The feasibility of climate action: Bridging the inside and the outside view through feasibility spaces

J. Jewell & A. Cherp. (2023). The feasibility of climate action: Bridging the inside and the outside view through feasibility spaces. WIREs Climate Change. DOI: https://doi.org/https://doi.org/10.1002/wcc.838

The feasibility of different options to reduce the risks of climate change has engaged scholars for decades. Yet there is no agreement on how to define and assess feasibility. We define feasible as “do-able under realistic assumptions.” A sound feasibility assessment is based on causal reasoning; enables comparison of feasibility across climate options, contexts, and implementation levels; and reflexively considers the agency of its audience. Global climate scenarios are a good starting point for assessing the feasibility of climate options since they represent causal pathways, quantify implementation levels, and consider policy choices. Yet, scenario developers face difficulties to represent all relevant causalities, assess the realism of assumptions, assign likelihood to potential outcomes, and evaluate the agency of their users, which calls for external feasibility assessments. Existing approaches to feasibility assessment mirror the “inside” and the “outside” view coined by Kahneman and co-authors. The inside view considers climate change as a unique challenge and seeks to identify barriers that should be overcome by political choice, commitment, and skill. The outside view assesses feasibility through examining historical analogies (reference cases) to the given climate option. Recent studies seek to bridge the inside and the outside views through “feasibility spaces,” by identifying reference cases for a climate option, measuring their outcomes and relevant characteristics, and mapping them together with the expected outcomes and characteristics of the climate option. Feasibility spaces are a promising method to prioritize climate options, realistically assess the achievability of climate goals, and construct scenarios with empirically-grounded assumptions.