Income and investment, not energy policy, are driving GHG emission intensities

Abstract

Global greenhouse gas (GHG) emissions continue to rise but, at the same time, emission intensities associated with domestic consumption and territorial production have declined albeit at vastly different rates across economies. To identify the socioeconomic factors that drive this cross-country variation, we combine input–output modelling with panel data analysis. Using the World Input–Output Database, we estimate GHG intensities separately for domestic consumption and for territorial production. For the regression analysis, we consider several socioeconomic factors that capture development features, exposure to international trade, as well as energy prices and GHG-relevant programmes. Our results show that development-type factors, such as per capita income, capital-labour ratios, and investments, are the primary drivers of cross-country differences. Energy prices and domestic GHG policies are not major drivers. We also find that reductions in intensities are primarily through changes in techniques rather than compositional changes in the structure of economies.

KEYWORDS:

• Greenhouse gas
• emissions intensity
• technology change
• structural change

Acknowledgements

We would like to thank the editor and the three anonymous referees for their constructive comments and feedback on the previous versions of the paper. We also thank Derek Hermanutz and Nick Macaluso for useful discussions and comments on the earlier versions of the paper. Views expressed in this paper are solely those of the author(s) and may differ from official Bank of Canada views. No responsibility for them should be attributed to the Bank.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 IPCC (2007) defines territorial emissions. ‘National inventories include greenhouse gas emissions and removals taking place within national territory and offshore areas over which the country has jurisdiction’.

2 The WOID database (Timmer, 2012) used in this paper is consistent with the aggregate data used by other sources. For instance, in the WIOD, global emissions increased 26.7% while global emissions reported by Climate Watch Data (Climate Watch Historical GHG Emissions, 2021) increased 25.6% (Total GHG without Land-Use Change and Forestry).

3 The WIOD project was funded by the European Commission, Research Directorate General as part of the 7th Framework Programme, Theme 8: Socio-Economic Sciences and Humanities.

4 Emissions data on the latter two are not directly available hence the equation is solved to determine it.

5 Note that the WIOD tables used in this paper are symmetric industry by industry matrices. Since a single industry is producing a single commodity, we use the term interchangeably.

6 The symmetric input–output tables in the WIOD data used in this analysis implies that a single output is produced by a sector. This implies that the emissions intensity at sector level and commodity level are equivalent.

7 One can further decompose technical intensity into changes in energy mix and efficiency improvement. However, given insufficient detail available in the WIOD, we do not undertake this exercise.