Exploring synergistic and individual causal effects of rare earth elements and renewable energy on multidimensional economic complexity for sustainable economic development

The ongoing global energy transition has highlighted the instrumental role of rare earth elements (REEs) in achieving green growth through the deployment of renewable energy technologies, establishing a hypothetical progressive synergistic association between them. Therefore, testing this hypothetical synergy and its potential causal effects on economic and technological proxies based on empirical foundations becomes imperative in addressing rampant climatic change. First, we employed Deep Learning algorithms to explore the combined effects of this synergy on Multidimensional Economic Complexity (MEC) as an index using panel data of the largest producers of REEs from 1990 to 2023. Second, Temporal Causal Modelling (TCM) was employed to unveil the directional causality between REEs and renewable energy, as well as their causal roles in driving economic complexities. The deep learning models accurately predicted the positive role of this synergistic relationship in promoting overall MEC. Then, the results of TCM reveal that REE production increases renewable energy use and reduces CO 2 emissions. Renewable energy promotes Research and Technology complexities while REE production affects them indirectly through renewables. It implies that harnessing this synergy to enhance economic complexities can, in turn, bolster green economic and technological development while mitigating CO 2 emissions and addressing pervasive climate change.