Modelling the bioeconomy : Emerging approaches to address policy needs

Pyka, A.; Cardellini, G.; Meijl, H. van; Verkerk, P.J.


With its update of the Bioeconomy Strategy and the Green Deal, the European Commission committed itself to a transformation towards a sustainable and climate-neutral European Union. This process is characterised with an enormous complexity, which policymaking needs to acknowledge for designing transition pathways. Modelling can support policymaking in dealing with uncertainty and complexity. This article reviews emerging and new developments and approaches to model the development of the bioeconomy. We focused our review on how bioeconomy modelling addresses key enabling factors related to (i) climate change, (ii) biodiversity, (iii) circular use of biomass, (iv) consumer behaviour related to biomass and bioproducts use, and (v) innovation and technological change. We find that existing modelling frameworks offer large possibilities for extensions and considerations for analysing short-run impacts related to climate change and circularity, and to lesser degree for biodiversity, and we identify possibilities for developing further the existing bioeconomy models. However, addressing key processes related to societal and technological changes is more challenging with existing/conventional modelling approaches, as they specifically relate to how innovations transform economic structures and how consumers learn and change their preferences and what kind of dynamics are to be expected. We indicate how emerging modelling techniques such as Agent-Based Modelling could improve and complement existing bioeconomy modelling efforts by allowing for the consideration of structural change and, more generally, transformation of the economic metabolism. This modelling approach eclecticism asks for a better description of modelling targets, a sound reflection on the meaning of time horizons and a closer cooperation between the different research communities. Furthermore, it will benefit from the developments in big data and artificial intelligence from which we expect valuable guideposts for designing future modelling strategies.