Publish or Perish – do French hospitals disclose their greenhouse gas emissions for vertical differentiation?
Bibliographic record
Abstract
French legislation requires large and medium-sized hospitals to publicly report their greenhouse gas (GHG) emissions. Yet, many hospitals fail to comply with this regulation, while others report voluntarily. The organizational drivers behind this behavior remain underexplored. This study examines whether hospitals disclose their GHG emissions as part of a broader strategy to differentiate themselves-similar to how they report patient satisfaction scores to signal quality. We explore whether carbon reporting is used as a vertical differentiation strategy in the French healthcare system. We used a mixed-methods approach. First, we analyzed national administrative data to test whether reporting GHG emissions is associated with reporting patient satisfaction scores. Second, we conducted semi-structured interviews with hospital managers to understand the motivations behind emissions reporting. Quantitatively, we found no significant association between the two types of reporting. Hospitals do not appear to use GHG emissions disclosure and patient satisfaction scores as part of the same signaling strategy. Qualitative findings confirmed that GHG reporting is primarily driven by internal factors such as executive leadership, process improvement, and organizational values, rather than external differentiation or patient demand. Carbon reporting in French hospitals is not currently used as a differentiation strategy. Stronger regulatory enforcement is needed to ensure compliance. In addition, hospitals require support-through methodological guidance, training, and the development of dedicated sustainability roles-to integrate environmental performance into their management systems and contribute meaningfully to healthcare decarbonization.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".