A Comparative Study of Firm-Level Climate Change Mitigation Targets in the European Union and the United States
Bibliographic record
Abstract
While the debate on global and national carbon emission targets has dominated every major climate change conference, setting firm-level climate change mitigation targets has become an increasingly important issue. In this paper we present illustrative evidence on cross-country and cross-industry differences of the firm-level mitigation targets among some of the largest corporations in the European Union (EU) and the United States (US) with regard to five aspects, i.e., target adoption, target metric, target scope, target stringency, and target completion. We find that overall 25% of the firms have not set up emission targets. The EU firms are significantly more likely to use intensity targets than the US firms. The EU firms are twice as likely as the US firms to incorporate indirect emissions from the supply chain into the scope of their targets. The Energy and Materials sectors in the EU set significantly more stringent targets than their US counterparts. The energy sectors of the EU and the US in general have not made satisfactory progress toward accomplishment of the targets. Based on these findings, we discuss the most pressing issues that should be addressed by policymakers and firm managers in different regions and sectors with regard to target-setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".