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This study focused on 26 cities within the Yangtze River Delta urban agglomeration and employed a comprehensive analytical approach, utilizing the Super-SBM model, Malmquist index, Gini coefficient, standard deviation ellipse model, and Tobit model based on statistical data from 2010 to 2023. The study systematically analyzed regional disparities, dynamic evolutionary characteristics, and the factors influencing water environment governance efficiency. Key findings included that the overall water governance efficiency in the Yangtze River Delta urban agglomeration exhibited a fluctuating upward trend, with a notable dip in 2022 attributed to a decline in scale efficiency (SE), with an average value of 1.121. Spatially, water governance efficiency followed a "high-west, low-east" pattern: western cities experienced significant efficiency gains through ecological compensation and green industrial transfer, while eastern cities relied on technological innovation to drive improvements. The largest intergroup disparity in water environment governance efficiency occurred between the Anhui Province and Zhejiang Province, with an average value of 0.067. Within the Jiangsu-Shanghai region, the internal equilibrium was the highest, with an average value of 0.055. The hyper-variation density contributed the most to the overall difference, with an average annual value of 38.053. Over the study period, the center of gravity of the standard deviation ellipse for water environment governance efficiency shifted southwestward, suggesting a trend toward a more aggregated spatial distribution. Population size, urbanization level, and industrial structure showed significant negative correlations with water environment governance efficiency in the Yangtze River Delta urban agglomeration. In contrast, innovation levels, environmental management, openness to external markets, and industrial scale positively drove water environment governance efficiency.

, authors=Hui-shan FU¹, Jia-lin LI^{1, 2, *}, Yong-chao LIU^{1, 2}, Luo-dan CAO¹, Xiao ZHU¹, Jiang-tao KONG¹, authorsList=Hui-shan FU, Jia-lin LI, Yong-chao LIU, Luo-dan CAO, Xiao ZHU, Jiang-tao KONG, authorCompany=null, correspAuthors=Jia-lin LI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314923104934134316, articleId=1314923104376291882, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=长三角城市群水环境治理效率的区域差异及动态演进, columnId=1234106419604157142, journalTitle=中国环境科学, columnName=环境影响评价与管理, runingTitle=null, highlight=null, articleAbstract=

以长三角城市群26个城市为研究对象，基于2010~2023年统计数据，综合运用Super-SBM模型、Malmquist指数、Dagum基尼系数、标准差椭圆模型以及Tobit模型，系统分析了水环境治理效率的区域差异、动态演进特征以及影响因素．研究发现，长三角城市群治理效率整体呈波动上升趋势，2022年因规模效率（SE）下降水环境治理效率出现低谷，水环境治理效率均值为1.121．长三角城市群水环境治理效率的空间分布呈现“西高-东低”分化，西部城市因生态补偿与产业绿色转移效率提升显著，东部城市依赖技术创新实现效率提高．长三角城市群水环境治理效率的整体空间差异中，安徽省-浙江省水环境治理效率组间差异最大，其均值为0.067；在组内差异对比中苏沪地区内部均衡性最优，均值为0.055；超变密度贡献率对整体差异的贡献率最大，年均值为38.053．研究期内，长三角城市群水环境治理效率的标准差椭圆重心向西南迁移，空间分布向相对聚集转变．人口规模、城镇化水平和产业结构对长三角城市群水环境治理效率呈现显著负向关联，创新水平、环境管理、对外开放程度和工业规模对长三角城市群水环境治理效率起到推动作用．

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傅慧珊（2002-），女，浙江温州人，宁波大学硕士研究生，主要研究方向：水生态环境评估．2969115162@qq.com.

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傅慧珊（2002-），女，浙江温州人，宁波大学硕士研究生，主要研究方向：水生态环境评估．2969115162@qq.com.

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