Industry perceptions of government interventions: generating an energy efficiency norm
Bibliographic record
Abstract
The world has been grappling with energy efficiency for decades. Much attention has been focused on how government can encourage energy efficiency, but there has been essentially none on industry perspectives of which government interventions are necessary to encourage these actions to become the norm. We address this gap through a study of industry views as to which government interventions prompt corporate actors to adopt energy efficiency measures across three industries (building and construction, energy/utilities, and hospitality) in Canada and the United Kingdom. Our findings demonstrate that industry responses mirror recent literature on the need for a mixture of policy tools. Where our findings depart from this literature is that we find a strong endorsement of the use of information provided by government and antipathy towards the use of economic instruments to engender new norms of behaviour. This finding is particularly significant given that much of the literature focuses on the benefits of economic instruments in advancing sustainability goals. We also find the express norms found in command and control instruments are, in the views of industry actors, necessary to make a shift from energy efficiency actions being carried out only by leaders within the industry to these actions becoming standard.
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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 | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".