Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model

Authors

DOI:

https://doi.org/10.4108/ew.5808

Keywords:

Carbon emissions, Forecast, Path planning, Multilayer Perception Model, MLP, Scenario analysis method

Abstract

INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carbon emission prediction.

OBJECTIVES: This study models and forecasts carbon emissions in Jiangsu Province, one of China's largest industrial provinces, aiming to forecast whether Jiangsu province will achieve the two-carbon target on time plan and provide feasible pathways and theoretical foundations for achieving dual carbon goals.

METHODS: Based on the analysis of the contributions of relevant indicators using the Grey Relational Analysis method, a comprehensive approach integrating the STIRPAT model, Logistic model, and ARIMA model is adopted. Ultimately, an MLP prediction model for carbon emission variations is established. Using this model, simulations are conducted to analyze the carbon emission levels in Jiangsu Province under different scenarios from 2021 to 2060.

RESULTS: The time to reach carbon peak and the likelihood of achieving carbon neutrality vary under three scenarios. Under the natural scenario of no human intervention, achieving carbon neutrality is not feasible. While under human-made intervention scenarios including baseline and intervention scenarios, Jiangsu Province is projected to achieve the carbon neutrality target as scheduled, attaining the peak carbon goal, however, proves challenging to realize by the year 2030.

CONCLUSION: The MLP model exhibits high accuracy in predicting carbon emissions. To expedite the realization of dual carbon goals, proactive government intervention is necessary.

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Author Biography

Chengyu Li, Liaoning Technical University

Public Policy Research Center, Peking University, Beijing

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Published

16-04-2024

How to Cite

1.
Zhao N, Li C. Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model. EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 16 [cited 2024 May 20];11. Available from: https://publications.eai.eu/index.php/ew/article/view/5808