Cross-border Dependencies Among Critical Infrastrucures: Econometric Analysis of Canada-U.S. Vulnerabilities
Bibliographic record
Abstract
This paper explores the application of econometric input-output (I-O) modeling as a method for assessing cross-border critical infrastructure interdependencies (CII), with a focus on Canadian and U.S. sectors. Current Canadian policy frameworks lack quantitative tools and detailed industry-level definitions needed to assess CII risks rigorously. By integrating I-O data from Statistics Canada and the U.S. Bureau of Economic Analysis, the study identifies economic vulnerabilities that span national boundaries, revealing how disruptions—such as to Canadian energy exports or U.S. digital services—can produce cascading impacts across multiple infrastructure sectors. Through a series of sectoral visualizations, the paper demonstrates how round-trip interdependencies and feedback loops amplify infrastructure risks, and how econometric analysis can support more empirical, standardized, and defensible risk assessments. Implications include the potential for better-targeted emergency preparedness strategies, improved public safety outcomes, and more effective cross-border resilience investments. While limitations remain—particularly regarding time-to-impact, substitution effects, and socio-environmental dimensions—the findings underscore the urgent need for quantitative, system-wide approaches to national and international CI risk governance. Recommendations are provided for policymakers, private sector risk managers, national security assessors, and emergency management professionals to integrate I-O modeling into practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".