Urban innovation capacity is increasingly critical to city development, and quantitative assessments of innovative cities’ innovation capability can be achieved via the composite index method, which fully integrates multidimensional indicators. This study develops a hierarchical data envelopment analysis (H-DEA) method to establish a composite index evaluation model for innovation capacity, which features flexible and objective two-level indicators—an advantage that avoids subjective weight assignment and adapts well to the hierarchical structure of innovation evaluation indicators. The proposed H-DEA model is applied to evaluate 67 innovative cities in China, yielding composite scores and rankings that are further compared with those from the traditional weighting method. Sensitivity analysis is conducted by adjusting different upper and lower bounds of the H-DEA model to verify its robustness. Additionally, these 67 cities are divided into four regions, with region-specific weights assigned to the evaluation indicators in the model. The results show that the eastern region has the highest average innovation capacity (0.3783), where technological innovation (weight 0.27) serves as a key driving force; the western region has the lowest average innovation capacity (0.3235), and its innovative cities should prioritize improving outcome transformation capacity (weight 0.1357). Overall, technological innovation receives the highest average weight (0.2422), while outcome transformation capacity gets the lowest (0.1647).
Zhang et al. (Tue,) studied this question.