Investigating the Determinants of the Growth of the New Energy Industry: Using Quantile Regression Approach

Abstract

Expanding the supplies of new energy can not only reduce CO2 emissions, but also alleviate energy shortage. This paper applies the quantile regression to investigate the new energy industry in China. The results show that economic growth exerts the greatest effect on the new energy industry in the lower 10th quantile province. This is because these provinces have the developed economies, demand for a higher ecological environment and new energy resources. Foreign energy dependence has a minimal impact on the new energy industry in the 25th–50th quantile province, due to their minimal oil importation. The contribution of technological progress to the upper 90th quantile province is the lowest, because their R&D capabilities are the weakest. The impact of energy consumption structure decreases in steps from the lower 10th quantile provinces to the upper 90th quantile provinces. The agricultural sector promotes the new energy industry in most provinces.

Assessing the Impact of Transportation Infrastructure on Rural Residents' Income: Using the Quantile Regression Approach

“…The theory of the Q-Q graph points out that if the Q-Q graph of a variable sequence coincides with the X=Y line, and this economic sequence is normally distributed. On the contrary, this economic sequence is a skewed distribution, including left-skewed distribution or right-skewed distribution (Xu and Lin, 2023). The Q-Q graph has the advantages of intuitive results and easy judgment.…”