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The research aims to identify and predict the factors influencing pro-environmental behavioral intention using machine learning techniques.

May 13, 2026Scientific ReportsOpen Access

Predicting pro-environmental behavioral intention using interpretable machine learning

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Randomized trial investigates determinants of pro-environmental behavioral intention, highlighting key predictors and their interactions.

The research aims to identify and predict the factors influencing pro-environmental behavioral intention using machine learning techniques.
Utilized survey data from the 2021 wave of the Chinese General Social Survey (CGSS).
Compared predictive performance of nine machine learning algorithms, identifying Random Forest as the most accurate.
Developed an interpretable model using Shapley additive explanations (SHAP) to assess 20 key predictors.
Environmental concern was identified as the most influential predictor of pro-environmental behavioral intention.
Other significant predictors included environmental attitude, connection to nature, environmental knowledge, government performance perception, and environmental responsibility.
Analysis revealed heterogeneity across different subsamples and interaction effects among key variables.

Li et al. (2026) studied this question.

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