Abstract
Bioenergy is expected to play an important role in future energy supply. However, increased implementation of large scale bioenergy production could have significant adverse effects. Strong improvement in spatially explicit potential and impact analyses are required to allow for effective certification, sound planning of sustainable investments in the future and
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good governance of land use and the agricultural sector. This thesis aims to examine how potentials, costs, and environmental impacts of bioenergy production can be assessed, taking into account the avoidance of indirect land use change and the spatiotemporal variability of the biophysical and socio-economic context. As biomass yields, production costs, logistics, and environmental impacts are strongly related to location specific biophysical conditions (e.g. agro-ecological suitability, availability of infrastructure, soil properties, climate conditions etc); spatially explicit assessment of land availability for bioenergy crops is an important precondition for the design bioenergy of supply chains and logistics and for the assessment of bioenergy production potentials and environmental and socio-economic impacts. In this study a new land use change model (called PLUC) is designed to assess the development in land availability for bioenergy crops on a detailed spatial level (1 km2), taking into account the dynamics and uncertainties of key drivers of land use change. The major advantage of this model framework is its ability to run spatio-temporal Monte Carlo analyses based on stochastic input parameters in order to evaluate uncertainty propagation spatially explicitly. In addition, methods are developed to assess environmental impacts of the implementation of bioenergy in a spatially explicit way. The impacts included are green house gas (GHG) emissions during the lifecycle and related to land use change, soil quality, water use and water quality and biodiversity. The developed spatiotemporal GHG module in conjunction with the PLUC model allows for spatially explicit and dynamic modelling of GHG emissions of the entire agricultural sector that result from changes in land use and management related to the implementation of bioenergy crop production. The economic viability of bioenergy production depends on the competitive advantage of bioenergy crop production compared to other land uses and the competitiveness of bioenergy production compared to the reference energy system. The costs of biomass production and conversion change over time due to technological learning. Integrating the projections on technological learning in the cost calculations, interlinked with the spatiotemporal land use modelling, allows for the spatial and temporal explicit evaluation of the economic performance of bioenergy supply chains. The methods and models developed provide an approach to identify ex-ante the areas where implementation of bioenergy production is or could become economically viable and the areas with little negative or even positive environmental impacts. The integration of these models and methods enables the identification of go and no-go areas for bioenergy production from an economic and environmental point of view and when competition for land is to be avoided
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