Economic Models of Biofuels and Policy Analysis

1. Economic Models of Biofuels and Policy Analysis John Miranowski,* Professor of Economics Iowa State University *With Alicia Rosburg, Research Assistant Jittinan Aukayanagul, Post-Doctoral Associate

2. Economic Models of Biofuels and Policy Analysis • Economic challenges of cellulosic feedstock supply, logistics, and modeling efforts • Importance of corn yield growth and fertilizer use to land use change • Handling of risk and uncertainty in biofuel investment decisions

3. Cellulosic Ethanol Industry Policy Challenges • How do we establish biomass feedstock supply chain for commercial, cellulosic biofuel plant? • Logistics of biomass supply • Transportation and storage of low-stability, low-density material • Direct delivery vs. intermediate consolidator in feedstock supply chain • How do we develop technology and platforms for cellulosic biofuel production? • Conversion of multiple feedstocks • Yield of biomass feedstock

4. Market Viability • What intervention to create sustainable market? • Maximum price processor is willing to pay must equal minimum price biomass supplier is willing to accept for market to exist • Considering commercial scale plant and MC of supplying last unit to biofuel plant • If WTA>WTP => government incentive (tax or carbon credit, or mandate to make WTA=WTP

5. Supplier WTA for Biomass Baseline (2009; No policy incentives)

6. Gap between Supplier WTA and Processor WTP Baseline (2009, No policy incentives)

7. Gap between Supplier WTA and Processor WTP Blender’s Credit (2009, $75 oil)

8. Carbon Credit Needed for Feedstock Market Baseline (2009, No policy incentives)

9. Land Use Change Controversy • Modeling indirect land use change controversial • Pecuniary externality captured in market • Do not target ILUC for other energy forms • Attempting to measure the unmeasureable • Economic models focus on existing cropland, conversion, and expansion • Yield growth • Crop and input substitution • Changing cropping practices • Cropland expansion

10. Corn Yields, Fertilizer, and Land Use Dynamics • Study of differing rates of corn yield growth, nitrogen use, and cropland needs • Yield increases reduce total cropland needs but impact on land use depends on yield increase, fertilizer increase, and prices

13. State Trends – AR Model

15. Implications of Nitrogen Use • Nitrogen use model • N rates based on historical data using trend model • N use per acre has been increasing slowly, if at all • Will corn yield increases require greater N use rates than forecast below? • Important in GHG LCA for corn ethanol in RFS.2

17. Summary • AR model yield projections are higher than LT • State level yield increase rates in important corn states may meet 2030 industry yield targets and production • Nitrogen use per bushel of corn will likely continue to decline as will nitrous oxide emissions • Implications for cellulosic feedstock land available and GHG emissions

18. Risk and Uncertainty in the Industry • Capacity build-out rate for cellulosic ethanol plants needs to be twice capacity build-out rate in corn ethanol industry at peak to meet RFS.2 mandates for 2022 • Cellulosic ethanol plants are a risky investment • Feedstock supply • Input, output, and co-product prices • Yield and technology or platform • Market and policy (RFS.2) uncertainty • Opportunities to lay-off risk are lacking due uncertainty

19. Corn Ethanol Study • Corn ethanol plant investment has higher thresholds (>NPV) to trigger investment due to risk and uncertainty • Economies of scale in ethanol plants make it optimal to invest in larger plants until transportation diseconomies offset scale economies (similar to oil refineries) • Prior to 2005 and higher oil prices, smaller scale plants were the industry norm due to capital subsidies for smaller plants. These plants “leveled the playing field” for smaller plants and local investors, and served as model for dry mill expansion

20. Jump-Starting Cellulosic Industry • Capital as opposed to output subsidies for early cellulosic ethanol plant investment? • Magnitude of shock required to spur firms to invest in cellulosic industry may be large, especially given size and uncertainty surrounding RFS.2 • Number of firms in industry relative to magnitude of shock will determine number of new cellulosic plants that will enter or exit

21. Thank you! • Any questions, comments, discussion?

22. Carbon Credit Needed for Feedstock Market by Oil Price Baseline (2009, No policy incentives)

23. Simple LR Breakeven Model: • Processor Derived Demand for Biomass: • Supplier Marginal Cost of Producing Biomass: WTA = {(CES+COpp)/YB+CHM+CNR+CS+DFC+DVC*D} • Market Viability if: WTP ≥ WTA

24. Current Biofuel Policy Incentives • Renewable Fuels Standard (RFS.2) • 36 billion gallons by 2022 • Maximum of 15 billion gallons of corn-based ethanol counts • 21 billion gallons of advanced biofuels by 2022 • Low Carbon Fuel Standard (LCFS) • Minimum GHG reduction standards (relative to 2005) • Food, Conservation, and Energy Act of 2008 (FCEA) • $1.01 per gallon tax credit for cellulosic ethanol production • 2008 Farm Bill - Biomass Crop Assistance Program (BCAP) • Up to $45 per ton of biomass for collection, harvest, storage and transportation (CHST payments) – 2 year limit • Payments up to 75% of establishment costs for perennial crops • If current policy incentives are continued, cellulosic biofuels will play a key role in renewable energy • Corn stover, woody biomass, perennial grasses

25. Simulation for Carbon Price Needed to Sustain a Switchgrass Market by RegionProducer’s Credit Only($75/barrel oil, 23 MPG, 2009)

26. U.S. Yield – AR Model • Annual yield increase rate from data: • 1970-2009: 1.70% • 1999-2009: 1.92% • Annual yield increase rates needed to achieve proposed yield targets by 2030: • 260 by 2030: 2.18% • 280 by 2030: 2.54% • 300 by 2030: 2.88%

27. U.S. Yield – Linear Trend • Annual yield increments from data (bu/year): • 1970-2009: 1.92 • 1999-2009: 2.80 • Annual yield increments needed to achieve proposed yield targets by 2030: • 260 by 2030: 4.51 • 280 by 2030: 5.47 • 300 by 2030: 6.42