iEnEx.io is a framework and web-based tool designed to facilitate the evaluation of the complex interplay between energy inequity, environmental justice, and climate change mitigation through the reduction of cooling demand. The tool is specifically tailored to assist planners and designers in Chicago, IL in assessing the potential for energy savings in urban buildings. iEnEx.io is predicated on a five-step workflow, which leverages big data and machine learning techniques to construct the model.

The tool is intended to offer a nuanced understanding of the trade-offs inherent in the application of natural ventilation in urban buildings, given the attendant health risks associated with exposure to outdoor-origin indoor PM2.5 concentrations. Through the integration of machine learning algorithms and big data, iEnEx.io is able to provide a comprehensive and dynamic analysis of the interactions between energy equity, environmental justice, and climate change mitigation. As such, it represents an important contribution to the field of sustainability science, providing planners and designers with a powerful tool for evaluating and implementing strategies for mitigating the impact of climate change in urban environments.