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Related Experiment Video

Updated: May 14, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimizing urban green spaces using a decision-support model for carbon sequestration and ecological connectivity.

Hyemee Hwang1, Daseul Kim1, Sanghyuck Kim2

  • 1Department of Landscape Architecture and Rural Systems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.

Journal of Environmental Management
|May 4, 2025
PubMed
Summary

Optimizing urban green spaces (UGSs) requires balancing multiple goals. This study presents a model to improve ecological connectivity by 7.57% while meeting carbon and budget targets, aiding sustainable urban planning.

Keywords:
Carbon sink potentialDecision-makingEcological networkNSGA-IIUrban green space

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Area of Science:

  • Urban ecology
  • Environmental planning
  • Decision science

Background:

  • Urban green spaces (UGSs) are crucial for ecological health and human well-being in cities.
  • Balancing the diverse functions of UGSs is challenging due to spatial constraints and stakeholder needs.
  • Integrated planning is essential to maximize the benefits of UGSs.

Purpose of the Study:

  • To introduce a multi-objective decision-support model for optimizing UGS planning.
  • To simultaneously address carbon sequestration, ecological connectivity, and cost constraints in UGS development.
  • To provide a framework for balancing competing objectives in urban green space strategies.

Main Methods:

  • Development of a decision-support model integrating multiple objectives.
  • Application of the non-dominated sorting genetic algorithm II (NSGA-II) to identify Pareto-optimal solutions.
  • Simulation and validation of optimal scenarios for UGS planning.

Main Results:

  • The model achieved a 7.57% improvement in ecological connectivity.
  • Carbon reduction and budgetary targets were met concurrently with connectivity improvements.
  • The study demonstrated effective balancing of trade-offs between UGS quantity and strategic placement.

Conclusions:

  • The decision-support framework enables rapid simulation and validation of UGS planning scenarios.
  • It provides a scientific basis for balancing competing objectives in UGS development.
  • The approach promotes sustainable urban environments by optimizing UGS benefits.