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本案例以天津市为例，探索基于机器学习算法的区域物流业碳排放量预测方法。首先，从统计年鉴采集天津市物流业碳排放量相关数据，对数据进行描述性统计分析，对相关特征变量进行相关性分析，检验是否存在多重共线性问题。其次，针对存在的多重共线性问题，采用Lasso回归进行特征选择，识别影响天津市物流业碳排放量的关键影响因子/特征。然后，基于不同的机器学习算法，例如，支持向量机、神经网络等，构建碳排放量预测模型，并对模型的预测性能进行比较评估。最后，选择预测性能较好的模型对天津市2021年和2022年的物流业碳排放量进行预测，并提出针对性的碳减排对策建议。

" ["minPrice"]=> string(4) "0.00" ["maxPrice"]=> string(4) "0.00" ["discountId"]=> string(1) "0" ["images"]=> array(3) { ["large"]=> string(76) "http://www.chinadatacase.com/files/course/2023/10-20/1402102bdaa3765841.jpeg" ["middle"]=> string(76) "http://www.chinadatacase.com/files/course/2023/10-20/1402102be3dc964415.jpeg" ["small"]=> string(76) "http://www.chinadatacase.com/files/course/2023/10-20/1402102bea32394595.jpeg" } ["ratingNum"]=> string(1) "0" ["rating"]=> string(1) "0" ["hitNum"]=> string(3) "611" ["hotSeq"]=> string(1) "0" ["maxPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["minPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["minDisplayPrice"]=> string(4) "0.00" ["maxDisplayPrice"]=> string(4) "0.00" ["minDisplayPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["maxDisplayPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["canManage"]=> bool(false) ["peopleShowNum"]=> string(3) "113" ["isMember"]=> bool(false) ["status"]=> string(9) "published" ["orgId"]=> string(1) "1" ["orgCode"]=> string(2) "1." ["recommendWeight"]=> string(1) "0" ["recommendedTime"]=> string(1) "0" ["createdTime"]=> string(25) "2023-10-11T15:18:21+08:00" ["updatedTime"]=> string(25) "2025-12-10T06:03:49+08:00" ["product"]=> array(7) { ["id"]=> string(4) "1819" ["targetType"]=> string(6) "course" ["title"]=> string(96) "“碳”测未来——基于机器学习的区域物流业碳排放量预测实验教学案例" ["owner"]=> string(1) "8" ["createdTime"]=> string(10) "1697008701" ["updatedTime"]=> string(10) "1698738114" ["target"]=> array(17) { ["id"]=> string(4) "1919" ["type"]=> string(6) "normal" ["title"]=> string(96) "“碳”测未来——基于机器学习的区域物流业碳排放量预测实验教学案例" ["subtitle"]=> string(0) "" ["summary"]=> string(799) "

本案例以天津市为例，探索基于机器学习算法的区域物流业碳排放量预测方法。首先，从统计年鉴采集天津市物流业碳排放量相关数据，对数据进行描述性统计分析，对相关特征变量进行相关性分析，检验是否存在多重共线性问题。其次，针对存在的多重共线性问题，采用Lasso回归进行特征选择，识别影响天津市物流业碳排放量的关键影响因子/特征。然后，基于不同的机器学习算法，例如，支持向量机、神经网络等，构建碳排放量预测模型，并对模型的预测性能进行比较评估。最后，选择预测性能较好的模型对天津市2021年和2022年的物流业碳排放量进行预测，并提出针对性的碳减排对策建议。

" ["cover"]=> array(3) { ["large"]=> string(76) "http://www.chinadatacase.com/files/course/2023/10-20/1402102bdaa3765841.jpeg" ["middle"]=> string(76) "http://www.chinadatacase.com/files/course/2023/10-20/1402102be3dc964415.jpeg" ["small"]=> string(76) "http://www.chinadatacase.com/files/course/2023/10-20/1402102bea32394595.jpeg" } ["status"]=> string(9) "published" ["studentNum"]=> string(3) "113" ["discountType"]=> string(8) "discount" ["discount"]=> string(2) "10" ["minCoursePrice"]=> string(4) "0.00" ["maxCoursePrice"]=> string(4) "0.00" ["defaultCourseId"]=> string(4) "1937" ["productId"]=> string(4) "1819" ["goodsId"]=> string(4) "1819" ["minCoursePrice2"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["maxCoursePrice2"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } } } ["extensions"]=> array(3) { [0]=> string(8) "teachers" [1]=> string(14) "recommendGoods" [2]=> string(10) "isFavorite" } ["specs"]=> array(1) { [0]=> array(26) { ["id"]=> string(4) "1837" ["goodsId"]=> string(4) "1819" ["targetId"]=> string(4) "1937" ["title"]=> string(0) "" ["seq"]=> string(1) "1" ["status"]=> string(9) "published" ["price"]=> string(4) "0.00" ["coinPrice"]=> string(4) "0.00" ["usageMode"]=> string(7) "forever" ["usageDays"]=> string(1) "0" ["usageStartTime"]=> string(1) "0" ["usageEndTime"]=> string(1) "0" ["buyableStartTime"]=> string(1) "0" ["buyableEndTime"]=> string(1) "0" ["buyableMode"]=> NULL ["buyable"]=> string(1) "1" ["maxJoinNum"]=> string(1) "0" ["services"]=> array(0) { } ["priceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["displayPrice"]=> string(4) "0.00" ["displayPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["isMember"]=> bool(false) ["access"]=> array(2) { ["code"]=> string(14) "user.not_login" ["msg"]=> string(15) "用户未登录" } ["hasCertificate"]=> bool(false) ["learnUrl"]=> string(43) "http://www.chinadatacase.com/my/course/1937" ["teachers"]=> array(1) { [0]=> array(6) { ["id"]=> string(1) "8" ["nickname"]=> string(7) "mingzhu" ["title"]=> string(6) "教师" ["uuid"]=> string(40) "66f930e6f2d349b45f48f24e125e05d3a92fb8d1" ["destroyed"]=> string(1) "0" ["avatar"]=> array(3) { ["small"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/09105602405b816729.png" ["middle"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/091056023132077454.png" ["large"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/0910560220f8152273.png" } } } } } } “碳”测未来——基于机器学习的区域物流业碳排放量预测实验教学案例 - 中国经管实验教学案例平台

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