The Politics of Funding Urban Infrastructure in Canada and the United States: Implications for Resilience and Sustainability
Bibliographic record
Abstract
Infrastructure is a core municipal expenditure and an important component of local climate change strategies. There is a general consensus that cities never have enough funding to meet their infrastructure needs. Attention is rarely paid, however, to how cities fund and finance infrastructure, which can vary meaningfully between cities in different jurisdictions. While U.S. cities are highly reliant on issuing voter-approved municipal bonds to finance infrastructure, Canadian cities often depend on transfers from higher levels of government. This dissertation uses a mixed-method comparative approach to investigate how the policies governing cities’ capital revenue sources (which I label “multilevel fiscal institutions”) shape local infrastructure policy processes and climate change outcomes. Using an original dataset of infrastructure investments in four North American cities dating back to 1957 (Calgary, Edmonton, San Antonio and Austin) and drawing on in-depth qualitative interview data from Calgary, AB, and San Antonio, TX, I argue that how cities fund and finance infrastructure is critical to what they build, with important implications for local investments in sustainability and resilience. Multilevel fiscal institutions set the capital revenue options available to cities, which in turn influence accountability channels and power distribution. I find that local policy actors (namely, local bureaucrats) respond to these power and accountability dynamics by shaping policy processes and structuring investment choices in order to advance the projects that are best poised to secure the highest levels of funding. Because different projects are more likely to advance under different capital revenue arrangements, multilevel fiscal institutions have important consequences for infrastructure outcomes and how cities choose to advance their sustainability objectives. These findings highlight the various democratic trade-offs involved in different multilevel fiscal arrangements and push back against the commonly held assumption that more fiscal autonomy is inherently good. While local fiscal autonomy and voter approval requirements can heighten citizen influence and accountability over infrastructure investment processes, they may not be conducive to equity and sustainability over the long-term. I close with policy recommendations and areas for further research.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".