Optimal planning of urban greening system in response to urban microenvironments in a high-density city using genetic algorithm: A case study of Tianjin

• Design an UGS optimization model to maximize the multiple eco-benefits. • Map the spatial distribution of microenvironment factors based on ES theory. • Achieve a spatial optimization scheme of multi-scale green spaces in central Tianjin. • Integrate the results into land use planning to support decision-making. • Balance the model's usability and accuracy to enable replication in other cities. Recently, the impact of urban greening system (UGS) patterns on mitigating microenvironment problems has received extensive attention. However, the relative UGS optimal models cannot take into account both usability and spatial accuracy, thus the applicability is limited in the multi-objective decision contexts. We developed a spatial model to identify the optimal locations of new green spaces with respect to multiple eco-benefits, especially considering the complex built-environment of high-density cities. In the model, we assessed the spatial distribution of microenvironment indicators such as air pollution, heat island effect and noise, as well as the status quo of buildings and UGS. An optimal model was thus designed based on genetic algorithm to maximize the multiple eco-benefits of UGS. The model thus developed was applied to Tianjin as a case study. As a result, a successful optimization of locations for multi-scale green spaces was achieved and visualized in GIS. While ensuring the accuracy of the results, the model requires a relatively small amount of data and expertise to enable assessment in complex decision contexts. It is hoped to further develop customized models based on this framework and integrate the results into the cities land use plan.