Responding to the barriers in climate adaptation planning among transport systems: Insights from the case of the port of Montreal
Bibliographic record
Abstract
With the accelerating pace of climate change, there has been no scarcity of research, in recent years, that assess climate risks and cost-effectiveness of adaptation measures in the transport sector. Nevertheless, existing literature associated with adaptation planning for climate change is still at an embryonic stage with little attention on certain potential dilemmas. Understanding such, this paper focuses on the question of how to respond to the barriers in climate adaptation planning in transport systems. This is achieved mainly through reviewing the literature in transport adaptation to climate change impacts to summarize eights conditions (potential barriers) that the shortage of those might lead to the failure of climate adaptation planning. Next, those conditions are examined by a historical case study between 2014 and 2015 on the Canadian port of Montreal's experience in tackling the Great Lakes and St. Lawrence River's dropping water level. The findings, via semi-structured interviews with affiliated senior experts, closely mirror the enablers influencing the success of a climate adaptation plan, revealing the impediments and opportunities in the existing and future planning. It offers constructive recommendations on how to improve the port of Montreal’s, and ports and transport infrastructures in general, process and practice of adaptation planning. The study strives to bridge the research gaps and provide decision-makers with a novel thinking pattern and workable recommendations from design, implementation to the reconstruction of adaptation planning and facilitate a paradigm shift in broader sustainable transport management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".