Carbon Emission
Carbon emission research focuses on accurately measuring, predicting, and mitigating greenhouse gas emissions, primarily carbon dioxide. Current research employs diverse methods, including data fusion techniques combining satellite observations with ground-level measurements and sophisticated machine learning models (e.g., neural networks, deep reinforcement learning, and gradient boosting machines) to improve emission estimations and optimize mitigation strategies across various sectors (transportation, manufacturing, data centers). These advancements offer refined insights for policymaking, enabling more targeted interventions to reduce emissions and contribute to climate change mitigation efforts, with applications ranging from urban planning to supply chain optimization.
Papers
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models
Jinbo Wen, Ruichen Zhang, Dusit Niyato, Jiawen Kang, Hongyang Du, Yang Zhang, Zhu Han
A Knowledge-driven Memetic Algorithm for the Energy-efficient Distributed Homogeneous Flow Shop Scheduling Problem
Yunbao Xu, Xuemei Jiang, Jun Li, Lining Xing, Yanjie Song