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<p>The logistics sector is one of the significant contributors to carbon emissions in China, making the improvement of carbon emission efficiency (CEE) an urgent priority. This study leverages a quasi-natural experiment and employs a spatial difference-in-differences (SDID) model to examine the impact of the national Logistics Hub Layout and Construction Plan (LHP) on urban CEE. The results reveal that the LHP significantly improves local CEE, while generating negative spatial spillover effects on neighboring cities—particularly those located within a 350&#xa0;km radius. Spatial mechanism analysis reveals that logistics hubs primarily enhance CEE through industrial structure optimization, technological innovation, logistics expansion, and logistics workforce development. The findings remain robust across various model specifications, including those using the debiased machine learning difference-in-differences (DMLDID) approach and placebo tests. Heterogeneity analysis based on a spatial difference-in-difference-in-difference (SDDD) framework reveals that seaport hubs yield the greatest local gains in CEE, while production service hubs impose the strongest negative spillovers on neighboring areas. Moreover, composite hub cities are more effective at mitigating carbon spillovers compared to single hub cities. This study offers valuable insights for promoting energy-efficient urban development and regionally coordinated emission reduction strategies.</p>

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Logistics hub policies and carbon emission efficiency: insights into emission reduction in China

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Hua Yao,
  • Xinlian Yu,
  • Haijun Mao,
  • Huansong Zhang,
  • Russell Thompson

摘要

The logistics sector is one of the significant contributors to carbon emissions in China, making the improvement of carbon emission efficiency (CEE) an urgent priority. This study leverages a quasi-natural experiment and employs a spatial difference-in-differences (SDID) model to examine the impact of the national Logistics Hub Layout and Construction Plan (LHP) on urban CEE. The results reveal that the LHP significantly improves local CEE, while generating negative spatial spillover effects on neighboring cities—particularly those located within a 350 km radius. Spatial mechanism analysis reveals that logistics hubs primarily enhance CEE through industrial structure optimization, technological innovation, logistics expansion, and logistics workforce development. The findings remain robust across various model specifications, including those using the debiased machine learning difference-in-differences (DMLDID) approach and placebo tests. Heterogeneity analysis based on a spatial difference-in-difference-in-difference (SDDD) framework reveals that seaport hubs yield the greatest local gains in CEE, while production service hubs impose the strongest negative spillovers on neighboring areas. Moreover, composite hub cities are more effective at mitigating carbon spillovers compared to single hub cities. This study offers valuable insights for promoting energy-efficient urban development and regionally coordinated emission reduction strategies.