# Insight from Review Articles of Life Cycle Assessment for Buildings
## 3 Results
### Findings of Qualitative Analysis
#### 3.2.3 Findings from Highly Cited Papers

Table 8: Key findings, challenges/future research directions from 30 highly cited papers. 2. Many studies concluded that the LCA results of different cases lack comparability, mainly due to the complexity and diversity of building LCA [117; 122; 124]. (1) Differences in system boundaries definitions, i.e., the coverage of life cycle stages and inconsistency of functional units. For example, some studies focused on \"Cradle to Gate\" and others focused on \"Cradle to Grave\". (2) Differences in data sources and assumptions, i.e., background databases, geographical and climatic conditions, technical parameters, and scenario assumptions. For example, material production, transportation, and energy consumption data may come from different databases, such as Ecoinvent, GaBi, China's local database, etc., which cause the difference in results. (3) Differences in methodological and model selection, i.e., process-based LCA (PLCA) and input-output LCA (IO-LCA) and hybrid LCA, impact assessment model, and software tool selection. For example, the data sources, coverage and modeling are different for PLCA, IO-LCA, and hybrid LCA. (4) Differences in building characteristics and design, i.e., building function and types, material and technology selection, and regional adaptive design. For example, the energy usage patterns and intensities of buildings may vary significantly for buildings with different functions, such as office buildings, residential buildings, and hospital buildings. (5) Differences in the time span and data timeliness, i.e., the assumptions of buildings service life, parameter update due to technology upgradation. For example, the building lifespan used in most cases ranges from 50 years to 120 years. In addition, a large amount of data (i.e., material production, transportation, energy consumption, etc.) is required for building LCA, while the data is usually scattered and difficult to collect precisely in actual projects, especially the embodied carbon data in the supply chain. To overcome this challenge, much effort should be made to develop a set of standardized guidelines and methodology on the boundary scoping, methodology choices, and data inventories so as to establish benchmarks for building LCA. 3. The integration of BIM technology in LCA is widely acknowledged as it can achieve the visualization and dynamic management of the entire life cycle data of buildings, improving design efficiency and data accuracy. Nevertheless, the major issues can be summarized as follows: (1) The lack of a unified standard for data interfaces between different BIM software and LCA tools leads to obstacles in software compatibility and data exchange. (2) It is difficult to synchronize the energy consumption and maintenance data during the building operation stage to the BIM model in real time, which may lead to the deviation of LCA results from the actual situation. Furthermore, the data of the demolition and recycling stages are often ignored due to the lack of coordination of the industrial chain. (3) The integration of BIM-LCA requires professionals to master architectural modeling, LCA methods, and data analysis techniques simultaneously. However, the current talent reserve in the industry is insufficient and thus limits its promotion. The following solution paths are proposed: (1) Adopt unified data standards and long-term archiving formats; establish a digital twin for the entire life cycle and continuously record the changes in the building's status. (2) Develop automated data synchronization tools to collect operation and maintenance data in real time and update models using the Internet of Things; combine machine learning to predict future trends and generate dynamic LCA input parameters at multiple time nodes; develop an LCA tool integrating uncertainty analysis.