The application obstacles of BIM technology in green building project and its key role path analysis

Abstract

The purpose of this study is to identify and analyze the application barriers and internal mechanisms of BIM technology in green building projects. It is found that BIM technology can effectively integrate information and optimize management, but its application still faces economic externalities and requires government intervention to promote its development. Through the analysis of five aspects of technology, economy, management, policy and environment, we identified 16 influencing factors and focused on nine key factors, such as insufficient technology maturity and high-cost issues. Research shows that policy and management factors play a fundamental role in BIM adoption and influence economic effectiveness through complex interactions. Based on the ISM-ANP coupling model, we propose that the government should encourage technological innovation and personnel training to promote the effective application of BIM technology in green buildings. This study provides a theoretical reference for the application of BIM and green building on a global scale.

Introduction

Since the reform and opening up, China’s construction industry has been booming and the market scale has been expanding. According to the National Bureau of Statistics, the total output value of the construction industry in 2023 reached 31.59 trillion-yuan, accounting for 25.06% of the GDP, showing its importance in the national economy^1,2,3. However, rapid development has also brought about problems such as high energy consumption, heavy pollution and low efficiency, which has aroused social attention to the sustainable development of the construction industry⁴. In response to these challenges, green building, as an emerging development direction, has been widely valued. In the context of "Carbon peak, Carbon neutral", Building Information Modeling (BIM) technology, as an advanced digital building design and management tool, can realize the integration and sharing of information throughout the life cycle of green building projects^5,6. With the advantages of BIM, the future construction industry will better realize the efficient use of resources and environmental protection, and promote the comprehensive development of green buildings⁷. Although BIM technology has been introduced successively in China, it is still in the transition stage from primary to intermediate, and model sharing has not been fully realized⁸. Therefore, in the promotion of green buildings, it is particularly necessary to promote the application of BIM.

The research and application of BIM technology in green buildings provides a practical path for the construction industry to achieve sustainable development goals and promote the transformation and upgrading of the construction industry⁹. Liang et al.⁷ found that in green building construction, collision detection in BIM can save up to 10% of the contract value and reduce the construction schedule by 7%. Javier et al.¹⁰ in the indoor renovation case of the Diagnosis and treatment center of Jain University Hospital, reduced the annual energy consumption of the renovated building by 120.94 kwh/m² through thermal simulation, daylight simulation and energy analysis. Lin et al.¹¹ in the energy-saving renovation project of the central public retail market of the new store in Taiwan reduced the load of the air conditioning system by 100 kwh through reasonable application of BIM. With the deepening of the international research on BIM in the design and construction of green buildings, the international research focus on BIM in green buildings has begun to shift to the application of BIM in environmental monitoring and green building certification evaluation. Wong et al.¹² pointed out that combining BIM with Internet of Things (IOT) technology allows real-time monitoring of environmental indicators such as energy consumption, water use, and indoor air quality. Chen et al.¹³ developed a framework by integrating the Autodesk Revit API and Google Maps API to facilitate the integration of BIM with online mapping services for site selection and traffic analysis certification for LEED green building evaluation. These studies provide more efficient and sustainable solutions for green building projects, pushing the construction industry in a greener direction^14,15.

However, there are various barriers to the application of BIM technology in green building projects. In terms of technology integration, the effective implementation of BIM technology relies on integration with other related technologies, such as the Internet of Things (IoT) and building energy analysis software^16,17. Existing research has shown that the lack of integration between different technologies can lead to barriers to information flow, which limits the efficiency of BIM in the full life cycle management of green buildings¹⁸. In addition, despite the power of BIM, its effective use still requires expertise. At present, there is a widespread shortage of BIM professionals in the construction industry, resulting in a lack of understanding and application of BIM technology by project teams. Hall et al.¹⁹ pointed out that strengthening personnel training and skill upgrading is the key to promoting BIM application. In terms of initial investment and economic benefits, many enterprises are cautious about the adoption of BIM because the short-term economic benefits of BIM technology are not obvious^20,21. At the same time, since BIM projects involve multiple participants, the use of different software tools and platforms causes difficulties in data sharing, which in turn affects the efficiency of collaborative work in projects^22,23. At present, the lack of unified laws, regulations and industry standards on the application of BIM technology in green buildings restricts its widespread promotion. Liu et al.²⁴ showed that the risk of data leakage may affect the smooth progress of projects, which requires effective protection measures in the industry.

In summary, domestic research still mainly focuses on the promotion of BIM in green building, and mostly focuses on qualitative analysis, lacking systematic and targeted. In the field of green building in China, the application of BIM is relatively new, and the time period of related research is relatively short. It mainly focuses on “methodology”, providing guidance and suggestions for domestic application by drawing on international theories. This kind of research is often limited to specific projects and fails to comprehensively cover the whole industry.

Therefore, based on the current situation of the industry in which the application and promotion of BIM in green buildings are hindered, this study uses the analysis method of ISM-ANP to sort out and analyze various influencing factors that affect the application of BIM in green buildings, reveal the relationship between them, explore the key influencing factors and action paths, and provide specific suggestions for the promotion of BIM in green buildings. The main contents are as follows:

• 1. Conducted "literature research → Expert interview → Comprehensive analysis" to establish the factors and systems that hinder the application of BIM technology in green building projects.

• 2. Based on the ISM optimized by DEMATEL, a multi-level hierarchical structure model is established and the relationship between influencing factors is analyzed.

• 3. Based on the ISM-ANP coupling model, the action path of the key influencing factors of BIM application in green buildings is analyzed.

Methods

Optimal interpretive structure model based on DEMATEL

ISM is a system analysis tool designed to help decision-makers clarify and understand the hierarchical structure of problems. It has the ability to deal with a large number of elements and complex relationships, and can clearly reveal the hierarchy and internal relationships of the system through qualitative analysis, so as to make the structure of the complex system clearer and more concise^25,26. The traditional ISM method mainly creates simple binary adjacency matrix, which can only represent unidirectional relationship, and it is difficult to meet the modeling requirements of complex system relationship²⁷.

On the basis of ISM method, decision tests and evaluation experiment are introduced to allow the interaction between system elements, and provide a more refined tool to measure the strength of these relationships²⁸. The improved ISM based on DEMATEL introduces the direct influence relationship matrix, which makes the influence relationship more operable. It can not only reflect the existence of the relationship, but also quantify its strength, so as to make the system analysis more accurate²⁹. In addition, the method allows the introduction of thresholds, screening out important influence paths and eliminating redundant paths, making the model more concise and informative³⁰. This improvement has significant application potential in solving complex problems, especially in large-scale system analysis, and provides a more powerful tool for system analysis and complex problem solving³¹. Therefore, an optimized interpretation structure model based on DEMATEL was constructed (as shown in Fig. 1). The specific modeling steps are as follows:

Fig. 1

Modeling process of DEMATEL-ISM.

• 1. Clarify the system elements, denoted as S₁, S₂… … S_n.

• 2. Construct influence matrix X

  $$X = \left[ {\begin{array}{*{20}c} 0 & {a_{12} } & \cdots & {a_{1n} } \\ {a_{21} } & 0 & \cdots & {a_{2n} } \\ \vdots & \vdots & \vdots & \vdots \\ {a_{n1} } & {a_{n2} } & \cdots & 0 \\ \end{array} } \right]$$

  (1)

• 3. The normalized influence matrix G is calculated.

  $$G = \frac{1}{{\mathop {\max }\limits_{1 \le i \le n} \mathop \sum \limits_{j = 1}^{n} a_{ij} }}X$$

  (2)

• 4. Calculate the comprehensive influence matrix T;

  $$T = G + G^{1} + G^{2} + ... + G^{n} = G(I - G)^{ - 1}$$

  (3)

  where I is the identity matrix of n × n;

• 5. Calculate the overall influence matrix H;

  $$H = T + I$$

  (4)

• 6. The threshold λ is introduced, and the value of the threshold λ can be appropriately enlarged or reduced according to the actual situation, where α and β are respectively the mean value and standard deviation of the elements of the overall influence matrix T;

  $$\lambda = \alpha + \beta$$

  (5)

• 7. According to the setting of λ, the reachable matrix M is calculated, where 1 indicates that there is an action relationship and 0 indicates that there is no action relationship.

  $$m_{ij} = \left\{ {\begin{array}{*{20}c} {1,h_{ij} \ge \lambda } \\ {0,h_{ij} < \lambda } \\ \end{array} } \right.(i,j = 1,2,3,...,n)$$

  (6)

• 8. According to the concepts of reachability set and leading set, the reachability set of an element Si is the set of elements represented by all column elements of row i value 1 in the reachability matrix, and its leading set is the set of elements represented by all row elements of column i value 1. By comparing the intersection T(Si) of elements Si with its reactable set R(Si), the top-level element set can be determined, that is, the elements satisfying the condition T(Si) = R(Si), and then the layered elements are eliminated to obtain a new matrix M1. This operation is repeated until all elements are stratified.

• 9. Construct the ISM based on the hierarchical relationship of elements.

Analytic network process model

ANP is a multi-criteria decision analysis method for decision making and evaluation of complex problems, designed to deal with problems that not only involve multiple decision criteria, but also have interdependencies^32,33. Figure 2a shows a typical ANP structure. The ANP method first divides the elements of the problem into two main parts: the control layer and the network layer. The control layer mainly covers the objective and decision criteria of the problem, in which all decision criteria are regarded as independent of each other and constrained by the objective elements. Although this layer may not directly include the decision criteria, it must contain at least one objective, and the weight between the criteria can be determined by the Analytic Hierarchy Process (AHP). The network layer contains all the elements in the control layer, forming a network structure that interacts with each other^9,34. Figure 2b shows the ANP construction process, which is as follows:

Fig. 2

ANP analysis method.

• 1. Construct ANP structure model

By analyzing the objective and evaluation criteria, the purpose of decision making is clarified, and the interaction between the internal elements of the system is identified.

• 2. Establish a judgment matrix

Based on the relationship between elements, a judgment matrix is formed to evaluate the relative importance of elements, and the relative importance of elements is scored by the established scale Table 1.

Table 1 Scale table of judgment matrix.

In the control layer of ANP model, let the element set be ${P}_{1},{P}_{2},...{P}_{m}$, there are element groups ${C}_{1},{C}_{2},...{C}_{N}$, where ${C}_{i}$ has elements ${e}_{i1},{e}_{i2},...,{e}_{i{n}_{i}}$, ${n}_{i}$ is the number of elements in element group ${C}_{i}$. The element ${P}_{s}$ in the control layer is taken as the criterion, and the element ${e}_{jl}$ in the element ${C}_{j}$ in the network layer is taken as the secondary criterion, and the element in the element group ${C}_{i}$ is compared by indirect dominance according to its influence on ${e}_{jl}$, that is, the judgment matrix is constructed. When solving the judgment matrix, consistency detection should be performed to ensure that C.R. The value is less than 0.1.

$$C.{\text{R}}. = \frac{C.I.}{{R.I.}},C.{\text{I}}. = \frac{{\lambda_{\max } - n}}{n - 1}$$

(7)

where C.I. is the consistency index; ${\lambda }_{\text{max}}$ is the largest eigenroot; n is the order of the judgment matrix; R.I. is the average random consistency index.

The judgment matrix is constructed under the P_s control layer criterion. By the characteristic root method and sorting vector ${\left.\left(\begin{array}{cccc}{w}_{i1}^{(jl)},& {w}_{i2}^{(jl)},& \cdots ,& {w}_{i{n}_{i}}^{(jl)}\end{array}\right.\right)}{\prime}$, as ${W}_{ij}$:

$$W_{ij} = \left[ {\begin{array}{*{20}c} {w_{i1}^{(j1)} } & {w_{i1}^{(j2)} } & \cdots & {w_{i1}^{{(jn_{j} )}} } \\ {w_{i2}^{(j1)} } & {w_{i2}^{(j2)} } & \cdots & {w_{i2}^{{(jn_{j} )}} } \\ \vdots & \vdots & \vdots & \vdots \\ {w_{{in_{i} }}^{(j1)} } & {w_{{in_{i} }}^{(j2)} } & \cdots & {w_{{in_{i} }}^{{(jn_{j} )}} } \\ \end{array} } \right]$$

(8)

Here the column vector in ${W}_{ij}$ is the ${C}_{i}$ element ${e}_{i1},{e}_{i2},...,{e}_{i{n}_{i}}$ pairs ${C}_{j}$ elements ${e}_{j1},{e}_{j2},...,{e}_{j{n}_{j}}$ influence degree ranking vector. In this way, the supermatrix W under ${P}_{s}$ can be obtained:

$$W = \left[ {\begin{array}{*{20}c} {W_{11}^{{}} } & {W_{i1}^{{}} } & \cdots & {W_{1N}^{{}} } \\ {W_{21}^{{}} } & {W_{22}^{{}} } & \cdots & {W_{2N}^{{}} } \\ \vdots & \vdots & \vdots & \vdots \\ {W_{NN}^{{}} } & {W_{NN}^{{}} } & \cdots & {W_{NN}^{{}} } \\ \end{array} } \right]$$

(9)

The subblock ${W}_{ij}$ of the supermatrix is column normalized, but W is not. Therefore, taking ${P}_{s}$ as the criterion, the importance of each element group under ${P}_{s}$ to criterion ${C}_{j}(j=\text{1,2},...,N)$ is compared. The ordering vector component corresponding to the element group unrelated to ${C}_{j}$ under ${P}_{s}$ is zero, and the weight matrix is thus obtained:

$$A = \left[ {\begin{array}{*{20}c} {a_{11} } & {a_{12} } & \cdots & {a_{1N} } \\ {a_{21} } & {a_{22} } & \cdots & {a_{2N} } \\ \vdots & \vdots & \vdots & \vdots \\ {a_{N1} } & {a_{N2} } & \cdots & {a_{NN} } \\ \end{array} } \right]$$

(10)

Weighting the elements of the supermatrix W gives $\overline{W }=({\overline{W} }_{ij})$, where:

$$\overline{W}_{ij} = a_{ij} (W_{ij} )i = 1,2, \cdots N;j = 1,2, \cdots N$$

(11)

$\overline{W }$ is a weighted supermatrix whose column sum of 1 is called a column random matrix.

By iteratively calculating the weighted supermatrix until it reaches a stable state, the limit ordering vector of each element in the network layer relative to a specific element is obtained, and the final influence ordering is established.

$$\overline{W}^{\infty } = \mathop {\lim }\limits_{t \to \infty } \overline{W}^{t}$$

(12)

ISM-ANP coupling model

By transforming the element system into a multi-level structure, ISM clearly shows the hierarchical relationship and mutual influence among various elements, but the ISM model cannot well reflect the relative importance of elements. The ANP method emphasizes quantitative analysis, assigning weight to each element and quantifying the influence relationship between them. The combination of ISM and ANP takes full advantage of their respective strengths. ISM analysis method can clarify the system structure qualitatively and understand the interaction between elements. Using ANP analysis method, the relative importance of each factor can be evaluated quantitatively, and an operational quantitative index can be provided for the influence degree of each factor³⁵. This combined application not only makes the problem analysis more comprehensive, but also fully combines subjective judgment and objective data.

In green buildings, BIM application factors form a complex multi-level system, which is suitable for using ISM-ANP method to analyze its interaction³⁶. ISM can establish a multi-level hierarchical structure model of the influencing factor system of BIM application in green buildings, and show the hierarchical structure and functional relationship of the influencing factor system. The ANP analysis method is used to calculate the importance weights of each influencing factor, and it is integrated into the ISM in a visual way to explore the node position and function relationship of influencing factors in the ISM³⁷. By combining these two methods, we can better reveal the key influencing factors and action paths of BIM application and promotion in green buildings³⁸. At the same time, provide stronger support for decision makers to promote the integration between green building and BIM, and promote the development of sustainable construction.

Application and analysis

Identification of influencing factors and system construction

This study is based on the enterprise level to study the influencing factors of BIM application in green buildings, so as to better reflect the actual situation. On the “China Knowledge Network” and “Web of Science”, the subject keywords are used to screen the literature, and the steps of “ Literature research → Expert interview → Comprehensive analysis ” are followed to identify the influencing factors system of BIM application in green buildings.

By sorting out and summarizing the original influencing factors table of BIM application in the construction industry, this paper initially identified 47 original influencing factors from five aspects: technology, economy, management, policy and environment.

However, there are homogeneous and overlapping factors. In order to ensure the rationality, completeness and representativeness of the selection of influencing factors, this paper invited ten experts with rich experience in the field of green building and BIM technology to conduct semi-structured interviews as interview subjects to select and revise the initial influencing factor system. The influencing factor system of BIM application in green buildings with 16 indicators from 5 aspects was identified (see Table 2).

Table 2 Influencing factor system of BIM application in green buildings.

Multi-level hierarchical structure model construction

Analysis of influencing factors

In order to obtain the strength of the relationship between the influencing factors of BIM application in green buildings, questionnaires were distributed to professionals and researchers engaged in BIM and green building related work through online and offline methods. Considering the unfavorable situation for the overall results, this paper invited 10 experts from universities, research institutions and enterprises with rich knowledge and experience in the field of green building and BIM technology to evaluate the relationship between various influencing factors, and then eliminated invalid questionnaires based on the data of expert interviews. A total of 179 effective questionnaires were obtained in this study, with an effective rate of 75.53%. The summary of basic information is shown in Fig. 3.

Fig. 3

Statistical data of valid questionnaires.

S_ij was used to represent the influence degree of factor S_i on S_j, and the scale of influence relationship intensity was from 0 to 5, and the influence degree gradually increased. The proportion of respondents who believe that S_i has an impact on S_j is P. When 0% ≤ P(S_ij) < 20%, the S_ij is assigned 1; when 20% ≤ P(S_ij) < 40%, the S_ij is assigned 2; when 40% ≤ P(S_ij) < 60%, the S_ij is assigned 3; when 60% ≤ P(S_ij) < 80%, the S_ij is assigned 4; when 80% ≤ P(S_ij) < 100%, the S_ij is assigned a value of 5. After processing the questionnaire data, the direct impact matrix X was constructed, as shown in Table 3. It can be seen that there is a complex interaction relationship within the influencing factor system of BIM application in green buildings.

Table 3 Direct impact matrix.

The solution of reachable matrix

• 1. Comprehensive influence matrix

The normalized direct influence matrix G is calculated according to Eq. (2), and then the comprehensive influence matrix T is calculated according to Eq. (3) with the help of MATLAB R2023a software (Program code is shown in Appendix I). As shown in Appendix Table 13, and its mean α is 0.0608 and standard deviation β is 0.0773.

• 2. Overall influence matrix

According to Eq. (4), the overall influence matrix H is calculated, as shown in Appendix Table 14

• 3. Reachable matrix

The reachable matrix M is calculated according to Eq. (6), as shown in Table 4. In order to simplify the model, λ is used here as the threshold, and the threshold is calculated as 0.1381 according to the Eq. (5).

Table 4 Reachable matrix M.

Construct the DEMATEL-ISM model

Based on Section "Optimal interpretive structure model based on DEMATEL", the relevant sets are calculated, and the results are shown in Table 5. By analyzing the accessibility matrix, the interaction and hierarchical relationship between each factor are determined. The results show that S5 and S6 are at the top of the model, and then the factor set of each level is further subdivided, from the top to the bottom are {S2, S4, S7, S8, S15}, {S1, S9, S16}, {S3, S11}, {S10}, {S12, S14} and {S13}. Based on this, a multi-level hierarchical structure model is built, as shown in Fig. 4. The circle is the influencing factor; A one-way arrow indicates that there is an influence relationship between factors; The dashed lines are the boundaries that divide the different levels.

Table 5 Calculation of related sets.

Fig. 4

Multi-layer hierarchical structure model.

Determination of key influencing factors

• 1. Network structure model construction

Establish the network structure model of influencing factors of BIM application in green buildings (as shown in Fig. 5). The top layer of the model is targeted as "barriers to BIM adoption in green buildings", while the network layer is composed of five main groups of influencing factors: economic, technical, environmental, regulatory and policy factors. The specific influencing factors within these groups are labeled Si, which contains a total of 16 individual influencing factors. Based on the ANP network structure model of BIM application in green buildings, Super Decisions software was used to first define Cluster and Node, and then establish the interrelation and feedback mechanism among influencing factors. The software will automatically generate the network structure diagram according to the established connection and feedback mechanism.

Fig. 5

Network structure model.

• 2. Judgment matrix construction

Due to space limitations, this paper cannot show the judgment matrix of pairwise comparison of all experts’ influencing factors. Therefore, this study takes an expert’s partial judgment matrix as an example to illustrate the steps of ANP analysis.

When analyzing the influencing factors of BIM application in green buildings, the main criterion is set as "BIM application advancement in green buildings is hindered", and a specific factor group is used as a sub-criterion to assess the strength of the influence of other factor groups on it. For example, the technical factor group is used to evaluate the strength of influence of other factor groups on the technical factor group based on this criterion. As shown in Table 6, the judgment matrix data between the factor groups is input into the Super Decisions software to calculate the importance weight and C.R of other factor groups on the technical factor group. Be worth. The judgment matrix of this group, C.R. = 0.029, is less than 0.1 and passes the consistency test. Among them, the "technical factor group pair" has the greatest influence on the "technical factor group" with the relative weight of 0.36039, and the "management factor group" has the least influence on the "technical factor group" with the relative weight of 0.07643. Similarly, the remaining four factor groups are input into the Super Decisions software as the judgment matrix of this criterion to calculate the weight of the factor groups respectively. After obtaining the result data of the judgment matrix between the five factor groups, the weighted matrix M was obtained by applying Eq. 10, as shown in Table 7.

Table 6 Judgement matrix with technical factor group as the secondary criterion.

Table 7 Weighted matrix M.

The pairwise comparison judgment matrix between specific influencing factors takes a certain influencing factor in a certain group as the secondary criterion to analyze the importance of relevant factors in the group or related factors in other groups to the factor. For example, taking "imperfect policy incentive mechanism for green building BIM application S13" as the sub-criterion, the relative weight of factors within the technical factor group to factor S13 was calculated, as shown in Table 8. The judgment matrix of this group, C.R. = 0.070, is less than 0.1 and passes the consistency test. In the group of technical factors, "lack of S3 in local BIM core software for green buildings" has the greatest impact on S13, with a relative weight of 0.53487; "Insufficient maturity of BIM in green building performance analysis S1" has the second impact on S13, with a relative weight of 0.35037; "Insufficient interoperability and compatibility of BIM with green building design tools S2" has the least impact on S13, with a relative weight of 0.11476.

Table 8 The judgment matrix among factors within the technical factor group under the sub-criterion S13.

• 3. Solving the ultimate supermatrix

In view of the complex relationship among the influencing factors, in order to accurately evaluate the relative importance of these factors, it is necessary to deal with the stability of the weighted supermatrix. This study uses Super Decisions software to calculate complex hypermatrices. By applying the Eq. 12 to solve the limit supermatrix, the importance of each influencing factor is measured effectively.

In the Super Decisions software, by entering the pairwise judgment matrix between each factor and performing corresponding operations, the software automatically calculates the unweighted supermatrix of the influencing factors of BIM application in green buildings. The weighted supermatrix reflects the direct influence of each element as a secondary standard. However, these factors often show a complex indirect interaction. The ultimate supermatrix of influencing factors of BIM application in green buildings can be obtained by executing corresponding operations in Super Decisions software. The ultimate supermatrix is exported in.txt format and sorted through Excel tables. Specifically, as shown in Appendix Table 15, each column of the ultimate supermatrix reflects the importance of all indicators to the overall goal.

• 4. Ranking the importance weight of influencing factors

According to the judgment matrix of Expert 1, the Super Decisions software executes the Priorities calculation command, and the software will automatically calculate the ranking result of the importance of the influencing factors of BIM application in green buildings of Expert 1, and calculate the analysis results of other experts in the same way, as shown in Table 9.

Table 9 Summary of expert importance analysis results.

After the results of the importance of the factors affecting BIM application in green buildings are obtained, the entropy weight method is used to process the results. With the help of Python 3.8 software (see Appendix II for the code), the entropy and corresponding weights of each expert are calculated, and the results are summarized in Table 10. Then, weighted average processing is performed on the importance weights of each influencing factor, and the final importance weights of the influencing factors of BIM application in green building are obtained, as shown in Table 11.

Table 10 Summary of expert weights.

Table 11 Weight of influencing factors of BIM application in green buildings.

Discussion and suggestions

Hierarchical analysis of the relationship between influencing factors

After establishing the multi-layer hierarchical structure model of BIM application in green buildings, the structure level of ISM analysis is adjusted by analyzing the internal function relationship of the model. Reassign the hierarchy position of some elements and combine the hierarchy with common characteristics to make the hierarchy more logical and representative, so as to analyze the structural hierarchy of the element system more effectively. According to Fig. 4, the following adjustments were made: S7 was moved from the second layer to the first layer and set as a separate layer, with the first layer being economic factors; S9 is moved from the third to the fourth floor, while the second and third floors are merged into one floor, the second floor is dominated by technical and environmental factors; The fourth and fifth layers will be merged, and the third layer will focus on management factors; S13 is adjusted from the seventh layer to the sixth layer, and is set as an independent layer, and the fourth layer is a policy factor. In the adjusted model, the influencing factor system of BIM application in green building is divided into four levels, and its internal relationship follows the rule of "Policy factor → Management factor → Technical and environmental factor → Economic factor", as shown in Fig. 6.

Fig. 6

The adjusted hierarchical structure model.

• 1. Analysis of the first layer influencing factors

The initial layer of the multi-layer hierarchical structure model of the influencing factors system of BIM application in green buildings is constituted by economic factors, including the exorbitant cost of utilizing BIM in green buildings (S5), the lack of evident investment return from BIM in green buildings (S6) and the protracted cost recovery cycle of BIM application in green buildings (S7). The first-floor factors exert a direct or indirect influence on other level factors, and can have a direct impact on the application of BIM in green buildings without reliance on other factors.

Construction companies are driven by the pursuit of optimal economic outcomes. The integration of BIM, however, entails significant initial investments, including software acquisition and personnel training, which could potentially exert a detrimental impact on project economics in the early stages. Although BIM has the potential to enhance design and construction efficiency and reduce the cost of errors, it is challenging to translate these benefits into immediate financial returns. Consequently, enterprises tend to prioritize short-term benefits while overlooking the long-term advantages of BIM. In a highly competitive market environment, enterprises may be reluctant to adopt BIM technology due to its high initial investment, particularly during periods of economic uncertainty.

In summary, while BIM is perceived as a valuable tool for advancing green building development, its implementation may be at odds with the economic benefit maximization objective of construction enterprises. Consequently, economic considerations emerge as a pivotal factor influencing the adoption of BIM in green buildings.

• 2. Analysis of the second layer influencing factors

The second layer of barriers to the adoption of Building Information Modelling (BIM) in green building projects is primarily constituted by technical and environmental factors, along with other types of factors. From a technical standpoint, a number of challenges impede effective implementation. Firstly, the functionality of current BIM software is often limited, with a lack of comprehensive capabilities for energy analysis and sustainable material selection, which restricts design innovation. Secondly, incompatibility issues between different BIM software result in the loss of information, which has a detrimental impact on team collaboration and increases the risks associated with both the construction and operational phases. Furthermore, the operational intricacy of the majority of BIM interfaces presents a considerable obstacle, rendering these tools challenging for those without the requisite training to navigate effectively, and thus impeding effective communication and collaboration.

Environmental factors also play a pivotal role, particularly the dearth of interdisciplinary expertise, given that the integration of green building and BIM technology is still in its infancy. The market’s limited number of professionals with expertise in both design and construction impedes the development of relevant skills. This is further compounded by an education system that fails to meet the interdisciplinary demands of the industry. This deficit of qualified professionals increases the cost of acquiring professional BIM services. Moreover, the constraints of the conventional design-bid-build delivery model impede effective lifecycle collaboration and information transmission, as each discipline typically utilizes discrete software solutions. This separation is further compounded by the reluctance of professionals, who are accustomed to conventional methods, to adapt to the requirements of BIM. This ultimately complicates the implementation of BIM in construction projects. Finally, the inherent time and effort required to apply BIM in green building projects, coupled with the lack of incentives for stakeholders, often leads them to prefer traditional working methods. This restricts the full potential of BIM in construction projects.

• 3. Analysis of the third layer influencing factors

The third layer is primarily constituted of management factors, with the incorporation of additional types of factors. A significant challenge is the lack of a robust implementation and management system, which can give rise to a number of substantial difficulties. Firstly, difficulties in coordination among project teams frequently result in inefficiencies. A well-structured management system is therefore essential for clarifying execution paths and delineating responsibilities, thereby reducing confusion. Secondly, an inadequate risk management system impedes the timely processing of data and information, thereby increasing the probability of project failure. Additionally, there is a dearth of professional teams with the requisite skills, as the absence of systematic training precludes them from keeping pace with industry advancements, thereby constraining the effective application of BIM. Moreover, the absence of senior management endorsement can impede project advancement and the distribution of resources, as managers may fail to recognize the significance of BIM in the context of green construction. The provision of top management support is of paramount importance for the successful integration of BIM, particularly in relation to decision-making, the allocation of resources and the training of personnel. A restricted comprehension of green building principles may additionally serve to reduce support, which could have a detrimental effect on the implementation of BIM. Given that BIM integration is still in its infancy and characterized by complex software operations and rapidly evolving technology, there is a pressing need for extensive learning and training opportunities to enable adaptation to emerging trends.

Furthermore, additional factors, such as the absence of locally-developed BIM core software, contribute to elevated procurement and maintenance costs. Moreover, the discrepancy between China’s green building standards and those of other countries necessitates further adaptations to ensure effective software utilization. Foreign software may not align with China’s regional characteristics and sustainable development requirements, and there are variations in regulations.

• 4. Analysis of the fourth layer influencing factors

The fourth layer is primarily concerned with policy-level factors. As the foundation of the model, these factors in the fourth layer exert a direct or indirect influence on all the factors in the upper layer.

BIM applications for green buildings are subject to a number of legal and compliance-related considerations. The imperfect legal system presents enterprises with significant challenges in ensuring project compliance, thereby increasing legal risks. Furthermore, the involvement of multiple parties in BIM projects, coupled with the absence of a clear legal basis for responsibility sharing and dispute resolution, may give rise to disputes. Furthermore, the BIM process entails the involvement of a considerable amount of intellectual property and privacy information. The absence of regulations renders it challenging to ensure the security and lawful utilization of data, which in turn gives rise to disputes. Furthermore, environmental and sustainability factors are challenging to support due to the lack of adequate legislation, which affects the effectiveness of BIM applications.

The implementation of BIM in green buildings necessitates the provision of governmental incentives. Frequently, the free-market development results in a lag, and organizations are more inclined to reduce costs than to adopt new technologies. It can be seen that there is still scope for improvement in the incentive mechanism for the application of green building BIM in China. To illustrate, the lack of unified policy makes it challenging for enterprises to devise an overarching strategy. Furthermore, the extended policy implementation cycle dampens the enthusiasm of enterprises and hinders their ability to swiftly garner support.

Furthermore, the absence of BIM standards and implementation guidelines for green buildings has a considerable impact on projects. The lack of a unified standard affects software interoperability, such as energy simulation software that requires data exchange with other BIM models. In the absence of standards, data transmission is challenging, which can impair the precision of decision-making. Additionally, it is difficult to ensure data quality, which can result in inaccurate energy assessments and suboptimal material selection, thereby impeding the achievement of the desired outcome.

Analysis of key influencing factors and action paths

• 1. Determination of key influencing factors

There are a total of 16 factors in the BIM application influencing factor system in green buildings, and the calculated average importance weight is 0.0625. This paper finds that the importance weight of some influencing factors is significantly higher than the average value, while that of some factors is significantly lower than the average value, indicating that there are significant hierarchical differences in the importance of influencing factors. Therefore, the influential factors whose importance weight exceeds the average value are regarded as key influential factors in this paper, as shown in Table 12.

Table 12 Summary of key influencing factors.

• 2. Integrated modeling of ISM and ANP

Based on Section "Multi-level hierarchical structure model construction", a multi-level hierarchical structure model of influencing factors of BIM application in green buildings is constructed. After obtaining the weight of various influencing factors of BIM application in green buildings through ANP analysis, the ANP analysis results are integrated into the ISM in a visual form, as shown in Fig. 7. In the model, each factor node displays the factor number, weight value and weight ranking in the order from top to bottom, and the color depth of the node is used to represent the weight size, which is convenient for the analysis of the model. This dual methodology not only ensures the reliability of the research conclusions, but also strengthens its academic and practical guiding value, and explores the key influencing factors and action paths of BIM application in green buildings through the method of ISM-ANP coupling.

Fig. 7

ISM-ANP combination model.

• 3. Analysis of Key Function Paths

According to the combination model of ISM-ANP, the key action path in this model presents a progressive relationship from the bottom to the top. In this paper, it is summarized as "Insufficient government intervention in the application of green building BIM → Insufficient high-level support and imperfect management system; Insufficient research and development of domestic BIM core technology combined with green building → BIM technology is difficult to innovate and spread in the field of green building; Lack of compound talents → Insufficient economic benefits of green building BIM application → Affecting BIM in green building. " As shown in Fig. 8. The specific analysis is as follows:

• 1. The economic benefits of BIM application in green buildings are insufficient, which directly affects its promotion. Economic factors are the top-level influencing BIM adoption, with the key factor being "high additional costs". Construction enterprises pursue profit maximization, which makes them face the contradiction of low short-term economic benefits when deciding whether to adopt BIM, which in turn affects the use of BIM.

• 2. The field of green building has yet to fully embrace the potential of BIM technology, and the dearth of qualified professionals continues to impede the realization of economic benefits. These two factors exert a direct influence on the economic consequences of BIM applications. The construction of green buildings necessitates technological innovation, and the supplementary costs incurred by BIM are contingent upon market supply and demand. The concentration of market control in the hands of a few firms will result in higher costs due to a lack of supply. Conversely, the widespread adoption of the technology will lead to a reduction in costs. The advancement of green buildings necessitates technological advancement, yet it demands a considerable investment of resources. Conversely, the construction industry is evolving towards informatics, and conventional training methodologies are unable to cultivate individuals who possess both project management capabilities and BIM operational abilities, resulting in a dearth of composite talents and further escalating the cost of utilizing BIM. Consequently, technological innovation, dissemination and the absence of interdisciplinary talents directly influence the economic benefits of BIM in green buildings.

• 3. The absence of substantial backing from senior management, the deficiencies in the organizational structure and the dearth of research and development activities pertaining to the integration of BIM core technology and green building practices in China have constrained the advancement and dissemination of BIM technology in the domain of green building, and have also resulted in a scarcity of multidisciplinary professionals.

Fig. 8

The application of BIM in green buildings affects the action path.

The lack of local BIM software for green buildings, inadequate BIM implementation and management systems, and insufficient support from senior management represent key factors that impede the advancement of BIM technology in the field of green building. The provision of top management support is of critical importance with regard to the adoption of BIM technology, particularly in the context of complex green building projects. In the absence of recognition by management of the value of BIM and green building, it is challenging to justify investment or support the effective implementation of BIM, which in turn affects the promotion of corporate culture and technology.

An imperfect management system results in a lack of strategic planning for BIM applications. In the context of green buildings, BIM is not merely a design tool; it also encompasses environmental analysis and energy optimization. In the absence of comprehensive management support, it is challenging to nurture interdisciplinary professionals who possess a comprehensive understanding of BIM. Consequently, BIM applications remain confined to the fundamental level, unable to permeate into domains such as energy management.

The domestic BIM software market is deficient in core software that aligns with the requirements of green buildings. Consequently, enterprises frequently rely on foreign software, which escalates the cost and complexity of projects. These challenges diminish the appeal of BIM.

• 4. The government’s inadequate involvement in the implementation of BIM in green buildings has resulted in a dearth of robust support, an imperfect management system and a paucity of research and development in the field of domestic BIM software. The absence of pertinent legal and policy support has impeded the uptake of BIM by enterprises. It is evident that policy support is of paramount importance in the dissemination of novel technologies. Financial incentives, such as government subsidies and tax breaks, can mitigate the perceived risk of enterprises adopting new technologies, thereby facilitating the enhancement of management systems. By establishing a legal framework and industry standards, the government cannot only stimulate the growth of the BIM field but also stimulate the domestic demand for high-quality BIM software. Consequently, the absence of government intervention has resulted in a dearth of high-level support, management systems and core software.

• 4. In-depth analysis of key influencing factors and action paths

From the perspective of the factors influencing the application of BIM in green buildings, the high cost of BIM represents a significant barrier to its implementation. The prevailing strategy for advancing BIM relies on government financial subsidies and tax incentives, with the objective of reducing the economic burden on enterprises and fostering greater enthusiasm for the adoption of BIM. For instance, a directive from the Ministry of Housing and Urban–Rural Development underscores the significance of policy backing and urges local governments to extend further incentives for intelligent construction. Furthermore, the State Council’s guideline underscores the necessity for the construction industry to achieve carbon peaking and carbon neutrality, and it endorses the advancement of green buildings through a multitude of policies. While these policies facilitate the implementation of BIM, they fail to address the underlying issue. Once the economic stimulus is withdrawn, the promotion of BIM may stagnate.

It is therefore evident that the promotion of BIM application in green buildings requires a combination of internal adjustments and external intervention in order to reduce dependence on external stimuli and overcome the barriers to promotion. The key influencing factors are the innovation of BIM technology and the lack of interdisciplinary talent. While external incentives may temporarily reduce costs, sustained cost reduction depends on technological progress and talent development.

In order to maintain the application of BIM in green buildings, it is essential that the government pays attention to market demand, encourages technological innovation and attaches importance to the training of multi-skilled talents. This will enable the effective reduction of production costs through the dual-wheel drive of technology and talent.

Application promotion suggestions

• 1. Policy aspect

According to the coupled model of ISM-ANP, the three key factors influencing BIM application in green buildings—"imperfect legal and policy support" (S12), "imperfect policy incentive mechanism" (S13), and "lack of BIM standards and implementation guidelines" (S14)—are all at the bottom, and their importance weights are above average. It shows that they are very important in promoting BIM applications.

It is incumbent upon the government, in its capacity as the industry regulator, to provide stronger legal support for the application of BIM in terms of policy. The extant legislation and regulatory framework is largely oriented towards promotion and encouragement, and there is a pressing need for more robust and explicit support measures. It would be prudent for the government to incorporate BIM application standards into the fundamental building regulations in order to guarantee their implementation and promotion. Such measures include the mandatory use of BIM for certain types of green building projects, such as large infrastructure or public welfare projects, while a gradual introduction path is set for smaller private investment projects, with the provision of financial incentives and technical support. As the adoption of BIM technology becomes more widespread and costs decrease, these measures can be extended to encompass a greater number of project types. This classification and phased approach can effectively promote the application of BIM in green buildings, while also accounting for the specific circumstances of different projects and avoiding undue burden on the industry. This approach can successfully achieve the standardization and widespread application of BIM.

• 2. Management aspect

In the context of green building projects, the establishment of an effective work management system represents a crucial step in promoting the application of BIM technology. It is incumbent upon governments and industry leaders to integrate BIM management requirements into traditional building management systems in order to achieve both technical and environmental goals.

By formulating policies and providing funds, the government can encourage the development of BIM management models that are appropriate for green buildings. This will facilitate the application of BIM in the design, construction and operation and maintenance stages, optimize the use of resources and reduce the environmental impact. Concurrently, there is a need to reinforce the education and training of BIM in order to enhance the proficiency of the industry in this field. It is recommended that enterprises employ BIM for innovative purposes, such as project simulation and visualization management utilizing virtual reality (VR) and augmented reality (AR) technologies, with the objective of enhancing the efficiency and quality of green building projects.

Furthermore, the establishment of standards and specifications for BIM application ensures the consistency and effectiveness of the technology in different projects, including data standards, sharing protocols and coordination processes. In addition to providing support at the policy level, the government must also promote the innovation of the management system and effectively promote the wide application of BIM technology in green building projects. This will improve project efficiency and sustainability, and pave the way for the transformation and upgrading of the construction industry.

• 3. Technical and environmental aspects

It is of paramount importance to promote the advancement and widespread adoption of BIM technology, while concurrently addressing the shortage of core software, in order to enhance the efficiency and sustainability of China’s construction industry, particularly within the context of green buildings. Nevertheless, there is a considerable discrepancy between the level of innovation and utilization of BIM technology in China and that observed in developed countries.

In order to bridge this gap, a number of measures can be implemented. Firstly, the government should establish clear policies and support mechanisms, such as tax incentives and financial subsidies, to encourage enterprises and research institutions to develop and utilize BIM technology. Secondly, international cooperation is essential. By actively collaborating with nations and institutions that have successfully implemented BIM, it will be possible to facilitate the introduction of advanced technologies and management practices, which should be tailored to the domestic market. Additionally, the government could establish an industry alliance centered around BIM to foster collaboration among construction companies, software developers, and academic institutions, thereby promoting technological innovation and standard-setting. Moreover, the establishment of a dedicated fund to incentivize enterprises to invest in original research and development of BIM technology for green buildings is crucial, particularly in supporting initiatives that enhance building energy efficiency. Finally, optimizing the incentive and evaluation mechanisms for technical personnel by aligning them with career development goals can stimulate innovation potential within the industry. Concurrently, in response to the dearth of demand for interdisciplinary talents, the government can reinforce BIM teaching and training within the educational system, guarantee that construction professionals are proficient in BIM and green building technology, and encourage enterprises to conduct internal talent training through financial support and tax incentives.

• 4. Economic incentive aspect

Building Information Modelling (BIM) is widely regarded as an effective tool for advancing the development of environmentally sustainable buildings. However, the integration of BIM with other construction technologies is still in its nascent stages. The initial costs of green buildings and BIM are considerable in comparison to traditional buildings. Consequently, government economic incentives are of paramount importance, particularly in terms of promoting the innovation and application of BIM technology.

It is recommended that the government establish a "Green Building BIM Innovation Fund" with the objective of providing subsidies for enterprises to utilize BIM in green building projects, and to support the adoption of efficient building information management systems and advanced building data analysis tools. Furthermore, the government may consider providing tax incentives to enterprises that utilize BIM in green building projects, thereby reducing the financial burden associated with the acquisition of BIM-related software, hardware and training resources.

It is further recommended that the application and innovation of BIM should be included in the incentive system of the construction industry. Enterprises should be encouraged to consider the performance of green buildings in the application of BIM when evaluating excellent projects. The aforementioned economic incentives can effectively promote the application of BIM in green buildings, stimulate the enthusiasm of enterprises for technological innovation, and promote the development of the entire construction industry in a greener, more efficient and sustainable direction.

Conclusion

This paper comprehensively identifies and analyzes the application obstacles and internal action mechanisms of BIM technology in green building projects, reveals the key factors and action paths of BIM application in green buildings, and draws the following conclusions based on the specific situation in China:

• 1. BIM technology can integrate key information and optimize building design and management, but the combination of the two is still in the exploratory stage and faces economic externalities, requiring government intervention and support to promote the effective application of BIM technology.

• 2. At present, the influencing factors of BIM application in green buildings mainly come from five aspects: technology, economy, management, policy and environment. Through the analysis, we identified 16 factors including technology maturity, high cost, inadequate management, etc., and constructed a system of influencing factors for BIM application in green buildings. In this system, nine key influencing factors are identified, such as the lack of compatibility between BIM and green building tools, high cost, imperfect management system, etc., which have a significant impact on the application effectiveness of BIM technology.

• 3. There are complex interactions within the influencing factor system of BIM application in green buildings. On the whole, the function law of the influencing factor system is as follows: "policy factor layer → management factor layer → technical and environmental factor layer → economic factor layer", which indicates that policy and management factors play a basic and guiding role in the application of green building BIM.

• 4. Through the analysis of the ISM-ANP coupling model, we identify the key role path, showing a progressive relationship from the bottom to the top. The lack of government intervention and high-level support may restrict the innovation and development of BIM technology, which in turn affects the economic benefits of green buildings. Therefore, we suggest that the government should encourage technological innovation and talent cultivation to promote the application of BIM in green buildings.

Although it is based on the research perspective of China, its analysis method and the key factors identified are also widely applicable in the field of green building in other countries, providing a reference perspective and method for relevant research on a global scale. This study provides a theoretical basis for promoting the international application of BIM technology in the field of green building, but in future studies, further exploration and quantification of the differences in policy background, market environment and technology adaptation in different countries are needed to improve the understanding of the role of BIM in global green building practice.