To evaluate the efficiency of innovation development in China’s high-tech manufacturing industry, this paper constructs a two-stage dynamic network cross-efficiency model. This model divides innovation activities into two stages: technology research and development and achievement transformation and introduces a 2-year lag period in the technology research and development stage and a 1-year lag period in the achievement transformation stage. It proposes the overall efficiency and efficiency models for each stage. The model was applied to 30 provinces in China, and the results showed that most provinces have achieved relatively ideal results in the overall efficiency and achievement transformation stage of high-tech manufacturing, while the efficiency in the technology research and development stage is generally lower than that in the achievement transformation stage. It is recommended that enterprises increase their R&D investments, break through technological barriers, and optimize the innovation chain.

To evaluate the effectiveness of the low-carbon innovation development of enterprises, this paper proposes a neutral dynamic network cross-efficiency model and introduces the bootstrap sampling method to correct the model. The model categorizes the low-carbon green innovation R&D activities of enterprises into two distinct stages, as follows: the green R&D stage and the results transformation stage. It then assesses the efficiency of each stage and provides an overall efficiency rating. The model has been applied to a sample of listed Chinese iron and steel enterprises (CISES). The results of the study show that the overall efficiency of low-carbon innovation and development of CISES is on the low side, with the highest efficiency achieved in the green R&D stage, which is less than the lowest efficiency attained in the transformation stage, and most of the enterprises are in the stage of high green R&D and low transformation of the results. The ability of marketization of the R&D results still needs to be strengthened.