基于PSO-BP神经网络的山西省碳排放预测

山西作为能源使用和碳排放大省，推动“双碳”战略对全国具有重要示范意义。基于IPCC（政府间气候变化专门委员会）排放系数法测算山西省2000—2020年的碳排放量，运用Tapio脱钩模型分析碳排放与经济发展之间的脱钩关系，利用LMDI法对影响碳排放变化的因素进行分解，采用PSO-BP神经网络模型对山西省的碳排放量进行模拟和预测。结果表明：2000—2020年山西省碳排放量呈增长趋势，碳排放强度呈下降趋势，脱钩系数为0.585，整体处于弱脱钩状态。经济增长是碳排放量增长的决定因素，而产业结构与能源强度的优化调整是抑制碳排放的主导因素。引入PSO（粒子群优化算法）有效提高了BP神经网络的预测精度。预测结果显示，在基准情景、低碳情景和强化低碳情景下，山西省碳排放分别于2032年、2029年和2027年达峰。针对预测结果，提出了相关政策建议。

Prediction of carbon emissions in Shanxi Province based on PSO-BP neural network

Shanxi, as a major province of energy use and carbon emission, has an important demonstration significance for the whole country to promote the "dual carbon" strategy. The carbon emissions of Shanxi Province from 2000 to 2020 were calculated based on IPCC emission coefficient method. Tapio decoupling model was used to analyze the decoupling relationship between carbon emissions and economic development, LMDI method was used to decompose the factors affecting carbon emission changes, and PSO-BP neural network model was used to simulate and forecast the carbon emissions of Shanxi Province. The results showed that the carbon emission in Shanxi Province increased during 2000-2020, while the carbon emission intensity decreased, and the decoupling coefficient was 0.585, indicating a weak decoupling state as a whole. Economic growth was the determining factor of carbon emission growth, and the optimization and adjustment of industrial structure and energy intensity was the leading factor to restrain carbon emission. The introduction of particle swarm optimization (PSO) improved the prediction accuracy of BP neural network effectively. The predicted results showed that carbon emissions in Shanxi Province would peak in 2032, 2029 and 2027 under three scenarios: baseline scenario, low carbon scenario and intensive low carbon scenario, respectively. In view of the forecast results, relevant policy suggestions were put forward.