Exploring the relationship between the digital economy and carbon intensity: Evidence from cities in China

This study investigates the nonlinear relationship between the digital economy and carbon intensity, a topic that remains underexplored in the existing literature. Drawing on data from 274 prefecture-level cities in China from 2011 to 2019, the analysis employs a Regression by Artificial Neural Network with Bootstrapping approach to identify a distinct three-stage pattern: carbon intensity undergoes minor changes at the early stage of digital development, grows steadily during the intermediate stage, and finally accelerates into rapid growth as digital expansion intensifies, indicating that China’s digital economy has yet to contribute to a clear decline in carbon intensity. Based on this framework, the moderating effects of industrial structure, energy mix, and technological innovation are further assessed. The results show that industrial upgrading nonlinearly enhances the decarbonization effect of digitalization. Importantly, the carbon-mitigation potential of digitalization depends on a clean energy mix, whereas coal dependence can prevent reductions and even trigger rebound emissions. Moreover, technological innovation initially strengthens the digital economy’s mitigation impact, while its moderating effect is constrained by diminishing marginal returns and clear threshold limitations. This study introduces a novel nonlinear estimation framework and offers city-level empirical evidence, underscoring the importance of coordinated policy efforts to align digital expansion with carbon reduction through industrial upgrading, energy transition, and innovation.