## 5 Discussion  
### Critical Analysis of Data-Driven CIM  
Data-driven CIM is considered a crucial pillar for the future development of smart cities. Its core advantage lies in the integration of GIS, BIM, and IoT technologies, enabling the automated acquisition, processing, and analysis of urban data to support urban planning, infrastructure management, and real-time decision making. However, despite its theoretical advantages in regard to efficiency and intelligence, the practical implementation of data-driven CIM remains constrained by technical, managerial, and societal challenges [74]. (1) Does data-driven CIM truly improve the urban management efficiency? One of CIM's primary objectives is to enhance urban management through intelligent automation. However, its actual effectiveness is often hampered by issues such as the data quality, computational capacity, and interdepartmental collaboration [75; 76]. For instance, in Singapore's Smart Nation Initiative, the government has attempted to integrate CIM with multi-dimensional data sources, including buildings, transportation, and energy systems, to achieve refined urban management [77]. However, in practice, the lack of standardized data formats and difficulties in cross-departmental data sharing have hindered the system's data integration efficiency, reducing the effectiveness of real-time management. In the case of smart traffic management, data inconsistencies across different sensors--such as cameras and traffic flow detectors--have led to data fusion challenges, ultimately affecting the accuracy and timeliness of decision making. Moreover, the implementation of CIM in small and medium-sized cities often faces high technological and financial costs. Particularly in developing countries, where the digital infrastructure remains underdeveloped, CIM systems may not necessarily be more efficient than the traditional urban management methods. Some smart city projects have reported unstable system performance due to the inadequate sensor quality and insufficient cloud computing resources, thereby undermining the anticipated efficiency gains. Thus, the value of CIM should be assessed based on the specific urban conditions rather than blindly promoted as a universal solution. (2) Is CIM truly capable of real-time data processing? A key selling point of data-driven CIM is its ability to process urban data in real time and support intelligent decision making. However, in reality, its \"real-time\" capabilities are often constrained by computational resources, network latency, and data processing limitations [43; 78]. For instance, during the Tokyo Olympics, the Japanese government leveraged CIM to optimize the urban traffic flows by integrating vehicle GPS data, public transportation information, and crowd monitoring systems. However, due to the massive volume of data and limited computational resources, delays in data processing affected the effectiveness of the real-time traffic optimization strategies. To address this issue, edge computing has been introduced into CIM frameworks to reduce latency and enhance the real-time performance. In Barcelona's smart city project,computational tasks were distributed across edge nodes, allowing data to be processed locally before being transmitted to the cloud, thereby alleviating the burden on the centralized computing resources. However, the widespread adoption of edge computing remains constrained by the high cost of infrastructure deployment and maintenance, making large-scale implementation challenging. Furthermore, CIM's decision-making mechanisms often rely on historical data and machine learning models, which may fail to adapt effectively to sudden disruptions or extreme environmental conditions. For example, during the 2023 Turkey earthquake, despite CIM being utilized for the disaster response, its reliance on pre-earthquake urban data limited its ability to accurately predict building collapse risks, impeding precise rescue operations.