Paying for Urban Infrastructure Adaptation in Canada: An Analysis of Existing and Potential Economic Instruments for Local Governments
Bibliographic record
Abstract
As is the case in many other countries in the Western hemisphere, local governments in Canada have a significant role to play in minimizing the impacts of climate change on their population, economy, and fiscal budgets. Simultaneously, local governments typically experience limited capacity, expertise, and limited financial resources.\nThis report examines a number of instruments that local governments in Canada may use to generate revenues in support of adaptation in general, and in support of the development of climate resilient infrastructure in particular. The report also examines instruments aimed at incentivizing behavioural changes at local levels that may reduce the need for public investments in adaptation, and could thereby reduce the need to generate revenues in support of such investments. The most effective combination of incentives and investments is likely to vary across local governments.\nFor local governments, it is recommended that they: include adaptation in long-term strategic planning using downscaled climate change projections; reduce incremental costs associated with climate change by incorporating adaptation actions into existing municipal processes (e.g., into infrastructure maintenance and replacement programs, or in updates of community plans); act strategically and be creative with the current tools available.\n
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".