Leveraging the capabilities of multinational firms to address climate change: a finance perspective
Bibliographic record
Abstract
Abstract Climate change and the associated issue of curbing carbon emissions have risen on the agenda of policymakers worldwide. However, global coordination on matters such as harmonized regulation has been subject to significant political frictions, and the large intergovernmental transfers needed to finance the transition of developing economies have proven hard to raise. Recently, there have been considerable responses to climate change from the private sector, with stakeholders placing more pressure on firms, and financial markets mobilizing increasingly more capital towards the reduction of negative externalities. We argue that although multinational enterprises (MNEs) have been a major contributor to the problem, they can be an important part of the solution – they have unique features that enable them to play an important role in the fight against climate change. MNEs have extensive and efficient internal markets for governance, financing, and technology, which enable them to circumvent country-specific frictions to climate action such as heterogeneous regulation, corruption, and the lack of technology. We analyze how different public and private incentive mechanisms could be designed to leverage MNEs’ unique features, realign their incentives, and engage their potential to play a role in decarbonizing the economy. Lastly, we discuss challenges, opportunities, and future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".