The STRAP framework is grouped into five themes (Figure 1): (a) Sustainability knowledge; (b) Perception and preferences; (c) Sustainability trade-offs; (d) Development pathways; and (e) Integrated assessment. The core of the framework is a sustainability concept which defines the determinants of economic stability (i.e. energy security, technology diffusion, market organisation), social equity (i.e. food security, welfare contribution, social exclusion), and ecological balance (i.e. feedstock options, resource capacity, land management). These determinants not only represent the complementary and/or competitive views on the use of first and second generation bioenergy crops for food and fuel production, but also capture the inherent potential contradictions and controversies in achieving a balance between the three sustainability dimensions (Acosta-Michlik et al. 2011). The framework summarises what are the data requirements for, the technical applications in, and knowledge generation from the STRAP approach, and how they link to one another in assessing trade-offs and pathways in bioenergy production.

STRAP framework

Figure 1 Thematic and methodical framework of the hybrid approach STRAP

(a) Sustainability knowledge

So far only limited research has been conducted to make comprehensive and in-depth inquiries on the local knowledge of sustainable bioenergy production (e.g. (Elghali et al. 2007, Buchholz, Volk, and Luzadis 2007). STRAP projects will contribute to building sustainability knowledge by identifying and combining different sources and types of information that are available to conduct an integrated assessment of bioenergy potentials in different regions. The information will include field data to be collected from local survey and interviews, time-series data to be collected from national and local statistical offices, and spatial data to be collected from administrative and research institutions. Field data is important because sustainability of bioenergy production depends largely on the perception and preferences of not only policy-makers but also local producers and consumers of bioenergy. In case of the latter, expectations on the demand and price of bioenergy are some of the important factors affecting preferences. Statistical data provides relevant historical evidence on the capacity and ability of countries to attain sustainability targets and objectives that are relevant to the development of the bioenergy sector. Maps based on geographical information system (GIS) provide useful information on the potential locations for bioenergy production particularly for countries where productive areas are limited due not only to poor ecological and physical conditions, but also to urban and industrial growths.

(b) Perception and preferences

Opinions on the sustainability of bioenergy are at odds because the institutional structure of its production is complex. Bioenergy production involves different products, different sectors and a range of actors interacting at and across different levels (Clancy 2008). Thus it not only provides opportunities to generate multiple benefits apart from energy generation, but also causes conflict with many interests due to these inter-linkages (Faaij 2006). Sustainable production should result in an equal distribution of not only economic, social and ecological benefits but also costs among the different sectors and actors participating in the bioenergy production system. Understanding society’s perception on these benefits and costs is essential for developing a stable bioenergy market. Policy should thus aim to collectively promote both modern technology (i.e. technical know-how) and improved awareness (i.e. social know-how) on bioenergy. Using field data the project will aim to understand public perception that influences policy preferences for a sustainable bioenergy production (Figure 1). Through application of conjoint and cluster analyses on collected field data (section 2.3.2b), preference weights on the sustainability determinants that is classified according to different population typologies will be estimated to generate not only new knowledge on policy preferences but also valuable input into the assessment of sustainability trade-offs.

(c) Sustainability trade-offs

To achieve development goals for a sustainable bioenergy production, policy is often confronted with the challenging task of setting priorities between a range of competing issues like social interests (e.g. food vs. fuel security), economic efficiency (e.g. local vs. commercial producers, domestic vs. imported products), technological outcomes (e.g. economic vs. environmental wellbeing), etc.. The necessity of trading-off between different policy options makes it difficult, or nearly impossible, to achieve a balance between economic, social, and environmental sustainability (e.g. (Gibson 2006, Spangenberg 2002). The potentials of bioenergy production will depend on public support, which in turn depends on the perception about its contributions to and impacts on economic stability, social equity, and ecological balance. But at the same time, a stable economic and an equitable social environment as well as productive resources are important to realise these potentials. Using statistical and spatial data the project will aim to analyse the trade-offs that affect the production potentials of bioenergy. Trade-off parameters will be estimated to assess the relative importance of, on the one hand, the sustainability determinants of economic stability, social equity, and ecological balance in developing the bioenergy sector and, on the other hand, the different sources of biomass (i.e. first or second generation) in increasing bioenergy production (Figure 1). The trade-off analysis of sustainability determinants and production activities will generate useful knowledge in the form of sustainability and production indices for bioenergy.

  • Sustainability determinant indices – These trade-off parameters will be based on the aggregate values of the different social, economic and ecological indicators. The values will range from 0 to 1, where 1 represent indicators that are most preferred to support the development of bioenergy production. The trade-off parameters for the sustainability determinants will be generated from statistical data and conjoint-based preference weights using fuzzy logic analysis.
  • Land-use conversion indices – These trade-off parameters from production activities are indices with values of 0 and 1 representing specific land use conversion pattern such as arable-to-forest or rice-to-maize, where particular focus will be given to conversion from food to bioenergy crops. These parameters will be generated through GIS-based overlay analysis of high-resolution historical land use and biophysical maps.

(d) Development pathways

A pathway is generally defined as a course or set of actions to achieve a goal or result. In the context of bioenergy, the course or set of actions are represented by the trade-off decisions on sustainability determinants and the goal or result is achieving bioenergy potentials. The higher the probabilities of land use conversion into bioenergy production, the higher are the bioenergy potentials. The development pathways for bioenergy measures the extent to which the potentials for bioenergy production can be realised in a particular country based on the trade-off decisions on social, economic and ecological goals of the society. In the STRAP approach, bioenergy pathways are diagrams showing the interconnections and interdependencies of the relevant (i.e. statistically significant) determinants of sustainability and their effects on the probabilities of converting land use into bioenergy. The diagrams are different from those in existing bioenergy literature describing the technical pathways for various bioenergy feedstocks (e.g. (Faaij 2006, Girard and Fallot 2006) because they are based on the logical analysis of the trade-offs among economic, social and ecological determinants of sustainability. In the proposed project bioenergy pathways are thus a logical, not a technical analysis. The bioenergy potentials will be estimated using logit analysis and the pathways to achieving these potentials will be assessed using path analysis.

(e) Integrated assessment

The key to the successful implementation of the STRAP approach is the stepwise integration of different types of information through application of distinct but mutually supporting analytical tools to generate new knowledge on the sustainability of bioenergy production. First, the integration of conjoint-generated preference weights, which informs about social perception on bioenergy, will improve the policy relevance of trade-offs parameters. Second, the integration of fuzzy-generated trade-offs parameters, which informs about policy preferences on sustainability, in logit analysis will reveal the most relevant social, economic and ecological determinants (e.g. or variables X1, …, Xn) that influence the probabilities of shifting land use into bioenergy crops (e.g. (P(Y)). And third, the integration of logit estimates into path analysis will generate additional knowledge (i.e. coefficients and arrows) on the sustainability of bioenergy production. The coefficients (e.g. β1, …, βn) measure the magnitude of influence of the determinants to the probabilities of land use conversion. The arrows (e.g. →, ↔, ←) indicate the direction of relationships between the determinants, provide valuable indications of sustainable pathways for bioenergy production, and inform on the possible conflicts among the different sustainability determinants.


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