Abstract
The present research purposes to investigate the derivers of ecological footprint in China by explaining the “environmental Kuznets curve”. It examines the short-term and long-term effect of non-renewable energy consumption, CO2 emission, and economic growth (GDP) on ecological footprint in China. To achieve this purpose, the current study uses secondary data of 36 years for China and applies time series “auto-regressive distributed lag” modeling to assess short range and long-range effects. Findings of the current study reveal that the CO2 emission significantly positively derives the ecological footprint in long run while the economic growth significantly negatively derives the ecological footprint in long run. It is further found that the CO2 emission significantly positively derives the ecological footprint in short run while the economic growth significantly negatively affects the ecological footprint in short run. It means that the CO2 emission is a positive short-range as well as long-range deriver of ecological footprint while the economic growth is a negative short-term and long-range predictor of ecological footprint in China. However, the non-renewable energy consumption has not been supported as the significant deriver of ecological footprint, neither in short range nor in long range. The current study has strong implications because it will help Chinese policymakers to control environmental degradation and China’s ecological footprint through better polices.
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Acknowledgements
This work was supported jointly by the National Social Science Fund of China (Grant NO. 12BJL074), and Key Research Program of Philosophy and Social Sciences of the Ministry of Education (12JZD034).
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XW proposed the initial conception, collected the data and completed the manuscript. XZ processed the data and completed the data calculation. LY gave lots of suggestions and revisions.
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Wang, X., Yan, L. & Zhao, X. Tackling the ecological footprint in china through energy consumption, economic growth and CO2 emission: an ARDL approach. Qual Quant 56, 511–531 (2022). https://doi.org/10.1007/s11135-021-01128-4
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DOI: https://doi.org/10.1007/s11135-021-01128-4