【摘要】 区域协同减排对于实现“双碳”目标意义重大，而这依赖于各个区域绿色创新效率有效测度及空间关联效应.针对经典的SBM-DEA模型，必须要忽视投入或产出的变动情况，采用全局的含非期望产出SBM-DEA模型并以此构建评价体系，对2004年—2019年中国省域层面绿色创新效率进行测评，表明中国省域绿色创新效率呈现波动上升，但存在明显区域差异.呈现出东部均值远高于全国水平，中西部、东北地区均低于全国水平的特征；采用空间自相关分析探究空间关联效应，表明整体呈现出“高-高”“低-低”空间正向自相关集聚特征；进一步通过经典的绿色创新“技术-市场-环境规制”驱动模型构建影响因素，采用空间杜宾模型空间关联效应影响因素检验，表明技术进步影响最强，环境规制次之，市场意识最弱.因此，通过技术创新、提高区际环境规制与市场意识的标准等是整体提升中国绿色创新效率的可行路径.更多还原

【Abstract】 Regional coordinated emission reduction is of great significance for achieving the “double carbon” goal, which depends on the effective measurement of green innovation efficiency and spatial correlation effect of each region. In view of the fact that the classic SBM-DEA model must ignore the changes in inputs or outputs, the SBM-DEA model with unexpected outputs is adopted as a whole, and an evaluation system is built to evaluate the green innovation efficiency at the provincial level in China from 2004 to 2019. It shows that the green innovation efficiency in China’s provinces has fluctuated and increased, but there are significant regional differences, showing that the average value in the east is far higher than the national level, while that in the central and western regions and northeast China is lower than the national level. The spatial auto-correlation analysis is used to explore the spatial correlation effect, which shows that the whole space presents a positive auto-correlation clustering feature of “high-high” and “low-low”. Furthermore, the classical green innovation “technology market environmental regulation” driving model is used to build influencing factors, and the spatial Dubin model is used to test the influencing factors, indicating that technological progress has the strongest impact, environmental regulation takes the second place, and market awareness is the weakest. It is a feasible way to improve China’s green innovation efficiency as a whole through technological innovation and improving the standards of regional environmental regulation and market awareness.更多还原