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Cellulosic Biofuel Potential with Heterogeneous Biomass Suppliers: An Application to
Switchgrass-based Ethanol
John MiranowskiProfessor of Economics, Iowa State University
with
Alicia Rosburg, Assistant Professor, University of Northern Iowa
Keri Jacobs, Assistant Professor, Iowa State University
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
Research Objectives
1. Develop a long run cost model of cellulosic biofuel production with local biomass suppliers and biofuel processors.
2. Identify marginal costs and biorefinery scales and locations of meeting biofuel targets (RFS2).
3. Evaluate policy and biofuel costs of meeting RFS2 with location differences in biomass production and processing costs.
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
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
Minimum ATC of SG ethanol by CRD
ATC($/gallon ethanol)
3.19 - 3.25
3.26 - 3.50
3.51 - 3.75
3.76 - 4.00
4.01 - 4.60
31
5
10 96
2
7
84
Trends in cost minimizing decisions
As aggregate biofuel production expands, MC increases.
1. Processing plant capacity decreases
2. Biomass transportation distance and costs increase
3. Landowner participation rate decreases because
• Biomass yields decrease • Suitable land for SG production decreases• Land opportunity costs increase
Estimated ethanol supply curve from switchgrass
0 1 2 3 4 5 6 7 8 9 103
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
Billion gallons per year
$/ga
llon
etha
nol
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 $145 per barrel
Note: Wholesale prices
Production (bgy)
Gasoline price ($/gallon) 5.05 5.25 5.46 5.88
Oil price ($/barrel) 147 153 158 171
Gasoline price with tax credit ($/gallon) 3.54 3.74 3.95 3.47
Oil price with tax credit ($/barrel) 103 109 114 127
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 $5.25/gge.
Thank you!
Comments or questions?
Extra slides
Empirical approach
1. Establish least-cost SG biofuel supply for each CRD and market supply curve based on aggregation of CRD least cost biofuel supplies.
2. Determine aggregate MC, along with biorefinery scales and locations, to meet RFS2 production goals.
Spatial variation in cost-minimizing decisions
Heterogeneity between and within local biomass markets creates significant variation in the cost-minimizing decisions
(mgy) (miles)
($/dt)
($/gal)
% of Total Production
All biorefineries
Average 52 35 18.6 3.73
Range 9 – 117 22 – 51 4 – 58 3.19 – 4.57
Top 25% of biorefineries 86 31 12 3.38 41%
Bottom 25% of biorefineries 31 38 31 4.17 15%
Supply curve sensitivity
Switchgrass Yield Available biomass cropland
0 2 4 6 8 10 122.5
3
3.5
4
4.5
5
5.5
Billion gallons per year
$/ga
llon
Low switchgrass yield
Baseline switchgrass yieldHigh switchgrass yield
0 2 4 6 8 10 12 143
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
Billion gallons per year
$/g
allo
n
Low dA
BaselineHigh d
A
Supply curve sensitivity
Variable transportation cost Economies of scale
0 2 4 6 8 10 12 143
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
Billion gallons per year
$/g
allo
n
Low (t = 0.50)
Baseline (t = 0.70)High (t = 1.00)
0 2 4 6 8 10 12 143
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
Billion gallons per year
$/ga
llon
High economies (k = 0.60)
Baseline (k = 0.75)Low economies (k = 0.90)
Supply curve sensitivity
Alternative transportation models
0 2 4 6 8 10 123
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
Billion gallons per year
$/ga
llon
Baseline
Diminishing participationAverage hauling distance