What cause regional inequality of technology innovation in renewable energy? Evidence from China

Regional inequality of technology innovation associated with renewables may lead to insufficient innovation growth over the long-term, which impedes the deployment of renewable energy and slows down low-carbon transition. This paper aims to explore the major contributors to regional inequality of technology innovation in renewable energy by taking China as a representative example. Three groups of variables including regional attributes, supporting policies, and local official characteristics are proposed as potential explanatory factors. A set of panel data covering both the photovoltaic and wind energy sectors in 31 Chinese provinces from 2008 to 2017 are collected for the empirical study. Gini coefficient is used to quantify the inequalities of renewable technology innovation, while Shapley decomposition method is applied to evaluate the contribution of each significant factors. The results show that the overall Gini coefficients of photovoltaic and wind technology innovation in China are 0.65 and 0.62, respectively, indicating medium-level regional technology innovation inequality in both sectors. Knowledge stock (46.44%), R&D expenditure (36.56%), green fixed industrial investment (13.53%), electricity consumption (1.33%), and the energy-related experience of local officials (0.05%) are significant contributors to regional inequality of photovoltaic innovation. Knowledge stock (45.20%), R&D expenditure (37.90%), electricity consumption (8.63%), feed-in tariffs (1.02%), and the tenure of local officials (0.18%) are significant contributors to regional inequality of wind innovation. Therefore, eliminating regional inequality of technology innovation in renewables should start with balancing regional R&D expenditure and knowledge stock.

Automated summary

This paper examines the regional inequality of technology innovation in renewable energy, using China as a representative example. The study identifies several significant factors and recommends balancing regional R&D expenditure and knowledge stock as a key strategy to reduce the inequality.