绿色创新效率测度及时空分异特征：以长江经济带制造业为例

Measurement and Spatial-temporal Variation of Green Innovation Efficiency: Taking the Manufacturing Industry in the Yangtze River Economic Belt as an Example

摘要:

有别于已有研究，采用超效率EBM模型测算纳入能源要素的区域绿色创新效率，并从空间相关角度，采用探索性空间数据分析（ESDA）方法分析区域绿色创新效率的时空差异。以长江经济带制造业为例，运用2008－2019年有关面板数据构建绿色创新效率测算指标体系，实证结果发现：（1）各年份内长江经济带制造业绿色创新效率值均小于1，未实现有效生产，其中下游地区的创新效率最高，中上游地区的创新效率较低；（2）各省市制造业绿色创新效率空间差异明显，且与其经济发展水平的空间分布格局表现出协调一致性；（3）各省市制造业绿色创新效率在空间上表现出正的自相关性，存在空间集聚特征。根据研究结论，分别从提升绿色创新水平、强化空间集聚效应和推动区域趋同化发展3个角度提出促进长江经济带制造业绿色创新的对策建议。

Abstract:

Different from the existing studies, this paper adopts super efficiency EBM model to measure the regional green innovation efficiency which considers energy factors, and uses the exploratory spatial data analysis (ESDA) method to analyze spatial-temporal variation of regional green innovation efficiency from the spatial correlation perspective. Taking the manufacturing industry in the Yangtze River Economic Belt as an example, the paper uses the panel data from 2008 to 2019 to construct the measurement index of green innovation efficiency, empirical results indicate that: (1)The green innovation efficiency of manufacturing industry in the Yangtze River Economic Belt is less than 1, which reflects the failure to achieve efficient production, and among them, the green innovation efficiency of manufacturing industry in the downstream region is the highest, and that in the middle and upstream region is poor. (2)The green innovation efficiency of manufacturing industry has obvious spatial heterogeneity among different regions in the Yangtze River Economic Belt, and the spatial distribution pattern with its economic development level is coordination. (3)The green innovation efficiency shows positive spatial autocorrelation with spatial agglomeration characteristics. On this basis, the paper proposes policy implications for contributing green innovation of manufacturing industry in the Yangtze River Economic Belt from the perspectives of improving the level of green innovation, strengthening the spatial clustering effect and promoting regional convergence development.