FAO estimates of net forest emissions and removals, 1990–2025

IF 13.6 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Francesco N. Tubiello,Anne Branthomme,Griffiths Obli-Laryea,Michelle Piccoli,Nidal Ramadan,Rocio Danica Condor,Nathan Wanner,Rachele Brivio,Örjan Jonsson,Giulia Conchedda,Anssi Pekkarinen
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引用次数: 0

Abstract

FAO officially collects, analyzes and disseminates forest data by country via the Forest Resources Assessment (FRA) program since the late 1950s. Since 2015, FAO uses a subset of the FRA data, namely forest area and carbon stocks, to compute and disseminate in FAOSTAT net forest emissions/removals estimates, as part of its efforts to assess emissions from agrifood systems. This paper presents the most recent update of the FAO net emission/removal estimates, based on estimates of carbon stock change from all pools, i.e., above- and below-ground living biomass, litter, dead wood and soils, covering over 200 countries and territories for the period 1990–2025. Results show that forests acted globally as net carbon sinks since the 2000s, on average ranging from −1.0 to -2.3GtCO2yr-1 over the most recent decade 2015–2025. This net flux was a result of substantial carbon removals on forest land over the same period, ranging from −3.8 to -6.1GtCO2yr-1, only partially counterbalanced by emissions from net forest loss of 2.8–3.8 Gt CO 2  yr −1  – used herein as a proxy of deforestation. Annex I (AI) and non-Annex I (NAI) countries contributed equally to the estimated forest removals, whereas 90 % of emissions from net forest loss were generated in NAI countries. Comparisons of the FAO estimates with data submitted independently by countries to the United Nations Framework Convention (UNFCCC), as well as with output from the Global Carbon Budget, showed good agreement at global, AI and NAI levels, including for major forest countries. At the same time, important discrepancies between FAO, UNFCCC and global carbon model data were identified in about a dozen countries. Improved convergence of evidence is achievable through dedicated statistical development programs in specific countries, to support international implementation and monitoring of effective climate mitigation actions. The FAO estimates and country data collected from UNFCCC were disseminated in FAOSTAT for 194 countries and 42 territories, with regional and global aggregates, in annual time-series covering the period 1990–2025. They were made available as open access at https://doi.org/10.5281/zenodo.20085666 (Tubiello and Obli-Laryea, 2026).
粮农组织对1990-2025年森林净排放量和清除量的估计
自1950年代末以来,粮农组织通过森林资源评估(FRA)计划正式收集、分析和传播各国的森林数据。自2015年以来,粮农组织利用森林资源评估数据的一个子集,即森林面积和碳储量,在粮农组织统计数据库中计算和传播森林净排放/清除估计数,作为其评估农业粮食系统排放工作的一部分。本文介绍了粮农组织净排放/清除估计数的最新更新,其依据是对1990-2025年期间200多个国家和地区的所有碳库(即地上和地下的活生物量、凋落物、枯死木和土壤)的碳储量变化估计数。结果表明,自2000年代以来,森林在全球范围内发挥了净碳汇的作用,在2015-2025年的最近十年中,平均碳汇量在- 1.0至-2.3亿吨二氧化碳年-1之间。这一净通量是同一时期林地大量碳清除的结果,范围为- 3.8至-6.1亿吨二氧化碳年-1,仅部分被森林净损失2.8-3.8亿吨二氧化碳年-1的排放抵消——本文用其作为森林砍伐的代用指标。附件一(人工智能)和非附件一(人工智能)国家对估计的森林砍伐贡献相同,而森林净损失排放的90%是由人工智能国家产生的。将粮农组织的估计与各国独立提交给《联合国气候变化框架公约》(UNFCCC)的数据以及全球碳预算(Global Carbon Budget)的数据进行比较,显示出在全球、人工智能和人工智能水平上的良好一致性,包括主要森林国家。与此同时,在大约十几个国家发现了粮农组织、联合国气候变化框架公约和全球碳模型数据之间的重大差异。通过在特定国家实施专门的统计发展计划,以支持有效的气候减缓行动的国际实施和监测,可以改善证据的趋同。粮农组织的估计和从《联合国气候变化框架公约》收集的国家数据在粮农组织统计数据库中以1990-2025年期间的年度时间序列传播,涉及194个国家和42个领土,包括区域和全球总量。它们在https://doi.org/10.5281/zenodo.20085666上开放获取(Tubiello和obi - laryea, 2026)。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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