AI-Driven Climate Intelligence for Sustainable Futures: Advancing Environmental Modeling and Resource Optimization

Authors

https://doi.org/10.48313/iee.v1i3.59

Abstract

The increasing complexity of climate systems demands more robust, adaptive analytical approaches than those provided by conventional modeling techniques. This study examines the role of machine learning in improving climate change modeling and advancing environmental sustainability. By leveraging large-scale, heterogeneous environmental data, machine learning enables more precise predictions of climate dynamics, including temperature variations, extreme weather events, and emission patterns. It also supports efficient resource management by optimizing energy systems, enhancing the integration of renewables, and improving environmental monitoring through real-time data analysis. Beyond predictive capabilities, the study highlights the contribution of machine learning to sustainable practices, including precision agriculture, water resource management, and waste optimization. However, the effectiveness of these approaches depends on addressing key challenges, including data quality limitations, model interpretability, and the high computational demands of advanced algorithms. Ethical considerations related to data governance and the environmental costs of computation are also discussed. The findings suggest that integrating machine learning with traditional climate science can significantly strengthen policy formulation and sustainability strategies. Strengthening interdisciplinary collaboration, improving data infrastructure, and promoting transparent, energy-efficient modeling practices are essential to fully realize the potential of machine learning to address climate challenges.

Keywords:

Machine learning, Climate change modeling, Environmental sustainability, Predictive analytics, Resource optimization

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Published

2026-05-24

Issue

Vol. 1 No. 3 (2025): Innovations in Environmental Economics

Section

Articles

How to Cite

AI-Driven Climate Intelligence for Sustainable Futures: Advancing Environmental Modeling and Resource Optimization. (2026). Innovations in Environmental Economics , 1(3), 196–206. https://doi.org/10.48313/iee.v1i3.59

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