Impact of agricultural-based biofuel production on greenhouse gas emissions from land-use change: Key modelling choices Luis Panichelli n,1 , Edgard Gnansounou Bioenergy and Energy Planning Research Group (GR-GN - INTER - ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 18, CH-1015 Lausanne, Switzerland article info Article history: Received 23 March 2014 Received in revised form 25 August 2014 Accepted 12 October 2014 Keywords: Biofuel Land-use change Greenhouse gas emission Modelling choice Policy abstract Recent regulations on biofuels require reporting of greenhouse gas (GHG) emission reductions related to feedstock-specific biofuels. However, the inclusion of GHG emissions from land-use change (LUC) into law and policy remains a subject of active discussion, with LUC–GHG emissions an issue of intense research. This article identifies key modelling choices for assessing the impact of biofuel production on LUC–GHG emissions. The identification of these modelling choices derives from evaluation and critical comparison of models from commonly accepted biofuels–LUC–GHG modelling approaches. The selection and comparison of models were intended to cover factors related to production of agricultural-based biofuel, provision of land for feedstock, and GHG emissions from land-use conversion. However, some fundamental modelling issues are common to all stages of assessment and require resolution, including choice of scale and spatial coverage, approach to accounting for time, and level of aggregation. It is argued here that significant improvements have been made to address LUC–GHG emissions from biofuels. Several models have been created, adapted, coupled, and integrated, but room for improvement remains in representing LUC–GHG emissions from specific biofuel production pathways, as follows: more detailed and integrated modelling of biofuel supply chains; more complete modelling of policy frameworks, accounting for forest dynamics and other drivers of LUC; more heterogeneous modelling of spatial patterns of LUC and associated GHG emissions; and clearer procedures for accounting for the time-dependency of variables. It is concluded that coupling the results of different models is a convenient strategy for addressing effects with different time and space scales. In contrast, model integration requires unified scales and time approaches to provide generalised representations of the system. Guidelines for estimating and reporting LUC–GHG emissions are required to help modellers to define the most suitable approaches and policy makers to better understand the complex impacts of agricultural-based biofuel production. & 2014 Elsevier Ltd. All rights reserved. Contents 1. Introduction ........................................................................................................ 345 2. Methodological approach ............................................................................................. 345 2.1. Selection of modelling approaches ................................................................................ 345 2.2. Overview of selected modelling choices ............................................................................ 346 2.3. Overview of modelling approaches................................................................................ 347 3. Modelling biofuel supply.............................................................................................. 348 3.1. Accounting for market dynamics and interactions ................................................................... 348 Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/rser Renewable and Sustainable Energy Reviews http://dx.doi.org/10.1016/j.rser.2014.10.026 1364-0321/& 2014 Elsevier Ltd. All rights reserved. Table of abbreviations: AB, agent-based; AEZ, agro ecological zone; CET, constant elasticity of transformation; dLUC, direct land-use change; EPA, Environmental Protection Agency; EPPA, Emissions Prediction and Policy Analysis; EU, European Union; GE, general equilibrium; GES, greenhouse gas emission saving; GHG, Greenhouse gas; GTAP, global trade analysis project; iLUC, indirect land-use change; KLUM, Kleines Land-Use Model; LUC, land-use change; MAGNET, modular applied general equilibrium tool; PE, partial equilibrium; POLYSIS, Policy Analysis System; RED, Renewable Energy Directive; RFS, Renewable Fuel Standard; SD, system dynamics; USA, United States of America n Corresponding author. Tel.: þ337 45 97 13 65. E-mail address: lpanichelli@gmail.com (L. Panichelli). 1 Present address: Rr. Pjeter Bogdani 13, Prishtina 10000, Kosovo. Renewable and Sustainable Energy Reviews 42 (2015) 344–360