Article(id=1284878803533008901, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_yX7EH8Bu, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.1346, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1784268578656, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268578655, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268578655, creator=admin, updateTime=1784268578655, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', 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LSTM)相结合的模型，以提高碳排放量的预测精度。首先，使用可拓展的随机性环境影响回归模型(Stochastic T Impacts by Regression on PAT,STIRPAT)选取年末总人口数量、人均GDP等影响因素；其次，通过皮尔逊相关系数和LASSO回归模型进行初步和二次筛选；再次，对数据进行预处理并进行MEMD分解，进而对分解出的不同频率的IMF分量以及残差项RES分别建立预测模型；最后，使用SSA对LSTM模型的神经元个数、迭代次数、学习率等参数进行寻优，并将数据汇总整合，对比分析其他预测模型的预测结果。结果显示，LASSO-MEMD-SSA-LSTM模型的预测值与实际值最为接近，其平均绝对误差、均方根误差、平均相对百分比误差分别为8.45%、8.32%、0.85%,均优于其他模型。, authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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