Data-driven modelling for CO2 emission reduction in bike-sharing systems: multi-scale estimation and key determinants
Data-driven modelling for CO2 emission reduction in bike-sharing systems: multi-scale estimation and key determinants
Bike-sharing possesses the potential for CO
2 emission reduction through modal shift effects. However, existing research overlooks the influence of other highly-correlated urban system components such as public transport, Points of Interest (POIs), and socio-economic factors on bike-sharing CO
2 emission reductions (BCERs). Utilizing a large-scale dataset comprising over 170 million trip records, 6 million POI data points and 0.4 million check-ins, we achieve a multi-scale estimation of BCER and explore the impact of urban system factors on BCER potential at the grid level, which is a pioneering work of urban-system-level BCER analysis. Firstly, we estimate city-wide BCER using Bayesian prior probability and cross-scale allocation strategies. Secondly, we apply SHapley Additive exPlanation (SHAP) methods to identify the key determinants affecting BCER. Finally, we perform BCER pattern identification based on feature attribution differences and validate the results using various statistical significance tests to enhance interpretability and reliability. The framework's robustness is validated through the aforementioned real-world dataset and the underlying case study in Beijing, China. The key determinants of BCER include work-related POI density, bus passenger flow, and bicycle lane density, contributing 20.5%, 9.5%, and 7.2% to the total attribution, respectively. Additionally, urban functional and public transport factors significantly influence BCER, while urban perceptual factors are not significant. Notably, areas classified as Pattern I (commercial core areas) and Pattern II (within the 5th Ring Road) show substantial emission reduction potential, accounting for 23.8% and 42.8%, respectively. These findings facilitate the formulation of targeted and effective emission reduction strategies, promoting sustainable urban mobility.
Bike-sharing CO reduction, Data-driven interpretation, Emission reduction pattern, Key determinant, Multi-scale estimation, Urban systems
Zhu, Bing
59a36f7c-f707-43ed-b6ee-fec2ab8d0fb6
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Zhu, Zheng
0afdb2bb-da9c-45f2-a707-15893d5fcba4
Lee, Der-Horng
29e4b33a-f0fd-4a98-be16-11508a77df4d
Chen, Xiqun (Michael)
a1755726-d9d3-4e01-99d8-959b88c0c8b2
Hu, Simon
268a8229-41b0-4e3b-9acf-5a1dee29606c
21 February 2025
Zhu, Bing
59a36f7c-f707-43ed-b6ee-fec2ab8d0fb6
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Zhu, Zheng
0afdb2bb-da9c-45f2-a707-15893d5fcba4
Lee, Der-Horng
29e4b33a-f0fd-4a98-be16-11508a77df4d
Chen, Xiqun (Michael)
a1755726-d9d3-4e01-99d8-959b88c0c8b2
Hu, Simon
268a8229-41b0-4e3b-9acf-5a1dee29606c
Zhu, Bing, Kaparias, Ioannis, Zhu, Zheng, Lee, Der-Horng, Chen, Xiqun (Michael) and Hu, Simon
(2025)
Data-driven modelling for CO2 emission reduction in bike-sharing systems: multi-scale estimation and key determinants.
Journal of Cleaner Production, 495, [144974].
(doi:10.1016/j.jclepro.2025.144974).
Abstract
Bike-sharing possesses the potential for CO
2 emission reduction through modal shift effects. However, existing research overlooks the influence of other highly-correlated urban system components such as public transport, Points of Interest (POIs), and socio-economic factors on bike-sharing CO
2 emission reductions (BCERs). Utilizing a large-scale dataset comprising over 170 million trip records, 6 million POI data points and 0.4 million check-ins, we achieve a multi-scale estimation of BCER and explore the impact of urban system factors on BCER potential at the grid level, which is a pioneering work of urban-system-level BCER analysis. Firstly, we estimate city-wide BCER using Bayesian prior probability and cross-scale allocation strategies. Secondly, we apply SHapley Additive exPlanation (SHAP) methods to identify the key determinants affecting BCER. Finally, we perform BCER pattern identification based on feature attribution differences and validate the results using various statistical significance tests to enhance interpretability and reliability. The framework's robustness is validated through the aforementioned real-world dataset and the underlying case study in Beijing, China. The key determinants of BCER include work-related POI density, bus passenger flow, and bicycle lane density, contributing 20.5%, 9.5%, and 7.2% to the total attribution, respectively. Additionally, urban functional and public transport factors significantly influence BCER, while urban perceptual factors are not significant. Notably, areas classified as Pattern I (commercial core areas) and Pattern II (within the 5th Ring Road) show substantial emission reduction potential, accounting for 23.8% and 42.8%, respectively. These findings facilitate the formulation of targeted and effective emission reduction strategies, promoting sustainable urban mobility.
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Accepted/In Press date: 5 February 2025
e-pub ahead of print date: 15 February 2025
Published date: 21 February 2025
Keywords:
Bike-sharing CO reduction, Data-driven interpretation, Emission reduction pattern, Key determinant, Multi-scale estimation, Urban systems
Identifiers
Local EPrints ID: 499267
URI: http://eprints.soton.ac.uk/id/eprint/499267
ISSN: 0959-6526
PURE UUID: 750e12dc-85d0-4a04-a798-d37e68d64cfa
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Date deposited: 13 Mar 2025 17:33
Last modified: 15 Mar 2025 02:53
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Contributors
Author:
Bing Zhu
Author:
Zheng Zhu
Author:
Der-Horng Lee
Author:
Xiqun (Michael) Chen
Author:
Simon Hu
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