<p>The rapid growth of the takeaway industry has generated considerable takeaway packaging waste (TPW). Current research focuses on the environmental impacts from takeaway packaging at the regional or city level, which may offer limited implications for localized targeted policymaking. In this study, a high-resolution gridded dataset is provided for annual estimates of TPW in China at a grid resolution of 1 km × 1 km. Seven takeaway packaging categories and their usage probabilities are considered in the estimation, and the results are spatially distributed at the grid scale based on the geographic scope of takeaway delivery. The dataset reveals that a total of 366.66 kt of TPW were generated in China in 2018. The top 10% of grids with the highest TPW generation accounted for 64% of the national TPW. The TPW hotspots are typically located in commercial and residential areas. This dataset could promote effective management of TPW, and support strategies to reduce municipal solid waste in line with the “Zero-waste City” program.</p>

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A high resolution gridded dataset for takeaway packaging waste in China

  • Pingda Lu,
  • Ya Zhou,
  • Qiuli Liu,
  • Aiqun Guan,
  • Zhifeng Yang

摘要

The rapid growth of the takeaway industry has generated considerable takeaway packaging waste (TPW). Current research focuses on the environmental impacts from takeaway packaging at the regional or city level, which may offer limited implications for localized targeted policymaking. In this study, a high-resolution gridded dataset is provided for annual estimates of TPW in China at a grid resolution of 1 km × 1 km. Seven takeaway packaging categories and their usage probabilities are considered in the estimation, and the results are spatially distributed at the grid scale based on the geographic scope of takeaway delivery. The dataset reveals that a total of 366.66 kt of TPW were generated in China in 2018. The top 10% of grids with the highest TPW generation accounted for 64% of the national TPW. The TPW hotspots are typically located in commercial and residential areas. This dataset could promote effective management of TPW, and support strategies to reduce municipal solid waste in line with the “Zero-waste City” program.