基于SVR-SHAP算法的新质生产力驱动因素分析——以美丽中国建设先行区为例Analysis of the Driving Factors of New Quality Productive Forces Based on SVR-SHAP Algorithm——Take the Beautiful China Building Pilot Zone as an Example
魏峰,张怡,王晶茹,孙巳玉
摘要(Abstract)：
2024年1月，中共中央、国务院印发了《关于全面推进美丽中国建设的意见》,提出要以京津冀、长江经济带、粤港澳大湾区、黄河流域和长三角地区等五个区域为重点，建设美丽中国先行区。美丽中国建设离不开新质生产力作用的发挥，弄清新质生产力的驱动因素是助推美丽中国建设的重要环节。由此，本文首先采取投影寻踪模型(RAGA-PP)精准测度了2012—2022年美丽中国建设五大先行区的新质生产力水平，接着以文本分析构建其驱动因素，并在比较九种机器学习模型性能的基础上选用SVR-SHAP算法考察驱动因素重要性排序及交互作用。研究发现，五大先行区新质生产力水平稳步上升但区域间差异逐年增大；新质生产力提升的关键驱动因素是市场需求和数字经济；驱动因素间具有显著的交互作用；先行区中黄河流域数字经济的驱动作用相对较弱。据此本文提出，建设美丽中国需加强区域间协同发展，坚持激发市场需求与促进数字经济并行，重视驱动因素间的交互作用和依赖关系，并且积极推进黄河流域的数字化建设。
关键词(KeyWords)： 新质生产力;美丽中国建设;五大先行区;RAGA-PP算法;SVR-SHAP算法
基金项目(Foundation):
作者(Author): 魏峰,张怡,王晶茹,孙巳玉
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- (1)这9种模型包括六种非线性机器学习方法和三种线性机器学习方法,分别为支持向量回归(SVR)、随机森林(RF)、K-近邻(KNN)、梯度提升回归树(GBRT)、极端梯度提升树(XGB)、决策树(DT)、岭回归(Ridge Regression)、套索回归(Lasso)和弹性网回归(EN)。以上9种机器学习模型涵盖了常见类型的统计学习模型,模型成熟、结构可靠且可解释性强。
- (2)本文分别采取MATLAB程序进行RAGA-PP方法、Python程序进行熵权法,测得2012—2022年各省新质生产力水平。
- (3)该图可向作者联系索取。
- (4)相关地图可向作者联系索取。
- (5)该部分与SHAP算法均使用Python程序,可向作者索取。
- (6)SVR方法虽然是参数方法,但是其对模型参数进行了L2惩罚,这大幅减小了指标间的共线性的负面影响。另外,相比LASSO算法,SVR更能处理非线性数据和噪声数据,具有较高的预测准确性和稳健性,基于SVR对异常值不敏感且数据集为中等规模,综合考虑下,本文选择SVR模型进行后续研究。
- (7)C:决定模型复杂度;epsilon:决定模型容忍度;gamma决定核函数参数。
- (8)alpha:弹性网的正则化强度;L1_ratio:拉格朗日乘子与惩罚项之比。
- (9)max_depth:最大深度;min_samples_leaf:内部节点再划分所需的最小样本数;min_samples_split:叶子节点最少需要包含的样本数。
- (10)本文均按照平均SHAP绝对值的大小进行排序。
- (11)五大先行区新质生产力水平的熵权法测算结果见附录。