External uncertainty and raw material price in energy transition: Implications for green development

The sharp fluctuations of critical raw material prices for energy transition (CRMPET) and the escalating global uncertainties (GU) have raised substantial concerns about the stability and development of the green economy (GE). Understanding the impacts of these uncertainties is crucial for the global cooperation and formulating effective energy transition strategies. This study aims to investigate the dynamic interactions among CRMPET, GU, and GE over time. Given the complexity and constantly changing nature of these relationships, a time-varying parameter vector autoregressive (TVP-VAR) model was employed to capture the temporal evolution of parameters, along with wavelet analysis to examine the interactions of variables across different frequencies. These methods enabled the identification of the dynamic relationships among CRMPET, GU, and GE from June 2012 to the end of 2023. The main findings are as follows: (1) An initial surge in CRMPET intensifies GU and stimulates GE. However, these effects diminish over time, indicating that the pressures exerted by CRMPET on GU and GE are temporary; (2) GU drives up CRMPET in the short term, but exerts a suppressive effect on prices in the long term. Conversely, GU significantly inhibits GE in the short to medium term, although it eventually accelerates energy transitions and promotes long-term green economic growth; (3) Unlike previous studies, GE suppresses CRMPET in the medium to long term. This suggests that economies of scale and technological advancements associated with GE can reduce the costs of critical raw materials. However, it is important to note that GE may also trigger global resource tensions, exacerbating GU in the medium term. The main contributions and novelty are as follows: (1) It provides a dynamic, time-varying perspective to analyze the interactions among CRMPET, GU, and GE; (2) The application of TVP-VAR and wavelet analysis introduces a novel methodological framework for studying complex, time-dependent systems; (3) By quantifying the impacts of these factors, the study offers valuable data-driven support for policymakers and investors in managing uncertainties and formulating strategies.