Digital governance and the low-carbon transition: evidence from double machine learning
Abstract Global warming caused by carbon emissions significantly threatens ecosystems and sustainable development. Adopting new measures is essential to realize the low-carbon transition. Digital governance provides fresh impetus for climate change mitigation and carbon neutrality. Although several...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025-06-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-05144-9 |
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| Summary: | Abstract Global warming caused by carbon emissions significantly threatens ecosystems and sustainable development. Adopting new measures is essential to realize the low-carbon transition. Digital governance provides fresh impetus for climate change mitigation and carbon neutrality. Although several studies have analysed the effect of digital governance on total carbon emissions, the relationship between digital governance and carbon emission intensity remains poorly understood. In particular, the long-term relationship between China’s digital governance and low-carbon remains unclear. On the basis of panel data covering 282 prefecture-level cities in China from 2008 to 2022, our study combines principal component analysis with double machine learning to evaluate the effect of digital governance on the low-carbon transition. Our findings indicate that digital governance promotes the low-carbon transition in the short term, but it may hinder the low-carbon transition in the long term. The mechanistic analysis reveals that digital governance influences the low-carbon transition through green technology innovation and industrial upgrading. Furthermore, the effects of digital governance on the low-carbon transition are stronger in the eastern region, resource-based cities, and areas with high population concentrations. Our study deepens the understanding of China’s government digitalization and low-carbon transition, providing policymakers with crucial insights for achieving green and high-quality development. |
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| ISSN: | 2662-9992 |