Sustainable Banking and Bank Stability in Nigeria: Empirical Evidence from Deposit Money Banks
Bibliographic record
Abstract
We investigated the impact of sustainable banking practices on bank stability in the Nigerian banking sector. We focused on data from 2012 to 2022, which were extracted from the balance sheets of deposit money banks in Nigeria. We employed the Dynamic Ordinary Least Squares (DOLS) estimator with E-Views to analyze the data. Our findings show that environmental emissions and waste reduction have minimal effects on bank assets, capital adequacy, and liquidity, though they do not directly cause financial instability. Investments in environmental innovation reduce asset growth and increase liquidity constraints but lower non-performing loans, emphasizing a trade-off between sustainability and stability. Environmental resource use efficiency remains neutral regarding asset stability and capital adequacy but poses liquidity challenges. Social welfare investments have little impact on asset growth and profitability, potentially reducing financial stability. Human resource development improves capital adequacy and liquidity strengthening bank stability, while community investments aid societal growth but create liquidity pressures. Macroeconomic factors like GDP growth and inflation are significant, yet economic growth does not always increase bank assets, whereas inflation increases non-performing loans. Sustainable banking in Nigeria is evolving; therefore, there is a need for robust regulation, financial incentives for compliance, a high level of awareness, and alignment between banking operations and sustainability principles.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".