Climate finance spillovers and entrepreneurship in developing countries
Bibliographic record
Abstract
Abstract Research Summary We conduct a multicountry analysis and show that there is a strong and significant positive relationship between climate finance and entrepreneurship, even after controlling for conventional macroeconomic and institutional factors commonly reported in the literature. Specifically, a 10% increase in climate finance is linked with a 2% increase in entrepreneurial activity across most countries. There are important heterogeneities in this nexus as it relates to fossil fuel exporting countries—the main “ losers ” from a global move away from fossil fuels. We find that although fossil fuel exporting countries exhibit notably faster rates of entrepreneurship growth, the interaction with climate finance in these countries is negatively related to entrepreneurial activity. This finding holds across different types of climate finance—adaptation and mitigation—highlighting its robustness. Managerial Summary It is often suggested that more finance will lead to more entrepreneurship. We conduct a multicountry analysis and add nuance to this notion. We find that although there is a strong and significant positive relationship between climate finance and entrepreneurship in most countries, this is not always true for fossil fuel exporting countries. Fossil fuel exporting countries, despite experiencing faster entrepreneurial growth, exhibit a negative interaction between climate finance and entrepreneurship. For managerial practice, these results emphasize the importance of targeted strategies in deploying climate finance. Policymakers and investors should consider nuanced approaches that address the specific economic dependencies and regulatory environments of fossil fuel exporting countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".