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Short-Term Land use Planning and Optimal Subsidies

  • L. M. Briceño-Arias
  • F. MartínezEmail author
Article
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Abstract

Urban planning is a complex problem which includes choosing a social objective for a city, finding the associated optimal allocation of agents and identifying instruments like subsidies to decentralize this allocation as a market equilibrium. We split the problem in two independent steps. First, we find the short-term optimal allocation for a social objective and, second, we derive subsidies that reproduce this optimal allocation as a market equilibrium. This splitting is supported by a fundamental result asserting that the optimal allocation of any social objective can be decentralized by applying feasible subsidies, which can be computed even in the case with location externalities and transportation costs. In the first step, we compute the optimal allocation using an algorithm to solve a convex urban planning problem, which is applicable to a wide class of objective functions. In the second step, we compute optimal subsidies in several political situations for the planner, like budget constraints and limited impact on specific agents, zones, rents and/or utilities. As an example, we simulate a prototype city which aims at improving social inclusion.

Keywords

Convex optimization Land use planning problem Location subsidies Urban segregation 

Notes

Acknowledgments

This work is supported by the Complex Engineering Systems Institute, ISCI (ICM-FIC: P05-004-F, CONICYT: FB0816), by project FONDECYT 11140360, and by “Programa de financiamiento basal” from the Center for Mathematical Modeling, Universidad de Chile. The authors thanks C. Vigouroux for his help with simulations.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Departamento de MatemáticaUniversidad Técnica Federico Santa MaríaSantiagoChile
  2. 2.Departamento de Ingeniería CivilUniversidad de Chile e Instituto Sistemas Complejos de IngenieríaSantiagoChile

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