A new look at China’s environmental quality: how does environmental sustainability respond to the asymmetrical behavior of the competitive industrial sector?

Abstract

ABSTRACTThis research aims to expand the existing literature by bringing a new perspective to China's environmental quality. Existing literature concerning China's environmental sustainability disregards competitive industrial performance, which allows the analysis of Sustainable Development Goal-9. The study investigates the impact of competitive industrial performance, economic growth, and renewable energy consumption on the environmental quality (represented by load capacity factor) within the load capacity curve framework. Considering the positive and negative shocks in industrial competitiveness, the study runs the novel asymmetric ARDL with Fourier terms method to examine the non-linear connection between competitiveness and the environment. Empirical analysis shows that industrial competitiveness asymmetrically affects environmental quality and that the load capacity curve hypothesis is invalid in China. It is also evident that renewable energy contributes to environmental quality. Based on these findings, the study presents sustainable development policies for China within the framework of SDG targets. In this context, country's rapid growth and higher-order industrial competitiveness strategies need to be redesigned in order to increase the share of high-tech activities in total manufacturing and the portion of renewable energy in total energy use. Accordingly, the Chinese policymakers' primary objectives should be regulations that guide fundamental principles for enhancing environmental quality while preserving the country's rapid growth and competitive advantage in the industrial process.KEYWORDS: Chinacompetitive industrial performanceFourier transformsrenewable energysustainable development goals Disclosure statementNo potential conflict of interest was reported by the authors.Availability of data and materialsThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable requestNotes1. The time period is dictated by data availability. Data on the CIP variable is only available from 1990 while the data on the LCF ends in 2018. Hence, while the CIP determines the starting year of the data set, the LCF determines its ending year.Additional informationFundingNo funding was received to assist with the preparation of this manuscript.