Investment decision model for CO2 utilization projects: An empirical study on CO2 mineralization curing

The investment decision of CO 2 utilization projects faces complexity arising from sunk costs, return uncertainty, timing flexibility, and divergent sub-technology learning rates. Existing research largely focuses on single-dimensional uncertainty analysis, which fails to adequately address the combined effects of technological, carbon price, and market uncertainties, leading to potentially inaccurate investment valuations. To address this limitation, this study proposes an integrated analytic framework that integrates a component-based technological learning model with a trinomial tree model and Geometric Brownian Motion, which can simultaneously capture the dynamics of carbon and product prices, and account for heterogeneous learning rates across key technological components, and determine the optimal project investment timing under uncertainty. Applying this framework to a carbonation-cured concrete case study reveals an optimal investment window before 2040, empirical results show that under a high learning scenario,.the operational cost of emerging components decreases by up to 46.2 %, higher than that of other mature components. CCU product price volatility increases the critical carbon price, while a positive drift rate significantly reduces the investment threshold, though its impact diminishes beyond a drift rate of 0.05. Ultimately, the real options model generates a valuation premium of up to ¥6.1 billion compared to the static NPV, validating the value of deferred flexibility. Investment timing analysis reveals a delay of 3–4 years under volatility and exhibits a non-monotonic shift with increasing drift. This method provides quantitative guidance for low-carbon technology investment under uncertainty. • Develop a real options model linking tech progress with market uncertainties. • Propose a component-based heterogeneous learning curve for carbon utilization. • Reveal how learning rates significantly affect the optimal investment timing.