Going green in the Norwegian fossil fuel sector? The case of sustainability culture at Equinor
Bibliographic record
Abstract
As the effects of climate change continue to impact society, fossil fuel sector organisations are seen as principal contributors to the climate crisis. In the hopes of mitigating climate change at the source, we gain access to one of the largest fossil fuel organisations in Norway and conduct an exploratory case study investigation into their business practices, green ambitions, and notable results. Our analysis of executive interviews, confidential in-house documentation, media releases, corporate social responsibility (CSR) reports, and grey papers suggests that a strong sustainability-oriented organisational culture can contribute to reversing ‘business as usual’ practices towards seeking strategic greener solutions. Such results are partly achieved by strong responsible leaders at the organisational helm in combination with a sustainability-oriented national culture. Additionally, we critically question the secrecy surrounding the case organisation’s ‘choice’ and ‘format’ of promotion and support for their operational status quo (e.g. greenwashing), to challenge the insider perspective unearthed herein. In sum, the study contributes to the newer and under-investigated field of green human resource management by better identifying the role of organisational culture as a critical lever in bringing about much-needed greener organisational policies, and offering a critical analysis less seen in green human resource management (HRM).
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".