Artificial Intelligence in Environmental Management: Emerging Trends and Challenges

Abstract

Artificial intelligence (AI) is moving from a supporting data-processing tool toward a
decision infrastructure for environmental management. This article reports an original
secondary-indicator analysis that quantifies how recent advances in AI strengthen
environmental monitoring, forecasting, and intervention design while also generating
new management burdens. The analysis integrates public climate, emissions, and
energy indicators with reported performance markers from state-of-the-art AI systems
in weather forecasting, Earth observation, flood prediction, biodiversity monitoring,
water management, air quality, and waste systems. Two original composite indicators
were developed: the AI Environmental Management Capability Index (AEMCI) and
the Challenge Burden Score (CBS). The results show that weather and climate-risk
management has the highest AEMCI because AI models provide rapid global forecasts,
probabilistic uncertainty estimates, and plausible operational pathways. Earth
observation is the second strongest domain because multimodal geospatial foundation
models are improving transfer across land-cover, disaster-response, and crop-
monitoring tasks. Water, air-quality, and biodiversity applications are advancing rapidly
but remain constrained by sparse sensors, limited ground truth, ecological bias, and
incomplete governance integration. The same evidence also reveals a sustainability
paradox: AI can improve environmental management while increasing electricity,
water, carbon, and land pressures through expanding data-centre infrastructure. The
article concludes that high-impact AI for environmental management requires domain
validation, transparent uncertainty, ecological-footprint accounting, human-in-the-loop
governance, and deployment models that prioritize public environmental value over
generic automation.