Integrating AI in Energy Efficiency, Natural Hazards and Ecological Resilience: A Python Case Study

Abstract

The integration of Artificial Intelligence (AI) into sustainability science offers significant potential for improving prediction accuracy, resource allocation, and decision-making across environmental domains. This study develops a unified, Python-based AI methodology and applies it to three representative challenges: energy consumption forecasting, wildfire occurrence prediction, and ecological resilience assessment. For each case, publicly available datasets were preprocessed through outlier removal, missing-value imputation, and feature normalization. Appropriate machine learning models-linear regression, logistic regression, and multiple linear regression-were implemented to predict key outcomes, with performance evaluated using accuracy, coefficient of determination (R² ), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The study deliberately employs baseline, interpretable AI models-linear regression, logistic regression, and multiple linear regression-to ensure methodological transparency and highlight the generalizable workflow rather than algorithmic complexity. The results demonstrate that a standardized AI workflow can be adapted to diverse environmental contexts, while at the same time it maintains interpretability and strong predictive performance. The novelty of this work lies in its methodological generalization, enabling cross-domain application, promoting reproducibility, and providing a scalable decision-support tool for researchers and policymakers. The proposed framework offers a practical pathway for integrating AI into sustainability planning, bridging the gap between domain-specific studies and transferable, replicable solutions.