Identifying contributing factors to China's declining share of renewable energy consumption: no silver bullet to decarbonisation

Abstract

Renewable energy consumption (REC) holds the key to sustainable development. Therefore, many studies have considered the role of REC. However, the factors influencing the REC share in total energy usage (SREC) are not well investigated. Especially, the factors of China's fast-shrinking SREC are understudied. This research void on the world's largest renewable energy producer and consumer, i.e., China's decreasing SREC, is alarming and requires thorough investigation. Our study intends to fill this gap by analyzing the factors of China's decreasing SREC. The study uses both the conventional (descriptive and directional correlational analyses) and some unconventional (automatic linear modeling (ALM) and Artificial neural network (ANN) multilayer perceptron (MLP)) approach to investigate the factors of China's decreasing SREC. The initial hypothesis testing and most reliable model validation were achieved via directional correlational (Pearson and Spearman) and ALM analyses. The ANN MLP (two hidden layers) indicated that the most critical factor is "Combustible renewables and waste," with a 100% normalized importance. It was followed by "urbanization (64.2%), gross savings (56.1%), and alternative and nuclear energy (38%)," respectively. It is suggested that the Chinese government and private investors prioritize their investments based on factors' importance ranking.

Keywords: Artificial neural network (ANN); Automatic linear modeling (ALM); Environment; Pearson and Spearman rank correlation; Regression analysis; Sustainability; IBM SPSS.