Efficiency Calculation and Analysis of Green Economy Based on Improved Fixed Effects Model

Abstract

The discrepancy between global economic growth and environmental degradation has become increasingly pronounced, and the calculation of green economic efficiency has become a key issue. In response to the problem of traditional fixed effects models neglecting important heterogeneity factors in measuring green economic efficiency, this study proposes an improved fixed effects model combined with long short-term memory neural network classification algorithm. The results showed that the model’s recall in the classification task was 0.9813, and the average area under the working characteristic curve of the subjects was 0.975, which was significantly better than the comparison algorithm; Multiple regression analysis indicted that capital stock had a remarkable negative influence on technical efficiency values, while energy consumption and financial agglomeration had a significant positive impact; Regions with per capita gross domestic product exceeding 60000 yuan, urbanization rate exceeding 60%, and tertiary industry proportion exceeding 50% generally had higher efficiency in green gross domestic product. The smallest value of the explanatory Variable for energy consumption intensity was 0.02, and the biggest value was 0.14, thus the fluctuation of energy consumption intensity in the sample was relatively small, which meant that most enterprises were relatively efficient in energy use. This model effectively improves the accuracy and explanatory power of green economy efficiency measurement, reveals the endogenous driving force of green economic growth, and support the policy formulation.

Keywords

• Green economy
• efficiency measurement
• fixed effect model
• LSTM
• SBM model