Comparison of uncertainty quantification techniques for national greenhouse gas inventories

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.