Climate Change Mitigation and Internationalization: The Competitiveness of Multinational Corporations
Bibliographic record
Abstract
Abstract In recent years, the debate about climate change and the competitiveness of multinational corporations (MNCs) has increased. Decision makers in MNCs often face ambiguities on how their business competitiveness could be impacted by their actions to mitigate climate change. By combining knowledge from the field of climatology with the management literature, this study suggests that climate change mitigation can enhance an MNC's competitiveness. We test the hypotheses using longitudinal panel data on US MNCs from 2001 to 2009. We find that MNCs that implement climate change mitigation are likely to see significant increase in sales effectiveness and product leadership but no significant increase in return on equity. Further, the positive influence of mitigation on sales effectiveness and product leadership is found to be more strongly positive when the MNC's internationalization is high. Hence, mitigation efforts positively impact at least two dimensions of competitiveness Ñ sales effectiveness and product leadership, particularly when internationalization is high. © 2013 Wiley Periodicals, Inc.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".