There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good ...There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend.The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso.It can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction accuracy.In this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis.Firstly,we used Lasso regression to reduce the dimensionality of the data.Because the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–2019.Finally,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal revenue.The experimental results show that the combined model has a good effect in financial revenue forecasting.展开更多
Urban Comprehensive Carrying Capacity(UCCC)can describe the development of a city and is an important guarantee for its sustainable development.Production-Living-Ecological Space(PLES)is an important guideline for the...Urban Comprehensive Carrying Capacity(UCCC)can describe the development of a city and is an important guarantee for its sustainable development.Production-Living-Ecological Space(PLES)is an important guideline for the optimal development of China's land space in the new era.Based on the PLES,we construct a UCCC index system and analyzed the spatial and temporal distribution characteristics of the UCCC of each city in Zhejiang Province from 2006 to 2020.In addition,a coupling coordination degree model and an obstacle degree model are introduced to analyze the coupling coordination degree of the development of PLES of each city and the main constraint indicators,respectively,and a GM(1,1)model is used to predict the UCCC of each city in Zhejiang Province from 2021 to 2025.The study indicates that(1)the UCCC of each city is steadily increasing,with a growth rate of over 30%in 15 years.(2)As of 2020,the highest degree of coupling coordination reaches only 0.508,and it remains a major challenge for cities to improve the coordination degree of land use.(3)Some production space and living space indicators are the main obstacle indicators hindering the development of the UCCC in Zhejiang Province.(4)The forecast results show that the UCCC in each city will still maintain an upwards trend in the following 6 years and will gradually reach a high carrying capacity level.展开更多
The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for the healthy and sustainable development of urban agglom...The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for the healthy and sustainable development of urban agglomerations. Focusing on the Beijing-Tianjin-Hebei(BTH) urban agglomeration, this study constructs an urban resilience evaluation system based on four subsystems: economy, society, infrastructure, and ecology. It uses the entropy method to measure the urban resilience of the BTH urban agglomeration from 2000 to 2018.Theil index, standard deviation ellipse, and gray prediction model GM(1,1) methods are used to examine the spatio-temporal evolution and dynamic simulation of urban resilience in this urban agglomeration. Our results show that the comprehensive evaluation index for urban resilience in the BTH urban agglomeration followed a steady upward trend from 2000 to 2018,with an average annual growth rate of 6.72%. There are significant differences in each subsystem’s contribution to urban resilience;overall, economic resilience is the main factor affecting urban resilience, with an average annual growth rate of 8.06%. Spatial differences in urban resilience in the BTH urban agglomeration have decreased from 2000 to 2018, showing the typical characteristic of being greater in the central core area and lower in the surrounding non-core areas. The level of urban resilience in the BTH urban agglomeration is forecast to continue increasing over the next ten years. However, there are still considerable differences between the cities. Policy factors will play a positive role in promoting the resilience level. Based on the evaluation results, corresponding policy recommendations are put forwar to provide scientific data support and a theoretical basis for the resilience construction of the BTH urban agglomeration.展开更多
基金This research was funded by the National Natural Science Foundation of China(No.61304208)Scientific Research Fund of Hunan Province Education Department(18C0003)+2 种基金Research project on teaching reform in colleges and universities of Hunan Province Education Department(20190147)Changsha City Science and Technology Plan Program(K1501013-11)Hunan Normal University University-Industry Cooperation.This work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open project,grant number 20181901CRP04.
文摘There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend.The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso.It can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction accuracy.In this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis.Firstly,we used Lasso regression to reduce the dimensionality of the data.Because the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–2019.Finally,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal revenue.The experimental results show that the combined model has a good effect in financial revenue forecasting.
基金supported by the National Natural Science Foundation of China[grant number 42071429]the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources[grant number KF-2023-08-19].
文摘Urban Comprehensive Carrying Capacity(UCCC)can describe the development of a city and is an important guarantee for its sustainable development.Production-Living-Ecological Space(PLES)is an important guideline for the optimal development of China's land space in the new era.Based on the PLES,we construct a UCCC index system and analyzed the spatial and temporal distribution characteristics of the UCCC of each city in Zhejiang Province from 2006 to 2020.In addition,a coupling coordination degree model and an obstacle degree model are introduced to analyze the coupling coordination degree of the development of PLES of each city and the main constraint indicators,respectively,and a GM(1,1)model is used to predict the UCCC of each city in Zhejiang Province from 2021 to 2025.The study indicates that(1)the UCCC of each city is steadily increasing,with a growth rate of over 30%in 15 years.(2)As of 2020,the highest degree of coupling coordination reaches only 0.508,and it remains a major challenge for cities to improve the coordination degree of land use.(3)Some production space and living space indicators are the main obstacle indicators hindering the development of the UCCC in Zhejiang Province.(4)The forecast results show that the UCCC in each city will still maintain an upwards trend in the following 6 years and will gradually reach a high carrying capacity level.
基金Innovation Research Group Project of National Natural Science Foundation of China,No.42121001。
文摘The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for the healthy and sustainable development of urban agglomerations. Focusing on the Beijing-Tianjin-Hebei(BTH) urban agglomeration, this study constructs an urban resilience evaluation system based on four subsystems: economy, society, infrastructure, and ecology. It uses the entropy method to measure the urban resilience of the BTH urban agglomeration from 2000 to 2018.Theil index, standard deviation ellipse, and gray prediction model GM(1,1) methods are used to examine the spatio-temporal evolution and dynamic simulation of urban resilience in this urban agglomeration. Our results show that the comprehensive evaluation index for urban resilience in the BTH urban agglomeration followed a steady upward trend from 2000 to 2018,with an average annual growth rate of 6.72%. There are significant differences in each subsystem’s contribution to urban resilience;overall, economic resilience is the main factor affecting urban resilience, with an average annual growth rate of 8.06%. Spatial differences in urban resilience in the BTH urban agglomeration have decreased from 2000 to 2018, showing the typical characteristic of being greater in the central core area and lower in the surrounding non-core areas. The level of urban resilience in the BTH urban agglomeration is forecast to continue increasing over the next ten years. However, there are still considerable differences between the cities. Policy factors will play a positive role in promoting the resilience level. Based on the evaluation results, corresponding policy recommendations are put forwar to provide scientific data support and a theoretical basis for the resilience construction of the BTH urban agglomeration.
