Government digital transformation, resource curse and green total factor energy efficiency in Chinese cities
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DOI: 10.1016/j.resourpol.2024.105002
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Cited by:
- Tang, Jiaomei & Li, Wanting & Hu, Jiahan & Ren, Yayun, 2025. "Can government digital transformation improve corporate energy efficiency in resource-based cities?," Energy Economics, Elsevier, vol. 141(C).
- Zhang, Zhiyuan & Zhao, Jialin & Sahut, Jean-Michel & Song, Yang & Guesmi, Khaled, 2026. "The impact of artificial intelligence on urban energy consumption," Technovation, Elsevier, vol. 150(C).
- Xinle Zheng & Linrong Yu & Qi Liu & Rui Xu & Junyan Tang & Xinyuan Yu & Kun Lv, 2025. "Digital Government Construction, Bidirectional Interaction Between Technological and Spiritual Civilization, and Achieving Dual Control of Sustainable Energy: Causal Inference Using Spatial DID and Dual Machine Learning," Sustainability, MDPI, vol. 17(11), pages 1-43, May.
- Feng, Yanchao & Liu, Gaoxiang & Meng, Xiangxu & Jiang, Kai & Huang, Rongbing & Zhang, Ci & Shi, Jiaxin & Pan, Yuxi, 2024. "How does digital government affect carbon intensity at the global level? New perspective of resource allocation optimization," Resources Policy, Elsevier, vol. 94(C).
- Zi-Han Liu & Zheng-Zheng Li & Oana Ramona Lobonț & Kai-Hua Wang, 2025. "How Does Fintech Affect Green Total Factor Energy Efficiency? Evidence from 240 Cities in China," Sustainability, MDPI, vol. 17(19), pages 1-25, September.
- Zhong, Kaijun & Lei, Yalin & Zhao, Jun & Jiang, Yong, 2025. "How to enhance China's total-factor energy efficiency via digital-real economy integration: New evidence from dynamic QCA analysis," Energy Economics, Elsevier, vol. 148(C).
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