Artificial Intelligence for environmental sustainability: a systematic review of applications, impact pathways, and governance implications

The escalating urgency of climate change and environmental degradation has intensified demand for scalable solutions that strengthen system resilience and support progress toward the Sustainable Development Goals. Artificial intelligence offers substantial potential to enhance environmental monitoring and decision support, yet the evidence remains dispersed across domains, methods, and industry contexts. This study provides a systematic review of AI for environmental sustainability, synthesizing evidence from 493 identified journal articles. The review examines how AI is applied to environmental sustainability and identifies the main barriers affecting its outcomes. It uses an impact-pathway framework to explain how institutional capacity and governance shape the environmental effects of AI deployment. Findings indicate that AI applications concentrate in infrastructure-intensive sectors, particularly transportation, communication, and electric, gas, and sanitary services, followed by cross-cutting domains spanning IT-enabled environmental applications, water systems, and resource management. Across sectors, supervised and unsupervised learning, deep learning, forecasting, anomaly detection, and optimization-oriented approaches are most prevalent, while text-intensive approaches such as natural language processing (NLP) and sequence mining remain comparatively underexplored. The review shows that net sustainability outcomes depend on the balance between measurable environmental gains and the operational and lifecycle burdens of AI deployment. The proposed framework identifies governance capacity, Green AI engineering, and infrastructure choices as factors that shape this balance.