The Impact of Urban Form on Carbon Emission Efficiency Under Public Transit-Oriented Development: Spatial Heterogeneity and Driving Forces

Urban form optimization is crucial for controlling carbon emissions. Taking Shenzhen as a case study with 2022 data, this research constructs a multidimensional indicator system covering land use, functional mix, transportation structure, and spatial layout. It incorporates both static (inventory-based) and dynamic (transit-based) carbon efficiency metrics to capture complementary urban emission patterns. We employed OLS, GWR, and quantile regression methods to identify key influencing factors, spatial variations, and their impact on carbon emission efficiency. Results show that (1) compact road infrastructure and dense transit systems in the southwestern core contribute to higher efficiency, whereas extensive green coverage in eastern areas facilitates carbon sequestration; (2) elevated population and building densities in central zones are linked with lower efficiency, implying the necessity for balanced spatial redistribution and peripheral infrastructure enhancement; (3) despite comprehensive transit electrification, further improvements in network density and accessibility are essential to enhance urban low-carbon outcomes. These results establish a basis for optimizing urban spatial layout and reducing carbon emissions.

Authors

Institutions

Publication Details

Journal
Land
Published
2025-05-29
DOI
https://doi.org/10.3390/land14061172
Citations
2
Primary Topic
Urban Transport and Accessibility
Type
article
Field-Weighted Citation Impact
4.35

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Impact of Urban Form on Carbon Emission Efficiency Under Public Transit-Oriented Development: Spatial Heterogeneity and Driving Forces

X.‐J. Li, Tengxiang Pan, Xuecai Dong, Chun Zhang
2 citations
Land
Urban Transport and Accessibility
4.35
article

The Impact of Urban Form on Carbon Emission Efficiency Under Public Transit-Oriented Development: Spatial Heterogeneity and Driving Forces

X.‐J. Li, Tengxiang Pan, Xuecai Dong, Chun Zhang
article en
2 citations

Abstract

Urban form optimization is crucial for controlling carbon emissions. Taking Shenzhen as a case study with 2022 data, this research constructs a multidimensional indicator system covering land use, functional mix, transportation structure, and spatial layout. It incorporates both static (inventory-based) and dynamic (transit-based) carbon efficiency metrics to capture complementary urban emission patterns. We employed OLS, GWR, and quantile regression methods to identify key influencing factors, spatial variations, and their impact on carbon emission efficiency. Results show that (1) compact road infrastructure and dense transit systems in the southwestern core contribute to higher efficiency, whereas extensive green coverage in eastern areas facilitates carbon sequestration; (2) elevated population and building densities in central zones are linked with lower efficiency, implying the necessity for balanced spatial redistribution and peripheral infrastructure enhancement; (3) despite comprehensive transit electrification, further improvements in network density and accessibility are essential to enhance urban low-carbon outcomes. These results establish a basis for optimizing urban spatial layout and reducing carbon emissions.

LandVol. 14(6)
Beijing Jiaotong University (CN), Peng Cheng Laboratory (CN), City University of Macau (MO)
National Natural Science Foundation of China
Openalex Percentile: Top 7%
Urban Transport and Accessibility
4.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.