科学评估工业水资源效率并识别其影响因素是缓解水资源供需矛盾的重要前提。基于非期望产出的超效率EBM（Epsilon-based measure）模型测度了沿黄九省2010—2019年的工业水资源效率，然后构建Tobit模型分析其全局影响因素，在此基础上选取显著性影响因素纳入时空地理加权回归模型，探究各影响因素的时空异质性。结果表明：沿黄九省工业水资源效率均值为0.77，未达到有效水平，但呈现出波动上升态势。各省份之间效率差异明显，山东最高（1.064），宁夏最低（0.424）。在影响因素的宏观层面，技术水平和工业化程度对工业水资源效率攀升有正向促进作用；水资源禀赋、社会发展水平、工业用水强度和政府规制程度皆存在抑制效应，但显著程度不同，且高显著性影响因素的效应具有不同的时间演化趋势；在微观层面，各省份工业水资源效率的主导影响因素及其效应存在空间差异。

Scientifically evaluating the efficiency of industrial water resources and identifying its influencing factors is an important prerequisite for alleviating the current contradiction between water supply and demand. Based on the super-efficiency EBM model of undesired output， we measure the industrial water resources efficiency along the Yellow River and nine provinces from 2010 to 2019， and then construct a Tobit model to analyze its global influencing factors. On this basis， significant influencing factors are selected and assessed in space and time. Geographically weighted regression model is used to explore the spatiotemporal heterogeneity of each influencing factor. The results show that the overall average value of industrial water resources efficiency along the Yellow River and nine provinces is 0.77， which has not reached the effective level， but shows a fluctuating upward trend. The efficiency difference between provinces is obvious， and Shandong is the highest （1.064）， and Ningxia is the lowest （0.424）. At the macro level of influencing factors， the technological level and the degree of industrialization have a positive effect on improving the overall industrial water resources efficiency. In addition， the effects of highly significant influencing factors have different temporal evolution trends. At the micro level， the main influencing factors and their roles of industrial water resources efficiency in different provinces are spatially different. Based on this， we put forward relevant improvement suggestions for each province.