The Impact of Macroeconomic Variables on Credit Risk: Evidence Regarding Sustainable Lending in ASEAN Countries
Bibliographic record
Abstract
This research examines the hypotheses that macroeconomic variables and environmental factors impact credit risk.This study focuses on the relationship between macroeconomics, environmental issues, and credit risk in ASEAN countries.This study applies a panel Autoregressive Distributed Lag (ARDL) model to data from the World Bank for the 2008-2019 period.The variables studied include non-performing loans (NPLs), Gross Domestic Product (GDP) growth, interest rates, inflation, and carbon emissions.The research results show that environmental factors do not affect NPLs, while macroeconomic factors do.GDP growth and inflation reduce NPLs while rising credit interest rates increase NPLs.The results imply credit risk is not considered sustainable lending.Credit risk does not consider environmental degradation as measured by increases in carbon emissions.From cross-country evidence, the effect of environmental degradation on credit quality is not found in all countries.Indeed, if environmental quality is considered, environmental degradation will be detrimental to operational and financial performance, especially for heavily polluting firms.However, poor ecological quality will harm operational and financial performance.In addition, business entities within the framework of sustainable loans have collateral consequences for business activities aimed at reducing pollution, which has implications for increasing costs.For decision-makers, both regulators and banks, in the future, banking credit distribution must seriously consider implementing sustainable lending through green credit policy schemes.Green credit schemes need to involve collaboration with banks and incorporate environmental factors into the loan portfolio.Empirically implementing green credit can improve bank financial performance and firm environmental, social, and governance (ESG) performance.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".