BioEnergy Research

, Volume 9, Issue 2, pp 566–577 | Cite as

Analyzing Economic and Environmental Performance of Switchgrass Biofuel Supply Chains

  • T. Edward YuEmail author
  • Burton C. English
  • Lixia He
  • James A. Larson
  • James Calcagno
  • Joshua S. Fu
  • Brad Wilson


This study optimized the net present value (NPV) of profit of various switchgrass-based ethanol supply chains and estimated associated greenhouse gas (GHG) emissions in west Tennessee. Three configurations of feedstock harvesting and storage, including a large round baler system, a large square baler system, and a chopping/densification system, were evaluated. A mixed-integer mathematical programming model incorporating high-resolution spatial data was used to determine the optimal locations and capacities of cellulosic ethanol plants and feedstock preprocessing facilities, and associated feedstock-draw areas by maximizing the NPV of profit over 20 years. The optimized outputs were then used to estimate the GHG emissions produced in the biofuel supply chain (BSC) per year. The study shows that BSC configurations have important implications for the economic and environmental performance of the system. The harvest and storage configurations affect the locations of conversion and preprocessing facilities, and associated feedstock-draw areas, hence impacting the cost and emissions of both feedstock and biofuels transportation. The findings suggest the BSC system that harvests feedstock with forage choppers and utilizes stretch-wrap balers to increase feedstock density has the highest NPV of profit. The BSC system that uses large square balers for harvest and storage emits the lowest amount of GHGs per year. In addition, the sensitivity analysis suggests that biofuel price and scaling factor of facility capital was influential to the economics of BSC systems. The breakeven price of biofuel for the three BSCs was around $0.97 L−1.


Cellulosic ethanol Switchgrass Supply chains Net present value GHG emissions 



This project was funded by the US Department of Transportation (grant no. DT0S5907G00050). We would acknowledge the comments and edits provided by Dr. Roland Roberts and Mr. Robert Menard for the manuscript. We are also grateful for research assistance by Ms. Jia Zhong. The usual disclaimer applies.

Compliance with Ethical Standards


This study was funded by US Department of Transportation (grant no. DT0S5907G00050).

Conflict of Interest

The authors declare that they have no competing interests.


  1. 1.
    U.S. Congress (2007) Energy independence and security act of 2007. Available at: . Accessed 5 March 2013
  2. 2.
    U.S. Department of Energy (2007) Roadmap for bioenergy and biobased products in the United States. Report of the biomass research and development technical advisory committee. U.S. Department of Energy, Biomass Research and Development Initiative, WashingtonGoogle Scholar
  3. 3.
    An H, Wilhelm WE, Searcy SW (2011) Biofuel and petroleum-based fuel supply chain research: a literature review. Biomass Bioenergy 35:3763–3774CrossRefGoogle Scholar
  4. 4.
    De Meyer A, Cattrysse D, Rasinmaki J, van Orshoven J (2014) Methods to optimise the design and management of biomass-for-bioenergy supply chains: a review. Renew Sust Energ Rev 31:657–670CrossRefGoogle Scholar
  5. 5.
    Yu TE, Wang Z, English BC, Larson JA (2014) Designing a dedicated energy crop supply system in Tennessee: a multiobjective optimization analysis. J Agric Appl Econ 46(3):357–373Google Scholar
  6. 6.
    Sharma B, Ingalls R, Jones C, Khanchi A (2013) Biomass supply chain design and analysis: basis, overview, modeling, challenges, and future. Renew Sust Energ Rev 24:608–627CrossRefGoogle Scholar
  7. 7.
    Kumar A, Sokhansanj S (2007) Switchgrass (Panicum vigratum, L) delivery to a biorefinery using integrated biomass supply analysis and logistics (IBSAL) model. Bioresour Technol 98:1033–1044CrossRefGoogle Scholar
  8. 8.
    Sokhansanj S, Mani S, Turhollow A, Kumar A, Bransby D, Lynd L, Laser M (2009) Large-scale production, harvest and logistics of switchgrass (Panicum Virgatum L) – current technology and envisioning a mature technology. Biofuels Bioprod Biorefin 3:124–141CrossRefGoogle Scholar
  9. 9.
    Larson JA, Yu T, English BC, Mooney DF, Wang C (2010) Cost evaluation of alternative switchgrass producing, harvesting, storing, and transporting systems and their logistics in the southeastern US. Agric Financ Rev 70:184–200CrossRefGoogle Scholar
  10. 10.
    Zhang J, Osmani A, Awudu I, Gonela V (2013) An integrated optimization model for switchgrass-based bioethanol supply chain. Appl Energy 102:1205–1217CrossRefGoogle Scholar
  11. 11.
    Kaliyan N, Morey RV, Tiffany DG (2015) Economic and environmental analysis for corn stover and switchgrass supply logistics. Bioenerg Res 8:1433–1448CrossRefGoogle Scholar
  12. 12.
    Daystar J, Gonzalez CR, Venditti RA, Treasure T, Abt R, Kelley S (2014) Economics, environmental impacts, and supply chain analysis of cellulosic biomass for biofuels in the southern US: pine, eucalyptus, unmanaged hardwoods, forest residues, switchgrass, and sweet sorghum. Bioresources 9:393–444Google Scholar
  13. 13.
    Jäppinen E, Korpinen OJ, Ranta T (2013) The effects of local biomass availability and possibilities for truck and train transportation on the greenhouse gas emissions of a small-diameter energy wood supply chain. Bioenerg Res 6:166–177CrossRefGoogle Scholar
  14. 14.
    Archer DW, Johnson M (2012) Evaluating local crop residue biomass supply: economic and environmental impacts. Bioenerg Res 5:699–712CrossRefGoogle Scholar
  15. 15.
    Jäppinen, E, Korpinen OJ, Ranta T (2011) Effects of local biomass availability and road network properties on the greenhouse gas emissions of biomass supply chain. ISRN Renewable Energy. Article ID 189734: 6 pagesGoogle Scholar
  16. 16.
    Wright L, Turhollow A (2010) Switchgrass selection as a “model” bioenergy crop: a history of the process. Biomass Bioenergy 34:851–868CrossRefGoogle Scholar
  17. 17.
    Humbird, D, Davis R, Tao L, Kinchin C, Hsu D, Aden A, Schoen P, Lukas J, Olthof B, Worley M, Sexton D, Dudgeon D (2011) Process design and economics for biochemical conversion of lignocellulosic biomass to ethanol. National Renewable Energy Laboratory and Harris Group. Technical Report No. NREL/TP-5100-47764 MayGoogle Scholar
  18. 18.
    McLaughlin SB, Kszos LA (2005) Development of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States. Biomass Bioenergy 28:515–535CrossRefGoogle Scholar
  19. 19.
    Mooney DF, Larson JA, English BC, Tyler DD (2012) Effect of dry matter loss on profitability of outdoor storage of switchgrass. Biomass Bioenergy 44:33–41CrossRefGoogle Scholar
  20. 20.
    Yu TE, He L, English BC, Larson JA (2014) GIS-based optimization for advanced biofuels supply chains: a case study in Tennessee. Lecture Notes Manag Sci 6:217–227Google Scholar
  21. 21.
    Song F, Zhao J, Swinton SM (2011) Switching to perennial energy crops under uncertainty and costly reversibility. Am J Agric Econ 93(3):768–783CrossRefGoogle Scholar
  22. 22.
    Jager HI, Baskaran LM, Brandt CC, Davis EB, Gunderson CA, Wullschleger SD (2010) Empirical geographic modeling of switchgrass yields in the United States. GCB Bioenergy 2:248–257CrossRefGoogle Scholar
  23. 23.
    U.S. Department of Agriculture, National Agricultural Statistics Service (2011) CropScape−Cropland Data Layer Database. Available at: Accessed 18 February 2013
  24. 24.
    U.S. Department of Agriculture. Nature Resources Conservation Service (2012) Soil survey geographic (SSURGO) database. Available at: Accessed 25 April 2012
  25. 25.
    U.S. Department of Agriculture, National Agricultural Statistics Service (2010) Crop values: 2010 summary. Available at: Accessed 29 November 29 2013
  26. 26.
    USDA NASS (2012) Tennessee farm facts. Vol. 12;14. Available at:
  27. 27.
    University of Tennessee Extension (2009) Guideline switchgrass establishment and annual production budgets over three year planning horizon, E12-4115-00-001-08, Knoxville, TN. Available at: Accessed 9 July 2013
  28. 28.
    U.S. Census Bureau, Geography Division, Geographic Products Branch. (2012) Topologically integrated geographic encoding and referencing (TIGER/Line®) Shapefiles. Available at: Accessed 5 November 2012
  29. 29.
    Höltinger S, Schmidt J, Schönhart M, Schmid E (2014) A spatially explicit techno-economic assessment of green biorefinery concepts. Biofuels Bioprod Biorefin 8:325–341. doi: 10.1002/bbb.1461 CrossRefGoogle Scholar
  30. 30.
    Wang MC, Saricks C, Santini D (1999) Effects of fuel ethanol use on fuel-cycle energy and greenhouse gas emissions. U.S. Department of Energy, Argonne National Laboratory, Center for Transportation Research, ArgonneGoogle Scholar
  31. 31.
    U.S. Energy Information Administration (2014) Tennessee state energy profile. Available at: Accessed 10 July 2014
  32. 32.
    Donahue DJ, Meyer S, Thompson W (2010) RIN risks: using supply and demand behavior to assess risk in the markets for renewable identification numbers used for renewable fuel standard compliance. Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MOGoogle Scholar
  33. 33.
    Parton WJ, Hartman M, Ojima D, Schimel D (1998) DAYCENT and its land surface submodel: description and testing. Glob Planet Chang 19:35–48CrossRefGoogle Scholar
  34. 34.
    Argonne National Laboratory (2013) The greenhouse gases, regulated emissions, and energy use in transportation model (GREET). Available at: Accessed 5 March 2013
  35. 35.
    U.S. Energy Information Administration (2014) State electricity profiles. Available at: Accessed 6 May 2014
  36. 36.
    Intergovernmental Panel on Climate Change (2011) IPCC special report on renewable energy sources and climate change mitigation. Prepared by Working Group III of the IPCC. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Seyboth K, Matschoss P, Kadner S, Zwickel T, Eickemeier P, Hansen G, Schlömer S, von Stechow C (eds). Cambridge University Press, New York, NY, USAGoogle Scholar
  37. 37.
    Hsu D, Inman D, Heath GA, Wolfrum EJ, Mann MK, Aden A (2010) Life cycle environmental impacts of selected US ethanol production and use pathways in 2022. Environ Sci Technol 44:5289–5297CrossRefGoogle Scholar
  38. 38.
    U.S. Environmental Protection Agency (EPA) Development of emission rates for heavy-duty vehicles in the motor vehicle emissions simulator MOVES2010—final report. Assessment and Standards Division Office of Transportation and Air Quality: EPA-420-B-12-049. August 2012. Available at:
  39. 39.
    Jack MW (2009) Scaling laws and technology development strategies for biorefineries and bioenergy plants. Bioresour Technol 100:6324–6330CrossRefGoogle Scholar
  40. 40.
    Kocoloski M, Griffin WM, Matthews HS (2011) Impacts of facility size and location decisions on ethanol production cost. Energy Policy 39:47–56CrossRefGoogle Scholar
  41. 41.
    Haque M, Epplin FM, Biermacher JT, Holcomb RB, Kenke PL (2014) Marginal cost of delivering switchgrass feedstock and producing cellulosic ethanol at multiple biorefineries. Biomass Bioenergy 66:308–319CrossRefGoogle Scholar
  42. 42.
    Khanna M, Dhungana B, Brown JC (2008) Costs of producing miscanthus and switchgrass for bioenergy in Illinois. Biomass Bioenergy 32:482–493CrossRefGoogle Scholar
  43. 43.
    Kwon HY, Mueller S, Dunn JB, Wander MM (2013) Modeling state-level soil carbon emission factors under various scenarios for direct land use change associated with United States biofuel feedstock production. Biomass Bioenergy 55:299–310CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • T. Edward Yu
    • 1
    Email author
  • Burton C. English
    • 1
  • Lixia He
    • 1
  • James A. Larson
    • 1
  • James Calcagno
    • 2
  • Joshua S. Fu
    • 2
  • Brad Wilson
    • 1
  1. 1.Agricultural & Resource EconomicsUniversity of TennesseeKnoxvilleUSA
  2. 2.Civil and Environmental EngineeringUniversity of TennesseeKnoxvilleUSA

Personalised recommendations