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• W4317542908 abstract "Abstract Green innovation is a critical support to combat climate change arising from greenhouse gas emissions generated by energy consumption. It is an essential way to achieve resource storage, carbon emissions reduction, and sustainable development goals in China. Based on an environmental framework defined as the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, this study aimed to empirically check the impact of green innovation (GI), per capita GDP (PGDP), population density (PD), environmental regulations (ER), energy consumption (EC), and industrial structure upgrading (ISU) on CO 2 emissions (CO 2e ). For this purpose, a sample dataset covering the 30 provincial regions in mainland China from 2005 to 2019 was analyzed using the Fixed Effects and System Generalized Method of Moment (SYS-GMM) Methodology. The empirical results showed that CO 2e in the current period were further aggravated due to the agglomeration effect of CO 2e from the previous period. The data analysis indicated that GI, ER, and ISU all exert a significant inhibitory effect on CO 2e , whereas PGDP, PD, and EC had a positive effect on carbon emissions when dynamic relationships were analyzed. In the regional heterogeneity test, the current model also revealed that the impact of GI on diminishing CO 2e was more pronounced in the east-central region, but not in the west. It is suggested that policymakers in China not only design differentiated policies in response to regional heterogeneity, but also focus on the decisive role of green technology application, environmental protection, and green transformation of industrial structure in curbing CO 2e ." @default.
• W4317542908 created "2023-01-20" @default.
• W4317542908 creator A5073529072 @default.
• W4317542908 creator A5087929857 @default.
• W4317542908 date "2023-01-20" @default.
• W4317542908 modified "2023-09-27" @default.
• W4317542908 title "An assessment of the green innovation, environmental regulation, energy consumption, and CO2 emissions dynamic nexus in China: Fresh insights based on the GMM model" @default.
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• W4317542908 doi "https://doi.org/10.21203/rs.3.rs-2356825/v1" @default.
• W4317542908 hasPublicationYear "2023" @default.
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