US-China Tensions and renewable energy markets: A Dynamic Bayesian Inference Approach

Babak Naysary and Abdul Ghafoor

No 44, CAFE Working Papers from Centre for Accountancy, Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University

Abstract: This paper examines the impact of US-China trade tensions on renewable energy markets from 2010-2024. Using a Dynamic Bayesian Inference approach, we quantify time-varying sensitivities of four NASDAQ renewable energy indices: Solar, Wind, Geothermal, and Green Economy. Our findings reveal significant heterogeneity in market responses. These results suggest that certain renewable sectors have developed effective resilience mechanisms to geopolitical risks.

Keywords: Dynamic Bayesian Inference; US-China trade tensions; renewable energy (search for similar items in EconPapers)
Date: 2026-09-30
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