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Food Security

, Volume 6, Issue 5, pp 685–699 | Cite as

Land use representation in a global CGE model for long-term simulation: CET vs. logit functions

  • Shinichiro Fujimori
  • Tomoko Hasegawa
  • Toshihiko Masui
  • Kiyoshi Takahashi
Original Paper

Abstract

Land use is one of the key elements in global computable general equilibrium models for food security and agricultural assessment. Constant elasticity transformation (CET) or logit functions have been used to allocate land. CET has the advantage that it is easily handled by modeling tools. However, it does not maintain area balance, whereas logit does. This article compares both functions in future scenarios and evaluates area balance violations of land use area made by CET. We found that agricultural goods production and land use were similar with CET and logit functions. The area balance violation generated by CET was large and heterogeneous across regions, but was small for the aggregated world total. In conclusion, the logit approach was preferable to the CET approach if any scenario assumption, such as consumption preference, changed by much from the base year, or if the main focus of the study was region-specific variables rather than global aggregates.

Keywords

Logit function CET function Computable general equilibrium model Land use Agricultural economic model 

Notes

Acknowledgments

This study was supported by the “Global Environmental Research Fund” S-10, and 2–1402 of the Ministry of the Environment of Japan. The authors would like to acknowledge the generosity of these funds. Finally, we wish to thank two anonymous reviewers for their comments which substantially improved the paper.

Supplementary material

12571_2014_375_MOESM1_ESM.doc (233 kb)
ESM 1 (DOC 235 kb)

References

  1. Avetisyan, M., Baldos, U., & Hertel, T. W. (2011). Development of the GTAP Version 7 Land Use Data Base. GTAP Research Memorandum (Vol. 19).Google Scholar
  2. Bruinsma, J. (2010). The resource outlook to 2050: by how much do land, water and crop yields need to increase by 2050?, Expert meeting on how to feed the world in 2050.Google Scholar
  3. Dimaranan, B. V. (2006). Global trade, assistance, and production: the GTAP 6 data base. In D. B.V. (Ed.). Center for Global Trade Analysis, Purdue University.Google Scholar
  4. FAO (2013). FAOSTAT. In FAO (Ed.). Rome, Italy.Google Scholar
  5. Fujimori, S., & Matsuoka, Y. (2011). Development of method for estimation of world industrial energy consumption and its application. Energy Economics, 33(3), 461–473. doi: 10.1016/j.eneco.2011.01.010.CrossRefGoogle Scholar
  6. Fujimori, S., Masui, T., & Matsuoka, Y. (2012). AIM/CGE [basic] manual. Discussion paper series: Center for Social and Environmental Systems Research, National Institute Environemntal Studies.Google Scholar
  7. Fujimori, S., Masui, T., & Matsuoka, Y. (2014). Development of a global computable general equilibrium model coupled with detailed energy end-use technology. Applied Energy, 128, 296–306. doi: 10.1016/j.apenergy.2014.04.074.CrossRefGoogle Scholar
  8. Golub, A. A., & Hertel, T. W. (2012). Modeling land-use change impacts of biofuels in the gtap-bio framework. Climate Change Economics, 03(03), 1250015. doi: 10.1142/S2010007812500157.CrossRefGoogle Scholar
  9. Golub, A. A., Henderson, B. B., Hertel, T. W., Gerber, P. J., Rose, S. K., & Sohngen, B. (2013). Global climate policy impacts on livestock, land use, livelihoods, and food security. Proceedings of the National Academy of Sciences, 110(52), 20894–20899. doi: 10.1073/pnas.1108772109.CrossRefGoogle Scholar
  10. Gurgel, A., Reilly, J. M., & Paltsev, S. (2007). Potential Land Use Implications of a Global Biofuels Industry. Journal of Agricultural & Food Industrial Organization, 5(2), article 9, doi: 10.2202/1542-0485.1202.
  11. Hasegawa, T., Fujimori, S., Shin, Y., Takahashi, K., Masui, T., & Tanaka, A. (2014). Climate change impact and adaptation assessment on food consumption utilizing a New scenario framework. Environmental Science and Technology, 48(1), 438–445. doi: 10.1021/es4034149.PubMedCrossRefGoogle Scholar
  12. Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., et al. (2011). Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change, 109(1–2), 117–161. doi: 10.1007/s10584-011-0153-2.CrossRefGoogle Scholar
  13. IEA (2009a). Energy balances for non-OECD countries. In OECD/IEA (Ed.). Paris, France.Google Scholar
  14. IEA (2009b). Energy balances for OECD countries. In OECD/IEA (Ed.). Paris, France.Google Scholar
  15. IIASA (2012). Shared Socioeconomic Pathways (SSP) Database Version 0.9.3. https://secure.iiasa.ac.at/web-apps/ene/SspDb.
  16. Kyle, P., Luckow, P., Calvin, K., Emanuel, W., Nathan, M., & Zhou, Y. (2011). GCAM 3.0 Agriculture and Land Use: Data Sources and Methods. PACIFIC NORTHWEST NATIONAL LABORATORY.Google Scholar
  17. Laborde, D., & Valin, H. (2012). Modeling land-use changes in a global cge: assessing the eu biofuel mandates with the mirage-biof model. Climate Change Economics, 03(03), 1250017. doi: 10.1142/S2010007812500170.CrossRefGoogle Scholar
  18. Lofgren, H., Harris, R. L., & Robinson, S. (2002). A standard computable general equilibrium (cge) model in gams. MICROCOMPUTERS IN POLICY RESEARCH (Vol. 5): International Food Policy Research Institute.Google Scholar
  19. Monfreda, C., Ramankutty, N., & Hertel, T. (2008). Global Agricultural Land Use Data for Climate Change Analysis (Routledge explorations.Google Scholar
  20. Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., et al. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747–756. doi: 10.1038/nature08823.PubMedCrossRefGoogle Scholar
  21. Msangi, S., Ewing, M., Rosegrant, M. W., & Zhu, T. (2010). Biofuels, Food Security, and the Environment: A 2020/2050 Perspective. 65–94, doi: 10.1007/978-3-642-04615-5_4.
  22. OECD. (2010). Input–output tables. Paris: Organization for Economic Cooperation and Development.Google Scholar
  23. Robinson, S., van Meijl, H., Willenbockel, D., Valin, H., Fujimori, S., Masui, T., et al. (2014). Comparing supply-side specifications in models of global agriculture and the food system. Agricultural Economics, 45(1), 21–35. doi: 10.1111/agec.12087.CrossRefGoogle Scholar
  24. Rutherford, T. F. (1995). Extension of GAMS for complementarity problems arising in applied economic analysis. Journal of Economic Dynamics and Control, 19(8), 1299–1324. doi: 10.1016/0165-1889(94)00831-2.CrossRefGoogle Scholar
  25. Sands, R., & Leimbach, M. (2003). Modeling agriculture and land Use in an integrated assessment framework. Climatic Change, 56(1–2), 185–210. doi: 10.1023/A:1021344614845.CrossRefGoogle Scholar
  26. Schmitz, C., van Meijl, H., Kyle, P., Nelson, G. C., Fujimori, S., Gurgel, A., et al. (2014). Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agricultural Economics, 45(1), 69–84. doi: 10.1111/agec.12090.CrossRefGoogle Scholar
  27. Schumacher, K., & Sands, R. D. (2006). Innovative energy technologies and climate policy in Germany. Energy Policy, 34(18), 3929–3941. doi: 10.1016/j.enpol.2005.09.022.CrossRefGoogle Scholar
  28. Thepkhun, P., Limmeechokchai, B., Fujimori, S., Masui, T., & Shrestha, R. M. (2013). Thailand’s Low-Carbon Scenario 2050: The AIM/CGE analyses of CO2 mitigation measures. Energy Policy, 62, 561–572. doi: 10.1016/j.enpol.2013.07.037.CrossRefGoogle Scholar
  29. UN (2007a). National Accounts Main Aggregates Database. In U. Nations (Ed.). New York, USA.Google Scholar
  30. UN (2007b). United Nations Commodity Trade Statistics Database. In U. Nations (Ed.). New York, USA.Google Scholar
  31. UNIDO (2009). INDSTAT2 - 2009 edition. In U. N. I. D. O. (UNIDO) (Ed.). Vienna, Austria.Google Scholar
  32. von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., et al. (2014). Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison. Agricultural Economics, 45(1), 3–20. doi: 10.1111/agec.12086.CrossRefGoogle Scholar
  33. Wise, M., & Calvin, K. (2011). GCAM 3.0 Agriculture and Land Use; Technical Description of Modeling Approach. PNNL (Vol. 20971).Google Scholar
  34. You, L., S.Crespo, Guo, Z., Koo, J., Ojo, W., Sebastian, K., et al. (2012). Spatial Produciton Allocation Model (SPAM) 2000 Version 3 Release 2. http://MapSPAM.info. Accessed 10/01 2012.

Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2014

Authors and Affiliations

  • Shinichiro Fujimori
    • 1
  • Tomoko Hasegawa
    • 1
  • Toshihiko Masui
    • 1
  • Kiyoshi Takahashi
    • 1
  1. 1.Center for Social and Environmental Systems ResearchNational Institute for Environmental StudiesTsukubaJapan

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