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基于消费的碳账户的不确定性。

Uncertainty of Consumption-Based Carbon Accounts.

机构信息

Institute of Environmental Sciences CML , Leiden University , Einsteinweg 2 , 2333 CC Leiden , Netherlands.

Programme for Industrial Ecology, Energy and Process Technology Department , NTNU , NO-7491 Trondheim , Norway.

出版信息

Environ Sci Technol. 2018 Jul 3;52(13):7577-7586. doi: 10.1021/acs.est.8b00632. Epub 2018 Jun 13.

Abstract

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 contributors to global CBCA uncertainty are the electricity sector data globally and Chinese national data in particular. We anticipate that this work will provide practitioners an approach to understand CBCA uncertainties and researchers compiling MRIOs a guide to prioritize uncertainty reduction efforts.

摘要

消费型碳账户(CBCA)追踪一个地区的最终需求如何因全球经济网络中的供应链而在其他地方导致碳排放,同时考虑到国际贸易。尽管 CBCA 作为理解和量化气候缓解努力责任的一种方法非常重要,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,对全球经济及其温室气体排放的随机多变量模型进行实证校准,以确定全球 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,而且随着排放量的增加,不确定性不会显著降低。我们发现,忽略数据相关性(即独立抽样)的偏差是显著的,不确定性被系统地低估了。我们发现,CBCA 和源 MRIO 表都表现出不同国家之间部门层面数据之间的强相关性。最后,我们发现,导致全球 CBCA 不确定性的最大因素是全球电力部门数据以及中国国家数据。我们预计,这项工作将为从业者提供一种理解 CBCA 不确定性的方法,并为编制 MRIO 的研究人员提供一个优先考虑降低不确定性努力的指南。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ac/6150677/22cbf3449014/es-2018-006329_0001.jpg

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