Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS, and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness in China. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility toward these associations in the context of the COVID-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (
n
= 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness in China. Recent research shows that supply chain professionals, policymakers, managers, and researchers are turning to formal EMS, BDA-AI, and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.
Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility towards these associations in the context of the Covid-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness. Recent research shows that supply chain professionals, policy makers, managers and researchers are turning to formal EMS, BDA-AI and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.
The purpose of this study is to determine during the COVID-19 epidemic effects on wind and green energy and control the raising the cost of utilizing wind energy to power for country energy plants using the Levelized Cost of Energy methods. Objective 1) The COVID-19 pandemic can be provided through green financial policies such as coal pricing, transferable green certificates, and loans for wind energy markets. Objective 2) examined the cost of wind energy in china before and after the COVID-19 outbreak, using data from 100 wind energy projects constructed between 2014 and 2020. Based on results, wind energy's fixed average cost of electricity fell from 0.98 Chinese yuan in 2014 to 0.79 Chinese Yuan in March 2019, and subsequently to 0.75 Chinese Yuan in 2020, a 13.99 percent increase. Other results average electricity generation price down to 0.79 Yuan, 0.99 Yuan, and 0.79 Yuan and average carbon oxide emissions was 50 Yuan/ton increase. The green fiscal policies will be required during the COVID-19 epidemic to promote wind energy generation investment.
The main aims of the current study are to determine during the COVID-19 epidemic effects on renewable and green energy and control the raising the cost of utilizing renewable energy to power for country energy plants using the Levelized Cost of Energy methods. Objective 1) The COVID-19 pandemic can be provided through green financial policies such as coal pricing, transferable green certificates, and loans for renewable energy markets. Objective 2) examined the cost of offshore wind power in china before and after the COVID-19 outbreak, using data from 100 offshore wind power projects constructed between 2000 and 2020. Based on results, wind power's fixed average cost of electricity fell from 0.98 Chinese yuan in 2000 to 0.79 Chinese RMB in March 2019, and subsequently to 0.75 Chinese RMB in 2020, a 13.99 percent increase. Other results average electricity generation price down to 0.79 RMB, 0.99 RMB, and 0.79 RMB and average carbon oxide emissions was 50 RMB/ton increase. The green fiscal policies will be required during the COVID-19 epidemic to promote offshore wind energy generation investment.
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