Predictors of climate change policy preferences: A machine learning approach

Abstract

Climate change poses a significant threat to planetary and civilizational health. Fossil fuel taxation policies are an effective climate change mitigation strategy. Yet, public opinion research indicates that support for these climate change mitigation measures vary greatly across nations. Here, using machine learning techniques on a set of nationally representative surveys of 22 European countries and Israel, we determine the relative influence of a large number (151 predictors) of individual-level attitudes, beliefs, perceptions, and behaviors from six groups (i.e., [1] media and social trust, [2] politics, [3] subjective well-being, social exclusion, religion, national and ethnic identity, [4] attitudes towards climate change, [5] energy security and energy preferences, [6] welfare attitudes, [7] human values) and socio-demographic factors, as well as national-level indices of social, economic, ecological, and environmental development. Across nations, feeling personally responsible for reducing climate change, being concerned about climate change, and willingness to reduce one’s own energy usage are the three most frequent and most robust individual predictors of climate change mitigation policy support (vs. opposition). However, other key factors associated with policy support, such as general political- and welfare attitudes, highlight the need to develop tailored climate communication strategies for individual groups and nations. Moreover, results show substantial within-country variation when comparing undecided, supportive, and opposed respondents’ key predictors, indicating different sets of underlying psychological motivations. Fossil fuel consumption per capita and economic, environmental, ecological development indicators predict between-country dissimilarities.