Representing spatial technology diffusion in an energy system optimization model

In this study, we develop a series of technology diffusion formulations that endogenously represent empirically observed spatial diffusion patterns. We implement these formulations in the energy system optimization model MESSAGE to assess their implications for the market penetration of low-carbon electricity generation technologies. In our formulations, capacity growth is constrained by a technology's knowledge stock, which is an accumulating and depreciating account of prior capacity additions. Diffusion from an innovative core to less technologically adept regions occurs through knowledge spillover effects (international spillover effect). Within a cluster of closely related technologies, knowledge gained through deployment of one technology spills over to other technologies in the cluster (technology spillover effect). Parameters are estimated using historical data on the expansion of extant electricity technologies. Based on our results, if diffusion in developing regions relies heavily on earlier deployment in advanced regions, projections for certain technologies (e.g., bioenergy with carbon capture and storage) should be tempered. Our model illustrates that it can be globally optimal when innovative economies deploy some low-carbon technologies more than is locally optimal as it helps to accelerate diffusion (and learning effects) elsewhere. More generally, we demonstrate that by implementing a more empiricaly consistent diffusion formulation in an energy system optimization model, the traditionally crude-or nonexistent-representation of technology diffusion in energy-climate policy models can be significantly improved. This methodologicl improvement has important implications for the market adoption of low-carbon technologies