Cross sector land use modelling framework

  • Torbjörn Jansson
  • Martha Bakker
  • Baptiste Boitier
  • Arnaud Fougeyrollas
  • John Helming
  • Hans van Meijl
  • Pieter J. Verkerk


The purpose of the model component in SENSOR is to quantify the effects of a comprehensive set of policies on land use. The need to include interaction between sectors as well as a high level of detail for each sector calls for a combination of sector specific and sector wide models. This chapter describes the modelling system, with emphasis on the linking of the models to a coherent system. Five sectors of significant importance for land use are modelled individually: Forestry, agriculture, urban land use, transport infrastructure and tourism. All models are connected as sub-modules to an economy-wide partial econometric model. In addition, a land cover model is used to disaggregate land use down to 1 km grid resolution.

The linking of such a diverse set of models in a consistent way poses conceptual as well as practical issues. The conceptual issues concern questions such as which items of the models to link, how to obtain a stable joint baseline scenario, and how to obtain a joint equilibrium solution for all models simultaneously in simulation. Practical issues concern the actual implementation of the conceptually sound linkages and provision of a workable technical solution. In SENSOR, great care has been taken to develop a sound linkage concept.

The linked system allows the user to introduce a shock in either of the models, and the set of results will provide a joint solution for all sectors modelled in SENSOR. In this manner, the models take a complex policy scenario as argument and compute a comprehensive set of variables involving all five sectors on regional level, which in turn forms a basis for distilling out the impact on sustainability in the form of indicators. Without the extensive automation and technical linkages, it would not have been possible to obtain a joint equilibrium, or it would have required exorbitant amounts of working time.


Model linking sustainable land use cross sector modelling iterative recalibration 


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  1. Alig, RJ, Kline JD and Lichtenstein M (2004) Urbanization of the US landscape: looking ahead in the 21st century, Landscape and urban planning 69: 219–234CrossRefGoogle Scholar
  2. Angel S, Sheppard SC and Civco DL (2005) The dynamics of global urban expansion, Transport and urban development department, The World Bank, Washington D. C.Google Scholar
  3. Armington PS (1969) A Theory of Demand for Products Distinguished by Place of Production. IMF Staff Papers 16: 159–178Google Scholar
  4. Brécard D, Fougeyrollas A, Le Mouël P, Lemiale L, Zagamé P, (2006), Macro-economic consequences of European research policy: Prospects of the Nemesis model in the year 2030, Research Policy 35(7): 910–924CrossRefGoogle Scholar
  5. Britz W (ed) (2005). CAPRI Modelling System Documentation. Available on (October 2006)
  6. Böhringer C, Rutherford TF (2006). Combining top-down and bottom-up analysis in energy policy analysis. ZEW, Discussion paper 06-07Google Scholar
  7. Deaton A, Muellbauer J (1980) An almost ideal demand system. The American Economic Review, 80(3): 312–326Google Scholar
  8. Di Pasquale D and Wheaton WC (1994) Housing market dynamics and the future of housing prices, Journal of urban economics 35: 1–27CrossRefGoogle Scholar
  9. EEA (2006) How much bioenergy can Europe produce without harming the environment? EEA report no. 7/2006. European Environment Agency, CopenhagenGoogle Scholar
  10. FAO (2006) Global forest resources assessment 2005, Progress towards sustainable forest management. FAO forestry paper 147. Food and Agricultural Organization of the United Nations, RomeGoogle Scholar
  11. Grant JH, Hertel TW, Rutherford TF (2006) Extending general equilibrium to the tariff line. Paper prepared for the Ninth Annual Conference on Global Trade Analysis, June 15–17, Addis Ababa, EthiopiaGoogle Scholar
  12. Heckelei T, Britz W (2005) Models based on Positive Mathematical Programming: state of the art and further extensions. In: F. Arfini (ed): Modelling Agricultural State of the Art and New Challenges. Proceedings of the 89th European Seminar of the European Association of Agricultural Economists. Parma (Italy). February 3–5, 2005. Monte Università Parma Editore. Parma, 48–73Google Scholar
  13. Hertel TW (2004) Global trade analysis, Cambridge: Cambridge university pressGoogle Scholar
  14. Howitt RE (1995) Positive mathematical programming. American Journal of Agricultural Economics, 77: 329–342CrossRefGoogle Scholar
  15. Karjalainen T, Pussinen A, Liski J, Nabuurs GJ, Eggers T, Lapveteläinen T, Kaipainen T (2003) Scenario analysis of the impacts of forest management and climate change on the European forest sector carbon budget. Forest Policy and Economics 5: 141–155CrossRefGoogle Scholar
  16. Klijn JA, Vullings LAE, Berg Mvd, van Meijl H, van Lammeren R, van Rheenen T, Veldkamp A, Verburg PH, Westhoek H, Eickhout B (2005) The EURURALIS study: Technical document, Alterra-rapport 1196, Alterra, WageningenGoogle Scholar
  17. Kuhlman T (2008) Scenarios — driving forces and policies. In: Helming K, Tabbush P, Perez-Soba M (eds). Sustainability impact assessment of land use changes. Springer, 131–157Google Scholar
  18. Liski J, Palosuo T, Peltoniemi M, Sievanen R (2005) Carbon and decom-position model Yasso for forest soils. Ecological Modelling 189: 168–182CrossRefGoogle Scholar
  19. Mayer CJ and Somerville CT (2000) Residential construction: using the urban growth model to estimate housing supply, Journal of urban economics 48: 85–109CrossRefGoogle Scholar
  20. Meijl H, van Rheenen T, Tabeau A, Eickhout B (2006) The impact of different policy environments on land use in Europe, Agriculture, Ecosystems and Environment 114: 21–38CrossRefGoogle Scholar
  21. MNP and WUR (2007) EURURALIS 2.0: a scenario study on Europes rural areas to support policy discussionGoogle Scholar
  22. Muth, R. F. (1972). The derived demand for urban residential land, Urban studies 8: 243–254CrossRefGoogle Scholar
  23. Nabuurs GJ, Päivinen R, Schanz H (2001) Sustainable management regimes for Europe’s forests-a projection with EFISCEN until 2050. Forest Policy and Economics 3: 155–173CrossRefGoogle Scholar
  24. Nowicki P, van Meijl H, Knierim A, Banse M, Helming J, Margraf O, Matzdorf B, Mnatsakanian R, Reutter M, Terluin I, Overmars K, Verhoog D, Weeger C, Westhoek H (2006) Scenar 2020-Scenario study on agriculture and the rural world. Contract No. 30-CE-0040087/00-08. European Commission, Directorate-General Agriculture and Rural Development, BrusselsGoogle Scholar
  25. Ortiz RA (2005) “Transport Sector Modelling in the Context of the SENSOR Project”, SENSOR project internal document, available at
  26. Ortiz RA (2006) “Transport Infrastructure Module: preliminary results”, SENSOR project internal document, available at
  27. Rausch S, Rutherford TF (2007) Computation of equilibria in OLG models with many heterogeneous households. University of Duisburg-Essen, Department of economics, June 2007 (, downloaded in September 2007)
  28. Schelhaas MJ, van Brusselen J, Pussinen A, Pesonen E, Schuck A, Nabuurs GJ, Sasse V (2006a). Outlook for the development of European forest resources. A study prepared for the European forest sector outlook study (EFSOS). Geneva timber and forest discussion paper 41. ECE/TIM/DP/41. UNECE/FAO, Timber Section, Geneva, New YorkGoogle Scholar
  29. Schelhaas MJ, Eggers J, Lindner M, Nabuurs GJ, Päivinen R, Schuck A, Verkerk PJ, van der Werf DC, Zudin S (2007) Model documentation for the European Forest Information Scenario model (EFISCEN 3.1.3). Alterra report 1559 and EFI technical report 26. Alterra and European Forest Institute, Wageningen and JoensuuGoogle Scholar
  30. Schelhaas, MJ, Varis S, Schuck A, Nabuurs GJ (2006b). EFISCEN’s European Forest Resource Database. European Forest Institute, Joensuu.
  31. Verburg PH, Ritsema van Eck J, de Nijs TCM, Visser H, de Jong K (2004). A method to analyse neighbourhood characteristics of land use patterns. Computers, Environment and Urban Systems 28(6), 667–690CrossRefGoogle Scholar
  32. Walker, R and Solecki, W (2004). Theorizing land-cover and land-use change: The case of the Florida Everglades and its degration, Annals of the Association of American geographers 94(2): 311–328CrossRefGoogle Scholar
  33. Yrjölä T (2002). Forest management guidelines and practices in Finland, Sweden and Norway, Rep. No. 11. European Forest Institute, Joensuu, FinlandGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Torbjörn Jansson
    • 1
  • Martha Bakker
    • 2
  • Baptiste Boitier
    • 3
  • Arnaud Fougeyrollas
    • 3
  • John Helming
    • 1
  • Hans van Meijl
    • 1
  • Pieter J. Verkerk
    • 4
  1. 1.LEI, Agricultural Economics Research InstituteWageningen URThe HagueThe Netherlands
  2. 2.Wageningen UniversityWageningenThe Netherlands
  3. 3.ERASME laboratoryEcole CentraleParisFrance
  4. 4.European Forest InstituteJoensuuFinland

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