AI-Driven Environmental Decision-Making: Integrating Business Intelligence and Computer Science for Sustainable Development

Abstract

Abstract 
Combining Artificial Intelligence (AI) and Business Intelligence (BI) presents an effective framework in developing data-driven 
environmental decision-making. The architecture proposed is a congruence of machine learning models, real-time data analytics, 
and user-friendly BI dashboards to solve complex sustainability problems. Measurable results in terms of air quality emissions, 
reliable forecasts in climate change, and sustainable allocation of renewable resources: empirical implementations in urban 
air quality forecasts, smart irrigation systems, and renewable energy resource allocation display improved prediction accuracy. 
as well as demonstrating an enhanced optimization of resources and resource transparency. Incorporating modularity, data 
interoperability, and adaptive feedback, the framework is not only scalable but also relevant in diverse realms of the 
environment. Moreover, the participatory interfaces of decision support are incorporated with greater stakeholder participation. 
and legitimacy of governance. The study creates an appealing combination of technical innovation and problem-relevant 
research through a versatile and intelligent system augmenting the sustainable development goals. It also highlights major 
implementation issues, including algorithm opacity, ethical issues, and specificity of domain, and determines research directions 
on the way to climate-resilient and real-time edge AI and digital infrastructures of the future.