Ambiguity and model misspecification with potentially disruptive mitigation options

This paper aims to explore the impact of ambiguity, ambiguity aversion, and model misspecification on mitigation dynamics when several mitigation options are considered. It develops a continuous-time endogenous-growth economic model allowing for ambiguity and model misspecification on (i) climate and investment dynamics and (ii) uncertainty around technological jumps for potentially disruptive decarbonisation technologies. The model further innovates by considering a relative degree of technology richness, by representing emission-free capital, carbon intensity reductions and negative-emission technologies. Given the high dimensionality of the model and the inherent difficulties encountered in optimal control in the presence of misspecification corrections, we solve the model using a recent deep learning method, the Deep-Galerkin Method with Policy Iteration Algorithm (DGM-PIA), proposed by Al-Aradi et al. (2022). We are able to satisfactorily approximate a solution to a complex, highly non-linear problem in a fraction of the time required by traditional methods. Our preliminary findings suggest that misspecification and ambiguity aversion can lead to a range of transition strategies, including reduced reliance on uncertain technologies, such as negative-emission mitigation options.