Artificial intelligence (AI) is increasingly integrated into environmental management to address challenges related to climate change, biodiversity loss, pollution control, and sustainable resource use. This study systematically reviews recent scientific literature and policy reports to evaluate both the environmental benefits and ecological costs associated with AI technologies. Using a comparative analytical framework, the research assesses AI applications in environmental monitoring, predictive modelling, and operational optimization alongside the environmental impacts of AI infrastructure, including energy consumption, water use, electronic waste generation, and critical mineral extraction. The findings indicate that AI significantly enhances environmental decision-making by improving forecasting accuracy, optimizing resource efficiency, and enabling real-time ecosystem monitoring. However, these advantages are counterbalanced by substantial environmental burdens arising from energy-intensive data centers, growing e-waste streams, and resource-intensive hardware production. The study identifies a clear sustainability paradox in which AI-driven environmental solutions may inadvertently intensify ecological pressures. To mitigate these risks, the paper recommends the development of energy-efficient algorithms, transition to renewable-powered data centers, implementation of circular economy practices for AI hardware, responsible sourcing of critical minerals, and the integration of environmental accountability into AI governance frameworks. The results emphasize that without sustainable design and policy oversight, AI expansion may undermine environmental progress. Conversely, when aligned with sustainability principles, AI can serve as a powerful tool for advancing global environmental resilience and long-term planetary health.
Rajamani Krishna (Sat,) studied this question.