文章编号： 1672-5328（2025）05-0024-11

郑登耀1，孔奥2
(1. 重庆交通大学交通运输学院，重庆400074；2.江西省交通设计研究院有限责任公司，江西南昌330052)

摘要： 建成环境对城市轨道交通出行碳减排效率的影响存在显著空间异质性，如何精准识别核心与 外围组团之间的差异化驱动机制，已成为多中心组团城市低碳规划面临的关键难题。以重庆市为 例，基于城市轨道交通IC 卡数据，首先分析出行特征，继而采用“自下而上”法进行碳排放测 算，基于OLS，GWR和MGWR模型，构建城市轨道交通出行碳排放与建成环境要素之间的关系模 型，最终系统分析各要素对碳排放的影响。模型对比结果表明，MGWR模型拟合效果最优，其调 整后的R2 较OLS和GWR模型分别提高48.13%和3.11%。研究发现，建成环境对碳排放的影响具有 显著空间分异：在核心组团，POI 密度、公共汽车线路数和距主要商圈距离对碳减排呈现显著正向 影响，而城市轨道交通车站周边平均坡度则表现为抑制作用；在外围组团，距商圈距离的影响最为 突出，POI密度与车站周边平均坡度等因素影响相对较弱。

关键词： 城市轨道交通；碳排放；多中心组团；建成环境；MGWR模型；重庆市

中图分类号： U491

文献标识码：A

The Impact of the Built Environment in Polycentric Agglomeration-Based Cities on Carbon Emissions from Rail Transit Travel: A Case Study of Chongqing

ZHENG Dengyao1, KONG Ao2
(1. School of Transportation, Chongqing Jiaotong University, Chongqing 400074, China; 2. Jiangxi Communications Design and Research Institute Co., Ltd., Nanchang Jiangxi 330052, China)

Abstract： The influence of the built environment on the carbon reduction efficiency of urban rail transit travel exhibits significant spatial heterogeneity. How to accurately identify the differentiated driving mechanism between core and peripheral agglomerations has become a critical challenge for low- carbon planning in polycentric agglomeration-based cities. Using Chongqing as a case study and based on IC card data from urban rail transit, this paper first analyzes travel characteristics and subsequently applies a bottom-up approach to measure carbon emissions. Based on OLS, GWR, and MGWR models, a relationship model is developed between carbon emissions from rail transit travel and built environment factors, followed by a systematic analysis of the impact of each factor on carbon emissions. Comparative results demonstrate that the MGWR model achieves the best fit, with the adjusted R2 values 48.13% and 3.11% higher than those of the OLS and GWR models, respectively. The findings reveal a significant spatial differentiation in the influence of the built environment on carbon emissions: in core agglomerations, POI density, number of bus routes, and distance to major commercial districts exert significant positive effects on carbon reduction, whereas the average slope around rail transit stations serves as a suppressive factor. In peripheral agglomerations, distance to commercial districts emerges as the most prominent factor, while influences from POI density and station-area slope are relatively weak.

Keywords： urban rail transit; carbon emissions; polycentric agglomerations; built environment; MGWR model; Chongqing