From Energy Burden to Efficiency Gain: Nonlinear and Spatial Effects of Digital Infrastructure on Carbon Emission Efficiency

Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development.

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Publication Details

Journal
Sustainability
Published
2026-08-25
DOI
https://doi.org/10.3390/su18178689
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
0.00
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article

From Energy Burden to Efficiency Gain: Nonlinear and Spatial Effects of Digital Infrastructure on Carbon Emission Efficiency

Yuqing Lu, Xingqiu Hu, Ruichen Yin
Sustainability
Energy, Environment, Economic Growth
article

From Energy Burden to Efficiency Gain: Nonlinear and Spatial Effects of Digital Infrastructure on Carbon Emission Efficiency

Yuqing Lu, Xingqiu Hu, Ruichen Yin
article en

Abstract

Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development.

SustainabilityVol. 18(17)
Hohai University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Energy, Environment, Economic Growth
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