Journal article
Do common shocks drive changes in aggregate emissions intensity?
- Abstract:
- In the UK, aggregate emissions intensity has declined by about a factor of two over the last three decades. Prior research attributes most of this decline to reductions within industries rather than shifts in the composition of economic activity. This paper investigates whether such within-industry progress primarily reflects industry-specific factors or common forces operating across industries. Using a newly constructed panel of UK industry-level GHG emissions and gross value added for 1990–2022, we estimate a block-level dynamic factor model that decomposes changes in emissions intensity into global, block-level, and idiosyncratic components. We find that industry-specific factors account for the majority of variation in emissions intensity changes, though common shocks, either global or at the level of groups of industries, play a smaller but non-negligible role. We further show how patterns of co-movement partly reflect the way that emissions are recorded at the activity level and allocated to industries, a feature with implications for interpreting industry-level decarbonization dynamics.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 2.5MB, Terms of use)
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- Publisher copy:
- 10.1016/j.jeem.2026.103384
Authors
+ UK Research and Innovation
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- Funder identifier:
- https://ror.org/001aqnf71
- Grant:
- ES/W010356/1
- Publisher:
- Elsevier
- Journal:
- Journal of Environmental Economics and Management More from this journal
- Volume:
- 139
- Article number:
- 103384
- Publication date:
- 2026-06-18
- DOI:
- EISSN:
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1096-0449
- ISSN:
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0095-0696
- Language:
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English
- Keywords:
- Pubs id:
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2434914
- Local pid:
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pubs:2434914
- Source identifiers:
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W4413850031
- Deposit date:
-
2026-07-21
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier Inc.
- Copyright date:
- 2026
- Rights statement:
- © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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