Artificial intelligence-based digital transformation and environmental sustainability - Di Wu; Yuge Zhang; Lihong Zhao (PLoS ONE, 2026)
AI’s rapid growth presents significant potential for enhancing environmental sustainability and driving digital transformation across various sectors. AI applications are scattered, uneven data integration is complex, and defined metrics are lacking, limiting its ability to support sustainable practices. In search of a holistic and interoperable solution, this study suggests Green AI, an innovative framework that blends AI, blockchain, and the IoT to address sustainability issues. This work aims to provide a scalable and secure architecture that enhances energy efficiency, reduces carbon emissions, and improves environmental monitoring accuracy. Green AI utilizes blockchain to securely manage real-time data from smart grids, environmental sensors, and energy markets via IoT devices, ensuring energy transaction transparency and accountability. Advanced machine learning algorithms maximize renewable energy integration and estimate energy demand, enabling proactive decision-making. Its unified design improves energy system sustainability and provides a reproducible model for cost-effective resource management, making this study unique. The Green AI framework guides academics, policymakers, and industry stakeholders in utilizing intelligent technology for sustainable development, thereby creating a greener and more resilient future.