Building Climate Adaptation Capacity: A Pedagogical Model for Training Civil Engineers
Bibliographic record
Abstract
Civil engineers play a central role in climate change adaptation, as they are responsible for designing and managing infrastructure that supports societal resilience. However, professional education has not kept pace with the growing demand for sustainability competencies. This paper proposes a pedagogical model for capacity building that equips engineers with the skills needed to integrate climate adaptation into their daily practice. Semi-structured interviews with stakeholders across Canada identified four pedagogical pillars of effective training: appreciation of climate risks, reflective practice, project-based learning, and design thinking. These were synthesized into the Model for Climate Change Adaptation through Appreciation and Engagement, which emphasizes both technical proficiency and transversal competencies such as collaboration, critical reflection, and ethical responsibility. By grounding climate knowledge in authentic, workplace-based contexts, the model bridges sustainability learning and engineering practice through a scalable training framework. It supports the advancement of Quality Education (SDG 4), Sustainable Cities and Communities (SDG 11) and Climate Action (SDG 13), while offering practical guidance to universities, professional associations, and policymakers seeking to accelerate climate adaptation in engineering education.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".