Does International Green Finance Accelerate Green Innovation? Catalysts for Fostering CO2 Reduction in Developing Economies
Bibliographic record
Abstract
While domestic green finance is widely recognized for its role in fostering green innovation and supporting climate change mitigation, the impact of international green finance (IGF) remains critical, particularly for developing economies where external finance inflows can catalyse transitions toward low-carbon development. This study investigates the long-run and short-run effects of IGF on green innovation and further examines the influence of green innovation on carbon dioxide (CO2) emissions across a panel of 76 developing countries from 2000 to 2019. Using second-generation panel cointegration and the vector error correction mechanism, our findings reveal a nonlinear long-run relationship between IGF and total innovation, indicating that IGF must exceed a threshold before significantly boosting total innovation in developing economies. We also identify an inverted U-shaped relationship between IGF and green innovation, in which the positive effects of IGF diminish beyond a certain point. Crucially, IGF emerges as a significant driver of CO2 emissions reduction in both the short- and long-run. While total innovation is associated with increased emissions over the long term, green innovation contributes to a substantial and sustained decrease in CO2 emissions. These results emphasize the need to design targeted policies that prioritize green innovation and scale up IGF to support sustainable growth in developing 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".