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<p>Clarifying the interactions between ecological functions (EF) and agricultural functions (AF) is crucial for achieving a win-win balance between ecological protection and food security within a low-carbon development framework. However, studies on the trade-offs and optimization of EF and AF at the grid scale in highly urbanized regions of China remain scarce. This study proposes an integrated framework of “function assessment-trade-off quantification-factor identification-targeted optimization” by combining multi-source datasets, including land use, net primary productivity (NPP), socio-economic data, and soil water storage. First, EF and AF in the Chang-Zhu-Tan Metropolitan Area (CZTMA) were evaluated using these datasets. Then, trade-off characteristics were then identified via Spearman correlation and root mean square error (RMSE) analysis, and key driving factors were detected using the Geographical Detector model. Finally, three land-use optimization objectives were defined: maximizing ecological benefits, maximizing agricultural benefits, and minimizing carbon emissions. Six major land-use types were treated as optimization units, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to generate multi-objective land-use allocation scenarios. Results indicate that: (1) EF are more prominent in mountainous and hilly areas, while high-value AF are concentrated in the western region; (2) A weak trade-off exists between EF and AF, largely determined by fiscal policy and urbanization. The public fiscal balance ratio is the most significant factor (q = 0.5079); (3) Compared with the current situation, the low-carbon (LC) scenario can reduce carbon emissions by 1.15% and increase ecological benefits by 3.65%, whereas the ecological-dominant (ED) scenario can enhance ecological benefits by 19.02%. By quantifying key trade-offs and generating spatially explicit land-use allocations, this study translates multi-objective spatial optimization into policy-relevant evidence. The identified priority areas provide a scientific basis for national land planning, ecological compensation, and other low-carbon land management strategies, thereby strengthening the link between optimized outputs and practical policy implementation.</p>

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Spatial trade-off and optimization of ecological and agricultural functions under low-carbon orientation: a case study of Chang-Zhu-Tan Metropolitan Area

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Yilin Li,
  • Xianchao Zhao,
  • Mingyang Ma,
  • Xiaoyan Ma,
  • Xin Li

摘要

Clarifying the interactions between ecological functions (EF) and agricultural functions (AF) is crucial for achieving a win-win balance between ecological protection and food security within a low-carbon development framework. However, studies on the trade-offs and optimization of EF and AF at the grid scale in highly urbanized regions of China remain scarce. This study proposes an integrated framework of “function assessment-trade-off quantification-factor identification-targeted optimization” by combining multi-source datasets, including land use, net primary productivity (NPP), socio-economic data, and soil water storage. First, EF and AF in the Chang-Zhu-Tan Metropolitan Area (CZTMA) were evaluated using these datasets. Then, trade-off characteristics were then identified via Spearman correlation and root mean square error (RMSE) analysis, and key driving factors were detected using the Geographical Detector model. Finally, three land-use optimization objectives were defined: maximizing ecological benefits, maximizing agricultural benefits, and minimizing carbon emissions. Six major land-use types were treated as optimization units, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to generate multi-objective land-use allocation scenarios. Results indicate that: (1) EF are more prominent in mountainous and hilly areas, while high-value AF are concentrated in the western region; (2) A weak trade-off exists between EF and AF, largely determined by fiscal policy and urbanization. The public fiscal balance ratio is the most significant factor (q = 0.5079); (3) Compared with the current situation, the low-carbon (LC) scenario can reduce carbon emissions by 1.15% and increase ecological benefits by 3.65%, whereas the ecological-dominant (ED) scenario can enhance ecological benefits by 19.02%. By quantifying key trade-offs and generating spatially explicit land-use allocations, this study translates multi-objective spatial optimization into policy-relevant evidence. The identified priority areas provide a scientific basis for national land planning, ecological compensation, and other low-carbon land management strategies, thereby strengthening the link between optimized outputs and practical policy implementation.