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Environmental burden and health inequity in China’s road-based express delivery

Nature Cities volume 2, pages 825–834 (2025)Cite this article

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Abstract

Urban e-commerce growth has driven unprecedented expansion in express delivery services, yet their cross-regional environmental and health consequences remain poorly understood. Here we present a novel spatially explicit assessment of emissions and their environmental burden in China’s express delivery sector by integrating large-scale shipping records, geospatial modeling and atmospheric chemical transport models. In 2021, express delivery transportation emitted 23.9-Mt CO2-equivalent and 166.4-kt atmospheric pollutant equivalents, creating substantial environmental inequality. These emissions and associated health impacts disproportionately affect key transit regions connecting major urban agglomerations, which handled only 12.7% of parcels but accounted for 37.3% of the total emissions, with 75.2% of their air-pollution-related premature deaths from other regions’ delivery activities. Express-delivery-related pollution caused 5,100 premature deaths in 2021, yet implementing synergistic mitigation strategies could prevent over 256,000 cumulative premature deaths by 2050, underscoring the need for sustainable logistics that balance urban convenience with environmental externalities.

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Fig. 1: Spatial distribution and flow patterns of express delivery in China for 2021.
Fig. 2: Spatial distribution of GHG and APE emissions from express delivery transportation in China.
Fig. 3: Emission and health burden inequalities in express delivery sector.
Fig. 4: Projections of express delivery transportation emissions and health impacts through 2050 under various scenarios.

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Data availability

The gridded road-based express delivery emissions inventory data we developed are openly available via Zenodo at https://doi.org/10.5281/zenodo.15726553 (ref. 48). Road network data are from OpenStreetMap (https://www.openstreetmap.org/); meteorological data are from ECMWF Reanalysis v. 5 (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) and NASA MERRA-2 (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/); population data are from LandScan (https://landscan.ornl.gov/); anthropogenic emissions are from ABaCAS-EI v. 2.0 (ref. 42); and air quality monitoring data are from the China National Environmental Monitoring Centre (https://quotsoft.net/air/).

Code availability

The analysis was conducted using ArcGIS v. 10.8, Python 3.11 and MATLAB R2021b. Route identification, emission calculations and spatial allocation were performed using existing ArcGIS Network Analyst and Spatial Analysis tools. Custom code for the gridded transportation distance calculations and Monte Carlo uncertainty analysis we developed are deposited via Zenodo at https://doi.org/10.5281/zenodo.15726553 (ref. 48). Air quality simulations used GEOS-Chem v. 13.4.0 and WRF-Chem v. 4.6.0, with source codes freely available at https://geoschem.github.io/ and https://github.com/wrf-model/WRF/releases, respectively.

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Acknowledgements

We acknowledge the Air Benefit and Cost and Attainment Assessment System—Emission Inventory team (ABaCAS-EI, China) for making their data publicly available. We express our gratitude to the developers of GEOS-Chem for openly sharing their source code. This work was supported by the National Natural Science Foundation of China (no. 42377393 to B.L. and no. 42021004 to H. Liao); Jiangsu Science Fund for Carbon Neutrality (no. BK20220031 to H. Liao), and the NUIST-Harvard Joint Laboratory for Air Quality and Climate (JLAQC).

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Authors and Affiliations

  1. State Key Laboratory of Climate System Prediction and Risk Management, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China

    Baojie Li, Hong Liao, Ke Li, Lei Chen, Yang Yang, Xipeng Jin, Yongqi Zhao & Jianbing Jin

  2. Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

    Jintai Lin

  3. Department Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, Germany

    Cheng Gong

  4. State Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing, China

    Huan Liu

  5. School of Urban Planning and Design, Peking University Shenzhen Graduate School, Peking University, Shenzhen, China

    Yan Li

  6. College of Oceanography, Hohai University, Nanjing, China

    Teng Wang

  7. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Ruijun Dang & Daniel J. Jacob

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Contributions

B.L., H. Liao and D.J.J. designed the research. B.L., H. Liao, K.L. and J.L. performed the research. C.G., Y.L., L.C., Y.Y., X.J., Y.Z. and T.W. analyzed the data. B.L., H. Liao, K.L., H. Liu and D.J.J. wrote the paper. J.J. and R.D. reviewed the paper.

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Correspondence to Hong Liao.

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Nature Cities thanks Enrico Pisoni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 GHG footprint from parcels originating in the PRD region.

Map created using ArcGIS (v. 10.8, Esri), with administrative boundaries from the Standard Map Service System, Ministry of Natural Resources of China (http://bzdt.ch.mnr.gov.cn/index.html).

Extended Data Fig. 2 Provincial allocation of air pollution-related premature mortality attribution (deaths).

Arrows flow from provinces where deaths occur to those responsible for the emissions causing these health impacts. The attribution allocation reveals substantial cross-provincial impacts, as exemplified by Jiangxi, where 94.5% of premature deaths are associated with other regions’ express delivery activities.

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Supplementary Sections 1–5, Figs. 1–17 and Tables 1–6.

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Statistical source data for Fig. 1b.

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Statistical source data for Fig. 3a,c.

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Statistical source data for Fig. 4.

Source Data Extended Data Fig. 2

Statistical source data for Extended Data Fig. 2.

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Li, B., Liao, H., Li, K. et al. Environmental burden and health inequity in China’s road-based express delivery. Nat Cities 2, 825–834 (2025). https://doi.org/10.1038/s44284-025-00300-3

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