Global research dynamics on energy supply chain risks under geopolitical, economic and climate policy uncertainty: a bibliometric analysis

Purpose This research aims to examine the global academic landscape of energy supply chain risk within the framework of geopolitical risk, economic policy uncertainty and climate policy uncertainty. It explores the intellectual structure, overarching research topics and trends that are shaping the development of this fast-developing area. Design/methodology/approach To achieve its objective, the authors used bibliometric techniques to analyse a data set of 344 publications from the Web of Science Core Collection between 2014 and 2025. The use of advanced bibliometric techniques enabled the authors to map the intellectual structure of the field; identify the common themes and trends across the corpus; and examine the collaborations amongst researchers. Findings This study reveals a marked and continuous rise in research on energy supply chain risks, highlighting its increasing importance and interdisciplinary scope. The literature is primarily structured around three key themes: uncertainty in energy systems, the integration of sustainability and climate policy and the design and optimisation of supply chains. Findings also emphasise the growing role of uncertainty management, environmental performance, financial instability and simulation-based modelling in enhancing risk management. Overall, the field has shifted from efficiency-focused approaches towards resilience-oriented energy systems capable of adapting to complex, interconnected and evolving disruptions across global supply chains. Originality/value This study contributes to the literature by providing an integrated bibliometric framework that connects geopolitical, economic and climate policy uncertainties within energy supply chain research. Unlike previous studies that examined these dimensions separately, this study highlights the transition from efficiency-oriented models towards resilience-driven and sustainability-oriented systems while identifying emerging research directions related to digitalisation, predictive modelling and sustainable energy transitions.