Carbon Disclosure Project Supply Chain Program Membership and Reported Incidents
Bibliographic record
Abstract
Firms are increasingly blamed for environmental supply chain incidents revealed in media reports, which harms firms’ reputations and economic returns. Therefore, firms may be increasingly motivated to publicly signal commitment to mitigating environmental impacts in their supply chains, such as by gaining membership in the Carbon Disclosure Project Supply Chain Program (CDP SCP). CDP SCP is a well-regarded voluntary initiative with high public visibility that helps member firms (buyers) engage their suppliers in environmental impact mitigation. However, while a buyer’s membership might curtail media reports through a supplier engagement mechanism, it could also amplify scrutiny through a spotlight mechanism, prompting salient questions about membership outcomes. What is the relationship between CDP SCP membership and subsequent reported environmental supply chain incidents? What are the dominant mechanisms? We address these questions in our research using a novel dataset assembled from various archival sources including the CDP SCP, RepRisk, and FactSet. Our quasi-experimental design employs coarsened exact matching and a regression difference-in-differences model to compare outcomes across 124 CDP supply chain program members and 1,148 control firms, comprising a panel of 12,622 firm-year observations. We find strong and robust support for the net positive effect of CDP supply chain program membership on reported environmental supply chain incidents. Additional analyses reveal the underlying mechanisms: while CDP supply chain membership causes positive changes in environmental performance at the supplier-level of analysis, it also attracts the attention of external stakeholders in ways that have been shown to be detrimental to buyers’ environmental reputation and economic returns.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".