Urban parks, a type of urban green space, help mitigate environmental pollution and climate change by absorbing and storing atmospheric carbon. Optimizing their carbon-sink capacity requires thoughtful plant community design considering multiple factors. This study analyzed South Korean urban parks using QGIS and i-Tree Eco, integrating satellite imagery with field surveys at both spatial and tree scales. Park spaces were classified into six types based on the biotope criteria established in this study. Random forest regression was applied to each type to identify key variables influencing annual carbon sequestration and storage. The relationship between maturity and sequestration was examined for ten dominant tree species, offering insights for plant selection. Higher tree coverage and more deciduous species were linked to efficiency in carbon sequestration and storage. While variable importance varied slightly across biotope types, tree density was most influential for sequestration, and diameter at breast height and age were key for storage. These findings provide integrated insights into short-term sequestration and long-term storage, as well as strategic directions for structuring plant communities in urban ecosystems. The study offers empirical evidence for designing carbon-efficient urban parks, contributing to sustainable landscape strategies.

As urbanization intensifies, the ecological area ratio (EAR) has become an essential tool for assessing ecological performance in urban development projects. However, conventional EAR systems remain largely prescriptive and surface-oriented, lacking adaptability to diverse planning contexts. This study proposes a probability-based EAR reference table developed from empirical data collected across six representative large-scale urban development districts. EAR values were statistically analyzed for 16 land-use types to construct a reference table using mean and quartile indicators. The table was then applied to seven newly planned towns to evaluate its predictive utility. The results showed that predicted EAR values closely aligned with institutional targets and revealed meaningful internal variation depending on land-use composition. Green space and parks showed the highest ecological contributions, while multi-family housing, despite moderate unit-area performance, played a key stabilizing role due to its large spatial footprint. Correlation analyses further demonstrated that EAR composition varied across housing supply types, shaped by differing regulatory frameworks and design priorities. By transitioning EAR from a uniform ratio to a data-driven, probabilistic guideline, this study offers both a practical estimation tool and a strategic planning aid. The findings provide actionable insights for more adaptive, equitable, and ecologically robust urban development practices.