Optimizing China's power mix under heterogeneous risk preferences: Roles of carbon pricing, energy storage, and CCS toward 2060

China's low-carbon power transition faces risks from renewable intermittency and fossil fuel price volatility. Existing studies often assume risk-neutral policymakers, neglecting heterogeneous risk preferences. This study proposes a risk-preference-based dynamic programming model to optimize China's power generation mix toward 2060, integrating three risk scenarios with technology portfolios, carbon pricing, energy storage, and carbon capture and storage (CCS). Results show medium-risk scenarios achieve optimal balance, reducing cumulative emissions by 132–135 billion tons CO2 (2020–2060) with ¥0.42 trillion cost savings, driven by 83.6 % renewable penetration and phased coal-to-gas transitions. High-risk scenarios require ≥6000 GW wind/solar capacity and 15 % energy storage (saving ¥1.7 trillion) but face scalability challenges, while low-risk strategies retain 12.2 % gas-fired generation with diminishing CCS returns. Carbon pricing proves most effective in medium-risk contexts, enabling an additional 2.34 billion tons CO2 reduction, whereas expanding CCS capacity beyond 40 Mt/year yields limited benefits. The transition follows three phases: extreme reduction (2020–2030, 32 % emission cuts via coal phase-out), rapid reduction (2030–2045, renewable-storage-CCS deployment), and stable reduction (2045–2060, near-zero emissions via renewables dominance). This study provides policymakers with a quantitative framework to balance risk preferences, economic costs, and decarbonization goals.