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Designing a Dedicated Energy Crop Supply System in Tennessee: A Multiobjective Optimization Analysis

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  • Yu, T. Edward
  • Wang, Zidong
  • English, Burton C.
  • Larson, James A.

Abstract

A multiobjective optimization model integrating with high-resolution geographical data was applied to examine the optimal switchgrass supply system in Tennessee that considers both feedstock cost and greenhouse gas (GHG) emissions in the system. Results suggest that the type of land converted into switchgrass production is crucial to both plant gate cost and GHG emissions of feedstock. In addition, a tradeoff relationship between cost and GHG emissions for the switchgrass supply is primarily driven by the type of land converted. The imputed cost of lowering GHG emissions in the feedstock supply system was also calculated based on the derived tradeoff curve. Biofuel production from lignocellulosic biomass (LCB) is being advocated as an alternative to fossil-based transportation fuels in the United States. LCB-based biofuel production has the potential to mitigate greenhouse gas (GHG) emissions from the transportation sector and to enhance rural economic activity through more intense use of agricultural lands (English et al., 2006). The Renewable Fuel Standard (RFS) established in 2005 and revised in the Energy Independence and Security Act 2007 mandates 21 billion gallons of advanced biofuel (other than ethanol derived from corn starch) available for transportation use by 2022 with 16 billion gallons to be produced from LCB feedstock (U.S. Congress, 2007). Based on the recently revised One Billion Ton Update study (U. S. Department of Energy, 2011), considerable LCB feedstock, including dedicated energy crops, will be required to fulfill this goal. Notwithstanding the potential availability of LCB feedstock to meet the mandate, the cost of LCB feedstock will be an important factor influencing the sustainability of an LCB-based biofuel industrial sector.

Suggested Citation

  • Yu, T. Edward & Wang, Zidong & English, Burton C. & Larson, James A., 2014. "Designing a Dedicated Energy Crop Supply System in Tennessee: A Multiobjective Optimization Analysis," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 46(3), pages 357-373, August.
    • Handle: RePEc:cup:jagaec:v:46:y:2014:i:03:p:357-373_03
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    References listed on IDEAS

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    1. James A. Larson & Tun‐Hsiang Yu & Burton C. English & Daniel F. Mooney & Chenguang Wang, 2010. "Cost evaluation of alternative switchgrass producing, harvesting, storing, and transporting systems and their logistics in the Southeastern USA," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 70(2), pages 184-200, August.
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    1. Nepal, Sandhya & Tran, Liem T., 2019. "Identifying trade-offs between socio-economic and environmental factors for bioenergy crop production: A case study from northern Kentucky," Renewable Energy, Elsevier, vol. 142(C), pages 272-283.
    2. Sharma, Bijay P. & Yu, T. Edward & English, Burton C. & Boyer, Christopher N. & Larson, James A., 2020. "Impact of government subsidies on a cellulosic biofuel sector with diverse risk preferences toward feedstock uncertainty," Energy Policy, Elsevier, vol. 146(C).
    3. Zhong, Jia & Yu, T. Edward & Larson, James A. & English, Burton C. & Fu, Joshua S. & Calcagno, James, 2016. "Analysis of environmental and economic tradeoffs in switchgrass supply chains for biofuel production," Energy, Elsevier, vol. 107(C), pages 791-803.
    4. Rosburg, Alicia & Miranowski, John & Jacobs, Keri, 2016. "Modeling biomass procurement tradeoffs within a cellulosic biofuel cost model," Energy Economics, Elsevier, vol. 58(C), pages 77-83.
    5. He-Lambert, Lixia & English, Burton C. & Lambert, Dayton M. & Shylo, Oleg & Larson, James A. & Yu, T. Edward & Wilson, Bradly, 2018. "Determining a geographic high resolution supply chain network for a large scale biofuel industry," Applied Energy, Elsevier, vol. 218(C), pages 266-281.
    6. Nguyen, Trung H. & Granger, Julien & Pandya, Deval & Paustian, Keith, 2019. "High-resolution multi-objective optimization of feedstock landscape design for hybrid first and second generation biorefineries," Applied Energy, Elsevier, vol. 238(C), pages 1484-1496.

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    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

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