Digital Businesses in the Manufacturing Sector and the Green Economy: Empirical Evidence from EU Countries DOI
Emilia Herman

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 127 - 140

Published: Jan. 1, 2025

Language: Английский

Revisiting the relationship between geopolitical risk and ecological footprint: A comprehensive analysis based on dual machine learning DOI
Chen Zhang,

Qiang Wang,

Rongrong Li

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124125 - 124125

Published: Jan. 20, 2025

Language: Английский

Citations

0
Eco-intelligent production: intelligent manufacturing and industrial green transition DOI
Xiaoli Hao, Yuhong Li, Kun Wang

et al.

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Language: Английский

Citations

0
Identification and Prioritization of Critical Success Factors of a Lean Six Sigma–Industry 4.0 Integrated Framework for Sustainable Manufacturing Using TOPSIS DOI Open Access

Arish Ibrahim,

Gulshan Kumar

Sustainability, Journal Year: 2025, Volume and Issue: 17(3), P. 1331 - 1331

Published: Feb. 6, 2025

The relationship between Lean Six Sigma, Industry 4.0 and sustainable manufacturing has been evaluated only to a limited extent within this domain of the published literature. A DMAIC-DMADV-based framework along with phase-by-phase implementation path is proposed in study integrate Sigma technologies for achieving manufacturing. paper also focused on identifying prioritizing critical success factors framework. identified through literature review are ranked using multi-decision criteria technique TOPSIS, input from selected experts across various companies. results highlight that most important enablers set clear sustainability goals, regularly monitor progress have skilled workforce. findings provide actionable guidance practitioners, contributes existing body knowledge by offering comprehensive methodology Further research must focus validation diverse industrial settings refining assessment model enhance its adaptability.

Language: Английский

Citations

0
How the digital economy promotes urban–rural integration through optimizing factor allocation: theoretical mechanisms and evidence from China DOI Creative Commons
Yuchen Lu,

Jiakun Zhuang,

Chenlu Yang

et al.

Frontiers in Sustainable Food Systems, Journal Year: 2025, Volume and Issue: 9

Published: Feb. 28, 2025

The digital economy plays an increasingly crucial role in bridging the gap between urban and rural areas. This study investigates how development of can foster integrated areas by optimizing factor allocation, with emphasis on its potential to narrow urban-rural divide. aims examine impact integration, focusing particularly mediating optimized allocation. Using panel data from 30 Chinese provinces 2011 2022, we construct indicators for integration. analysis employs a two-way fixed-effects model, effect spatial Durbin model explore evolution Findings suggest that enhances integration both directly indirectly. It contributes indirectly optimizes allocation labor, capital, land, technology, information, further promoting convergence. effects these mechanisms exhibit significant threshold heterogeneity. These results underline importance accelerating mobility as key strategies Policy implications focus enhancing efficiency resource across accelerate balanced development.

Language: Английский

Citations

0
Digital Businesses in the Manufacturing Sector and the Green Economy: Empirical Evidence from EU Countries DOI
Emilia Herman

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 127 - 140

Published: Jan. 1, 2025

Language: Английский

Citations

0