html5-img
1 / 17

John Miranowski Professor of Economics, Iowa State University with

Cellulosic Biofuel Potential with Heterogeneous Biomass Suppliers: An Application to Switchgrass-based Ethanol. John Miranowski Professor of Economics, Iowa State University with Alicia Rosburg , Assistant Professor, University of Northern Iowa

nigel-berry
Télécharger la présentation

John Miranowski Professor of Economics, Iowa State University with

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Cellulosic Biofuel Potential with Heterogeneous Biomass Suppliers: An Application to Switchgrass-based Ethanol John Miranowski Professor of Economics, Iowa State University with Alicia Rosburg, Assistant Professor, University of Northern Iowa Keri Jacobs, Assistant Professor, Iowa State University

  2. Motivation • Biofuel expansion • U.S. RFS2 – 16 billion gallons of cellulosic biofuel by 2022 • Economics of cellulosic biofuel differs from conventional fuel and first-generation biofuel • Non-commoditized feedstock • Location-specific economic trade-offs

  3. Research Objectives • Develop a long run cost model of cellulosic biofuel production with local biomass suppliers and biofuel processors. • Identify marginal costs and biorefinery scales and locations of meeting biofuel targets (RFS2). • Evaluate policy and biofuel costs of meeting RFS2 with location differences in biomass production and processing costs.

  4. Features of conceptual model • Consider only long run costs prior to capital investment • Account for economic tradeoff • Economies in processing • Diseconomies in feedstock procurement (e.g., transportation) • Biomass supplies differ within and between local markets which dictate economies of biofuel processing • Breakeven aggregate production is driven by the long run price of crude oil or gasoline

  5. Application to switchgrass • Biorefinery conversion • Biochemical conversion of biomass to ethanol – Kazi et al. (2010) • Conversion scale factor • Assume processing plant runs at annual capacity • Biomass production • Potential land available for SG – CRD land use data (USDA) • SG production costs and yields – Khanna et al. (2011) • Storage and transportation cost assumptions – Rosburg & Miranowski (2011) • Marginal opportunity cost of biomass cropland – CRP offers

  6. Minimum ATC of SG ethanol by CRD

  7. Trends in cost minimizing decisions As aggregate biofuel production expands, MC increases. • Processing plant capacity decreases • Biomass transportation distance and costs increase • Landowner participation rate decreases because • Biomass yields decrease • Suitable land for SG production decreases • Land opportunity costs increase

  8. Estimated ethanol supply curve from switchgrass

  9. Market conditions to support biofuel production from SG • 2016 RFS2 cellulosic biofuel mandate of 4.25 bgy • EIA 2012 oil price forecasts for 2022 and 2035: $129 and $145 per barrel Note: Wholesale prices

  10. Conclusions • Local production environments play an important role in aggregate cost of cellulosic biofuel production. • Biofuel production costs vary significantly across locations. • Given SG land use assumptions, the cost of satisfying 2016 cellulosic biofuel mandate (4.25 bgy) is $5.25/gge.

  11. Thank you!Comments or questions?

  12. Extra slides

  13. Empirical approach • Establish least-cost SG biofuel supply for each CRD and market supply curve based on aggregation of CRD least cost biofuel supplies. • Determine aggregate MC, along with biorefinery scales and locations, to meet RFS2 production goals.

  14. Spatial variation in cost-minimizing decisions Heterogeneity between and within local biomass markets creates significant variation in the cost-minimizing decisions

  15. Supply curve sensitivity Switchgrass YieldAvailable biomass cropland

  16. Supply curve sensitivity Variable transportation costEconomies of scale

  17. Supply curve sensitivity Alternative transportation models

More Related