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Related Experiment Videos

Predicting pro-environmental behavioral intention using interpretable machine learning.

Jing Li1,2, Meng Liu3,4, Meng Zhang5

  • 1School of Management, Tianjin University of Commerce, Tianjin, China. janelwz0909@163.com.

Scientific Reports
|May 11, 2026
PubMed
Summary

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This summary is machine-generated.

Machine learning identified key drivers of pro-environmental behavioral intention (PEBI). Environmental concern, attitude, and connection to nature significantly influence PEBI, offering insights for sustainability efforts.

Area of Science:

  • Environmental Science
  • Computer Science
  • Social Science

Background:

  • Understanding pro-environmental behavioral intention (PEBI) is crucial for addressing environmental sustainability challenges.
  • Existing research often relies on traditional statistical methods, potentially limiting the depth of analysis.
  • The application of advanced machine learning in this domain remains relatively underexplored.

Purpose of the Study:

  • To identify and rank the key determinants of pro-environmental behavioral intention (PEBI) using machine learning.
  • To compare the predictive accuracy of various machine learning algorithms for PEBI.
  • To provide an interpretable model for understanding the factors influencing PEBI.

Main Methods:

  • Utilized survey data from the 2021 Chinese General Social Survey (CGSS).
Keywords:
CGSSDeterminant modelMachine learningPro-environmental behavioral intention

Related Experiment Videos

  • Employed and compared nine machine learning algorithms, including Random Forest, for predictive modeling.
  • Applied Shapley additive explanations (SHAP) analysis for model interpretability and feature importance.

    • Main Results:

    • The Random Forest model demonstrated the highest predictive accuracy for PEBI.
    • Environmental concern emerged as the most significant positive predictor of PEBI.
    • Other key predictors included environmental attitude, connection to nature, environmental knowledge, government performance perception, and environmental responsibility.

    Conclusions:

    • Machine learning, particularly Random Forest with SHAP analysis, offers a powerful approach to understanding PEBI determinants.
    • Environmental concern is the primary driver of PEBI, underscoring the importance of fostering ecological awareness.
    • The findings provide valuable insights for designing targeted interventions to promote sustainable behaviors.