Institutions make a difference: assessing the predictors of climate policy stringency using machine learning

Abstract Despite the urgent need for ambitious national climate policies to reduce carbon emissions, their implementation lacks stringency. This lack of policy stringency is driven by a complex combination of a country’s numerous politico-economic, institutional and socio-economic characteristics. While extant studies aim at estimating causal effects between a selection of such characteristics and policy stringency, we examine the importance of a comprehensive set of predictors that underlie such empirical models. For this purpose, we employ machine-learning methods on a data set covering 22 potential predictors of policy stringency for 95 countries. Conditional random forests suggest that the most important predictors of policy stringency are of institutional nature: freedom (of press, media, associations, and elections), governmental effectiveness, and control of corruption. Further, accumulated local effects plots suggest that the relationship between some predictors, e.g. freedom or education, and policy stringency is highly non-linear.