The Dynamics of Carbon and Energy Intensity in a Model of Endogenous Technical Change

In recent years, a large number of papers have explored different attempts to endogenise technical change in climate models. The obvious reason is that technical change is widely considered the main route to achieving a significant reduction in global GHG emissions. This recent literature has emphasized that four factors - two inputs and two outputs - should play a major role when modelling technical change in climate models. The two inputs are Ramp;D investments and Learning by Doing, the two outputs are energy-saving and fuel switching. Indeed, Ramp;D investments and Learning by Doing are the main drivers of a climate-friendly technical change that eventually affect both energy intensity and fuel-mix. In this paper, we present and discuss an extension of the FEEM-RICE model in which these four factors are explicitly accounted for. In our new specification of endogenous technical change, an index of technical progress depends on both Learning by Researching and Learning by Doing. This index enters the equations defining energy intensity (i.e. the amount of carbon energy required to produce one unit of output) and carbon intensity (i.e. the level of carbonization of primarily used fuels). This new specification is embodied in the RICE 99 integrated assessment climate model and then used to generate a business as usual scenario and to analyze the relationship between climate policy and technical change. Sensitivity analysis is performed on different key parameters of the energy module in order to obtain crucial insights into the relative importance of the main channels through which technological changes affects the impact of human activities on climate. In addition, the effectiveness of different possible ways of combining Learning by Researching and Learning by Doing is also investigated