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 - PP Abdul Majeed, Anwar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Climate change is a pressing global issue that needs immediate attention. The primary cause of climate change is global warming resulting from the anthropogenic emissions of greenhouse gases (GHGs). The combustion of fuels required to meet the energy demand accelerates carbon emissions, contributing to the increase of ambient GHGs. In this study, we investigated the prediction of carbon dioxide (CO2, the most important GHG) emissions resulting from energy consumption in China using Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), based on the energy consumption data and annual CO2 emissions per unit of energy data from 1965 to 2022. The results indicate that the LSTM model outperforms the others.
AB - Climate change is a pressing global issue that needs immediate attention. The primary cause of climate change is global warming resulting from the anthropogenic emissions of greenhouse gases (GHGs). The combustion of fuels required to meet the energy demand accelerates carbon emissions, contributing to the increase of ambient GHGs. In this study, we investigated the prediction of carbon dioxide (CO2, the most important GHG) emissions resulting from energy consumption in China using Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), based on the energy consumption data and annual CO2 emissions per unit of energy data from 1965 to 2022. The results indicate that the LSTM model outperforms the others.
KW - Climate change
KW - Energy consumption
KW - Long Short-Term Memory
KW - Support Vector Regression
UR - http://www.scopus.com/inward/record.url?scp=85211334084&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70687-5_22
DO - 10.1007/978-3-031-70687-5_22
M3 - Conference Proceeding
AN - SCOPUS:85211334084
SN - 9783031706868
T3 - Lecture Notes in Networks and Systems
SP - 206
EP - 213
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P. P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
Y2 - 6 December 2023 through 8 December 2023
ER -
