Flood risk and land-use governance in Quebec, Canada: Fifty years of crises and institutional reform
Bibliographic record
Abstract
Over the past fifty years, Quebec’s flood risk governance has undergone successive reforms driven by major disasters, from the floods of 1974 and 1976 to those of 2017 and 2019. Drawing on legislative archives, policy documents, cartographic records, and media sources, this paper reconstructs how these critical junctures reshaped institutions, from the first Canada-Quebec floodplain agreements to the recent transitional regime and forthcoming third-generation maps. Using a historical institutionalist framework combined with insights from political ecology, we examine how crises have acted as catalysts for reform while also reinforcing path dependencies. Early hazard-based approaches focused on probabilistic mapping and infrastructure defence, while later initiatives introduced land-use restrictions, standardized cartographic methods, and eventually risk-based models integrating hazard, exposure, and vulnerability. Recent reforms further expand this scope to include adaptation and river mobility, supported by high-resolution LiDAR mapping and watershed-scale planning. Yet major challenges persist, including regulatory rigidity, tensions between provincial norms and local contexts, and uneven attention to social vulnerability, even if ongoing reforms begin to address some of these gaps. Through a political ecology lens, these shifts reveal how regulatory change intersects with power, equity, and knowledge: who defines risk, whose expertise prevails, and how costs and restrictions are distributed across territories. Quebec’s evolving trajectory highlights both the opportunities and limits of event-driven reform while disasters accelerate innovation, they can also entrench uneven governance outcomes. By situating Quebec within broader debates on adaptive risk governance and resilience, this study offers transferable lessons for jurisdictions facing climate-induced flood risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".