Regulating Green Finance and Managing Environmental Risks in the Conditions of Global Uncertainty
Notice bibliographique
Résumé
This paper’s goal was to determine the state of green financing and reveal the main aspects of its regulation and influence on environmental risk management in the conditions of the growth of global uncertainty. Based on the sample that contains the top 10 countries of the world with a higher level of green economic capabilities in 2024, by the assessment for developed and developing countries in isolation, we performed regression analysis of the following: (1) Dependence of environmental costs of GDP on the volume of green investments; (2) Dependence of the volume of green investments on the application of the measures of state regulation of green finance. As a result, we proved that in developed countries, the growth of the activity of green investing in the economy leads to a reduction in the environmental costs of GDP, and in developing countries, an increase in the environmental costs of GDP. Unlike developed countries, in which green investments are not determined by the influence of the factors of state regulation, the implementation of the measures of state regulation of green finance in developing countries ensures the inflow of green investments into the economy. This paper’s novelty, compared to the existing literature, is that it discloses previously unknown differences in the character of the influence of the factors of state regulation of green finance on green investments in the economy and differences in the consequences of the activity of investing for environmental risks in different categories of countries (in particular, differences between developed and developing countries) and at different phases of the economic cycle (in the conditions of relative stability and in the conditions of global instability). The established regularities of the development of green finance under the influence of state regulation measures in developed and developing countries will raise the precision of forecasting and planning of this development in support of green economic growth and decarbonization. The revealed differences between developed and developing countries will allow forming a strategy of development of green finance in each category of countries, given their specifics, and thus, achieving the growth of these strategies’ effectiveness. The proposed policy implications for the reduction in environmental risks through the improvement of state regulation of green finance in developed and developing countries, given their revealed specifics, have practical significance.
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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 ».