Journal
GONDWANA RESEARCH
Volume 125, Issue -, Pages 150-167Publisher
ELSEVIER
DOI: 10.1016/j.gr.2023.08.006
Keywords
Nuclear energy; Geothermal energy; Agriculture development; Carbon emissions ecological footprint; QARDL approach; China
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This paper investigates the impact of nuclear energy, geothermal energy, agriculture development, and urbanization on carbon emissions and ecological footprint in China. The findings show statistically significant correlations between these factors and environmental degradation, while agricultural development has a pollution-mitigation impact.
The global issue of atmospheric variations and global warming caused by diverse anthropogenic behaviors is a global concern. There is apprehension about preserving an uncontaminated atmosphere and attaining optimal nuclear and geothermal energy utilization with agriculture sector development. In this regard, this paper investigates the influence of nuclear energy, geothermal energy, agriculture development, and urbanization on carbon emissions and ecological footprint from 1990Q1 to 2019Q4 in the case of China. The findings of unit root tests reveal that all variables are stationary at first integration order, and cointegration test findings confirm the presence of long-run relationships among series. The quantile autoregressive distributive lag (QARDL) method findings demonstrate that nuclear energy, geothermal energy, and urbanization statistically correlate with CO2 emissions and ecological footprint across all quantiles, indicating that these determinants have contributed to environmental degradation. Whereas agricultural development has a statistically significant and negative influence on the environment, implying that agriculture has a pollution-mitigation impact. Based on these empirical findings, several policy implications are presented to preserve environmental quality to achieve the 2030 Sustainable Development Goals (SDGs) target.(c) 2023 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
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