管理评论

›› 2015, Vol. 27 ›› Issue (12): 57-64.

• 经济与金融管理 • 上一篇    下一篇

基于Bootstrap与神经网络模型的浦东新区土地收储增值收益分配研究

何芳, 王小川, 张皓   

  1. 同济大学经济与管理学院, 上海 200092
  • 收稿日期:2013-10-24 出版日期:2015-12-30 发布日期:2015-12-25
  • 作者简介:何芳,同济大学经济与管理学院教授,博士生导师;王小川,同济大学经济与管理学院,博士;张皓,同济大学经济与管理学院,硕士。
  • 基金资助:

    国家自然科学基金项目(71473179)。

Research on Allocating Land Value-added Income Allocation in Pudong Land Reserve System Using Bootstrap-Elman Neural Network

He Fang, Wang Xiaochuan, Zhang Hao   

  1. School of Economics and Management, Tongji University, Shanghai 200092
  • Received:2013-10-24 Online:2015-12-30 Published:2015-12-25

PDF (PC)

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摘要：

在土地紧缺和城市更新背景下,低效城市土地的收储盘活极为迫切,而其关键是如何进行土地收储增值收益的合理分配。本文在剖析土地增值收益分配机理基础上,提出土地增值优先偿付公私基本利益、净增值收益阶梯分享的分配机制,并采集浦东新区土地收储案例数据,运用数理模型进行了分配的定量研究。结果表明,第一梯级原使用权人分享比例为56.5%,第二梯级其分享比例45.6%。这一结果符合低梯级价段原产权人分享多的分配思想。本文提出的"优先偿付阶梯分享"方法,同时兼顾了公平与效率,丰富了土地收益分配理论,并为政府创新土地收储收益分配方式提供依据。

关键词: 土地收储, 增值收益, Bootstrap, Elman神经网络

Abstract:

In the context of land storage and urban redevelopment, it is critical to purchase, store and make better use of inefficient urban land usage and the key lies in how to reasonably allocate the income from land value addition. In this paper, we first analyze the land value-added benefit distribution mechanism and put forward the idea that the value addition should satisfy the priority of payment of public and private interests and the ladder allocation method based on net income. Mathematical models are adopted for quantitative researches on Pudong district land increment income. The results show that the original property user shared ratio is 56.5% in the first rung while 45.6% in the second rung which is in line with the principal the government share higher proportion in upper rung. Priority of payment and ladder allocation method can realize the unification of fairness and efficiency, deepen the land value-added income allocation mechanism in theory and provide a basis for the government to innovate the revenue allocation in practice.

Key words: land purchase and storage, land value-added income, Bootstrap, Elman neural network