Author(s)

Qingnian Wang, Chunwei Guo

Corresponding Author

Qingnian Wang

Abstract

As a new model of world economic trade collaboration, global value chain (GVC) connects the economies of various countries, bringing a steady stream of opportunities to each economy in the value chain. While enjoying the development dividend, all countries are also dealing with common problems: global warming. As a high energy consumption industry, manufacturing industry produces a large amount of CO2 in production activities. Therefore, analyzing the evolution law of GVC participation in China's manufacturing industry and exploring its impact on carbon emissions will help to explore the energy-saving and carbon reduction mode of manufacturing industry to achieve "carbon neutrality" and the countermeasures to improve the international competitiveness of China's manufacturing industry, in order to pave the way for the development of high-quality foreign trade with "high value and low emission". Theoretically, this paper analyzes the influence mechanism of GVC insertion on China's manufacturing CO2 emissions from three aspects: scale, technology and structural effects. In the empirical aspect, the degree of GVC embeddedness and carbon emissions of China's manufacturing industry from 2000 to 2018 are calculated, and the impact of GVC embeddedness on China's manufacturing carbon emissions is studied by using OLS method. The results show that the value of GVC embeddedness in China's manufacturing industry shows an "M" trend from 2000 to 2018.The overall embeddedness of GVC in China's manufacturing industry is high, and the backward embeddedness is much higher than the forward embeddedness. GVC embeddedness has a positive impact on China's manufacturing carbon emissions. Finally, policy suggestions are put forward from the perspective of actively embedding GVC high-end links, accelerating industrial transformation and upgrading and optimizing energy structure.

Keywords

Global Value Chain, Carbon Emissions, Effect, Gvc Participation Index, Multiple Regression