A review of the application of machine learning in carbon emission . . . A systematic literature search was conducted on the Web of Science database for articles published between 2020 and 2024 Initial searches using the keywords “machine learning” and “carbon emissions” returned 64 pages of records, while the combination “carbon neutrality” and “machine learning” yielded 11 pages of records
Artificial Intelligence-Powered Carbon Emissions Forecasting . . . Keywords: Artificial Intelligence, Predictive Analytics, Carbon Emissions Forecasting, Green Finance, Sustainable Supply Chain 1 Introduction In today’s rapidly evolving global market, supply chain management (SCM) has become a crucial determinant of business success (Uddin et al , 2023b) Companies worldwide are continually
AI-Driven Predictive Analytics for Carbon Emission Reduction in . . . Keywords: AI-Driven, Predictive analytics, Carbon emission reduction, Machine learning, Sustainable production 1 Introduction Industrial manufacturing is one of the largest contributors to global carbon emissions, significantly impacting climate change and environmental sustainability (Onukwulu et al , 2022) The sector is responsible for
Designing and Deploying AI Models for Sustainable Logistics . . . The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed logistics and supply chain management, particularly in the pursuit of sustainability and eco-efficiency This study explores AI-based methodologies for optimizing logistics operations in the USA, focusing on reducing environmental impact, improving fuel efficiency, and minimizing costs
Research on the application of deep learning in low-carbon supply chain . . . Dataset: This study utilized a self-collected LCSC dataset, encompassing the supply chain operations and carbon emissions data of a large manufacturing company from January 1, 2019, to December 31, 2023 The dataset spans 1,826 days and includes comprehensive information on daily carbon emissions (measured in tons of CO2 equivalent
A Comparative Analysis of 3Pl Logistics for Deteriorating Products: a . . . This study presents a comparative analysis of machine learning based models to predict demand and carbon emission forecasting We propose a novel methodology to transform the raw supply chain data to use it in Convolution neural network (CNN) model These data are then classified into Low, Medium, High Categories
A Hybrid Model with Applying Machine Learning Algorithms and . . . This study has presented a hybrid machine learning and mathematical programming approach that enjoys a high prediction accuracy and is implemented with small data to predict the emission of greenhouse gases (C O 2, N 2 O, C H 4, and Fluorinated-Gas) Data in the energy sector affecting GHG emissions and also related data to the rates of GHG
AI for climate change: unveiling pathways to sustainable . . . - Springer This research attempts to amplify the precision of greenhouse gas (GHG) emission predictions through a comprehensive examination of machine learning regression models The study addresses a critical gap in the existing literature by conducting an in-depth comparative analysis of various algorithms, including traditional linear models, decision trees, random forest, and advanced boosting
Machine Learning Models for Prediction of Scope 3 Carbon Emissions For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions In this paper, we train machine learning algorithms on 15 reported types of scope 3 emissions The models
A Full Life Cycle Prediction Method for Supply Chain Carbon Emissions . . . In order to refine the company's carbon peak target indicators, establish a dynamic monitoring system for the indicators, and track the progress of the company's and each unit's emission reduction targets, this paper proposes a full life cycle prediction method for supply chain carbon emissions based on integrated deep learning The method adopts a combination of gray prediction model and deep
Designing and Deploying AI Models for Sustainable Logistics . . . accuracy in forecasting supply chain demand, leading to more informed planning and effective resource allocation Furthermore, Anonna et al (2023) delve into the capabilities of machine learning in predicting CO2 emissions, providing valuable insights that can shape sustainable policies for the logistics sector [1]
Green supply chain transformation and emission reduction based on . . . The effects of machine learning investment and its risk on the supply chain competition are analyzed and the equilibria outcomes in both cases are compared In our study, we pay special attention to the asymmetric competition between a trad-itional supply chain and a transformed supply chain that is willing to adopt machine-