Do feed-in tariffs unlock green finance? A panel study of banking sector assets and renewable energy consumption across 66 countries around the world
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DOIhttp://dx.doi.org/10.21511/bbs.20(3).2025.03
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Article InfoVolume 20 2025, Issue #3, pp. 27-44
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Type of article: Research Article
Abstract
The mobilization of banking sector capital is increasingly viewed as a pivotal component of the global transition to renewable energy sources (RES), given the sector’s capacity to finance capital-intensive projects. However, banks typically favor investments and lending opportunities that offer predictable cash flows and low default risk, characteristics often lacking in RES projects without policy support. This study investigates whether the development of the banking sector facilitates the uptake of RES and how feed-in tariffs (FiTs), which provide guaranteed purchase periods and stable prices, modify this relationship. Using a panel dataset of 66 countries (selected based on data availability, allowing robust results that may be cautiously applied to countries with comparable financial and institutional contexts) from 2000 to 2020, fixed effects regression models with time dummies and robust standard errors are employed. The analysis finds that banking sector development alone does not lead to increased consumption from RES (coefficient = 0.0011, p = 0.950), suggesting that banks are reluctant to invest in renewables due to the lack of mechanisms to guarantee returns. The standalone introduction of FiTs is associated with a temporary decrease in RES uptake (coefficient = −5.07; p < 0.001), likely reflecting initial market distortions. However, when FiTs are implemented in countries with a more significant economic role of banks, the interaction yields a significant positive effect (coefficient = 0.0412; p < 0.001), indicating that FiTs reduce investment risk and unlock bank financing for RES. The model explains 20.8% (R2=0.208) of within-country variation, and fixed effects vary substantially, underscoring structural differences across countries.
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JEL Classification (Paper profile tab)G21, Q42, O13, Q48, C23
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References50
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Tables8
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Figures0
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- Table 1. Overview of variables and data sources used in the empirical analysis
- Table 2. Descriptive statistics for key variables
- Table 3. Outputs of fixed effects and random effects models
- Table 4. Fixed effects regression results with cluster-robust standard errors
- Table 5. Fixed effects panel regression model with time (year) effects
- Table 6. Fixed effects panel regression model with year effects, using cluster-robust standard errors
- Table 7. Country-specific fixed effects from the fixed effects panel regression model
- Table A1. List of countries
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