Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models

Lisirui Tang, Peng Zhao^*, Anwar P. P. Abdul Majeed

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding

Original language	English
Title of host publication	The 11th International Conference on Robot Intelligence Technology and Applications (RiTA2023)
Publisher	Springer
Publication status	Published - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models",

author = "Lisirui Tang and Peng Zhao and {P. P. Abdul Majeed}, Anwar",

year = "2023",

language = "English",

booktitle = "The 11th International Conference on Robot Intelligence Technology and Applications (RiTA2023)",

publisher = "Springer",

}

Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models. / Tang, Lisirui; Zhao, Peng; P. P. Abdul Majeed, Anwar.
The 11th International Conference on Robot Intelligence Technology and Applications (RiTA2023). Springer, 2023.

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding

TY - GEN

T1 - Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models

AU - Tang, Lisirui

AU - Zhao, Peng

AU - P. P. Abdul Majeed, Anwar

PY - 2023

Y1 - 2023

M3 - Conference Proceeding

BT - The 11th International Conference on Robot Intelligence Technology and Applications (RiTA2023)

PB - Springer

ER -