[Predicting Multidimensional Carbon Emissions in Jiangsu Province Based on the BP Neural Network]

Under the "dual carbon" goal， promoting energy conservation and emission reduction is the key to high-quality economic development. Through innovative analysis， we aim to analyze and predict the influencing factors of carbon emissions in Jiangsu Province from multiple dimensions and provide targeted strategies to reduce carbon emissions. Based on the STRIPAT extended model and LMDI model， we construct an index system of influencing factors of carbon emissions in Jiangsu Province and analyze the impact of different indicators on carbon emissions from multiple dimensions. Using ridge regression and factor analysis methods， we obtain the correlation and contribution rate between carbon emissions and various indicators and predict the carbon emissions in Jiangsu Province using the BP neural network algorithm. The results showed that the ranking of the influencing factors of carbon emissions in Jiangsu Province was： energy consumption， GDP， population， proportion of added value of the tertiary industry， energy consumption structure， proportion of added value of the secondary industry， and proportion of added value of the primary industry. Among them， the proportion of added value of the primary industry and the proportion of added value of the secondary industry had a restraining effect on the growth of carbon emissions， while the remaining factors had a promoting effect. At the same time， according to the prediction results， Jiangsu Province should adjust its industrial and energy structure between 2025 and 2035， increasing the proportion of non-fossil energy to 30%， reducing unit CO₂ emissions by 28.6%， and achieving carbon peak. Around 2050， increasing the proportion of non-fossil energy to 50% and reducing unit energy consumption by 46.1% will lead to a rapid decline in CO₂ emissions. Eventually， around 2060， the proportion of non-fossil energy will exceed 80%， unit energy consumption will decrease by 54.6%， and CO₂ emissions will decrease by 77.9%， achieving carbon neutrality.