Review of material flow analysis and its application under carbon neutralization target: a bibliometric perspective

Lina Liu, Jiansheng Qu, Xuemei Li, Qin Liao, Yibo Niu
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Abstract

Material flow analysis (MFA) could provide methodological support for quantifying material carbon flow and carbon emissions in the pursuit of carbon neutrality. Despite the extensive publication of MFA studies in academic journals, significant challenges remain on MFA and its application in carbon emission (MFA-CE) research, including identifying emerging research trends. This paper reviews MFA and MFA-CE research based on bibliometric analysis of data from WOS (Web of Science) platform, spanning from 1991 to 2022. We find that over the last 32 years: (1) Both MFA and MFA-CE research share similar article characteristics, such as rapid and active fluctuations in trends, with high-output countries primarily being the USA, China, and others; (2) MFA and MFA-CE are multidisciplinary fields, showing the fastest growth in Environmental Studies and Economics research; (3) Highly cited papers mainly focus on global material flow, environmental impact, and recycling. Notably, high citation analysis shows that both MFA and MFA-CE research have garnered substantial attention since 2014; and (4) Combining MFA with other methods would help identify material flow, such as carbon material flow. Additionally, the future perspectives of MFA-CE research were summarized: increasing interdisciplinary cooperation; a growing emphasis on multi-scale research; and enhanced availability and application of data.
碳中和目标下的物质流分析及其应用回顾:文献计量学视角
物质流分析(MFA)可为量化物质碳流和碳排放提供方法支持,以实现碳中和。尽管在学术期刊上发表了大量的 MFA 研究,但 MFA 及其在碳排放(MFA-CE)研究中的应用仍面临着巨大挑战,包括确定新出现的研究趋势。本文基于对 WOS(Web of Science)平台数据的文献计量分析,回顾了从 1991 年到 2022 年的 MFA 和 MFA-CE 研究。我们发现,在过去的 32 年中:(1)MFA 和 MFA-CE 研究都有相似的文章特点,如趋势波动迅速而活跃,高产出国家主要是美国、中国和其他国家;(2)MFA 和 MFA-CE 是多学科领域,其中环境研究和经济学研究增长最快;(3)高被引论文主要集中在全球物质流、环境影响和循环利用等方面。值得注意的是,高被引分析表明,自 2014 年以来,MFA 和 MFA-CE 研究都获得了大量关注;以及(4)将 MFA 与其他方法相结合将有助于识别物质流,如碳物质流。此外,还总结了 MFA-CE 研究的未来前景:加强跨学科合作;日益重视多尺度研究;提高数据的可用性和应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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