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

Lisirui Tang; Peng Zhao; Anwar PP Abdul Majeed

doi:10.1007/978-3-031-70687-5_22

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

Lisirui Tang
, Peng Zhao
, Anwar PP Abdul Majeed^*

^*Corresponding author for this work

Xi'an Jiaotong-Liverpool University

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

Abstract

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.

Original language	English
Title of host publication	Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
Editors	Anwar P. P. Abdul Majeed, Eng Hwa Yap, Pengcheng Liu, Xiaowei Huang, Anh Nguyen, Wei Chen, Ue-Hwan Kim
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	206-213
Number of pages	8
ISBN (Print)	9783031706868
DOIs	https://doi.org/10.1007/978-3-031-70687-5_22
Publication status	Published - 29 Nov 2024
Event	11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023 - Taicang, China Duration: 6 Dec 2023 → 8 Dec 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1133 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
Country/Territory	China
City	Taicang
Period	6/12/23 → 8/12/23

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 13 Climate Action

Keywords

Climate change
Energy consumption
Long Short-Term Memory
Support Vector Regression

Access to Document

10.1007/978-3-031-70687-5_22

Cite this

Tang, L., Zhao, P., & PP Abdul Majeed, A. (2024). Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models. In A. P. P. Abdul Majeed, E. H. Yap, P. Liu, X. Huang, A. Nguyen, W. Chen, & U.-H. Kim (Eds.), Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications (pp. 206-213). (Lecture Notes in Networks and Systems; Vol. 1133 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-70687-5_22

Tang, Lisirui ; Zhao, Peng ; PP Abdul Majeed, Anwar. / Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models. Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. editor / Anwar P. P. Abdul Majeed ; Eng Hwa Yap ; Pengcheng Liu ; Xiaowei Huang ; Anh Nguyen ; Wei Chen ; Ue-Hwan Kim. Springer Science and Business Media Deutschland GmbH, 2024. pp. 206-213 (Lecture Notes in Networks and Systems).

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

abstract = "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.",

keywords = "Climate change, Energy consumption, Long Short-Term Memory, Support Vector Regression",

author = "Lisirui Tang and Peng Zhao and \{PP Abdul Majeed\}, Anwar",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023 ; Conference date: 06-12-2023 Through 08-12-2023",

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doi = "10.1007/978-3-031-70687-5\_22",

language = "English",

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series = "Lecture Notes in Networks and Systems",

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Tang, L, Zhao, P & PP Abdul Majeed, A 2024, Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models. in APP Abdul Majeed, EH Yap, P Liu, X Huang, A Nguyen, W Chen & U-H Kim (eds), Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. Lecture Notes in Networks and Systems, vol. 1133 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 206-213, 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023, Taicang, China, 6/12/23. https://doi.org/10.1007/978-3-031-70687-5_22

Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models. / Tang, Lisirui; Zhao, Peng; PP Abdul Majeed, Anwar.
Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. ed. / Anwar P. P. Abdul Majeed; Eng Hwa Yap; Pengcheng Liu; Xiaowei Huang; Anh Nguyen; Wei Chen; Ue-Hwan Kim. Springer Science and Business Media Deutschland GmbH, 2024. p. 206-213 (Lecture Notes in Networks and Systems; Vol. 1133 LNNS).

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

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 - 2024/11/29

Y1 - 2024/11/29

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 - https://www.scopus.com/pages/publications/85211334084

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 -

Tang L, Zhao P, PP Abdul Majeed A. Predicting Carbon Dioxide Emissions from Energy Consumption in China with Long Short-Term Memory and Support Vector Regression Models. In Abdul Majeed APP, Yap EH, Liu P, Huang X, Nguyen A, Chen W, Kim UH, editors, Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. Springer Science and Business Media Deutschland GmbH. 2024. p. 206-213. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-70687-5_22

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

Abstract

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UN SDGs

Keywords

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