Climate policy decision making in contexts of deep uncertainty - from optimisation to robustness

Abstract Integrated assessment models are often used to evaluate the role of different technologies in meeting global climate goals. Such models have been criticised for failing to address the deep uncertainties and plurality of values that are fundamental to energy transitions. One consequence is that model scenarios overwhelmingly depend on large-scale carbon dioxide removal to hold warming to below 2 °C. Here we propose an alternative approach using Scenario-Focused Decision Analysis (SFDA) as methods that embrace decision making under deep uncertainty. SFDA can accommodate a range of value sets and perspectives, and most importantly can integrate value-based decision-making in designing climate policy. We specifically consider Robust Decision Making (RDM) as an exemplar of SFDA for developing climate policy. We outline an iterative five-stage framework for RDM using the role of carbon dioxide removal in long-term mitigation pathways as an example. The five steps comprise (i) participatory definition of goals, values, potential policy options and uncertainties; (ii) modelling the performance of policy portfolios across a wide range of future scenarios; (iii) visualisation and identification of portfolio vulnerabilities; (iv) analysis of trade-offs; and (v) development of policy strategies. SFDA, and specifically RDM, provide untapped opportunities for diverse actors to explore alternative mitigation pathways and evaluate the robustness of climate policy choices through “deliberation with analysis”. In relation to carbon dioxide removal methods, RDM provides a framework for evaluating their potential for safely meeting climate goals in a societally acceptable manner.