Transport Sector Impacts of a Border between Ireland and Northern Ireland after a Hard Brexit
Bibliographic record
Abstract
More than half of British voters chose to leave the European Union (EU) leading to a series of negotiations between the United Kingdom and the EU. The withdrawal of the UK from the EU is widely referred to as Brexit. As the only country that shares a land border with the UK, the impact of Brexit on Ireland is expected to be greater than on any other European country. The objective of the research is to evaluate the potential impact of Brexit on the transport sector in Ireland at a micro level by focusing on cross‐border commuters and by also assessing the impact on road freight transport. Potential crossing scenarios are examined at six crossing locations. Assuming a hard border is implemented, each crossing is modelled in VISSIM, a microscopic traffic flow simulation software, using traffic data from Transport Infrastructure Ireland (TII) and dwell time estimated based on the US–Canada border crossings. Six scenarios are considered to determine the impact on cross‐border traffic at different flow conditions and with varying levels of technology used in border infrastructure leading to short versus long processing times. The paper evaluates travel measures including delays, queue lengths and emissions. The worst‐case scenario has a vehicle delay of 18.4 min and the highest delay‐associated costs across all locations modelled are estimated at €60.7 million per year. Estimated emissions generated at the border crossings raise concerns about environmental impacts of a hard Brexit. Interviews with stakeholders emphasized the critical role of technology in reducing the impact of a hard Brexit on cross‐border commuters and on the freight sector. A key finding is the importance of using technology tools to facilitate controls and reduce processing times. The results indicate that technology use leads to significant time and cost savings as well as reduced environmental impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".