2.4.1.
Modeling actors’ heterogeneity
Most models have an aggregated representation of actors independently of the model scale. This is a problem if the model needs to account for actors’ heterogeneity, for example, to evaluate government support to the infant industry or small agricultural producers. Indeed, equilibrium models are typically aggregated models. General equilibrium models represent the whole economy through aggregated economic actors at the national level. The GTAP model, for example, represents the global economy as a multi-region economy. Each regional economy is represented by a representative household that maximises utility and a set of producers of specific goods and services that maximise profit. Each good is assumed to be produced by a single representative firm in each region. While this aggregation level is necessary to handle global models, actors’ homogeneity makes models less suitable to account for key country- and context- specific differences. The Brazilian Biodiesel Program, for instance, explicitly supports small agricultural producers (Ministry of Agriculture 2006). The Argentinean biodiesel policy (SyCDNA 2006) explicitly supports small and medium biodiesel producers. Accounting for these policy constraints may significantly influence biofuel production patterns such as the location of feedstock production.
Actors´ heterogeneity is better represented in agent based models. As ABMs focus on simulation of actors’ decisions, they overcome some limitations of equilibrium models mainly by including different actors’ types and individual decision making processes, for example, in a study by Rossetti et al. (2009) the market diffusion of second generation biofuels is based on forecasting investor attractiveness for second generation biofuels technologies. Products are differentiated based on multiple attributes such as price, quality, or environmental performance. Consumers are treated as independent entities with heterogeneous preferences and behaviour capable of both learning and behavioural change. On the other hand, the Stanford-Carnegie Biofuels Project uses agent-based modeling to assess the effects of sugarcane-based ethanol production on land-use distribution in northern Brazil. The model simulate farmer response to increased demand for sugar cane and the displacement effects on competing land cover classes (Fernandez 2008). The level of detail required to account for actors’ heterogeneity however, makes these models more suitable for regional or local applications.
Actors’ expectations and bounded rationality are typically well represented in SD models. System dynamics allows modeling of the actors’ decision processes in a more realistic way by accounting for delays and incomplete information (Smith and van Ackere 2002). Current applications to biofuel diffusion processes however, have relied on relatively simple models with an aggregated representation of actors. In Stamboulis and Papachristos (2008), for instance, the simple SD diffusion model is divided in 3 sectors: biomass production, biofuel production and biofuel use. Biomass production is centred on the provision of land. Biofuels production is represented by the investment on production capacity. Biofuel use is based on the availability of retailing sites and the consumers’ demand. Each process is represented by
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an aggregated actor (i.e. the farmer, the biofuel producer, the consumer) that determines the dynamics of the transition to biofuels.
2.4.2.
Aggregation of sectors and regions
Most global models also aggregate regions and economic sectors. In the case of general equilibrium models, as they focus on the whole economy, their representation of sectors and regions is highly aggregated. In the EPPA model, for example, due to its focus on climate change policies, regions were aggregated in Annex B and Non-Annex B countries of the Kyoto Protocol and sectors were aggregated as energy, non-energy and advanced energy technologies. The current disaggregation of countries and sectors constrains the model’s ability to assess land-use changes in specific countries. On the other hand, the GTAP model is a multi-regional model of 16 regions and 21 sectors. This GTAP 6 database accounts for 87 countries/regions and 57 products/sectors. However, the level of sectors aggregation do not allow to model for example, decisions on biofuel feedstock type, especially for the case of biodiesel where oilseeds and oleaginous fruits are treated as a single aggregate as well as vegetable oils.
This type of aggregation that is suitable for assessing global impacts is less suitable to assess specific biofuel pathways. This occurs because it is impossible to track a specific biofuel supply chain. Current general equilibrium models only account for sugarcane-beet ethanol, coarse-grains ethanol (GTAP, LEITAP), wheat ethanol (LEITAP), and average lingo- cellulosic ethanol (EPPA) production pathways. Feedstock selection by a biofuel producer within the country is not possible. Moreover, regional specificities are not accounted for, constraining the ability of global models to assess specific biofuel supply chains. On the other hand, biofuels eligibility for the EU-RED, for example, specifies GES for specific biofuel production pathways. Therefore complementary models may be developed focusing on specific biofuel supply chains.
With respect to land-use change, aggregation also results in some limitations. Land aggregation is not suitable to study the eligibility of regions as feedstock production areas. In the EU-RED, for example, command and control instruments are used to regulate feedstock location. This criterion implies for example, that feedstock cannot be cultivated in forest land with more than 30% canopy cover. Accounting for this criterion will imply disaggregating forest land in different types. Some work is being done to overcome this limitation by adapting existing land-use databases (Carré et al. 2010; Hiederer et al. 2010). However, their integration into models and policy analysis is still missing.
Finally, in some cases the biofuel policy aims to promote cultivation in certain land-use types. In the EU-RED for example, a credit is given for energy crops cultivation in set aside land. Consequently the land-aggregation level should specify a set-aside land-use category. These constraints and incentives can significantly change land-use allocation patterns. Unfortunately, this is not considered in current models as land-use aggregation does not allow performance of such an analysis.