Modelling economic growth, carbon emissions, and fossil fuel consumption in China: Cointegration and multivariate causality

Zhihui Lv; Amanda M.Y. Chu; Michael McAleer; Wing Keung Wong

doi:10.3390/ijerph16214176

Modelling economic growth, carbon emissions, and fossil fuel consumption in China: Cointegration and multivariate causality

Zhihui Lv
, Amanda M.Y. Chu
, Michael McAleer
, Wing Keung Wong^*

^*Corresponding author for this work

Econometrics

Northeast Normal University
The Education University of Hong Kong
Asia University Taiwan
China Medical University Taichung
Hang Seng University of Hong Kong

Research output: Contribution to journal › Article › Academic › peer-review

69 Citations (Scopus)

Abstract

Most authors apply the Granger causality-VECM (vector error correction model), and Toda–Yamamoto procedures to investigate the relationships among fossil fuel consumption, CO emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, CO emissions, and fossil fuel consumption in China from 1965–2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing CO emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing CO emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, CO emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.

Original language	English
Article number	4176
Journal	International Journal of Environmental Research and Public Health
Volume	16
Issue number	21
DOIs	https://doi.org/10.3390/ijerph16214176
Publication status	Published - 29 Oct 2019

Bibliographical note

Funding Information:
This research was funded by Northeast Normal University, Education University of Hong Kong (project number RG 66/2018-2019R), Asia University, China Medical University Hospital, Hang Seng University of Hong Kong, Research Grants Council of Hong Kong (Project Number 12500915) and Ministry of Science and Technology, Taiwan (MOST, Project Numbers UGC/IIDS14/P01/17 and 106-2410-H-468-002 and 107-2410-H-468-002-MY3). The fourth author would like to thank Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research has been supported by Northeast Normal University, The Education University of Hong Kong, Asia University, China Medical University Hospital, The Hang Seng University of Hong Kong, the Research Grants Council of Hong Kong (Project Number 12500915), and Ministry of Science and Technology (MOST, Project Numbers 106-2410-H-468-002 and 107-2410-H-468-002-MY3), Taiwan.

Funding Information:
Acknowledgments: The fourth author would like to thank Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research has been supported by Northeast Normal University, The Education University of Hong Kong, Asia University, China Medical University Hospital, The Hang Seng University of Hong Kong, the Research Grants Council of Hong Kong (Project Number 12500915), and Ministry of Science and Technology (MOST, Project Numbers 106-2410-H-468-002 and 107-2410-H-468-002-MY3), Taiwan.

Funding Information:
Funding: This research was funded by Northeast Normal University, Education University of Hong Kong (project number RG 66/2018-2019R), Asia University, China Medical University Hospital, Hang Seng University of Hong Kong, Research Grants Council of Hong Kong (Project Number 12500915) and Ministry of Science and Technology, Taiwan (MOST, Project Numbers UGC/IIDS14/P01/17 and 106-2410-H-468-002 and 107-2410-H-468-002-MY3).

Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy
SDG 8 Decent Work and Economic Growth

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10.3390/ijerph16214176

Cite this

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title = "Modelling economic growth, carbon emissions, and fossil fuel consumption in China: Cointegration and multivariate causality",

abstract = "Most authors apply the Granger causality-VECM (vector error correction model), and Toda–Yamamoto procedures to investigate the relationships among fossil fuel consumption, CO emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, CO emissions, and fossil fuel consumption in China from 1965–2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing CO emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing CO emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, CO emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.",

author = "Zhihui Lv and Chu, \{Amanda M.Y.\} and Michael McAleer and Wong, \{Wing Keung\}",

note = "Funding Information: This research was funded by Northeast Normal University, Education University of Hong Kong (project number RG 66/2018-2019R), Asia University, China Medical University Hospital, Hang Seng University of Hong Kong, Research Grants Council of Hong Kong (Project Number 12500915) and Ministry of Science and Technology, Taiwan (MOST, Project Numbers UGC/IIDS14/P01/17 and 106-2410-H-468-002 and 107-2410-H-468-002-MY3). The fourth author would like to thank Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research has been supported by Northeast Normal University, The Education University of Hong Kong, Asia University, China Medical University Hospital, The Hang Seng University of Hong Kong, the Research Grants Council of Hong Kong (Project Number 12500915), and Ministry of Science and Technology (MOST, Project Numbers 106-2410-H-468-002 and 107-2410-H-468-002-MY3), Taiwan. Funding Information: Acknowledgments: The fourth author would like to thank Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research has been supported by Northeast Normal University, The Education University of Hong Kong, Asia University, China Medical University Hospital, The Hang Seng University of Hong Kong, the Research Grants Council of Hong Kong (Project Number 12500915), and Ministry of Science and Technology (MOST, Project Numbers 106-2410-H-468-002 and 107-2410-H-468-002-MY3), Taiwan. Funding Information: Funding: This research was funded by Northeast Normal University, Education University of Hong Kong (project number RG 66/2018-2019R), Asia University, China Medical University Hospital, Hang Seng University of Hong Kong, Research Grants Council of Hong Kong (Project Number 12500915) and Ministry of Science and Technology, Taiwan (MOST, Project Numbers UGC/IIDS14/P01/17 and 106-2410-H-468-002 and 107-2410-H-468-002-MY3). Publisher Copyright: {\textcopyright} 2019 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2019",

month = oct,

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doi = "10.3390/ijerph16214176",

language = "English",

volume = "16",

journal = "International Journal of Environmental Research and Public Health",

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T1 - Modelling economic growth, carbon emissions, and fossil fuel consumption in China

T2 - Cointegration and multivariate causality

AU - Lv, Zhihui

AU - Chu, Amanda M.Y.

AU - McAleer, Michael

AU - Wong, Wing Keung

N1 - Funding Information: This research was funded by Northeast Normal University, Education University of Hong Kong (project number RG 66/2018-2019R), Asia University, China Medical University Hospital, Hang Seng University of Hong Kong, Research Grants Council of Hong Kong (Project Number 12500915) and Ministry of Science and Technology, Taiwan (MOST, Project Numbers UGC/IIDS14/P01/17 and 106-2410-H-468-002 and 107-2410-H-468-002-MY3). The fourth author would like to thank Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research has been supported by Northeast Normal University, The Education University of Hong Kong, Asia University, China Medical University Hospital, The Hang Seng University of Hong Kong, the Research Grants Council of Hong Kong (Project Number 12500915), and Ministry of Science and Technology (MOST, Project Numbers 106-2410-H-468-002 and 107-2410-H-468-002-MY3), Taiwan. Funding Information: Acknowledgments: The fourth author would like to thank Robert B. Miller and Howard E. Thompson for their continuous guidance and encouragement. This research has been supported by Northeast Normal University, The Education University of Hong Kong, Asia University, China Medical University Hospital, The Hang Seng University of Hong Kong, the Research Grants Council of Hong Kong (Project Number 12500915), and Ministry of Science and Technology (MOST, Project Numbers 106-2410-H-468-002 and 107-2410-H-468-002-MY3), Taiwan. Funding Information: Funding: This research was funded by Northeast Normal University, Education University of Hong Kong (project number RG 66/2018-2019R), Asia University, China Medical University Hospital, Hang Seng University of Hong Kong, Research Grants Council of Hong Kong (Project Number 12500915) and Ministry of Science and Technology, Taiwan (MOST, Project Numbers UGC/IIDS14/P01/17 and 106-2410-H-468-002 and 107-2410-H-468-002-MY3). Publisher Copyright: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2019/10/29

Y1 - 2019/10/29

N2 - Most authors apply the Granger causality-VECM (vector error correction model), and Toda–Yamamoto procedures to investigate the relationships among fossil fuel consumption, CO emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, CO emissions, and fossil fuel consumption in China from 1965–2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing CO emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing CO emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, CO emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.

AB - Most authors apply the Granger causality-VECM (vector error correction model), and Toda–Yamamoto procedures to investigate the relationships among fossil fuel consumption, CO emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, CO emissions, and fossil fuel consumption in China from 1965–2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing CO emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing CO emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, CO emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.

UR - https://www.scopus.com/pages/publications/85074368741

U2 - 10.3390/ijerph16214176

DO - 10.3390/ijerph16214176

M3 - Article

C2 - 31671848

AN - SCOPUS:85074368741

SN - 1661-7827

VL - 16

JO - International Journal of Environmental Research and Public Health

JF - International Journal of Environmental Research and Public Health

IS - 21

M1 - 4176

ER -

Modelling economic growth, carbon emissions, and fossil fuel consumption in China: Cointegration and multivariate causality

Abstract

Bibliographical note

UN SDGs

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