Research on Neutral Dynamic Network Cross-Efficiency Modeling for Low-Carbon Innovation Development of Enterprises

Zhiying Liu, Danping Wang (), Wanrong Xie, Jian Ma and Aimin Yang ()
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Zhiying Liu: College of Science, North China University of Science and Technology, Tangshan 063210, China
Danping Wang: College of Science, North China University of Science and Technology, Tangshan 063210, China
Wanrong Xie: College of Science, North China University of Science and Technology, Tangshan 063210, China
Jian Ma: College of Science, North China University of Science and Technology, Tangshan 063210, China
Aimin Yang: College of Science, North China University of Science and Technology, Tangshan 063210, China

Sustainability, 2024, vol. 16, issue 22, 1-18

Abstract: 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.

Keywords: low-carbon innovation development efficiency; dynamic networks; neutral cross-efficiency; bootstrap sampling methods (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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