Toward carbon neutrality? legitimizing carbon-neutral disengagement in agri-food organizations
Bibliographic record
Abstract
The aim of this article is to analyze the perceptions held by business leaders in the agri-food sector with regard to carbon neutrality pledges, and how these leaders justify their organizations' lack of commitment or relative passivity in this area. The results of a qualitative study carried out by using semi-structured interviews with 34 business leaders show that—despite the strategic importance of climate issues, the vulnerability of the agri-food sector to climate risks and growing institutional pressure to reduce and offset greenhouse gas (GHG) emissions—most respondents remain skeptical or even distrustful of the relevance and credibility of carbon neutrality targets. The findings show a core tension between recognizing the importance of the principle of carbon neutrality and adopting specific, measurable and short-term commitments. The rationalizations used to legitimize the absence of a clear commitment to reduce, measure and offset these organizations’ carbon footprint revolve around five main neutralization techniques: navigating resources and strategic constraints, claiming to engage in exemplary organizational practices, debunking carbon neutrality principles, tackling measurement challenges and ambiguities, and scapegoating large emitters. This study contributes to the literature on corporate carbon neutrality commitments, on impression management strategies and neutralization techniques, and on sustainability performance measurement and organizational accountability on climate issues. • Agri-food leaders are skeptical of the credibility of carbon neutrality targets. • Agri-food leaders are open to the principle of carbon neutrality. • Agri-food leaders criticize the integration of the principle of carbon neutrality. • Neutralization techniques are used to legitimize the absence of climate targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| 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".