A MERGE model with endogenous technological change and the cost of carbon stabilization

Abstract

Two stylized backstop systems with endogenous technological learning (ETL) are introduced in the “model for evaluating regional and global effects” (MERGE): one for the electric and the other for the non-electric markets. Then the model is applied to analyze the impacts of ETL on carbon-mitigation policy, contrasting the resulting impacts with the situation without ETL. We model research and development (R&D) spending and learning subsidies for the demonstration and deployment stage as control variables, and we investigate the ability of this extra spending to create path-dependent experience and knowledge to aid in the implementation of carbon-free technologies. Based on model estimations and sensitivity analyses, we conclude that increased commitments for the development of new technologies to advance along their learning curves has a potential for substantial reductions in the cost of mitigating climate change and thereby helping to reach safe concentrations of carbon in the atmosphere.