Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series-Reference-Cited by-同舟云学术

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Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series

Published:2024-08 Issue: Volume:367 Page:123368
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Liu Xiao,Hu Qunpeng,Li Jinsong,Li Weimin,Liu Tong,Xin Mingjun,Jin Qun^ORCID

Publisher

Elsevier BV

Reference41 articles.

1. CO2 emissions from renewable and non-renewable electricity generation sources in the G7 countries: static and dynamic panel assessment;Voumik;Energies,2023

2. A new model for the use of renewable electricity to reduce carbon dioxide emissions;Mostafaeipour;Energy,2022

3. Moving beyond the NDCs: ASEAN pathways to a net-zero emissions power sector in 2050;Handayani;Appl Energy,2022

4. A review and analysis of renewable energy policies and CO2 emissions of Pakistan;Qudrat-Ullah;Energy,2022

5. Time series forecasting using a hybrid ARIMA and neural network model;Zhang;Neurocomputing,2003

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Driver Analysis and Integrated Prediction of Carbon Emissions in China Using Machine Learning Models and Empirical Mode Decomposition;Mathematics;2024-07-11

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