Regional Population Size and the Cost of Municipal Environmental Protection Services: Empirical Evidence from Ontario
Bibliographic record
Abstract
With the passage of City of Toronto Act, the Government of Ontario created a ‘megacity’ by amalgamating six lower tier municipalities with the upper tier Regional Municipality of Metropolitan Toronto. The controversy over the megacity bill and similar proposals for amalgamation in other Ontario regions has rekindled a debate over whether larger city-regions supply services at a lower cost per capita than smaller regions. Through numerous public pronouncements and television advertisements, the Government argued that larger city-regions reduce political and administrative overlap, operate more efficiently, and subsequently reduce municipal government expenditures. The savings result in lower property taxes, making amalgamated communities more attractive locations for new business investment. Despite considerable public debate, neither the Government nor its critics say much about the potential environmental consequences and related environmental protection expenditures resulting from amalgamation. As in many jurisdictions, environmental protection represents a major area of program spending for municipal governments in Ontario. These outlays absorb approximately 18 % of total municipal expenditures across Ontario, with some municipalities that have high per capita expenditures such as the District of Muskoka spending as much as 26 % of their total budget (Ministry of Municipal Affairs (MMA) 1993a). If the ‘bigger is better’ argument holds for environmental programs in Ontario, some economy of scale should be present in the existing expenditure data. In this context we pursue two research objectives. First, we review the literature on the relationships between population size and environmental costs to provide a rationale for the research. Second, we analyse the empirical relationship between environmental expenditures and population size in Ontario to inform current policy debates and contribute empirical evidence to the scholarly discourse. The next section contains the literature review and conceptual framework. This is followed by a description of the data and methods. Presentation and discussion of the results follow this section. The paper concludes with a summary and recommendations for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".