Green lending in Kazakhstan: Bank-level drivers, volumes, stability channels, and short-horizon forecasts (2015–2024)

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Type of the article: Research Article

Abstract
Green lending growth can support bank resilience and is therefore relevant to Kazakhstan’s pathway to carbon neutrality by 2060. The study created a panel of banking years (2015–2024) and assessed the relationships between banks’ regulatory compliance, digitalization, borrowers’ ESG performance, and green loan volumes using multivariate models. The research provides short-term forecasts using compressed ARIMAX and policy scenarios. Moreover, 20 purposively selected semi-structured interviews (commercial bank executives, SME owners, customers, and policy experts) and a national survey of 850 adult bank customers / SME owners led by the author were added. Across preferred specifications, regulatory eligibility and borrower ESG are consistently positive: policy support is associated with KZT 7-9 billion more green credit per bank year, and each one-point increase in borrower ESG is associated with KZT 0.34-0.38 billion higher volumes. Digitalization is positive but model-sensitive, strengthening within-bank variation; larger banks extend more green credit, consistent with capacity advantages. The results are interpreted through three stability channels: improved screening/asset quality, portfolio tilt toward taxonomy-aligned exposures, and funding access without making solvency claims. Scenario paths suggest aggregate green lending could reach KZT 80-96 billion by 2027 under aligned policy-ESG-digital conditions; under weak support, it may stagnate near KZT 49-55 billion. Findings motivate the development of a binding taxonomy with standardized disclosures, a national ESG scorecard registry, and inclusive digital rails to enhance SME and rural uptake.

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    • Figure 1. Correlation matrix of green loan determinants
    • Figure 2. Green loan over time
    • Figure 3. ACF and PACF
    • Figure 4. Forecasted green loan volumes in Kazakhstan (2025–2027) based on the ARIMAX model
    • Table 1. Econometric model specification (multivariate OLS)
    • Table 2. Descriptive statistics of key variables
    • Table 3. Base OLS regression results (without bank assets)
    • Table 4. Complete OLS regression results (with bank assets as control)
    • Table 5. Fixed effects regression results (robust SE)
    • Table 6. Scenario-based forecasts of green loan volumes in Kazakhstan (2025–2027)
    • Table 7. ARIMAX (1,1,1) regression results
    • Table 8. Regression results for ARIMA (1,1,1)
    • Table 9. Scenario-based forecasts for green loan volumes (2025–2027)
    • Table 10. Matrix of scenario assumptions and institutional levers
    • Table 11. Thematic summary of qualitative findings
    • Table A1. TWFE robustness grid (bank & year FE; SEs clustered at bank level)
    • Table A2. Sensitivity to unobservables (Oster δ bounds) for key coefficients
    • Table A3. Multicollinearity diagnostics (OLS)
    • Table A4. ARIMAX parameters and residual diagnostics (full-exog spec)
    • Table A5. Residual checks
    • Table A6. Scenario assumption grid
    • Table A7. Forecast sensitivity to ±1 SD in D and E (2025–2027)
    • Conceptualization
      Arifioglu Abdurrahman Zeki, Azhar Nurmagambetova, Aliya Nurgaliyeva, Altynay Assanova, Diana Alisheva
    • Data curation
      Arifioglu Abdurrahman Zeki
    • Validation
      Arifioglu Abdurrahman Zeki
    • Writing – original draft
      Arifioglu Abdurrahman Zeki, Azhar Nurmagambetova, Aliya Nurgaliyeva, Altynay Assanova, Diana Alisheva
    • Writing – review & editing
      Arifioglu Abdurrahman Zeki, Azhar Nurmagambetova, Aliya Nurgaliyeva, Altynay Assanova, Diana Alisheva
    • Formal Analysis
      Azhar Nurmagambetova
    • Project administration
      Aliya Nurgaliyeva
    • Supervision
      Aliya Nurgaliyeva
    • Methodology
      Altynay Assanova
    • Resources
      Altynay Assanova
    • Visualization
      Diana Alisheva
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Banks and Bank Systems
Banks and Bank Systems