‐specialist. A crucial aspect during the developing phase is how these groups interact and collaborate together. Also, different steps can be distinguished in policy‐relevant integrated modelling, including the definition of policy themes, problems, options and indicators (which is going to define the relevant scale and resolution); the selection of an appropriate model; model integration (e.g. designing a software architecture, connecting the individual models, adaptation and rebuilding); the translation of scientific knowledge into information relevant for the policy‐maker; and the calibration and validation of the model and the data that is used.


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She indicated that for a land use modelling PSS to be useful, it has to be able to support policy‐relevant questions. Important elements in this regard are the user‐friendliness of the tool in entering input, viewing output and analysing results; that it is spatially explicit, the possibility to operate on different scales and resolutions, the incorporation and integration of actual data and process knowledge from different disciplines, the flexibility of the system to be extended with other modules over time and the dynamism of the feedback loops between individual models.

Regarding the structure of the PSS presented, Mr. De Smedt (EC) requested more detail on the linkage between the different modelling of RIKS and their functionality. Ms. van Delden (RIKS) explained that GEONAMICA is the software environment, where the different models are plugged in depending on the policy questions to be assessed. The PSS is the final executable, the full application that is delivered to the end‐user and where the policy options can be introduced. The end‐user has the access to the models and if necessary, new data and models can be introduced.

Regarding the operation at different scales of the models presented, Mr. Verburg (Wageningen University) requested to know how the coupling of the different scales was done in the presented tools. In particular, he highlighted that the feedback between the local, regional and national levels and the global can considerably increase the modelling time. Ms. van Delden (RIKS) explained that, for example in LUMOCAP, the feedback exists between the local, regional and national levels, but botton‐up feedback with the global scale.

Mr. Kuhlman (LEI/ Wageningen University) raised the issue of the user‐friendliness of PSS. He argue that while in the morning sessions it has been generally agreed that most modelling tools can only be operated by trained modellers, the modelling tools presented by RIKS seemed to allow operation by non specialist. In this regard, Ms. van Delden (RIKS) replied that this type of user‐friendly tools to be use directly by non‐experts is possible, although training is required so that they can interpret the results and the uncertainties.

8. INTEGRATING MODELS AND INDICATOR RULES FOR LAND USE IMPACT ASSESSMENT AND POLICY ANALYSIS

Mr. Koomen (SPINlab) gave a presentation on the use of indicators in land‐use models for impact assessment and policy analysis. He explained that from a modelling perspective, different types of indicators can be distinguished depending on the type of information that is used for their estimation and their level of aggregation. The first type is the pure land‐use indicators (information available within the model) that are used to characterise land changes. This type of indicators consists of quantitative measures used to interpret, compare, and evaluate different scenario and simulations of land‐use changes. Different types of land‐use indicators can be distinguished including general composition metrics (that quantify the variety and abundance of land‐use types without considering their spatial character), spatial configuration metrics (that refer to the spatial distribution of the various land‐use types and focus on their individual patches, i.e. areas of a specific land‐use type), structural metrics (those that measure the physical composition or configuration of the land‐use patch mosaic without explicit reference to an ecological process) and functional metrics (those that


explicitly measure landscape pattern in a manner that is functionally relevant to the organism or process under consideration).

A second type of indicators can be the so‐called enhanced land‐use indicators (for which further data is required from other external sources .i.e. models), which are used to evaluate more complex parameters, such as, for example, the risk of floods. Finally, Mr. Koomen also described and gave examples of the dedicated impact assessment tools, used for assessing sustainability in general. This is the case, for example, of the Dutch Outlook (1 and 2) that has estimated, with the help of a land‐use modelling tools, impacts on people (flood risk) the environment (biodiversity and landscape quality), and the economy (accessibility, business climate and costs).

9. SCENARIO DEVELOPMENT

Mr. Verburg (Wageningen University) gave a presentation on scenarios for land use modelling. He started by introducing the concept of policy scenarios, their utility and the different types, including predictive scenarios ( extrapolation of current trends and processes, business‐as‐usual and reference scenarios, supplemented with policy options), exploratory scenarios (constructing alternative, plausible futures as trends or shocks), and normative scenarios (describing desired futures and the events that can lead to such futures). Also he provided some examples of European modelling projects using the different type of scenarios.

He continued explaining how scenarios are developed (narrative development, quantification of drivers, how drivers can be translated into model setting, etc), and discussing the requirements and challenges for scenario development in terms of credibility, saliency and legitimacy).

He concluded that the choice of one or another type of scenario will depend on the specific use of the modelling tool and the context and that there is not one‐size‐fits‐all scenario for land use modelling. In general, he highlighted that model should be flexible enough to deal with scenario requirements and that modellers should pay special attention in the development their development by dealing with aspects of credibility, saliency, and legitimacy.

10. WHAT IS BEING DONE IN OTHER AREAS

Mr. Zondag (Significance/Delft University of Technology) gave a presentation on what is being done in other areas and the link between land‐use modelling for environmental impacts and land‐use modelling in other contexts.

He started by presenting examples of land‐use modelling in other fields, particularly in the fields of transport and water resources managements. Regarding transport interaction models, Mr. Zondag highlighted that they became increasingly helpful in the context of urban sprawl and urbanisation. In these cases, transport and spatial planning are going to be essential to facilitate and define the new developments. Land use and transport interaction models allow analysing the relationship between transport and associated land changes. The experience with this type of models started approximately 40 years ago and they have been applied in many different regions and cities in Europe. Land use in transport models refers to land itself as well as to its objects (e.g. housing units)


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and activities (people, jobs). In general, this type of models operate at the city and regional levels using zones as spatial units (1000‐2000 zones), although some models are already operating at the national level. Mr. Zondag continued describing the TIGRIS XL transport interaction models as an example. This model has been applied in interaction with the Land Use Scanner in a major scenario study ‘The Netherlands in the Future’ conducted by the Environmental Assessment Agency in NL. It contains different economic models (labor market, housing market, etc.), which is considered to be very important. A great level of detail was necessary (e.g. different dwellers and housing types were considered) and the running times of the model was elevated. This is mainly due to the fact that congestion was analysed, which is a local phenomenon that requires a lot of data. One interesting output of this model was the accessibility benefits in monetary terms of different land‐use strategies in comparison with the trend scenario.

Regarding land‐use changes in the context of water resources management, Mr. Zondag highlighted that land‐use projection models were essential in the context of water resources planning. He provided an example of the application of land‐use models in the island of Java, where the total conservation area for water was estimated taking into account the zoning needed for erosion and run‐off conservation. Furthermore, the same land‐use modeling tool was used to calculate the impacts of water conservation policies on other sectors like housing or agriculture.

He continued by discussing some general issues related to spatial modelling based on its experience. He argued that simulating spatial markets and market players (both in the supply and demand side) was crucial as land‐use changes are strongly driven by changes in spatial markets, such as housing‐, labor‐, transport‐ or agriculture markets. He also highlighted that as different processes occur at different spatial scale levels, it is always better to integrate these scale levels and spatial processes in land‐use modeling. Also, he indicated that land‐use modelling is by nature a multi sector activity as different sectors compete for scarce land resources.

Another important issue raised by Mr. Zondag was the utilization of incremental versus equilibrium approaches. In this regard, he argued that in his opinion, a general equilibrium does in reality not exist in space, and therefore, he was in favor of an incremental approach and using equilibrium techniques to solve specific markets. Furthermore, he highlighted that the difference between an integrated model and a tool linking models is not always very clear as an integrated framework can still have a modular set‐up. He finally highlighted that the linkage of models can be complex due to problems resulting from overlap, the level of aggregation of the information, limiting policy responsiveness, etc.

11. DISCUSSION ON THE REQUIREMENTS FOR AN EU MODELLING FRAMEWORK

Following the previous presentations, BIO/SPINlab moderated a discussion on the possible options for an EC land‐use modelling framework.

The discussion started addressing the issue of the possible options for the architecture of the framework. Two main possibilities were further discussed by participants: a “one system covering all”, which could also be regarded as a stand‐alone light version of a land‐use modelling framework able to replace existing components, or an integrated framework that uses existing components. In


this regard, Ms. van Delden (RIKS) explained that in order to define what type of modelling tool is necessary, either ‘heavy’ or ‘light’ systems, it is necessary to discus first with the end‐user. Depending on the level of detail required, it would be possible to define the type of models that are required, which in turn will define the data requirements, the complexity of the system and the running time required. In some situations, an application that integrates both types of models will be feasible.

Mr. Verburg (Wageningen University) agreed that for some applications a ‘somewhere in the middle’ solution would be possible but not for all, as it not always possible to date to integrate all kind of models to address certain issues in the same framework (for example, as in the case of CAPRI) due to the complexity in the feedbacks that is required between components and also because of the lack of transparency in terms of the modelling chain. He highlighted that, as questions to be addressed and the level of detail vary widely, what it would be appropriate would be to build a model capable of integrating one or the other type of models depending of the question being addressed in a coherent and consisted manner. In any case, he expressed to be in favour of using existing modelling tools that have already been validated and applied for addressing policy questions.

In this regard, participants generally agreed on the fact that there are certain existing modelling tools that have already been applied in Europe and that should be considered in the context of the EU modelling framework for land use, such as in the case of TRANSTOOLS or TREMOVE for transport (Mr. Zondag, Mr. Delsalle) or CAPRI for agriculture (Mr. Verburg).

The issue of the type of end‐user that would be using this modelling framework and the type of tool that would be more suitable was also addressed during the discussion. Mr. Delsalle (EC) expressed his doubts about the usefulness and adequacy of the results obtained from user‐friendly PSS in the context of policy analysis, as thorough analysis and the participation of experts are usually required in policy assessment. In this regard, Mr. Verburg (Wageningen University) explained his experience in the development of this type of tool in the context of the EURURALIS project. He indicated that it was complicated to develop such tool and that even with a very simplified version, some kind of assistance from experts was necessary. In his opinion, PSS interface should allow policy makers to overview the process and to access results related to possible futures, but this type of tool should mainly be used for policy discussion but it is not a real decision support tool. Ms. van Delden (RIKS) indicated that in their experience, technicians in the administration are the main end‐user of this type of tools. They are in charge of running and calibrating them are provide outcome to policy‐makers. Mr. Kuhlman (LEI/ Wageningen University) highlighted that in the framework of the SENSOR projects, they have seen that there is not such a thing as ‘end‐user’ in the Commission. He also indicated that depending on the organisation that is commissioning the modelling tool, the focus will be put in one or another type of tool. For example, in the case of JRC, there interest was the development of a tool linking different existing models and not a stand‐alone user‐friendly tool.

Regarding the question of whether the modelling tool to be developed should focus on questions related to specific sectors or should it address more integrated or general issues affecting different sectors, it was generally agreed by participants that it should be a multi‐sector tool given the fact that land‐use modelling is in its nature a multi sector activity.


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Another important issue that was raised was the approach that should be taking when developing this type tool in the future. Ms. Borsboom‐van Beurden (Netherlands Environmental Assessment Agency) argued that the most efficient approach would be to prioritise policy questions to be addressed and to develop the tool accordingly. In a second phase, it would be possible to adapt the created model to new policy questions. Mr. Zondag (Significance/Delft University of Technology) added that it will be advisable to create a list of policy questions to be addressed and the aspects that would be relevant to analyse for each of them. It was also remarked by different attendants (e.g. Mr. Zondag and Ms. van Delden) that the process of developing the tool is in some cases more important that the results of the modelling tool themselves, also for policy makers and it allows to discuss the different drivers of land use changes and to better understand the process. This is one of the reasons why the participation of policy‐makers in the process is so important.

Finally, the issue of future research needed to better meet current analytical needs in the Commission was addressed. Several issues were highlighted during the discussion:

• More research is required to improve our knowledge about the relationship between land‐use changes and the resulting environmental impacts (Mr. Kuhlman,LEI/ Wageningen University). Also more research is required to develop the methodology for assessing the resulting environmental impacts.

• Social and economic impacts are not addressed to an appropriated detail in most existing modelling tools, particularly at the regional and local scales. Mr. Verburg (Wageningen University). Further research would also investigate better ways to accommodate behaviour patterns and human preferences in modelling tools, which cause important impacts on land‐use changes. Appropriate indicators should be developed in this regard.

• The link between changes in land use and their impacts into landscape is not very well known (Mr. Kuhlman,LEI/ Wageningen University). This is one example where the scaling issues becomes important.

• Awareness rising is necessary about the current state of modelling tools, their potential, policy analytical needs, and the needed development among policy‐makers and also scientist (Mr. Delsalle, EC).


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APPENDIX 4 – LAND‐USE/LAND‐COVER CLASSES IN MODELS AND DATABASES

In the PRELUDE project, the following land‐use/cover classes were considered:

• Urban

• Cropland

• Grassland

• Biofuels crops

• Forests

• Abandoned land

As it happens with the PRELUDE classes, land‐use and land‐cover are mixed in the classification used in CORINE. The following classes are considered under CORINE1.

• Artificial surfaces

o Urban fabric

Continuous urban fabric

Discontinuous urban fabric

o Industrial, commercial and transport units

Industrial or commercial units

Road and rail networks and associated land

Port areas

Airports

o Mine, dump and construction sites

Mineral extraction sites

Dump sites

Construction sites

o 1.4 Artificial, non‐agricultural vegetated areas

Green urban areas

Sport and leisure facilities

• Agricultural areas

o Arable land

Non‐irrigated arable land

Permanently irrigated land

1 Corine Land‐cover 2006 (http://terrestrial.eionet.europa.eu/CLC2006/)


Rice fields

o 2.2 Permanent crops

Vineyards

Fruit trees and berry plantations

Olive groves

o 2.3 Pastures

Pastures

o 2.4 Heterogeneous agricultural areas

Annual crops associated with permanent crops

Complex cultivation patterns

Land principally occupied by agriculture, with significant areas of natural vegetation

Agro‐forestry areas

• Forest and semi‐natural areas

o Forests

Broad‐leaved forest

Coniferous forest

Mixed forest

o Scrub and/or herbaceous vegetation associations

Natural grasslands

Moors and heath land

Sclerophyllous vegetation

Transitional woodland‐shrub

o Open spaces with little or no vegetation

Beaches, dunes, sands

Bare rocks

Sparsely vegetated areas

Burnt areas

Glaciers and perpetual snow

• Wetlands

o Inland wetlands

Inland marshes

Peat bogs

o Maritime wetlands


45

Salt marshes

Salines

Intertidal flats

• Water bodies

o Inland waters

Water courses

Water bodies

o Marine waters

Coastal lagoons

Estuaries

Sea and ocean

Both SENSOR’s SIAT and the modelling framework used for the SCENAR projects use the classification of the CLUE‐s model, which derivates from the CORINE European land‐cover map, which has been aggregated. The following land‐use and land‐cover classes were used:

• Built‐up area

• Arable land (non‐irrigated)

• Grassland

• (semi‐)Natural vegetation (including natural grasslands, scrublands, regenerating forest below 2 m, and small forest patches within agricultural landscapes)

• Inland wetlands

• Glaciers and snow

• Irrigated arable land

• Recently abandoned arable land (i.e. “long fallow”; includes very extensive farmland not reported in agricultural statistics, herbaceous vegetation, grasses and shrubs below 30 cm)

• Permanent crops

• Forest

• Sparsely vegetated areas

• Beaches, dunes and sands

• Salines

• Water and coastal flats


• Heather and moorlands

• Recently abandoned pasture land (includes very extensive pasture land not reported in agricultural statistics, grasses and shrubs below 30cm)

The SENSOR’s SIAT does also consider biofuel crops, which are usually part of (non‐irrigated) arable land and therefore not shown in the produced maps.

Regarding the Land‐use Scanner, developed by VU/SPINlab, the following types of land‐use are distinguished in the present version of the model:

• Urban:

o residential,

o industrial,

o roads,

o railways,

o airports.

• Agriculture:

o pasture,

o corn,

o arable land (potatoes, beets, cereals),

o flower bulbs,

o orchards,

o cultivation under glass,

o and other agriculture.

• Natural areas:

o wood,

o nature.

• Water.

The EURURALIS modelling tool considers 8 different land‐use types, which are based and aggregates the categories of the CLUE‐s model. These are:

• Built‐up area

• Non‐irrigated arable land

• Permanent pastures


47

• Forest, nature and natural grassland

• Inland wetlands

• Abandoned farmland

• Integrated arable land

• Other land‐use types considered static during the simulation (including glaciers, beaches, coastal wetlands, etc.)

The MOLAND model (JRC) uses a classification of land‐use types that are the same of CORINE Land‐cover European database but with a more detailed level of land‐use class and mainly focused on urban areas. The twenty‐four land‐use classes used in the MOLAND urban growth model, comprise nine “active functions” (the urban land‐uses which change in direct response to urban growth), eight “passive functions” (which participate in the land‐use dynamics, but change in response to land taken or abandoned by the active functions), one ”transitional state”( represents a transition between two functions such as for example construction site), and six “fixed features” (which do not change, but affect the dynamics of the active land‐uses). All twenty‐four land‐use classes used by the MOLAND model are listed below:

• Nine active functions

o Residential continuous dense urban fabric

o Residential continuous medium‐dense urban fabric

o Residential discontinuous urban fabric

o Residential discontinuous sparse urban fabric

o Industrial areas

o Commercial units

o Public and private services

o Port areas

o Abandoned land

• Eight passive functions

o Arable land

o Permanent crops

o Pastures

o Heterogeneous agricultural areas

o Forests


o Shrub

o Sparsely vegetated areas

o Wetlands

• One transitional state

o Construction site

• Six fixed features

o Road and rail network

o Airport

o Mineral extraction sites

o Dump sites

o Artificial non‐agricultural vegetated areas

o Water bodies


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APPENDIX 5 – DRIVING FORCES CONSIDERED IN EXISTING MODELS

For the PRELUDE project, different stakeholders were gathered to categorise driving forces that influence different land‐use types. 20 driving forces were identified (EEA, 2007):

• Subsidiarity

• Environmental Awareness

• Policy intervention

• Economic growth

• Settlement density

• International trade

• Population growth

• Daily mobility

• Ageing society

• Self‐sufficiency

• Immigration

• Technological growth

• Internal migration

• Agricultural intensity

• Health concern

• Climate change

• Social equity

• Renewable energy

• Quality of life

• Human behaviour

For the SCENAR project, a fundamental objective has been to identify what are the long‐term drivers that the Scenar 2020 study had to work with in developing a vision for the future of agriculture and the rural world in the European Union. The main drivers identified in the framework of SCENAR project for the modelling exercise were:

• Exogenous drivers to the EU policy‐making system

o Demography


o Macro‐economic growth

o World agricultural markets

o Consumer preferences

o Quality of life

o Human and Animal Health concerns

o Agri‐technology

• Endogenous drivers to the EU policy‐making system

o Trade policy and agricultural policy

o EU agricultural policy

o Environmental Policy (Impact on Agriculture)

o Enlargement

o WTO and other international agreements

In the case of the SENSOR project, the following drivers were considered in the first SIAT model finalised in 2006:

• Population growth,

• Participation rate in the labour force, for the same area,

• Economic growth in the world outside the EU,

• The world oil price and

• Expenditure on research & development in the EU

Three additional drivers, namely policies, institutions and cultural preferences are kept constant in the baseline scenarios, but are to be considered through policy scenarios and sectoral analyses.

The Land‐use Scanner model takes into consideration the socio‐economic conditions (e.g., economic growth, population, etc), policy (e.g. spatial policy, nature policy, etc), agriculture trends (e.g. rural land pricing, agricultural production, etc), and external pressures (e.g. urbanization, recreational developments, etc).

The EURURALIS considers political developments, macro‐economic growth, demographic developments climate change and technology as the main driving forces for land‐use changes.


51

APPENDIX 6 – NON‐EXHAUSTIVE LIST OF PROJECTS AND PROGRAMMES RELATED TO LAND‐USE CHANGE

Project Goals Research Scope Project period

Website

EU‐

AGRICU

LTURE

AND FISHER

IES SCENAR 2020 • To identify future trends and driving forces that will be

the framework for the European agricultural and rural economy on the horizon of 2020

Scenario study on agriculture and the rural world

http://ec.europa.eu/agriculture/publi/reports/scenar2020/index_en.htm

EU 6

TH FP

COCONUT(effeCts Of land‐use Changes ON ecosystems to halt loss of biodiversity dUe to habitat destruction, fragmenTation and degradation)

• To understand the effect of land‐use change on biodiversity in Europe

• To provide an evidence‐base to underpin policy for landscape management

Impacts of land‐use changes on biodiversity in Europe

2006‐2008 www.coconut‐project.net/

DECOIN (Development and Comparison of Sustainability Indicators)

• to evaluate the existing methods and analytical frameworks in order to assess the progress towards sustainable development,

• To elaborate on forecasts and scenarios, and to identify inter‐relationships between selected unsustainable trends in the EU, and

• To carry out a detailed analysis on the inter‐relationships between selected unsustainable trends and to provide a prototype tool for the analysis and for forecasting.

2006‐2007 www.tukkk.fi/tutu/decoin/



Website

ELME (European Lifestyles and Marine Ecosystems)

• To assess the consequences of current human lifestyles on Europe's regional seas

• To create a predictive model of key environmental problems

• To form an integrated vision of the future state of Europe's seas following the application of alternative policy options

Modelling of environmental problems in Europe's regional seas

2003‐2007

www.elme‐eu.org

EXIOPOL • To synthesise and develop further estimates of the external costs of key environmental impacts for Europe;

• To set up an environmentally extended (EE) Input‐Output (I‐O) framework in which as many of these estimates as possible are included, allowing the estimation of environmental impacts and external costs of different economic sector activities, final consumption activities and resource consumption for countries in the EU;

• Apply the results of the external cost estimates and EE I‐O analysis for the analysis of policy questions of importance.

EU Policy fields 2007‐2010 www.feem‐project.net/exiopol/

FORSCENE (Forecasting Framework and Scenarios to Support the EU Sustainable Development Strategy)

To develop an analytical framework for consistent environmental sustainability scenario building (forecasting, back casting, simulation) in areas such as water, soil, biodiversity, waste and natural resources.

Policy‐oriented research, Scientific support to policies, Integrating and Strengthening the European Research Area

2005‐2008 www.forescene.net/Project.htm

FORWAST • To inventorise the historically cumulated physical mapping of the 2007‐?


53


Website

stock of materials in EU‐27 (EU‐25 plus Romania and Bulgaria), and to forecast the expected amounts of waste generated, per resource category, in the next 25 years.

• To assess the life‐cycle wide environmental impacts from different scenarios of waste prevention, recycling and waste treatment in the EU‐27.

environmental issues of waste generation and management in Europe

INSEA (Integrated Sink Enhancement Assessment)

To develop analytical tools to assess economic and environmental effects of enhancing carbon sink and greenhouse gas abatement measures on agricultural and forest lands

Greenhouse gases mitigation in forestry and agriculture

www.insea‐eu.info/

KASSA (Knowledge assessment and sharing on sustainable agriculture)

To building up a comprehensive knowledge base on sustainable agricultural practices, approaches and systems in support of stakeholders initiatives in different world regions.

Sustainable agricultural practices in Europe

2004‐2007 kassa.cirad.fr

MATISSE (Methods and Tools for Integrated Sustainability Assessment)

To achieve a step‐wise advance in the science and application of Integrated Sustainability Assessment (ISA) of EU policies

Tools available for conducting Integrated Sustainability Assessments.

2005‐2008 www.matisse‐project.net/projectcomm/

MEA‐SCOPE(Micro‐economic instruments

• To further development of the multifunctionality concept for European agriculture;

• To develop quantitative tool for assessment of the

sustainability of agriculture and forestry in rural areas

2004‐2007 www.mea‐scope.org/



Website

for impact assessment of multifunctional agriculture to implement the Model of European Agriculture)

multifunctionality impacts of CAP reform options;

• To answer policy‐relevant questions for the implementation of the multifunctionality concept;

• To demonstrate the operability of the integrated assessment framework;

• To generate scientific knowledge on specific questions regarding multifunctionality of agriculture, particularly with respect to spatial scale and regional differences.

METHODEX (Methods and data on environmental and health externalities: harmonising and sharing of operational estimates)

To advance best practice in external cost assessment, and extend the ExternE analysis to agriculture, industry, waste and other sectors

Environmental and health externalities of agriculture, industry, waste and other sectors

2004‐2007

MODELKEY To develop interlinked and verified diagnostic and predictive modelling tools as well as innovative field and laboratory methods generally applicable to European freshwater and marine ecosystems

Models for Assessing and Forecasting the Impact of Environmental Key Pollutants on Marine and Freshwater Ecosystems and Biodiversity

2005‐2010 www.modelkey.ufz.de/index.php?en=3138

MODELS (MOdel To develop advanced economic modelling methodologies and Economic, social, and environmental

2006‐2009 www.ecmodels.eu/


55


Website

Development for the Evaluation of Lisbon Strategies)

to support the European Commission services to operate this suite of advanced computer‐based economic modelling tools in order to perform comprehensive impact assessments of the policy agenda set for the Lisbon strategy and the related regulation and legislative proposals of the European Union.

renewal and sustainability in Europe

PLUREL (Peri‐urban Land‐Use Relationships)

To develop the new strategies and planning and forecasting tools that are essential for developing sustainable rural‐urban land‐use relationships.

Strategies and Sustainability Assessment Tools for Urban – Rural Linkages

2007‐2010 www.plurel.net/Default.aspx?id=4

SCENES (Water Scenarios for Europe and for Neighbouring States)

To develop and analyse a set comprehensive scenarios of Europe's freshwater futures up to 2025,

Future of water in Europe

2006‐2010 www.environment.fi/default.asp?node=20627&lan=en

SEAMLESS (System for Environmental and Agricultural Modelling; Linking European Science and Society)

To assess and compare, ex‐ante, alternative agricultural and environmental policy options.

Environmental and agriculture modelling covering a full range of scales (farm to EU and global)

2005‐2009 www.seamless‐ip.org/

SENSOR (Tools for Environmental, Social and Economic Effects of Multifunctional Land‐use in European Regions)

To develop science based ex‐ante Sustainability Impact Assessment Tools (SIAT) to support decision making on policies related to multifunctional land‐use in European regions.

Impact assessment of land‐use changes

2004‐2008 www.sensor‐ip.org/



Website

SustainabilityA‐Test • Support the definition and implementation of the EU Sustainable Development Strategy by describing, assessing and comparing tools that can be used to measure or assess sustainable development; and thus

• Improve the scientific underpinning of sustainable development impact assessment

Assessments tools used to assess a policy's contribution to sustainable development

2004‐2006 ivm5.ivm.vu.nl/sat/

EFFORWOOD To provide methodologies and tools that will, for the first time, integrate Sustainability Impact Assessment of the whole European Forestry‐Wood Chain (FWC), by quantifying performance of FWC, using indicators for all three pillars of sustainability; environmental, economic and societal.

Sustainability impacts of modifications of Forestry‐Wood Chains

2005‐2009 87.192.2.59/eforwood/default.aspx

SELMA (Spatial Deconcentration of Economic Land‐use and Quality of life in European Metropolitan Areas),)

To design urban planning and management strategies to ensure the maintenance of quality of life in European metropolitan areas

Assessing the impacts of sprawling urban economies

selma.rtdproject.net/


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Website

FARO‐EU (Foresight Analysis of Rural areas Of Europe)

• To analyse recent trends and patterns in the evolution of rural areas in Europe from a territorial perspective

• To forecast the likely future evolution of identified trends and patterns in the medium term

• To identify the forms of governance and institutional cooperation or partnerships which have been influential in developing, codifying and diffusing information and communication technologies in different types of rural areas

Rural Development in the EU

Jan 2007‐Dec 2008)

http://www.faro‐eu.org/

NitroEuropeIP • To establish robust datasets of N fluxes and net greenhouse‐gas exchange (NGE) in relation to C‐N cycling of representative European ecosystems

• To quantify the effects of past and present global changes (climate, atmospheric composition, land‐use/land‐management) on CN cycling and NGE,

• To simulate the observed fluxes of N and NGE

• To quantify multiple N and C fluxes for contrasting European landscapes

Nitrogen cycle 2006‐2011 http://www.nitroeurope.eu

INTERR

EG ESPON (European

Spatial Planning Observation Network)

Applied research and studies on territorial development and spatial planning seen from a European perspective in support of policy development

Spatial planning in EU territory

ESPON programme until 2013

www.espon.eu/

OTH

ER

ETC‐LUSI(European Topic Centre Land‐use and Spatial

To improve national information systems related with land‐ use and spatial information

Information systems in Europe

terrestrial.eionet.europa.eu/index_html



Website

Information)

LUCAS (land‐use/ cover area frame survey) Lot 1

To generate indicators enabling the monitoring of impact of the CAP reform and rural development.

land‐use/ cover area in Europe

2006 www.lucaslot1.com/new_sites/About.aspx

LUMOCAP To develop an operational tool for evaluating changes in land‐use and their impact on the rural landscape according to a Common Agricultural Policy (CAP) orientation.

Land‐use changes in rural landscapes in EU territory

2005‐2008 www.riks.nl/projects/LUMOCAP

PRELUDEproject (PRospective environmental analysis of land‐use development in Europe)

To develop coherent scenarios that describe plausible future

developments for land‐use in EU‑25 plus Norway and Switzerland and their potential environmental impacts for the period 2005–2035.

Land‐use changes and environmental impacts in EU25+ Norway and Switzerland

2005‐2007 reports.eea.europa.eu/technical_report_2007_9/en/technical_report_9_2007.pdf

TRANSUST.SCAN (Scanning Policy Scenarios for the Transition to Sustainable Economic Structures)

To scan a wide range of policy scenarios as to their relevance for the European Sustainable Development Strategy in view of Extended Impact Assessment.

European Sustainable Development Strategy

www.transust.org/

EURURALIS To develop an interactive, user‐friendly meta‐model for a balanced discussion on the future of the European rural area (25 EU countries) from the perspective of sustainable development in the coming decades (time horizon app. 30 years).

European rural area (25 EU countries)

2003‐2007 http://www.eururalis.eu/

Land‐use Scanner (LUMOS)

To provide theoretical foundations and practical adaptations of land‐use models for the Netherlands in order to improve and stimulate the use of these models in (spatial) policy

Spatial planning in Netherlands

http://www.lumospro.nl/


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Website

discussions

CLUE To make a spatially explicit, multi‐scale, quantitative description of land‐use changes through the determination and quantification of the most important (assumed) bio‐geophysical and human drivers of agricultural land‐use on the basis of the actual land‐use structure.

Land‐Use and Land‐Cover Change (LUCC) Project

• To obtain a better understanding of global land‐use and land‐cover driving forces.

• To investigate and document temporal and geographical dynamics of land‐use and land‐cover.

• To define the links between sustainability and various land‐uses.

• To understand the inter‐relationship between LUCC, biogeochemistry and climate.

http://www.geo.ucl.ac.be/LUCC/


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APPENDIX 7 – PRE‐PROCESSING PHASE IN SELECTED EXISTING LAND‐USE MODELLING FRAMEWORKS

Model/modelling framework

Policy questions considered Drivers/Data Policy Scenarios Developers/Application References

MedAction PSS

Physical, economic and social aspects of land degradation and desertification, sustainable farming and water resources.in Northern Mediterranean coastal watersheds.

Wide range of hypothesis and scenarios (climatic, economic, social, etc.) including historic rainfall and temperature data, soil moisture, crop prices, water demand, erosion and sedimentation and the salinisation of the soils, etc.

Afforestation, grazing regulations, zoning, construction of check dams, construction of infrastructure (dams, roads, channels), etc.

Commissioned by European Union and developed by the Research Institute for Knowledge Systems (RISK).. Applied to the Guadalentın river basin in Spain, Alentejo (Portugal), Val d'Agri (Italy) and Lesbos (Greece). Other future applications include MedAction is the updated version of MODULUS, and is currently being further developed in FP‐6 project DeSurvey.

http://www.riks.nl/projects/MedAction

(van Delden et al. 2005)

LUMOCAP Changes in land‐use and their impact on the rural landscape according to a Common Agricultural Policy (CAP) orientation.

Different policy, socio‐economic ‐ and bio‐physical drivers. Models require pan‐European databases (CLC, FSS, FADN, Natura 2000, Image 2000, Soils, NDVI series, MARS meteo, EUROSTAT socio‐economic data, etc.

Different levels of market support and farm income.

Commissioned by European Commission, DG‐RTD and developed by a consortium of 4 partners, coordinated by RIKS: JRC, (Italy); KU Leuven (Belgium) IUNG (Poland); and RIKS (The Netherlands).

http://www.riks.nl/projects/LUMOCAP

SENSOR SIAT The Policies related to multifunctional land‐use in Europe in general, including non‐monetary (such as the EU soil strategy) to monetary instruments such as taxes

‐Population growth,

‐Participation rate in the labour force, for the same area,

‐Economic growth in the world outside the EU,

For the reform of the CAP, the scenarios show the possible impact of changes in the level of farm income support and market support.

Integrated Project in the 6th Framework Research Programme of the European Commission

http://www.sensor‐ip.org/




and subsidies –e.g. subsidies to promote renewable energies). The reform of the Common Agriculture Policy (CAP) is the first policy case for which model outputs have been produced within the SENSOR project

‐The world oil price and

‐Expenditure on research & development in the EU

‐Policy development

EURURALIS Europe’s future agriculture and rural areas

‐Policy developments

‐World trade

‐Demography

‐Climate change

‐Technology development

Four policy options t that comprise different levels of market support, farm payments, rural development support, and nature conservation

The EURURALIS project was initiated by the Wageningen University and Research Center and commissioned by the Dutch Ministry of Agriculture, Nature and Food Quality

http://www.eururalis.eu/

SEAMLESS Agricultural and environmental policies. In particular, SEAMLESS‐IF has been applied and tested in two Test Cases, one focusing on assessment of Common Agricultural Policy reforms and trade liberalisations as a consequence of WTO negotiations, and a second on assessing local implementations of

A wide range of drivers are considered including climate change, environmental policies, rural development options, an enlarging EU, an international competition.

Models require pan‐European databases for environmental, economic and social issues. environmental data (soils, altitude and climate), farming

Policy scenario that have been taken into account in the prototype 1 include:

‐Reduction of import tariffs

‐Elimination of export subsidies

‐Expansion of tariff rate quotas

‐Specific bilateral trade agreements (not considered for Prototype 1)

The project is funded by the 6th EU Framework Programme and is coordinated by Wageningen University

http://www.seamless‐ip.org/


63



environmental directives and consequences of agro‐technical innovations.

data and socio‐economic data.


65

APPENDIX 8 – NON‐EXHAUSTIVE INVENTORY OF EXISTING SECTOR‐SPECIFIC AND GLOBAL MODELS RELATED TO LAND‐USE

MODEL Themes covered

Drivers considered Indicators Geographical coverage Temporal coverage Analytical technique

Target group/Applications

ASTRA (ASsessment of TRAnsport strategies)2

‐ transport ‐air and climate change (in relation to transport)

‐ key economic indicators as GDP and employment ‐ population ‐transport demand

‐ Traffic volume, vehicle fleet and CO2/NOx emissions

‐ coverage: EU15 ‐resolution: NUTS II

‐ base year: 1995 ‐ time horizon: 2026 ‐time steps: annual

‐ system dynamics model

DG Transport

CAPRI‐Dynaspat (Common Agricultural Policy Regionalised Impact)3

‐ agriculture ‐ water, air and climate (i.e. agriculture‐related) ‐land‐use (cropping shares at 1x1 km grid)

‐ agricultural policies in the CAP ‐ market prices of agricultural products ‐ feed, N,P,K fertiliser, diesel or plant protection costs by agricultural activity

‐ areas cropped ‐ livestock (herd sizes), ‐ nutrient balances (N, P, K), ‐emissions (ammonia, methane and N2O) PLUS energy use in agriculture

‐ coverage: EU27 plus NO plus 6 Western Balkan countries (note that trade of agricultural products is simulated in global module) ‐ resolution: regional level (administrative regions, NUTS2) plus 1x1 km grid (crop level, animal stocking densities, yields, N balances and further indicators), currently EU15 (EU25 foreseen for late spring 2007)

‐ base year: 2002 (three year averages 2001‐2003), time series at regional level from 1985 onwards (EU‐10: 1990, Western Balkans: 1995) ‐ time horizon: 5 to 10 years, some analysis until 2020 (e.g. SCENAR 2020 study for DG‐AGRI, SENSOR IP), typically final year only

‐partial equilibrium model

EU commission (DG‐AGRI) is using the model for policy impact analysis. Several research institutions (UBONN,NILF, SLI, LEI, FAL, Univ. Galway) use the model for different projects (i.a. for DG‐AGRI, DG/ENV, EAA, Norwegian Ag. Council, Ag. Ministry Ireland, seed and biochemical manufacturers).

2 More information on ASTRA at: http://www.iww.uni‐karlsruhe.de/ASTRA/ 3 More information on CAPRI at: http://www.ilr1.uni‐bonn.de/agpo/staff/jansson/capriwp03‐03.pdf





PHOENIX plus4

‐ demography ‐ assumptions on fertility, mortality and migration (from previous studies)

‐ demography ‐ coverage: global (in Europe 40 countries detailed) ‐resolution: country level, disaggregated to 0,5 x 0,5 degree grid cells

‐ base year: 1950 / 2000 (i.e. latest population data) ‐ time horizon: 2100 ‐time steps: annual

‐systems dynamic modelling approach

The PHOENIX model has been used, among others, in the IMAGE model, in National Environmental Outlooks, in UNEP's Global Environmental Outlook, IPCC SRES and EURURALIS.

EcoSense5 ‐ air pollution ‐ energy (i.e. related to air pollution from energy use) ‐environment and health (i.e. related to air pollution)

‐ information on power plants (e.g. location, type of technology) ‐ emissions, e.g. NOX, SO2, PM2.5, PM10, NMVOC, GHG, heavy metals

‐ concentration levels of primary and secondary particles and ozone ‐ receptor exposure (i.e. population, crops, building material) ‐ physical impacts resulting from exposure to airborne pollutants ‐ (damage) costs due to impacts on human health, crops, building materials, ecosystems, and due to climate change.

‐ Coverage: Europe including North Africa (EcoSense‐Web). EcoSense can be / has been used in other regions, e.g. China, Brasil, Russia ‐ Resolution: local scale: (polar‐stereographic) grids with resolutions of 10 km x 10 km; regional scale: (polar‐stereographic) grids with resolutions of 50 km x 50 km; hemispheric scale: (polar‐stereographic) grids with resolutions of 100 km x 100 km covering the Northern

‐ no time‐steps / time horizon The EcoSense model is an impact assessment model without forecasts) ‐yearly average values

‐ modelling framework combines and links various modelling and impact assessment approaches

ExternE project partners; policy makers and researcher dealing with air pollution in Europe. Currently being applied in NEEDS (New Energy Externalities Development for Sustainability) project.

4 More information on PHONIX at: http://www.mnp.nl/phoenix/ 5 More information on EcoSense at: http://www.ier.uni‐stuttgart.de/forschung/modmeth/ecosense/ecosense.html


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Hemisphere.

EFISCEN6 (European Forest Information Scenario Model)

‐ forestry ‐ biodiversity (i.e. forestry related) ‐climate change (i.e. forestry related)

‐EFFISCEN inventory database (i.e. forest types can be separated based on administrative unit, ownership, tree species and site class; see http://www.efi.int/databases/efiscen/intro.php ‐ forest management (tree mortality, yield, and felling regime) ‐ market demand for round wood

‐ tree species distribution ‐ area, growing stock, increment, harvest level and age class distribution ‐ information on carbon stocks in biomass and soil

‐ coverage: EU27 + CH, NO ‐resolution: national to provincial level (depends on inventory database)

‐ base year: depends on inventory database ‐ time horizon: 50 to 60 years ‐time steps: 5 years

‐area‐based matrix modelling approach

The model is designed to be used by various users such as policy and decision makers, researchers as well as the general public. Currently being used in the EU FP6 projects EFORWOOD and ADAM.

GLOBIO (Global methodology for mapping human impacts on the biosphere)7

‐biodiversity ‐ land‐use

‐ interaction between policy strategies and changes in land‐use (agriculture, forestry, settlements), climate changes, infrastructure, fragmentation and nitrogen disposition (i.e. derived from existing scientific studies) ‐ satellite imagery

‐ maps land areas that are directly urbanised; directly converted; fragmented and under conversion; or still relatively intact ecosystems ‐ecosystem impacted by infrastructure development

‐ coverage: global ‐resolution: visualisation at 1km x 1km grid

‐ base year: 2002 ‐time horizon: 2032 / 2050

based on impact zones using regression analysis based on historic or predicted rates of growth in infrastructure

based on impact zones using regression analysis based on historic or predicted rates of growth in infrastructure

6 More information on EFISCEN available at: http://www.efi.int/projects/efiscen 7 More information on GLOBIO available at: http://www.globio.info





RAINS (Regional Air pollution Information and Simulation)8

‐ air ‐ energy (as related to air) ‐ transport (as related to air) ‐agriculture (as related to air)

‐ economic development ‐sectoral activity (for agriculture, transport, energy, fuels and others)

‐ emissions of sulphur dioxide (SO2), nitrogen oxides (NOX), ammonia (NH3), non‐methane volatile organic compounds (NMVOC), particulate matter (PM) ‐ air pollution effect of energy consumption, transport and agriculture ‐health impact and acidification

‐ coverage: almost all European countries, incl. the European part of Russia ‐ National versions available for Italy and the Netherlands (also RAINS versions for other regions, e.g. Asia, available) ‐ resolution: country‐level (can be linked with finer resolution dispersion models)

‐ base year: 2000 ‐ time horizon: 2020 ‐time steps: 5 year time steps

‐linear programming model / impact assessment model

European Commission (e.g. the model was used in preparation of the 1998 EU directive on air quality and emissions). The model is also used by a large number of governments, universities, and research institutes.

SCENES9 ‐ transport ‐ assumptions on transport infrastructure and pricing tools ‐socio‐economic development

‐ traffic volumes for passenger transport (i.e. per population group, trip purpose, zone, mode, time and distance) ‐ traffic volumes for freight transport (i.e. freight generation, freight movement, etc.)

‐ coverage: EU 15 plus BG, CZ, EE, HU, LT, LV, PL, SI, SL, RO, CH, NO ‐resolution: county level plus 244 ‘zones’ (based on NUTS2)

‐ base year: 1995 ‐ time horizon: 2020 ‐time steps: annual

The model is used by the European Commission (EXPEDITE, MC‐ICAM, IASON, TIPMAC, SPECTRUM). The model has also been used in various European studies on transport issues.

TREMOVE10 ‐ transport ‐ air (i.e. transport‐related emissions)

‐ BAU transport activity forecast (currently from SCENES model) ‐ BAU transport prices forecast

‐ demand for passenger km (pkm) / ton km (tkm) per transport type ‐ total fleet and number

‐ coverage: EU15 countries plus CZ, HU, PL, SI + CH, NO ; extension ongoing to CY, EE, LV, LT , SL, MT,

‐ base year: 1995 ‐: 2020 (extension to 2030 ongoing) ‐ time steps: annual

partial equilibrium model

TREMOVE 1 was primarily used in the EU Auto‐Oil II programme. TREMOVE 2.0 to 2.4 were used in the EU CAFE programme

8 More information on RAINS at: http://www.iiasa.ac.at/rains 9 More information on SCENES at: http://www.iww.uni‐karlsruhe.de/SCENES/download.html 10 More information on TREMOVE at: http://www.tremove.org


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‐ energy (i.e. transport‐related energy consumption and WTT emissions)

‐ road pricing, public transport pricing ‐ size and composition of vehicle stock in the baseyear(s) ‐ policies on emission standards, subsidies for cleaner cars etc.

of km for each year according to vehicle type and age ‐ fuel consumption and emissions from transport ‐ cost to society associated with emission reduction scenarios

SK, BG, HR, RO, TR. ‐ resolution: output by country, each country is split up in three regions ‐ one metropolitan city ‐ an aggregate urban region (all other cities) ‐ non‐urban region

(Euro 5 emission standards, maritime policies, etc.) and for the EU policy on car CO2 emissions. Also some national applications and applications in cooperation with automobile manufacturers (FP5 projects).

NEMESIS (New Econometric Model for Environmental and Sustainable development and Implementation Strategies)11

‐energy, ‐environment ‐economy

‐economic (prices, tax systems, trade, government expenditure) ‐demographic (population dynamics and population structure) ‐technological innovation

‐evolution of main macro‐economic indicators (GDP growth, GDP deflator, households final consumption, consumer price index, interest rates, rate of technical progress, and value of sectoral production and value‐added). The Energy/Environmental module allows for: ‐ the land‐use by sector ‐ total Fuel Quantities ‐ emissions ‐ investments in New Plants / equipment ‐ direct energy and

‐coverage: EU27 (plus Norway, USA and Japan)

Neo‐keynesian structure.

This project belongs to the RTD activities of a generic nature: Socioeconomic Aspects of Environmental Change in the Perspective of Sustainable Development : Tools and Methodologies for Socio‐economic Impacts. The model has been applied, for example in the SENSOR project.

11 More information on NEMESIS at: http://www.nemesis‐model.net/





environmental costs to each Sector


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APPENDIX 9 – NON‐EXHAUSTIVE LIST OF EXISTING LAND‐USE MODELS

Model Themes covered

Drivers considered Indicators Geographical coverage

Temporal coverage

Model type Target group/Open source

Land‐use Scanner

‐ Land‐use changes,

‐ Water, ‐ Urban areas, ‐ Agriculture, ‐ Biodiversity, ‐ Transport

‐ Socio‐economic conditions (e.g., economic growth, population, accessibility and other common location factors),

‐ Policies (e.g. spatial policy, nature policy),

‐ Agriculture trends (e.g. rural land pricing, agricultural production,),

‐ Physical conditions (e.g. topography, soil types, and groundwater levels).

Flexible. It can model, for example, ecological impacts (loss of natural areas, or specific habitats), landscape impacts (fragmentation or loss of open space, deterioration of landscape quality, land‐use diversity), urbanisation patterns (urban sprawl containment), flood risk (in terms of potential damage).

Coverage : national (NL), supranational (Rhine and ELbe catchment Resolution: 100m or 500m grid)

Anywhere between 2040‐2100

Comparatively static model, logit (economics) and mathemetical optimisation model

The model was developed in close cooperation between the Netherlands Environmental Assessment Agency, the Vrije Universiteit Amsterdam and Object Vision (OV). Applications of this model include, amongst others: the simulation of future land‐use following different scenarios, an outlook for the prospects of agricultural land‐use in the Netherlands, and assessments of flood risk and water shortage in the Netherlands. Open source




Temporal coverage


CLUE (including: CLUE, CLUE‐s, Dyna‐CLUE) (Conversion of Land‐Use change and its Effects)

‐ Land‐use ‐ Agriculture ‐ Urbanisation

‐ Land‐use maps, remote sensing of land‐cover or census data on land‐use‐ Demographic change land‐use requirements (based on trends, scenarios, or macro‐economic modelling)

‐ Spatial policies (assumed) location factors

‐ land‐use change ‐ coverage: EU 27 ‐ resolution: 1km by 1 km grid

Time horizon: 20‐40 years

Empirical‐statistical model/ Spatial dynamics model

The CLUE model has been used by a large number of both universities and governmental research institutes from all over the world. Case study versions for a variety of regions exists. In EUruralis, GEO4, SENSOR, NITRO‐Europe). Open source

LandSHIFT (Land Simulation to Harmonize and Integrate Freshwater Availability and the Terrestrial Environment)

‐ Land use ‐ Agriculture

‐ Agricultural production ‐ Socio‐economic aspects (population and environmental policy) ‐ Landscape ‐ Current land use

‐ Land‐use types (location and quantity of change) ‐ Crop production ‐ Population density Ongoing work concentrates on further model development and testing. The next steps include the coupling of LandSHIFT to the International food market and trade model (IMPACT) from IFPRI and to the LPJ model from

Coverage: Continental/Global coverage

2000‐2050 Cellular automata Developed by the Centre for Environmental Systems Research – University of Kassel First applications of the LandSHIFT model include a simulation study on land use changes for the African continent and an analysis of the potential impact of bioenergy production on natural land in India. A prototype version of the LandSHIFT model is available.


73



Temporal coverage


PIK to cover forest productivity and composition of potential vegetation.

GEOMOD Originally designed to simulate the loss of tropical forests and to estimate the resulting carbon dioxide emissions

Biophysical and socio‐economic factors (e.g. elevation, slope, soils and distance from rivers, roads and already established settlements)

‐ Land‐use dynamics (location and rate of change)

Spatially explicit Developped by SUNY College of Environmental Science and Forestry with funding from the US Department of Energy, Carbon Dioxide Research Program, Atmospheric and Climatic Change Division

SLEUTH Urbanisation Four factors considered: spontaneous growth (neighbourhood; suitability); diffusive growth (slope determined); edge growth neighbourhood); road influenced growth.

‐ Urban growth ‐ Land‐use changes (Residential, commercial, mixed use, industrial, other)

Coverage : San Francisco and Washington/ Baltimore region, Mid Atlantic. Region. Possibility for urban regions in general. User defined

1900–2100 Cellular automata/ spatially explicit

Developed by the University of California (USA). Over 35 SLEUTH applications to cities worldwide have been conducted, in a large variety of planning contexts such as to the growth of informal settlements in Africa and to urban encroachment on waste disposal sites in Brazil. Open source




Temporal coverage


IIASA‐LUC Various socio‐economic and biophysical drivers considered. Model based on multi‐sector input‐output tables and detailed agro‐ecologic

Projections to 2025

Integrated /Hybrid model and/or Optimisation model

Develeoped by the IIASA. Application in China: agro‐ecological analysis for detailed pixels; integrated analysis for 8 regions

Constrained cellular automata

Land‐use changes

Land use, suitability, zoning status and accessibility vis‐a`‐vis the transportation infrastructure.

Land‐use changes. Depending on the sectoral models they are linked to, several indicators are possible.

Coverage: From cities to the national level

± 30 years Cellular Automata model and/or Integrated/ Hybrid model

Used in several modelling frameworks12 such as RamCo, LOV, MedAction PSS , SIMLUCIA, Environmental Explorer

CURBA Land‐use change in urban areas

Land‐use types, topographic and hydrologic features, transportation networks, zoning, and various socioeconomic data (e.g., population and employment levels).

Urban/Non‐Urban or 10‐urban density classes

Coverage: Regional (38 counties in California) Resolution: 1 ha

User defined (output to specific times: 2020, 2060, 2100)

Logit model Developed in the University of California (USA)

LUCAS Land‐use change

Socio‐economic module (transportation networks, topographic information, land cover, and population density)

Probability of change in land cover, the landscape change and impact on species habitat

Coverage: User defined. Resolution: up to 90 meter cell size

User defined time steps (default: 100 yrs/5 yr. Intervals)

Spatial stochastic model

Developed by the University of Tennessee. The LUCAS model has been used in the Little Tennessee River Basin in Tennessee.

12 NOTE: Demands for land use are calculated in sectoral models at an aggregated spatial level

August 2008 European Commission, DG ENV Scoping study on modelling of EU land‐use choices

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APPENDIX 10 – SET OF CRITERIA FOR ASSESSING LAND‐USE MODELS

Criteria Description

Relevancy Does the model provide pertinent information that matches the analytical needs of the Commission?

Simulation of multiple land‐use changes Does the model allow simulating different types of land‐use changes simultaneously?

Spatial resolution What grid size and/or level of detail can be modelled?

Simulation at multiple scales Does the model allow for cross‐scale analysis?

Applicability for decision making Can the model results aid decision making for policy makers?

Time horizon Does the model take into consideration policies’ lifetime?

Can the model project the outcomes for multiple time periods?

Assessment of environmental impacts of land‐use choices

Does the model take into account different environmental, economic and social issues?

Does the model illustrate cross‐sectoral side effects of land‐use policies?

Can the model project outcomes for multiple variables (e.g. land‐use, transportation, employment, housing, and environmental)?

Third‐Party Use How extensively has this model been used in “real‐world” situations (models used primarily in academic settings or used more extensively in real‐world settings?

Can other parties easily use the existing frameworks, or are they protected by, for example, copyright restrictions on the applied software?

Accuracy and precision Are the projections generated by the model reliable to a degree that is useful to the Commission?

Certainty of databases What it reliability of the data that is used to run the model?

Certainty of the output results Are the projections generated by the model reliable to a degree that is useful to the Commission?

Validation and internal checks Does the Commission have the technical expertise required to calibrate the model?

Does the model allows to easily add new information in order to enable the user to introduce his/her own knowledge to the model

Transferability Can the model be applied to locations other than the one(s) for which it was originally developed?


Criteria Description

Utilisation Is the model user‐friendly?

Expertise required for its utilisation Do the end‐users in different DGs need a special technical expertise required to use the model?

Or can anybody with basic IT experience use the model?

Time to apply and simulate model What it is the amount of time and labor needed to run the model for a typical scenario?

Level of complexity of the output ‐ Understandability of the results

Are the output of the model easy to interpret or special technical tools are required?

Linkage Potential Can the model be linked to other models currently in use by, or of interest to, the Commission (ability of a model to join with other tools, including geographic information systems (GISs), other models, or presentation software)?

Technical requirements

Able to be run with common hardware and software configurations

Are the model and the computer requirements (hardware, software) needed to support the system available easily?

Intensity of data needs Is the model data intensive and/or require a certain scale of data to provide reliable results?.

Availability of data The data necessary to run the model is provided along with or can be procured easily?

Time/costs needed for data acquisition Is the necessary data publicly available? If not, what can be the potential costs of obtaining this data?

August 2008 European Commission, DG ENV

Scoping study on modelling of EU land‐use choices 77

APPENDIX 11 – EU POLICY ANALYTICAL NEEDS

Issue Policy Context Policy Options Potential impacts/indicators to be assessed through modelling

Water scarcity ‐ Water Framework Directive (Directive 2000/60/EC)

‐ Communication on water scarcity and droughts (COM(2007) 414 final)

‐ Construction of desalination plants

‐ Dam constructions ‐ Investments in water treatment technologies

‐ Restriction on water use

‐ Changes in water price

‐ Programmes promoting the reduction of water consumption

‐ Revision of the WFD ‐ Transfer of water from other regions/countries

‐ Areas facing possible water management problems due to water shortage

‐ Increase or decrease of areas with significant reduction of water availability

‐ Erosion ‐ Desertification ‐ Irrigation water usage ‐ Amount of water from outside the region

‐ Water prices ‐ Amount of water resources available in aquifers

Floods ‐ Water Framework Directive

‐ Recent EU Floods Directive

‐ Revision of the WFD ‐ Introduction of measures to reduce the probability of flooding as required by the Directive

‐ Urban development

‐ Floods probability, ‐ Flood damage, ‐ Potential casualties ‐ Costs

Impact of changes in agriculture

‐ CAP reform ‐ Implementation of the different environmental directives.

‐ Directive on the promotion of the use of biofuels and other renewable fuels for transport

‐ In 2007, the EU target for biofuels was increased to 10 % level by 2020.

‐ Changes in farm income support

‐ Changes in market support (e.g. elimination of export subsidies or reduction of import tariffs)

‐ Changes in water prices

‐ Changes in nature conservation targets

‐ Rural development support

‐ Subsidising bioenergy ‐ Agro‐technical innovations production in Europe

‐ Afforestation

‐ N‐surplus ‐ nitrate balance ‐ Soil biodiversity ‐ Visual landscapes changes ‐ Competition for land by biofuels and agriculture

‐ Erosion ‐ Decline in organic matter ‐ Soil contamination (local and diffuse)

‐ Soil sealing ‐ Agricultural income ( GDP/capita)

‐ Employment ‐ Crop diversity ‐ Carbon sequestration ‐ Irrigation water usage ‐ Land abandonment

Soil ‐ Thematic Strategy on Soil Protection

‐ Proposal for Soil

‐ Afforestation ‐ Grazing regulation ‐ Measures to reduce

‐ Erosion rate ‐ Decline in organic matter ‐ Soil contamination (local


Issue Policy Context Policy Options Potential impacts/indicators to be assessed through modelling

Framework Directive

soil sealing ‐ Measures to limit the introduction of dangerous substances into the soil

and diffuse) ‐ Soil sealing ‐ Soil compaction ‐ Carbon sequestration ‐ Floods and landslides ‐ Soil salinity ‐ Decline in soil biodiversity

Biodiversity ‐ Birds Directive ‐ Habitats Directive ‐ Biodiversity Communication

‐ Ecosystem accounting

‐ Cost of biodiversity loss

‐ Changes in nature conservation targets

‐ Extension of the NATURA network

‐ Forest creation and management

‐ Different levels of support of

‐ Changes in the surface of protected areas

‐ Area of sensitivity areas ‐ Number of endangered species (Red List)

‐ Natural vegetation change ‐ Landscape fragmentation ‐ Cost of biodiversity loss ‐ Loss of recreational areas

Impacts of transportation networks

Green paper on trans‐European transport network

‐ Accessibility ‐ Congestion ‐ Air pollution ‐ Fragmentation or loss of open spaces

‐ Soil sealing ‐ Water run‐off ‐ Erosion

Climate change ‐ White Paper on adaptation

‐ Greenhouse Gas Emission Trading Scheme (Directive 2003/87/EC)

‐ Introduction of measures for adaptation

‐ Likely use at a specific location in the future under different climate conditions.

‐ Land‐use impacts of adaptation options, e.g. green infrastructure for ecosystems resilience, water retention, flood prevention

‐ Desertification ‐ Crop diversity Crop change

Urban Sprawl Thematic Strategy on the Urban Environment

‐ Regional/local urban development plans

‐ Increase of land prices

‐ Fragmentation or loss of open spaces

‐ Increase distance from residence to work

‐ Air pollution ‐ Soil sealing ‐ Recreational areas

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