Beyond Hard-to-Abate: Matching Models to Transition Bottlenecks
Abstract
In energy-intensive industry, the difficulty of decarbonising is not a uniform property of the emissions involved but a property of the pathway, conditional on identifiable bottlenecks at three interlocking levels. At the process level, heterogeneous products and coupled processes multiply the low-carbon option space. At the firm level, financing premiums and coordination failures across the supply chain govern who invests and when. At the sector level, competition is shaped by trade policy and security of supply, and the politics of the transition by the regional employment these industries sustain. These bottlenecks reinforce one another across levels, so the difficulty becomes a property of the configuration rather than of any single constraint. Representing this configuration demands matching the "right" model to each bottleneck. Dominant optimisation frameworks are built for system-level analyses and resolve process detail well but collapse firm heterogeneity into a single planner and treat demand and global competition as exogenous rather than as co-evolving with the transition. Path-dependent first-mover investments and their regional employment effects fall outside cost-optimising frameworks with exogenous demand, leaving the firm level largely missing. Across sectors, shared inputs such as hydrogen and methanol create reinforcing learning curves that only models with endogenous, shared learning capture. We therefore argue for a shift in framing, from hard-to-abate, a property of emissions, to hard-to-transition, a property of the pathway, where viable solutions exist but have not been deployed. The reframe converts the problem into an actionable task, matching each bottleneck to a model that can address it.
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BibTeX
@misc{schirren2026beyond,
title = {{Beyond Hard-to-Abate: Matching Models to Transition Bottlenecks}},
author = {Lukas Schirren and Leander Müller and Alaa Al Khourdajie and Lukas Gast and Florian Egli and Adam Hawkes},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5cf8b},
url = {https://eartharxiv.org/repository/view/14869/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Hard-to-Abate: Matching Models to Transition Bottlenecks
AU - Lukas Schirren
AU - Leander Müller
AU - Alaa Al Khourdajie
AU - Lukas Gast
AU - Florian Egli
AU - Adam Hawkes
PY - 2026
DA - 2026/09/08
PB - EarthArXiv
DO - 10.31223/x5cf8b
UR - https://eartharxiv.org/repository/view/14869/
AB - In energy-intensive industry, the difficulty of decarbonising is not a uniform property of the emissions involved but a property of the pathway, conditional on identifiable bottlenecks at three interlocking levels. At the process level, heterogeneous products and coupled processes multiply the low-carbon option space. At the firm level, financing premiums and coordination failures across the supply chain govern who invests and when. At the sector level, competition is shaped by trade policy and security of supply, and the politics of the transition by the regional employment these industries sustain. These bottlenecks reinforce one another across levels, so the difficulty becomes a property of the configuration rather than of any single constraint. Representing this configuration demands matching the "right" model to each bottleneck. Dominant optimisation frameworks are built for system-level analyses and resolve process detail well but collapse firm heterogeneity into a single planner and treat demand and global competition as exogenous rather than as co-evolving with the transition. Path-dependent first-mover investments and their regional employment effects fall outside cost-optimising frameworks with exogenous demand, leaving the firm level largely missing. Across sectors, shared inputs such as hydrogen and methanol create reinforcing learning curves that only models with endogenous, shared learning capture. We therefore argue for a shift in framing, from hard-to-abate, a property of emissions, to hard-to-transition, a property of the pathway, where viable solutions exist but have not been deployed. The reframe converts the problem into an actionable task, matching each bottleneck to a model that can address it.
ER -