An innovative breakthrough in urban sustainability has emerged from the National University of Singapore’s College of Design and Engineering, presenting a powerful new tool that leverages artificial intelligence to map carbon emissions at the building level across multiple cities. This open-source model, spearheaded by Assistant Professor Filip Biljecki and his research team, offers unprecedented granularity in understanding how buildings contribute to urban carbon footprints, ultimately empowering policymakers with data-driven insights to sculpt more effective, equitable decarbonisation strategies. The research findings, published in the esteemed journal Nature Sustainability, mark a significant leap forward in urban climate science and planning.
The model distinguishes itself by estimating the operational carbon emissions of individual buildings at vast city-wide scales, surpassing previous methodologies that often depended on proprietary datasets, which hindered flexibility and global applicability. Department of Architecture PhD candidate Winston Yap, who led the study, emphasized the model’s adaptability, explaining how it can be utilized seamlessly across cities with varying data availability. This breakthrough opens new avenues for cities worldwide, especially those lacking comprehensive carbon accounting infrastructures, to track their building emissions with rigor and precision.
Applying this model to over 500,000 buildings across five diverse urban environments – Singapore, Melbourne, Manhattan in New York City, Seattle, and Washington DC – the researchers achieved remarkable explanatory power, accounting for as much as 78% of emission variations within these cities. This achievement represents a major technical milestone, harnessing a blend of open data sources such as satellite imagery, street-level photos, population maps, and climate data. These diverse inputs fuel a sophisticated graph neural network, a cutting-edge deep learning technique capable of capturing complex spatial interdependencies between urban elements.
What sets this AI-driven model apart is its ability to dissect the intricate interplay between urban form, socioeconomic factors, and energy consumption, highlighting nuances that more simplistic analyses often overlook. The research reveals that building emissions are influenced far beyond just physical size or density. Instead, they are intimately shaped by localized factors — including urban planning legacies, microclimates, and economic conditions — changing how energy is consumed in unique urban fabrics and neighborhoods.
A particularly striking finding of the study pertains to the complex relationship between density patterns and emissions. The data suggests that while taller, densely clustered buildings generally achieve better energy efficiency per square meter, dense urban cores are also subject to intensified cooling demands linked to urban heat island effects. Intriguingly, suburban regions characterized by sprawling low-rise developments contribute disproportionately to total carbon emissions, rivaling city centers in their environmental impact. These insights challenge traditional assumptions and signal the need for multifaceted urban sustainability policies.
Furthermore, the investigation uncovered alarming disparities in emissions intensity across socioeconomic strata. In most cities assessed, affluent neighborhoods demonstrated significantly higher per capita emissions compared to lower-income areas. Manhattan’s data was telling; a mere handful of large, luxury buildings accounted for over fifty percent of all building-related emissions in the city. This deepens ongoing policy debates about environmental justice, underscoring the risk of uniform carbon pricing accidentally placing disproportionate burdens on economically vulnerable communities that often reside in less efficient housing stock.
Assistant Professor Biljecki elucidated the stakes of these inequities: “Uniform carbon pricing or blanket regulations risk placing an unfair burden on lower-income communities that may already be struggling with older, less efficient infrastructure.” This recognition compels a shift toward place-based strategies that account simultaneously for carbon intensity and social vulnerability, promoting climate action that is both effective and socially equitable.
Technically, the integration of various geospatial datasets is orchestrated through graph neural networks (GNNs), which excel in modeling relational and spatial data. GNNs enable the model to capture not just isolated building attributes but also the relational context within the urban landscape—such as proximity to roads, neighboring building types, and the configuration of green spaces. This holistic spatial understanding allows the model to predict emissions more precisely by considering the multifaceted interactions within densely interconnected city systems.
The open-source nature of the model is a deliberate and meaningful choice by the researchers, aiming to democratize access to advanced urban carbon accounting tools. By relying only on publicly available data and releasing their codes openly, the team paves the way for cities worldwide—including those with limited resources or restricted data environments—to participate in global decarbonisation efforts. This openness resonates with the principles of open science, fostering transparency, collaboration, and acceleration of research impact.
In practical terms, city governments and urban planners equipped with this model can perform detailed emissions audits, pinpointing hotspots and identifying the specific drivers of carbon intensity at the building level. This spatially precise intelligence facilitates the design of targeted interventions, such as prioritizing energy retrofits in identified high-emission districts or adjusting zoning codes and urban planning regulations to mitigate heat island effects and optimize building efficiency.
Moreover, the inclusion of socioeconomic data helps to align climate action with social equity objectives, enabling policymakers to tailor incentives or support mechanisms where they are most needed. Such data-driven, localized approaches are vital to craft fair decarbonisation pathways that avoid exacerbating existing social inequalities while achieving ambitious sustainability goals.
The collective implications of this research underscore a critical narrative: urban sustainability is inherently complex and context-dependent, requiring sophisticated analytical tools that move beyond aggregate city-wide metrics to embrace fine-grained spatial heterogeneity. The fusion of AI, geospatial data, and urban science in this project represents a paradigm shift toward smarter, fairer, and more actionable carbon management.
Looking forward, the potential applications of this framework extend beyond carbon accounting alone. The modeling techniques could be adapted to estimate other environmental burdens of urban environments or integrated into digital twins of cities for dynamic, real-time sustainability planning. The research team’s commitment to open science invites continual refinement and collaborative expansion, promising a future where cities can not only understand their carbon footprints but actively navigate the complexities of sustainable transformation.
For stakeholders invested in combating climate change at the urban scale, this research is a beacon of innovation and equity—demonstrating how open data and AI can unlock new frontiers in sustainability science. As cities continue to grow and evolve, such tools will be indispensable for meeting climate commitments in ways that are not only efficient but just.
Subject of Research: Not applicable
Article Title: Revealing building operating carbon dynamics for multiple cities
News Publication Date: 15-Aug-2025
Web References: http://dx.doi.org/10.1038/s41893-025-01615-8
Image Credits: College of Design and Engineering at NUS
Keywords: Urban planning, Climate change mitigation, Architectural design, Carbon emissions
