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Optimizing Taiwan’s Renewable Energy Mix: A Regression and Principal Component Analysis Approach Under Climate Change Challenges

Author

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  • Mei-Mei Lin

    (Department of Hospitality Management, Tung Nan University of Technology, New Taipei City 222, Taiwan)

  • Fu-Hsiang Kuo

    (Department of Hospitality Management, Tung Nan University of Technology, New Taipei City 222, Taiwan
    Department of Finance, National Yunlin University of Science and Technology, Yunlin County, Douliu 64002, Taiwan)

Abstract

Amid rising global energy demand and Taiwan’s transition toward a non-nuclear and low-carbon future, identifying an optimal renewable energy (RE) mix has become essential. This study analyzes eight RE sources using a three-model framework—Pearson correlation, Stepwise Regression Analysis (SRA), and Principal Component Analysis (PCA)—based on 60 monthly observations from 2019 to 2023. The results show that geothermal energy (GE) and solar photovoltaics (SP) exhibit strong positive correlations with total RE generation. Both SRA and PCA consistently identify conventional hydropower (CH), SP, and offshore wind power (OSW) as Taiwan’s most effective RE combination, while PCA provides superior predictive performance and reduces multicollinearity. In contrast, OWP, SB, BG, and WTE show limited contribution to overall RE output. Policy recommendations suggest prioritizing SP under resource constraints, and jointly expanding CH, SP, and OSW when resources permit, to achieve a balanced and sustainable RE structure.

Suggested Citation

  • Mei-Mei Lin & Fu-Hsiang Kuo, 2025. "Optimizing Taiwan’s Renewable Energy Mix: A Regression and Principal Component Analysis Approach Under Climate Change Challenges," Sustainability, MDPI, vol. 17(24), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:24:p:10894-:d:1811210
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