Measuring the impact of technological innovation, green energy, and sustainable development on the health system performance and human well-being: Evidence from a machine learning-based approach

Alaeddine Mihoub; Montassar Kahia; Mohannad Alswailim; Alaeddine Mihoub; Montassar Kahia; Mohannad Alswailim

doi:10.3934/environsci.2024035

AIMS Environmental Science

2024, Volume 11, Issue 5: 703-722. doi: 10.3934/environsci.2024035

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Measuring the impact of technological innovation, green energy, and sustainable development on the health system performance and human well-being: Evidence from a machine learning-based approach

1.
Department of Management Information Systems, College of Business and Economics, Qassim University, Saudi Arabia
2.
Department of Economics, College of Business and Economics, Qassim University, Saudi Arabia

Received: 23 June 2024 Revised: 05 August 2024 Accepted: 22 August 2024 Published: 27 August 2024

Health performance and well-being are crucial elements of Saudi Arabia's Vision 2030, aiming to improve the overall quality of life and promote a prosperous community. Within this context, this study intended to examine the impact of recent innovations, logistical measures, Information and Communication Technology (ICT) diffusion, environmental quality improvements, economic growth, and green (renewable) energy exploitation on health performance and well-being, in Saudi Arabia from 1990 to 2022, by implementing machine learning models (random forest and gradient boosting) and regression algorithms (ridge and lasso). Overall, the findings of machine learning models indicate a strong impact of digital connectivity on health spending by internet users, with scores of 0.673 and 0.86. Further, economic growth also influences health costs but to a lesser extent, with scores of 0.145 and 0.082. Mobile user penetration and CO₂ emissions have moderate to low importance, suggesting nuanced interactions with health expenditure. Patent applications and logistics performance show minimal impact, indicating a limited direct influence on health costs within this study. Similarly, the share of renewable energy is negligible, reflecting its minimal impact on the analyzed data. Finally, regression analyses using ridge and lasso models confirmed similar trends, further validating these findings. Limitations and several policy implications are also debated.
- health expenditure,
- innovation technology,
- renewable energy,
- sustainable growth,
- ICT access,
- economic well-being
Citation: Alaeddine Mihoub, Montassar Kahia, Mohannad Alswailim. Measuring the impact of technological innovation, green energy, and sustainable development on the health system performance and human well-being: Evidence from a machine learning-based approach[J]. AIMS Environmental Science, 2024, 11(5): 703-722. doi: 10.3934/environsci.2024035

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Abstract

Health performance and well-being are crucial elements of Saudi Arabia's Vision 2030, aiming to improve the overall quality of life and promote a prosperous community. Within this context, this study intended to examine the impact of recent innovations, logistical measures, Information and Communication Technology (ICT) diffusion, environmental quality improvements, economic growth, and green (renewable) energy exploitation on health performance and well-being, in Saudi Arabia from 1990 to 2022, by implementing machine learning models (random forest and gradient boosting) and regression algorithms (ridge and lasso). Overall, the findings of machine learning models indicate a strong impact of digital connectivity on health spending by internet users, with scores of 0.673 and 0.86. Further, economic growth also influences health costs but to a lesser extent, with scores of 0.145 and 0.082. Mobile user penetration and CO₂ emissions have moderate to low importance, suggesting nuanced interactions with health expenditure. Patent applications and logistics performance show minimal impact, indicating a limited direct influence on health costs within this study. Similarly, the share of renewable energy is negligible, reflecting its minimal impact on the analyzed data. Finally, regression analyses using ridge and lasso models confirmed similar trends, further validating these findings. Limitations and several policy implications are also debated.

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