Climatic Change

, Volume 112, Issue 3–4, pp 1085–1100 | Cite as

Climate change and agriculture in computable general equilibrium models: alternative modeling strategies and data needs

Article

Abstract

Agricultural sectors play a key role in the economics of climate change. Land as an input to agricultural production is one of the most important links between economy and the biosphere, representing a direct projection of human action on the natural environment. Agricultural management practices and cropping patterns exert an enormous effect on biogeochemical cycles, freshwater availability and soil quality. Agriculture also plays an important role in emitting and storing greenhouse gases. To consistently investigate climate policy and future pathways for the economic and natural environment, a realistic representation of agricultural land use is essential. Top—down Computable General Equilibrium (CGE) models have increasingly been used for this purpose. CGE models simulate the simultaneous equilibrium in a set of interdependent markets, and are especially suited to analyze agricultural markets from a global perspective. However, modeling agricultural sectors in CGE models is not a trivial task, mainly because of differences in temporal and geographic aggregation scales. This study surveys some proposed modeling strategies and highlights different tradeoffs involved in the various approaches. Coupling of top-down and bottom-up models is found to be the most applicable for comprehensive analysis of agriculture in prism of climate change. However, linking interdisciplinary data, methods and outputs is still the major obstacle to be solved for wide-scale implementation.

References

  1. Abdula R (2005) Climate change policy of bio-energy: a computable general equilibrium analysis. Paper presented at the International Conference on Energy, Environment and Disasters, Charlotte, NC, USA, July 24–30Google Scholar
  2. Alcamo J, Leemans R, Kreileman E (eds) (1998) Global change scenarios of the 21st century: results from the Image 2.1 Model. Elsevier Science, The NetherlandsGoogle Scholar
  3. Baltzer K, Kløverpris J (2008) Improving the land use specification in the GTAP Model IFS working paper 2008–2. Institute of Food and Resource Economics, DenmarkGoogle Scholar
  4. Banse M, Grethe H (2008) Top down, and a little bottom up: modelling EU agricultural policy liberalization with LEITAP and ESIM. Paper presented at the GTAP Conference, Helsinki, Finland, June 12–14Google Scholar
  5. Berrittella M, Hoekstra AY, Rehdanz K, Roson R, Tol RSJ (2007) The economic impact of restricted water supply: a computable general equilibrium analysis. Water Res 42:1799–1813CrossRefGoogle Scholar
  6. Bosello F, Zhang J (2005) Assessing climate change impacts: agriculture. FEEM Working Paper, 94Google Scholar
  7. Brooks J, Dewbre J (2006) Global trade reforms and income distribution in developing countries. Journal of Agricultural and Development Economics 3(1):86–111Google Scholar
  8. Bruinsma J (ed) (2003) World agriculture: towards 2015/2030. FAO, London: EarthscanGoogle Scholar
  9. Burniaux JM (2002) Incorporating carbon sequestration into CGE models: A prototype GTAP model with land uses. GTAP Resource 1144. Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, Indianapolis, USAGoogle Scholar
  10. Burniaux JM, Lee HL (2003) Modelling land use changes in GTAP. Paper presented at the Sixth Annual Conference on Global Economic Analysis. The Hague, The Netherlands, June 12–14Google Scholar
  11. Darwin R, Tsigas M, Lewandrowski J, Raneses A (1995) World agriculture and climate change: Economic adaptations. Agricultural Economic Report 703. Natural Resources and Environment Division, Economic Research Service, US Department of Agriculture, Washington DCGoogle Scholar
  12. Darwin R, Tsigas M, Lewandrowski J, Raneses A (1996) Land use and cover in ecological economics. Ecol Econ 17:157–181CrossRefGoogle Scholar
  13. Golub A, Hertel TW, Lee HL, Ramankutty N (2006) Modeling land supply and demand in the long run. Paper presented at the Ninth Annual Conference on Global Economic Analysis, Addis Ababa, Ethiopia, June 15–17Google Scholar
  14. Golub A, Hertel TW, Sohngen B (2009) Land use modeling in recursively-dynamic GTAP framework. In: Hertel TW, Rose S, Tol RSJ (eds) Economic analysis of land use in global climate change policy. Routledge, AbingdonGoogle Scholar
  15. Gurgel A, Reilly JM, Paltsev S (2007) Potential land use implications of a global biofuels industry. J Agr Food Ind Organ 5(2) Article 9Google Scholar
  16. Hertel TW (1997) Global trade analysis. Cambridge University Press, CambridgeGoogle Scholar
  17. Hertel TW, Tsigas ME (1988) Tax policy and U.S. agriculture: a general equilibrium approach. Am J Agr Econ 70(2):289–302CrossRefGoogle Scholar
  18. Hertel TW, Lee HL, Rose S, Sohngen B (2009a) Modeling land-use related greenhouse gas sources and sinks and their mitigation potential. In: Hertel TW, Rose S, Tol RSJ (eds) Economic analysis of land use in global climate change policy. Routledge, AbingdonGoogle Scholar
  19. Hertel TW, Rose S, Tol RSJ (2009b) Land use in computable general equilibrium models: an overview. In: Hertel TW, Rose S, Tol RSJ (eds) Economic analysis of land use in global climate change policy. Routledge, AbingdonGoogle Scholar
  20. Hsin H, van Tongeren F, Dewbre J, van Meijl H (2004) A new representation of agricultural production technology In GTAP. Paper presented at the 7th Annual Conference on Global Economic Analysis, Washington DC, USA, June 17–19Google Scholar
  21. Hubacek K, van den Bergh JCJM (2006) Changing concepts of land in economic theory: from single to multi-disciplinary approaches. Ecol Econ 56:5–27CrossRefGoogle Scholar
  22. Ianchovichina E, McDougall R (2001) Structure of dynamic GTAP. Center for Global Trade Analysis, Technical Paper 17Google Scholar
  23. Ianchovichina E, Darwin R, Shoemaker R (2001) Resource use and technological progress in agriculture: a dynamic general equilibrium analysis. Ecol Econ 38:275–291CrossRefGoogle Scholar
  24. Ignaciuk AM (2006) Economics of multifunctional biomass systems. PhD dissertation, Wageningen University, The NetherlandsGoogle Scholar
  25. IMAGE team (2001) The IMAGE 2.2 implementation of the SRES scenarios: A comprehensive analysis of emissions, climate change and impacts in the 21st century. RIVM CD-ROM publication 481508018, National Institute for Public Health and the Environment, Bilthoven, The NetherlandsGoogle Scholar
  26. IPCC (1997) Revised 1996 IPCC guidelines for national inventories. Reference Manual for Agriculture. NGGIP PublicationGoogle Scholar
  27. IPCC (2001) Climate change 2001. Third Assessment Report of the IPCC. Cambridge University PressGoogle Scholar
  28. IPCC (2007) Climate change 2007: mitigation of climate change. Fourth Assessment Report of the IPCC.Cambridge University PressGoogle Scholar
  29. Jorgenson DW, Wilcoxen PJ (1990) Environmental regulation and U.S. economic growth. The Rand Journal 21(2):314–340CrossRefGoogle Scholar
  30. Keeney R, Hertel TW (2005) GTAP-AGR: a framework for assessing the implications of multilateral changes in agricultural policies. GTAP Technical Paper 24Google Scholar
  31. Klijn JA, Vullings LAE, van den Berg M, van Meijl H, van Lammeren R, van Rheenen T A, Veldkamp, Verburg PH (2005) The EURURALIS study: technical document. Alterra-rapport 1196., Alterra, WageningenGoogle Scholar
  32. Lee HL (2004) Incorporating agro-ecological zoned data into the GTAP Framework. Paper presented at the Seventh Annual Conference on Global Economic Analysis, The World Bank, Washington DC, USA, June 17–19Google Scholar
  33. Lee HL, Hertel TW, Rose S, Avetisyan M (2009) An integrated global land use data base for CGE analysis of climate policy options. In: Hertel TW, Rose S, Tol RSJ (eds) Economic analysis of land use in global climate change policy. Routledge, AbingdonGoogle Scholar
  34. McKibbin WJ, Sachs JD (1991) Global linkages: macroeconomic interdependence and cooperation in the world economy. The Brookings Institution, WashingtonGoogle Scholar
  35. McKibbin WJ, Wang Z (1998) The G-cubed (Agriculture) model: a tool for analyzing US agriculture in a globalizing world. Brooking Discussion Papers in International Economics, 139Google Scholar
  36. McKibbin WJ, Wilcoxen P (1998) The theoretical and empirical structure of the GCUBED model. Econ Model 16(1):123–148CrossRefGoogle Scholar
  37. Robidoux B, Smart M, Lester J, Beausejour L (1989) The agriculture expanded getmodel: Overview of Model Structure. Unpublished, Department of Finance, Ottawa, CanadaGoogle Scholar
  38. Ronneberger K, Berrittella M, Bosello F, Tol RSJ (2009) KLUM@GTAP: Spatially-explicit, biophysical land use in a computable general equilibrium model. In: Hertel TW, Rose S, Tol RSJ (eds) Economic analysis of land use in global climate change policy. Routledge, AbingdonGoogle Scholar
  39. Sohngen B, Mendelsohn R (2006) A sensitivity analysis of carbon sequestration. In: Schlezinger M (ed) Human-induced climate change: an interdisciplinary assessment. Cambridge University Press, CambridgeGoogle Scholar
  40. Stern N (ed) (2006) The economics of climate change: the stern review. H.M. Treasury, UKGoogle Scholar
  41. Tan G, Shibasaki R, Matsumora K, Rajan KS (2003) Global research for integrated agricultural land use change modeling. Asia GIS Conference 2003 Publications. Wuhan Science and Technology Conference and Exhibition CenterGoogle Scholar
  42. van Meijl H, van Rheenen T, Tabeau A, Eickhout B (2006) The impact of different policy environments on agricultural land use in Europe. Agric Ecosyst Environ 114(1):21–38CrossRefGoogle Scholar
  43. Wong GY, Alavalapati JRR (2003) The land-use effects of a forest carbon policy in the US. Forest Pol Econ 5:249–263CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Fondazione Eni Enrico MatteiVeneziaItaly
  2. 2.Natural Resource & Environmental Research CenterUniversity of HaifaHaifaIsrael
  3. 3.Department of Economics and ManagementThe Max Stern Academic College Of Emek YezreelJezreel ValleyIsrael
  4. 4.Dip. Scienze EconomicheUniversitá Cá Foscari di VeneziaVeneziaItaly
  5. 5.IEFEUniversità BocconiMilanoItaly

Personalised recommendations