Estimating the uncertainty of the greenhouse gas emission accounts in global multi-regional input–output analysis

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Simon Schulte, Arthur Jakobs, Stefan Pauliuk
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引用次数: 0

Abstract

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.
估算全球多地区投入产出分析中温室气体排放账户的不确定性
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。最近进行的数据库间比较显示,基于全球多区域投入产出分析的结果存在差异,温室气体排放账户是造成差异的主要原因。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,这些研究往往对原始数据的不确定性做出简单化的假设,并忽略了分类变量之间的相关性。在此,我们根据 EXIOBASE V3 的分辨率编制了 2015 年的温室气体排放账户,涵盖 CO2、CH4 和 N2O 排放。我们将不确定性从原始数据(即《联合国气候变化框架公约》(UNFCCC)和 EDGAR 清单)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。我们解决了以往研究的两个局限性。首先,我们利用了提交给《联合国气候变化框架公约》(UNFCCC)的国家清单报告(NIR)中的权威原始数据不确定性估算,以及最近关于 EDGAR 排放清单不确定性的研究,而不是简单化的假设。其次,我们通过从 Dirichlet 分布中采样,考虑了数据分解导致的固有相关性。我们的结果表明,国家一级温室气体排放账户的中位变异系数(CV)分别为:二氧化碳 4%、甲烷 12%、一氧化二氮 33%。就二氧化碳而言,拥有重要国际航空或海运部门的较小经济体的变异系数高达 94%,马耳他就是如此。在部门层面,不确定性更高,二氧化碳、甲烷和一氧化二氮的不确定性中位数分别为 94%、100% 和 113%。总体而言,从温室气体排放账户到温室气体足迹的传播过程中,不确定性会降低,这可能是由于全球供应链的排放分布及其不确定性造成的抵消效应。在国家层面,我们的温室气体排放核算与 EXIOBASE 排放核算和经合组织的官方数据基本一致,但在部门层面存在差异,需要进一步研究。我们在 https://doi.org/10.5281/zenodo.10041196(Schulte et al., 2023)上提供了我们的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的不确定性的用户使用。
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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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