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Record W2253827978 · doi:10.55016/ojs/sppp.v7i1.42466

The Free Ride is Over: Why Cities, and Citizens, Must Start Paying For Much-Needed Infrastructure

2014· article· en· W2253827978 on OpenAlexaffabout
Philip Bazel, Jack Mintz

Bibliographic record

VenueThe School of Public Policy Publications · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBusinessFinanceTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Canada’s roads, bridges, wastewater treatment centres and sewer systems are already past their prime. On average, and across the country, these key elements of municipal infrastructure are now past the halfway point of their useful lifespans. In the next 10 to 15 years, Canadian cities will face some very expensive bills for replacing critical infrastructure. But there are better means of funding needed infrastructure than raising local taxes or pleading with the provincial and federal governments for more transfers. A better solution, for many reasons, is user fees. Municipal governments have already managed to extract a great deal of infrastructure funding out of higher levels of governments. In the last 50 years, the portion of municipal revenues provided by federal and provincial transfers has increased from under 30 per cent to roughly 45 per cent, while the portion of revenues representing local property taxes has fallen from over 50 per cent to roughly 35 per cent. Yet, federal-provincial government funding is a seriously flawed means of funding local infrastructure. Money gathered at the federal and provincial level is placed in municipal coffers, breaking the chain of political accountability for the outcomes. This alters the spending priorities of municipal governments as they are inclined to favour those projects with federal or provincial subsidies attached. Such subsidies lower the political costs for local governments allowing municipalities to maintain artificially low taxes for their constituents by spending federal or provincial tax revenue. This is essentially having their cake and eating it too, and on a policy level, we are left to wonder how decision making surrounding municipal priorities is affected. These projects come at a discount to local governments, but not their constituents who will ultimately pay through federal or provincial income taxes. Most importantly, this kind of funding results in unpriced infrastructure access and contributes to the over-usage of infrastructure. It is only rational for residents to live further from work by taking full advantage of underpriced highways, bridges, transit and other infrastructure that someone else pays for, exacerbating congestion and commuting times in Canada’s major cities. If Canadian cities are serious about replacing aging infrastructure and, just as importantly, alleviating the evergrowing problem of traffic congestion and urban sprawl, then cities must begin making proper use of user fees, and charging citizens for the use of the infrastructure they value. As it stands, while urban centres around the world increasingly embrace user fees, Canadians remain stuck in their old ways. Aside from international crossings, in 2012, Canada had only eight tolled bridges, and less than 0.25 per cent of Canada’s paved public roads were tolled. In Alberta, across the five largest cities, user fees (combined with sales) only make up an average of less than 25 per cent of municipal program operational revenues. User fees would bring a predictable and dedicated revenue stream that would allow municipalities to responsibly take on the debt required to invest in much-needed infrastructure projects. Canadian city governments already have the tools they need to keep up with their pressing infrastructure needs; they need only find the creativity and political will to make use of them.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.299
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2014
Admission routes2
Has abstractyes

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