Energy Efficiency and Norm Compliance Drivers Amongst Industry Decision-makers: Evidence of Intersectionality and the Role of Morality
Bibliographic record
Abstract
Purpose: This article explores industry decision-makers’ motivation for energy efficiency (EE) actions. Our research question is: why do industry decision-makers feel obligated to comply with norms or engage in EE actions? More specifically, what types of norm compliance drivers are they responding to? Methodology: We use a two-country (United Kingdom and Canada) survey of managers and executives in three key sectors – building and construction, hospitality, and utilities to explore the presence of norm compliance driver typologies that motivate EE actions. Findings: Drawing on existing theoretical frameworks, we define four types of norm compliance drivers related to industry action: custom, third-party, moral, and social. Our results show evidence of all four, with moral as the most common norm compliance driver. Our findings also point to intersectionality: the presence of more than one type of norm compliance driver in reasoning for action. Originality: The emphasis on the underlying drivers of norm obligations as a motivation for decision-makers within industry related to EE action makes this article novel. Doing so from the perspective of industry actors is also original. Practical implications: Many of the responses related to moral norm compliance drivers are tied to larger environmental issues, such as climate change, which contributes to understanding how to trigger industry action on large global issues. Social implications: The finding that moral drivers are a significant proportion of the underlying force behind norm compliance, coupled with the understanding that many of these statements point to larger sustainability goals, suggests policymakers need to take a closer look at how they motivate industry.
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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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".