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在我国经济发展方式发生转变的新时代背景下，新发展理念成为引领经济增长与社会发展的指挥棒。党的二十大报告中指出加快发展方式绿色转型与实施创新驱动发展战略。绿色创新成为贯彻落实新发展理念，融合绿色转型与创新驱动的重要手段，能够有效破解资源与环境束缚，为促进经济高质量发展提供强大动力。长三角城市群作为我国经济发展与改革开放的排头兵，经济总量约占全国1/4,以其为对象研究绿色创新在该区域的发展情况有助于为东部地区乃至全国的绿色创新发展提供良好范例。

基于此，本文首先从长三角城市群41个地级市的绿色创新投入、期望产出、非期望产出现状出发，利用超效率-SBM模型测度出长三角城市群的绿色创新效率并利用K-均值聚类方法将41个地级市的绿色创新效率划分为高、中、低效率地区；接着利用核密度估计分析长三角城市群整体与江苏、浙江、安徽的绿色创新效率的动态演变与绝对差异变化；利用Dagum基尼系数分析上述区域的相对差异变化并对差异来源进行分解；其次构建面板Tobit模型，从经济、制度、技术三个角度筛选出七个影响因素，在对七个影响因素对绿色创新效率的作用机理进行分析的基础上，实证分析出影响因素对长三角城市群整体以及高、中、低效率地区绿色创新效率的影响结果。主要得出如下结论：

（1）长三角城市群各地区绿色创新的投入与期望产出均在逐步增长，非期望产出大幅下降，同时也存在如部分城市创新资源投入不足、资源投入不平衡、发展差异较大等问题；

（2）长三角城市群整体与上海市、江苏省、浙江省绿色创新效率处于增长趋势，正逐步接近有效值，安徽省绿色创新效率有小幅下降。上海市绿色创新效率始终高于其他地区，接下来依次是江苏省、安徽省、浙江省；

（3）通过核密度估计发现，长三角城市群绿色创新效率呈现“中心小幅右移，但峰度较宽”的特征，说明域内各地区绿色创新效率呈增长趋势，但绝对差异仍然较大。通过测算Dagum基尼系数发现，长三角城市群整体以及江苏省和浙江省内绿色创新效率相对差异有所下降，安徽省内绿色创新效率相对差异有所上升，江苏省与浙江省之间相对差异缩小，但安徽省与其余两省相对差异扩大，对差异来源进行分解发现，2015年之前，造成绿色创新效率区域差异的来源按贡献率依次为：区域间差异、区域内差异、超变密度；2015年之后变为超变密度、区域内差异、区域间差异；

（4）在对影响因素进行实证分析时发现，对长三角城市群整体来说，经济发展、对外开放、政府支持对绿色创新效率呈负向显著影响，产业结构、环境规制、受教育水平、信息化水平呈正向显著影响；在分区域分析时，经济发展对高效率地区起正向显著影响，对中、低效率地区起负向显著影响；对外开放对中、低效率地区起负向显著影响，对高效率地区影响不显著；产业结构对高效率地区起正向显著影响，对中、低效率地区影响不显著；环境规制对所有区域都呈显著正向影响；政府支持对高效率地区起正向显著影响，对中、低效率地区起负向显著影响；受教育水平对中、低效率地区起正向显著影响，对高效率地区不显著；信息化水平对高、中效率地区起正向显著影响，对低效率地区不显著；

（5）基于上述结论，本文提出如下建议：第一，加强区域协调能力；第二，优化经济结构；第三，提高对外开放水平；第四，提高技术创新能力；第五，建立和完善绿色创新政策体系。

In the contemporary era marked by the transition of China's economic development paradigm, the novel developmental paradigm has emerged as the guiding principle for promoting economic prosperity and societal progress. The lecture of the 20th National Congress of the Communist Party of China underscored the imperative of Expediting the environmental-friendly reorientation of our development approach and fostering a strategy driven by innovation.Embodying the novel developmental paradigm, green innovation has emerged as a pivotal approach for integrating environmental sustainability with innovation-led progress, thus effectively implementing the new development concept., which can effectively break the constraints of resources and environment, and provide a strong impetus for promoting high-quality development. As the vanguard of China's reform and opening up , the study of green innovation in the Yangtze River Delta city cluster is helpful to provide a good example for the development of green innovation in the eastern region and even the whole country.

Drawing upon the present circumstances regarding green innovation inputs, anticipated outputs, and unintended outputs across 41 prefecture-level cities within the Yangtze River Delta urban .the paper use the Super-SBM model to assess the efficiency of green innovation within this region. And uses the K-means clustering method to divide the green innovation efficiency of 41 prefecture-level cities into high, medium and low efficiency areas. Secondly, to further investigate the dynamic evolution and absolute variation in green innovation efficiency among the study area, as well as Jiangsu, Zhejiang, and Anhui provinces, kernel density estimation was utilized. Additionally, the Dagum Gini coefficient was employed to explore the variations in relative differences within these regions and dissect the sources of these disparities.Secondly, a panel Tobit model is constructed to screen out seven influencing factors from the three perspectives of economy, system and technology. After analyzing the influence mechanism of seven factors on green innovation efficiency, an empirical study is conducted to explore the impact of these factors on the green innovation efficiency of the study area , as well as on the high, medium, and low efficiency regions within it. The main conclusions are as follows:

(1) Within the various regions of the study area, the input and anticipated outputs related to green innovation are exhibiting a gradual upward trend, whereas the unintended outputs are experiencing a notable decrease.At the same time, there are also some problems, such as insufficient input of innovation resources in some cities, imbalance of resource input, and large differences in development.

(2) The green innovation efficiency of the study area as a whole is in a growing trend with that of Shanghai, Jiangsu and Zhejiang provinces, and is gradually approaching the effective value, while the green innovation efficiency of Anhui Province has a slight decline. Shanghai's green innovation efficiency is always higher than other regions, followed by Jiangsu Province, Anhui Province and Zhejiang Province.

(3) Utilizing kernel density estimation, it is observed that the green innovation efficiency within the study area exhibits a distinct pattern of "a slight shift of the center towards the right, accompanied by an increase in kurtosis."indicating that the green innovation efficiency of all regions in the region shows an increasing trend, but the absolute difference is still large. By calculating Dagum Gini coefficient, it is found that the relative difference of green innovation efficiency in the Yangtze River Delta urban agglomeration as a whole and in Jiangsu and Zhejiang provinces decreases, while the relative difference of green innovation efficiency in Anhui Province increases, and the relative difference between Jiangsu and Zhejiang Province narrates, but the relative difference between Anhui and the other two provinces expands. The decomposition of the sources of difference shows that, before 2015, According to the contribution rate, the sources of regional differences in green innovation efficiency are: inter-regional differences, intra-regional differences, and super-variable density; After 2015, it became super-variable density, intra-regional difference and inter-regional difference;

(4) In the empirical analysis of the influencing factors, It is discovered that, in terms of the Yangtze River Delta urban agglomeration as a collective entity, the level of economic development, opening up and government support have a significant negative impact on green innovation efficiency, Meanwhile, the industrial composition and educational attainment exert a noteworthy positive influence. In the sub-regional analysis, it is evident that economic prosperity has a positive and substantial impact on the regions with high efficiency, and a negative and significant impact on the medium and low efficiency areas. Exposure to external markets has a notable negative influence on regions with medium and low efficiency, but has no significant impact on the high efficiency areas. The industrial composition exhibits a positive and significant influence on regions with high efficiency, but has no significant effect on the medium and low efficiency areas. Government support has a positive and significant impact on high efficiency areas and a negative and significant impact on medium and low efficiency areas. The education level has a positive and significant effect on the middle and low efficiency areas, but not on the high efficiency areas. The information level has a significant positive effect on the high and low efficiency areas, but not on the medium area;

(5) Drawing upon the aforementioned conclusions, this paper offers the following recommendations.: First, strengthen regional coordination ability and shorten regional differences; Second, optimize the economic structure; Third, expand our openness to the external world further; Fourth, enhance technological innovation capabilities; Fifth, establish and improve the green innovation policy system.