SYSTEMATIC REVIEW article
Front. For. Glob. Change
Sec. Forest Management
Volume 8 - 2025 | doi: 10.3389/ffgc.2025.1649356
Global patterns in forest carbon storage estimation: Bibliometric analysis of technological evolution, accuracy gains and scaling challenges
Provisionally accepted
- 1Nanjing Forestry University, Nanjing, China
- 2Umea Universitet, Umeå, Sweden
- 3Jinling Institute of Technology, Nanjing, China
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Estimation of forest carbon (C) storage is essential for understanding the global C cycle, mitigating climate change, and developing carbon markets. However, systematic research on forest C storage estimation needs improving. Herein, a bibliometric and content review of literature published between 2008 and 2025 was conducted to synthesize temporal and spatial trends and to identify methodological advances and gaps in forest C-storage estimation. The results revealed that environmental sciences accounted for the largest share of publications (n = 718). The most productive institution and country were the Chinese Academy of Sciences (n = 208) and the United States (n = 691), respectively. Research progress in the field was categorized into three distinct stages since 2008. The early stage (2008-2012) was dominated by eddy covariance, satellite remote sensing, and airborne radar. The middle stage (2013-2017) was characterized by greater use of process-based and statistical simulation models. In the later stage (2018-2025), techniques such as random forest (RF), machine learning and biomass mapping became more widely used. Over this period, model performance improved substantially, especially the coefficient of determination (R2) increased from 0.62 to 0.97 for the TRIPLEX-Flux C-exchange model and from 0.63 to 0.97 for RF models. Spatially, most studies addressed local-to-regional scales, whereas large-scale or global assessments remain limited. This synthesis clarifies methodological trajectories and persistent gaps that can guide the development and wider deployment of forest C-storage estimation approaches and support evidence-based climate policy and C-market design.
Keywords: forests, Carbon storage estimation, Bibliometrics, biomass, Citespace
Received: 18 Jun 2025; Accepted: 17 Sep 2025.
Copyright: © 2025 Feng, Liu, Haibo, Cui, Zhou, Ma, Liu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hu Haibo, huhaibo@njfu.edu.cn
Peng Cui, cui.peng@umu.se
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