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Research project (§ 26 & § 27)
Duration : 2018-12-01 - 2021-11-30

The coffee value chain is rather long: Typically, coffee is grown by smallholder farmers, traded through buyers to roasters and retailer until it reaches the consumers. Challenges along the chain include poverty among coffee producers, little flow of information between the chain actors as well as concentration of power among a little number of gate keepers, usually located in the Global North. Therefore, some actors co-established relational value chains aimed to increase communication and learning processes between value chain actors, based on direct contacts or internet platforms. Following Polanyi’s concept of social embeddedness, relational coffee value chains are trying to increase the social proximity of actors. The research aims to scrutinize strategies to create social proximity in geographically long coffee value chains and to understand their effects on challenges of the coffee market. The embeddedness concept should be operationalized by creating a scale to measure social proximity. The extent/quality of proximity for conventional and relational coffee value chains should be assessed. Three case studies of relational value chains and one control case of a conventional chain are selected for comparison. Using convention theory, the analysis of conventions present at each nod of the value chain should give insights into how economic actions are justified and if/how social proximity is related to the homogeneity of quality perceptions of the chain actors. The assumption is that in chains with a higher level of social proximity, actors share more homogenous quality perceptions which might improve organoleptic coffee quality and environmental/social production standards among producers. The case study will also look into possible challenges that could result from increased social proximity. This PhD study aims to contribute insights into the potential of socially proximate and relational coffee value chains to address challenges in the coffee sector.
Research project (§ 26 & § 27)
Duration : 2018-07-01 - 2019-09-30

This study employs models to analyse the effectiveness of selected measures in the Programme of Rural Development on farm income and climate in the Austrian agriculture. It includes analyses on omitting payments for less favoured areas as well as scenarios of alternative payment design. The results will be used in evaluating the current Programme of Rural Development as well as in designing the new programme.
Research project (§ 26 & § 27)
Duration : 2018-08-01 - 2023-07-31

Recent global integrated modelling studies indicate low intensities in trade of energy commodities between global regions in a future low-carbon global energy system. Also, research based on modelling indicates that deep greenhouse-gas emission cuts are possible in fully electrified renewable energy systems on a continental or country scale from a techno-economic perspective. However, these modelling efforts partly neglect drivers of globalization and may therefore wrongly project regionalization of energy systems. In particular, (i) new, easily tradable, low-cost renewable fuels (e.g. solar & electric fuels), (ii) global bio-physical variability of renewables (e.g. solar radiation and freshwater availability), and (iii) regional differences in social land-use restrictions associated with the expansion of energy infrastructure can cause an increase of trade flows in the energy sector. We aim at better understanding how the spatial configuration of renewables in low-carbon energy systems is affected by these drivers and develop a cutting-edge, open-source global renewable energy model that combines elements of energy system and land-use modelling. It takes into account bio-physical conditions for renewable fuel and electricity production, social land availability restrictions, and a map of existing energy infrastructure at unprecedented level of detail. Our approach integrates open data sources from public institutions, user-generated GIS data, and social networks. Existing models for Europe and Brazil are used for validation. Qualitative interviews in local case studies complement the global model and increase our understanding of land-use restrictions on the local scale. Our project has impacts beyond energy systems analysis: in particular the identification of winning and losing regions in a global renewable energy system is highly relevant in climate change mitigation negotiations, and the generated spatial indicators and maps enable many potential applications.

Supervised Theses and Dissertations