Does Artificial Intelligence Bring to Renewable Energy Innovation?Yes, Empirical Investigation for 51 Countries? DOI
Haijie Wang, Shuai Jin, Chun‐Ping Chang

et al.

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 16

Published: Oct. 16, 2024

The development of high-tech industries, represented by artificial intelligence (AI), plays an important role in driving renewable energy innovation (REI). This paper analyzes the effects and mechanisms AI on REI, using data from 51 countries 1993 to 2019. results show that can promote REI increasing research (R&D) investment, improving labor productivity institutional quality. impact varies depending level type AI, national income, extent digital infrastructure. In addition, constructing a spatial Durbin model, this also shows inhibits neighboring while promoting at home. view this, should make full use advantages increase investment R&D field RE, improve efficiency RE production use, establish international cooperation framework for among effort REI.

Language: Английский

How does data factor marketization influence urban carbon emission efficiency? A new method based on double machine learning DOI
Neng Shen, Jingwen Zhou, Guoping Zhang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106106 - 106106

Published: Dec. 1, 2024

Language: Английский

Citations

5

Beyond the Core: Pollution Dynamics in Peripheral Cities Amidst Deepening Functional Division in Urban Agglomerations DOI

You Wu,

Wanyu Xu,

Jun Zha

et al.

Emerging Markets Finance and Trade, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: Jan. 6, 2025

Peripheral cities play a crucial role in the innovative development of urban agglomerations (UAs). However, environmental impacts on peripheral during UA are not well understood. Utilizing data from Yangtze River Delta China 2003 to 2021, this study investigates pollution dynamics amidst deepening functional division within UAs. Furthermore, we identify mechanisms and contextual factors shaping these dynamics. Our nonlinear panel regression model reveals an inverted U-shaped trend emissions both scale density as deepens. Moreover, results mediation effect models show that economic expansion driven by significantly increases cities, whereas industrial structure optimization production technology upgrade caused contribute reduction cities. Further analyses indicate enhanced transportation infrastructure improved information network connectivity can amplify positive effects These insights for developing effective systems fostering sustainable

Language: Английский

Citations

0

The panel threshold analysis of digitalization on manufacturing industry’s green total factor productivity DOI Creative Commons
Lu Liu,

Yulong Xin,

Bei Liu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 5, 2025

Green and sustainable development of manufacturing industry has become the developing trend, green total factor productivity is an important indicator for measuring growth industry. The rapid digitalization provides new opportunities improvement industry's (IGTFP). However, there exists two different perspectives "digital promotion effect" inhibition presently, which causes paradox". That is, may have a non-linear effect on IGTFP. Therefore, this study focuses level aims to test threshold impact Furthermore, tries explore boundary conditions applicability driving sub-industries from perspective heterogeneous technological innovation abilities in industries. empirical based panel data 29 industries China 2012–2019. results show that: (1) Digitalization significant IGTFP, research are still robust according changing variable algorithm carrying Winsorize test. (2) Industry (ITI) plays role process (3) Heterogeneity analysis shows that mechanism varies depending differentiated intensity pollution degree. policymakers should formulate strategies.

Language: Английский

Citations

0

Greening the energy industry: An efficiency analysis of China's listed new energy companies and its market spillovers DOI
Xiaohang Ren, Shen Wang, Weifang Mao

et al.

Energy Economics, Journal Year: 2025, Volume and Issue: unknown, P. 108414 - 108414

Published: March 1, 2025

Language: Английский

Citations

0

Digital–real economy integration and urban low-carbon development in China DOI

Zhenhua Xu,

W. Xu, Daleng Xin

et al.

Economic Analysis and Policy, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0

Digital economy, technological progress, and carbon emissions in Chinese provinces DOI Creative Commons

Yuyan Shen,

Guoliang Wang, Xudong Wu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 3, 2024

Language: Английский

Citations

4

A novel DEA-Tobit-SD assessment framework and application of provincial-level carbon emission embracing regional heterogeneity DOI Creative Commons

Pingyuan Shi,

Yingxin Zhang, Yan Meng

et al.

Carbon Neutrality, Journal Year: 2025, Volume and Issue: 4(1)

Published: Jan. 9, 2025

Abstract Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, system dynamics (SD) model analyze the influence factors evaluate provincial while considering differences. The DEA method assesses province's development resource allocation efficiency. Based on results, provinces’ key influencing can be derived combining with regression sensitivity of SD. Policies are then selected based these gauge their effectiveness. SD used simulate emissions under different policy scenarios future. results present obvious characteristics among provinces. Qinghai's potential has been preliminarily explored as an example. Energy structure, industry energy intensity, forest coverage, R&D input intensity its main emission. sink plays significant role. integrated scenario not linear sum all other scenarios. To ensure completion neutralization goal, further adjustments long-term extra measures needed.

Language: Английский

Citations

0

Climate physical risks: catalyst or constraint for the convergence of the digital and low-carbon economies? DOI Creative Commons

Ya Ru Cui,

Bo Yang

Data Science and Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0

Does the Integration of the Digital Economy and the Real Economy Enhance Urban Green Emission Reduction Efficiency? Evidence from China DOI

Guoguang Pang,

Lin Li,

Dong Guo

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106269 - 106269

Published: March 1, 2025

Language: Английский

Citations

0

How digital economy mitigates urban carbon emissions: the green facilitative power of industrial coagglomeration DOI
Jie Huang, B. Zheng, Minzhe Du

et al.

Applied Economics, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: April 6, 2025

Language: Английский

Citations

0