Jesse Jenkins

Ruike Lyu, Anna Li, Jianxiao Wang et al.

In many countries, declining demand in energy-intensive industries such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock energy-intensive industries' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining energy-intensive industries overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also provide an opportunity for seasonally complementary labor deployment across the aluminum smelting and thermal power generation sectors, offering a potential pathway for mitigating socio-economic disruptions caused by industrial restructuring and energy decarbonization.

Mohamed Atouife, Jesse Jenkins

Forecasting the cost evolution of emerging clean technologies is crucial for informed policy, investment, and decarbonization decisions, yet it remains deeply uncertain. Learning curves, which link cost declines to cumulative deployment, are widely used for technological cost forecasting. However, applying them to emerging technologies is challenging due to parametric uncertainty in learning rates, which are scarce and highly uncertain, and structural uncertainty stemming from multiple plausible learning frameworks. Using water electrolysis as a case study, we evaluate how different learning structures, from shared to fragmented learning across technology variants and regions, alter expected cost paths. We interrogate model assumptions that represent contrasting industrial realities, including competition among electrolyzer variants and supply chain fragmentation associated with protectionism and industrial policy. We find that plausible modeling choices generate widely different trajectories, with materially different implications for policy design and technology strategy. We argue for routinely applying multiple learning frameworks to explore decision spaces and stress-test conclusions for scale-up planning, national industrial strategy, and energy-systems modeling.