{"title":"A glance at solid waste disposal research progress using bibliometric analysis","authors":"Lvhan Zhu, Zixiao Wu, Dongsheng Shen, Foquan Gu, Lulu Wang, Yuyang Long","doi":"10.1007/s10163-025-02273-w","DOIUrl":null,"url":null,"abstract":"<div><p>Solid waste disposal is a complex field. Based on bibliometric analysis method, the research progress of solid waste disposal was evaluated with Web of Science core database, VOSviewer and Citespace. It shows that the strongest burst keywords, neural networks first appeared in 2008 and have been developing ever since. Then, the models and higher heating value appeared after 2016 and 2020, respectively. In 2008–2024, 70 % of the top journals and 30 % of the top paper related with energy production prediction. The hot spots are municipal solid waste (59.5 %), resource generation and heat treatment (54.4 %), neural network (77.3 %), and optimization and prediction (58 %). Overall, energy production prediction and sustainable development are the main research progress of the field by introducing machine learning method. This research provides reference value of the future research on solid waste disposal.</p></div>","PeriodicalId":643,"journal":{"name":"Journal of Material Cycles and Waste Management","volume":"27 4","pages":"2699 - 2709"},"PeriodicalIF":3.0000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Material Cycles and Waste Management","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10163-025-02273-w","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Solid waste disposal is a complex field. Based on bibliometric analysis method, the research progress of solid waste disposal was evaluated with Web of Science core database, VOSviewer and Citespace. It shows that the strongest burst keywords, neural networks first appeared in 2008 and have been developing ever since. Then, the models and higher heating value appeared after 2016 and 2020, respectively. In 2008–2024, 70 % of the top journals and 30 % of the top paper related with energy production prediction. The hot spots are municipal solid waste (59.5 %), resource generation and heat treatment (54.4 %), neural network (77.3 %), and optimization and prediction (58 %). Overall, energy production prediction and sustainable development are the main research progress of the field by introducing machine learning method. This research provides reference value of the future research on solid waste disposal.
固体废物处理是一个复杂的领域。基于文献计量分析方法，利用Web of Science核心数据库、VOSviewer和Citespace对固体废物处理的研究进展进行了评价。它表明，最强的突发关键词，神经网络首次出现在2008年，并一直在发展。2016年和2020年之后分别出现了模型和更高的热值。2008-2024年，70%的顶级期刊和30%的顶级论文与能源生产预测有关。城市生活垃圾（59.5%）、资源生成与热处理（54.4%）、神经网络（77.3%）和优化与预测（58%）是研究热点。总的来说，通过引入机器学习方法，能源生产预测和可持续发展是该领域的主要研究进展。本研究对今后固体废物处理的研究具有参考价值。
期刊介绍：
The Journal of Material Cycles and Waste Management has a twofold focus: research in technical, political, and environmental problems of material cycles and waste management; and information that contributes to the development of an interdisciplinary science of material cycles and waste management. Its aim is to develop solutions and prescriptions for material cycles.
The journal publishes original articles, reviews, and invited papers from a wide range of disciplines related to material cycles and waste management.
The journal is published in cooperation with the Japan Society of Material Cycles and Waste Management (JSMCWM) and the Korea Society of Waste Management (KSWM).
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