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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
Utilizing network optimization to mitigate rising greenspace exposure inequalities in Chinese cities from 2000 to
Rundong Feng1,2, Bin Chen2,3, Shenghe Liu1
1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
None:
Urban greenspaces enhance human well-being and promote sustainable development in rapidly urbanizing regions by delivering vital ecosystem services, including cooling, air purification, and recreation. In China, where cities accommodate a large share of the population amid persistent environmental pressures, disparities in greenspace exposure pose a major obstacle to equitable access; these disparities arise from geographic, climatic, socioeconomic, and landscape factors. Although awareness of such inequalities is growing, their long-term trajectories, demographic and city-scale patterns, and viable spatial optimization approaches remain largely unexplored. Here we show that greenspace exposure inequality across 246 Chinese cities increased by 25 % from 2000 to 2020 and is projected to rise further by 12.2-15.7 % by 2050 under middle-of-the-road and fossil-fueled development scenarios, disproportionately affecting older, less-educated women and megacity residents. Geodetector and random forest analyses reveal that this rise results from interactions among greenspace coverage, population density, and patch connectivity, which explain 83.9 % of the inequality. A network-based optimization approach that improves patch connectivity-without expanding total greenspace-can reduce disparities by 10.3-20.8 %, with greater efficacy in high-inequality cities and among vulnerable populations. Our results highlight how precise landscape interventions can advance social equity in greenspace access, supporting Sustainable Development Goal 11 for inclusive, resilient urban environments.
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