Adaptation investments for transport resilience: trends and recommendations
Bibliographic record
Abstract
Climate change, extreme weather and flooding threaten to increase damage and disruption to our transport networks and the services that they provide. There is increased need for adaptation to maintain current asset conditions and services, and a strategic requirement to prioritise such investments in adaptation to reduce future risks. Physical network risks will not be evenly distributed across nations (e.g. due to geographical and climate change patterns), and some regions will require more investment and adaptive interventions than others to maintain services due their vulnerability to natural hazards. Comparatively, the distribution of investment for transport infrastructure does not have a uniform spatial distribution, and can favour schemes that reduce congestion on networks with high demand without considering the actual risk of being impacted. These two issues, if unchallenged, will present an unfavourable future for areas with high network risks and low transport demand that will widen spatial inequality or resilience, mobility and potential for economic growth. This study advances a methodological framework to analyse the spatial distribution of flood risk on UK road and rail networks in the light of potential bias of regional investment. Using GIS mapping, network data and risk analysis, regional futures are categorised and discussed. There is a clear North/South divide in transport networks at risk from potential coastal and fluvial flooding, with southern regions having 10-30% of their network situated in known flood risk areas. Investment in transport infrastructure is also disproportionately favoured towards regions with high transport demand, and peripheral regional such as Wales and the South West are at risk from increase disparity from high flood risk networks and a low potential for investment. The study provides preliminary evidence for the need to consider assessment approaches for long-term investment in resilience, drawing recommendations for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".