Santeramo, Fabio Gaetano and Searle, Stephanie (2018): Linking soy oil demand from the US Renewable Fuel Standard to palm oil expansion through an analysis on vegetable oil price elasticities. Forthcoming in: Energy Policy
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Abstract
The United States (US) Renewable Fuel Standard and California’s Low Carbon Fuel Standard support the use of soy biodiesel and renewable diesel in the transport fuel supply for climate mitigation. However, linkages between the markets for soy oil and palm oil, which is associated with very high land use change emissions, could negatively affect the climate performance of soy-based biofuels. This study estimates the own and cross-price elasticities for the supply of soy and palm oils in the US using country-level data from 1992 to 2016 under rational expectations, through a seemingly unrelated regressions system of equations. We find a positive cross-price elasticity of palm oil import with respect to soy oil price and a positive reaction of supply of soy oil to increase in prices of palm oil. These results suggest that US biofuel policies may underestimate substitution between soy and palm oils and thus overestimate the climate benefits from soy-based biofuel.
Item Type: | MPRA Paper |
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Original Title: | Linking soy oil demand from the US Renewable Fuel Standard to palm oil expansion through an analysis on vegetable oil price elasticities |
English Title: | Linking soy oil demand from the US Renewable Fuel Standard to palm oil expansion through an analysis on vegetable oil price elasticities |
Language: | English |
Keywords: | Biofuel; Price elasticity; Oils market; SURE |
Subjects: | O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O13 - Agriculture ; Natural Resources ; Energy ; Environment ; Other Primary Products P - Economic Systems > P2 - Socialist Systems and Transitional Economies > P28 - Natural Resources ; Energy ; Environment Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation > Q21 - Demand and Supply ; Prices Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q41 - Demand and Supply ; Prices Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q42 - Alternative Energy Sources |
Item ID: | 90248 |
Depositing User: | Prof. Fabio Gaetano Santeramo |
Date Deposited: | 29 Nov 2018 08:17 |
Last Modified: | 26 Sep 2019 09:08 |
References: | 1. Angrist J.D., Imbens, G.W., Rubin, D.B., 1996. Identification of causal effects using instrumental variables. Journal of the American Statistical Association. 91(434), 444-455. 2. ARB, 2010. Final Regulation Order. Subchapter 10. Climate Change. Article 4. Regulations to Achieve Greenhouse Gas Emission Reductions. Subarticle 7. Low Carbon Fuel Standard. 3. ARB, 2015. Staff Report: Appendix I: Detailed Analysis for Indirect Land Use Change. 4. Chen, X., Önal, H., 2016. Renewable energy policies and competition for biomass: Implications for land use, food prices, and processing industry. Energy Policy. 92, 270-278. 5. Cui, J., Martin, J. I., 2017. Impacts of US biodiesel mandates on world vegetable oil markets. Energy Economics. 65, 148-160. 6. Dahl, C., T.E. Duggan, T.E., 1996. US energy product supply elasticities: A survey and application to the US oil market. Resource and Energy Economics. 18(3), 243-263. 7. Goddard, E.W., Glance, S., 1989. Demand for fats and oils in Canada, United States and Japan. Canadian Journal of Agricultural Economics. 37(3), 421-443. 8. Gohin, A. and Chantret, F., 2010. The long-run impact of energy prices on world agricultural markets: The role of macro-economic linkages. Energy Policy, 38(1), 333-339. 9. Imbens G. 2014. Instrumental variables: An econometrician’s perspective (No. w19983). National Bureau of Economic Research. 10. Kojima, Y., J. Parcell, Cain, J., 2016. A Global Demand Analysis of Vegetable Oils for Food and Industrial Use: A Cross-Country Panel Data Analysis with Spatial Econometrics. In 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts (No. 235744), Agricultural and Applied Economics Association. 11. Labandeira, X., Labeaga, J. M., López-Otero, X., 2017. A meta-analysis on the price elasticity of energy demand. Energy Policy. 102, 549-568. 12. Labys, W.C., 1973. Dynamic commodity models: Specification, estimation and simulation. Lexington: Heath Lexington Books. 13. Labys, W.C., 1977. Multicommodity substitution patterns in the international fats and oils market. European Review of Agricultural Economics. 4(1), 75-84. 14. Malins, C., Searle, S., Baral, A., 2014. A Guide for the Perplexed to the Indirect Effects of Biofuel Production. ICCT report. 15. Miettinen, J., Hooijer, A., Tollenaar, D., Page, S., Malins, C., Vernimmen, R., Shi, C., & Liew, S. C., 2012. Historical analysis and projection of oil palm plantation expansion on peatland in Southeast Asia. Washington, DC: International Council on Clean Transportation. 16. Nelson B., Searle, S., 2016. Projected availability of fats, oils, and greases in the US. ICCT Working Paper. 17. Nerlove, M., 1972. Lags in economic behavior. Econometrica: Journal of Econometric Society. 221-251. 18. Nerlove, M., 1979. The dynamics of supply: retrospect and prospect. American Journal of Agricultural Economics. 61(5), 874-888. 19. Page, S. E., Morrison, R., Malins, C., Hooijer, A., Rieley, J. O., & Jauhainen, J., 2011. Review of peat surface greenhouse gas emissions from oil palm plantations in Southeast Asia. Washington, DC: International Council on Clean Transportation. 20. Petrenko, C., Paltseva, J., Searle, S., 2016. Ecological Impacts of Palm Oil Expansion in Indonesia. Washington, DC: International Council on Clean Transportation. 21. Purcell, L. C., Salmeron, M., & Ashlock, L., 2000. Chapter 19: Soybean facts [PDF]. Arkansas Soybean Production Handbook – MP197. Little Rock, AR: University of Arkansas Cooperative Extension Service. p. 1. 22. Roberts, M.J., Schlenker, W., 2013. Identifying supply and demand elasticities of agricultural commodities: implications for the US ethanol mandate. American Economic Review. 103, 2265-2295. 23. Santeramo, F.G. 2015. A cursory review of the identification strategies Agricultural and Food Economics. 3(1), 3-24. 24. Sorda, G., Banse, M., Kemfert, C., 2010. An overview of biofuel policies across the world. Energy policy. 38(11), 6977-6988. 25. US EIA, 2018. Monthly Biodiesel Production Report: With data for March 2018. 26. US EPA, 2010. 40 CFR Part 80 Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program; Final Rule. 27. US EPA, 2012. Notice of Data Availability Concerning Renewable Fuels Produced From Palm Oil Under the RFS Program. [EPA–HQ–OAR–2011–0542; FRL–9608–8]. 28. USDA ERS 2016. Database on Commodity and Food Elasticities. 29. USDA FAS, 2017a. EU-28 Biofuels annual: EU Biofuels Annual 2018. 30. USDA FAS, 2017b. Oilseeds: World markets and trade. 31. Valin, H., Peters, D., van den Berg, M., Frank, S., Havlik, P., Forsell, N., & Hamelinck, C. (2015). The land use change impact of biofuels consumed in the EU: Quantification of area and greenhouse gas impacts. Report commissioned by the European Commission. 32. Yen, S.T., Chern, W.S., 1992. Flexible demand systems with serially correlated errors: fat and oil consumption in the United States. American Journal of Agricultural Economics. 74(3), 689-697. 33. Zellner, A., 1962. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American statistical Association. 57(298), 348-368. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/90248 |