Optimization of dynamic incentive for the deployment of carbon dioxide removal technology: A nonlinear dynamic approach combined with real options

Abstract Due to the high adoption cost, large uncertainty, and ignorance of the positive externalities for private entities, additional incentives are needed for the development of carbon dioxide removal (CDR) technology. And there is a trade-off between the government and investors on how to ensure the effectiveness of the incentive policy and optimally allocate subsidized capital. This paper proposes a nonlinear dynamic programming model that combines real options method to study the optimization of dynamic subsidies for CDR technology. Using the endogenous learning effect, technological advance, and technology applicability, we modeled the investor decisions under uncertainty, as well as the government's effective use of incentive policies. Our model is available for deriving the development path of CDR technology with optimized subsidies and research and development (R&D) input across multiple periods. We use China's carbon capture and storage (CCS) development as a case study. The results show that, unlike other kinds of low-carbon technology such as renewable energy, the subsidy level of CCS may not decrease in the future because of rising trend of fuel costs and worse technology applicability in large-scale deployment. The achievement of large-scale CCS development will rely more on second-generation CCS. The levelized policy cost of incentivizing CCS technology in China can be high, and thus the target should be prudently set based on an evaluation of its socioeconomic burden. A supplementary measure that caps the CCS installation in each period is recommended to prevent excessive development.