Jay P. Kesan and Hsiao-shan Yang University of Illinois, Energy Biosciences Institute (EBI)

1. Mandatory Demand as a Policy Instrument: The Case of the Renewable Fuel Standard (RFS) Biofuel Program Jay P. Kesan and Hsiao-shan Yang University of Illinois, Energy Biosciences Institute (EBI)

2. Motivation • A mandatory demand regime is a policy instrument without a high budget obligation • Subsidies and tax credits are the most commonly used policy instruments, but they require either government funding or foregone government revenue • The RFS has three distinct policy goals: • Enhancing U.S. energy security; • Producing environmental benefits; and • Stimulating rural economic development

3. The RFS2’s Volumetric Mandates Billions of Gallons

4. Motivation • Focusing on the policy goal of enhancing U.S. energy security, the RFS is used to promote biofuels over traditional fossil fuels • To evaluate if the RFS (i.e., a mandatory demand regime) is an effective policy instrument to promote an infant industry (i.e., the biofuels industry), we need to examine: • Whether the RFS incentivizes biofuel plant growth? • Whether plant production efficiency increases with plant size?

5. Methods & Main Goals Methods: • Fixed effect OLS model • To what extent does the RFS affect plant growth? • Survival analysis • Efficiency is a necessary condition for plant survival • Using plant survival as a proxy for efficiency, to what extent is plant size related to production efficiency? The main goals of this study are to: • Discuss the potential economic impact of a mandatory demand regime as a policy instrument • Evaluate the effectiveness of the RFS

6. Economic Impacts of Large-scale Mandatory Demand • The mandatory demand regime is only effective in product markets that are in the early stage of their product life-cycle: • Mandated demand produces a higher product price in the short-run • This higher price should result in a greater optimal capital input and R&D investment • Increasing demand should also induce more market entry • Higher competition should force firms to increase their production efficiency

7. Data Description • The data in our study consists of 225 bioethanol production facilities • 204 plants producing corn-based bioethanol • 21 plants producing bioethanol with either potato or beverage waste as a feedstock • Each plant is listed in the Renewable Fuel Association’s (RFA) annual Ethanol Industry Outlook in the year 2000 or later • The period of our study is from 2000 to 2011

8. Data Description • Variables:

9. Number of Operating Plants and Firms • The figure shows that the growth in number of plants is greater than the growth in number of firms • This implies that firms expanded through new plant construction

10. Growing Plant-level Capacity • The figure shows the growth in average plant-level capacity

11. Production Capacity Expansion by Existing and New Plants (mgy) • Before 2009, the bioethanol industry’s expansion has mainly been achieved through new plant construction • After 2009, the industry has grown through existing plant expansion

12. Empirical Plant Capacity Distributions • The three distributions are all right-skewed • The plant capacity distribution has shifted toward higher capacity from 2002-2010

13. Empirical Plant Capacity Descriptive Statistics • The Plant Capacity from 2001-2011 are all right-skewed and leptokurtic distributions

14. Fixed-effect OLS Regression Using the Plant Capacity as the Dependent Variable • The Hausman Statistics shows that the fixed effect model dominates • The RFS has a significant positive effect on plant capacity

15. Insights from Estimation Results The ethanol price affects plant capacity positively The positive correlation between the number of plants in a state and plant capacity implies that increasing the number of plants raises the competition among plants and then only large capacity plants are efficient enough to stay in the market The statistical result is consistent with the fact that bioethanol plants were hit by a nation-wide recession in 2008

16. Hazard Function by Plant Size • Hazard functions differ with plant size • Small and medium size plants face a greater risk of plant cessation or closure

17. Estimated Hazard Ratios • Estimated hazard ratios for small and medium size plants are greater than the estimated ratio for large plants.

18. Insights from Estimation Results • Plant size, ethanol demand growth, and corn price affect the risk of plant cessation or closure • Small and medium size plants face a greater hazard ratio than large size plants, given other things being equal • Ethanol demand growth increases hazard • Increasing demand raises the competition among firms and then only efficient firms survive • Plant hazard ratio is positively affected by corn price, which represents the bioethanol input price

19. Relationship between the RFS and Plant Survival • Plant Survival is significantly affected by the RFS • Plant hazard rate is lower with the RFS

20. Ratio of Fuel-Ethanol Rack Price to Unleaded Gasoline Rack Price (per gallon; F.O.B. Omaha, NE) • The relative volumetric price of ethanol to gasoline falls over time and dips below 1 in 2008 and 2010, which shows the increasing competitiveness of bioethanol • Source: Nebraska Ethanol Board

21. Summary of Empirical Results • Findings: • The ethanol plant-level data supports plant-level economies of scale; large size (greater than 90 mgy) seems optimal when plant survival is used as proxy for efficiency • The RFS significantly affects ethanol plant growth • The falling relative volumetric price of ethanol to gasoline shows the increasing competitiveness of bioethanol • Evaluation of the RFS: • A mandatory demand regime as a policy instrument is effective in the early stage of a product’s life cycle • Plant-level economies of scale are observed in the first-generation bioethanol industry

22. Implications for Biofuel Regulations • We need to be very cautious when applying our analysis to the emerging advanced and cellulosic biofuel industries • The effectiveness of the policy instrument might differ depending on which stage of the industry life-cycle the industry is in • For example, traditional bioethanol and cellulosic biofuels were in different stages of their industry-life cycle at the time the RFS was implemented (i.e., traditional bioethanol had already reached commercial scale) • Additional policy instruments might be justified to help advanced and cellulosic biofuels reach commercial scale so that the RFS might then begin to produce the effects we see with the first-generation ethanol industry

23. Thank you for your attention.Questions?

24. Backup Slides

25. Fuel-Ethanol and MTBE Consumption • Ethanol replaced MTBE rapidly as a gasoline additive between 2002 and 2007

26. Chow Breakpoint Test • U.S. Ethanol consumption from 1980-2010 • AR(1) series of ethanol consumption • The F-statistics suggest the existence of structural change in 2006 with a 99% confidence level