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The achievement of climate neutrality is a fundamental goal for cities in the next 30 years. In order to achieve this goal, this research focuses on a novel tree carbon sequestration utilizing point clouds. Using a multi-algorithm workflow, tree information is extracted to calculate carbon storage from airborne and mobile laser scanning data, using Helsinki as a test case. The study employs local maximum and seeded region growing algorithms to detect tree locations and crown extents from aerial point clouds. A Python script is generated using the DBSCAN algorithm to extract tree point clouds and trunk diameters. The established allometric equations are utilized to calculate the carbon sequestration of trees. The results are integrated into the digital platform, filling the gap in urban digital twins' carbon storage information. This innovative approach will contribute significantly to urban planning and decision-making for sustainable cities in the face of climate challenges.