Optimal abatement technology adoption based upon learning-by-doing with spillover effect

We specify a general model of the dynamic abatement technology adoption process and show how the accumulated experience among them can alter the adoption timing. Learning-by-doing (LBD) effect is considered to describe the reduction of the costly price to the adoption. Moreover, spillovers effect which reflects how accumulated experience can be shared among different technologies, is integrated into the learning process. To ensure the specification convincing, some reasonable assumptions are considered such as one abatement technology must conduct its own experience in order to realize the spillover. Besides, to investigate how the abatement process is influenced by the technology allocation under the spillovers effect, an endogenous emission path is considered to necessitate a meaningful optimization problem. Finally, apart from obtaining some basic properties of the model, we implement an empirical study to verify the effectiveness of the model. Data from the iron industry in China is used to specify the process, which can highlight the spillovers effect among different technologies from a sector angle. In the results, we find that intrinsic mechanism in the model results in the interplay among abatement coefficient, spillover factor and the growing rate of the emission etc. Collaborative relationships between variables are discussed as well.