Improving Energy Efficiency: The Significance of Normativity
Bibliographic record
Abstract
Abstract The failure of the global community to effectively address many large-scale environmental challenges calls into question the existing regulatory approaches. A large number of these challenges are diffuse issues which have, over the years been targeted by significant and sizable regulatory frameworks and yet the challenges persist—energy efficiency is one such issue and is the focus of this article. Increasing monitoring or enforcement to achieve improvements in regulatory compliance is too expensive in the context of diffuse problems due to the scale and costs such activities would entail. We suggest a focus on the fit between regulatory frameworks and norm creation may identify more fruitful routes to regulatory reform. Drawing on the ‘interactional account of law’ as a framework, this research uses new empirical data from a survey and a set of interviews to investigate the failure of energy efficiency regulatory frameworks at achieving energy efficient norms of behaviour in industry. We look at Canada and the UK as our case studies and our emphasis is on industry actors as they represent a significant and yet understudied area of society. We find that though existing regulatory structures seem adequate to generate general shared understandings around obligations to engage in energy efficiency actions, more specific shared practice around actually engaging in these actions remains elusive, resulting in a failure to engender norms of behaviour. These failures, we suggest, link directly to an inadequate fit between the regulatory tools and Fuller’s criteria for the internal morality of law.
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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 | 0.053 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.066 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 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".