Using the mission-oriented innovation policy system (MOIPS) for research and development (R&D) at the national level, this study explains how and when R&D spending on sustainable energy technologies drives the clean energy transition (CET).
The study utilises second-generation unit root tests, the Westerlund cointegration test, quantile-quantile plots, method-of-moments quantile regression, instrument-variable quantile regression, panel quantile regression and Dumitrescu and Hurlin panel causality analysis to test cause-and-effect and moderation models.
The empirical analysis shows that technology-related research and development spending significantly accelerate the CET across all quantiles. However, the constructive role of technology-related spending is more pronounced in lower quantiles than in higher ones. Moreover, moderation analysis reveals that economic globalisation (EGL) and artificial intelligence (AI) serve as boundary conditions that spur technological spending to achieve CET objectives. In addition, Dumitrescu and Hurlin’s panel causality analysis exhibits unidirectional causality from technological spending to CET.
This study contributes to methodological rigour, the literature and the context. First, it provides valuable findings on the heterogeneous impact of sustainable energy technological R&D spending on the CET by utilising second-generation method of moment quantile regression analysis to ensure methodological rigour. Second, the results offer insight into how heterogeneously EGL and AI moderate the relationship between sustainable energy technological R&D spending and the CET by linking the MOIPS framework, thereby advancing theoretical understanding. Third, this study has implications for policymakers, demonstrating that investing in sustainable energy technological R&D programs with the assistance of AI and EGL accelerates the CET, helping Organisation for Economic Co-operation and Development (OECD) nations meet the NetZero+ and United Nations Sustainable Development Goal(s).
