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Abstract As a highly concentrated residential area, urban development and population concentration have caused serious environmental pollution problems that threaten the safety of the water and atmospheric resources that humans rely on for survival. To address this issue, the importance of urban green space (UGS) has become increasingly prominent. This paper collected data related to UGS (green space coverage, vegetation type, environmental quality, population distribution, etc.) for processing, used the entropy algorithm to build an ecological environment assessment model, and then used the particle swarm optimisation algorithm to optimise the model accordingly. Finally, a decision support system was proposed for UGS ecological environment planning, which comprehensively considered future environmental changes. Through comparison before and after the application of decision support system, this paper tested and verified several indicators such as green space coverage, biological diversity index, and climate adaptability. Among them, after the application of the decision support system, the green space coverage rate has increased year by year, and many indicators in the biological diversity index have improved significantly. The average climate adaptability of traditional UGS planning was 70 %, while the average climate adaptability of decision support system green space planning was 90 %, which has been significantly improved. The outcome shows that the system has a notable effect in improving the climate adaptation and ecological quality of the city.
Sijing Feng (Sat,) studied this question.