Evaluation of Green Technology Innovation Efficiency, Regional Differences and Influencing Factors of Industrial Enterprises in China: Based on a Two-Stage Perspective
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Keywords

Green technology innovation efficiency
Super-efficient EBM-ML model
Dagum Gini coefficient model
Spatial Durbin model

DOI

10.26689/pbes.v9i1.13378

Submitted : 2026-01-11
Accepted : 2026-01-26
Published : 2026-02-10

Abstract

The symposium on industrial green and low-carbon development held by the Ministry of Industry and Information Technology in January 2024 emphasized the need to steadily promote carbon reduction in the industrial sector, and improving the efficiency of green technology innovation in industrial enterprises has important practical significance in promoting their green transformation and upgrading. Therefore, this article uses inter-provincial panel data from 2005 to 2022, and constructs super efficiency EBM model, ML index model, Dagum Gini coefficient model, and spatial Durbin model to measure, decompose, analyze the sources of differences and influencing factors in the two-stage efficiency of industrial enterprises. The results show that the efficiency of technology research and development is higher than the efficiency of technology transformation, and the efficiency level of each stage is directly proportional to the economic development level of the region. The scale efficiency level of each stage remains stable at 0.9 or above, and the low pure efficiency is an important reason for the significantly low efficiency. The efficiency level of each stage shows an increasing trend from 2005 to 2022, and the efficiency level of each stage in the eastern region is higher than that of other regions. The efficiency level of China’s research and development stage shows a good development trend, but there is insufficient coordination between technological efficiency and technological progress in the transformation stage, and there are significant bottlenecks in the technological progress index. The differences in efficiency levels between different stages mainly come from the differences in efficiency levels between regions, with more significant differences between the eastern region and other regions. The industrial structure and market competitiveness have a significant promoting effect on efficiency levels, while environmental regulations have a significant inhibitory effect on efficiency levels.

References

Liu J, Zhang Y, Li J, et al., 2024, A Study on the Improvement of Innovation Efficiency of National High-Tech Industrial Development Zones in Sichuan and Chongqing. Journal of Chongqing College of Arts and Sciences (Social Science Edition), 43(6): 80–95.

Liang W, Wang W, Liu Z, 2022, Analysis of Coupling and Coordination of New Urbanization and Logistics Industry in Yangtze River Delta. Journal of Chongqing College of Arts and Sciences (Social Science Edition), 41(3): 54–69.

Tang Q, 2020, Measurement and Empirical Research on the Efficiency of Circulation Industry. Journal of Chongqing College of Arts and Sciences (Social Science Edition), 39(4): 50–59.

Liu D, 2019, Research on Financing Efficiency of Listed Companies in Textile Industry based on SBM-Malmquist-Tobit Model. Journal of Chongqing College of Arts and Sciences (Social Science Edition), 38(5): 24–36.

Lin S, Wang Q, Guan H, 2023, Dynamic Evaluation of Green Technology Innovation Efficiency of Chinese Industrial Enterprises. Statistics and Decision Making, 39(16): 163–1.

Fan D, Wu X, 2022, A Study on the Spatio-Temporal Evolution Characteristics and Coordination of Green Technology Innovation Efficiency in Chinese Industry. Exploration of Economic Issues, 2022(12): 1–15.

Wu K, Qu H, Pan L, et al., 2023, Evaluation of Green Technology Innovation Efficiency of Chinese Industrial Enterprises based on Malmquist Index. Journal of Heilongjiang Engineering Institute, 37(3): 35–39.

Wang S, Lin X, Zhang W, et al., 2023, Research on the Impact of Green Credit on the Efficiency of Green Technology Innovation in Chinese Industry. Statistics and Information Forum, 38(4): 88–102.

Yuan P, Dong X, 2023, A Study on Spatio-Temporal Differences and Identification of Causes of Industrial Green Technology Innovation Efficiency in the Yellow River Basin. Journal of Jinan University (Social Science Edition), 33(5): 93–105.

Huang J, Ma C, Zeng G, 2025, Environmental Regulation and the Efficiency of Agricultural Green Technology Innovation: Based on a Two-Stage Perspective of Innovation. Journal of China Agricultural University, 30(5): 230–247.

Cao Z, Su J, 2025, Research on the Spatio-Temporal Evolution Pattern and Influencing Factors of Green Technology Innovation Efficiency. Journal of Tongling College, 24(1): 42–47.

Hou X, 2024, Spatio-Temporal Evolution of Green Transportation Technology Innovation Efficiency and Influencing Factors in Chengdu-Chongqing City Cluster. Science and Industry, 24(24): 61–70.

Yu S, Wang Y, Zeng J, et al., 2021, Research on Technological Innovation Efficiency and Driving Factors of High-Tech Industries in Beijing-Tianjin-Hebei under Innovation Value Chain. Science Decision, 2021(7): 77–90.

Chen A, 2023, Evaluation of Green Technology Innovation Efficiency in Chinese Manufacturing Industry under the Background of “Double Carbon”. Modern Industrial Economy and Informatization, 13(11): 293–296.

Lv T, Ma C, Tang T, et al., 2023, Environmental Regulation, Technological Innovation and Energy Intensity of Industrial Enterprises. Statistics and Decision Making, 39(10): 59–64.

Wang H, Ding C, Ren Z, 2025, Research on the Factors Influencing Green Technology Innovation Efficiency of Enterprises Empowered by New Quality Productivity and the Improvement Path. Research on Coal Economy, 45(3): 140–148.

Zou X, 2024, The Impact of Green Financial Policy on the Efficiency of Green Technology Innovation in Industrial Enterprises. Times Economy and Trade, 21(12): 125–128.

Fang Z, Bai H, Bilan Y, 2020, Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries. Sustainability, 12(1): 146.

He Y, Cai D, 2021, Analysis of Green Technology Innovation Efficiency and its Influencing Factors of Industrial Enterprises in Yangtze River Delta. Chongqing Social Science, 2021(1): 49–63.

Yan H, Xiao J, Feng B, 2022, Evaluation of Industrial Green Technology Innovation Efficiency and Analysis of its Influencing Factors in the Yangtze River Economic Belt. Statistics and Decision Making, 38(12): 96–101.

Dagum C, 1997, A New Approach to the Decomposition of the Gini Income Inequality Ratio. Empirical Economics, 22(4): 515–531.

Chung Y, Färe R, Grosskopf S, 1997, Productivity and Undesirable Outputs: A Directional Distance Function Approach. Journal of Environmental Management, 51(3): 229–240.

Hou J, Wang G, Chen J, 2020, External Knowledge Sourcing, Knowledge Accumulation and Green Growth of Chinese Industry: A Study of Dynamic Heterogeneous Threshold Effects. Research Management, 41(3): 91–100.

Sun F, Jiang Y, 2018, Measurement of Regional R&D Capital Stock in China: 1978–2015. Statistical Research, 35(2): 99–108.

Jiang Y, Sun F, 2016, Measurement of R&D Capital Stock in China: 1952–2014. Research on Quantitative and Technical Economics, 33(7): 112–129.

Zhang J, Wu G, Zhang J, 2004, Estimation of Interprovincial Physical Capital Stock in China: 1952–2000. Economic Research, 2004(10): 35–44.