The nonlinear relationship between digital economy and environmental pollution: evidence from 287 cities in China

In the context of global digital transformation and the transition toward green development, the environmental impact of the digital economy requires further systematic investigation. Utilizing panel data from 287 Chinese cities (2013–2022) and leveraging big data techniques, this study examines the nonlinear effects of the digital economy on environmental pollution by employing panel threshold regression and machine learning. Our results reveal a significant inverted U-shaped relationship, alongside pronounced technological heterogeneity. Specifically, green digital technologies monotonically suppress pollution without a threshold effect, while non-green digital technologies exhibit a more marked inverted U-shaped pattern. Significant regional heterogeneity is also evident, with a lower threshold value in more developed regions compared to less developed ones. Mechanism analysis shows that the initial phase of digital infrastructure expansion aggravates pollution, whereas in later stages, technology-driven industrial upgrades facilitate energy conservation and emission reduction. These findings provide empirical support for formulating staged and targeted policies to steer the digital economy toward sustainable development.