Asymmetric effects of Eco-innovation and Human Capital development in realizing Environmental Sustainability in China: Evidence from Quantile ARDL framework

Cheng Jin, Asif Razzaq, Faiza Saleem and Avik Sinha

MPRA Paper from University Library of Munich, Germany

Abstract: The present study investigates the dynamic and asymmetric impacts of eco-innovation and human capital development on ambient pollution by validating the Environment Kuznets Curve (EKC) hypothesis in China from 1988Q1 to 2018Q4. The findings confirm non-normality and structural breaks in data. Thus, Quantile Autoregressive Distributive Lag (QARDL) model and Granger Causality-in-Quantiles are applied to address non-linearity and structural breaks. The long-run results exhibit that eco-innovation and human capital have a significant negative relationship with carbon emissions, mainly from lower (0.05) to medium (0.5) quantiles and medium (0.50) to higher (0.95) emissions quantile. Moreover, economic growth contributes to higher emissions across all quantiles. In contrast, the square of economic growth has a significant negative association with emissions, confirming the validity of EKC from medium (0.40) to higher (0.95) quantiles. Lastly, Granger causality confirms a two-way causality between eco-innovation, human capital, and carbon emissions, and a one-way causality from human capital, economic growth to carbon emissions. These findings offer valuable policy recommendations.

Keywords: Eco-innovation; Human capital; Environmental sustainability; Carbon emissions; Quantile ARDL (search for similar items in EconPapers)
JEL-codes: Q5 (search for similar items in EconPapers)
Date: 2021, Revised 2021
New Economics Papers: this item is included in nep-cse, nep-ene, nep-env, nep-sbm and nep-tra
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Journal Article: Asymmetric effects of eco-innovation and human capital development in realizing environmental sustainability in China: evidence from quantile ARDL framework (2022)
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