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Heterogeneous effects of urbanization, economic growth, and energy consumption on carbon emissions in China: evidence from a PVAR model

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Abstract

An improved level of urbanization may slow the problem of environmental degradation and achieve green economic development. This study examined the use of the panel vector autoregressive (PVAR) model in discussing the relationship between economic growth, urbanization, energy consumption, and carbon emissions based on annual data from 30 Chinese provinces (municipalities and autonomous regions) for the period of 2000–2019. Meanwhile, the short- and long-term dynamics of urbanization, economic growth, and carbon emissions are better captured by using impulse response function tools. The PVAR model is able to perfectly solve the problem of endogeneity between variables, which has been mostly ignored in the energy environment literature. The results of the study are as follows. First, urbanization has the strongest impact on carbon emissions in the eastern region and a weaker impact on other regions. The impact of economic growth on environmental quality lessens gradually from the western and north-eastern regions to the central region and then the eastern region, with a clear regional gradient. Second, the causal relationships between urbanization and carbon emissions and between energy consumption and carbon emissions are unidirectional and significant only in the eastern region. All four regions have a unidirectional causal relationship between economic growth and carbon emissions. Unidirectional causality between urbanization and economic growth exists in the eastern, western, and north-eastern regions. Third, the impulse response results show that urbanization has more short-term than long-term effects on carbon emissions in the eastern region, both long- and short-term effects in the west and north-east, and only short-term effects in the central region. Economic growth and energy consumption in the central and north-eastern regions improve environmental quality in the short term until the third lag period, which shows the beginning of deteriorating environmental quality.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

PVAR:

Panel vector autoregression

CO2 :

Carbon dioxide

IPCC:

Intergovernmental Panel on Climate Change

GDP:

Gross domestic product

EKC:

Environmental Kuznets curve

STIRPAT:

Stochastic impacts by regression on population, affluence, and technology

LLC:

Levin lin chu

ADRL:

Autoregressive distributed lag

DOLS:

Dynamic ordinary least squares

CCR:

Canonical cointegration regression

FMOLS:

Fully modified least squares

PERGDP:

GDP per capita

SSA:

Sub-SaharanSaharan Africa

SYS GMM:

System Generalized Method of Moments

VAR:

Vector autoregressive

ADF:

Augmented dickey-fuller

SAARC:

South asian association for regional cooperation

IPS:

Im pesaran shin

BIC:

Bayesian information criterion

MIC:

Modified information criterion

QIC:

Quinn information criterion

GHS:

Greenhouse gas

References

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Acknowledgements

We are grateful to Editor-in-Chief Andreas Langousis and the three reviewers for their valuable suggestions and helpful comments, which have made our study more comprehensive.

Author information

Authors and Affiliations

Authors

Contributions

Yangyang Zhang: conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; writing - original draft; validation; visualization; writing - review & editing. Wenxia Hong: supervision; validation; visualization; suggestions; writing - review & editing. Qiaran Huang: visualization; formal analysis. Chuanyu Liu: investigation; formal analysis.

Corresponding author

Correspondence to Yangyang Zhang.

Ethics declarations

Ethics approval and consent to participate

This study involves the macro-data of the human economy and society. All the data are from the official statistical yearbook. The data collection process is in line with ethical and moral standards. The research method of this study is PVAR econometric method, and there is no need for ethical approval and animal experiment content. The author guarantees that the process, content, and conclusion of this study do not violate the theory and moral principles.

Conflict of interest

The authors declare no competing interests.

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Appendix 1. PVAR model rewriting results

Appendix 1. PVAR model rewriting results

$$\Delta Ln\left({gdp}_{it}\right)={A}_{1i}+\sum \nolimits_{j=1}^m{a}_{1j}\Delta Ln\left({gdp}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{b}_{1j}\Delta Ln\left({ur}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{c}_{1j}\Delta Ln\left({ec}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{d}_{1j}\Delta Ln\left({c}_{i,t-j}\right)+{\beta}_{1t}+{\gamma}_{1i}+{\varepsilon}_{1 it}\kern1em$$
(3)
$$\Delta Ln\left({ur}_{it}\right)={A}_{2i}+\sum \nolimits_{j=1}^m{a}_{2j}\Delta Ln\left({gdp}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{b}_{2j}\Delta Ln\left({ur}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{c}_{2j}\Delta Ln\left({ec}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{d}_{2j}\Delta Ln\left({c}_{i,t-j}\right)+{\beta}_{2t}+{\gamma}_{2i}+{\varepsilon}_{2 it}$$
(4)
$$\Delta Ln\left({ec}_{it}\right)={A}_{3i}+\sum \nolimits_{j=1}^m{a}_{3j}\Delta Ln\left({gdp}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{b}_{3j}\Delta Ln\left({ur}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{c}_{3j}\Delta Ln\left({ec}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{d}_{3j}\Delta Ln\left({c}_{i,t-j}\right)+{\beta}_{3t}+{\gamma}_{3i}+{\varepsilon}_{3 it}$$
(5)
$$\Delta Ln\left({c}_{it}\right)={A}_{4i}+\sum \nolimits_{j=1}^m{a}_{4j}\triangle Ln\left({gdp}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{b}_{4j}\Delta Ln\left({ur}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{c}_{4j}\Delta Ln\left({ec}_{i,t-j}\right)+\sum \nolimits_{j=1}^m{d}_{4j}\Delta Ln\left({c}_{i,t-j}\right)+{\beta}_{4t}+{\gamma}_{4i}+{\varepsilon}_{4 it}$$
(6)

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Zhang, Y., Hong, W., Huang, Q. et al. Heterogeneous effects of urbanization, economic growth, and energy consumption on carbon emissions in China: evidence from a PVAR model. Air Qual Atmos Health 16, 2471–2498 (2023). https://doi.org/10.1007/s11869-023-01419-9

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  • DOI: https://doi.org/10.1007/s11869-023-01419-9

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