Abstract
Green geospatial analytics has become a pivotal enabler in many regional planning frameworks of their sustainability agendas and is regularly appearing as an important aspect of environmental governance and economic development. In the context of spatial decision systems, the rising number of environmental constraints, the growing demand for precision-based indicators, and the necessity to compete on sustainability-driven benchmarks, there is an urgent need for suitable decision-support models. The purpose is to examine how questions of technological adoption, data modeling, and sustainability orientation are made integral parts of these analytical systems. This study aims to investigate the interaction between geospatial intelligence and enterprise-level performance in environmental monitoring, the predictive effect of eco-indicators, and the structural effect of land-use adaptation. The analysis situates the enterprises’ planning strategies in transitional economies, or even emerging contexts, to examine whether the advancement of green growth still relies on a combination of evidence-based modeling and participatory decision-making. AHP, regression is embedded in a TOPSIS–driven evaluation framework in which they play the role of analytical instruments that identify inter-variable associations and forecasting shapes to create decision-ready insights. The results demonstrate (1) that the effect of infrastructure density on sustainability in pilot zones was not statistically significant, while geospatial innovation had a strong positive correlation with growth potential, and land classification had a moderate negative correlation with pollution levels in urban clusters; The study contributes to an understanding of the layered functions and adaptive roles geospatial analytics takes in digital and ecological settings and what it is that these tools find effective in addressing sustainability as a policy-guided transformation.