Prediction of drivers’ impact on green supply chain management using deep learning algorithm

Nowadays, for controlling the organizations’ environmental impact and attaining performance enhancement in industries, green supply chain management (GSCM) plays a significant role. This work considers four major drivers and five primary dimensions for the deployment of GSCM systems in industries. T...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 30; H. 3; S. 8062 - 8079
Hauptverfasser:	Merneedi, Anjibabu, Palisetty, Ramesh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023 Springer Nature B.V
Schlagworte:	Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Belief networks consciousness Consumers Cost reduction Deep Learning Development policy Earth and Environmental Science Ecotoxicology Energy consumption Environment Environmental Chemistry Environmental Health Environmental impact Genetic algorithms human resources India Industry Knowledge management Leather Leather & leather products leather industry Machine learning Manufacturing Optimization techniques Pareto optimum Performance enhancement Pollution prediction Product life cycle Research Article risk supply chain Supply chain management Supply chain sustainability Supply chains Surveys Sustainability sustainable technology Urbanization Waste Water Technology wastes Water Management Water Pollution Control India Tamil Nadu India Urbanization Risk and security consciousness GSCM Restructuring GSCM
ISSN:	0944-1344, 1614-7499, 1614-7499
Online-Zugang:	Volltext
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