As Climate Risks Intensify: How Do Key Digital Technologies Promote Pollution Reduction and Carbon Mitigation? Evidence From China
Shengling Zhang et al.
Abstract
Key digital technology (KDT) serves as a critical technological foundation for advancing pollution reduction and carbon mitigation (PRCM), while increasingly reshaping regional environmental governance capacity. However, the institutional fluctuations and governance uncertainties induced by intensifying climate risks have introduced both challenges and opportunities for the environmental effectiveness of KDT. Drawing on panel data from 276 prefecture‐level cities in China between 2011 and 2021, this study investigates the impact of KDT on PRCM synergy across heterogeneous regional contexts. The findings reveal that KDT significantly enhances the degree of PRCM synergy, highlighting its dual environmental benefits. Moreover, climate risks play differentiated moderating roles in this process. Specifically, climate transition risks amplify the PRCM effect of KDT, reflecting the greater marginal adaptability of technological governance under institutional risk scenarios. In contrast, the overall moderating role of climate physical risks is limited. Heterogeneity analysis suggests that under transition risks, KDT possesses compensatory capacities to address institutional uncertainty, whereas under physical risks, its effectiveness is conditioned by regional structural characteristics and governance capacity. Mechanism analysis further demonstrates that KDT influences PRCM synergy through a chain pathway encompassing “source prevention–process control–end‐stage blocking.” Spatial effect analysis indicates a siphon effect of KDT, with significant negative spillovers on the PRCM synergy of adjacent regions, implying the risk of widening inter‐regional disparities in environmental governance outcomes.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.