| 基于BP神经网络的江苏省多维度碳排放预测 |
| 摘要点击 1580 全文点击 87 投稿时间：2024-05-19 修订日期：2024-07-31 |
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| 中文关键词 碳排放量影响因素 多维度 LMDI模型 岭回归 BP神经网络 |
| 英文关键词 index system of factors affecting carbon emissions multiple dimensions LMDI model ridge regression BP neural network |
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| 中文摘要 |
| “双碳”目标下，推进节能减排是经济高质量发展的关键. 通过创新提出在多维度视角下对江苏省的碳排放量进行影响因素分析和预测，针对性给出降低碳排放的策略. 基于STRIPAT扩展模型和LMDI模型，构建江苏省碳排放量影响因素指标体系，多维度分析不同指标因素对碳排放量的影响. 运用岭回归和因子分析方法，得到碳排放量与各指标间的关联度和贡献率，采用BP神经网络算法对江苏省碳排放量进行预测. 结果表明，江苏省碳排放量影响因素程度排名为：能源消耗量、GDP、人口、第三产业增加值占比、能耗结构、第二产业增加值占比和第一产业增加值占比. 其中第一产业增加值占比和第二产业增加值占比这两个因素对碳排放量的增长起到了抑制作用，其余因素均为促进作用. 同时根据预测结果，江苏省应当在2025~2035年间调整产业和能源结构，将非化石能源占比增至30%，单位CO2排放下降28.6%，实现碳达峰. 在2050年前后，将非化石能源占比提高到50%，单位能耗下降46.1%， 则CO2排放进入快速下降阶段. 最终，在2060年前后将非化石能源占比超过80%，单位能耗下降54.6%，CO2排放减少77.9%，达到碳中和. |
| 英文摘要 |
| 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. |
