Mapping the Recycling Potential of Bottom Ashes from Waste-to-Energy Plants toward Circular Economy: Evidence from China

  • Jiayue Zhang
  • , Yi Zhang
  • , Zheng Hao Leong
  • , Yixiang Zhang
  • , Tan Chen
  • , Fan Fei
  • , Zongguo Wen*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The rapid expansion of waste-to-energy (WtE) facilities underscores the need for sustainable management of incinerator bottom ash (IBA) to advance circular economy goals. However, China’s national potential, economic, and environmental performance in IBA recycling remain underexplored. This study employs a data-driven machine learning model to assess the recycling potential and spatial distribution of various IBA components, based on a survey of 177 WtE plants and 45 IBA recovery facilities. Using life cycle assessment and cost-benefit analysis with Monte Carlo sampling under the price uncertainties, we evaluate IBA recycling performance across six environmental impact categories and two economic indicators. China produces approximately 92.11 Mt of IBA annually, including 0.13 Mt of copper, 0.90 Mt of aluminum, 3.87 Mt of iron, and 83.85 Mt of aggregates. Recycling IBA from 1 ton of waste reduces greenhouse gas emissions by 49.34 kg CO2equivalent, conserves 21.88 kg of fossil fuels, and mitigates the impacts of mineral extraction and toxicity. Economically, IBA recycling generates net revenues of 0.94–15.37 USD per ton of waste, comparable to waste disposal fees, enhancing the financial sustainability of WtE. This study establishes China’s first national IBA recycling inventory, emphasizing its role in climate mitigation, energy savings, and economic viability, and outlining a pathway for sustainable waste management within a circular economy framework.

Original languageEnglish
Pages (from-to)20805-20816
Number of pages12
JournalEnvironmental Science and Technology
Volume59
Issue number38
DOIs
Publication statusPublished - 30 Sept 2025
Externally publishedYes

Keywords

  • circular economy
  • incineration bottom ash
  • machine learning
  • waste-to-energy

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