EFFECTS OF SECTOR AGGREGATION ON CO2 MULTIPLIERS IN MULTIREGIONAL INPUT–OUTPUT ANALYSES

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How researchers see this paper

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The Water-Energy-Food Nexus in East Asia: A tele-connected value chain analysis using inter-regional input-output analysis (2018, methodology): “This research shares shortcomings particular to all IOA studies in regards to data uncertainty due to sectoral aggregation error (see Lenzen, 2011; Steen-Olsen et al., 2014).”
To RAS or not to RAS? What is the difference in outcomes in multi-regional input–output models? (2016, unknown): “Steen-Olsen et al. (2014) investigate differences in consumption-based emissions that are due to aggregations of the table, while Geschke et al. (2014) investigate the differences that may occur due to different workflows for harmonising the data.”
EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi‐Regional Input‐Output Tables (2018, methodology): “One way to overcome this problem is to aggregate the available EE MRIO tables into a common classiﬁcation scheme (Steen-Olsen et al. 2014).”
Global Sustainability Accounting—Developing EXIOBASE for Multi-Regional Footprint Analysis (2014, methodology): “Due to aggregation errors inherent to IO modeling [27], a focus was placed on increasing the product and industry detail of the model.”