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Modeling Bioenergy, Land Use, And Ghg Emissions With Fasomghg: Model Overview And Analysis Of Storage Cost Implications

Author

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  • ROBERT H. BEACH

    (Environmental, Technology, and Energy Economics Program, 3040E Cornwall's Road, P. O. Box 12194, Research Triangle Park, NC 27709-2194, USA)

  • YUQUAN W. ZHANG

    (Environmental, Technology, and Energy Economics Program, 3040E Cornwall's Road, P. O. Box 12194, Research Triangle Park, NC 27709-2194, USA)

  • BRUCE A. MCCARL

    (Department of Agricultural Economics, Texas A&M University, 2124 TAMU, College Station, TX 77843-2124, USA)

Abstract

Biofuels production has increased rapidly in recent years due to higher petroleum prices as well as heightened concerns regarding climate change and energy security. However, because commercially viable biofuels are currently produced primarily from agricultural feedstocks, higher production volumes increase pressure on land resources. Thus, large-scale biofuels production has important implications for the forest and agriculture sectors, land use, trade, and net greenhouse gas (GHG) emissions. Competition for land is expected to continue growing in the future as mandated biofuels volumes increase along with rising demand for food, feed, and fiber, both domestically and internationally. In response to heightened concern regarding impacts such as indirect land-use change and higher food prices, the U.S. policy is focusing on second-generation (cellulosic) feedstocks to contribute the majority of the mandated increase in biofuels volume through 2022. However, there has been little work exploring the logistics of supplying these feedstocks or examining feedstock mix and net GHG effects of combining renewable fuels mandates with climate policy. In this paper, we apply the recently updated Forest and Agricultural Sector Optimization Model with GHGs (FASOMGHG) to explore the implications of alternative assumptions regarding feedstock storage costs and carbon price for renewable energy production mix, land use, and net GHG emissions. The model is used to quantify the magnitude and regional distribution of changes in the optimal mix of bioenergy feedstocks when accounting for storage costs. In addition, we find that combining the biofuels volume mandate with a carbon price policy has additional implications for feedstock mix and provides a substantially larger net reduction in GHG than a renewable fuels mandate alone.

Suggested Citation

  • Robert H. Beach & Yuquan W. Zhang & Bruce A. Mccarl, 2012. "Modeling Bioenergy, Land Use, And Ghg Emissions With Fasomghg: Model Overview And Analysis Of Storage Cost Implications," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 1-34.
    • Handle: RePEc:wsi:ccexxx:v:03:y:2012:i:03:n:s2010007812500121
    DOI: 10.1142/S2010007812500121
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    References listed on IDEAS

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    1. Abbott, Philip C. & Hurt, Christopher & Tyner, Wallace E., 2009. "What's Driving Food Prices? March 2009 Update," Issue Reports 48495, Farm Foundation.
    2. Fraas, Arthur & Johansson, Robert, 2009. "Conflicting Goals: Energy Security vs. GHG Reductions under the EISA Cellulosic Ethanol Mandate," RFF Working Paper Series dp-09-24, Resources for the Future.
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    Cited by:

    1. Jianhong Mu & Anne Wein & Bruce McCarl, 2015. "Land use and management change under climate change adaptation and mitigation strategies: a U.S. case study," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(7), pages 1041-1054, October.
    2. Ruiqing Miao & Madhu Khanna, 2017. "Effectiveness of the Biomass Crop Assistance Program: Roles of Behavioral Factors, Credit Constraint, and Program Design," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 39(4), pages 584-608.
    3. Miao, Ruiqing & Khanna, Madhu, 2017. "Costs of meeting a cellulosic biofuel mandate with perennial energy crops: Implications for policy," Energy Economics, Elsevier, vol. 64(C), pages 321-334.
    4. Sabrina Spatari & Alexander Stadel & Paul R. Adler & Saurajyoti Kar & William J. Parton & Kevin B. Hicks & Andrew J. McAloon & Patrick L. Gurian, 2020. "The Role of Biorefinery Co-Products, Market Proximity and Feedstock Environmental Footprint in Meeting Biofuel Policy Goals for Winter Barley-to-Ethanol," Energies, MDPI, vol. 13(9), pages 1-15, May.
    5. Miao, Ruiqing & Khanna, Madhu, 2015. "Costs of Meeting the Cellulosic Biofuel Mandate with an Energy Crop with Establishment Cost and Yield Risk: Implications for Policy," 2015 Conference, August 9-14, 2015, Milan, Italy 212458, International Association of Agricultural Economists.
    6. Galik, Christopher S., 2015. "Exploring the determinants of emerging bioenergy market participation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 107-116.
    7. Latta, Gregory S. & Sjølie, Hanne K. & Solberg, Birger, 2013. "A review of recent developments and applications of partial equilibrium models of the forest sector," Journal of Forest Economics, Elsevier, vol. 19(4), pages 350-360.
    8. Plevin, Richard J. & Delucchi, Mark A. & O’Hare, Michael, 2017. "Fuel carbon intensity standards may not mitigate climate change," Energy Policy, Elsevier, vol. 105(C), pages 93-97.
    9. Weiwei Wang, 2022. "Agricultural and Forestry Biomass for Meeting the Renewable Fuel Standard: Implications for Land Use and GHG Emissions," Energies, MDPI, vol. 15(23), pages 1-21, November.
    10. Kivevele, Thomas & Kichonge, Baraka, 2024. "Techno-economic evaluation of transesterification processes for biodiesel production from low quality non-edible feedstocks: Process design and simulation," Energy, Elsevier, vol. 297(C).
    11. Wang, Weiwei & Khanna, Madhu & Dwivedi, Puneet, 2013. "Optimal Mix of Feedstock for Biofuels: Implications for Land Use and GHG Emissions," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150736, Agricultural and Applied Economics Association.

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    More about this item

    Keywords

    Agriculture; bioenergy; biomass storage; FASOMGHG; forests; GHG mitigation; transportation costs; C61; Q15; Q18;
    All these keywords.

    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
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

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