An open-source artificial intelligence model to accurately map the carbon emissions of buildings across multiple cities could become a powerful new tool to help policymakers plan targeted and equitable decarbonisation strategies.
The model, developed by researchers at the College of Design and Engineering (CDE) at the National University of Singapore (NUS), offers city planners a detailed picture of how building carbon emissions are distributed and what drives them, with a view to helping authorities design smarter, fairer strategies to cut emissions.
The model is the result of research led by Assistant Professor Filip Biljecki from the Department of Architecture at CDE. The team's findings were published on 15 August 2025 in the journal Nature Sustainability.
"Our model estimates operational carbon emissions of individual buildings at the scale of entire cities," said Department of Architecture PhD student Winston Yap, Lead Author of the study.
"Unlike previous approaches that rely on proprietary data, our open approach is designed to be transferable across cities, including those with different data availability conditions."
Applied to data mapping over half a million buildings in five cities - Singapore, Melbourne, New York City (Manhattan), Seattle and Washington DC - the researchers say their model explained up to 78 per cent of the variation in emissions. The results revealed significant differences in how emissions are distributed within cities and identified key factors that influence building energy use, including urban form, planning history, and income levels.
"Building emissions are not just about size or density, they're deeply shaped by the unique context of each city, from its planning legacy to climate and economic conditions," said Asst Prof Biljecki. "By using only open data, we've built a flexible framework that cities around the world can use to better understand their carbon footprint and plan more effective responses."
One of the key insights from the study is the complex relationship between building density and carbon emissions. While taller buildings tend to be more energy-efficient per unit area due to economies of scale, dense urban cores may also experience higher cooling demands due to urban heat island effects. Suburban areas, typically associated with detached low-rise buildings, were found to be significant contributors to total emissions, sometimes rivalling those of city centres.
The research also uncovered stark inequalities. In most cities studied, wealthier neighbourhoods were found to have disproportionately high per capita emissions. In Manhattan, for example, more than half of total building emissions were attributed to just a handful of large buildings.
"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," said Asst Prof Biljecki. "Our findings highlight the need for place-based strategies that take both emissions intensity and socioeconomic vulnerability into account."
The framework integrates diverse data sources including satellite imagery, street view photos, population maps, road networks, and local climate data using graph neural networks, a form of deep learning that captures spatial relationships between urban elements.
By making their approach entirely open, the researchers say they want to support global efforts to reduce emissions from the built environment and to help cities meet their climate targets.
"This work demonstrates the potential of open science and AI to accelerate urban sustainability," said Asst Prof Biljecki. "It's not just about understanding where emissions come from, but also ensuring that climate action is both effective and fair."
