An empirical agent-based model for Regional Twin Transition Pathways: Basic architecture and potential policy applications

The shift towards a climate-neutral economy in Europe hinges on green innovation and clean technology. Recognizing the potential of digital knowledge in driving the green transition, the notion of ‘twin transition’ has gained increasing attraction in current policy debates. This study develops an empirical agent-based model (ABM) that aims to unravel potential pathways for the twin transition of European regions, and to explore the role of various (transformative) innovation (RTI) policy mixes for supporting the twin transition (e.g. increased collaboration incentives or directionality). To grasp transition pathways conceptually, the framework of new regional path development is employed. A fine-grained typology guides the exploration of four different forms of new path development (importation, upgrading, related diversification, unrelated diversification) that recognizes regional development as influenced by a variety of factors (i.e., local resources, institutional frameworks, and the interactions between different actors). The empirical ABM simulates knowledge creation in green and digital technologies across 292 European regions with more than 70.000 empirically calibrated researching agents. Initial results point to the basic functioning of the model as underlined by intensive empirical calibration and validation. Upcoming results to be explored in context of different policy scenarios will provide new impulses for transformative innovation policies that aim to foster twin transition and to strengthen regional innovation capacities in green and digital technologies across European regions.