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

2025-05-14

AI Summary

Addressing the challenge of jointly optimizing embodied and operational carbon emissions in urban sustainable development—and the lack of actionable, multi-objective trade-off tools for non-expert decision-makers—this paper introduces EcoSphere: the first policy-aware, building-level carbon-cost co-optimization framework designed for non-specialist stakeholders. EcoSphere integrates computer vision and natural language processing (CV/NLP) for material identification from street-level and satellite imagery, high-resolution building stock census data, and geospatial AI to construct a scenario-driven, life-cycle carbon-cost coupling model. Empirical validation in Chicago and Indianapolis demonstrates that EcoSphere quantitatively evaluates the effectiveness of diverse policy interventions, achieving up to 32% carbon reduction while maintaining construction cost feasibility. The framework significantly enhances both the scientific rigor and accessibility of low-carbon urban retrofitting decisions.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Optimize carbon emissions and costs in urban development
• Evaluate embodied and operational carbon with construction costs
• Assess policy impacts on emissions using data-driven tools

Innovation

• Uses computer vision and NLP for building categorization
• Simulates policy scenarios for carbon mitigation
• Integrates high-resolution data for emissions baseline