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Uncertaintyof Consumption-Based Carbon Accounts

机译:不确定基于消费的碳账户

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摘要

Consumption-based carbon accounts (CBCAs) track how final demand in a region causes carbon emissions elsewhere due to supply chains in the global economic network, taking into account international trade. Despite the importance of CBCAs as an approach for understanding and quantifying responsibilities in climate mitigation efforts, very little is known of their uncertainties. Here we use five global multiregional input-output (MRIO) databases to empirically calibrate a stochastic multivariate model of the global economy and its GHG emissions in order to identify the main drivers of uncertainty in global CBCAs. We find that the uncertainty of country CBCAs varies between 2 and 16% and that the uncertainty of emissions does not decrease significantly with their size. We find that the bias of ignoring correlations in the data (that is, independent sampling) is significant, with uncertainties being systematically underestimated. We find that both CBCAs and source MRIO tables exhibit strong correlations between the sector-level data of different countries. Finally, we find that the largest contributorsto global CBCA uncertainty are the electricity sector data globallyand Chinese national data in particular. We anticipate that this workwill provide practitioners an approach to understand CBCA uncertaintiesand researchers compiling MRIOs a guide to prioritize uncertaintyreduction efforts.
机译:基于消费的碳账户(CBCA)会在考虑国际贸易的情况下,跟踪一个地区的最终需求如何导致全球经济网络中的供应链导致其他地方的碳排放。尽管CBCA作为了解和量化减缓气候变化责任的一种方法很重要,但对其不确定性知之甚少。在这里,我们使用五个全球多区域投入产出(MRIO)数据库来经验性地校准全球经济及其温室气体排放的随机多变量模型,以便确定全球CBCA不确定性的主要驱动因素。我们发现,国家CBCA的不确定性在2%至16%之间变化,并且排放的不确定性不会随其大小而显着降低。我们发现忽略数据相关性(即独立采样)的偏见很明显,不确定性被系统地低估了。我们发现CBCA表和MRIO源表在不同国家的部门级数据之间都显示出很强的相关性。最后,我们发现最大的贡献者全球CBCA不确定性是全球电力行业数据尤其是中国国家数据。我们期望这项工作将为从业者提供一种了解CBCA不确定性的方法和研究人员编制MRIO来确定不确定性的优先级减少努力。

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