The Effect of Monetary Policy and Private Investment on Green Finance: Evidence from Hungary
Bibliographic record
Abstract
The objective of this study was to examine the effect of monetary policy and private investment on green finance in the case of Hungary. The study used an explanatory research design and a quantitative research approach. Quarterly secondary time series data over 8 years (2013–2020) were utilized. More specifically, the study used Johnson co-integration test and vector error correction model to investigate the long and short-run relationship among variables. The study’s findings imply that monetary policy, as measured by interest rates and the broad money supply, has a mixed effect on the level of green financing. Interest rates, in particular, have a negative and significant relationship with green finance in both the long and short run. However, a broad money supply has a positive but insignificant relationship with green finance in the long run. Private investment has a positive and significant relationship with green financing in both the long and short run. The study also used inward and outward foreign direct investment, and greenhouse gas as a control variable of the study. The study finding implies that inward foreign direct investment has a positive and significant relationship with green financing in both the long and short run. On the other hand, outward foreign direct investment and the level of greenhouse gas have a negative and significant relationship with green finance in both the long and short run. The study also discovered that over time series, disturbance in domestic private investment was the most determinant factor in forecast error variance of green financing. In addition, the result of document analysis shows that the majority of Hungarian credit institutions are dealing with their corporate strategy rather than their sustainability strategy. Hence, progressive approaches are needed from the credit institution to frame their strategy under the concept of sustainable development goals. The finding of this study will contribute to the existing literature on the study area, provide suggestions on green finance and green monetary policy approaches, provide implications on key stakeholders of green financing, as well as the experience of different economies. The study advises central banks, credit institutions, and regulatory authorities to consider both neoliberal and reformist approaches of green finance and green monetary policies in aid to increase green investment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".