Leveraging Machine Learning to Understand Environmental Tax Opposition in Different Regions and Periods
High environmental taxes face global opposition despite their broad recognition as essential for environmental protection. This study employs the random forest algorithm and the ISSP Environment surveys from 2020 (N=43,678) and 2010 (N=27,032) across 28 countries to identify factors associated with opposition to environmental tax increases. Simultaneously analyzing 41 individual-level factors and tax system characteristics,… Continue reading Leveraging Machine Learning to Understand Environmental Tax Opposition in Different Regions and Periods