Climate Inaction in Business Management: An Exploratory Review of the Literature
Bibliographic record
Abstract
Climate change is considered as one of the major threats for the international community due to its negative consequences in the financial, social, and environmental issues. Companies, who are considered as an essential element in the mitigation process, have exerted corporate inactivity to address climate change that has led to the increment of the greenhouse gas (GHG) emissions, contributing to climate change over the last decade. The objective of this review is to explore, summarize, and analyze the state of knowledge in the business and management literature about climate inaction that guides future researches to diminish this corporate inactivity, enhancing the practices aimed to reduce such emissions. The review was developed through the narrative method in order to acquire a broad perspective of the phenomenon through the examination of 24 articles from the Web of Science from 1998 to 2018. Our findings indicate that climate inaction is nascent and fragmented literature where the company is identified as one of the main actors, being this approach developed from different perspectives that guide to decrease such corporate inactivity, and motivating the corporate action. The inclusion of the concept of climate inaction might lead to an understanding of the mechanisms for climate mitigation, providing a guide for future research in the field of environmental performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".