Exploring the Spatial Heterogeneity and Influence Factors of Daily Travel Carbon Emissions in Metropolitan Areas: From the Perspective of the 15-min City

Abstract

Most of the residents’ daily travel is concentrated within their 15-min walking distance. In China, derived from the 15-min city concept, the 15-min walkable area is often referred to as the 15-min pedestrian-scale neighborhood, and it has become a basic planning unit. Understanding the factors that influence the built environment of the 15-min pedestrian-scale neighborhood on the residents’ daily travel carbon emissions is critical to reduce urban carbon emissions. There may be spatial heterogeneity in daily travel carbon emissions as a dependent variable due to the spatial heterogeneity of built environment factors. Therefore, this study used data from the Wuhan City Resident Travel Survey to describe the spatial pattern of daily travel carbon emissions among Wuhan residents. The study examined the spatial heterogeneity of daily travel carbon emissions and explored the spatial differentiation of the built environment’s impact on daily travel carbon emissions within the 15-min pedestrian-scale neighborhood of the residents using spatial autocorrelation analysis and multi-scale geo-weighted regression (MGWR). The results indicate that Wuhan residents’ daily travel carbon emissions show an increasing circle structure from the center outward. In general, built environment elements in the 15-min pedestrian-scale neighborhood are closely related to the daily travel carbon emissions, and the direction and degree of impact of the built environment varies spatially. This study provides empirical evidence for controlling transportation carbon emissions.

Accurate Identification of Micro-scale Urban Functional Zones from the Perspective of Dialectical Materialism

“…POI data consists of spatial coordinates and attribute information associated with specific geographic locations [17][18][19] . Many existing studies have successfully utilized POI data as an efficient tool for identifying urban functional zones [20][21][22] . For example, Miao et al 23 obtained massive POI data from social media and applied the hierarchical clustering algorithm to identify urban functional zones.…”

Accurate Identification of Micro-scale Urban Functional Zones from the Perspective of Spatial Dialectic

“…POI data consists of spatial coordinates and attribute information associated with specific geographic locations [17][18][19] . Many existing studies have successfully utilized POI data as an efficient tool for identifying urban functional zones [20][21][22] . For example, Miao et al 23 obtained massive POI data from social media and applied the hierarchical clustering algorithm to identify urban functional zones.…”