The Role of Innovation Development in Advancing Green Finance
Bibliographic record
Abstract
This study aims to investigate how innovation development drives green finance in the Visegrad countries by analyzing the role of R&D investments, high-tech trade, and patent activity in attracting greenfield investments. Using a vector autoregression (VAR) model with data from 2007 to 2022, this study employs forecasting techniques, impulse response functions, and variance decomposition analyses to assess the dynamic relationship between innovation and green financial flows. The findings reveal that R&D expenditures are the strongest driver of green investments, explaining over 93% of the variance in Poland and Hungary. High-tech trade significantly influences investment trends, contributing up to 84% of the variance in the Czech Republic, while patent applications initially boost greenfield investments but show diminishing returns over time. Although innovation-driven investments remain stable overall, the impact of trade and patents varies across countries, reflecting regional differences. This study identifies key challenges, such as commercialization gaps and policy disparities, highlighting the need for targeted financial and innovation policies. To sustain green finance growth, policymakers should expand R&D funding, strengthen trade infrastructure, and enhance intellectual property commercialization. Additionally, financial institutions and investors should play a more active role in developing green investment markets to support long-term economic resilience and sustainability.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".