Abstract

Abstract Facing the dual pressure of domestic environmental pollution and international climate change mitigation, China, the world’s largest emitter of carbon emissions, has proposed a “double carbon” target of 3060. Fujian Province is a clean energy province and a major economic province in southeastern China, with inherent energy conservation and emission reduction advantages. However, as industrialization is still in progress and urbanization is accelerating, the problems of low industrial level and sloppy development are still prominent, and the future low-carbon transformation will be difficult and long. Based on the theory of industrial structure optimization and the theory of low carbon economy, this paper analyzes the influence mechanism of industrial structure optimization on carbon emission from three perspectives: efficiency improvement, technological progress, and structural adjustment. Secondly, an econometric model is constructed by combining the environmental Kuznets curve, and the time series data of Fujian Province from 2000-2021 are analyzed empirically to derive the optimization path of industrial structure on carbon emissions in Fujian Province through co-integration analysis, multiple regression, mediating effect analysis, and robustness testing methods. The results of this paper show that from the structure of energy consumption in Fujian Province, coal and oil consumption account for 72% of the total consumption, which is the main cause of carbon emissions in Fujian Province. The mediating effect and stability analysis of the optimization path through the benchmark regression model shows that the p-value variance is 0.0862, and there is no significant difference between them. The optimization path proposed in this paper has significant utility for carbon emission reduction, improves the efficiency of energy use, strengthens the introduction of FDI and industrial structure optimization in various regions of Fujian Province in a targeted manner, and provides a reference for decision-making for global industrial structure optimization and upgrading.

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