Linking Economic Growth, Urbanization, and Environmental Degradation in China: What Is the Role of Hydroelectricity Consumption?

Tomiwa Sunday Adebayo; Mary Oluwatoyin Agboola; Husam Rjoub; Ibrahim Adeshola; Ephraim Bonah Agyekum; Nallapaneni Manoj Kumar

doi:10.3390/ijerph18136975

Author(s)

Tomiwa Sunday Adebayo
Mary Oluwatoyin Agboola
Husam Rjoub
Ibrahim Adeshola
Ephraim Bonah Agyekum

Related Research Unit(s)

School of Energy and Environment

Detail(s)

Original language	English
Article number	6975
Journal / Publication	International Journal of Environmental Research and Public Health
Volume	18
Issue number	13
Online published	29 Jun 2021
Publication status	Published - Jul 2021

Link(s)

DOI	DOI https://doi.org/10.3390/ijerph18136975 Final Published version
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Attachment(s)	Documents 78908811.pdf Final Published version, 1.99 MB, PDF Publisher's Copyright Statement This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-85108789551&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(b83828fd-3b91-44f6-9be4-1956abf0dfac).html

Abstract

Achieving environmental sustainability has become a global initiative whilst addressing climate change and its effects. Thus, this research re-assessed the EKC hypothesis in China and considered the effect of hydroelectricity use and urbanization, utilizing data from 1985 to 2019. The autoregressive distributed lag (ARDL) bounds testing method was utilized to assess long-run cointegration, which is reinforced by a structural break. The outcome of the ARDL bounds test confirmed cointegration among the series. Furthermore, the ARDL revealed that both economic growth and urbanization trigger environmental degradation while hydroelectricity improves the quality of the environment. The outcome of the ARDL also validated the EKC hypothesis for China. In addition, the study employed the novel gradual shift causality test to capture causal linkage among the series. The advantage of the gradual shift causality test is that it can capture gradual or smooth shifts and does not necessitate previous information of the number, form of structural break(s), or dates. The outcomes of the causality test revealed causal connections among the series of interest.