Bibliographic record
Abstract
The goal of this thesis is to produce a benchmarking template that can be applied by municipalities across Canada to reduce greenhouse gas emissions from their operations. This template will provide a common set of indicators for benchmarking municipal emissions. It will attempt to link the academic literature to existing conditions and practices within municipalities. It also provides original insight through interviews with municipal officials and municipal policy experts. Research has shown that municipalities can mitigate climate change. Municipalities have authority to enact policies which reduce greenhouse gas emissions. While many municipalities have taken action, benchmarking initiatives still do not exist to allow for direct comparison of municipalities. Following a review of academic literature interviews were held with a panel of nine municipal policy experts to assess existing programs and a proposed benchmarking template. An indicator set with nine categories and 18 individual indicators measuring corporate and community GHG emissions was developed through consultations with the panel. A questionnaire was sent to 32 municipalities with a response rate of 25%. Based on the results participating municipalities were compared against one another to determine best practices and areas for improvement. Indicators for residential densities, municipal building heating, solid waste, and municipal buildings and operations had the highest tCO An examination of indicator set development, improved methods for modelling community emissions, assessment of the benefits of climate action and municipal networking for small municipalities would build upon this 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 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 | 0.032 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".