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

被引：0

作者：

Zhang, Hui ^{[1
]}

Fahlevi, Mochammad ^{[2
]}

Aljuaid, Mohammed ^{[3
]}

Beser, Nazife Ozge ^{[4
]}

Cabas, Meral ^{[5
]}

Lominchar, Jose ^{[5
]}

机构：

[1] Qilu Normal Univ, Sch Econ & Management, Jinan, Peoples R China

[2] Bina Nusantara Univ, Management Dept, BINUS Online Learning, West Jakarta, Indonesia

[3] King Saud Univ, Coll Business Adm, Dept Hlth Adm, Riyadh, Saudi Arabia

[4] Agri Ibrahim Cecen Univ, Fac Econ & Adm Sci, Agri, Turkiye

[5] Univ Distancia Madrid UDIMA, Fac Ciencias Jurid, Madrid, Spain

来源：

RESOURCES POLICY | 2024年 / 92卷

关键词：

Sustainable development; FINTECH; Resource efficiency; Environmental technologies; Trade; Institutional quality; PANEL-DATA; FINANCIAL DEVELOPMENT; EMPIRICAL-EVIDENCE; ECONOMIC-GROWTH; TRADE; ENERGY; ENVIRONMENT; INTEGRATION; GOVERNANCE; EMPLOYMENT;

D O I：

10.1016/j.resourpol.2024.105017

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：13

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