Green Energy, Economic Growth, and Innovation for Sustainable Development in OECD Countries

This study explores the interrelationship between green energy adoption, economic growth, and innovation in promoting sustainable development within OECD countries. Using a random forest regression model, the research analyzes secondary data from 2013 to 2022 to identify the most significant contrib...

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Bibliographic Details
Published in:Sustainability 2024-11, Vol.16 (22), p.10113
Main Authors: Zhao, Tianhao, Shah, Syed Ahsan Ali
Format: Article
Language:English
Subjects:
Alternative energy sources
Clean technology
Economic growth
Efficiency
Energy industry
Energy resources
Environmental impact
Environmental protection
Fossil fuels
GDP
Green technology
Gross Domestic Product
Growth
Human Development Index
Hypotheses
Industrial development
Innovations
Literature reviews
Machine learning
Renewable resources
Sustainable development
Technological change
Variables
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Summary:This study explores the interrelationship between green energy adoption, economic growth, and innovation in promoting sustainable development within OECD countries. Using a random forest regression model, the research analyzes secondary data from 2013 to 2022 to identify the most significant contributors to sustainable development. The random forest model was selected for its ability to handle non-linear relationships and feature importance ranking, providing a comprehensive understanding of the variables’ impacts. The analysis reveals that green energy adoption has the strongest influence on the human development index (HDI), with an importance score of 0.43, followed by gross domestic product (GDP) and the global innovation index (GII). These findings underscore the pivotal role of green energy adoption, amplified by economic growth and technological innovation, in advancing sustainable development. While the study focuses on OECD countries, the insights offer valuable implications for global sustainability initiatives. The evidence supports the argument that prioritizing green energy, supported by economic and innovative drivers, is crucial for achieving broader sustainable development goals. This research provides a methodological contribution by demonstrating the effectiveness of machine learning models in analyzing complex sustainability data and offers empirical evidence that informs policy and future research in a broader context.
ISSN:2071-1050
2071-1050
DOI:10.3390/su162210113