Intelligent Environmental Monitoring: Business Intelligence and AI Framework for Ecological Decision-Making Using Public Sustainability Data

There's increasing demand for intelligent, predictive tools to support sustainability decision-making, as the world faces growing challenges related to climate change and other environmental threats. This research proposes a hybrid framework that merges Business Intelligence (BI) and Artificial Intelligence (AI), allows the processing of ecological footprint and capacity utilization data, when powered by open access information from Ecuador. Three experiments illustrate the synergistic power of descriptive analytics, trend detection, and unsupervised machine learning (e.g., Principal Component Analysis (PCA) and KMeans clustering) in generating insights on environmental indicators. Results highlight an absolute dominance of ecological pressure from fishing zones, croplands and pasturelands, while pasturelands and urban are the fastest-growing environmental footprints. Through observing patterns of land use changes and distributions, the AI-based clustering revealed hidden ecological profiles across different years and lands which could be used as evolving classification models for ecological risk assessment. This study offers a replicable and scalable model, using real environmental data, unlike prior literature which has focused heavily on either remote sensing or systems at the enterprise level. It addresses important gaps by making predictive sustainability analysis available to governments and institutions lacking advanced infrastructure. The paper ends with a series of strategic advice and future trends, including moving away from working with IoT data, creating scenario models and geospatial BI dashboards. Our findings contribute to the field of environmental data science by providing an actionable, interpretable, and transparent decision-support tool that aligns with both national and global sustainability goals.