Assessment of the causal links between energy, technologies, and economic growth in China: An application of wavelet coherence and hybrid quantile causality approaches

Abstract

It is undeniable that energy and technology play a vital role in driving the economic growth of a country. This paper's objective is to check the unexplored causal links between electricity generation and GDP growth in China by using a multivariate framework that contains technologies in the production or processing of goods, gross capital formation, labour force, financial sector development, and primary energy consumption as supplementary factors in growth model using the updated data. Applying new advances in time series data analysis, the study used wavelet coherence and hybrid non-parametric quantile causality approaches to analyze whether these factors predict GDP growth. We observed that technologies, capital formation, labor force, energy consumption, financial sector development, and electricity generation propel GDP growth in China. The quantile causality in mean and variance also corroborates that all these factors are causatives of GDP growth across their entire distribution. The Breitung-Candelon Spectral Granger-causality method further sustains the robustness of these results. Our findings provide valuable insight to policymakers to pave the way for these causative factors, mainly promoting electricity projects and adapting new technologies to propel economic growth in China.