As governmental climate policy efforts have expanded, evaluating their effectiveness has become increasingly challenging due to numerous coexisting policies that complicate isolating individual impacts. This paper explores methodologies designed to explicitly model all climate “policy parameters.” By integrating Bayesian priors, we regularize the estimation model, incorporating additional information to ensure that only policies meeting a certain threshold of evidence are considered. At the same time, by employing propensity score matching we ensure causal identification in a context of scarce data with respect to the number of parameters to estimate, which constitutes a methodological challenge.

Applying our methodology to the analysis of 47 different climate policy measures in 40 countries over 32 years in four policy sectors (1,737 individual policies), we identify those policies being consistently effective under various contextual conditions and examine their emission reduction potential in greater detail. Our findings provide decision-makers with insights into the most likely effective policy measures for achieving emission reductions and equip scholars with an innovative tool for evaluating policies within the context of expanding policy portfolios.