Biofuel policy in the United States is transitioning away from corn towards second-generation biofuels in part because of the debate over environmental damages from indirect land use change. We combine a spatially explicit parcel level model for land use change in Washington State with simulations for biofuel policy aimed at utilizing forest residue as feedstock. Using a spatially explicit model provides greater precision in measuring net returns to forestland and development and indicates which areas will be most impacted by biofuel policy. The effect of policy is simulated via scenarios of increasing net returns to forestry and of siting feedstock-processing plants. Our results suggest that forestland will increase from such a policy, leading to a net reduction in atmospheric carbon from indirect land use change. This is in contrast to the experience of corn ethanol where the change in carbon emissions is potentially positive and large in magnitude. 1. Introduction Biofuels policy in the United States developed as a plan to move towards energy independence and reduce the environmental externalities associated with fossil fuels. The policy was largely successful at creating a thriving domestic ethanol industry as the combination of high fuel prices and generous subsidies led to significant amounts of corn devoted to ethanol production. The recent spike in food prices starting in 2007, and research on emissions from indirect land use change [1], spurred debate over the net benefits of ethanol policy. The combination of biofuel mandates with high fuel prices, along with economic growth in Asia and a decline of the USA dollar, is thought to have contributed to price increases for several staple food commodities [2]. However, there is no consensus on the extent that price increases are directly due to biofuel mandates. Using an aggregate calorie index, Roberts and Schlenker [3] estimate that the USA ethanol mandate increased food prices by 20–30%, while in a computable general equilibrium model (CGE) Timilsina et al. [4] find small effects of biofuel policy for all crops except for sugar. There is also debate regarding the emissions from land use change associated with biofuel growth. The widely cited paper by Searchinger et al. [1] suggests that impacts from land use change dominate the environmental effects of biofuel policy. Several studies [5–7] argue that estimates for land use change are sensitive to modeling assumptions, and accounting for crop yield growth substantially ameliorates cropland conversion. Public discourse over the potentially
References
[1]
T. Searchinger, R. Heimlich, R. A. Houghton et al., “Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change,” Science, vol. 319, no. 5867, pp. 1238–1240, 2008.
[2]
C. P. Timmer, ADB Economics Working Paper Series Causes of High Food Prices, Manilla, Philippines, 2008.
[3]
M. J. Roberts and W. Schlenker, “Identifying supply and demand elasticities of agricultural commodities: implications for the US Ethanol Mandate,” American Economic Review. In press.
[4]
G. R. Timilsina, J. C. Beghin, D. Van der Mensbrugghe, and S. Mevel, “The impacts of biofuels targets on land-use change and food supply: a global CGE assessment,” Agricultural Economics, vol. 43, no. 3, pp. 315–332, 2012.
[5]
J. Dumortier, D. J. Hayes, M. Carriquiry, et al., “Sensitivity of carbon emission estimates from indirect land-use change,” Applied Economic Perspectives and Policy, vol. 33, no. 3, pp. 428–448, 2011.
[6]
J. F. Fabiosa, J. C. Beghin, F. Dong, A. Elobeid, S. Tokgoz, and T. H. Yu, “Land allocation effects of the global ethanol surge: predictions from the international FAPRI model,” Land Economics, vol. 86, no. 4, pp. 687–706, 2010.
[7]
R. Keeney and T. W. Hertel, “The indirect land use impacts of United States biofuel policies: the importance of acreage, yield, and bilateral trade responses,” American Journal of Agricultural Economics, vol. 91, no. 4, pp. 895–909, 2009.
[8]
Energy Information Administration, Biofuels Issues and Trends, Washington, DC, USA, 2012.
[9]
A. Hallam, I. C. Anderson, and D. R. Buxton, “Comparative economic analysis of perennial, annual, and intercrops for biomass production,” Biomass and Bioenergy, vol. 21, no. 6, pp. 407–424, 2001.
[10]
M. Popp and R. Hogan Jr., “Assessment of two alternative switchgrass harvest and transport methods,” in Proceedings of the Farm Foundation Bioenergy Conference, St. Louis, Mo, USA, 2007.
[11]
W. E. Tyner, “The integration of energy and agricultural markets,” Agricultural Economics, vol. 41, supplement 1, pp. 193–201, 2010.
[12]
I. Hascic and J. Wu, “Land use and watershed health in the United States,” Land Economics, vol. 82, no. 2, pp. 214–239, 2006.
[13]
C. Langpap, I. Hascic, and J. J. Wu, “Protecting watershed ecosystems through targeted local land use policies,” American Journal of Agricultural Economics, vol. 90, no. 3, pp. 684–700, 2008.
[14]
E. G. Irwin and J. Geoghegan, “Theory, data, methods: developing spatially explicit economic models of land use change,” Agriculture, Ecosystems and Environment, vol. 85, no. 1–3, pp. 7–23, 2001.
[15]
R. N. Lubowski, A. J. Plantinga, and R. N. Stavins, “What drives land-use change in the United States? A national analysis of landowner decisions,” Land Economics, vol. 84, no. 4, pp. 529–550, 2008.
[16]
R. N. Lubowski, Determinants of Land-Use Transitins in the United States: Econometric Estimation of a Markov Model, Forestry and Agriculture Greenhouse Gas Modeling Forum, Washington, DC, USA, 2002.
[17]
J. Behan, K. McQuinn, and M. J. Roche, “Rural land use: traditional agriculture or forestry?” Land Economics, vol. 82, no. 1, pp. 112–123, 2006.
[18]
M. Polyakov and D. Zhang, “Property tax policy and land-use change,” Land Economics, vol. 84, no. 3, pp. 396–408, 2008.
[19]
D. L. McFadden, Conditional Logit Analysis of Quantitative Choice Models, Academic Press, New York, NY, USA, 1973, Edited by: P. Zarembka.
[20]
L. W. Rogers and A. G. Cooke, The 2007 Washington State Forestland Database, Seattle, Wash, USA, 2010.
[21]
M. Brady and E. Irwin, “Accounting for spatial effects in economic models of land use: recent developments and challenges ahead,” Environmental and Resource Economics, vol. 48, no. 3, pp. 487–509, 2011.
[22]
R. D. Robertson, G. C. Nelson, and A. De Pinto, “Investigating the predictive capabilities of discrete choice models in the presence of spatial effects,” Papers in Regional Science, vol. 88, no. 2, pp. 367–388, 2009.
[23]
X. Wang, K. M. Kockelman, and J. D. Lemp, “The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data,” Journal of Transport Geography, vol. 24, pp. 77–88, 2012.
[24]
Y. Wang, K. M. Kockelman, and X. Wang, “Anticipation of land use change through use of geographically weighted regression models for discrete response,” Transportation Research Record, vol. 2245, pp. 111–123, 2011.
[25]
J. Perez-Garcia, E. Oneil, T. Hansen et al., Washington Forest Biomass Supply Assessment, Olympia, Wash, USA, 2012.
[26]
J. Johansson, J.-E. Liss, T. Gullberg, and R. Bj？rheden, “Transport and handling of forest energy bundles-advantages and problems,” Biomass and Bioenergy, vol. 30, no. 4, pp. 334–341, 2006.
[27]
T. Tahvanainen and P. Anttila, “Supply chain cost analysis of long-distance transportation of energy wood in Finland,” Biomass and Bioenergy, vol. 35, no. 8, pp. 3360–3375, 2011.
