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

Abstract

The past few years have seen the emergence of several global multiregional input–output (MRIO) databases. Due to the cost and complexity of developing such extensive tables, industry sectors are generally represented at a rather aggregate level. Currently, one of the most important applications of input–output analysis is environmental assessments, for which highly aggregate sectors may not be sufficient to yield accurate results. We experiment with four of the most important global MRIO systems available, analyzing the sensitivity of a set of aggregate CO₂ multipliers to aggregations in the MRIO tables used to calculate them. Across databases, we find (a) significant sensitivity to background system detail and (b) that sub-sectors contained within the same aggregate MRIO sector may exhibit highly different carbon multipliers. We conclude that the additional information provided by the extra sector detail may warrant the additional costs of compilation, due to the heterogeneous nature of economic sectors in terms of their environmental characteristics.

Keywords:

• MRIO databases
• Aggregation
• CO₂ multipliers

Acknowledgements

This work was performed while K.S.O. and A.O. were visiting the Centre for Integrated Sustainability Analysis at the University of Sydney, Australia.

FUNDING

The work was supported by the Australian Research Council (ARC) under its Discovery Projects DP0985522 and DP130101293. KSO and EGH were in part supported by the EU FP7 project CARBON Cap (contract 603386). The contribution from AO formed part of the programme of the UK Energy Research Centre and was supported by the UK Research Councils under Natural Environment Research Council award NE/G007748/1.

SUPPLEMENTAL DATA

Supplemental material for this article is available via the supplemental tab on the article's online page at http://dx.doi.org/10.1080/09535314.2014.934325

Notes

The territorial approach, which is adopted in the Kyoto Protocol, is similar but slightly less comprehensive than the production approach, because emissions from international shipping and aviation are not allocated to any country.

The coefficient of variation is the same as relative standard deviation, i.e. standard deviation divided by the mean.

Note that in the special case of the Eora database, n is variable. Furthermore, the matrix dimensions are larger for Eora because Eora contains supply and use tables instead of input-output tables for some regions.

Note that this would also require that the environmental extensions are true and equal across all the MRIO models, an assumption that is perhaps more dubious (Peters et al., 2012). For recent work on this, see Owen et al. (2014).