Management Responses to Climate Change: An Analysis of Scholarly Recommendations
Bibliographic record
Abstract
ABSTRACT This article analyzes management scholars' recommendations for business managers on how to respond to climate change. It distinguishes recommendations by whether they propose responsiveness to climate change that aims for adaptation, mitigation, and resilience as short‐term orientations as well as transformation and regeneration as long‐term orientations. The analysis of 192 practical implications in 91 articles suggests that managers are recommended to make short‐term oriented adjustments to their business by adapting to the challenges and contributing to the mitigation of greenhouse gas emissions. There are fewer recommendations on what measures managers should take to make businesses more resilient to the effects of climate change, how managers should contribute to the transformational needs of society, and how business models could be designed that support long‐term climate mitigation. The article provides an overview of the recommendations in the reviewed social scientific management literature and discusses further avenues for understanding and advancing the role of business toward climate‐friendly markets and society.
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 | 0.053 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".