基于文献计量分析的大数据驱动城市固体废物监管研究进展

利用文献计量学方法,对Web of Science核心合集数据库中2010—2020年大数据驱动固体废物监管的研究论文开展综述研究,通过对发文量、发文机构、出版物、关键词等文献指标统计分析,全面了解研究现状,掌握发展趋势,洞悉前沿热点,以期为推进城市固体废物信息化和智能化管理提供科学依据。研究发现:在检索时间段内发文数量呈逐年上升趋势,但总发文量相对较少,共计83篇,说明该研究属于新兴、前沿性的领域;发表载体主要包括期刊论文、会议论文和综述论文3种类型,论文主要在Sustainability、Journal of Cleaner Production和Waste Management等期刊发表,且引用频次较高;既有研究主要考虑数据工程和数据科学2个维度的应用,以实现固体废物全生命周期的节点管控,前者关注数据源获取以记录废物的流向与流量信息,后者通过对各类大数据进行建模分析,为提升固体废物管控效率提供决策支持。

A bibliometric analysis of research development regarding big data-driven municipal waste management

The bibliometric approach was used to develop a holistic review on the research progress of big data driven municipal waste management. By using the literatures retrieved by the core collection database of Web of Science during the period of 2010-2020, a number of bibliometric indicators were incorporated into statistical analysis, including number of articles, research institutions, source journals and keywords, to understand its research status, grasp its development trend and identify the research hotspots, so as to provide a scientific basis for promoting the informatization and intelligent management of municipal solid waste management. The results showed that: The number of articles published increased year by year during the predefined time period, but the total number of articles published was relatively small, a total of 83, indicating that the research still belonged to a new and cutting-edge field. Publication carriers mainly included journal articles, conference articles and review articles, among which articles were mainly published in journals such as Sustainability, Journal of Cleaner Production, and Waste Management and had high citation frequency. Existing studies mainly considered the application of two dimensions of data engineering and data science to achieve the node management and control of the whole lifecycle of municipal waste, in which the former mainly focused on data source acquisition to record the flow direction and information of the waste lifecycle, and the latter provided decision support for improving management efficiency through modeling and analyzing all kinds of big data.