Artificial Intelligence in Environmental Conservation: Applications and Challenge

Arya Chatterjee

Artificial Intelligence in Environmental Conservation: Applications and Challenge

Author(s)	Arya Chatterjee
Country	India
Abstract	The convergence of AI and environmental conservation presents transformative opportunities for addressing the interconnected challenges of climate change, resource depletion, and pollution. This comprehensive review synthesises peer‑reviewed research published between 2024 and 2026 across six critical domains: energy conservation and building performance optimisation, carbon footprint reduction, climate resilience and early warning systems, sustainable agriculture, pollution control, and machine learning integration with life cycle assessment (LCA). Key findings reveal that AI‑based control systems achieve HVAC energy savings of up to 37% and emissions reductions of 21%, while LSTM neural networks demonstrate exceptional accuracy (R² = 0.98) for climate temperature forecasting. In agriculture, AI‑driven precision techniques reduce irrigation water use by 20‑25% and nitrogen application by up to 31 kg ha⁻¹, with disease detection accuracy exceeding 95%. However, the environmental paradox of AI itself is substantial—training large models like GPT‑4 emits 12,456‑14,994 tons of CO₂. Emerging paradigms including physics‑informed machine learning, explainable AI, digital twins, and federated learning address long‑standing challenges of reliability, transparency, and scalability. Persistent obstacles include data fragmentation, model generalizability across contexts, the "black box" nature of deep learning, and computational demands. The review concludes with a six‑dimensional sustainable AI framework and identifies future research priorities: hybrid modelling approaches, standardised evaluation metrics, improved uncertainty quantification, and responsible AI deployment. When applied thoughtfully, AI can accelerate the transition to a more sustainable and resilient future, but human expertise, high‑quality data, supportive policy, and commitment to sustainable AI practices remain essential.
Keywords	Artificial Intelligence, Machine Learning, Environmental Sustainability, Carbon Footprint Reduction, Climate Resilience
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-08

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International Journal For Multidisciplinary Research

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Artificial Intelligence in Environmental Conservation: Applications and Challenge

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