Uncontrolled urban expansion presents significant challenges to green spaces, leading to increased land surface temperature, and carbon emissions. This study emphasizes the importance of predicting urban growth, monitoring LST, and assessing green space suitability to mitigate these impacts in Bojnourd City, Iran. This research aims to enhance land use planning by employing the SLEUTH model and integrating landscape features to evaluate and compare urban growth scenarios. The study consisted of five main stages: monitoring LULC and LST changes, utilizing the InVEST Carbon Storage and Sequestration model for carbon stock mapping, assessing green space suitability, simulating urban growth scenarios up to 2050, and prioritizing scenarios using landscape metrics and TOPSIS. Five scenarios were analyzed: Low Carbon City, Compact Urban Growth, Historical Urban Growth, Uncontrolled Urban Growth, and Green City. Landscape metrics were utilized to assess the environmental consequences of each scenario. The results demonstrate that the Compact Urban Growth scenario, which focuses on land conservation, achieved the highest environmental sustainability score of 0.818, followed by the Green City scenario at 0.72, and the Low Carbon City scenario at 0.55. These findings highlight the effectiveness of the SLEUTH model in guiding urban planners toward decisions that promote sustainable, low-carbon urban development.

中文翻译：

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加强减缓气候变化和可持续城市增长的决策


不受控制的城市扩张给绿地带来了重大挑战，导致地表温度升高和碳排放。本研究强调了预测城市增长、监测 LST 和评估绿色空间适宜性以减轻伊朗 Bojnourd 市这些影响的重要性。本研究旨在通过采用 SLEUTH 模型并整合景观特征来评估和比较城市增长情景，从而加强土地利用规划。该研究包括五个主要阶段：监测 LULC 和 LST 变化、利用 InVEST 碳储存和封存模型进行碳储量测绘、评估绿色空间适宜性、模拟到 2050 年的城市增长情景，以及使用景观指标和 TOPSIS 确定情景的优先级。分析了五种情景：低碳城市、紧凑型城市增长、历史城市增长、不受控制的城市增长和绿色城市。景观指标用于评估每种情景的环境影响。结果表明，专注于土地保护的紧凑型城市增长情景获得了最高的环境可持续性得分，为 0.818，其次是绿色城市情景 0.72 分和低碳城市情景 0.55 分。这些发现突出了 SLEUTH 模型在指导城市规划者做出促进可持续、低碳城市发展的决策方面的有效性。