The transition to low-carbon technologies is precipitating a structural shift in global commodity markets, replacing fossil fuel intensity with critical mineral intensity. This study investigates the heterogeneous transmission mechanisms of energy transition shocks in the United States copper and Chinese lithium markets, employing a Structural Vector Autoregression identified via Non-Gaussian Maximum Likelihood (NGML). By exploiting the heavy-tailed distributions of commodity shocks, we achieve data- driven identification without imposing arbitrary recursive restrictions. Our empirical results reveal a profound structural dichotomy. In the US, copper behaves as a mature, financialized upstream commodity, primarily driven by cost-push factors rather than short-term downstream renewable demand. Conversely, the Chinese lithium market exhibits pronounced structural inertia and counter-intuitive negative price response to renewable energy shocks, attributed to state-mediated policy forward guidance.
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