PROGRAMA: DESARROLLO ECONÓMICO Y GENERACIÓN DE EMPLEO POR UN MEJOR FUTURO

In recent years, the number of reviews dealing with biofuels, LUC and GHG emissions has grown sharply. While the literature on these issues is vast, a critical assessment of key modeling issues to address the linkage among these three issues has not been performed. Therefore this chapter critically reviews key modeling choices to assess the impact of biofuel production on land-use changes and GHG emissions. The review builds on selected models that have been already used for this purpose.

Several authors provide comprehensive reviews of land-use modeling approaches (Briassoulis 2000; Lesschen et al. 2005; Stratus Consulting Inc 2005). Moreover, economic approaches to assess the impact of agricultural policies have also been extensively reviewed (Hallam 1987; van Tongeren et al. 2002; Gardebroek and Oude Lansink 2008; Tschirhart 2009). Consistent reviews of agricultural sector models applied to the assessment of land-use changes induced by energy crops demand have also been performed (van Tongeren et al. 2002; Gnansounou and Panichelli 2008; Witzke et al. 2008; CBES 2009; Demirbas 2009). Parker et al. (2002) and Mathews and Goldsztein (2009) give extensive reviews of agent-based models applied to land-use change modeling, but little work has been done to analyse the impact of biofuels production (Happe et al. 2004; Bao Le et al. 2008). GHG emissions balances of biofuel pathways are treated in the literature, stating the main methodological challenges and how LUC can be integrated into LCA (Baitz et al. 2000; Brentrup et al. 2004; Canals et al. 2007; Gnansounou et al. 2009). Finally, Larson (2006), Cherubini (2009) and Malça and Freire (2010) provide consistent reviews of GHG emission balances of biofuels.

Table 2-1 gives a general overview of the selected models. The models in Table 2-1 are used to explore how key modeling issues are being represented in current modeling approaches. The model class and focus refers to the specific models that have been reviewed.

Equilibrium models apply theory of general (or partial) equilibrium explaining the relation between supply, demand and prices through the satisfaction of a set of simultaneous equilibrium equations (Hertel and Tsigas 1997). While general equilibrium models represent the whole economy and the interactions between different sectors, partial equilibrium models gain in a detailed description of a specific sector (or sectors) and finds equilibrium prices for a specific market (or a limited set of markets). Selected general equilibrium models include the Global Trade Analysis Project (GTAP)2, the Agricultural Economics Research Institute Trade Analysis Project (LEITAP)3, the Emissions Predictions and Policy Analysis model (EPPA)4, the Dynamic Applied Regional Trade (DART)5 model, and the Future Agricultural Resources 2 _{https://www.gtap.agecon.purdue.edu/models/} 3 _{http://www.lei.wur.nl/UK/newsagenda/Dossiers/Biobased_economy.htm} 4 _{http://globalchange.mit.edu/igsm/eppa.html} 5 http://bsi.fsu.edu

MODELING CHOICES: REVIEW

Model (FARM) (Darwin 1998). Most partial equilibrium models dealing with biofuels and land-use change are agricultural models, namely FAPRI6, FASOM (Adams et al. 1996) (and their global and European versions, GLOBIOM7 and EUFASOM (Schneider et al. 2008) respectively), IMPACT8, AgLink (Conforti and Londero 2001), CAPRI9, ESIM (Banse et al. 2005), and energy sector models, such as POLE10 and PRIMES (NTUA 2008) among others.

Table 2-1. Overview of selected models.

Models Type Class Focus

GTAP, LEITAP, EPPA, DART, FARM

General equilibrium

Static or recursive dynamic, non-spatial, economic, aggregated actors, policy oriented

Global, supply-demand-trade, policy analysis

AgLink, ESIM, FAPRI, CAPRI, IMPACT, PEM, POLE, PRIMES

Partial equilibrium

Recursive dynamic, non- spatial, economic, aggregated actors, policy oriented

Global, supply-demand-trade, policy analysis for the agricultural and energy sectors

GLOBIOM, EUFASOM, FASOM, LUCEA, P&G, POLYSIS

Optimisation

Recursive dynamic, non- spatial, linear programming, aggregated actors, policy oriented

Profit/welfare maximisation, policy analysis

CLUE, LANDShift, KLUM Spatial

Spatial, cellular automata, remote sensing,

empirical-statistical, disaggregated actors

Land allocation, spatial patterns

EPIC, IMAGE Biophysical Spatial, Static

Calibration of bio-physical parameters, estimation of bio- physical variables

C&S, B&S, G4M Agent-based Spatial, dynamic, local, disaggregated actors

Individual heterogeneous actors decision

S&G, GLUE, TIMER, BDM, BSM

System dynamics

Non-spatial, dynamic, aggregated actors, policy oriented

Time delays, feedbacks,

policy analysis, biofuels diffusion

GREET, Ecoinvent, GHGenius Life Cycle Analysis Static, non-spatial, feedstock specific, national- regional

GHG emissions balance

Optimisation models aim to optimally allocate resources by maximising or minimising an objective function, generally an economic objective function of profit, utility or welfare. The Land Use Change Energy and Agriculture Model (LUCEA47), the Regional Environment and Agriculture Programming Model (REAP) and the Policy Analysis System (POLYSIS11) models are currently being applied to estimate the impact of bioenergy production on land-use change.

Spatially explicit models focus on the spatial allocation of land resources. Cellular automata (Batty et al. 1999), neural networks (Pijanowski et al. 2002), and remote sensing (Cardille and Foley 2003; de Barros Ferraz et al. 2005) are examples of land allocation techniques.

6 _{http://www.fapri.iastate.edu/models/}

7 _{http://www.iiasa.ac.at/Research/FOR/globiom.html} 8 _{http://www.ifpri.org/themes/impact/impactresearch.asp} 9 _{http://www.ec4macs.eu/home/capri-news.html}

Relevant models dealing with biofuels account for the Land-use Change and its Effects (CLUE), KLUM (Ronneberger 2006) and LandShift12 models.

Biophysical models aims to describe ecological and environmental processes. They assess for example, the impact of climate change on crop yields and land productivity. Relevant examples applied to biofuels account for the Environmental Policy Integrated Climate (EPIC) and the Integrated Model to Assess the Global Environment (IMAGE) models.

Agent-based models (ABM) focus on simulation of individual actors’ decisions. They account for local/regional actors’ behaviour, preferences and heterogeneity to simulate the emerging behaviour of the system. However, ABM applications to estimate impacts of biofuels production on land-use change are still scarce.

System dynamics models (SD) assess the time dependent behaviour of complex social systems. They focus on the identification of feedback structures to generate endogenous explanations of the system behaviour. Several system dynamics models are being used to simulate biofuel diffusion processes (Bantz and Deaton 2006; Bush et al. 2008; Malczynski et al. 2009).

Life cycle assessment models (LCA) evaluates the environmental impact of a product through the quantification of input and output flows. While attributional LCA (ALCA) assesses the average environmental properties of a particular product, consequential LCA (CLCA) assesses the consequence of a decision. At present CLCA is the adopted methodology to assess land-use impacts induced by biofuel production (Kløverpris et al. 2008a; Brander et al. 2009; Winrock 2009). GREET13, Ecoinvent14 and GHGenius15 are the main models being applied to develop attributional and consequential LCAs.

Key modeling issues to address biofuel production impact on GHG emissions from LUC include the following aspects:

§ Objective and scope of the model: The objective and scope of the modeling approach states clearly the intended purpose of the model and the dimension of the problem to which the modeling approach aims to address.

§ Level of representation of policies: The degree of detail on the representation of biofuel policies includes the modeling of policy objectives and instruments, which may include also the accompanying policies regulating the biofuel supply chain.

§ Actors’ representation and aggregation issues: Actors’ representation includes the definition of main actors to be considered and the level of aggregation.

§ Scale and system boundaries: Choice of spatial and temporal scale and system boundaries of the model determine which parameters and processes are included. § Spatial and time dynamics: Spatial patterns and the system evolution over time

include the spatial heterogeneity of land, the co-relation of land-use conversion pathways, the time horizon of the policy and delays in actors’ decisions.

12 _{http://www.usf.uni-kassel.de/} 13 http://www.transportation.anl.gov/software/GREET/ 14 _{http://www.ecoinvent.ch/} 15 _{http://www.ghgenius.ca/}

MODELING CHOICES: REVIEW

The next sections identify some limitations and improvements in current models to evaluate the impact of biofuel production on LUC and GHG emissions.