Enlarging or narrowing? Exploring the impact of energy poverty on carbon inequality in China DOI
Congyu Zhao, Zhai Xuan, Zhengguang Liu

et al.

Utilities Policy, Journal Year: 2024, Volume and Issue: 92, P. 101859 - 101859

Published: Nov. 25, 2024

Language: Английский

Artificial intelligence and carbon emissions inequality: Evidence from industrial robot application DOI
Congyu Zhao, Yongjian Li, Zhengguang Liu

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 438, P. 140817 - 140817

Published: Jan. 1, 2024

Language: Английский

Citations

26
Trade-induced carbon-economic inequality within China: Measurement, sources, and determinants DOI
Qingyuan Zhu, Chengzhen Xu, Chien‐Chiang Lee

et al.

Energy Economics, Journal Year: 2024, Volume and Issue: 136, P. 107731 - 107731

Published: June 24, 2024

Language: Английский

Citations

20
Bridging the Gap or Widening Disparity? Exploring the Impact of Low-carbon Energy Technology Innovation on Carbon Inequality in Chinese cities DOI
Senmiao Yang, Xiaohui He, Jianda Wang

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106146 - 106146

Published: Jan. 1, 2025

Language: Английский

Citations

2
Impact of government subsidies and carbon inclusion mechanism on carbon emission reduction and consumption willingness in low-carbon supply chain DOI
Li Fang,

Yuhang Guo,

Bin Liu

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 449, P. 141783 - 141783

Published: March 13, 2024

Language: Английский

Citations

12
The panacea of heatwaves: Can climate finance mitigate heatwave welfare costs? DOI Creative Commons
Congyu Zhao, Kangyin Dong, Rabindra Nepal

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105197 - 105197

Published: Jan. 1, 2025

Language: Английский

Citations

1
Contribution of green bonds and green growth in clean energy capacity under the moderating role of political stability DOI

Syed Sumair Shah,

Gulnora Murodova,

Anwar Khan

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122888 - 122888

Published: March 1, 2025

Language: Английский

Citations

1
Is fair carbon mitigation practicable in China? Insights from digital technology innovation and carbon inequality DOI
Senmiao Yang, Jianda Wang, Miaomiao Tao

et al.

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 108, P. 107608 - 107608

Published: July 23, 2024

Language: Английский

Citations

5
Digital finance, capital-biased and labor-biased technical progress: Important grips for mitigating carbon emission inequality DOI

Haoyue Wu,

Yingkai Yin,

Guoxiang Li

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 371, P. 123198 - 123198

Published: Nov. 6, 2024

Language: Английский

Citations

5
The window of opportunity: Estimating the role of climate finance on urban-rural electricity inequality DOI
Congyu Zhao, Zhai Xuan, Meng Yuan

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124153 - 124153

Published: Jan. 20, 2025

Language: Английский

Citations

0
Regional Disparities and Driving Factors of Residential Carbon Emissions: An Empirical Analysis Based on Samples from 270 Cities in China DOI Creative Commons

Xiangjie Xie,

Jing Wang, Mohan Liu

et al.

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 510 - 510

Published: Feb. 28, 2025

Residential carbon emissions (RCEs) have become a major contributor to China’s overall emission growth. A comprehensive analysis of the evolution characteristics regional disparities in RCEs at urban level, along with thorough examination driving factors behind and convergence, is crucial for achieving reduction goals within regions. This study calculates 270 cities China from 2011 2019 based on multiregional input–output tables explores differences spatiotemporal using Dagum Gini coefficient decomposition method kernel density estimation. On this basis, we examine an extended Stochastic Impacts by Regression Population, Affluence, Technology (STIRPAT) econometric model further analyze convergence introducing β-convergence model. The results are as follows: (1) disparity generally shows wave-like declining trend, primary source being between city tiers. (2) Kernel estimation that greater rank, larger disparity; RCE distribution third- lower-tier more concentrated. (3) Population density, population aging, education level significantly exert negative influence RCEs, whereas economic development number researchers, private cars positively correlated RCEs. (4) Each agglomeration’s exhibits significant β-convergence, but their differ across agglomerations. provides targeted policy recommendations achieve its effectively. cluster should tailor approach strengthen collaborative governance, optimize layouts, promote low-carbon lifestyles order facilitate transformation.

Language: Английский

Citations

0