Market dynamics, innovation, and transition in China's solar photovoltaic (PV) industry: a critical review

China's photovoltaic (PV) industry has undergone dramatic development in recent years and is now the global market leader in terms of newly added capacity. However, market diffusion and adoption in China is not ideal. This paper examines the blocking and inducement mechanisms of China's PV industry development from the perspective of technological innovation. By incorporating a Technological Innovation System (TIS) approach, the analysis performed here complements the previous literature, which has not grounded itself in a theoretical framework. In addition, to determine the current market dynamics, we closely examine market concentration trends as well as the vertical and horizontal integration of upstream and downstream actors (74.8% and 36.3%). The results of applying the TIS framework reveal that poor connectivity in networks, unaligned competitive entities and a lack of market supervision obstruct the development of China's PV industry. Therefore, we maintain that inducement mechanisms are required to instigate learning-by-doing capacities, which may help overcome blocking mechanisms and offset functional innovation deficiencies. In addition, policy implications are proposed for promoting the development of the PV industry in China

Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach

The purpose of this paper is to explain the sources of energy system lock-in. It presents a comparative analysis of the respective contributions of some features of increasing returns to adoption factors, i.e. learning-by-doing, learning-by-searching and returns to scale effects in explaining the technological change dynamics in the energy system. The paper is technically based on a critical analysis of the learning curve approach. Econometric estimation of learning and scale effects inherent to seven energy technologies were performed by the use of several learning curve specifications. These specifications permit to deal with some crucial issues related to the learning curve estimation which are associated with the problem of omitted variable bias, the endogeneity effects and the choice of learning indicators. Results show that dynamic economies from learning effects coupled with static economies from scale effects are responsible for the lock-in phenomena of the energy system. They also show that the magnitude of such effects is correlated with the technology life cycle (maturity). In particular, results point out that, 1) the emerging technologies exhibit low learning rates associated with diseconomies of scale which are argued to be symptomatic of the outset of the deployment of new technologies characterized by diffusion barriers and high level of uncertainty, 2) the evolving technologies present rather high learning rates meaning that they respond quickly to capacity expansion and R&D activities development, 3) conventional mature technologies display low learning rates but increasing returns to scale implying that they are characterized by a limited additional diffusion prospects.Technological change dynamics Energy system lock-in Increasing returns to adoption Learning effects Returns to scale effect Learning curve