The Effect of Monetary Policy and Private Investment on Green Finance: Evidence from Hungary
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».