Multi-objective Land Use Multi-Objective Optimisation Based on Carbon Balance Partitioning - A Case Study of Chang-Zhu-Tan City Cluster
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    Abstract:

    [Objective] To optimize the spatial land use resources of ChangZhuTan City Cluster from the perspective of carbon balance, and to provide theoretical support for the sustainable development of the region's society and economy. [Methods] Calculate the carbon sinks and carbon emissions of ChangZhuTan City Cluster from the perspective of land use, carry out carbon balance zoning based on the carbon ecological carrying capacity coefficient and the carbon economic contribution coefficient, and put forward the optimization strategy of land use for each type of cities and counties, and set up economic benefit, carbon sink benefit, compactness and conversion cost as the optimization function of the multi-objective algorithm, and then get the more scientific spatial structure of land use through the CoMOLA model. Optimization. [Results] From 2000 to 2020, the total carbon emission of ChangZhuTan urban agglomeration showed a rising trend year by year, and the carbon sink level of all districts and counties was relatively stable, but the overall trend was slightly decreasing; based on the coefficient of carbon ecological carrying capacity and the coefficient of carbon economic contribution, the ChangZhuTan urban agglomeration was divided into the carbon sink development area, the economic development area, the comprehensive optimization area, and the green development area; the optimized economic benefit of the land use increased by 31.2%, carbon sink increased by 6.3%, and compactness increased by 5.5%. [Conclusion] The optimized land resources can better meet the economic and ecological sustainable development of ChangZhuTan City Cluster, and the spatial layout of land use is more reasonable, which provides a basis for decision-making on the allocation of land resources in the future.

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History
  • Received:September 10,2024
  • Revised:October 29,2024
  • Adopted:October 30,2024
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