<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Accelerating the rational allocation and optimal integration of urban public resources plays an extremely important role in national and regional development. Based on the public resource allocation model of BP optimization and the analysis method of public resource allocation efficiency, this paper, on the basis of constructing the indicators of the public resource allocation model, calculates and analyzes the data of each indicator from 2012 to 2020 of four cities A, B, C and D in Z, to test the validity of the constructed model and to derive the efficiency of public resource allocation of these four cities. Lastly, the Tobit regression model is employed to explore the factors that influence public resource allocation. In terms of public resource allocation efficiency, 2017, 2018 and 2019 are the concentrated years in which the public resource allocation of the four cities reaches the effective value of 1. The average value of the public resource allocation efficiency of the four cities from 2012 to 2020 is around 0.9, which does not reach the effective value of 1, and the overall allocation efficiency is low. Among the 24 selected influencing factors, 20 influencing factors have a positive effect on public resource allocation efficiency, accounting for 83.3%, of which 11 factors have a significant effect, satisfying P &lt; 0.1.</p>
</abstract>

Accelerating the rational allocation and optimal integration of urban public resources plays an extremely important role in national and regional development. Based on the public resource allocation model of BP optimization and the analysis method of public resource allocation efficiency, this paper, on the basis of constructing the indicators of the public resource allocation model, calculates and analyzes the data of each indicator from 2012 to 2020 of four cities A, B, C and D in Z, to test the validity of the constructed model and to derive the efficiency of public resource allocation of these four cities. Lastly, the Tobit regression model is employed to explore the factors that influence public resource allocation. In terms of public resource allocation efficiency, 2017, 2018 and 2019 are the concentrated years in which the public resource allocation of the four cities reaches the effective value of 1. The average value of the public resource allocation efficiency of the four cities from 2012 to 2020 is around 0.9, which does not reach the effective value of 1, and the overall allocation efficiency is low. Among the 24 selected influencing factors, 20 influencing factors have a positive effect on public resource allocation efficiency, accounting for 83.3%, of which 11 factors have a significant effect, satisfying P < 0.1.

Analysis of public resource allocation model based on BP neural network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Analysis of public resource allocation model based on BP neural network"}

Accelerating the rational allocation and optimal integration of urban public resources plays an extremely important role in national and regional development....

Analysis of public resource allocation model based on BP neural network

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 18 dic 2023

Accettato: 28 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0175

Parole chiave
BP neural network, DEA model, Tobit regression model, Public resource allocation

© 2024 Maolin Yang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of public resource allocation model based on BP neural network

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 18 dic 2023

Accettato: 28 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0175

Parole chiaveBP neural network, DEA model, Tobit regression model, Public resource allocation

© 2024 Maolin Yang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
BP neural network, DEA model, Tobit regression model, Public resource allocation