Integrating Linear Programming and CLUE-S Modeling for Scenario-Based Land Use Optimization Under Eco-Economic Trade-Offs in Rapidly Urbanizing Regions

Mufeng Zhang, Qinghua Gong, Bowen Liu, Shengli Yu, Linyuan Yan, Yanqiao Chen and Jianping Wu ()
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Mufeng Zhang: Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Qinghua Gong: Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Bowen Liu: Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Shengli Yu: Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Linyuan Yan: Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Yanqiao Chen: Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Jianping Wu: Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China

Land, 2025, vol. 14, issue 8, 1-21

Abstract: Rapid urbanization has intensified eco-economic trade-offs, necessitating integrated optimization frameworks that balance development with environmental conservation in land use planning. Traditional methods often fail to optimize both objectives simultaneously, highlighting the need for systematic approaches addressing competing demands. This study develops an integrated linear programming (LP) and CLUE-S modeling framework using Guangzhou, a rapidly urbanizing megacity in China, as a case study. The methodology combines LP quantitative optimization with CLUE-S spatial allocation under dual objectives: maximizing ecosystem service value and economic benefits across four policy scenarios: ecological protection, cultivated protection, economic development, and balanced development. Data inputs include the 2020 land-use database, 12 socio-economic and biophysical driving factors, and territorial planning constraints. Results show that the coupled framework effectively balances urban expansion with ecological protection, reducing habitat fragmentation and preserving key ecological corridors compared with business-as-usual scenarios. Accuracy assessments further confirm the robustness and reliability of the framework. The integrated LP-CLUE-S framework captures land use dynamics and spatial constraints, providing a robust tool for territorial spatial planning. This approach offers actionable insights for reconciling development pressures with environmental conservation, contributing a replicable methodology for sustainable land resource management with strong transferability potential for other rapidly urbanizing regions facing similar eco-economic challenges.

Keywords: integrated LP-CLUE-S framework; eco-economic synergy analysis; land use optimization; scenario-based spatial planning; fast-growing metropolitan regions (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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