Multinational enterprises and greenhouse gas emissions: The dual reality of climate governance mechanisms
Bibliographic record
Abstract
Multinational enterprises (MNEs) have a collective carbon footprint that would rank fifth among nations, positioning them at the center of the debate on climate change mitigation. Framed through theories of temporality, stakeholder pressure, and signaling, this study examines whether and how MNEs have reduced greenhouse gas (GHG) emissions over time. Using longitudinal data over 15 years (2009–2023), we track absolute reductions in scope 1 (direct) and scope 2 (indirect) emissions across the world’s top non-financial MNEs headquartered in 30 countries. We find that 69% of firms reduced scope 1 emissions, with an even higher proportion reducing scope 2 emissions and emission intensities. Reductions accelerated after the 2015 Paris Agreement, though fewer than 45% achieved reductions consistent with international climate targets. Our results highlight the central role of sustained climate governance mechanisms including board oversight, climate strategy, emission targets, monetary incentives, and verification in driving emission reductions. These mechanisms are effective only among firms already on a path to reduce GHG emissions. In firms that increased emissions, governance mechanisms appeared largely symbolic, underscoring the risk of greenwashing in corporate climate governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".