Machine learning-based land-use regression models for predicting carbon dioxide concentrations in San Francisco Bay area
File(s)
Published version
Author(s)
Smith, Anna C
Li, Linfeng
Xiang, Jiansheng
Fang, Fangxin
Type
Journal Article
Abstract
Carbon dioxide (CO2) is a key driver of anthropogenic climate change and cities have been identified as major sources of emissions. Urbanization and land use change are associated with rising urban CO2 emissions, highlighting the need to study spatiotemporal trends in intraurban CO2 to inform sustainable city planning. This study investigates the use of land use regression (LUR) to predict intraurban CO2 concentrations, using data from the BEACO2N monitoring network in the San Francisco Bay Area. Additionally, LUR is compared to machine learning (ML) algorithms capable of capturing non-linear relationships, representing a two-fold novel contribution. Model performance is evaluated using reserved data from training sensors as well as unseen sensor locations. For training sensors, extreme gradient boosting (XGBoost) and a convolutional neural network (CNN) achieved the highest predictive accuracy (R²=0.58), outperforming traditional LUR (R²=0.34). XGBoost and CNN also outperformed traditional LUR for unseen sensor locations, accounting for up to 42% of the variability in observed CO2 concentrations. These models offer insight into urban land use and carbon dynamics, supporting more informed approaches to urban planning and decarbonization.
Date Issued
2025-09-26
Date Acceptance
2025-09-12
Citation
Environmental Earth Sciences, 2025, 84 (19)
ISSN
1866-6280
Publisher
Springer Science and Business Media LLC
Journal / Book Title
Environmental Earth Sciences
Volume
84
Issue
19
Copyright Statement
© The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41019194
PII: 12582
Subjects
CNN
Carbon dioxide
Land use regression
Urban
XGBoost
Publication Status
Published
Coverage Spatial
Germany
Article Number
539
Date Publish Online
2025-09-26