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An analysis of the effects of human capital on green growth: effects and transmission channels

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A Correction to this article was published on 20 December 2022

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Abstract

In this digitalized world, economies have energetically encouraged green transformation. The empirical findings regarding the nexus between human capital and green growth are relatively inconclusive. The study originally explores the effect of human capital on the green growth of a digital economy like China from 1991 to 2019. To investigate the model empirically, we have applied the ARDL technique. Our results indicate that there is a positive impact of different levels of education on the green growth of China in the long run. Regression results also show that renewable energy consumption, internet use, and financial development lead to expansion in green growth in the long run. Our findings can strengthen the belief of the Chinese government on the advancement of green growth.

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

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Acknowledgements

1. Key Project of Humanities and Social Sciences of Sichuan Provincial Department of Education: Title: Research on Flexible Response Strategies of Performance Appraisal in Tertiary Public Hospitals in the Context of New Medical Reform, Subject Number: SCYG2019-39.

2. Key Project of Humanities and Social Sciences of Sichuan Provincial Department of Education, Funded by Sichuan Hospital Management and Development Center: Title: Research on the Reform of Salary System in Public Hospitals under the Background of New Medical Reform Based on Equity Theory, Subject Number: SCYG2022-21.

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This idea was given by Daxu Liu. Daxu Liu, Guanzhuo Wang, and Cong Sun analyzed the data and wrote the complete paper. While Muhammad Tariq Majeed and Zubaria Andlib read and approved the final version.

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Correspondence to Zubaria Andlib.

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Liu, D., Wang, G., Sun, C. et al. An analysis of the effects of human capital on green growth: effects and transmission channels. Environ Sci Pollut Res 30, 10149–10156 (2023). https://doi.org/10.1007/s11356-022-22587-8

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