| 基于贝叶斯模型平均与机器学习的中国省域碳达峰预测 |
| 摘要点击 1340 全文点击 92 投稿时间：2024-04-12 修订日期：2024-07-25 |
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| 中文关键词 碳达峰 贝叶斯模型平均（BMA） 机器学习 碳排放核算 预测 |
| 英文关键词 carbon peak Bayesian model averaging （BMA） machine learning carbon emission accounting prediction |
| DOI 10.13227/j.hjkx.20250614 |
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| 中文摘要 |
| 采用2005~2021年的面板数据测算了中国30个省域化石能源消费和净电力消费的碳排放，基于贝叶斯模型平均法筛选碳排放关键影响因素，运用机器学习模型预测各省域碳达峰实施方案是否能确保其2030年前实现碳达峰. 结果表明，非化石能源消费和天然气消费分别占能源消费总量的比例、第一和第三产业比例以及私人汽车拥有量5个变量是省域碳排放的关键影响因素；装袋法、随机森林、支持向量机和BP神经网络这4种机器学习模型中，BP神经网络预测中国各省域碳排放的效果最佳；如果产业结构调整和重点领域能源消费延续2017~2021年的发展趋势，各省域碳达峰实施方案提出的能源消费结构优化目标可确保北京等21个省域2030年前实现碳达峰，内蒙古等9个省域则需进一步优化能源消费结构、加强产业结构调整以及控制重点领域能源消费才能在2030年前实现碳达峰. |
| 英文摘要 |
| Based on carbon emissions from consumption of fossil energy and electricity in 30 Chinese provinces from 2005 to 2021， together with key influencing factors of carbon emissions selected using Bayesian model averaging （BMA）， machine learning models were used to predict whether the plans for carbon peak in each province would ensure their accomplishment of carbon peak before 2030. The results showed that five variables， namely the proportion of non-fossil energy consumption and natural gas consumption to the total energy consumption， the proportion of the primary and tertiary industries， and the ownership of private cars， were key influencing factors on provincial carbon emissions. Among the four machine learning models of bagging， random forest （RF）， support vector machine （SVM）， and back propagation neutral networks （BPNN）， BPNN had the best performance in predicting provincial carbon emissions. If the industrial structure adjustment and energy consumed in key areas continue their trends from 2017 to 2021， the optimization targets of energy consumption structure proposed in each province's plan for carbon peak can ensure that 21 provinces such as Beijing will achieve carbon peak before 2030， while nine provinces such as Inner Mongolia need to further optimize the energy consumption structure， strengthen industrial structure adjustment， and control the amount of energy consumed in key areas to achieve carbon peak before 2030. |
