基于BP神经网络的区域双碳目标与路径规划研究

Research on Regional Dual Carbon Targets and Path Planning Based on BP Neural Network

摘要: 随着科学技术的发展，现代化带来了生活方式的便利，但也造成了环境污染问题，如冰川融化和温室效应。因此，实现碳达标和碳中和成为中国现代化经济发展的重要目标。本文旨在建立碳排放量与人口、GDP、能源消费量之间的预测模型。通过SPSS分析发现，能源消费量与各相关变量具有较强相关性。由于样本数量有限，采用MATLAB最小二乘法构建多元非线性回归模型，预测2020~2030年的能源消费量。之后，运用ARIMA(0,2,0)时间序列模型预测2030~2060年的能源消费量。同时，分别建立Logistic阻滞增长人口模型、ARIMA时间序列GDP预测模型、第二产业占比和能源结构的灰色GM(1,1)预测模型。最后，结合粒子群优化BP神经网络算法和灰色GM(1,1)模型，得出碳排放量的预测结果。研究表明，该模型能有效降低预测误差，实现对碳排放量的精准估计，为我国实现碳达标和碳中和目标提供有力支持。

Abstract: With the development of science and technology, modernization has brought convenience to lifestyles, but it has also caused environmental pollution problems, such as glacier melting and greenhouse effect. Therefore, achieving carbon standards and carbon neutrality has become an important goal of China’s modern economic development. This paper aims to establish a prediction model between carbon emissions and population, GDP, and energy consumption. Through SPSS analysis, it is found that energy consumption has a strong correlation with each related variable. Due to the limited number of samples, the MATLAB least squares method is used to construct a multivariate nonlinear regression model to predict energy consumption from 2020 to 2030. After that, the ARIMA(0,2,0) time series model is used to predict energy consumption from 2030 to 2060. At the same time, the Logistic retardation growth population model, the ARIMA time series GDP prediction model, and the gray GM(1,1) prediction model of the proportion of secondary industry and energy structure are established respectively. Finally, the prediction results of carbon emissions are obtained by combining the particle swarm optimization BP neural network algorithm and the gray GM(1,1) model. Research shows that the model can effectively reduce prediction errors, achieve accurate estimation of carbon emissions, and provide strong support for my country to achieve carbon standards and carbon neutrality goals.