Prediction of the effect of bike-sharing on urban carbon emission reduction: Evidence from Beijing

Bike-sharing is regarded as a green and economical public transport mode. However, how to scientifically predict its carbon emission reduction effect is still on the way. This article builds an assessment model for emission reduction of bike-sharing. The modeling is based on the rational person hypothesis that consumers take transportation consumption expenditure as a budget constraint, and get the optimal number of trips of shared bikes for utility maximization. Then, we take Beijing as an example to carry out empirical research, whereby data on key influencing factors were collected through questionnaires and situational analysis. Finally, it is predicted that the carbon emission reduction contributed by bike-sharing in Beijing will be approximately 522,540.32–1,135,957.21 tons in 2025–2030. This study can provide a benchmark theoretical model for urban emission reduction, as well as a reference for public transport policy and environmental policy formulation. • This article builds a model for emission reduction of shared bikes based on CES utility function and Lagrangian function. • This article predicted the carbon reduction of shared bicycles from 2025 to 2030 by comparing them with taxis. • This article collected data on people's perceptions of the environment and bike-sharing scenarios through a questionnaire. • This article considers the urban spatial structure and transportation cost, evaluates users' willingness and frequency.

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Publication Details

Journal
Journal of Urban Management
Published
2025-04-29
DOI
https://doi.org/10.1016/j.jum.2025.03.005
Citations
2
Primary Topic
Urban Transport and Accessibility
Type
article
Field-Weighted Citation Impact
4.35
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article

Prediction of the effect of bike-sharing on urban carbon emission reduction: Evidence from Beijing

Huijun Zhao, Zhong Lin Wang, Jiaming Luo, Fan Hu
2 citations
Journal of Urban Management
Urban Transport and Accessibility
4.35
article

Prediction of the effect of bike-sharing on urban carbon emission reduction: Evidence from Beijing

Huijun Zhao, Zhong Lin Wang, Jiaming Luo, Fan Hu
article en
2 citations

Abstract

Bike-sharing is regarded as a green and economical public transport mode. However, how to scientifically predict its carbon emission reduction effect is still on the way. This article builds an assessment model for emission reduction of bike-sharing. The modeling is based on the rational person hypothesis that consumers take transportation consumption expenditure as a budget constraint, and get the optimal number of trips of shared bikes for utility maximization. Then, we take Beijing as an example to carry out empirical research, whereby data on key influencing factors were collected through questionnaires and situational analysis. Finally, it is predicted that the carbon emission reduction contributed by bike-sharing in Beijing will be approximately 522,540.32–1,135,957.21 tons in 2025–2030. This study can provide a benchmark theoretical model for urban emission reduction, as well as a reference for public transport policy and environmental policy formulation. • This article builds a model for emission reduction of shared bikes based on CES utility function and Lagrangian function. • This article predicted the carbon reduction of shared bicycles from 2025 to 2030 by comparing them with taxis. • This article collected data on people's perceptions of the environment and bike-sharing scenarios through a questionnaire. • This article considers the urban spatial structure and transportation cost, evaluates users' willingness and frequency.

Journal of Urban ManagementVol. 14(4)
Guangdong University of Technology (CN), State Power Investment Corporation (China) (CN)
Climate action, Sustainable cities and communities
Openalex Percentile: Top 7%
Urban Transport and Accessibility
4.35
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Prediction of the effect of bike-sharing on urban carbon emission reduction: Evidence from Beijing — Huijun Zhao, Zhong Lin Wang, et al. · Journal of Urban Management (2025) | TGRS Research Map | TGRS