Facing the challenges of high complexity in Building Information Modeling (BIM) technology application and insufficient realization of its benefits, this study identifies key influencing factors of BIM effectiveness through the literature analysis and multi-case research. A comprehensive evaluation system is constructed, covering six dimensions—technical level and application capability, organizational and management, human capability, economic and market factors, project factors, and external environment—with 30 specific indicators. Based on 257 valid questionnaire responses, a Structural Equation Model is developed, and reliability/validity tests as well as model fit verification are conducted using SPSS and AMOS. The findings reveal that human capability factors (weight: 0.182) serve as the core driver for realizing BIM value. Technical conditions, project characteristics, and external environment exhibit balanced influences (each with a weight of 0.164), while economic market (weight: 0.167) and organizational management (weight: 0.159) require collaborative optimization to enhance synergy. A four-dimensional coordination system—”technology support-organizational collaboration-human capability-policy guidance”—is proposed based on these conclusions. Practical application demonstrates that this system improves BIM implementation efficiency by 23% and reduces project rework rates by 40%, providing quantifiable implementation pathways for construction enterprises to optimize resource allocation and advance digital transformation. The research aims to offer theoretical guidance and technical support for promoting the digital evolution of the construction industry.

The current green building assessment system suffers from issues such as insufficient coverage of smart indicators, significant biases in subjective weighting, and weak dynamic adaptability, which restrict the scientific promotion of green buildings. This study focuses on the gaps in the quantitative assessment of smart technologies in China’s green building evaluation standards (such as the current Green Building Evaluation Standard). While domestic standards are relatively well-established in traditional dimensions like energy conservation and environmental protection, there are fragmentation issues in the assessment of smart technologies such as the Internet of Things (IoT) and BIM. Moreover, the coverage of smart indicators in non-civilian building fields is significantly lower than that of international systems such as LEED and BREEAM. This study determined the basic framework of the evaluation indicator system through the Delphi method. Drawing on international experience and contextualized within China’s (GB/T 50378-2019) standards, it systematically integrated secondary indicators including “smart security,” “smart energy,” “smart design,” and “smart services,” and constructed dual-drive evaluation dimensions of “greenization + smartization.” This elevated the proportion of the smartization dimension to 35%, filling the gap in domestic standards regarding the quantitative assessment of smart technologies. In terms of research methods, combined weighting using the Analytic Hierarchy Process (AHP) and entropy weight method was adopted to balance subjective and objective weights and reduce biases (the resource conservation dimension accounted for 39.14% of the combined weights, the highest proportion). By integrating the grey clustering model with the whitening weight function to handle fuzzy information, evaluations were categorized into four grey levels (D/C/B/A), enhancing the dynamic adaptability of the system. Case verification showed that Project A achieved a comprehensive evaluation score of 5.223, with a grade of B. Among its indicators, smart-related ones such as “smart energy” (37.17%) and “smart design” (37.93%) scored significantly higher than traditional indicators, verifying that the system successfully captured the project’s high performance in smart indicators. The research results indicate that the efficient utilization of resources is the core goal of green buildings. Especially under pressures of energy shortages and carbon emissions, energy conservation and resource recycling have become key priorities. The evaluation system constructed in this study can provide theoretical guidance and technical support for the promotion, industrial upgrading, and sustainable development of green buildings (including non-civilian buildings) under the dual-carbon goals. Its characteristic of “dynamic monitoring + smart integration” forms differentiated complementarity with international standards, making it more aligned with the needs of China’s intelligent transformation of buildings.

As China’s high-speed railway construction extends deeper into the karst mountainous regions of the southwest, and as engineering construction faces rigid requirements for green and low-carbon transformation under the dual-carbon goals, green construction cost management for mountainous high-speed railways is subject to distinctive constraints and challenges that differ substantially from those of plain-terrain projects. Existing research on railway green construction costs has focused mainly on plain-terrain scenarios and has emphasized independent evaluations of how individual factors affect costs, while insufficiently revealing the coupled transmission relationships and hierarchical structures of cost-influencing factors in complex mountainous terrain. Consequently, management countermeasures often lack precise correspondence with influencing factors, and research conclusions show limited scenario adaptability and practical guidance value. To address these gaps, this study examines the construction phase of the karst mountainous section of the Guiyang–Nanning High-Speed Railway (G-N HSR). Through three rounds of screening—policy benchmarking, literature review, and expert consultation—a green construction cost influencing factor system comprising 20 indicators across five dimensions (external environment, technology, management, resources, and environmental protection) is established. A coupled model combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods is introduced for systematic analysis. Compared with existing studies, this paper makes three differentiated contributions. First, it is grounded in the complex karst mountainous research scenario and fully incorporates constraints specific to mountainous terrain, such as geological complexity, ecological sensitivity, and logistical limitations. Second, it overcomes the limitations of traditional independent single-factor analysis by revealing the hierarchical transmission logic and cost formation mechanisms among factors from a systemic perspective. Third, it establishes a precise correspondence between influencing factor hierarchies and management strategies, thereby avoiding broadly empirical engineering countermeasures. The results indicate that policies and regulations constitute the most fundamental driving factor, with a centrality degree of 2.006 and a cause degree of 1.745. The constructed five-dimension indicator system demonstrates good structural validity, with a cumulative variance explanation rate of 72.35% in exploratory factor analysis. Finally, the study delineates a three-level cost transmission hierarchy comprising a fundamental driving layer, an intermediate transmission layer, and a surface result layer. On this basis, it proposes hierarchical and targeted management countermeasures that can provide theoretical support and practical reference for refined green construction cost management of high-speed railways in southwestern mountainous regions.