Abstract
The past four decades have witnessed rapid growth in the rate of energy consumption in China. A great deal of energy consumption has led to two major issues. One is energy shortages and the other is environmental pollution caused by fossil fuel combustion. Since energy saving plays a substantial role in addressing both issues, it is of vital importance to study the intrinsic characteristics of energy consumption and its relationship with economic growth. The topic of the nexus between energy consumption and economic growth has been hotly debated for years. However, conflicting conclusions have been drawn. In this paper, we provide a novel insight into the characteristics of the growth rate of energy consumption in China from a multi-timescale perspective by means of adaptive time-frequency data analysis; namely, the ensemble empirical mode decomposition method, which is suitable for the analysis of non-linear time series. Decomposition led to four intrinsic mode function (IMF) components and a trend component with different periods. Then, we repeated the same procedure for the growth rate of China’s GDP and obtained four similar IMF components and a trend component. In the second stage, we performed the Granger causality test. The results demonstrated that, in the short run, there was a bidirectional causality relationship between economic growth and energy consumption, and in the long run a unidirectional relationship running from economic growth to energy consumption.
Highlights
The last four decades have witnessed rapid economic growth in China
The last is that we provide a novel insight into the short-run and long-run Granger causality relationships between the two variables by performing the multi-timescale Granger causality tests, unlike the traditional vector auto-regressive model (VAR) models and error correction models (ECM)
The ensemble empirical mode decomposition (EEMD) method is characterized by noise-assisted data analysis (NADA) that derives from the empirical mode decomposition (EMD) method that was first proposed by Huang et al [13,37]
Summary
The last four decades have witnessed rapid economic growth in China. In 2013, China became the second largest economy globally after the U.S all over the world. We attempt to apply the ensemble empirical mode decomposition (EEMD) method to investigate the intrinsic characteristics of the growth rate of China’s energy consumption and extract fluctuations with periods on multi-timescales and a non-linear overall trend. Another stream pertaining to the energy issue in academic circles has been to analyze the nexus between energy consumption and the economy, their causality relationships, by means of the Granger causality test. Zhang and Cheng [24] tested the causality relationship based on time-series data from 1960 to 2007 They found a unidirectional Granger causality running from economic growth to energy consumption.
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