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A Method for Assessing Urban Industrial Ecological Efficiency Using SBM-GML Model with Tax Reduction

Yuchen Guo, Jianwei Guo

Abstract


The industrial development of cities promotes social and economic development, but it also affects cities' ecological environments. To balance the relationship between the two, the country introduces corresponding tax reduction policies as an effective means of regulation. Therefore, to explore tax and fee reduction policies' specific impact on urban industrial ecological efficiency, the proposed text clustering model was first used in this experiment to cluster the tax and fee reduction policies issued by the government. Subsequently, the Slack-Based Measure-Global Malmquist Lunberger was constructed to measure urban industrial development's ecological efficiency. These experiments confirmed that policy text clustering models had different clustering accuracy on different datasets, with clustering accuracy reaching up to 80.95%, 87.13%, and 94.08% at iterations of 200, 500, and 1000. The regression coefficients for the main variables obtained from the clustering policy, including overall tax reduction and fee reduction, circulation tax reduction, income tax reduction, social expense reduction, and technological innovation tax reduction, were 0.117, 0.105, 0.269, 0.112, and 0.115, respectively. This indicated that these tax and fee reduction measures affected industrial ecological efficiency positively. Therefore, the proposed method can effectively cluster policy texts and measure the industrial ecological efficiency of cities, which has practical feasibility. This provides an effective path for promoting industry and the ecological environment's balanced development.

 

Doi: 10.28991/HIJ-2024-05-04-02

Full Text: PDF


Keywords


Tax Reduction Policy; LDA Text Clustering; SBM-GML; Industrial Ecological Efficiency; Measure.

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Full Text: PDF

DOI: 10.28991/HIJ-2024-05-04-02

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