China’s CO₂ Emission Prediction by Population and GDP Based on MLR and BP Neural Network

DOI

10.2991/aebmr.k.220502.073How to use a DOI?

Keywords

Population; GDP; CO2 emission; multiple linear regression (MLR); backpropagation (BP) neural network

Abstract

Carbon dioxide (CO₂) emission is the amount of greenhouse gas emitted in processes, such as trading, production, or transportation. With the industry development, carbon dioxide emission grows significantly. However, too much CO₂ emission will cause a series of environmental issues, such as climate warming, glacial melting, and sea-level rising. Hence, it is urgent to realize influencing factors and take corresponding measures to protect the environment. Previous research has found that population and GDP are two major factors that cause CO₂ emission to increase, so building models to predict CO₂ emission is feasible and necessary. This paper will test the correlation between China’s population, GDP, and CO₂ emission, then use multiple linear regression (MLR) and backpropagation (BP) algorithm to establish CO₂ emission prediction models by inputting population and GDP data, and finally compare the advantages and disadvantages of the two models. The research shows that the BP algorithm is more suitable for prediction and the result is more accurate.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Yaning Zhang
PY  - 2022
DA  - 2022/05/16
TI  - China’s CO₂ Emission Prediction by Population and GDP Based on MLR and BP Neural Network
BT  - Proceedings of the 2022 International Conference on Urban Planning and Regional Economy（UPRE 2022）
PB  - Atlantis Press
SP  - 410
EP  - 415
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220502.073
DO  - 10.2991/aebmr.k.220502.073
ID  - Zhang2022
ER  -