Land use representation in a global CGE model for long-term simulation: CET vs. logit functions
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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.
KeywordsLogit function CET function Computable general equilibrium model Land use Agricultural economic model
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.
- 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
- 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
- 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
- FAO (2013). FAOSTAT. In FAO (Ed.). Rome, Italy.Google Scholar
- 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
- 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
- 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.
- 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
- IEA (2009a). Energy balances for non-OECD countries. In OECD/IEA (Ed.). Paris, France.Google Scholar
- IEA (2009b). Energy balances for OECD countries. In OECD/IEA (Ed.). Paris, France.Google Scholar
- IIASA (2012). Shared Socioeconomic Pathways (SSP) Database Version 0.9.3. https://secure.iiasa.ac.at/web-apps/ene/SspDb.
- 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
- 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
- Monfreda, C., Ramankutty, N., & Hertel, T. (2008). Global Agricultural Land Use Data for Climate Change Analysis (Routledge explorations.Google Scholar
- 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.
- OECD. (2010). Input–output tables. Paris: Organization for Economic Cooperation and Development.Google Scholar
- UN (2007a). National Accounts Main Aggregates Database. In U. Nations (Ed.). New York, USA.Google Scholar
- UN (2007b). United Nations Commodity Trade Statistics Database. In U. Nations (Ed.). New York, USA.Google Scholar
- UNIDO (2009). INDSTAT2 - 2009 edition. In U. N. I. D. O. (UNIDO) (Ed.). Vienna, Austria.Google Scholar
- Wise, M., & Calvin, K. (2011). GCAM 3.0 Agriculture and Land Use; Technical Description of Modeling Approach. PNNL (Vol. 20971).Google Scholar
- 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.