The Role of Green Financial Sector Initiatives in the Low-Carbon Transition: A Theory of Change
Bibliographic record
Abstract
Green financial sector initiatives, including financial policies, regulations, and instruments, could play an important role in the low-carbon transition by supporting countries in the implementation of economic policies aimed to decarbonize their economy. Thus, it is fundamental to understand the conditions under which and the extent to which green financial sector initiatives could enable the scaling up of green investments and the achievement of national climate mitigation objectives, while, at the same time, avoiding unintended effects on macroeconomic and financial stability. However, this understanding is currently limited, in particular in the context of emerging markets and developing economies. This paper contributes to filling this knowledge gap by analyzing opportunities and challenges associated with the implementation of green financial sector initiatives. It also considers the specificities \nof green financial sector initiatives in emerging markets and developing economies, which are often characterized by budget constraints, debt sustainability concerns, and limited access to finance. The analysis focuses on green macroprudential policies, green monetary policies, and green public co-funding. For each green financial sector initiative, the paper qualitatively investigates the transmission channels through which it affects the availability and cost of capital for high- and low-carbon goods, but also investments, output, and greenhouse gas emissions, \nconsidering the design and implementation of the green financial sector initiative. For each green financial sector initiative, the paper further identifies its entry point in the economy and its direct and indirect impacts. Building on these insights, the paper develops a theory of change about the role of green financial sector initiatives in climate mitigation and in the low-carbon transition, identifying the criteria for applicability and conditions to maximize impact.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".