Hiran H. Lathabai, a researcher at Amrita CREATE in Kerala, India, has just mapped out a roadmap for how artificial intelligence and big data—what’s being called *environmental intelligence* (EI)—can accelerate progress on three of the United Nations’ most critical Sustainable Development Goals: climate action (SDG 13), life below water (SDG 14), and life on land (SDG 15).
Using a new “macro-meso-micro” assessment framework, Lathabai and his team analyzed over 1,200 peer-reviewed publications from the Dimensions database up to June 2025. Their findings reveal not just which technologies are gaining traction, but how they’re being woven into real-world decision-making—especially in areas like water quality sensing, renewable energy siting, and industrial emissions monitoring.
“What we’re seeing is a convergence,” Lathabai says. “Environmental intelligence isn’t just about collecting data—it’s about turning it into actionable insight at scale.” The study identifies five core EI topics—climate risk assessment, ecosystem and biodiversity monitoring, carbon accounting, pollution tracking, and sustainable urban systems—each with its own cluster of enabling technologies.
For the energy sector, this has immediate commercial implications. Renewable energy developers, for instance, can use EI tools to optimize solar and wind farm placement by integrating climate risk models, land-use data, and ecosystem constraints. “Instead of relying on static environmental impact statements, companies can now simulate thousands of scenarios in real time,” Lathabai explains. “That reduces permitting delays and improves investor confidence.”
The research also highlights emerging intersections with allied goals like clean water (SDG 6), affordable energy (SDG 7), and sustainable cities (SDG 11). For example, water quality sensors feeding into AI models can simultaneously inform climate adaptation strategies and industrial compliance reporting—turning compliance from a cost center into a strategic asset.
Published in *IEEE Access*, the study doesn’t just stop at mapping the landscape; it offers a reproducible framework aligned with global bibliometric standards. That means governments, NGOs, and private firms can now adopt this approach to prioritize R&D, allocate resources, and build capacity where it’s most needed.
In a world where environmental data is growing exponentially but meaningful action often lags, Lathabai’s work suggests a way forward: intelligence that is not only environmental, but also intelligent about the environment.