Abstract. Global multi-regional input–output (GMRIO) analysis is the standard tool to calculate consumption-based carbon accounts at the macro level. Recent inter-database comparisons have exposed discrepancies in GMRIO-based results, pinpointing greenhouse gas (GHG) emission accounts as the primary source of variation. A few studies have analysed the robustness of GHG emission accounts, using Monte Carlo simulations to understand how uncertainty from raw data propagates to the final GHG emission accounts. However, these studies often make simplistic assumptions about raw data uncertainty and ignore correlations between disaggregated variables. Here, we compile GHG emission accounts for the year 2015 according to the resolution of EXIOBASE V3, covering CO2, CH4 and N2O emissions. We propagate uncertainty from the raw data, i.e. the United Nations Framework Convention on Climate Change (UNFCCC) and EDGAR inventories, to the GHG emission accounts and then further to the GHG footprints. We address both limitations from previous studies. First, instead of making simplistic assumptions, we utilise authoritative raw data uncertainty estimates from the National Inventory Reports (NIRs) submitted to the UNFCCC and a recent study on uncertainty of the EDGAR emission inventory. Second, we account for inherent correlations due to data disaggregation by sampling from a Dirichlet distribution. Our results show a median coefficient of variation (CV) for GHG emission accounts at the country level of 4 % for CO2, 12 % for CH4 and 33 % for N2O. For CO2, smaller economies with significant international aviation or shipping sectors show CVs as high as 94 %, as seen in Malta. At the sector level, uncertainties are higher, with median CVs of 94 % for CO2, 100 % for CH4 and 113 % for N2O. Overall, uncertainty decreases when propagated from GHG emission accounts to GHG footprints, likely due to the cancelling-out effects caused by the distribution of emissions and their uncertainties across global supply chains. Our GHG emission accounts generally align with official EXIOBASE emission accounts and OECD data at the country level, though discrepancies at the sectoral level give cause for further examination. We provide our GHG emission accounts with associated uncertainties and correlations at https://doi.org/10.5281/zenodo.10041196 (Schulte et al., 2023) to complement the official EXIOBASE emission accounts for users interested in estimating the uncertainties of their results.

中文翻译：

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全球多区域投入产出分析中温室气体排放核算的不确定性估算


摘要。全球多区域投入产出（GMRIO）分析是在宏观层面计算基于消费的碳账户的标准工具。最近的数据库间比较暴露了基于 GMRIO 的结果的差异，明确指出温室气体 (GHG) 排放账户是变化的主要来源。一些研究分析了温室气体排放账户的稳健性，使用蒙特卡罗模拟来了解原始数据的不确定性如何传播到最终的温室气体排放账户。然而，这些研究经常对原始数据的不确定性做出简单化的假设，而忽略分类变量之间的相关性。在此，我们根据EXIOBASE V3的决议编制了2015年温室气体排放核算，涵盖CO2、CH4和N2O排放量。我们将原始数据（即联合国气候变化框架公约 (UNFCCC) 和 EDGAR 清单）的不确定性传播到温室气体排放账户，然后进一步传播到温室气体足迹。我们解决了之前研究的两个局限性。首先，我们没有做出简单化的假设，而是利用提交给 UNFCCC 的国家清单报告 (NIR) 的权威原始数据不确定性估计以及最近关于 EDGAR 排放清单不确定性的研究。其次，我们通过狄利克雷分布采样来解释由于数据分解而产生的固有相关性。我们的结果显示，国家层面温室气体排放的变异系数 (CV) 中位数为：CO2 为 4%，CH4 为 12%，N2O 为 33%。对于二氧化碳而言，拥有重要国际航空或航运业的较小经济体的 CV 高达 94%，如马耳他所示。在行业层面，不确定性较高，CO2 的中位 CV 为 94%，CH4 为 100%，N2O 为 113%。 总体而言，当从温室气体排放账户传播到温室气体足迹时，不确定性会减少，这可能是由于全球供应链中排放分布及其不确定性造成的抵消效应。我们的温室气体排放账户总体上与国家层面的官方 EXIOBASE 排放账户和经合组织数据一致，尽管部门层面的差异需要进一步检查。我们在 https://doi.org/10.5281/zenodo.10041196（Schulte 等人，2023）提供温室气体排放账户以及相关的不确定性和相关性，以补充官方 EXIOBASE 排放账户，供有兴趣估计其结果不确定性的用户使用。