Affiliations: [a] College of Economics & Management, Shandong University of Science and Technology, Qingdao, China
| [b]
Institute of Marine Economy and Culture, Shandong Academy of Social Sciences, Qingdao, China
Correspondence:
[*]
Corresponding author. Fan Wu, Shandong Academy of Social Sciences, Qingdao, China. E-mail: [email protected].
Abstract: There are many factors that need to be considered when planning a city’s green economy, so it is difficult to simulate the planning effect through manual models. In order to improve the effect of urban green economic planning, this paper improves the traditional algorithm and combines the principle of machine learning algorithm to build a model that can be used in urban green economic planning. Moreover, this paper considers the measurement of green economic efficiency from the perspective of input, expected output and undesired output. In addition, this paper compares and analyzes the green efficiency calculated by the SE-SBM model, including horizontal comparison analysis and vertical comparison analysis, and conducts model simulation analysis in combination with data simulation research. Finally, this paper sets the simulation area, combines the data to perform model performance analysis, summarizes the data with statistical analysis methods, and draws charts. The research results show that the model constructed in this paper has a certain effect and can be applied to the design stage of urban green planning.
Keywords: Genetic algorithm, improved algorithm, machine learning, green economy
