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 morepersistent 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.