Emissions trading and technology deployment in an energy-systems “bottom-up” model with technology learning

An important criterion in the analysis of climate policy instruments is their ability to stimulate the technological change necessary to enable the long-term shift towards a low-carbon global energy system. In this paper, some effects of emissions trading on technology deployment when technology learning is endogenized are examined with a multi-regional “bottom-up” energy-systems optimization MARKAL model of the global energy system. In this framework, due to the action of spillovers of learning, imposing emission constraints on a given region may affect the technology choice and emission profiles of other (unconstrained) regions. The effects depend on the geographical scale of the learning process but also on the presence of emissions trading, the regions that join the trade system and their timing for doing so. Incorporating endogenous technology learning and allowing for spillovers across regions appears as an important mechanism for capturing the possibility of induced technological change due to environmental constraints in “bottom-up” models.