Longitudinal Predictors of Perceived Climate Change Importance and Worry among Italian Youths: A Machine Learning Approach

The current study aimed to investigate the longitudinal predictors of perceived importance climate change and personal worry among Italian youths. Specifically, we used machine learning techniques examine predictive a wide range socio-demographic factors, political perceptions, attitudes on national European level (identity, attitudes, tolerance, support for democracy, authoritarianism, nationalism, trust), efficacy beliefs, social well-being, interest, different forms participation worry. In this study, collected data using questionnaire in two waves at one-year interval—in 2016 2017. Participants were 1288 young adults (61.3% female; 38.7% male) whose mean age was 19.18 (SD = 3.29) ranging between 15 30 years. Breiman’s random forest algorithm performed better than Friedman’s gradient boosting machines algorithm. revealed that age, tolerance toward migrants, refugees most important Other national/European identity, internal efficacy, self-efficacy, anti-democratic EU warmth, online civic participation.