@article{Zou2026, 
author = {Qiuxia Zou and Zeguo Yang and Qingqing Zhou and Xiaoping Wang and Huijie Hou and Xiong Shi and Yunsheng Deng and Huabo Duan and Jiakuan Yang},
title = {Industrial solid waste management systems: Model development and strategic implementation},
year = {2026},
journal = {Circular Economy},
volume = {5},
number = {2},
pages = {100192},
keywords = {Industrial solid waste, Machine learning, Stacked modeling, Sustainability, China},
url = {https://www.sciopen.com/article/10.1016/j.cec.2026.100192},
doi = {10.1016/j.cec.2026.100192},
abstract = {Industrial solid waste (ISW) is a critical issue for sustainable development. Strengthening ISW management is essential for promoting the clean production process and transforming industrial structures towards eco-friendly models. The present study developed an advanced management system to identify the influencing factors of ISW generation and conduct projections, supported by machine learning methods. The results show that the stacking model StackNet2 is the optimal model for China's ISW generation projection, with a mean absolute percentage error value of 6.65%. Scenario analyses reveal that China's ISW generation continues to rise overall in the baseline scenario and stabilizes at 7.1 (±0.36) billion tons in 2050, while the positive circular economy and technology-driven scenarios decrease it to approximately 3 billion tons with a 42% reduction. The study further uncovers dynamic variations in waste management efficiency, where utilization efficiency is not only influenced by technological and managerial strategies but also significantly depends on the scale of waste generation. Based on these predictions, the constructed ISW management system is expected to help policymakers optimize solid waste management decision-making.}
}