Study of Transportation Fuel Life Cycle Analysis: Review of Economic Models Used to Assess Land Use Effects

摘要

Life cycle assessments (LCA) have examined the energy inputs and greenhouse gas (GHG) emissions associated with transportation fuels since the 1980s. Estimates of the CO2 release from land use change (LUC) associated with biofuel crop production have only recently been incorporated into policy and the broader scientific literature. Agro-economic modeling systems were improved and configured to assess the effect of changes in the agricultural policies, e.g., biofuel production, on land use change and GHG emissions. Those models are used by regulators to assess the impacts of biofuel policies on global agriculture. The objective of this study is to provide an assessment of the key factors going into LUC analysis and how those factors affect the prediction of land use change. The four leading models used in biofuels policy were reviewed and key inputs for LUC were compared. The four models are: (1) Forest and Agricultural Sector Optimization Model (FASOM); (2) Food and Agricultural Research Institute (FAPRI) model; (3) Global Trade Analysis Project (GTAP); and (4) Modeling International Relationships in Applied General Equilbrium (MIRAGE BioFuel (BioF)) model The MIRAGE BioF model is a recent adaptation of the GTAP database used by the European Union. The identified drivers for the estimation of land use change are crop yield (price induced yield vs. yield projections), shifting of crop production, and demand mediating effects (e.g., the demand for beef decreases with an increase in beef prices). In addition, the cumulative effect of different biofuels and biofuel policies, petroleum prices (via additional biofuel demand), and global growth affect the overall demand for agricultural commodities. The largest uncertainties in LUC analysis are associated with the prediction of yield, especially on new and marginal land as well as the selection of land cover type. The shifting among agricultural commodities further complicates the analysis and adds a level of opacity to the modeling.