An inventory of industrial solid waste in 337 cities of China: Applying machine learning for data completion

Abstract

Abstract Rapid industrialization of China generated a massive quantity of waste, among them industrial solid waste contributed the biggest flow to some 60 gigatonnes (Gt) in the past two decades. A complete tempo-spatial dataset of industrial waste, however, is absent in many areas in China, due to numerous waste producers and insufficient statistical coverage. To fill up the gap, we collected current available data from thousands of sources. We further developed six machine learning models to complete the dataset across all the 337 cities in China for the period 1990–2022. Bayesian optimization was employed to obtain the best estimation model for each city and to enhance its performance and resilience. In addition to the aggregate waste amount, generation of six major subcategories of industrial waste, i.e., metallurgical slags, fly ash, furnace slags, coal gangue, tailings, and desulfurization gypsum, are presented for more than half of the cities in 2022. This dataset can help researchers and policymakers recognize and address challenges brought by industrial waste.