Firm and industry adaptation to climate change: a review of climate adaptation studies in the business and management field
Bibliographic record
Abstract
Abstract Firms and industries will have a central role in supporting societal adaptation to the physical impacts of climate change, especially in more directly affected sectors such as agriculture, forestry, construction, or transportation. However, the business and management field has repeatedly been criticized for its lack of engagement with climate change as a pressing issue, and adaptation to the physical impacts of climate change in particular. Our review of adaptation studies in the business and management field suggests that most firm and industry adaptation studies focus on how firms adjust to changing business conditions because of the emergence of new competitors, new products, and markets or because of changed political, economic, and legal conditions; they largely exclude firm adjustments to the changing dynamics of the natural environment. Studies on firm and industry adaptation to climate impacts specifically are beginning to emerge, but they are sparse. There is still little cross‐disciplinary work integrating findings from the natural sciences into business thinking. We also find few considerations of the implications and consequences of climate change for firms and industries to date. This article provides an overview over the existing literature on firm adaptation to climate change, outlines research gaps, and suggests pathways for future research. WIREs Clim Change 2013, 4:397–416. doi: 10.1002/wcc.214 This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".