Cities and greenhouse gas reduction: Policy makers or policy takers?
Bibliographic record
Abstract
A growing number of cities have set ambitious mid-century targets for greenhouse gas (GHG) emissions reduction and increased use of renewable energy. Using the municipal jurisdiction of Vancouver, Canada as a case study, we integrated an energy-economy model with an urban land-use and infrastructure model to test the possible actions resulting from policies potentially available to this city government in pursuit of its 2050 target of 100 percent renewable energy and an 80 percent reduction of GHG emissions. We found that, while cities like the one we studied have some important options for reducing energy use by their inhabitants, they may lack the authority to completely transform the energy system, especially for causing a wholesale switch to renewable energy for deep decarbonization. To achieve such ambitious energy and GHG targets, cities with jurisdictional powers comparable to the city we studied are dependent to some degree on complementary GHG and energy policies from senior levels of government.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".