Evaluating Green Innovation Efficiency of High-Tech Industries: Extended DEA Models Integrating Multiple Subjects and Prospect Theory

The green innovation development of high-tech industries is a crucial driver for enhancing China’s core competitiveness in science and technology. However, diverse preferences of multiple subjects often lead to inconsistent demands for the coordinated development of environmental protection and innovation efficiency. The purpose is to construct extended DEA models integrating environmental constraints and prospect theory to evaluate green innovation efficiency from multi-subject perspectives, clarify regional disparities, and provide targeted policy recommendations. By determining ideal production modes for each subject and calculating preference utility values, this paper conducts empirical analysis using provincial data of China’s high-tech industries in 2021, complemented by comparative and sensitivity analyses. Key findings include: (i) Green development policies significantly improve green innovation efficiency, but gaps exist between actual and ideal production modes across provinces. (ii) Regional disparities are pronounced: the Eastern exhibits the highest efficiency, followed by the Central and the Western. (iii) Government intervention effectively balances industrial innovation demands and citizen environmental concerns, reducing efficiency losses compared to single-subject evaluations. (iv) Sensitivity analysis reveals that stricter environmental constraints and loss aversion enhance utility values, particularly in underdeveloped regions. The tailored strategies are further proposed, including strengthening cross-regional collaboration, optimizing resource allocation, and refining policy tools to align multi-subject interests.