In the global effort to mitigate climate change, the parties of the United Nations Framework Convention on Climate Change (UNFCCC) are committed to producing annual reports on their national greenhouse gas (GHG) emissions. These reports are a valuable source of information. Among others, they can be used to measure the effectiveness of climate mitigation strategies over time. However, large parts of GHG inventories rely on estimated quantities and consequently, the reported figures are uncertain. Quantifying this uncertainty is crucial as it may affect our ability to distinguish the true trends from the intrinsic variability. In this study, five statistical techniques for uncertainty quantification, two of them being recommended by the Intergovernmental Panel on Climate Change (IPCC), were evaluated as to their ability to correctly estimate the variance. The standard Monte Carlo estimator, which is one of the two techniques recommended by the IPCC, tended to overestimate the true variance. It was no better than a naïve estimator. The propagation-based estimator, which is the other technique recommended by the IPCC, also tended to overestimate the true variance but to a lesser extent. Goodman’s estimator and a rescaled Monte Carlo estimator were both unbiased and consequently, they should be preferred when evaluating the performance of national climate mitigation policies.

在全球减轻气候变化的努力中，《联合国气候变化框架公约》（UNFCCC）的缔约方致力于就其国家温室气体（GHG）排放量编制年度报告。这些报告是有价值的信息来源。除其他外，它们可用于衡量缓解气候变化策略随时间推移的有效性。但是，很大一部分温室气体清单依赖估计数量，因此，报告的数字尚不确定。量化这种不确定性至关重要，因为它可能会影响我们从内在差异中区分真实趋势的能​​力。在这项研究中，采用了五种统计技术来量化不确定性，其中两种是政府间气候变化专门委员会（IPCC）推荐的，对他们正确估计方差的能力进行了评估。IPCC建议的两种技术之一是标准的蒙特卡洛估计器，它往往高估了真实的方差。这并不比单纯的估计好。IPCC推荐的另一种基于传播的估计器也倾向于高估真实方差，但幅度较小。古德曼（Goodman）的估计量和重新定标的蒙特卡洛（Monte Carlo）估计量均无偏见，因此，在评估国家减缓气候变化政策的绩效时应首选它们。IPCC推荐的另一种技术也倾向于高估真实差异，但幅度较小。古德曼（Goodman）的估计量和重新定标的蒙特卡洛（Monte Carlo）估计量均无偏见，因此，在评估国家减缓气候变化政策的绩效时应首选它们。IPCC推荐的另一种技术也倾向于高估真实差异，但幅度较小。古德曼（Goodman）的估计量和重新定标的蒙特卡洛（Monte Carlo）估计量均无偏见，因此，在评估国家减缓气候变化政策的绩效时应首选它们。