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- Publisher
- Taylor & Francis
- Copyright
- © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC
- ISSN
- 1949-8284
- DOI
- 10.1080/19498276.2023.2251982
- Publisher site
- See Article on Publisher Site
Abstract
Abstract We present a deep learning model estimating carbon dioxide equivalent (CO2e) emissions in the real estate sector. The model, which utilizes convolutional neural networks (CNNs) and image classification techniques, is designed to estimate CO2e emissions based on publicly available images of buildings and their corresponding emissions. Our findings show that the model has the ability to provide reasonably accurate estimations of CO2e emissions using images as the sole input. Notably, incorporating primary energy sources as additional input further improves the accuracy up to 75%. The creation of such a model is particularly important in the fight against climate change, as it allows for transparency and fast identification of buildings, contributing significantly to CO2e emissions in the building sector. Currently, information on emission intensity in the real estate sector is scarce, with only a few countries collecting and providing the required data. Our model can help reduce this gap and provide valuable insights into the carbon footprint of the real estate sector.
Journal
Journal of Sustainable Real Estate – Taylor & Francis
Published: Dec 31, 2023
Keywords: Deep learning; decarbonization; CO2 footprint; image classification