Predicting priority of environmental protection over economic growth using macroeconomic and individual-level predictors: Evidence from machine learning. (August 2022)

• Record Type: Journal Article
• Authors: Lou, Xiaobin; Lin, Ying; Li, Liman Man Wai
• Is Part Of: Journal of environmental psychology. Volume 82(2022)
• Publication Date: 2022-08
• Subjects: Environmental concern -- Macroeconomics -- Random forest -- Machine learning -- Personal value -- Identity -- Priority of environmental protection; Environmental psychology -- Periodicals; Environment -- Periodicals; Psychology -- Periodicals; 155.905
• DOI: 10.1016/j.jenvp.2022.101843
• Languages: English
• ISSNs: 0272-4944

Abstract

Abstract: Individual priority of environmental protection over economic growth is predicted by macroeconomic contexts, demographics, and psychological processes. Corresponding supportive evidence exists for each category of predictors, but limited work has synthesized them. The present study attempted to build a parsimonious model to properly predict this priority by simultaneously considering various predictors. According to prior knowledge, we selected seven macroeconomic, six demographical, and 20 psychological predictors from the World Values Survey (wave 6, 51, 348 participants from 47 countries/regions). Using the machine learning approach, we first screened these predictors using a series of variable selection algorithms; then, we trained random forest models to predict the individual environment protection priority. Eight predictors were retained as the most parsimonious predictor set, and the corresponding model achieved an estimated Out-Of-Bag error rate of 34.15%. The retained predictors were Gross Domestic Product growth rate, Gross Domestic Product per capita, carbon dioxide productivity, biospheric value, post-materialistic value, sense of responsibility, self-expansion identity, and attitude toward science and technology, listed in descending order of importance. Partial dependence plots revealed that most of these predictors were non-linearly related to the outcome. These findings highlight the importance of considering macroeconomic and psychological predictors and their non-linear effects for better understanding the antecedents of environmental concern. Highlights: A series of machine learning strategies were used for predicting environmental attitude. The most parsimonious prediction model includes 8 predictors, selected from 33 ones. The 8 predictors include macroeconomic, psychological, but not demographical factors. Many predictors were non-linearly related to the outcome.