Long-term distributional effects of carbon pricing – A novel modeling approach
In this paper, we present a new methodological approach to assess the prospective long-term distributional impacts of carbon pricing under heterogeneous technology adjustment and alternative price trajectories. We link an energy systems model with a micro-simulation model based on random forests to simulate household-level cost burdens of two stylized carbon price paths in the German heating and transportation sectors through 2040. Using data-driven projections of technology adoption, the model suggests that carbon pricing initially generates regressive cost burdens, which can be substantially mitigated through lump-sum revenue recycling. However, heterogeneous adoption rates give rise to pronounced within-income dispersion for both price paths, particularly among low-income households, where a subset remains net burdened even after redistribution. When adoption in multi-family homes is assumed to be slower -- reflecting structural barriers -- distributional outcomes become relatively more regressive. Comparing price trajectories, a higher carbon price path implies higher average burdens in 2030, followed by declining costs as investments in climate-neutral technologies accumulate, whereas a lower price path produces a flatter but more persistent burden profile through 2040. Overall, the framework highlights how assumptions about technology diffusion and price design critically shape the projected distributional consequences of carbon pricing.