Probabilistic Identification of Technology Tipping Points in Deeply Decarbonised Energy Systems

Abstract: Energy policy is often guided by a small set of least-cost pathways to net-zero emissions, despite wide uncertainty in technology performance, fuel prices, demand and weather. To avoid overstating confidence in any single pathway, we quantify the likelihood of alternative technology pathways and identify the assumptions driving divergence, including the conditions under which technologies reach critical tipping points in competitiveness. We couple a sector-linked national optimisation model with Monte Carlo sampling (10,000 runs) across two European power systems (Germany and Great Britain) to generate probability distributions of capacity expansion and robust cost thresholds for key technologies. Results reveal substantial ambiguity in the future roles of wind versus solar, gas with carbon capture, and negative-emissions options. Tipping points vary widely with system conditions, while cross-country differences highlight the role of institutional constraints and resource endowments. Britain exhibits an either-or decision around nuclear power, investing if costs in 2035 fall below EUR 4700/kW, otherwise favouring offshore wind. Germany's uncertainty centres on dispatchable low-carbon options: gas with carbon capture (below EUR 2100/kW), biomass with carbon capture (below EUR 4200/kW), or hydrogen if electrolysis is below EUR 560/kW. We reframe scenario analysis as risk management by linking uncertainty to cost targets and minimum deployment requirements for robust net-zero strategies.