Bibliographic record
Abstract
Abstract Research Summary This article examines whether and how firms adapt to physical exposures to climate change. I build a novel dataset that compiles information on the adaptation strategies of publicly traded companies around the globe and merge it with climate science data. I find that firms are sensitive to the nature and level of forecasted climate change exposures, and that they adapt more often and more completely to those most salient to their business. Increased physical climate exposure heightens the perceived impact of climate change, leading to a higher degree of adaptation. Furthermore, the positive relationship between firms' climate change exposure and their adaptation is stronger for firms with greater environmental, social, and corporate governance capabilities and those with longer time horizons. Managerial Summary Companies are increasingly exposed to the physical impacts of climate change, yet little is known about how they adapt to these long‐term, systemic, and uncertain changes. This study investigates corporate adaptation strategies in response to climate change. By analyzing climate science data and climate change disclosure information from publicly traded companies worldwide, I find that most firms do not adapt to different physical climate change exposures. They adapt more often and more completely when facing higher forecasted climate exposures. Furthermore, firms' environmental, social, and corporate governance capabilities and their time horizons influence their adaptation to greater climate exposures. These findings suggest that targeted interventions may be necessary to improve corporate adaptation to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".