AI for Environmental Sustainability: Advances, Challenges, and Future Directions

Artificial Intelligence (AI) has emerged as a transformative tool in environmental science, offering innovative solutions for monitoring, prediction, and decision-making. This paper provides a comprehensive review of AI applications in environmental sustainability, focusing on remote sensing, climate modeling, biodiversity conservation, water resource management, and renewable energy optimization. Key AI methodologies, including deep learning, natural language processing (NLP), generative AI, and reinforcement learning, are examined in the context of environmental challenges. Despite significant advancements, AI-driven environmental science faces several challenges, such as data scarcity, model interpretability, computational constraints, and interdisciplinary collaboration. Addressing these limitations requires improvements in data accessibility, the development of explainable AI models, and the implementation of energy-efficient computing techniques. Furthermore, ethical considerations related to data privacy and AI-driven decision-making must be carefully managed. Looking forward, the integration of AI with physics-based models, self-supervised learning, federated learning, and Green AI principles presents promising opportunities to enhance sustainability efforts. AI-driven policy support systems will also play a crucial role in shaping climate regulations and environmental governance. By overcoming current challenges and leveraging AI’s full potential, researchers and policymakers can advance global environmental sustainability and climate resilience.

• Publication year

  2025

• Venue

  International Journal of Artificial Intelligence for Science (IJAI4S)

• Publication date

  2025-03-17

• Fields of study

  Not labeled

• Identifiers
  DOI 10.63619/ijais.v1i1.006
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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