The model of White Supply Chain Management for sustainable performance in the food industry DOI Open Access
Waraporn Suksanchananun, Sebastian Kot, Wornchanok Chaiyasoonthorn

et al.

Equilibrium Quarterly Journal of Economics and Economic Policy, Journal Year: 2024, Volume and Issue: 19(4), P. 1405 - 1448

Published: Dec. 30, 2024

Research background: The evolving business sector, driven by environmental factors and social pressure such as natural capital, global competitiveness, etc., necessitates continuous improvement adaptation. study presents White Supply Chain Management (WSCM), which incorporates ethical, social, practices into supply chains to enhance competitiveness. WSCM expands on Green (GSCM) integrating principles of ethics responsibility towards achieving the SDGs. variables include pressure, ethical management corporate responsibility, promoting holistic sustainability across all chains. Purpose article: study's objectives were examine validity components in food analyze influence long-term effectiveness Food Industry, model see how it promotes business. Method: research used a quantitative survey design elicit responses from sample group 664 respondents, selected using lottery-based random sampling method with 2–3 key informants per factory, typically occupying middle high-level executive positions. test tool was structural equation model. Findings & value added: results show that sustainable performance (SUS) are much improved pressure. further improves SUS. findings emphasize need for sector stakeholders interact their publics (both internal external), maintain standards, leverage chain analytics transparency. Theoretically, societal drives through WSCM, therefore addressing issues outside conventional Management. focuses necessity implementing an integrated framework managing chain, comprising factors, advises future additional sectors investigate its effects sustainability.

Language: Английский

Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy DOI Open Access
Xiaoyan Peng, Xin Guan, Yanzhao Zeng

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 4111 - 4111

Published: May 14, 2024

This research contributes to the overarching objectives of achieving carbon neutrality and enhancing environmental governance by examining role artificial intelligence-enhanced multi-energy optimization in rural energy planning within broader context a sustainable economy. By proposing an innovative framework that accounts for geographical economic disparities across regions, this study specifically targets systems X County Yantai City, Y Luoyang Z Lanzhou City. Furthermore, it establishes foundation integrating these localized approaches into national carbon-neutral efforts assessments green total factor productivity. The comparative analysis demand, conservation, efficiency, metrics among counties underscores potential tailored solutions significantly advance low-carbon practices agriculture, urban development, industry. Additionally, insights derived from offer deeper understanding dynamics between government enterprise governance, empirically supporting Porter hypothesis, which postulates stringent policies can foster innovation competitiveness. coal-coupled biomass power generation model introduced work represents convergence economy principles financial systems, serving as valuable guide decision-making decisions aimed at consumption production. Moreover, importance resilient adaptable pathway evaluating emission trading markets promoting recovery strategies align with sustainability goals.

Language: Английский

Citations

8
Spatial effect in corporate intelligent manufacturing: empirical evidence from Chinese listed companies DOI
Xiaozhen Pan,

Siqi Lin

Applied Economics, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: April 7, 2025

Language: Английский

Citations

0
Can Intelligent Manufacturing Reduce Corporate Carbon Emissions? Empirical Evidence from China’s Listed Manufacturing Firms DOI
Xiaozhen Pan,

Siqi Lin

Emerging Markets Finance and Trade, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 12

Published: April 7, 2025

Language: Английский

Citations

0
Intelligent Green AI Technologies for Promoting Eco-Friendly and Sustainable Smart Cities DOI
Niraj Kumar Jha, Bharat Bhushan, Khursheed Aurangzeb

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 393 - 414

Published: Jan. 17, 2025

Recently, the adoption of artificial intelligence is gone through roof in every field. However, during model training and operation it consumes significant energy which poses a major challenge to environment sustainability goals. To overcome this term “Green AI” has emerged aims reduce environmental impacts AI. Another concept on rise “Smart City” improve quality life people but also keeping welfare mind. As main goal both Green AI Smart city achieve goals by improving their respective area be or efficiency models, need each other. In paper, we have explored what actually ways it. It highlights applications green various fields such as healthcare, intelligent transportation etc.. The paper further discussed future can hold for researchers make more sustainable.

Language: Английский

Citations

0
A hybrid Machine learning solution for redesigning sustainable circular energy supply chains DOI
Javad Sadeghi, Moein Qaisari Hasan Abadi, Karl R. Haapala

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 197, P. 110541 - 110541

Published: Sept. 7, 2024

Language: Английский

Citations

2
A Closed-Loop Dual-Channel Supply Chain Network for Leather Products: An Integrated Simulation Optimization Clustering Approach DOI

Beheshteh Moghadaspoor,

Mohammad Sheikhalishahi, Ali Bozorgi-Amiri

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112411 - 112411

Published: Oct. 1, 2024

Language: Английский

Citations

2
COVID-19 impact on wind and solar energy sector and cost of energy prediction based on machine learning DOI Creative Commons
Saheb Ghanbari Motlagh, Fatemeh Razi Astaraei, Mohammad Montazeri

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e36662 - e36662

Published: Aug. 24, 2024

This study examines the impact of COVID-19 pandemic on renewable energy sectors across seven countries through techno-economic analysis and machine learning (ML). In China, fraction decreased in grid-connected systems due to 14.6 % higher diesel fuel prices. They reduced grid electricity prices, with Cost Energy (COE) reductions driven by a 2.8 inflation decrease 3 discount rate cut. The increase adoption USA during was initial operational costs components, significant rise government policy changes, despite reduction sell-back prices rising capital annual expanded capacity. Canada noted shift standalone 50 lower PV 2 WT 48 cost rise, reducing COE except grid/WT scenarios. Germany managed costs, decreasing inflation. India HRESs sevenfold capacity increase, lowering COE. Japan saw stable minimal variation. Iran faced economic challenges 104 impacting decrease. Machine forecasts suggest that may cause an China effects.

Language: Английский

Citations

1
The Role of Technologies in Facilitating Circular Economy of China's E-Commerce Section DOI
Poshan Yu,

Z.M. Zhang,

Steve K. M. Wong

et al.

Advances in finance, accounting, and economics book series, Journal Year: 2024, Volume and Issue: unknown, P. 151 - 174

Published: Nov. 15, 2024

The burgeoning e-commerce sector in China stands at the crossroads of technological innovation and sustainable development, particularly within context circular economy. This paper explores multifaceted role that technologies play enhancing economic practices this rapidly evolving industry. primary questions addressed are: (1) What key are influencing China's economy sector? (2) How can we assess impact these on e-commerce? (3) broader applications potential challenges for scaling practices? Employing analytical capabilities CiteSpace, study identifies maps out pivotal facilitating a These include advancements big data analytics, blockchain supply chain transparency, IoT resource monitoring, AI-driven platforms waste reduction materials management. By highlighting interconnectedness technologies, offers comprehensive overview ecosystem propelling then transitions to an evaluative framework, using case studies gauge effectiveness technologies. examining real-world sector, provides qualitative insights into how technology not only drives efficiency sustainability but also engenders new business models consumer behaviors aligned with principles. Lastly, implications findings discussed, alongside opportunities. argues integration is instrumental green serves as catalyst competitiveness global scale. recommendations policymakers industry stakeholders leverage advance more resource-efficient,

Language: Английский

Citations

1
Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy DOI Open Access
Miroslav Despotović,

Matthias Glatschke

Published: May 26, 2024

The inherent "take-make-waste" of the current linear economy is a major contributor to exceeding planetary boundaries. transition circular (CE) and associated challenges opportunities requires fast, innovative solutions. Artificial Intelligence (AI) Machine Learning (ML) are poised play pivotal role in facilitating this by addressing increasing material extraction use, ultimately contributing more environmentally sustainable future. This article aims provide an overview state AI ML CE discuss their potential challenges. A literature survey conducted on Google Scholar, using targeted queries with predefined keywords search operators, revealed that number experimental scientific contributions related has grown significantly recent years. As volume research articles increased, so diversity methods algorithms featured publications. Furthermore, we found since 2020, there been 84% increase ML-related compared total such entries, 55% 2023, those published up 2023. indicates increasingly recognized as valuable tools for advancing CE, application continues grow steadily.

Language: Английский

Citations

0
The model of White Supply Chain Management for sustainable performance in the food industry DOI Open Access
Waraporn Suksanchananun, Sebastian Kot, Wornchanok Chaiyasoonthorn

et al.

Equilibrium Quarterly Journal of Economics and Economic Policy, Journal Year: 2024, Volume and Issue: 19(4), P. 1405 - 1448

Published: Dec. 30, 2024

Research background: The evolving business sector, driven by environmental factors and social pressure such as natural capital, global competitiveness, etc., necessitates continuous improvement adaptation. study presents White Supply Chain Management (WSCM), which incorporates ethical, social, practices into supply chains to enhance competitiveness. WSCM expands on Green (GSCM) integrating principles of ethics responsibility towards achieving the SDGs. variables include pressure, ethical management corporate responsibility, promoting holistic sustainability across all chains. Purpose article: study's objectives were examine validity components in food analyze influence long-term effectiveness Food Industry, model see how it promotes business. Method: research used a quantitative survey design elicit responses from sample group 664 respondents, selected using lottery-based random sampling method with 2–3 key informants per factory, typically occupying middle high-level executive positions. test tool was structural equation model. Findings & value added: results show that sustainable performance (SUS) are much improved pressure. further improves SUS. findings emphasize need for sector stakeholders interact their publics (both internal external), maintain standards, leverage chain analytics transparency. Theoretically, societal drives through WSCM, therefore addressing issues outside conventional Management. focuses necessity implementing an integrated framework managing chain, comprising factors, advises future additional sectors investigate its effects sustainability.

Language: Английский

Citations

0