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Can green finance, green technologies, and environmental policy stringency leverage sustainability in China: evidence from quantile-ARDL estimation

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Abstract

Environmental sustainability is an umbrella approach depending on various climatic and economic policies. In doing so, the current study empirically evaluates the role of green finance, eco-innovation, and environmental policy stringency on the ecological footprint in China. To meet the objectives, the novel quantile autoregressive distributed lag (QARDL) approach was employed from 2000 to 2017. The outcomes reveal heterogeneous associations between the proposed variables. Manifestly, the QARDL estimation results demonstrate a positive impact between eco-innovation, green finance, and environmental policy stringency with the ecological footprints of China; however, the extent of the relationship is quantile dependent. The outcomes are further validated through the Wald test of parameter constancy. The bi-direction causality is observed among all variables at several quantiles. The current study offers policymakers helpful suggestions on enhancing the positive effects of environmentally supported innovation, green finance, and stringent environmental policies on the ecosystem.

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Fig. 1

Source: Global Footprint Network

Fig. 2

Source: The CityUk analysis based on data of Refinitive workshop

Fig. 3

Source: Statista

Fig. 4

Source: Demiral et al. (2021)

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Data will be available upon the request to corresponding author.

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This paper is supported by XMUMRF/2023-C11/ISEM/0040 grant.

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We acknowledge that all the authors participated equally in this paper. Sahar Afshan worked on introduction. Tanzeela Yaqoob worked on analysis and grammar. Muhammad Saeed Meo worked on interpretation and policy recommendation. Bushra Hamid worked on conclusion, policies, and grammar.

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Correspondence to Muhammad Saeed Meo.

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Afshan, S., Yaqoob, T., Meo, M.S. et al. Can green finance, green technologies, and environmental policy stringency leverage sustainability in China: evidence from quantile-ARDL estimation. Environ Sci Pollut Res 30, 61726–61740 (2023). https://doi.org/10.1007/s11356-023-26346-1

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