Methodological foundations of policy-making in modelling transitions to sustainability at regional to global scale

Policy-makers currently face unprecedented challenges and uncertainty when taking decisions that simultaneously affect economic development, technology and the environment. It is not clear to policy-makers how to reconcile economic policy supporting growth with climate change mitigation, and it is not clear how effective policies are likely to be. This paper argues that informing policy-making using conventional equilibrium or optimisation modelling of technology and economics is not fine-grained enough to capture the complexities of real-world human behaviour and its diversity, leaving a wide uncertainty gap for policy-making. We suggest that the use of a dynamical methodology involving complexity science coupled to behavioural science with sophisticated uncertainty analysis can provide appropriate tools to better understand policy issues in sectors that involve a high degree of interaction (collective effects) or differentiation (heterogeneity/diversity) between agents. We argue that a better representation in models of these complex feedbacks between three critical interrelated areas could enable a paradigm shift for our understanding of how these aspects work together: technological change, the macroeconomy, and the natural environment. We identify four areas of environmental policy where the high degree of agent interactions and/or behavioural diversity makes their analysis impractical/inconsistent. These are (1) the effectiveness of policy for emissions reductions in consumer based sectors (2) the analysis of green growth, (3) cascading uncertainty across models and (4) cross-sectoral impacts of sector specific policies (e.g. biofuels). We suggest how a wider adoption of this approach could lead to a step change in our ability to address the complex policy problems raised by sustainability transitions.