Managing organizational carbon neutrality: A systematic review
Bibliographic record
Abstract
Abstract This article aims to analyze the challenges that arise for organizations when they make carbon neutrality commitments, the practices involved in implementing those commitments, and the impacts they have, based on a systematic review of empirical studies focused on the managerial and organizational implications of carbon neutrality initiatives. Through rigorous analysis of relevant research, this study maps the literature, highlighting, among other things, that research on the subject has mushroomed and is widely dispersed in terms of both geographical and disciplinary provenance; that the process of achieving organizational carbon neutrality has been undertheorized; and that there is a lack substantial critical examinations of carbon neutrality actions despite the greenwashing trends that have been observed in organizations' climate commitments. The results of the selected studies show the numerous challenges of managing carbon neutrality, mainly due to organizational obstacles and governance issues related to the lack of expertise, leadership, and reliable data to track climate performance rigorously as well as persistent uncertainties surrounding regulations and public policies in this area. Despite these difficulties, the conclusions of most studies remain optimistic about the positive financial, reputational, and institutional impacts of organizational carbon neutrality. The best practices identified in the literature revolve around actions related to leadership commitments, strategic planning, and innovation. This study provides an in‐depth understanding of the implications of carbon neutrality for managers and proposes avenues of improvement for future initiatives in this area. It also discusses contributions to the literature, significant research gaps, and resulting avenues for future research.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".