Impact of built environment on commuting carbon emissions using big data: a case study of Jinan’s main urban area

Abstract

Abstract Rapid urbanization and alterations in the built environment have exacerbated transportation energy consumption and environmental pollution, making transportation-related carbon emissions a significant barrier to low-carbon urban development. This study examines the influence of the built environment on commuting carbon emissions in the central urban area of Jinan, addressing the increasing challenge of transportation-induced emissions in rapidly urbanizing cities. Through the integration of multi-source big data, including travel trajectory, urban land use, and street view data, the research analyzes the spatial patterns of commuting behavior and emissions. Utilizing spatial autocorrelation, multiple linear regression, and geographically weighted regression (GWR), the study identifies critical factors influencing emissions, such as residential and commercial land area, transportation hubs, road network density, and floor area ratio. The results reveal that commuting emissions exhibit a monocentric pattern, with higher emissions in suburban areas due to lower population density and limited access to public transportation. Conversely, the central urban area of Jinan experience lower emissions, attributed to greater use of public transportation and shorter commuting distances. The GWR model uncovers spatial heterogeneity in the impact of the built environment, emphasizing the necessity for context-specific urban planning strategies. This research presents a comprehensive framework for reducing commuting carbon emissions, providing valuable insights for medium-sized cities striving to promote low-carbon transportation and optimize urban structures. The findings contribute to the formulation of targeted, data-driven policies for sustainable urban planning.

Keywords

• Commuting carbon emissions
• Built environment
• Geographical weighted regression
• Geographic detector