Balancing climate action for greater transport decarbonization: an avoid-shift-improve driven network data envelopment analysis framework
Balancing climate action for greater transport decarbonization: an avoid-shift-improve driven network data envelopment analysis framework
Transport decarbonisation requires allocating limited resources across competing strategies. The Avoid–Shift–Improve framework categorises these strategies to reduce transport's reliance on fossil fuels. This study develops an Avoid–Shift–Improve driven network data envelopment analysis (DEA) framework to measure county-level progress toward transport decarbonisation. The framework also serves as a decision-support tool for allocating resources efficiently to advance transport decarbonisation. Using data from Ireland's 26 counties, we integrate DEA with a Stackelberg leader–follower game and the best–worst method. The DEA component provides an objective, mathematically defined measure of relative efficiency. The best–worst method incorporates expert judgement on the economic and environmental impact of actions within the Avoid–Shift–Improve hierarchy. This hierarchical approach, reflecting expert consensus, prioritises transformational measures that reduce travel demand (Avoid), followed by strategies that shift remaining trips to low-carbon modes (Shift), while technological improvements (Improve) play a more limited role. Results reveal disparities in county performance. Dublin leads due to its relatively well-developed public transport and active mobility infrastructure. Smaller counties such as Longford and Leitrim also perform strongly despite their rural character. By contrast, large-area counties including Cork, Mayo, and Kerry underperform, reflecting structural challenges of dispersed settlement and high car dependency. The analysis highlights that larger counties achieve lower efficiency scores, while links with emissions and expenditure are weaker, underscoring the role of spatial scale and carbon lock-in in shaping outcomes. The framework is scalable to other regional and national contexts and can support economically rational, socially inclusive climate policy.
Avoid-shift-improve, Best-worst method, Data envelopment analysis, Just transition, Stackelberg leader–follower game, Sustainable transport, Transport decarbonisation
Hosseini, Keyvan
162ed175-bfa9-4dd9-bf3d-57f2e5bb5b4c
Assani, Saeed
68a9e4a0-b2f5-4133-9128-ca2c6cc73c0e
Stefaniec, Agnieszka
66b6b4a6-d73d-43de-a604-40094d303d1b
Papaphilippou, Philippos
3da942ad-4093-4b65-a6fc-7e90ca736abe
Charly, Anna
c4740733-6c2b-4693-abbc-bdc6a96b3664
Caulfield, Brian
800f1be0-f870-4b70-a540-9dd1a0b48640
28 March 2026
Hosseini, Keyvan
162ed175-bfa9-4dd9-bf3d-57f2e5bb5b4c
Assani, Saeed
68a9e4a0-b2f5-4133-9128-ca2c6cc73c0e
Stefaniec, Agnieszka
66b6b4a6-d73d-43de-a604-40094d303d1b
Papaphilippou, Philippos
3da942ad-4093-4b65-a6fc-7e90ca736abe
Charly, Anna
c4740733-6c2b-4693-abbc-bdc6a96b3664
Caulfield, Brian
800f1be0-f870-4b70-a540-9dd1a0b48640
Hosseini, Keyvan, Assani, Saeed, Stefaniec, Agnieszka, Papaphilippou, Philippos, Charly, Anna and Caulfield, Brian
(2026)
Balancing climate action for greater transport decarbonization: an avoid-shift-improve driven network data envelopment analysis framework.
Energy Economics, 158, [109322].
(doi:10.1016/j.eneco.2026.109322).
Abstract
Transport decarbonisation requires allocating limited resources across competing strategies. The Avoid–Shift–Improve framework categorises these strategies to reduce transport's reliance on fossil fuels. This study develops an Avoid–Shift–Improve driven network data envelopment analysis (DEA) framework to measure county-level progress toward transport decarbonisation. The framework also serves as a decision-support tool for allocating resources efficiently to advance transport decarbonisation. Using data from Ireland's 26 counties, we integrate DEA with a Stackelberg leader–follower game and the best–worst method. The DEA component provides an objective, mathematically defined measure of relative efficiency. The best–worst method incorporates expert judgement on the economic and environmental impact of actions within the Avoid–Shift–Improve hierarchy. This hierarchical approach, reflecting expert consensus, prioritises transformational measures that reduce travel demand (Avoid), followed by strategies that shift remaining trips to low-carbon modes (Shift), while technological improvements (Improve) play a more limited role. Results reveal disparities in county performance. Dublin leads due to its relatively well-developed public transport and active mobility infrastructure. Smaller counties such as Longford and Leitrim also perform strongly despite their rural character. By contrast, large-area counties including Cork, Mayo, and Kerry underperform, reflecting structural challenges of dispersed settlement and high car dependency. The analysis highlights that larger counties achieve lower efficiency scores, while links with emissions and expenditure are weaker, underscoring the role of spatial scale and carbon lock-in in shaping outcomes. The framework is scalable to other regional and national contexts and can support economically rational, socially inclusive climate policy.
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Accepted/In Press date: 27 March 2026
Published date: 28 March 2026
Additional Information:
Publisher Copyright:
© 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
Keywords:
Avoid-shift-improve, Best-worst method, Data envelopment analysis, Just transition, Stackelberg leader–follower game, Sustainable transport, Transport decarbonisation
Identifiers
Local EPrints ID: 510779
URI: http://eprints.soton.ac.uk/id/eprint/510779
ISSN: 0140-9883
PURE UUID: 23de6f58-537c-4e1c-b8c5-4a90ef628cd1
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Date deposited: 21 Apr 2026 16:58
Last modified: 25 Apr 2026 04:20
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Contributors
Author:
Keyvan Hosseini
Author:
Saeed Assani
Author:
Agnieszka Stefaniec
Author:
Philippos Papaphilippou
Author:
Anna Charly
Author:
Brian Caulfield
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