EcoSphere: A Decision-Support Tool for Automated Carbon Emission and Cost Optimization in Sustainable Urban Development

TL;DR

EcoSphere is a software tool that integrates high-resolution urban data and AI techniques to help policymakers optimize carbon emissions and costs in city development, promoting sustainable urban growth.

Contribution

The paper introduces EcoSphere, a novel decision-support platform combining data-driven modeling and AI to evaluate and optimize urban carbon emissions and costs.

Findings

EcoSphere accurately categorizes building characteristics using computer vision and NLP.

The tool effectively simulates policy scenarios to predict environmental and financial impacts.

Case studies demonstrate EcoSphere's utility in guiding sustainable urban development.

Abstract

The construction industry is a major contributor to global greenhouse gas emissions, with embodied carbon being a key component. This study develops EcoSphere, an innovative software designed to evaluate and balance embodied and operational carbon emissions with construction and environmental costs in urban planning. Using high-resolution data from the National Structure Inventory, combined with computer vision and natural language processing applied to Google Street View and satellite imagery, EcoSphere categorizes buildings by structural and material characteristics with a bottom-up approach, creating a baseline emissions dataset. By simulating policy scenarios and mitigation strategies, EcoSphere provides policymakers and non-experts with actionable insights for sustainable development in cities and provide them with a vision of the environmental and financial results of their decisions. Case studies in Chicago and Indianapolis showcase how EcoSphere aids in assessing policy impacts on carbon emissions and costs, supporting data-driven progress toward carbon neutrality.