An investigation of nexus between climate-related financial policy and energy security: novel evidence from global database

Abstract

Purpose This paper answers the question of whether climate-related financial policies (CRFP) enable the energy transition in the European region. Design/methodology/approach By using various econometric techniques (namely a panel-corrected standard errors [PCSE] model and a feasible generalized least square estimates [FGLS] model, this study examines a link between CRFP and energy security (ES). Findings Using seven indicators, this paper examines four parts of energy security: acceptability, availability, sustainability and developability. The author has performed econometric analyses on 17 European countries during the period 2010–2020 to reveal critical findings. The results show a relationship between CRFP and energy intensity, energy consumption, nonfossil energy consumption, renewable energy consumption and CO2 emissions. This finding suggests that CRFP involvement benefits the energy system’s acceptability, developability and sustainability. Moreover, the author observes long-term cointegration between CRFP and ES, and the findings validate their short-term and long-term effects. The author also finds that ES is influenced by past and future CRFP participation. Practical implications This study focuses on countries in a European Union (EU) region, which contribute significantly to secure ES and represent a varied spectrum of rich and emerging economies. Originality/value In this paper, the author contributes to the research in three ways. First, to the best of the author’s knowledge, this is the first empirical study to explore CRFP as a contributor to the security of the energy system. This research contributes to the existing body of information by investigating the influence of CRFP on environmental quality as assessed by various dimensions. Second, this paper uses a PCSE model based on cross-sectional dependence and stationarity tests. Furthermore, the findings can be further verified using FGLSs considering heteroscedasticity. Long-term and short-term impacts of autoregressive distributed lag methods were also investigated using pooled mean groups (PMG).