The multiple empowerment effects of digital transformation on carbon emissions in manufacturing industry from the prospective of factor allocation: Theoretical analysis and empirical evidence

Abstract

To enhance the value of data as a production factor in economic activities and encourage the mutually beneficial integration of digitalization and environmental sustainability in the manufacturing industry, a factor input intensity model empowered by data factor is constructed to quantitatively identify the multiple empowerment effects of digital transformation on carbon emissions in manufacturing, mainly including substitution and allocation innovation effects, and rebound effect. Empirical tests are also conducted using data from Chinese manufacturing between 2010 and 2019. It can be seen that the carbon reduction effect is determined by both substitution and allocation innovation effects. When the input intensity of the data factor increases to a certain threshold, there is a range of effective carbon reduction within which the rebound effect can be counteracted by the carbon reduction effect. As a result, the primary carbon reduction effect will gradually shift from the allocation innovation effect to the substitution innovation effect. Additionally, there is an inverted “U-shaped” nonlinear relationship between the data factor input intensity, innovation effects, and the carbon emission intensity of manufacturing. Moreover, in contrast to the same and significant inverted U-shaped relationship between data factor input intensity and the substitution innovation effect of energy factor across all types of sub-sector groups, there is significant heterogeneity in the result and direction of interaction relationships between data factor input intensity and the rest of the multiple empowerment effects.