The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach

Cheng-Feng Wu (), Shian-Chang Huang (), Chei-Chang Chiou (), Tsangyao Chang and Yung-Chih Chen ()
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Cheng-Feng Wu: Hubei University of Economics
Shian-Chang Huang: National Changhua University of Education
Chei-Chang Chiou: National Changhua University of Education
Yung-Chih Chen: National Yunlin University of Science and Technology

Computational Economics, 2022, vol. 60, issue 4, No 1, 1197-1220

Abstract: Abstract In this study, the relationship between electricity and growth of the economy is investigated by applying the newly-developed bootstrap autoregressive-distributed lag test with a Fourier function to examine both the causality and cointegration for China, India, and the United States (US). While it is not possible to detect a long-term cointegration relation among the economy's electricity and growth, the study findings demonstrate the contingency of the causality. The ensemble method in machine learning performs better than conventional methods as electricity is an independent indicator for forecast economics. Concerning the US, previous electricity consumption has a positive impact on the current nature of economic growth. In contrast, the consumption of electricity is negatively affected by the development of the economy. However, for China and India, positive and negative feedback can be observed, respectively. Due to the increased awareness of the environment's adverse effects, China should promote technologies that conserve energy and boost energy efficiency to achieve sustainable development in both environmental and economic terms. In India's context, broadening access to electricity has significance for residents in rural areas and enhances economic growth. It is recommended that policy-makers promote innovative technologies in the US, as the abundant natural and human resources can make valuable contributions to the society and development of the economy.

Keywords: Fourier approximation; Structural breaks; Bootstrap autoregressive-distributed lag (ARDL); Cointegration; Causality; Machine learning (search for similar items in EconPapers)
JEL-codes: C22 Q43 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-021-10097-7

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