Why is there an implementation gap in community energy planning?
Bibliographic record
Abstract
In community energy planning, a persistent disconnect has been observed between the targets and plans announced by local governments and the application of effective policy to reduce energy consumption and greenhouse gas (GHG) emissions. We use two methods to explore this implementation gap. First, we apply energy-economy modelling tools at the urban level to evaluate the effectiveness of various policy options available to local governments. Our case study for these exercises is the leading jurisdiction of Vancouver, British Columbia. Second, we report and analyze the results of a survey we administered to community energy practitioners in Canada. The modelling results point to jurisdictional reach as an important contributor to the implementation gap. We find that, while Vancouver can make significant progress by implementing policies that are clearly within its jurisdiction, the city is unlikely to meet its ambitious renewable energy and GHG emissions targets without the support of higher levels of government. The survey responses suggest that capacity limitations of local government also have a role in perpetuating the implementation gap.
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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 | 0.070 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".