| dc.description.abstract | This study contributes to the existing literature by investigating and confirming a range of diverse outcomes
related to the interplay of factors shaping the global energy transition (ET). Employing advanced methodologies,
including the extension of the QVAR technique to short-term (SR), medium-term (MR), and long-term (LR)
connectedness analysis, as well as the application of the CQ method to explore relationships within varying
market conditions and timeframes, the study examines the interconnectedness of critical variables: artificial
intelligence (AI), the Belt and Road Initiative (BRI), the Paris Agreement (PA), green technologies (GT),
geopolitical risk (GPR), and ET. The findings highlight several crucial insights. Firstly, all selected variables
demonstrate substantial interconnectedness across different time horizons, except for MR, which exhibits
comparatively weaker connectedness than SR and LR. Secondly, independent series reveal diverse impacts on ET
across various market conditions and periods. For example, in SR, most series produce mixed effects on ET, with
BRI having primarily adverse consequences and GPR predominantly yielding positive impacts. In MR, the influence of AI, PA, and GT on ET varies, while BRI enhances ET, and GPR essentially hampers it. Notably, in LR,
AI, BRI, PA, and GT significantly promote ET, while GPR disrupts its progress. Additionally, the study underscores the dynamic and time-varying nature of the relationships between AI, BRI, PA, GT, GPR, and ET across
different market conditions, thus holding essential implications for shaping global policies to foster sustainable
energy transitions. | en_US |
