An Exploration into the Municipal Capacity to Finance Capital Infrastructure
Bibliographic record
Abstract
Municipal governments own and maintain two-thirds of Canada’s stock of public infrastructure. This burden is met by municipalities within the parameters afforded to them by their respective provinces. As a result, municipalities throughout the country rely on three primary revenue streams: issuing debt, financing from dedicated revenue and transfers from higher levels of government. At the same time, strict rules on borrowing, sometimes self-imposed, have left municipalities with considerable unrealized borrowing capacity. Importantly, a shift towards increased borrowing, away from a reliance on intergovernmental grants, would reinforce the linkage between local government spending and accountability and keep spending priorities in order. This paper focuses on infrastructure spending in Alberta and Ontario to illuminate how municipalities in both provinces cope with demands to provide capital- and labour-intensive programs and services. In both provinces, transportation, environmental services and recreation and culture comprise the bulk of infrastructure expenditure. In Ontario, as of 2013, 18 of the largest municipalities held assets valued at $111.8 billion. After accumulated depreciation, those assets are now estimated to be worth $73.8 billion, having lost $38 billion in value since their acquisition — although municipalities’ diligence varies. Mississauga has preserved 82.6 per cent of its assets’ original cost; Thunder Bay has only managed 45.6 per cent. In Alberta, 21 of the largest municipalities held assets valued at $51.7 billion in 2013, although thanks to depreciation, their value is now estimated at $37.8 billion. Again, there is significant variability between municipalities, with Wood Buffalo having preserved 98.6 per cent of its assets’ original value, and Crowsnest Pass with 43.9 per cent. In both provinces, the older the municipality and the weaker its fiscal capacity, the lower the net book value of its capital assets. While an ongoing nation-wide shift to modified accrual accounting has encouraged municipalities to plan long-term, the legacy of past decisions means that substantial underinvestment in infrastructure exists, and that the net book value of municipal assets is generally below the cost of their acquisition. Through an examination of municipal budgeting and the revenue-generating means at municipalities’ disposal, this paper argues that fiscal policy reform is essential, if municipalities are to serve Canadians to the best of their abilities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".