PROGRAMA: TURISMO COMO EJE DE DESARROLLO POR UN MEJOR FUTURO

2.2.1.

Techno-economic impacts of biofuels production

Economic impacts of biofuels production are typically covered by general/partial equilibrium, optimisation and system dynamics models. Modeling techniques, however, vary between these approaches making them suitable for different purposes. In the context of biofuels, equilibrium models have mainly applied to assess micro-economic consequences of biofuels production. They assessed the impact of biofuel mandates on feedstock international prices given an exogenous shock in feedstock demand. SD models on the other hand addressed mainly the diffusion process of biofuels. They abstract from equilibrium conditions and study the necessary conditions needed to achieve the required biofuel mandate level. Optimisation models assessed social welfare implications of biofuel production.

Computable general equilibrium models are top-down models that link general equilibrium theory with realistic data of a given economy in order to find the supply, demand and price levels that support equilibrium across interconnected markets of an opened economy (Wing, 2004). Goods production is typically represented by nested constant elasticity of substitution (CES) functions including primary production factors and intermediate inputs. Through the introduction of market distortions they study the impact of a change in price on production, consumption and trade patterns. These models have been mainly used to analyse climate change and agricultural reform policies. The GTAP model, for instance was designed to analyse trade interaction in the global economy (Hertel and Tsigas 1997).

These models however were developed for other purposes. Consequently, significant adaptations have been required to include the biofuel sector. In the case of the reviewed general equilibrium models, the introduction of biofuels is mainly performed by linking the energy and agricultural sectors. The GTAP-E model (Burniaux and Truong 2002), for example, is an extended version of the GTAP model that has an explicit representation of the energy sector. GTAP-E has the same structure as GTAP, but its production structure includes a more detailed description of substitution possibilities among different sources of energy. Taheripour et al. (2008) have developed the GTAP-BIO database that explicitly includes biofuels as a sector. Biodiesel for instance, is linked to the vegetable oil and fats sector. Birur et al. (2008) further disaggregated the biofuel sector in the GTAP-E model. In the EPPA model, on the other hand, biomass energy has been introduced as a perfect substitute of fossil fuels in the refined oil sector (Reilly and Paltsev 2008). While this is an acceptable alternative to address current public debate on the global impact of biofuels production, a more reasonable approach would consist in developing models specifically designed to account for specificities in the biofuel sector.

To this end, system dynamic models focus on representing specific biofuel supply chains and the real diffusion process of biofuel technologies. In a study by Bantz and Deaton (2006), for instance, the SD model aims to understand the evolution of the biodiesel industry based on four modules representing the diesel, biodiesel, glycerol and biomass oil sectors. The model

governmental regulations and incentives driving supply and demand of each product in the biodiesel supply chain. Malczynski et al. (2009), on the other hand, developed the Biofuel Deployment Model (BDM), a dynamic supply chain model applied to the cellulosic ethanol industry. The model aims to understand how certain variables affect the cost and volume of ethanol production. Additionally, Bush et al. (2008) also focus on the cellulosic ethanol industry. They developed the Biomass Scenario Model (BSM) to simulate the evolution of the cellulosic ethanol supply chain industry in the US. The model accounts for competition in the oil market, vehicle demand for biofuels and government regulations over time. Specific government policies and external economic factors are evaluated to assess their impact on investment decisions in the cellulosic ethanol industry. However, the reviewed SD models rely on simple structures that mimic the overall behaviour of the system. While this can be an advantage to avoid models integration it can be a simplistic approach to quantitative analysed complex interactions such as the linkage among biofuels, LUC and GHG emissions.

Optimisation models are useful for efficiency analysis purposes. In these models the objective function to be maximised generally relies on a welfare measure. The REAP model, for example, is a mathematical programming model of US agriculture that maximises the net social benefit (Johansson et al. 2007). This welfare approach is useful to assess the economic externalities generated by biofuel production. Optimisation models can also be applied to assess environmental externalities, for example, in a study by Panichelli and Gnansounou (2008) a constrained non-linear optimisation model is used to perform efficiency analysis of biofuel production strategies with respect to the carbon pay-back time16. The model calculates GHG emissions from direct and indirect LUC based on assumptions about feedstock production and potential displacements of other activities. Similarly to equilibrium models, the main limitation here is the assumed optimality conditions. Complex dynamic systems normally do not behave in an optimal way. This is due for example, to the presence of non- linear behaviour, incomplete information and the bounded rationality of economic agents.

2.2.2.

Land-use change impacts of biofuel production

Impacts on land-use change are typically addressed through spatially explicit models. Spatial models allocate land based on historical land-use transitions or based on agro-ecological and infrastructure factors. Land allocation is mainly estimated through regression models of location variables (Verburg et al. 1999; Aguiar et al. 2007) or through Markov models of transition probabilities. Transition probabilities can be estimated mainly through satellite images classifications (Leeuwen et al. 2006; Vega et al. 2009) or statistical analysis (de Koning et al. 1998; Braimoh and Onishi 2007). The CLUE (Land-use Change and its Effects) model, for instance, is a geo-referenced model for the analysis of LUC (Veldkamp and Fresco 1996; Veldkamp and Lambin 2001). CLUE mainly uses regression analysis to estimate land- use transitions based on a set of variables that are assumed to guide land-manager decisions on land allocation. On the other hand, LandShift17, for example, is a spatially explicit land-use change model based on the integration of socio-economic and biophysical components of land-use systems. LandShift was used to estimate the impact of biofuels production on land allocation at a country level (Lapola et al. 2010; Schaldach et al. 2011).

16 _{Number of years that a biofuel should be used to offset emissions from land-use change on feedstock}

cultivation.

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The main limitation of spatially explicit models is that as they focus on location patterns, their representation of economic drivers of LUC is limited. This motivated the link of spatial explicit models with economic approaches. KLUM (Ronneberger 2006), for instance, is a land-use model that was linked to a modified version of the GTAP model to account for macro-economic variables driving land allocation decisions. The models are linked by replacing the land allocation mechanism of GTAP-EFL with KLUM. Land allocation depends on the profit maximisation decision of the land-owner in response to GTAP equilibrium prices and biophysical characteristics of land that define the crop yield. Crop yields are exogenously introduced by linking the KLUM model to the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model. Moreover, the CLUE18 model has also been linked to the GTAP model to account for macro-economic drivers of LUC (Hellmann and Verburg 2010, 2011).

In economic models, the traditional approach to allocate land among competing land-uses is based on the constant elasticity of transformation (CET) function (Powell and Gruen 1968). Darwin et al. (1996) proposed an approach relying on CET functions to represent substitution among crop sectors. Most land-use change models, such as FARM (Darwin 1998) and KLUM (Ronneberger et al. 2005) rely on this approach. The CET function postulates that land owners maximise total land profits by allocating their land among different uses, subjected to the availability of land and the possibility of transformation among them. The land supply elasticity varies as a function of the constant elasticity of transformation and the relative importance of a given activity, measured as land value (Hertel et al. 2008b). The GTAP-PEM model also follows this approach, based on the estimation of elasticities of substitution for OECD countries (OECD 2003). Additionally, Golub et al. (2008) also implemented this framework but they distinguish land substitution between different zones within each country using data on the agro-ecological characteristics of land to more precisely represent the potential reallocation of land. A problem with the CET function however is that as it allocates land based on land value, it is difficult to track land-use changes in physical units (Nassar et al. 2011). Gurgel et al. (2007) addressed this problem through a modified version of the CET function assuming that 1 hectare of land of one type is converted to 1 hectare of another type, and through conversion it takes on the productivity level of the new land-use.

From an economic perspective, the problem with the representation of managed land supply is that provided that native lands are generally not under economic use, it becomes difficult to estimate its economic value. Consequently, it becomes difficult to estimate the possibility of land transformation based on the conventional CET function. Probably for this reason most economic models have assumed land as a fixed input and allocate land only among economic uses (Hertel et al. 2008a). However, for the purpose of assessing land-use change impacts of biofuels production, this approach is not sufficient. Some improvements have been introduced in some economic models to assess the impact of the demand for agricultural commodities for example, the LINKAGE model incorporates some possible land expansion based on the variation of an aggregated land price (van der Mensbrugghe 2005). A study by van Meijl et al. (2006) moreover, suggested the use of biophysical data to calibrate land supply functions based on marginal productivity information. The advantage of this approach is that asymptotic limits to land expansion and decreasing returns to scale can be modeled explicitly (Tabeau et al. 2009).

The second issue to be considered in representing managed land expansion is the definition of which unmanaged land-uses are displaced by the expansion of the agricultural frontier (Nassar et al. 2011). This is especially important because the share of each unmanaged land-use on

cropland expansion largely determines the impact of biodiesel production on land-use change and GHG emissions (Edwards et al. 2010a). The conventional approach to estimate these shares is to assume that agricultural land expansion will follow the same patterns as historical land-use change trends. Studies by Al-Riffai et al. (2010) and Searchinger et al. (2008) respectively, applied this approach by assuming historical shares of native ecosystems to allocate agricultural production displaced by biofuels production. Alternatively, land supply functions can be specified by estimating land transformation elasticities for managed lands expansion into unmanaged lands. The transformation elasticity can be estimated for instance, using land-use transition probabilities (Ahmed et al. 2008). A conventional approach for this estimation is to use time series satellite images (López et al. 2001; Kamusoko et al. 2009). However this approach would require detailed geo-referenced data and additional computational efforts.

The impact of biofuels production on land-use change has also been studied through system dynamics simulation models. However, their treatment is significantly simplified. Yamamoto (1999; 2000; 2001), for instance, have applied SD to develop a global land-use and energy model (GLUE). The model evaluates the biomass resources potential for bioenergy production including land-use competition among various uses of biomass resources. Additionally, several SD models are being used to simulate the biofuel supply chain in the US including land in the feedstock-production phase and GHG emissions from indirect land-use changes (Monson 2008; Stamboulis and Papachristos 2008; Malczynski et al. 2009; West et al. 2009). A study by Sheehan (2009b), has focused on estimating global land-use changes induced by cellulosic bioethanol production in the US. The model however does not account for economics in the biofuel supply chain and focuses mainly on estimating the GHG emission balance of US ethanol including direct and indirect land-use change at the global level. At the regional level, the impact of bioenergy production on GHG emissions has been addressed in work by Szarka et al. (2008). Their model allows simulating the quantitative effects of regional biomass alternatives for energetic purpose in the Austrian-Hungarian cross- border area. Agricultural policies impact on land-use and food security at the regional level are also addressed in research undertaken by Saeed et al. (2000).

2.2.3.

GHG emission impact of biofuel production

The most appropriate and widely-applied methodology to determine the GHG balance of a biofuel pathway is the Life Cycle Assessment (LCA). This tool evaluates the environmental impact of a product through the quantification of input and output flows. The conventional approach is to use the so-called attributional LCA (ALCA). However this is a static approach and in consequence dynamic processes are not considered. Static modeling does not account for price variations, changes in demand or technological improvements. Consequently, several authors have applied the so called “Consequential LCA” (CLCA) to assess land-use impacts induced by biofuel policies (Kløverpris et al. 2008a; Brander et al. 2009; Winrock 2009). The CLCA evaluates the changes produced in a system as a consequence of a decision.

Three main models and inventory databases are being applied to develop attributional and consequential LCAs, namely, GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation)19, ecoinvent® and GHGenius20. The GREET model examines a large set of U.S. transportation fuels, including biofuels and vehicle systems. Ecoinvent® is mainly

19 _{http://www.transportation.anl.gov/software/GREET/} 20 _{http://www.ghgenius.ca/}

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a database of life cycle inventories (Frischknecht et al. 2005) mostly used in the EU. The database is composed of life cycle inventories of productions and processes and a database of life cycle impact assessment methods. It has been widely applied to perform LCA of biofuel pathways (Jungbluth et al. 2007). GHGenius is an LCA model for the transportation sector, maintained by Natural Resources Canada. It is a spreadsheet model that calculates the well-to- wheel GHG emissions of transportation fuels and technologies.

To date, these LCA tools have considered direct LUC and associated GHG emissions but required the integration with other modeling approaches or the expansion of existing models, for instance, in a study by Searchinger et al. (2008), the FAPRI international model21 is used to allocate displaced corn production for other purposes and soybean displaced from rotation in the same land. Converted land is assigned based on the proportion of lands that have been transformed into cropland in the past. This data is used as input for the GREET model to calculate the GHG balance of US corn-based bioethanol. On the other hand, the US EPA (EPA 2010b) developed an integrated approach linking several models. The impact of US biofuel mandates on global land-use demand is estimated by linking the GTAP-FAPRI models. Land-use changes at the country level are estimated through the FASOM optimisation model. Finally, GHG emissions from biofuels production and use are estimated based on the Winrock and GREET models. These integrated approaches have been mainly used to assess indirect LUC GHG emissions from biofuels productions. However, they do not account for the effect of GHG emissions restriction of biofuels production and exports.

Endogenous estimation of LUC GHG emissions have also been improved in economic models (Lee et al. 2005). While this approach is pertinent for macro-analysis of biofuel policies, their application to smaller scales of analysis will required a more detailed representation of the system components.