Regulating Green Finance and Managing Environmental Risks in the Conditions of Global Uncertainty
Bibliographic record
Abstract
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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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".