Governing AI Models in Urban Tree Digital Twins: A Civic Control Framework for Climate, Carbon, and Biodiversity Stewardship

Cities increasingly use artificial intelligence (AI) to map urban trees, estimate ecosystem states, forecast risk, and prioritize interventions. Yet technical capability does not establish who may authorize an intervention, how affected residents can challenge it, or whether its ecological outcome is verified. This concept paper uses an integrative conceptual synthesis, informed by theory-synthesis design, to connect urban-tree and digital-twin scholarship with AI accountability and adaptive management. It defines an AI-enabled Urban Tree Digital Twin as a time-aware representation linking observations and explicitly labeled inferences to authorized decisions, traceable actions, and outcome verification. The proposed Civic Control Framework connects five functions—Sense, Represent, Decide, Act, and Audit—to an institutional layer governing ownership, accountability, financing, and participation. A seven-step inference-to-action cycle and seven linked record types make the boundary between model advice and public authority explicit. Candidate model families are task-class examples, including task-specific networks, foundation segmentation models, and temporal forecasters, rather than a prescribed or validated stack. Separate climate, carbon, and biodiversity pathways specify the evidence needed for each claim. Hypothetical planting and contested-removal scenarios illustrate assurance gates, authorization, public challenge, and audit. The contribution is a normative and hypothesis-generating governance architecture; the scenario walkthroughs examine its intended logic without establishing empirical effectiveness.