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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Urban growth boundaries optimization under low-carbon development: Combining multi-objective programming and patch

Jingye Li1, Jean-Michel Guldmann2, Jian Gong3

  • 1Department of Land Resource Management, School of Public Administration, Hohai University, Nanjing, 211100, China.

Journal of Environmental Management
|April 27, 2023
PubMed
Summary

This study introduces a new framework for optimizing urban growth boundaries (UGBs) by balancing economic growth, ecological benefits, and carbon storage. The approach aids urban planners in making science-based decisions for sustainable urban development.

Keywords:
Cellular automataLow-carbon developmentMulti-objective optimizationUrban growth boundariesWuhan

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Area of Science:

  • Urban planning
  • Environmental science
  • Geographic information systems (GIS)

Background:

  • Urban sprawl necessitates effective urban growth boundary (UGB) strategies.
  • Optimizing UGBs requires balancing ecological, economic, and carbon storage considerations.
  • Current methods for UGB delimitation lack comprehensive optimization.

Purpose of the Study:

  • To present a novel framework for delimiting UGBs.
  • To integrate ecological, economic, and carbon storage benefits into UGB optimization.
  • To provide a science-based decision-making tool for urban planners.

Main Methods:

  • A multi-objective optimization algorithm incorporating aggregate land-use constraints.
  • A patch-level cellular automata simulation model for spatial land-use allocation.
  • A novel two-step adjustment method for UGB delineation.
  • Application of the framework to Wuhan, China.

Main Results:

  • Identified potential for strong economic growth in Caidian district under low-carbon development.
  • Demonstrated that maximizing carbon storage minimizes ecological benefit losses.
  • Validated the model framework and adjustment method for scenario definition and policy decisions.

Conclusions:

  • The proposed framework effectively balances competing urban development objectives.
  • Carbon storage should be a key consideration in urban growth planning.
  • The integrated approach supports evidence-based policy for sustainable urban expansion.