Confronting the Food–Energy–Environment Trilemma: Global Land Use in the Long Run


Economic, agronomic, and biophysical drivers affect global land use, so all three influences need to be considered in evaluating economically optimal allocations of the world’s land resources. A dynamic, forward-looking optimization framework applied over the course of the coming century shows that although some deforestation is optimal in the near term, in the absence of climate change regulation, the desirability of further deforestation is eliminated by mid-century. Although adverse productivity shocks from climate change have a modest effect on global land use, such shocks combined with rapid growth in energy prices lead to significant deforestation and higher greenhouse gas emissions than in the baseline. Imposition of a global greenhouse gas emissions constraint further heightens the competition for land, as fertilizer use declines and land-based mitigation strategies expand. However, anticipation of the constraint largely dilutes its environmental effectiveness, as deforestation accelerates prior to imposition of the target.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    This point requires additional clarification. The biophysical and ecological literature suggests that restoration of forest structure and plant species takes at least 30–40 years and usually many more decades (Chazdon 2008), costs several to ten thousands dollars per hectare (Nesshöver et al. 2009), and is only partially successful in achieving reference conditions (Benayas et al. 2009). Modeling restoration of biodiversity under these assumptions introduces greater computational complexity without making significant changes relative to findings presented in this study.

  2. 2.

    GHG emissions flows are also sequestered by atmospheric and ocean sinks. We ignore this complication as our model does not provide comprehensive accounting of all GHG emissions flows, and focuses on understanding emissions from land use and related sectors.

  3. 3.

    This doesn’t necessarily mean that biofuels are ’greener’ than fossil fuels. That will depend on the emissions associated with agricultural production and natural land conversion.

  4. 4.

    The most popular demand systems estimated in recent applied work are the homothetic Cobb-Douglas system (HCD), the linear expenditure system (LES), the constant difference of elasticities demand system (CDE), and the almost ideal demand system (AIDS).

  5. 5.

    One of well-known limitations of the AIDS system is that its budget shares fall outside \([0,1]\) interval. This frequently occurs when AIDS is applied to model the demand for staple food when income growth is large (Yu et al. 2004, p. 102).

  6. 6.

    We do not consider the bequest value of protected forests, as they cannot be “scrapped” in our model.

  7. 7.

    As this study focuses on optimal path of land based GHG emissions, the emissions from combustion of petroleum products are not shown in Fig. 2.

  8. 8.

    Direct comparison of model predictions of biofuels penetration is difficult due to considerable uncertainty in variety of factors, such as, e.g., evolution of biofuels’ production technologies, land access costs, yield growth rates, and energy demand projections. We show model sensitivity to these factors in counterfactual simulations below, and in Technical Appendix.

  9. 9.

    We show the model sensitivity to changes in other important model parameters in Technical Appendix.

  10. 10.

    Of course there are many factors contributing to a potential decline of highly uncertain fossil fuel costs (Pindyck 1999). Our choice of rising fossil fuel costs in this scenario is motivated by understanding global land use decisions under greater resource scarcity.

  11. 11.

    In 2008 New Zealand passed legislation to include commercial forestry sector in the emissions trading scheme. Regulation of other land-use emissions is expected to take place in 2015 (Source: the New Zealand’s Ministry of Agriculture and Forestry website: In 2010 the European Commission launched a public consultation on whether emissions and removals of greenhouse gases related to land use, land use change and forestry (LULUCF) should be covered by the EU’s target of cutting GHG emissions to 30 % below 1990 levels by 2020 (Source: the European Commission’s website:

  12. 12.

    The term “green paradox” was first introduced by Sinn (2008).

  13. 13.

    The size of intertemporal leakage is reduced to 37 % over the period of 200 years.

  14. 14.

    Lack of regional disaggregation also impedes our ability to speak to the heterogeneous impacts of climate change in light of geographic shifting of production as an adaptation strategy to changing climate (Nelson et al. 2009). Accounting for such shifts would further moderate the impact of adverse climate change on global land use.

  15. 15.

    The only exception is the interplay of GHG emissions target and higher discount rate, where the decline in natural land conversion is larger compared to the model baseline.


  1. Antoine B, Gurgel A, Reilly J (2008) Will recreation demand for land limit biofuels production? J Agric Food Ind Org 6(2):5

    Google Scholar 

  2. Babiker M, Gurgel A, Paltsev S, Reilly J (2009) Forward-looking versus recursive-dynamic modeling in climate policy analysis: a comparison. Econ Model 26(6):1341–1354

    Article  Google Scholar 

  3. Bahel E, Marrouch W, Gaudet G (2013) The economics of oil, biofuel and food commodities. Resour Energy Econ 35(4):599–617

    Article  Google Scholar 

  4. Benayas J, Newton A, Diaz A, Bullock J (2009) Enhancement of biodiversity and ecosystem services by ecological restoration: a meta-analysis. Science 325(5944):1121–1124

    Article  Google Scholar 

  5. Bouwman A, Kram T, Goldewijk K (2006) Integrated modelling of global environmental change: an overview of image 2.4. Technical report, Netherlands Environmental Assessment Agency, Bilthoven, The Netherlands

  6. Brown R, Rosenberg N, Hays C, Easterling W, Mearns L (2000) Potential production and environmental effects of switchgrass and traditional crops under current and greenhouse-altered climate in the central United States: a simulation study. Agric Ecosyst Environ 78(1):31–47

    Article  Google Scholar 

  7. Bulte E, van Soest D, Van Kooten G, Schipper R (2002) Forest conservation in Costa Rica when nonuse benefits are uncertain but rising. Am J Agric Econ 84(1):150–160

    Article  Google Scholar 

  8. Chakravorty U, Hubert M, Moreaux M, Nostbakken L (2011) Will biofuel mandates raise food prices? Working paper 2011–01, Department of Economics, University of Alberta

  9. Chakravorty U, Magné B, Moreaux M (2008) A dynamic model of food and clean energy. J Econ Dyn Control 32(4):1181–1203

    Article  Google Scholar 

  10. Chazdon R (2008) Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320(5882):1458–1460

    Article  Google Scholar 

  11. Choi S, Sohngen B, Rose S, Hertel T, Golub A (2011b) Total factor productivity change in agriculture and emissions from deforestation. Am J Agric Econ 93(2):349–355

    Google Scholar 

  12. Conrad J (1997) On the option value of old-growth forest. Ecol Econ 22(2):97–102

    Article  Google Scholar 

  13. Conrad J (2000) Wilderness: options to preserve, extract, or develop. Resour Energy Econ 22(3):205–219

    Article  Google Scholar 

  14. Cranfield J, Eales J, Hertel T, Preckel P (2003) Model selection when estimating and predicting consumer demands using international, cross section data. Empir Econ 28(2):353–364

    Article  Google Scholar 

  15. Daigneault AJ, Miranda MJ, Sohngen B (2010) Optimal forest management with carbon sequestration credits and endogenous fire risk. Land Econ 86(1):155–172

    Article  Google Scholar 

  16. Deaton A, Muellbauer J (1980) An almost ideal demand system. Am Econ Rev 70(3):312–326

    Google Scholar 

  17. Dunn J, Eason J, Wang M (2011) Updated sugarcane and switchgrass parameters in the GREET model. Technical report, Center for Transportation Research, Argonne National Laboratory

  18. EIA (2010) Annual energy outlook. Publication DOE/EIA-0383, U.S. Department of Energy

  19. Fearnside P (2000) Global warming and tropical land-use change: greenhouse gas emissions from biomass burning, decomposition and soils in forest conversion, shifting cultivation and secondary vegetation. Clim Change 46(1):115–158

    Article  Google Scholar 

  20. Fischer C, Salant S (2012) Alternative climate policies and intertemporal emissions leakage: quantifying the green paradox. Working paper RFF DP 12–16, Resources for Future

  21. Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC et al (2011) Solutions for a cultivated planet. Nature 478(7369):337–342

    Article  Google Scholar 

  22. Fujimori S, Masui T, Matsuoka Y (2012) AIM/CGE [basic] manual. Technical report 2012–01, Center for Social and Environmental Systems, NIES

  23. Golub A, Hertel T, Sohngen B (2009) Land use modeling in recursively-dynamic GTAP framework. In: Hertel T, Rose S, Tol R (eds) Economic analysis of land use in global climate change policy. Routledge, London, pp 235–278

    Google Scholar 

  24. Gurgel A, Cronin T, Reilly J, Paltsev S, Kicklighter D, Melillo J (2011) Food, fuel, forests and the pricing of ecosystem services. Am J Agric Econ 92(2):342–348

    Google Scholar 

  25. Gurgel A, Reilly J, Paltsev S (2007) Potential land use implications of a global biofuels industry. J Agric Food Ind Org 5(2):1–34

    Google Scholar 

  26. Hanoch G (1975) Production and demand models with direct or indirect implicit additivity. Econometrica 43(3):395–419

    Article  Google Scholar 

  27. Havlík P, Schneider UA, Schmid E, Böttcher H, Fritz S, Skalskỳ R, Aoki K, Cara SD, Kindermann G, Kraxner F et al (2011) Global land-use implications of first and second generation biofuel targets. Energy Policy 39(10):5690–5702

    Article  Google Scholar 

  28. Hertel T, Tyner W, Birur D (2010) The global impacts of biofuel mandates. Energy J 31(1):75–100

    Article  Google Scholar 

  29. Hertel TW, Burke MB, Lobell DB (2010) The poverty implications of climate-induced crop yield changes by 2030. Glob Environ Change 20(4):577–585

    Article  Google Scholar 

  30. Houghton R (2003) Revised estimates of the annual net flux of carbon to the atmosphere from changes in land use and land management 1850–2000. Tellus B 55(2):378–390

    Article  Google Scholar 

  31. Ianchovichina E, Darwin R, Shoemaker R (2001) Resource use and technological progress in agriculture: a dynamic general equilibrium analysis. Ecol Econ 38(2):275–291

    Article  Google Scholar 

  32. IPCC (2007a) Climate Change 2007: Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Synthesis Report, The Intergovernmental Panel on Climate Change, Geneva, Switzerland

  33. IPCC (2007) Summary for Policymakers. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 1–18

  34. Leroux A, Martin V, Goeschl T (2009) Optimal conservation, extinction debt, and the augmented quasi-option value. J Environ Econ Manage 58(1):43–57

    Article  Google Scholar 

  35. Long S (1991) Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: has its importance been underestimated? Plant Cell Environ 14(8):729–739

    Article  Google Scholar 

  36. Long SP, Ainsworth EA, Leakey AD, Nösberger J, Ort DR (2006) Food for thought: lower-than-expected crop yield stimulation with rising \({\text{ CO }}_2\) concentrations. Science 312(5782):1918–1921

    Article  Google Scholar 

  37. Lotze-Campen H, Müller C, Bondeau A, Rost S, Popp A, Lucht W (2008) Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agric Econ 39(3):325–338

    Google Scholar 

  38. Melillo JM, Reilly JM, Kicklighter DW, Gurgel AC, Cronin TW, Paltsev S, Felzer BS, Wang X, Sokolov AP, Schlosser CA (2009) Indirect emissions from biofuels: how important? Science 326(5958):1397–1399

    Article  Google Scholar 

  39. Meyer S, Binfield J, Westhoff P, Thompson W (2011) U.S. biofuels baseline: the impact of extending the $0.45 ethanol blenders credit, FAPRI-MU Report 07–11, Food and Agricultural Policy Research Institute at the University of Missouri-Columbia

  40. Moss RH, Babiker M, Brinkman S, Calvo E, Carter T, Edmonds JA, Elgizouli I, Emori S, Lin E, Hibbard K et al (2008) Towards new scenarios for analysis of emissions, climate change, impacts, and response strategies. Technical report PNNL-SA-63186, Pacific Northwest National Laboratory (PNNL). Richland, WA (US)

  41. National Research Council (2011) Renewable fuel standard: potential economic and environmental effects of U.S. biofuel policy. National Academies Press

  42. Nelson GC, Rosegrant MW, Koo J, Robertson R, Sulser T, Zhu T, other, (2009) Climate change: impact on agriculture and costs of adaptation. Technical report, International Food Policy Research Institute (IFPRI), Washington, DC

  43. Nesshöver C, Aronson J, Blignaut J, Lehr D, Vakrou A, Wittmer H (2009) Investing in ecological infrastructure. In: ten Brink P (ed) The economics of ecosystems and biodiversity: national and international policy making. An output of TEEB, Earthscan, pp 401–448

  44. Nordhaus WD (1973) The allocation of energy resources. Brookings Pap Econ Act 1973(3):529–576

    Article  Google Scholar 

  45. Odum E (1969) The strategy of ecosystem development. Science 164:262–270

    Article  Google Scholar 

  46. Paltsev S, Reilly J, Jacoby H, Eckaus R, McFarland J, Sarofim M, Asadoorian M, Babiker M (2005) The MIT emissions prediction and policy analysis (EPPA) model: version 4. Technical report, MIT Joint Program on the Science and Policy of Global Change

  47. Parry ML, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Change 14(1):53–67

    Article  Google Scholar 

  48. Pindyck RS (1999) The long-run evolutions of energy prices. Energy J 20(2):1–27

    Article  Google Scholar 

  49. Popp A, Dietrich J, Lotze-Campen H, Klein D, Bauer N, Krause M, Beringer T, Gerten D, Edenhofer O (2011) The economic potential of bioenergy for climate change mitigation with special attention given to implications for the land system. Environ Res Lett 6(3):034017

  50. Popp A, Lotze-Campen H, Bodirsky B (2010) Food consumption, diet shifts and associated non-\(\text{ CO }_{2}\) greenhouse gases from agricultural production. Glob Environ Change 20(3):451–462

    Article  Google Scholar 

  51. Popp D (2002) Induced innovation and energy prices. Am Econ Rev 92(1):160–180

    Article  Google Scholar 

  52. Reilly J, Paltsev S, Felzer B, Wang X, Kicklighter D, Melillo J, Prinn R, Sarofim M, Sokolov A, Wang C (2007) Global economic effects of changes in crops, pasture, and forests due to changing climate, carbon dioxide, and ozone. Energy Policy 35(11):5370–5383

    Article  Google Scholar 

  53. Richards K, Stokes C (2004) A review of forest carbon sequestration cost studies: a dozen years of research. Clim Change 63(1):1–48

    Article  Google Scholar 

  54. Rimmer M, Powell A (1996) An implicitly additive demand system. Appl Econ 28(12):1613–1622

    Article  Google Scholar 

  55. Rose SK, Ahammad H, Eickhout B, Fisher B, Kurosawa A, Rao S, Riahi K, van Vuuren DP (2012) Land-based mitigation in climate stabilization. Energy Econ 34(1):365–380

    Article  Google Scholar 

  56. Rosegrant MW, IMPACT Development Team (2012) International model for policy analysis of agricultural commodities and trade (IMPACT) model description. Technical report, International Food Policy Research Institute, Washington, DC

  57. Schmitz C, van Meijl H, Kyle P, Nelson GC, Fujimori S, Gurgel A, Havlik P, Heyhoe E, d’Croz DM, Popp A et al (2014) Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agric Econ 45(1):69–84

    Article  Google Scholar 

  58. Searchinger T, Heimlich R, Houghton R, Dong F, Elobeid A, Fabiosa J, Tokgoz S, Hayes D, Yu T (2008) Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319(5867):1238

    Article  Google Scholar 

  59. Sinn H (2008) Public policies against global warming: a supply side approach. Int Tax Public Financ 15(4):360–394

  60. Smulders S, Tsur Y, Zemel A (2014) Uncertain climate policy and the green paradox. In: Moser E, Semmler W, Tragler G, Veliov VM (eds) Dynamic optimization in environmental economics. Springer, Berlin

    Google Scholar 

  61. Sohngen B, Mendelsohn R (2003) An optimal control model of forest carbon sequestration. Am J Agric Econ 85(2):448–457

    Article  Google Scholar 

  62. Sohngen B, Mendelsohn R (2007) A sensitivity analysis of forest carbon sequestration. In: Schlesinger M (ed) Human-induced climate change: an interdisciplinary assessment. Cambridge University Press, Cambridge, pp 227–237

    Google Scholar 

  63. Stavins R (1999) The costs of carbon sequestration: a revealed-preference approach. Am Econ Rev 89(4):994–1009

    Article  Google Scholar 

  64. Steinbuks J, Timilsina GR (2014) Land-use change and food supply. In: Timilsina G, Zilberman D (eds) The impacts of biofuels on the economy, environment, and poverty. Springer, New York, pp 91–102

  65. USGAO (2003) Natural gas: domestic nitrogen fertilizer production depends on natural gas availability and prices. Report to the Ranking Democratic Member, Committee on Agriculture, Nutrition and Forestry, U.S. Senate GAO-03-1148, United States General Accounting Office

  66. Van der Mensbrugghe D (2013) The environmental impact and sustainability applied general equilibrium (ENVISAGE) model: version 8.0, processed. Technical report, FAO, Rome

  67. Wise M, Calvin K (2011) GCAM 3.0 agriculture and land use: technical description of modeling approach. Technical report, Pacific Northwest National Laboratory (PNNL), Richland, WA (US)

  68. Wise M, Calvin K, Thomson A, Clarke L, Bond-Lamberty B, Sands R, Smith S, Janetos A, Edmonds J (2009) Implications of limiting \({\text{ CO }}_2\) concentrations for land use and energy. Science 324(5931):1183

    Article  Google Scholar 

  69. Yu W, Hertel T, Preckel P, Eales J (2004) Projecting world food demand using alternative demand systems. Econ Model 21(1):99–129

    Article  Google Scholar 

Download references


We would like to thank Yongyang Cai, Ujjayant Chakravorty, Alla Golub, Kenneth Judd, Todd Munson, Paul Preckel, Brent Sohngen, Farzad Taheripour, Wally Tyner, two anonymous reviewers, and the participants of the 4th International Workshop on Empirical Methods in Energy Economics, the American Geophysical Union Annual Meetings, the American Economic Association Annual Meetings, Cowles Summer Conference “Macroeconomics and Climate Change”, and research seminars at Purdue University and the World Bank for their helpful suggestions and comments. We appreciate the financial support from the National Science Foundation, Grant 0951576 “DMUU: Center for Robust Decision Making on Climate and Energy Policy”.

Author information



Corresponding author

Correspondence to Jevgenijs Steinbuks.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2123 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Steinbuks, J., Hertel, T.W. Confronting the Food–Energy–Environment Trilemma: Global Land Use in the Long Run. Environ Resource Econ 63, 545–570 (2016).

Download citation


  • Biofuels
  • Climate change
  • Deforestation
  • Energy
  • Environment
  • Food
  • Forestry
  • GHG emissions
  • Global land use

JEL Classification

  • C61
  • Q15
  • Q23
  • Q26
  • Q40
  • Q54