Resources Policy, Journal Year: 2024, Volume and Issue: 99, P. 105383 - 105383
Published: Nov. 9, 2024
Language: Английский
Resources Policy, Journal Year: 2024, Volume and Issue: 99, P. 105383 - 105383
Published: Nov. 9, 2024
Language: Английский
Deleted Journal, Journal Year: 2024, Volume and Issue: 1(2), P. 157 - 174
Published: June 1, 2024
This study comprehensively evaluates the integration and effectiveness of green mining technologies within sector, specifically focusing on mitigating environmental impact traditional practices. The primary goal is to establish a sustainable model that significantly reduces energy consumption minimizes ecological disturbances. To achieve this, employs mixed-method approach, integrating quantitative data analysis from monitored sites qualitative insights industry experts. Key parameters include consumption, greenhouse gas emissions, reductions in chemical use. findings reveal effective leads significant lower improved waste management compared methods. Specifically, use electric vehicles renewable sources operations has resulted decreased carbon emissions usage across studied sites. research concludes practices, when supported by robust technological regulatory frameworks, not only enhance sustainability but also boost economic efficiency industry. recommends increased investment development calls for tighter oversight ensure widespread adoption optimization these
Language: Английский
Citations
38World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(1), P. 1999 - 2008
Published: Jan. 25, 2024
The burgeoning threat of climate change has spurred an increased reliance on advanced technologies to comprehend and mitigate its far-reaching consequences. Artificial Intelligence (AI) Machine Learning (ML) have emerged as indispensable tools in research, offering unprecedented capabilities for predictive modeling assessing environmental impact. This review synthesizes the current state AI ML applications emphasizing their role understanding repercussions. Predictive models leveraging algorithms demonstrated remarkable efficacy forecasting patterns, extreme weather events, sea-level rise. These incorporate vast datasets encompassing meteorological, geospatial, oceanic information, enabling more accurate predictions future scenarios. Moreover, AI-driven excel recognizing intricate patterns non-linear relationships within data, enhancing capacity simulate complex systems. Environmental impact assessment stands a critical facet techniques are proving instrumental this regard. facilitate analysis diverse ecological parameters, including deforestation rates, biodiversity loss, carbon sequestration dynamics. By discerning nuanced immense datasets, systems contribute direct indirect consequences ecosystems. Despite these advancements, challenges persist, such need standardized data formats, model interpretability, ethical considerations. Additionally, integration findings into policy frameworks remains crucial frontier. As intersection AI, ML, research evolves, continuous interdisciplinary collaboration is essential harness full potential safeguarding our planet's future. illuminates landscape applications, providing insights efficacy, challenges, contributions advancing sustainability.
Language: Английский
Citations
9Resources Policy, Journal Year: 2024, Volume and Issue: 93, P. 105055 - 105055
Published: May 24, 2024
Language: Английский
Citations
4Environmental Education Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15
Published: Feb. 18, 2025
Language: Английский
Citations
0Energy Policy, Journal Year: 2025, Volume and Issue: 202, P. 114554 - 114554
Published: March 17, 2025
Language: Английский
Citations
0Applied Energy, Journal Year: 2025, Volume and Issue: unknown, P. 124847 - 124847
Published: April 1, 2025
Language: Английский
Citations
0SAGE Open, Journal Year: 2025, Volume and Issue: 15(2)
Published: April 1, 2025
Green and sustainable corporate development promotes economic, environmental, social sustainability. It significantly enhances long-term profitability mitigates climate change. An essential avenue toward fostering green among enterprises lies in the organic amalgamation of digital technology with real economy, commonly called digital-real integration. Hence, a comprehensive exploration integration its impact on becomes imperative. This study endeavors to address this need by constructing two-sector four-factor model focusing production R&D. examines both factor-based technology-based technologies assess their influence enterprises, alongside scrutinizing bias technological progress from theoretical empirical standpoints. paper employs panel data Chinese A-share listed companies covering period 2000 2022 develop for analysis. The findings reveal that fusion positively contribute enterprises. However, exhibits an inverted U-shaped curve relationship, significant positive promotional effect is observed only when critical threshold not exceeded. Furthermore, through lens heterogeneity, grappling poor revenue status, backwardness, inadequate internal control, non-compliance SDG policy may encounter challenges executing digital-realistic fusion. Subsequent investigations indicate results capital skilled labor, favoring advancement. In light these findings, we propose following recommendations: (1) expedite innovation optimize industrial framework; (2) implement tax incentives fiscal policies enhance company’s profile promote advancement; (3) establish mandatory environmental regulations ensure compliance objectives. These strategies offer noteworthy paradigm could be adapted developing countries.
Language: Английский
Citations
0Developments in the Built Environment, Journal Year: 2025, Volume and Issue: unknown, P. 100669 - 100669
Published: April 1, 2025
Language: Английский
Citations
0Process Integration and Optimization for Sustainability, Journal Year: 2024, Volume and Issue: 8(5), P. 1411 - 1437
Published: June 25, 2024
Language: Английский
Citations
3Resources Policy, Journal Year: 2024, Volume and Issue: 97, P. 105288 - 105288
Published: Sept. 4, 2024
Language: Английский
Citations
2