Adverse Weather Events and Costs of Bank Loans in the 4.0 Digital Context: The Case of a Developing Country

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DOI: https://doi.org/10.28924/2291-8639-24-2026-179
Published: Jun 15, 2026
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Hien Thi Thu Hoang, Ha Thi Thu Do, Thuy Thu Pham, Adverse Weather Events and Costs of Bank Loans in the 4.0 Digital Context: The Case of a Developing Country, Int. J. Anal. Appl., 24 (2026), 179.

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Hien Thi Thu Hoang, Ha Thi Thu Do, Thuy Thu Pham

Abstract

The study examines how digital transformation and adverse weather events affect bank loan costs in Vietnam. Using an unbalanced panel of 20 commercial banks from 2012–2023, it constructs a Digital Transformation Index from annual report keywords and applies fixed-effects, system GMM, and two-way clustered regressions. Results show that digital transformation significantly reduces loan costs, suggesting gains in operational efficiency, lower information asymmetry, and reduced transaction and monitoring expenses. In contrast, climate-related variables, especially the climate change indicator, increase loan costs by elevating credit risk and provisioning needs. The positive, significant interaction between digital transformation and climate shocks indicates that the cost-reducing effect of digitalization is partially weakened under extreme weather conditions. GDP growth is associated with lower loan costs, while funding diversification and capitalization yield mixed effects; higher inflation raises lending costs, whereas larger bank size and income diversification help reduce them. Overall, the findings highlight the need to accelerate digital adoption while strengthening climate resilience to enhance credit affordability and financial stability in emerging economies.

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References

  1. Intergovernmental Panel on Climate Change (IPCC), Sixth Assessment Report, United Nations, (2023). https://www.ipcc.ch/assessment-report/ar6.
  2. O. de Bandt, L. Kuntz, N. Pankratz, F. Pegoraro, H. Solheim, et al., The Effects of Climate Change‐related Risks on Banks: A Literature Review, J. Econ. Surv. 39 (2024), 1553-1594. https://doi.org/10.1111/joes.12665.
  3. D. Gautam, Unlocking Adaptation Finance in Emerging Market and Developing Economies, Staff. Clim. Notes 2024 (2024), 1. https://doi.org/10.5089/9798400293290.066.
  4. Krungsri, Opportunities for Financial Institutions in Vietnam: Adaptation Amid Macroeconomic and Financial Risks, Bank of Ayudhya Public Company Limited, (2025). https://www.krungsri.com/en/research/research-intelligence/vietnam-financial-2025.
  5. World Bank, Vietnam Country Climate and Development Report, Washington, DC: World Bank, (2022). https://www.ifc.org/en/insights-reports/2022/vietnam-country-climate-and-development-report.
  6. H. Thomson, H.B. Zeff, R. Kleiman, A. Sebastian, G.W. Characklis, Systemic Financial Risk Arising from Residential Flood Losses, Earth's Futur. 11 (2023), e2022EF003206. https://doi.org/10.1029/2022ef003206.
  7. O. Nie, M. Regelink, D. Wang, Banking Sector Risk in the Aftermath of Climate Change and Environmental-Related Natural Disasters, Policy Research Working Paper No. 10326, World Bank, Washington, DC, 2023. https://pdfs.semanticscholar.org/d492/2bddde4dc6503e2af84530e6ba81b2e72cdf.pdf.
  8. Vietnamplus, Small and Medium-Sized Enterprises Focus on ESG Practices, (2025). https://thoibaotaichinhvietnam.vn/small-and-medium-sized-enterprises-focus-on-esg-practices-168045.html (retieved on 1 July 2025).
  9. X. Vives, Digital Disruption in Banking, Annu. Rev. Financ. Econ. 11 (2019), 243-272. https://doi.org/10.1146/annurev-financial-100719-120854.
  10. A. Fuster, P. Goldsmith‐Pinkham, T. Ramadorai, A. Walther, Predictably Unequal? the Effects of Machine Learning on Credit Markets, J. Finance 77 (2021), 5-47. https://doi.org/10.1111/jofi.13090.
  11. P. Gomber, J.A. Koch, M. Siering, Digital Finance and FinTech: Current Research and Future Research Directions, J. Bus. Econ. 87 (2017), 537-580. https://doi.org/10.1007/s11573-017-0852-x.
  12. H. Cui, Y. Li, S. Liang, Digital Transformation and the Cost of Bank Loans in China, Asian Rev. Account. 33 (2025), 695-724. https://doi.org/10.1108/ARA-04-2024-0119.
  13. B. Gershman, D. Rivera, Subnational Diversity in Sub-Saharan Africa: Insights from a New Dataset, J. Dev. Econ. 133 (2018), 231-263. https://doi.org/10.1016/j.jdeveco.2018.01.003.
  14. M. Shakil, M. Ali, T. Illahi, F. Ahmed, FinTech 5.0 and the Future of Global Finance: Harnessing Artificial Intelligence, Blockchain, and Big Data to Build Inclusive, Sustainable, and Resilient Financial Ecosystems, Inverge J. Soc. Sci. 4 (2025), 56-66. https://doi.org/10.63544/ijss.v4i4.179.
  15. L. Klapper, Expanding Financial Inclusion through Digital Financial Services: A Literature Review, Policy Research Working Paper No. 11008, World Bank, Washington, DC, 2024. https://documents1.worldbank.org/curated/en/099415112192435594/pdf/IDU-eb12eb3f-c2d2-45ad-b1b6-2a3f9ec30262.pdf (retrieved on 1 July 2025).
  16. Bank for International Settlements, The Effects of Climate Change-Related Risks on Banks: A Literature Review, (2022). https://www.bis.org/bcbs/publ/wp40.htm (retrieved on 1 May 2025).
  17. S. Battiston, Y. Dafermos, I. Monasterolo, Climate Risks and Financial Stability, J. Financ. Stab. 54 (2021), 100867. https://doi.org/10.1016/j.jfs.2021.100867.
  18. T. Le, T. Ngo, T.H. Ho, T.T. Do, Digitalisation and Bank Efficiency: Evidence from an Emerging Market, Asian Acad. Manag. J. Account. Financ. 21 (2025), 29-65. https://doi.org/10.21315/aamjaf2025.21.2.2.
  19. F. Modigliani, M.H. Miller, The Cost of Capital, Corporation Finance and the Theory of Investment, Amer. Econ. Rev. 48 (1958), 261-297. https://www.jstor.org/stable/1809766.
  20. M.Z. Frank, V.K. Goyal, Capital Structure Decisions: Which Factors Are Reliably Important?, Financ. Manag. 38 (2009), 1-37. https://doi.org/10.1111/j.1755-053X.2009.01026.x.
  21. T. Beck, A. Demirgüç‐Kunt, V. Maksimovic, Financial and Legal Constraints to Growth: Does Firm Size Matter?, J. Financ. 60 (2005), 137-177. https://doi.org/10.1111/j.1540-6261.2005.00727.x.
  22. J.R. Barth, G. Caprio, R. Levine, Bank Regulation and Supervision: What Works Best?, J. Financ. Intermediation 13 (2004), 205-248. https://doi.org/10.1016/j.jfi.2003.06.002.
  23. I. Love, M.S. Martínez Pería, How Bank Competition Affects Firms' Access to Finance, World Bank Econ. Rev. 29 (2014), 413-448. https://doi.org/10.1093/wber/lhu003.
  24. J.E. Stiglitz, A. Weiss, Credit Rationing in Markets with Imperfect Information, Amer. Econ. Rev. 71 (1981), 393-410. https://www.jstor.org/stable/1802787.
  25. A.N. Berger, G.F. Udell, Relationship Lending and Lines of Credit in Small Firm Finance, J. Bus. 68 (1995), 351-381. https://doi.org/10.1086/296668.
  26. H. Degryse, M. Kim, S. Ongena, Microeconometrics of Banking: Methods, Applications, and Results, Oxford University Press, Oxford, 2009.
  27. S. Claessens, J. Frost, G. Turner, F. Zhu, Fintech Credit Markets Around the World: Size, Drivers and Policy Issues, BIS Q. Rev. 2018 (2018), 29-49. https://www.bis.org/publ/qtrpdf/r_qt1809e.htm.
  28. B.N. Ashraf, Y. Shen, Economic Policy Uncertainty and Banks’ Loan Pricing, J. Financ. Stab. 44 (2019), 100695. https://doi.org/10.1016/j.jfs.2019.100695.
  29. T.D. Nguyen, M.N. Nguyen, How Do Banks Price Loans in the Context of Market Concentration? Empirical Evidence from Vietnam, Econ. Financ. Lett. 12 (2025), 329-337. https://doi.org/10.18488/29.v12i2.4232.
  30. M.H. Pham, N.M. Nguyen, Bank Funding Diversity, Risk and Profitability: Evidence from Vietnam in the Context of the COVID-19 Pandemic, Cogent Bus. Manag. 10 (2023), 2191305. https://doi.org/10.1080/23311975.2023.2191305.
  31. Q.T.T. Nguyen, L.T.H. Ho, D.T. Nguyen, Digitalization and Bank Profitability: Evidence from an Emerging Country, Int. J. Bank Mark. 41 (2023), 1847-1871. https://doi.org/10.1108/IJBM-03-2023-0156.
  32. G.G. Judge, W.E. Griffiths, R.C. Hill, H. Lutkepohl, T.C. Lee, The Theory and Practice of Econometrics, John Wiley & Sons, 1985.
  33. H. Gulen, M. Ion, Policy Uncertainty and Corporate Investment, Rev. Financ. Stud. 29 (2015), 523–564. https://doi.org/10.1093/rfs/hhv050.
  34. M. Arellano, S. Bond, Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Rev. Econ. Stud. 58 (1991), 277-297. https://doi.org/10.2307/2297968.
  35. M.A. Petersen, Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches, Rev. Financ. Stud. 22 (2008), 435-480. https://doi.org/10.1093/rfs/hhn053.