Causal heuristics for predicting co-benefits from decarbonization and sufficiency: presenting a multi-staged methodology based on Theories-of-Change and causal diagrams

Abstract Background Sufficiency is a critical but often overlooked decarbonization strategy. A significant challenge in promoting sufficiency is the difficulty in assessing its social co-benefits, such as improved health outcomes. This study presents a multistaged, iterative methodology designed to facilitate empirical investigations into the direct and indirect causal effects of sufficiency interventions. The primary purpose is to bridge the gap between an ex-ante impact hypothesis, potential impact models and evidence for plausible causal pathways. It helps to identify relevant variables and guides their statistical control. Results The methodology employs a four-step heuristic approach combining Inferences-to-the-Best-Explanation logic, Theory-of-Change models, Bayesian epistemology, and causal diagrams. Using the improvement of public transport systems as a case study, the methodology identifies two main pathways to health co-benefits: the reduction of pollutants leading to cleaner air and the increase in physical activity from walking or cycling to transit points. Bayesian credence assessments reveal that while the health benefits from reduced pollution and increased activity are highly credible, the initial “modal shift” from cars to public transport is not a trivial outcome. The results highlight that this shift is heavily moderated by the specific preferences and capabilities of car users. Causal diagrams further enable the distinction between direct and indirect effects, allowing researchers to identify which parameters must be controlled for in empirical studies to accurately predict health outcomes. Conclusions The proposed methodology provides a robust blueprint for establishing plausible impact relations in social impact assessments. It successfully closes the gap between qualitative case-oriented research and quantitative population-oriented evaluations. It follows a probabilistic epistemology and is thus open to different types of impact assessment methods, including both impact modelling and conventional statistics. By re-evaluation of pathways in light of evidence, the approach helps researchers avoid causal fallacies and biased expectations. Beyond transport, this iterative framework is adaptable to various sectors, including sustainable finance and scenario analysis, offering a versatile tool for designing more effective decarbonization policies and impact models.