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Confronting the food-energy-environment trilemma : global land use in the long run

Author

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  • Steinbuks, Jevgenijs
  • Hertel, Thomas W.

Abstract

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.

Suggested Citation

  • Steinbuks, Jevgenijs & Hertel, Thomas W., 2014. "Confronting the food-energy-environment trilemma : global land use in the long run," Policy Research Working Paper Series 6928, The World Bank.
  • Handle: RePEc:wbk:wbrwps:6928
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    References listed on IDEAS

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    1. Antoine Blandine & Gurgel Angelo & Reilly John M, 2008. "Will Recreation Demand for Land Limit Biofuels Production?," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 6(2), pages 1-29, December.
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    4. Gouel, Christophe & Hertel, Thomas, 2006. "Introducing Forest Access Cost Functions into a General Equilibrium Model," GTAP Research Memoranda 2215, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
    5. Guerrini, Luca, 2006. "The Solow-Swan model with a bounded population growth rate," Journal of Mathematical Economics, Elsevier, vol. 42(1), pages 14-21, February.
    6. Thomas W. Hertel & Kyle Stiegert & Harry Vroomen, 1996. "Nitrogen-Land Substitution in Corn Production: A Reconciliation of Aggregate and Firm-Level Evidence," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(1), pages 30-40.
    7. Sohngen, Brent & Golub, Alla & Hertel, Thomas, 2008. "The Role of Forestry in Carbon Sequestration in General Equilibrium Models," GTAP Working Papers 2610, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
    8. J. A. L. Cranfield & James S. Eales & Thomas W. Hertel & Paul V. Preckel, 2003. "Model selection when estimating and predicting consumer demands using international, cross section data," Empirical Economics, Springer, vol. 28(2), pages 353-364, April.
    9. Hertel, Thomas, 1997. "Global Trade Analysis: Modeling and applications," GTAP Books, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, number 7685.
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    1. repec:bla:rdevec:v:21:y:2017:i:2:p:213-236 is not listed on IDEAS
    2. Thomas W. Hertel & Jevgenijs Steinbuks & Wallace E. Tyner, 2016. "What Is the Social Value of Second Generation Biofuels?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 38(4), pages 599-617.
    3. Yongyang Cai & Kenneth Judd & Jevgenijs Steinbuks, 2017. "A nonlinear certainty equivalent approximation method for dynamic stochastic problems," Quantitative Economics, Econometric Society, vol. 8(1), pages 117-147, March.
    4. repec:bla:jageco:v:68:y:2017:i:3:p:611-631 is not listed on IDEAS
    5. Midmore, Peter, 2017. "The Science of Impact and the Impact of Agricultural Science," 91st Annual Conference, April 24-26, 2017, Royal Dublin Society, Dublin, Ireland 258614, Agricultural Economics Society.

    More about this item

    Keywords

    Climate Change Mitigation and Green House Gases; Environmental Economics&Policies; Climate Change Economics; Environment and Energy Efficiency; Energy and Environment;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q26 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Recreational Aspects of Natural Resources
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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