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.
Cite this article
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 -