Multi-scale synergy mechanism for measuring the return on investment in renewable energy: a study based on wavelet analysis of provincial panel data in China

This paper is set against the backdrop of the imbalances in global energy transformation and the uncertainties surrounding the return on investment (ROI) in renewable energy. Addressing the issues of existing research lacking dynamism in analysing the ROI of renewable energy, methodological limitations, and insufficient relevance to the Chinese context, this study utilises panel data from 31 provinces in China spanning from 2000 to 2025. By integrating wavelet power spectrum analysis, wavelet coherence analysis, and OLS regression models, it systematically explores the multidimensional dynamic driving mechanisms of various factors on the measurement of ROI in renewable energy across different time scales. This study reveals that technological innovation, regional green finance, industrial cluster level, and environmental policy intensity have a significant positive impact on the ROI of renewable energy. Specifically, technological innovation serves as a long-term core driving factor; regional green finance and industrial cluster-level synergies exert a leading effect in the medium term; and environmental policy intensity exerts a notable positive influence in the short term. This paper constructs a differentiated strategic system encompassing “technological breakthroughs, capital supply, and policy support”, providing guidance and evidence for optimising investment decisions and designing cross-cycle differentiated support policies in the renewable energy industry. It also enriches academic research on energy transformation and green investment in emerging economies and provides a reference for analysing the time-frequency correlation characteristics of variables.