The contemporary urban environment, being a complex system saturated with construction objects interconnected by engineering and social communications, contains numerous potential sources of hazardous technosphere situations. Preventing and mitigating their consequences becomes feasible only through timely automated monitoring of early warning signs and forecasting dynamics of development. At the same time, construction objects within the urban context consume significant material and energy resources, contributing to increased carbon emissions impacting the environment. Therefore, there is a pressing need for digital instruments capable of managing these processes across their entire lifecycle. In this regard, effective means of ensuring ecological safety in cities involves monitoring technical, organizational, and functional components of works conducted and planned for both construction and maintenance phases of urban infrastructure. Based on these measures, maintaining the carbon sustainability of urban immovable property and infrastructure funds becomes achievable when implemented within an adaptable City Information Model (CIM) tailored specifically for managerial tasks. The scientific novelty of the proposed research lies in developing scientific-methodological foundations for digital monitoring of current conditions and predicting the evolution of carbon state and resilience of constructed and operational urban objects and infrastructure integrated into a unified CIM. This approach serves as the basis for instrumentation aimed at managing ecological safety of construction objects. In the research, the technology of information modeling of city objects is constructed based on the author's factor space, incorporating monitoring and forecasting of conditions for realization and assessment of carbon sustainability of constructed and operated objects. This effort utilizes international databases regarding the carbon impact of construction materials and processes, along with analytical data derived from project estimates documentation of urban objects. Automated expert activity tools, including the integration of unmanned aviation systems, are utilized extensively. Algorithms for automated evaluation and forecasting of City carbon impact Indicator (CCII) are presented and to be used as a basepoint for unmanned city carbon analysis within city life cycle management. These algorithms aim to optimize recommended construction, restoration, or operational measures by leveraging results from drone surveillance, neural network detection, mapping, quantitative assessments, and dynamic parameter changes of objects. Ultimately, this allows for synthesizing optimal management decisions ensuring environmentally safe urban spaces towards the carbon homeostasis as an ultimate goal for modern city ecological management.