Explainable Machine Learning For Household Level Prediction Of Sustainable Consumption Behavior
Keywords:
Sustainable Consumption, Machine Learning, Explainable Artificial Intelligence, Lifestyle Data, Behavioral PredictionAbstract
Sustainable consumption behavior at the household level plays a critical role in addressing environmental challenges associated with increasing resource demand. Understanding the behavioral and lifestyle factors that influence such consumption can support more effective sustainability policies and targeted interventions. This study applies an explainable machine learning approach to predict sustainable household consumption behavior using lifestyle and resource use data. A dataset comprising 499 household respondents was analyzed, including demographic characteristics, environmental awareness, and monthly electricity and water consumption. Sustainable consumption behavior was formulated as a binary classification problem based on sustainability ratings. Logistic Regression and Random Forest models were developed and evaluated using standard classification metrics. The Logistic Regression model achieved an accuracy of 0.79, with balanced precision, recall, and F1-score values, while the Random Forest model demonstrated comparable performance and captured non-linear relationships among variables. Model explainability analysis revealed that environmental awareness, electricity consumption, and water consumption were the most influential predictors of sustainability classification. The findings demonstrate that explainable machine learning can achieve reliable predictive performance while maintaining interpretability, making it suitable for sustainability research, behavioral analysis, and evidence-based policy development.
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Copyright (c) 2026 Aliyu Ibrahim Ahmad, Muhammad Mansur Adam, Naja’atu B. Sanusi
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