Environmental Supply Chain Orientation and Science-Based Targets
Bibliographic record
Abstract
With growing urgency to engage corporations in climate change mitigation, there is a rising need to understand what factors enable firms to set meaningful carbon reduction commitments. Formed in 2015, the Science-Based Targets initiative (SBTi) works to support and verify corporate carbon reduction commitments that align with 1.5?C global warming limitations, as laid out in the Paris Agreement. Despite the existential risks associated with climate change, there is notable heterogeneity in the preparedness of firms to commit to necessary mitigation efforts. To better understand what factors enable firms to make SBTi commitments, we draw on the attention-based view of the firm and the sustainable supply chain literature. We propose that higher levels of firms’ awareness of their environmental supply chain impacts and risks – what we refer to as an environmental supply chain orientation – provides important knowledge and motivations for firms to make SBTi commitments. To test our hypotheses, we combine panel data from SBTi, CDP, RepRisk, Bloomberg, and Compustat, finding strong support for the relationship between environmental supply chain orientation and SBTi commitments. In doing so, we extend theory to the context of SBTi commitments and provide actionable insights to encourage more firms to meaningfully engage in climate change mitigation.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".