Published Paper Details:

Artificial Intelligence (AI) rising on the horizon, is rapidly reshaping environmental science and sustainability practice by converting increasingly large and heterogeneous environmental datasets into operational knowledge. This paper synthesizes recent bibliometric studies, Earth-observation platform reports, and climate-tech investment analyses from 2019-2024 to (1) characterize current AI applications in remote sensing, biodiversity monitoring, water and waste management, and energy optimization and (2) present a near-term outlook through 2030 based on observed trends. Key findings show accelerating publication and deployment activity in AI-environment research (notable growth since 2019), expanded access to Copernicus/Landsat data and in-cloud compute that lowers barriers for imagery-based AI workflows and a re-shaping of climate-tech funding with a rising share for AI-centered ventures. Representative operational areas now include automated land-cover change detection, camera-trap and acoustic species classification with human-in-the-loop validation and AI-driven demand/supply optimization in energy and water systems. Scenario forecasts (conservative CAGR assumptions) indicate approximately a doubling of AI-applied research outputs and significant growth in AI's share of climate-tech investment by 2030 -- contingent on continued open data access, cloud compute availability and cross-sector governance. The paper mainly highlights three enablers (open EO data and CDSE access, affordable cloud/edge compute and pre-trained model hubs, hybrid human-AI workflows) and three risks (biased ground truth, ecological model mis-specification, governance gaps). Policy and research recommendations include standardized labelled datasets and benchmarks for ecological tasks, investment in interpretability and human-in-the-loop systems and multi-stakeholder governance to secure equitable environmental outcomes.