By August 2016, 180 countries have signed the Paris Agreement and committed to holding the increase in the global average temperature to well below 2degC above pre-industrial levels. Abiding by the agreement will require a substantial reduction of emissions over the next few decades and near zero emissions of CO2 and other long-lived greenhouse gases by the end of this century. In this context, the decarbonisation of the global power sector is of strategic importance, because low-carbon electricity has system-wide benefits that go beyond the electricity sector, enabling significant reductions of CO2 emissions in the industry, transport and buildings sectors. To make the necessary changes depends partly on improving the analysis and estimates of the economics of climate change, and for that there is an urgent need for a new generation of models that give a more accurate picture of the potential decarbonisation pathways.

The technological transition towards a low carbon power sector depends on many uncertain factors, such as policy efficiency, renewable energy investment and availability of energy resources. The knowledge about how these uncertain factors interact, and the impacts on the technological evolution of the energy sector, are the key to creating successful policies for driving the economy towards a cleaner, low carbon society. In this context, the work presented here provides decarbonisation scenarios of the global power sector, under uncertain drivers of technological change, and in doing so, enables a better understanding of technology diffusion process in the power sector. The scenarios are created using the FTT:Power model, a representation of global power systems based on market competition, induced technological change and natural resource use and depletion. The scenarios analysed in this dissertation are focused on four drivers of technological change: energy policy, energy resource availability, learning and investment. The influence of uncertainty on each of these drivers is analysed in detail, through endogenous emission scenarios of the global power sector between 2016 and 2050. Emission pathways with uncertainty ranges, as well as policy recommendations, are presented as a result of the modelling exercise.