A high risk of overestimating emission reductions would be detrimental to the credibility of forest mitigation. But high-quality information on uncertainties in measuring emissions from forests is hard to obtain because of frequent shortcomings in uncertainty analyses. This paper aims to gauge what precision is achievable by examining data from several contexts (including data from 18 countries that have proposed jurisdictional mitigation programmes to the Forest Carbon Partnership Facility Carbon Fund). Countries reported random uncertainties in measuring forest carbon density (mostly 5–15% of the mean at the 90% confidence level), forest areas and their changes (mostly 0–20% for forest loss and forest degradation and 10–40% for forest gain), and greenhouse gas emissions (mostly 10–30%). It follows that uncertainties may be substantial in estimating emission reductions from forests and land-use change, and that these uncertainties entail significant risks of overestimation. I propose discount factors (between 9 and 44%) to conservatively adjust emission reduction estimates and reduce the overestimation risk. The paper concludes by pointing out that uncertainties are much lower for aggregate emission reductions of several programmes than they are for individual programmes. Discounting individual programmes’ emission reductions could therefore lead to understating the mitigation contribution that forests deliver.

高估减排量的高风险将不利于森林减缓的可信度。但是，由于不确定性分析经常存在缺陷，因此很难获得关于测量森林排放量不确定性的高质量信息。本文旨在通过检查来自多种背景的数据（包括来自向森林碳伙伴关系基金碳基金提出管辖缓解计划的 18 个国家的数据）来衡量可实现的精确度。各国报告了测量森林碳密度（主要是 90% 置信水平的平均值的 5-15%）、森林面积及其变化（主要是森林损失和森林退化的 0-20% 以及森林的 10-40%）的随机不确定性。增益）和温室气体排放（主要是 10-30%）。因此，在估算森林和土地利用变化的减排量时，不确定性可能很大，而这些不确定性带来了高估的重大风险。我建议使用折扣因子（在 9% 到 44% 之间）来保守地调整减排量估计并降低高估风险。本文最后指出，若干项目的总减排量的不确定性比单个项目的不确定性要低得多。因此，对个别计划的减排量进行折扣可能会导致低估森林提供的减缓贡献。我建议使用折扣因子（在 9% 到 44% 之间）来保守地调整减排量估计并降低高估风险。本文最后指出，若干项目的总减排量的不确定性比单个项目的不确定性要低得多。因此，对个别计划的减排量进行折扣可能会导致低估森林提供的减缓贡献。我建议使用折扣因子（在 9% 到 44% 之间）来保守地调整减排量估计并降低高估风险。本文最后指出，若干项目的总减排量的不确定性比单个项目的不确定性要低得多。因此，对个别计划的减排量进行折扣可能会导致低估森林提供的减缓贡献。