The Drivers of Corporate Climate Change Strategies and Public Policy
Bibliographic record
Abstract
Effective public policy to mitigate climate change footprints should build on data-driven analysis of firm-level strategies. This article’s conceptual approach augments the resource-based view (RBV) of the firm and identifies investments in four firm-level resource domains (Governance, Information management, Systems, and Technology [ GISTe]) to develop capabilities in climate change impact mitigation. The authors denote the resulting framework as the GISTe model, which frames their analysis and public policy recommendations. This research uses the 2008 Carbon Disclosure Project (CDP) database, with high-quality information on firm-level climate change strategies for 552 companies from North America and Europe. In contrast to the widely accepted myth that European firms are performing better than North American ones, the authors find a different result. Many firms, whether European or North American, do not just “talk” about climate change impact mitigation, but actually do “walk the talk.” European firms appear to be better than their North American counterparts in “walk I,” denoting attention to governance, information management, and systems. But when it comes down to “walk II,” meaning actual Technology-related investments, North American firms’ performance is equal or superior to that of the European companies. The authors formulate public policy recommendations to accelerate firm-level, sector-level, and cluster-level implementation of climate change strategies.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".