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Energy at risk: Exploring energy security in a geopolitically unstable world

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  • Jiang, Ping-Chuan
  • Feng, Gen-Fu
  • Chang, Chun-Ping

Abstract

This study utilizes data from 42 countries spanning from 1996 to 2021. The panel quantile regression model is applied to assess how geopolitical risk (GPR) affects energy security (ES) across varying ES levels. Empirical analysis reveals that, in nations with high levels of ES, GPR exerts a negative impact on ES, suggesting that heightened geopolitical tensions can compromise energy stability. Conversely, in nations with lower levels of ES, GPR has a positive impact, driving these nations to enhance their energy self-sufficiency. The findings reveal an inverted U-shaped change in the regression coefficients of GPR and ES, whereby the influence of GPR transitions from positive to negative as ES quantile rises. These results are robust to the inclusion of dynamic effects and additional control variables. Furthermore, supplementary analysis demonstrates that GPR incentivizes the adoption of renewable energy in countries with lower levels of ES. In conclusion, this study provides empirical insights to assist policymakers in designing differentiated energy policies.

Suggested Citation

  • Jiang, Ping-Chuan & Feng, Gen-Fu & Chang, Chun-Ping, 2025. "Energy at risk: Exploring energy security in a geopolitically unstable world," Economic Analysis and Policy, Elsevier, vol. 86(C), pages 231-244.
    • Handle: RePEc:eee:ecanpo:v:86:y:2025:i:c:p:231-244
    DOI: 10.1016/j.eap.2025.03.018
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    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • F50 - International Economics - - International Relations, National Security, and International Political Economy - - - General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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