Design of a systems analysis tools framework for the EU bio-based economy strategy
One of the biggest challenges facing global society today is the provision of food, water and energy in light of a growing world population. To cope with this, the EU counts on the bioeconomy.
With ist 2012 strategy the EU aims to pave the way towards a bioeconomy. Being aware of possible side effects, the EU counts on the bioecomy´s potential to create economic growth and jobs in rual, coastal and industrial areas, reduce the dependance on fossil fuels and improve the sustainability of production and processing.
The aim of the SAT-BBE project is the development of toolbox for monitoring the EU´s bioeconomy strategy. SAT-BBE will show how existing data, indicators and quantitative models can be used and extended in order to describe the bioeconomy as well as its desired and undesired effects (e.g. on food security or biodiversity).
SAT-BBE will propose how gaps in the set of existing instruments can be closed.
We take stock of existing data, modells and indicators that can be used for monitoring and evaluation of the EU´s bioeconomy strategy.
To this end, we work together with experts from seven national and international research institutes in the field of agricultural, fisheries and forestry economics, environmental management und food policy.
The focus of the Thünen Institute is the stocktaking and assessment of models in the area of agricultural and fisheries economics.
We propose to monitor and evaluate the development of the bioeconomy against the three dimensions of sustainability (economic, ecologic and social dimension). The gaps identified in the available set of data, models and indicators can be grouped into three themes: Spatial, temporal or thematic.
With respect to spatial gaps, a major shortcoming ist that much data is available on highly aggregated scales, while the analysis requires data at lower aggregation level.
For temporal gaps it can be concluded that often time series data is needed in order to track the evolution of a certain parameter. However, time series data are often not available for low aggregation levels.
With respect to thematic gaps we find large gaps with respect to the social dimension of the bioeconomy.