Is there a trade‐off between environmental performance and financial sustainability in microfinance institutions? Evidence from South and Southeast Asia
Bibliographic record
Abstract
Abstract The environmental performance of microfinance institutions (MFIs) has received considerable attention in recent years. However, the question of whether MFIs' involvement in environmental practices can drive their financial sustainability has yet to be addressed. This is the knowledge gap that we intend to investigate in the current study. Specifically, using a panel of 587 MFI‐year observations for the 2007–2014 period, we investigate whether pursuing proactive environmental strategies, individually and in aggregation, can improve the financial sustainability of MFIs in South and Southeast Asian countries. By analyzing aggregate environmental performance, we demonstrate that environmental performance adversely influences the financial performance of MFIs. This provides support for the trade‐off hypothesis that predicts that higher levels of environmental practices worsen firms' financial sustainability. Our results also show that the relationship between the individual environmental performance dimensions and financial performance of MFIs varies with the individual dimension of green practice being considered. Finally, we find that there is a significant variation in this relationship across MFI ownership types. The findings have valuable implications for practitioners, investors, and policymakers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".