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<p>While global transportation accounts for 23% of energy-related CO₂ emissions, current decarbonization strategies often neglect the transformative potential of local built environment design. We investigate how micro-level built environment improvements contribute to macro-level transitions in China’s transportation sector by integrating the Global Change Analysis Model with high-resolution (1 km) spatial analysis. We demonstrate that localized actions targeting density, diversity, design, destination accessibility, and distance to transit can reduce the structural transportation service demand potential for four-wheeled light-duty vehicles (TSD-LDV4W) by 21% nationally by 2060, approaching the ambitious SSP1 sustainability scenario. Interprovincial and urban-rural analyses reveal significant regional disparities in transition pathways driven by variations in economic conditions, technological capabilities, and geographical contexts, underscoring the pivotal role of meso-level institutional and structural characteristics in steering sustainability transitions. Machine learning quantitatively characterizes stark differences between historic pedestrian-centric cores and newly developed car-oriented districts, crucial for China’s transition from rapid urban expansion to urban renewal. Our findings demonstrate that neighborhood-scale interventions, including targeted infrastructure changes and local policy implementations, can substantially advance macro-level climate goals. These results suggest that context-sensitive strategies tailored to diverse development trajectories are essential for achieving low-carbon transportation transformation.</p>

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Diverse built environment pathways for bridging global ambitions with local initiatives in sustainable transportation transitions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Tao Wang,
  • Xin Tong,
  • Xiaolei Shi

摘要

While global transportation accounts for 23% of energy-related CO₂ emissions, current decarbonization strategies often neglect the transformative potential of local built environment design. We investigate how micro-level built environment improvements contribute to macro-level transitions in China’s transportation sector by integrating the Global Change Analysis Model with high-resolution (1 km) spatial analysis. We demonstrate that localized actions targeting density, diversity, design, destination accessibility, and distance to transit can reduce the structural transportation service demand potential for four-wheeled light-duty vehicles (TSD-LDV4W) by 21% nationally by 2060, approaching the ambitious SSP1 sustainability scenario. Interprovincial and urban-rural analyses reveal significant regional disparities in transition pathways driven by variations in economic conditions, technological capabilities, and geographical contexts, underscoring the pivotal role of meso-level institutional and structural characteristics in steering sustainability transitions. Machine learning quantitatively characterizes stark differences between historic pedestrian-centric cores and newly developed car-oriented districts, crucial for China’s transition from rapid urban expansion to urban renewal. Our findings demonstrate that neighborhood-scale interventions, including targeted infrastructure changes and local policy implementations, can substantially advance macro-level climate goals. These results suggest that context-sensitive strategies tailored to diverse development trajectories are essential for achieving low-carbon transportation transformation.