The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies

This study examined the quadratic role of renewable energy and economic growth on environment quality in Asia's five most populous countries: China, India, Indonesia, Pakistan, and Bangladesh. Previous literature has scarcely addressed these economies and ignores the quadratic role of GDP per c...

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Published in:Geographical Journal
Main Author: Khan S.; Jehan N.; Rauf A.; Nawaz F.; Erum N.
Format: Article
Language:English
Published: John Wiley and Sons Inc 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180894527&doi=10.1111%2fgeoj.12573&partnerID=40&md5=8cf1a6f190ebb1ffa67b99f14e661813
id 2-s2.0-85180894527
spelling 2-s2.0-85180894527
Khan S.; Jehan N.; Rauf A.; Nawaz F.; Erum N.
The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
2024
Geographical Journal
190
3
10.1111/geoj.12573
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180894527&doi=10.1111%2fgeoj.12573&partnerID=40&md5=8cf1a6f190ebb1ffa67b99f14e661813
This study examined the quadratic role of renewable energy and economic growth on environment quality in Asia's five most populous countries: China, India, Indonesia, Pakistan, and Bangladesh. Previous literature has scarcely addressed these economies and ignores the quadratic role of GDP per capita (GDPPC) and renewable energy (REN) together in a single study. Therefore, using STIRPAT model, we first added the quadratic term of GDPPC and then REN. In both cases, a quantile regression technique is utilised to identify the inclusive relationship between CO2 emissions and determining factors, considering different quantiles (0.25, 0.50, 0.75, 0.90, and 0.95) of CO2 emissions. The findings show that during the annual sample period of 1983–2019, urbanisation, GDPPC, non-renewable energy, and REN all have an impact on CO2 emissions. Urbanisation and REN have nominal effects, with a 1% change in these variables leading to a 0.19% and 0.05% change in CO2 emissions, respectively. Non-renewable energy and GDPPC are found to be main sources of CO2 emissions in the region. GDPPC is positively associated with CO2 emissions across all quantiles, but higher quantiles show a stronger correlation (i.e., GDPPC coefficients vary from 0.26 to 0.66). In addition, the results also revealed that the square term of GDPPC and REN significantly reduces CO2 emissions. This implies that our results support the environmental Kuznets curve (EKC) hypothesis; an inverted U-shaped is established for both GDPPC and REN. These results encourage policy makers to adopt renewable energy that is both growth and environment friendly. The information, practices and views in this article are those of the author(s) and do not necessarily reflect the opinion of the Royal Geographical Society (with IBG). © 2023 Royal Geographical Society (with the Institute of British Geographers).
John Wiley and Sons Inc
167398
English
Article

author Khan S.; Jehan N.; Rauf A.; Nawaz F.; Erum N.
spellingShingle Khan S.; Jehan N.; Rauf A.; Nawaz F.; Erum N.
The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
author_facet Khan S.; Jehan N.; Rauf A.; Nawaz F.; Erum N.
author_sort Khan S.; Jehan N.; Rauf A.; Nawaz F.; Erum N.
title The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
title_short The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
title_full The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
title_fullStr The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
title_full_unstemmed The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
title_sort The troika of energy consumption, economic growth, and CO2 emission: Quantile regression evidences for five Asian economies
publishDate 2024
container_title Geographical Journal
container_volume 190
container_issue 3
doi_str_mv 10.1111/geoj.12573
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180894527&doi=10.1111%2fgeoj.12573&partnerID=40&md5=8cf1a6f190ebb1ffa67b99f14e661813
description This study examined the quadratic role of renewable energy and economic growth on environment quality in Asia's five most populous countries: China, India, Indonesia, Pakistan, and Bangladesh. Previous literature has scarcely addressed these economies and ignores the quadratic role of GDP per capita (GDPPC) and renewable energy (REN) together in a single study. Therefore, using STIRPAT model, we first added the quadratic term of GDPPC and then REN. In both cases, a quantile regression technique is utilised to identify the inclusive relationship between CO2 emissions and determining factors, considering different quantiles (0.25, 0.50, 0.75, 0.90, and 0.95) of CO2 emissions. The findings show that during the annual sample period of 1983–2019, urbanisation, GDPPC, non-renewable energy, and REN all have an impact on CO2 emissions. Urbanisation and REN have nominal effects, with a 1% change in these variables leading to a 0.19% and 0.05% change in CO2 emissions, respectively. Non-renewable energy and GDPPC are found to be main sources of CO2 emissions in the region. GDPPC is positively associated with CO2 emissions across all quantiles, but higher quantiles show a stronger correlation (i.e., GDPPC coefficients vary from 0.26 to 0.66). In addition, the results also revealed that the square term of GDPPC and REN significantly reduces CO2 emissions. This implies that our results support the environmental Kuznets curve (EKC) hypothesis; an inverted U-shaped is established for both GDPPC and REN. These results encourage policy makers to adopt renewable energy that is both growth and environment friendly. The information, practices and views in this article are those of the author(s) and do not necessarily reflect the opinion of the Royal Geographical Society (with IBG). © 2023 Royal Geographical Society (with the Institute of British Geographers).
publisher John Wiley and Sons Inc
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language English
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