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A machine learning and quantile analysis of FINTECH and resource efficiency in achieving sustainable development in OECD countries

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  • Zhang, Hui
  • Fahlevi, Mochammad
  • Aljuaid, Mohammed
  • Beşer, Nazife Özge
  • Cabas, Meral
  • lominchar, Jose

Abstract

This study aims to examine the impact of fintech investments and resource efficiency on sustainable development in OECD countries between 2010 and 2019. Various estimation techniques, including the Method of Moments Quantile Regression (MMQREG), machine learning-based Kernel Regularized Least Squares (KRLS), and Generalized Method of Moments (GMM), have been utilized in this study. MMQREG and KRLS are both estimators that examine the relationship between variables in several qunatiles, increasing the reliability of the findings. The research results indicate that fintech investments and resource efficiency support sustainable development. Additionally, institutional quality, environmentally friendly technologies, and foreign trade are found to have a positive impact on sustainable development. These findings suggest that financial technology and resource management can play a significant role in promoting both economic and environmental sustainability.

Suggested Citation

  • Zhang, Hui & Fahlevi, Mochammad & Aljuaid, Mohammed & Beşer, Nazife Özge & Cabas, Meral & lominchar, Jose, 2024. "A machine learning and quantile analysis of FINTECH and resource efficiency in achieving sustainable development in OECD countries," Resources Policy, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:jrpoli:v:92:y:2024:i:c:s0301420724003842
    DOI: 10.1016/j.resourpol.2024.105017
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