Indirect land use changes of biofuel production - a review of modelling efforts and policy developments in the European Union

Abstract

The issue of indirect land use changes (ILUC) caused by the promotion of transport biofuels has attracted considerable attention in recent years. In this paper, we reviewed the current literature on modelling work to estimate emissions of greenhouse gases (GHG) caused by ILUC of biofuels. We also reviewed the development of ILUC policies in the EU. Our review of past modelling work revealed that most studies employ economic equilibrium modelling and focus on ethanol fuels, especially with maize as feedstock. It also revealed major variation in the results from the models, especially for biodiesel fuels. However, there has been some convergence of results over time, particularly for ethanol from maize, wheat and sugar cane. Our review of EU policy developments showed that the introduction of fuel-specific ILUC factors has been officially suggested by policymakers to deal with the ILUC of biofuels. The values proposed as ILUC factors in the policymaking process in the case of ethanol fuels are generally in line with the results of the latest modelling exercises, in particular for first-generation ethanol fuels from maize and sugar cane, while those for biodiesel fuels are somewhat higher. If the proposed values were introduced into EU policy, no (first-generation) biodiesel fuel would be able to comply with the EU GHG saving requirements. We identified a conflict between the demand from EU policymakers for exact, highly specific values and the capacity of the current models to supply results with that level of precision. We concluded that alternative policy approaches to ILUC factors should be further explored.

Figure 1

Review of modelled greenhouse gas (GHG) emissions due to indirect land use change (ILUC) of ethanol biofuels. All values were recalculated to a 20-year allocation base and are expressed as g CO₂-eq per MJ ethanol. Some studies show results as intervals (illustrated with lines), others as specific values (illustrated with dots). The studies were divided into two categories; economic modelling (E) and miscellaneous (other) modelling (M). FAPRI-CARD, Food Agricultural Policy Research Institute and Center for Agricultural and Rural Development; GTAP, Global Trade Analysis Project; MIRAGE, Modeling International Relationships in Applied General Equilibrium; IFPRI, International Food Policy Research Institute; LEITAP, the abbreviation indicates the extension of the GTAP model developed at the LEI (Landbouw Economisch Instituut) in The Hague; AGLINK, Worldwide Agribusiness Linkage Program.

Figure 2

Review of modelled greenhouse gas (GHG) emissions due to indirect land use change (ILUC) of biodiesel. All values were recalculated to a 20-year allocation base and are expressed as g CO₂-eq per MJ biodiesel. Some studies show results as intervals (illustrated with lines), others as specific values (illustrated with dots). The studies were divided into two categories; economic modelling (E) and miscellaneous (other) modelling (M). FAPRI-CARD, Food Agricultural Policy Research Institute and Center for Agricultural and Rural Development; GTAP, Global Trade Analysis Project; MIRAGE, Modeling International Relationships in Applied General Equilibrium; AGLINK, Worldwide Agribusiness Linkage Program. Note the different scale of the x-axis compared with Figure 1.

Figure 3

Estimates of total GHG emissions for ethanol fuels. Black bars show scientific estimates and include direct emissions as default values established in the Renewable Energy Directive (RED) and Fuel Quality Directive (FQD), and indirect land use change (ILUC) factors from the modelling exercises presented in Figure 1. Dashed black lines show policy estimates and include direct emissions as default values established in the RED and FQD and indirect emissions as estimated in [24].

Figure 4

Estimates of total GHG emissions for biodiesel fuels. Black bars show scientific estimates and include direct emissions as default values established in the Renewable Energy Directive (RED) and Fuel Quality Directive (FQD), and indirect land use change (ILUC) factors from the modelling exercises presented in Figure 2. Dashed black lines show policy estimates and include direct emissions as default values established in the RED and FQD and indirect emissions as estimated in [24].