Next Article in Journal
Leveraging Post-Disaster Windows of Opportunities for Change towards Sustainability: A Framework
Next Article in Special Issue
How Does Transformational Leadership Promote Innovation in Construction? The Mediating Role of Innovation Climate and the Multilevel Moderation Role of Project Requirements
Previous Article in Journal
The Role of Internet of Things (IoT) in Smart Cities: Technology Roadmap-oriented Approaches
Previous Article in Special Issue
Optimizing the Construction Job Site Vehicle Scheduling Problem
Open AccessFeature PaperArticle

Factors Affecting Green Residential Building Development: Social Network Analysis

1
School of Civil Engineering, Harbin Institute of Technology, Harbin 150001, China
2
School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(5), 1389; https://doi.org/10.3390/su10051389
Received: 17 April 2018 / Revised: 26 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018

Abstract

Green residential buildings (GRBs) are one of the effective practices of energy saving and emission reduction in the construction industry. However, many real estate developers in China are less willing to develop GRBs, because of the factors affecting green residential building development (GRBD). In order to promote the sustainable development of GRBs in China, this paper, based on the perspective of real estate developers, identifies the influential and critical factors affecting GRBD, using the method of social network analysis (SNA). Firstly, 14 factors affecting GRBD are determined from 64 preliminary factors of three main elements, and the framework is established. Secondly, the relationships between the 14 factors are analyzed by SNA. Finally, four critical factors for GRBD, which are on the local economy development level, development strategy and innovation orientation, developer’s acknowledgement and positioning for GRBD, and experience and ability for GRBD, are identified by the social network centrality test. The findings illustrate the key issues that affect the development of GRBs, and provide references for policy making by the government and strategy formulation by real estate developers.
Keywords: green residential building; social network analysis; influential factors; critical factors; development strategy green residential building; social network analysis; influential factors; critical factors; development strategy

1. Introduction

Resource shortage and environmental deterioration affect the development of the construction industry. It is estimated that, in China, the construction industry consumes 40–50% of raw materials and about 20% of energy [1]. Although many functions have been submitted to reduce energy consumption [2,3,4], there are great numbers of high-energy consuming buildings in China, many of which are currently under construction [1]. Green building (GB), as one of the best practices of sustainable development in the construction industry, has drawn much attention [5,6,7,8]. Many researches of GB have been analyzed from different aspects, for example: Technological innovation [9,10,11], energy saving [12,13,14], risk management [15,16,17,18], influential factors for development [19,20,21], policy incentives and regulations [22,23,24], and economical benefit [25,26,27] etc.
In particular, the energy consumption of residential buildings is far more than that of other types, accounting for about 70% of CO2 emissions of the whole construction industry, and affecting residents’ psychological and physical health [28,29]. In order to meet the national policy of China’s sustainable development and the strategy of low carbon economy, and to promote the transformation and development of Chinese real estate enterprises, the Chinese housing market has transferred into “the era of green residential buildings (GRBs)” [30,31]. However, in China, green residential building development (GRBD) is still in its infancy [32,33]. Many barriers exist in the development process, such as high hurdle rates for new developers [34], and lack of experience and financial incentive [35,36].
GRBs are different from general GBs. The general public GBs are inclined to adopt environmentally friendly technology with the support and initiative of the government [37]. The development of commercial buildings for residential use is usually conservative on energy saving and environment protection because of the benefit segmentation between developers and users [38]. GRBD is a complex process with multiple organizations and social backgrounds, and the developers, as a core stakeholder, play an important role in the whole process [39]. The whole process of GRBD is restricted by the elements of the environment, resources, and technology. The determination of the relationships between the influencing GRBD factors and the critical factors affecting GRBD will help to improve the enthusiasm of developers in China. Thus far, limited studies have explored how to encourage developers on the GRBD.
The paper contributes to the body of knowledge from three aspects: Firstly, based on the whole life cycle of GRBD and the perspective of real estate developers, 14 factors affecting development are determined from 64 preliminary factors of three main elements, and the framework is established. Secondly, the incidence matrix and adjacency matrix of 14 factors according to the method of questionnaire survey and social network analysis (SNA). The sociogram is helpful for analyzing the relationships and the impact between different factors. Finally, the critical factors are determined through the centrality of influential factors. The results of critical factors are conducive to promoting policy recommendations and development strategies for developers.

2. Literature Review

2.1. Factors Influencing GRBD

Since the concept of GBs was clearly defined in the United Nations Conference on Environment and Development in 1992, more and more researches on GBs have been carried out. Early researches focused on the concept, influence factors, and evaluation [40,41,42]. However, scholars have found that there are some differences in factors and evaluation systems in different types of GBs, and the researches on GRBs are particularly prominent [32,43].
GRB, which is one of the best practices of sustainable development in the architectural field, is an important branch of GB [40]. Many countries have done a lot of research into the construction of GRBs, for example: Singapore [44,45], UK [46], USA [40], China [47,48,49], and India [50]. There are many stakeholders in the process of GRBD, and the relationships between influential factors are extremely complex. The government actively promoting GRBD, the residents having some knowledge on GRB, and the maturing technology of GRB all affect GRBD uptake, however obstacles to its widespread adoption still exist [47,51]. In order to solve the obstacles, many factors have been studied from different elements.
A variety of environmental elements have a strong impact on GRBD. The conservatism of environmental policy and legal factors will affect the enthusiasm of developers. Proper implementation of fiscal incentives, preferential policy frameworks, and effective evaluation mechanisms will have beneficial effects on GRBD [32,52]. GRBs are different from general residential buildings, therefore, the natural environment and social environment will restrict site selection, construction, and resource utilization [53]. The implementation of GRB is also affected by the development of the regional economy and green technology [50].
GRBs, similar to general residential buildings, involve many stakeholders in the development process. The standard specification and strictness of examination and approval will promote the operation of the GRB market [54]. Considering the constraints of environmental elements, low cost and energy saving are encouraged from the beginning of design. “Sustainable designing and planning”, “education and awareness of GRB”, and “economic aspects relating to various costs” are the banks’ credit standards for GRBD [45,53]. The shortage and high price of green materials are also barriers for enterprises to develop GRBs [18]. Wong, et al. (2016) [55] proposed that a green material market, which is dominated by the government and cooperates with suppliers and developers, should be established to promote the implementation of green purchasing, to improve the quality, and to gradually reduce the cost. However, some studies have pointed out that the green purchasing imposed by the government will hinder the development of the green material market [56]. In addition to the government, banks, designers, developers, and material suppliers, effective supervisors do not only ensure the quality of the projects, but also reduce the delay of the process to ensure the confidence of consumers [57]. Consumers’ acknowledgement and demand for GRB is an important factor affecting GRBD [47,58]. Paying attention to the marketing of GRB, which is also a positive impact on GRBD, will also improve the public’s acquisition of environmental protection and GRB information [32,59].
Although many stakeholders are involved in GRBD, the successful implementation of GRB projects is closely related to the developers [60]. Li, et al. (2011) [44] explored the important GRB project management factors, from the perspective of architectural, engineering, and construction (AEC) firms, which were human resource-oriented factors, technical and innovation-oriented factors, support from designers and senior management, project manager’s competence, and coordination of designers and contractors. The critical factors for the success of GRB projects are “coordination of designers and contractors” and “technical and innovation-oriented factors”. Marker, et al. (2014) [61] argued that the improvement and change of employees’ cognition can effectively influence the development and application of GRB. Hwang, et al. (2016) [62] indicated that workers’ experience, technology, design changes, workers’ skill level, and planning and sequencing of work were the top five most critical factors affecting GRBD. However, Li, et al. (2014) [63] illustrated “experience and knowledge in GRB”, “organizational green culture”, and “innovation capability” were more important than other factors. Shen, et al. (2017) [64] confirmed this view with the empirical study in Thailand.

2.2. The Application of SNA in GRBD

SNA, which originated in the 1840s as an important branch of sociology, was used to study the social structure of a small fishing village in Norway in 1954, and to study the British social network in 1957 [65]. The purpose of SNA is to reveal the influence of network structure on group and individual function, starting with the interaction of structure and function. The specific practice is to explore the relationship between the actors in the social network and determine the relationship characteristics, so as to discover the influence of relationships to the organization [66,67].
The two most important components of SNA are the actors and relationships. Therefore, SNA can help us to understand the cooperative relationship between organizations in various fields [68]. There are few studies on the development and application of GRBs on SNA. A few studies are mainly from the perspective of stakeholders to analyze the risk network in the process of GRBD [17,69,70]. In fact, from the perspective of technology and combining BIM and SNA, the analysis of the life cycle energy of building will help to provide effective residential energy-saving design plan [71]. In addition to analyzing the risks in the development of construction projects, SNA can also analyze the factors affecting the development of the projects according to the interdependence of the stakeholders [72].
From the literature review, it can be seen that the studies on the influential factors of GRBD are rather fragmented and lack a systematic nature and unity. Although some studies involve stakeholders in GRBD, the whole life cycle of development is not considered. In addition, it has been proved that the SNA method can analyze the influential factors of the whole life cycle of construction projects, but the existing social network application of GRBs mainly focuses on the risk analysis. This paper, based on the whole life cycle of GRBD, will use SNA to explore relationships of influential factors of GRBs and identify the critical factors affecting GRBD. We expand the application of SNA in the study of GRBD.

3. Research Method

3.1. Identification of Factors Influencing GRBD

This paper, according to the perspective of real estate developers, explores the factors influencing GRBD, from environmental elements, resource elements, and capacity elements. Environmental elements refer to the external factors that are generated in the process of GRBD; resource elements refer to the factors that influence how developers obtain possible resources with personal effort during the whole developing process; and capacity elements refer to developers’ evaluation, objective, and ability [44]. The three dimensions are different, but they interact with each other, as shown in Figure 1.
Based on the above three dimensions, from the literature, laws, regulations, and policy standards, 64 preliminary factors affecting GRBD are chosen (Seeing in Appendix A). The questionnaire survey technique is a systematic method of data collection and has been widely adopted to collect professional views on sustainable construction research [73,74,75]. Two rounds of surveys were performed in this study. The first round of the questionnaire survey was used to select the influential factors from the 64 preliminary factors (the questionnaire is shown in Appendix B). The population of the questionnaires were all stakeholders in GRBD. Therefore, the questionnaires were distributed to professionals in the government, quality supervision departments, real estate development enterprises, research institutes, construction organizations, and relevant organizations. A total of 92 questionnaires were received and 3 invalid responses were removed due to being incomplete responses. The selected 89 valid samples have more than 10 years of experience in residential development and more than 3 years of experience in GRBD. The questionnaire used a 5-point Likert scale, showing that the degree of preliminary factors ranged from “very unimportant” (1) to “very important” (5) [76,77,78].
This study grouped preliminary factors into 18 groups from environmental elements, resource elements, and capacity elements (Seeing in Appendix B). In general, reliability is estimated by examining the consistency with which different items express the same concept [79]. In order to test the internal consistency among factors under each category, we used the Cronbach’s alpha scale. When the value of Cronbach’s alpha is 0.7 or higher, it normally indicates a reliable group classification set [77]. The Cronbach’s alpha scores of the 3 elements and 18 categories were calculated (Shown in Table 1). The Cronbach’s alpha coefficient for the 18 categories and 3 elements is larger than 0.7. Hence, the structure of survey is considered to be reliable.
In the next step, we chose the factor which had a mean score above 4 (meaning “important”) as the possible influential factor. The selected influential factors include Mandatory Policy for Developing (I1), Incentive Policy for Developing (I2), Local Economy Development Level (I7), Technology Level of GRBs (I19), Strictness of Examination and Approval (I21), Design Level of GRBs (I32), Technology Application in Design and Construction (I37), Acknowledgement for GRBD (I49), Family Income (I51), Incentive Policy for Purchasing (I52), Local Cooperation (I58), Development Strategy and Innovation Orientation (I60), Acknowledgement and Positioning for GRBD (I61), and Experience and Ability for GRBD (I62) (Seeing in Appendix A). Factor analysis was used to analyze the selected influential factors of GRBD with the SPSS software. The value of KMO is 0.701, and the Sig. of Bartlett’s test is 0.000, which satisfies the factor analysis [80]. Then, with the Varimax Rotation in the SPSS, there are 5 components for which the principal component of eigenvalues is greater than 1, and the accumulated variance is 80.862%. The rotated component matrix of influential factors of GRBD is shown in Table 2.
Then, based on the factor analysis, 5 primary indexes were named and 14 influential factors were renumbered, as shown in Table 3.

3.2. A Framework of Influential Factors for GRBD

SNA focuses on the interaction among factors, instead of relationships between factors and processes. According to the characteristics of 14 influential factors in GRBD, we proposed a framework (shown in Figure 2). Five aspects of primary indexes, interacting with each other, have different levels of effect on 5 phases of GRBD separately. The 5 phases include the project acquisition phase, project preparing phase, project designing phase, project constructing phase, and project selling phase [42].

3.3. Social Network of Influential Factors

3.3.1. Correlation Analysis of Influential Factors for GRBD

In order to use the SNA method to determine the relationship between influential factors and identify the critical factors, the first step is to determine the relationships between the 14 factors through association analysis. In this paper, we use the combination of the survey method and expert evaluation method to collect relevant data and establish a network analysis matrix. The network analysis matrix, known as the adjacency matrix, reflects whether a pair of actors are associated with the same matter, or whether a pair of subordinations are associated with each other due to a common actor. But the adjacency matrix must be transformed from the incidence matrix, that is, an “actor-actor” adjacency matrix must be transformed from an “actor-event” incidence matrix [65]. This paper is to establish the “actor-actor” adjacency matrix to express the close ties and interaction between the 14 factors. We construct the “actor-event” incidence matrix through the expert evaluation method.
In order to ensure the rigor of the results of the second questionnaire survey, the respondents to the second survey were the same as the respondents to the first survey. A total of 89 directional questionnaires were sent out to the respondents of the first questionnaire who produced valid samples, and all of them were received, with no invalid responses, in the second-round survey. The respondents have more than 10 years of experience in residential development and more than 3 years of experience in GRBD. In the second survey, we mainly asked respondents to evaluate the degree of impact of the 14 influential factors on the 5 developing phases (shown in Figure 2). Five matrixes were established for experts to score. Scores 0, 1, 2, and 3 corresponded to “no link”, “weak link”, “medium link”, and “strong link”. The data of the relation matrix of the 14 influential factors and development phases are shown in Table 4.
In order to determine the in-degree and out-degree of each factor, we transformed the “actor-event” incidence matrix into an “actor-actor” adjacency matrix. U1, U2, U3…U14 in the rows and columns represent the influential factors, and the numbers represent the degree of impact, for example, the number in the i row and j column is the impact degree of Ui to Uj (i, j = 1, 2, 3…14). It is calculated by the following formula:
Vij = Xi1Xj1 + Xi2Xj2 + Xi3Xj3 + Xi4Xj4 + Xi5Xj5
  • Vij: The impact degree of Ui to Uj;
  • Xi1 (Xj1): The impact degree of Ui (Uj) in the acquiring phase in the relation matrix;
  • Xi2 (Xj2): The impact degree of Ui (Uj) in the preparing phase in the relation matrix;
  • Xi3 (Xj3): The impact degree of Ui (Uj) in the designing phase in the relation matrix;
  • Xi4(Xj4): The impact degree of Ui (Uj) in the constructing phase in the relation matrix;
  • Xi5 (Xj5): The impact degree of Ui (Uj) in the selling phase in the relation matrix.
Since we concentrate on the relationships among factors, we ignore the direction of impact between two factors, which means the impact degree of Ui to Uj equals that of Uj to Ui [65,81]. Thus, the adjacency matrix is a symmetric matrix, shown in Table 5.

3.3.2. Centrality of Influential Factors for GRBD

The social network centrality can identify the critical factors in the social network of factors influencing GRBD, that is, the critical factors are closely linked to the other factors and have a greater impact on the other factors. The second step of this study is to identify the critical factors from 14 influential factors through the method of degree centrality, closeness centrality, and betweenness centrality.
Degree centrality, known as Freeman’s degree centrality, reflects how a factor is contacted in the social network local environment. This method calculates the number of factors directly connected to the specific factor, and neglects the indirect influence. Degree centrality expresses the extent to which each factor is connected with a specific factor in the local environment. It is calculated by the following formula [82,83]:
C D ( i ) = k = 1 g x ij ( i j ;   k = 1 , 2 g 1 )
k = 1 g x ij : The number of direct correlation between factor i and other factors k (the number of factors k is g−1).
Closeness centrality measures the distance between one factor and other factors in the social network. Closeness centrality is based on the proximity between the factors. The closer one factor is to other factors, the less it depends on other factors, for which the closeness centrality is high. It is calculated by the following formula [83,84]:
C C ( a ) = i = 1 n l ( i , a )
l ( i , a ) : The length of the shortest path between factor i and factor a.
Betweenness centrality is a measure of the intermediate degree of a factor located in other factors of the network, that is, the betweenness centrality represents the activity and importance of the factor in the network. A factor which has low degree centrality or closeness centrality may play an important role in mediating or coordinating. It means that the factor plays the role of the key channel and becomes the center of the network. Therefore, betweenness centrality can determine which factors become the center of the GRBD network and could be the critical factor by controlling more resources. It is calculated by the following formula [85,86]:
C B ( a ) = i = 1 n j = 1 n g ij ( a )
g ij ( a ) : The number of the shortest path of factors i and j passing factor a.

4. Results

4.1. Sociogram of Factors Influencing GRBD

Based on the adjacency matrix (shown in Table 3), we use the software Ucinet 6 to draw the sociogram and analyze the results of SNA. The operation steps are as follows. Firstly, we used the “Data-Spreadsheets-Matrix” to establish the adjacency matrix. Secondly, we used “NetDraw-File-Open-Ucinet dataset-Network” to draw the sociogram (the sociogram is shown in Figure 3). Every node represents a factor, and every line between two nodes represents the relation of these two factors. The thickness of the line represents the tightness of two factors. The thicker the line, the tighter the two factors are.
Figure 3 can reveal the relationship and tightness of factors, however, it is impossible to determine the critical factors affecting GRBD. We should further analyze the centrality of the social network.

4.2. Critical Factors for GRBD

For identifying the critical factors for GRBD, we take the centrality tests of 14 influential factors. Degree centrality, closeness centrality, and betweenness centrality are tested separately.
We used “Network-Centrality-Degree” to draw the degree centrality sociogram and analyze the result of degree centrality. The sociogram and result of degree centrality are shown in Figure 4 and Table 6.
The degree centrality of the network is 21.79% and the ratio of standard deviation to average is 1.917/10.571 = 18.135%, which means that the local integration degree of the network is good. But the degree of some factors is too low or too high, which should be analyzed. In Figure 4, the degree is higher and the node is bigger. Firstly, the development strategy and innovation orientation (U12) and developer’s acknowledgement and positioning for GRBD (U13) have the most frequent connections with other factors, as shown by their high scores. Secondly, local economy development level (U4) and experience and ability for GRBD (U14) have more frequent connections with other factors because of their relatively high scores (the NrmDegree are over 90). Finally, both incentive policy for purchasing (U11) and consumers’ income (U10) have the lowest scores, that is, they have weak links with other factors.
From the local network, development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), local economy development level (U4), and experience and ability for GRBD (U14), which are the four critical factors affecting GRBD.
In order to determine the degree of dependence and information exchange in the whole network, this paper carried out a closeness centrality test. We used “Network-Centrality-Closeness” to draw the closeness centrality sociogram and analyze the result of closeness centrality. The sociogram and result of closeness centrality are shown in Figure 5 and Table 7.
The closeness centrality of the network is 32.66% and the ratio of standard deviation to average is 1.917/15.429 = 12.425%, which means that the network has the characteristic of congregation. But we should notice that, in Figure 5, the size of the node represents Farness, that is, the nCloseness of the factor is higher, and the node is smaller. Therefore, if the node is smaller, the value and impact are greater, and the factor is located more centrally in the whole network.
From Figure 5 and Table 7, similar to the degree centrality analysis, development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), local economy development level (U4), and experience and ability for GRBD (U14) are the more independent factors in the whole network. They depend less on other factors and have strong connections with others. Incentive policy for purchasing (U11) and consumers’ income (U10) are further from other factors and their communication of information is restricted by the mediators. Therefore U12, U13, U4, and U14 are the critical factors affecting GRBD.
In order to determine which factors in the study can control more resources and play an important role in mediation or coordination, we carried out the betweenness centrality test. We used “Network-Centrality-Freeman betweenness-node betweenness” to draw the betweenness centrality sociogram and analyze the result of betweenness centrality. The sociogram and results of betweenness centrality are shown in Figure 6 and Table 8.
The betweenness centrality of the network is 2.46% and the ratio of standard deviation to average is 1.036/1.214 = 85.338%, which means that the dispersion degree of the network is large and the betweenness centrality is poor. The ability of most nodes to control information is poor, and only a few nodes, which are located in the important information path to control the main resources, are the hub of the whole network.
Table 8 illustrates that, firstly, development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), local economy development level (U4), and experience and ability for GRBD (U14), which are located on the major paths, are the key junctions of the network. Secondly, consumers’ income (U10), strictness of examination and approval (U3), and local cooperation (U5) have the lowest scores, that is, they do not control any resources.
From the result of betweenness centrality, the four factors which are the critical factors affecting GRBD are development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), local economy development level (U4), and experience and ability for GRBD (U14).
Based on the results of the three centrality tests, the results of degree centrality and closeness centrality are the same, but the result of betweenness centrality is slightly different. We conclude that the critical factors affecting GRBD are development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), local economy development level (U4), and experience and ability for GRBD (U14). It is worth noting that, although degree centrality and closeness centrality of consumers’ acknowledgement for GRBD (U9) are low, the betweenness centrality is high. This means some factors do not have a strong impact on other factors, and information exchange of these factors is subject to other factors, but they may occupy an important path in the network and have a certain capacity to control resources (such as U9). For such factors, we should focus on their ability to control resources, so as to keep the social network access among factors smooth.

5. Discussion

Generally, this paper determined the relationships of 14 factors influencing GRBD by SNA, and then identified four critical factors from the influential factors by the social network centrality test. These factors and the findings are discussed hereinafter.
From the central test results, development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), and experience and ability for GRBD (U14) are all the critical factors of GRBD, which coincide with the results of successful implementation of construction projects [60]. The results confirm the importance of innovation factors for GRBD, but the argument that the improvement and change of employees’ cognition is the critical factor is not verified [61]. In the literature review, “experience and knowledge in GRB” (similar to U14), “organizational green culture” (similar to U13), and “innovation capability” (similar to U12) are more important than other influential factors [63], and the results confirm this view.
Local economy development level (U4) is a critical factor affecting GRBD. But technology level of GRBs (U6) is not a critical factor, which is in different from the results of Vyas and Jha (2016) [50]. “Sustainable designing and planning” (similar to U7 and U8) and “education and awareness of GRB” (similar to U13) have impacts on the credit of GRBD. However, from the results of critical factors, “education and awareness of GRB” seems to have a greater impact [53]. Therefore, similar to GRBD in Singapore, the technical design and construction knowledge of GRBs in China are not lacking [45]. This means that the technical factors have some influence on GRBD, but they are not the critical factors.
From the perspective of policy, mandatory policy for developing (U1) and incentive policy for developing (U2) also have considerable impacts on GRBD, which is similar to previous research results) [32,52]. Strictness of examination and approval (U3) is a new influential factor we put forward, but the key to GRBD is not obvious. Incentive policy for purchasing (U11) and consumers’ income (U10) have low centrality on GRBD, but the betweenness centrality of consumers’ acknowledgement for GRBD (U9) is high. It illustrates that this factor, U9 plays an important role in information communication, which explains the reason for the formation of consumers’ environmental protection and the importance of information acquisition on GRBD [32,59]. This is a further development of the existing research.

6. Conclusions and Recommendation

This paper, based on the whole life cycle of GRBD and the perspective of real estate developers, identified 14 factors affecting development from 64 preliminary factors of three main elements, and established a framework. The 14 influential factors including the following: Mandatory policy for developing (U1), incentive policy for developing (U2), strictness of examination and approval (U3), local economy development level (U4), local cooperation (U5), technology level of GRBs (U6), design level of GRBs (U7), technology application in design and construction (U8), consumers’ acknowledgement for GRBD (U9), consumers’ income (U10), incentive policy for purchasing (U11), development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), and experience and ability for GRBD (U14).
The tightness and relationships between 14 factors are tested by SNA. According to the method of the social network centrality test, degree centrality, closeness centrality, and the betweenness centrality of the local economy development level (U4), development strategy and innovation orientation (U12), developer’s acknowledgement and positioning for GRBD (U13), and experience and ability for GRBD (U14) are all higher than other influential factors. This shows that these four factors, which control most resources in the social network, are critical factors. However, we should consider consumers’ acknowledgement for GRBD (U9) as an important influential factor. The betweenness centrality of U9 is higher than other factors (except the critical factors). This shows that even if U9 is not the key to control and influence in GRBD in the whole network, it may be a node with higher activity and more frequent information transmission in the network.
The relationships between these factors and the determination of critical factors will help real estate developers to better understand how to improve their business capabilities on GRBD. The results also reveal that the support of local governments will promote GRBD, but we should know more about the local economy to avoid unnecessary economic losses because of blindly following. This paper also provides some references for policymakers. The effective implementation of mandatory policies and incentive policies can promote GRBD in real estate developers. However, in the process of examination and approval, the technology, experience, and management ability of the developers need to be strictly determined to avoid losses caused by a lack of developers’ capacity. At the same time, the government, developers, and non-governmental organizations should encourage the public to know more about GRBD, and enhance the understanding of GRBs. It will promote the demands for GRBs to improve the supply by real estate developers.
The limitation of this paper is mainly due to the limited scope of the investigation and the limited number of questionnaires, so the results may have some limitations. But the analysis method has a certain value for research in other areas. In the future, a larger and different sample should be used to test the universality of the method. Further analysis by SNA, such as the structure tree, should be used to excavate the influential factors for GRBD. We will conclude with the more universal influential factors and critical factors for GRBD.
In the future, the main points of our research include the use of the SNA method to analyze the stakeholder relationships of GRBD, or to analyze the factors in the development of GRBs from the perspective of different stakeholders. In fact, there are many methods to analyze the influential factors and critical factors of GRBD. Therefore, we may compare the different methods of factor analysis to get a more suitable method to select and analyze the factors affecting GRBD. In addition, we may also study dynamic evolution management of GRBD based on the dynamic network.

Author Contributions

X.Y. put forward this research idea, conducted literature review, and established the framework. J.Z. participated in the data analysis and collated the draft manuscript. X.Z. contributed to the Author method of this research and provided some comments. All authors participated in final manuscript preparation and agreed to publish this paper.

Funding

This research was funded by National Natural Science Foundation of China (71473061).

Acknowledgments

Appreciation is due to the National Natural Science Foundation of China (71473061), and Harbin Institute of Technology and Central Queensland University for their help and support. The authors are grateful to anonymous referees and editors.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Preliminary factors affecting GRBD.
Table A1. Preliminary factors affecting GRBD.
Primary IndexesSecondary IndexesPreliminary FactorsSources
Environmental ElementsPolitical EnvironmentMandatory Policy for Developing (I1)[46,48,55,87]
Incentive Policy for Developing (I2)[41,48,56,64]
Industrial Policy (I3)[64,87]
Industrial Standard (I4)[40,53,55,87]
Monetary Policy (I5)[47,61,88]
Tax Policy (I6)[47,50]
Economic EnvironmentLocal Economy Development Level (I7)[53,64]
Supply and Demand in Market of GRBs (I8)[40,61]
Supply and Demand in Market of GRBs (I9)[49,61]
Inflation (I10)[18,89]
Social EnvironmentCity Planning (I11)[40,53]
Social Acknowledgement of GRBs (I12)[55,90]
Juristic EnvironmentLaw Health (I13)[48,64]
Enforcement Strictness (I14)[48]
Land System and Policy (I15)[53,64]
Natural EnvironmentGeological Condition (I16)[32,53]
Climate Condition (I17)[21,62]
Acquisition of Natural Resource (I18)[32,53]
Technical EnvironmentTechnology Level of GRBs (I19)[32,47,54,88]
Popularization and Application of Green Technology (I20)[32,53]
Resource ElementsGovernmentStrictness of Examination and Approval (I21)[55,64,87]
Reliability of Quality Control (I22)[18,64,87]
Marketing AgencyAcknowledgement of GRBs (I23)[90]
Judgement Ability of GRBs (I24)[55,88]
Marketing Ability of GRBs (I25)[88]
BankCredit Policies for Developing (I26)[18,47,48,88]
Credit Policies for Purchasing (I27)[18,48,88]
Research InstituteDriving Force of Economy (I28)[40,44]
Research Ability of GRBs (I29)[40,87]
Resource ElementsDesignerDriving Force of Economy (I30)[40,55]
Reconnaissance of Construction Site (I31)[91,92]
Design Level of GRBs (I32)[40,62,69]
Technology and Material Application (I33)[21,55,87,88]
Social Responsibility (I34)[21,87]
BuilderAcknowledgement of Green Construction (I35)[44,91]
Management Ability of Green Construction (I36)[21,57,87]
Technology Application in Design and Construction (I37)[21,87]
Biding Price (I38)[55,62]
Coordination with Designer (I39)[91]
Appointed Subcontractors (I40)[21,55]
SupervisorAcknowledgement of Green Construction (I41)[44,47]
Regulation Ability of Green Construction (I42)[87]
Regulation Experience of Green Construction (I43)[21,55,87]
Professional Ethic (I44)[87]
SupplierPrices of Building Material and Facilities (I45)[87,93]
Quality of Building Material and Facilities (I46)[21,54,87]
Certificate AuthorityEvaluation Ability of GRBs (I47)[44,87]
Professional Ethic (I48)[87]
ConsumerAcknowledgement for GRBD (I49)[42,44,90]
Environmental Conscious (I50)[33,53,55]
Family Income (I51)[58,94]
Incentive Policy for Purchasing (I52)[41,47,48]
Information Acquisition of GRBs (I53)[18,44,88]
Living Habit (I54)[40,49]
Education Level (I55)[40,58]
Personality Characteristics (I56)[40,58]
Local SocietyLocation and Strategy of Local Development (I57)[47,48,49,64]
Local Cooperation (I58)[47,49,64,87]
Capacity ElementsDeveloperDriving Force of Economy (I59)[37,44,55]
Development Strategy and Innovation Orientation (I60)[53,54,55,62]
Acknowledgement and Positioning for GRBD (I61)[47,55,90]
Experience and Ability for GRBD (I62)[21,44,90]
Financing of the Project (I63)[44,62]
Management for GRBD (I64)[21,44,90]

Appendix B. Questionnaire on Factors Affecting GRBD

Dear madam or sir,
It is appreciated to fill out the questionnaire on factors affecting the green residential building development (GRBD). The private information you have completed is only for academic study, and will not be disclosed to the public. Thank you very much for your support and cooperation!
This questionnaire is divided into two parts. The first part is your private information, and the second part is degree of the factors affecting GRBD. In accordance with the importance of each factor, you should tick in the corresponding space with single election. The degree of importance is divided into “very unimportant”(1), “unimportant”(2), “average”(3), “important”(4), and “very important”(5).
Part 1:
1. Nature of work:
□Government □Quality supervision department □Real estate development enterprises
□Research institutes □Construction organizations □Relevant organizations
2. Years of experience in residential development:
□Below 10 years □ 10 years–20 years □Above 20 years
3. Years of experience in the green residential building development.
□Below 3 years □ 3 years–5 years □Above 5 years
Part 2:
Please fill in the information according to your work experience and actual situation of development of green residential buildings (GRBs). Please make a comparison between the scores after completing the questionnaire to ensure the differences between the factors.
Table A2. The scale of preliminary factors affecting GRBD.
Table A2. The scale of preliminary factors affecting GRBD.
ElementsCategoryNo.Preliminary Factors12345
Environmental ElementsPolitical Environment1Mandatory Policy for Developing
2Incentive Policy for Developing
3Industrial Policy
4Industrial Standard
5Monetary Policy
6Tax Policy
Economic Environment7Local Economy Development Level
8Supply and Demand in Market of GRBs
9Supply and Demand in Market of GRBs
10Inflation
Social Environment11City Planning
12Social Acknowledgement of GRBs
Juristic Environment13Law Health
14Enforcement Strictness
15Land System and Policy
Natural Environment16Geological Condition
17Climate Condition
18Acquisition of Natural Resource
Technical Environment19Technology Level of GRBs
20Popularization and Application of Green Technology
Resource ElementsGovernment21Strictness of Examination and Approval
22Reliability of Quality Control
Marketing Agency23Acknowledgement of GRBs
24Judgement Ability of GRBs
25Marketing Ability of GRBs
Bank26Credit Policies for Developing
27Credit Policies for Purchasing
Research Institute28Driving Force of Economy
29Research Ability of GRBs
Designer30Driving Force of Economy
31Reconnaissance of Construction Site
32Design Level of GRBs
33Technology and Material Application
34Social Responsibility
Builder35Acknowledgement of Green Construction
36Management Ability of Green Construction
37Technology Application in Design and Construction
38Biding Price
39Coordination with Designer
40Appointed Subcontractors
Supervisor41Acknowledgement of Green Construction
42Regulation Ability of Green Construction
43Regulation Experience of Green Construction
44Professional Ethic
Supplier45Prices of Building Material and Facilities
46Quality of Building Material and Facilities
Certificate Authority47Evaluation Ability of GRBs
48Professional Ethic
Consumer49Acknowledgement for GRBD
50Environmental Conscious
51Family Income
52Incentive Policy for Purchasing
53Information Acquisition of GRBs
54Living Habit
55Education Level
56Personality Characteristics
Local Society57Location and Strategy of Local Development
58Local Cooperation
Capacity ElementsDeveloper59Driving Force of Economy
60Development Strategy and Innovation Orientation
61Acknowledgement and Positioning for GRBD
62Experience and Ability for GRBD
63Financing of the Project
64Management for GRBD
Note: Green residential buildings (GRBs); Green residential building development (GRBD).

References

  1. China Association of Building Energy Efficiency (CABEE). Research Report on Building Energy Consumption in China; China Association of Building Energy Efficiency (CABEE): Beijing, China, 2017. [Google Scholar]
  2. Mills, E. Building commissioning: A golden opportunity for reducing energy costs and greenhouse gas emissions in the United States. Energy Effic. 2011, 4, 145–173. [Google Scholar] [CrossRef]
  3. Zhao, H.X.; Magoulès, F. A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 2012, 16, 3586–3592. [Google Scholar] [CrossRef]
  4. Cho, S.W.; Kim, J.J. Zero energy potential of high-rise residential buildings. J. Asian Archit. Build. Eng. 2015, 14, 641–648. [Google Scholar] [CrossRef]
  5. Ye, L.; Cheng, Z.; Wang, Q.; Lin, H.; Lin, C.; Liu, B. Developments of green building standards in China. Renew. Energy 2015, 73, 115–122. [Google Scholar] [CrossRef]
  6. Collinge, W.O.; Thiel, C.L.; Campion, N.A.; Al-Ghamdi, S.G.; Woloschin, C.L.; Soratana, K. Integrating life cycle assessment with green building and product rating systems: North American perspective. Procedia Eng. 2015, 118, 662–669. [Google Scholar] [CrossRef]
  7. Xie, X.; Lu, Y.; Gou, Z. Green building pro-environment behaviors: Are green users also green buyers? Sustainability 2017, 9, 1703. [Google Scholar] [CrossRef]
  8. Zuo, J.; Pullen, S.; Rameezdeen, R.; Bennetts, H.; Wang, Y.; Mao, G. Green building evaluation from a life-cycle perspective in Australia: A critical review. Renew. Sustain. Energy Rev. 2017, 70, 358–368. [Google Scholar] [CrossRef]
  9. Zhai, X.Q.; Wang, R.Z.; Dai, Y.J.; Wu, J.Y.; Ma, Q. Experience on integration of solar thermal technologies with green buildings. Renew. Energy 2008, 33, 1904–1910. [Google Scholar] [CrossRef]
  10. Bayraktar, M.E.; Arif, F. Venture capital opportunities in green building technologies: A strategic analysis for emerging entrepreneurial companies in South Florida and Latin America. J. Manag. Eng. 2013, 29, 79–85. [Google Scholar] [CrossRef]
  11. Chan, A.P.C.; Darko, A.; Effah, E.A. Strategies for promoting green building technologies adoption in the construction industry—An international study. Sustainability 2017, 9, 969. [Google Scholar] [CrossRef]
  12. Cheng, T.C.; Cheng, C.H.; Huang, Z.Z.; Liao, G.C. Development of an energy-saving module via combination of solar cells and thermoelectric coolers for green building applications. Energy 2011, 36, 133–140. [Google Scholar] [CrossRef]
  13. Shazmin, S.A.A.; Sipan, I.; Sapri, M.; Ali, H.M.; Raji, F. Property tax assessment incentive for green building: Energy saving based-model. Energy 2017, 122, 329–339. [Google Scholar] [CrossRef]
  14. Zhang, Y.; Kang, J.; Jin, H.; Sciubba, E. A review of green building development in China from the perspective of energy saving. Energies 2018, 11, 334. [Google Scholar] [CrossRef]
  15. Hwang, B.G.; Zhao, X.B.; See, Y.L.; Zhong, Y. Addressing risks in green retrofit projects: The case of Singapore. Proj. Manag. J. 2015, 46, 76–89. [Google Scholar] [CrossRef]
  16. Zhao, X.B.; Hwang, B.G.; Gao, Y. A fuzzy synthetic evaluation approach for risk assessment: A case of Singapore’s green projects. J. Clean. Prod. 2016, 115, 203–213. [Google Scholar] [CrossRef]
  17. Yang, R.J.; Zou, P.X.W.; Wang, J. Modelling stakeholder-associated risk networks in green building projects. Int. J. Proj. Manag. 2016, 34, 66–81. [Google Scholar] [CrossRef]
  18. Hwang, B.G.; Shan, M.; Supa’at, N.N.B. Green commercial building projects in Singapore: Critical risk factors and mitigation measures. Sustain. Cities Soc. 2017, 30, 237–247. [Google Scholar] [CrossRef]
  19. Hsieh, T.T.; Lai, K.P.; Chiang, C.M.; Ho, M.C. Eco-efficiency model for green building material in a subtropical climate. Environ. Eng. Sci. 2013, 30, 555–572. [Google Scholar] [CrossRef]
  20. Suh, S.; Tomar, S.; Leighton, M.; Kneifel, J. Environmental performance of green building code and certification systems. Environ. Sci. Technol. 2014, 48, 2551–2560. [Google Scholar] [CrossRef] [PubMed]
  21. Hwang, B.G.; Zhao, X.B.; Tan, L.L.G. Green building projects: Schedule performance, influential factors and solutions. Eng. Constr. Archit. Manag. 2015, 22, 327–346. [Google Scholar] [CrossRef]
  22. Choi, E. Green on buildings: The effects of municipal policy on green building designations in America’s central cities. J. Sustain. Real Estate 2010, 2, 1–21. [Google Scholar]
  23. Eichholtz, P.; Quigley, J.M. Green building finance and investments: Practice, policy and research. Eur. Econ. Rev. 2012, 56, 903–904. [Google Scholar] [CrossRef]
  24. Kuo, C.F.J.; Lin, C.H.; Hsu, M.W. Analysis of intelligent green building policy and developing status in Taiwan. Energy Policy 2016, 95, 291–303. [Google Scholar] [CrossRef]
  25. Robert, R.; Melissa, M.B.; Nuri, M.G.; Kim, L.N. The economic benefits of green buildings: A comprehensive case study. Eng. Econ. 2006, 51, 259–295. [Google Scholar]
  26. Sweitzer, G.E. Green building acoustics: Scoring user performance factors. J. Acoust. Soc. Am. 2009, 125, 2504. [Google Scholar] [CrossRef]
  27. Gabay, H.; Meir, I.A.; Schwartz, M.; Werzberger, E. Cost-benefit analysis of green buildings: An Israeli office buildings case study. Energy Build. 2014, 76, 558–564. [Google Scholar] [CrossRef]
  28. Anand, S.; Vrat, P.; Dahiya, R.P. Application of a system dynamics approach for assessment and mitigation of CO2 emissions from the cement industry. J. Environ. Manag. 2006, 79, 383–398. [Google Scholar] [CrossRef] [PubMed]
  29. Li, D.; Cui, P.; Lu, Y. Development of an automated estimator of life-cycle carbon emissions for residential buildings: A case study in Nanjing, China. Habitat Int. 2016, 57, 154–163. [Google Scholar] [CrossRef]
  30. Huang, Z.; Yuan, H.; Shen, L. Contribution of promoting the green residence assessment scheme to energy saving. Energy Policy 2012, 51, 374–381. [Google Scholar] [CrossRef]
  31. Ning, Y.; Li, Y.; Yang, S.; Ju, C. Exploring socio-technical features of green interior design of residential buildings: Indicators, interdependence and embeddedness. Sustainability 2016, 9, 33. [Google Scholar] [CrossRef]
  32. Ye, L.; Cheng, Z.; Wang, Q.; Lin, W.; Ren, F. Overview on green building label in China. Renew. Energy 2013, 53, 220–229. [Google Scholar] [CrossRef]
  33. Wang, X.; Altan, H.; Kang, J. Parametric study on the performance of green residential buildings in China. Front. Archit. Res. 2015, 4, 56–67. [Google Scholar] [CrossRef]
  34. Deng, Y.; Wu, J. Economic returns to residential green building investment: The developers’ perspective. Reg. Sci. Urban Econ. 2013, 47, 35–44. [Google Scholar] [CrossRef]
  35. Gou, Z.; Prasad, D.; Lau, S.Y. Are green buildings more satisfactory and comfortable? Habitat Int. 2013, 39, 156–161. [Google Scholar] [CrossRef]
  36. Shi, Q.; Lai, X.; Xie, X.; Zuo, J. Assessment of green building policies—A fuzzy impact matrix approach. Renew. Sustain. Energy Rev. 2014, 36, 203–211. [Google Scholar] [CrossRef]
  37. Zhou, N.; Mcneil, M.; Levine, M. Assessment of building energy-saving policies and programs in China during the 11th five-year plan. Energy Effic. 2012, 5, 51–64. [Google Scholar] [CrossRef]
  38. Ouyang, J.; Gao, L.; Yan, Y.; Hokao, K.; Ge, J. Effects of improved consumer behavior on energy conservation in the urban residential sector of Hangzhou, China. J. Asian Archit. Build. Eng. 2009, 8, 243–249. [Google Scholar] [CrossRef]
  39. Qian, Q.K.; Chan, E.H.W.; Visscher, H.; Lehmann, S. Modeling the green building (GB) investment decisions of developers and end-users with transaction costs (TCS) considerations. J. Clean. Prod. 2015, 109, 315–325. [Google Scholar] [CrossRef]
  40. Tinker, A.; Kreuter, U.; Burt, R.; Bame, S. Green construction: Contractor motivation and trends in Austin, Texas. J. Green Build. 2006, 1, 118–134. [Google Scholar] [CrossRef]
  41. Circo, C.J. Using mandates and incentives to promote sustainable construction and green building projects in the private sector: A call for more state land use policy initiatives. Penn State Law Rev. 2007, 112, 731. [Google Scholar]
  42. Glavinich, T.E. Contractor’s Guide to Green Building Construction; John Wiley & Sons: Hoboken, NJ, USA, 2008. [Google Scholar]
  43. Zuo, J.; Zhao, Z.Y. Green building research-current status and future agenda: A review. Renew. Sustain. Energy Rev. 2014, 30, 271–281. [Google Scholar] [CrossRef]
  44. Li, Y.Y.; Chen, P.H.; Chew, D.A.S.; Teo, C.C.; Ding, R.G. Critical project management factors of AEC firms for delivering green building projects in Singapore. J. Constr. Eng. Manag. 2011, 137, 1153–1163. [Google Scholar] [CrossRef]
  45. Hwang, B.G.; Tan, J.S. Green building project management: Obstacles and solutions for sustainable development. Sustain. Dev. 2012, 20, 335–349. [Google Scholar] [CrossRef]
  46. Williams, K.; Dair, C. What is stopping sustainable building in England? Barriers experienced by stakeholders in delivering sustainable developments. Sustain. Dev. 2007, 15, 135–147. [Google Scholar] [CrossRef]
  47. Shi, Q.; Zuo, J.; Huang, R.; Huang, J.; Pullen, S. Identifying the critical factors for green construction—An empirical study in China. Habitat Int. 2013, 40, 1–8. [Google Scholar] [CrossRef]
  48. Zhang, Y.; Wang, J.; Hu, F.; Wang, Y. Comparison of evaluation standards for green building in China, Britain, United States. Renew. Sustain. Energy Rev. 2017, 68, 262–271. [Google Scholar] [CrossRef]
  49. Shen, L.; Zhang, Z.; Zhang, X. Key factors affecting green procurement in real estate development: A China study. J. Clean. Prod. 2017, 153, 372–383. [Google Scholar] [CrossRef]
  50. Vyas, G.S.; Jha, K.N. Identification of green building attributes for the development of an assessment tool: A case study in India. Civ. Eng. Environ. Syst. 2016, 33, 313–334. [Google Scholar] [CrossRef]
  51. Ofori, G.; Kien, H.L. Translating Singapore architects’ environmental awareness into decision making. Build. Res. Inf. 2004, 32, 27–37. [Google Scholar] [CrossRef]
  52. Shen, L.Y.; Tam, V.W.Y.; Tam, L.; Ji, Y.B. Project feasibility study: The key to successful implementation of sustainable and socially responsible construction management practice. J. Clean. Prod. 2010, 18, 254–259. [Google Scholar] [CrossRef]
  53. Illankoon, I.M.C.S.; Tam, V.W.Y.; Le, K.N.; Shen, L. Key credit criteria among international green building rating tools. J. Clean. Prod. 2017, 164, 209–220. [Google Scholar] [CrossRef]
  54. Lam, P.T.; Chan, E.H.; Poon, C.; Chau, C.; Chun, K. Factors affecting the implementation of green specifications in construction. J. Environ. Manag. 2010, 91, 654–661. [Google Scholar] [CrossRef] [PubMed]
  55. Wong, J.K.W.; Chan, J.K.S.; Wadu, M.J. Facilitating effective green procurement in construction projects: An empirical study of the enablers. J. Clean. Prod. 2016, 135, 859–871. [Google Scholar] [CrossRef]
  56. Yang, W.F.; Zhang, Y.L. Research on factors of green purchasing practices of Chinese. J. Bus. Manag. Econ. 2012, 3, 222–231. [Google Scholar]
  57. Elrazek, M.E.A.; Bassioni, H.A.; Mobarak, A.M. Causes of delay in building construction projects in Egypt. J. Constr. Eng. Manag. 2008, 134, 831–841. [Google Scholar]
  58. Mi, J.K.; Oh, M.W.; Kim, J.T. A method for evaluating the performance of green buildings with a focus on user experience. Energy Build. 2013, 66, 203–210. [Google Scholar]
  59. Liu, Y.; Hong, Z.; Zhu, J.; Yan, J.; Qi, J.; Liu, P. Promoting green residential buildings: Residents’ environmental attitude, subjective knowledge, and social trust matter. Energy Policy 2018, 112, 152–161. [Google Scholar] [CrossRef]
  60. Korkmaz, S.; Riley, D.; Horman, M. Piloting evaluation metrics for sustainable, high performance building project delivery. J. Constr. Eng. Manag. 2010, 19, 877–885. [Google Scholar] [CrossRef]
  61. Marker, A.W.; Mason, S.G.; Morrow, P. Change factors influencing the diffusion and adoption of green building practices. Perfom. Improv. Quart. 2014, 26, 5–24. [Google Scholar] [CrossRef]
  62. Hwang, B.G.; Zhu, L.; Ming, J.T.T. Factors affecting productivity in green building construction projects: The case of Singapore. J. Manag. Eng. 2016, 33, 04016052. [Google Scholar] [CrossRef]
  63. Li, Y.Y.; Chen, P.H.; Chew, D.A.S.; Teo, C.C. Exploration of critical resources and capabilities of design firms for delivering green building projects: Empirical studies in Singapore. Habitat Int. 2014, 41, 229–235. [Google Scholar] [CrossRef]
  64. Shen, W.; Tang, W.; Siripanan, A.; Lei, Z.; Duffield, C.F.; Wilson, D. Critical success factors in Thailand’s green building industry. J. Asian Archit. Build. Eng. 2017, 16, 317–324. [Google Scholar] [CrossRef]
  65. Lin, N. Social Resources and Instrumental Action on Social Structure and Network Analysis; Lin Sage Publications: London, UK, 1982. [Google Scholar]
  66. Knoke, D.; Yang, S. Social Network Analysis (Quantitative Applications in the Social Sciences); Sage: Los Angeles, CA, USA, 2008. [Google Scholar]
  67. Ernstson, H.; Sverker, S.; Elmqvist, T. Social movements and ecosystem services-the role of social network structure in protecting and managing urban green areas in Stockholm. Ecol. Soc. 2008, 13, 3545–3549. [Google Scholar] [CrossRef]
  68. Dickinson, J.L.; Crain, R.L.; Reeve, H.K.; Schuldt, J.P. Can evolutionary design of social networks make it easier to be ‘green’? Trends Ecol. Evol. 2013, 28, 561–569. [Google Scholar] [CrossRef] [PubMed]
  69. Yang, R.J.; Zou, P. Stakeholder-associated risks and their interactions in complex green building projects: A social network model. Build. Environ. 2014, 73, 208–222. [Google Scholar] [CrossRef]
  70. Yu, T.; Shen, G.Q.P.; Shi, Q.; Lai, X.D.; Li, C.Z.D.; Xu, K.X. Managing social risks at the housing demolition stage of urban redevelopment projects: A stakeholder-oriented study using social network analysis. Int. J. Proj. Manag. 2017, 35, 925–941. [Google Scholar] [CrossRef]
  71. El-Diraby, T.; Krijnen, T.; Papagelis, M. Bim-based collaborative design and socio-technical analytics of green buildings. Automat. Constr. 2017, 82, 59–74. [Google Scholar] [CrossRef]
  72. Mok, K.Y.; Shen, G.Q.; Yang, R.J.; Li, C.Z. Investigating key challenges in major public engineering projects by a network-theory based analysis of stakeholder concerns: A case study. Int. J. Proj. Manag. 2017, 35, 78–94. [Google Scholar] [CrossRef]
  73. Zhao, X.B.; Hwang, B.G.; Hong, N.L. Identifying critical leadership styles of project managers for green building projects. Int. J. Constr. Manag. 2016, 16, 150–160. [Google Scholar] [CrossRef]
  74. Wu, P.; Song, Y.; Wang, J.; Wang, X.; Zhao, X.B.; He, Q. Regional variations of credits obtained by LEED 2009 certified green buildings—A country level analysis. Sustainability 2017, 10, 20. [Google Scholar] [CrossRef]
  75. Hwang, B.G.; Shan, M.; Looi, K.Y. Key constraints and mitigation strategies for prefabricated prefinished volumetric construction. J. Clean. Prod. 2018, 183, 183–193. [Google Scholar] [CrossRef]
  76. Ming, S.; Chan, A.P.C.; Yun, L.; Yi, H. Investigating the effectiveness of response strategies for vulnerabilities to corruption in the Chinese public construction sector. Sci. Eng. Ethics 2015, 21, 683–705. [Google Scholar]
  77. Zhao, X.; Chang, T.; Hwang, B.G.; Deng, X. Critical factors influencing business model innovation for sustainable buildings. Sustainability 2017, 10, 33. [Google Scholar] [CrossRef]
  78. Hwang, B.G.; Shan, M.; Phua, H.; Chi, S. An exploratory analysis of risks in green residential building construction projects: The case of Singapore. Sustainability 2017, 9, 1116. [Google Scholar] [CrossRef]
  79. De Vaus, D. Analyzing Social Science Data; SAGE Publications: London, UK, 2002. [Google Scholar]
  80. Kaiser, H.F. A second generation little jiffy. Psychometrika 1970, 35, 401–415. [Google Scholar] [CrossRef]
  81. Landherr, D.M.O.A.; Friedl, D.M.B.; Heidemann, J. A critical review of centrality measures in social networks. Bus. Inform. Syst. Eng. 2010, 2, 371–385. [Google Scholar] [CrossRef]
  82. Freeman, L.C. Centrality in social networks conceptual clarification. Soc. Netw. 1978, 1, 215–239. [Google Scholar] [CrossRef]
  83. Freeman, L.C.; Roeder, D.; Mulholland, R.R. Centrality in social networks: II. Experimental results. Soc. Netw. 1979, 2, 119–141. [Google Scholar] [CrossRef]
  84. Okamoto, K.; Chen, W.; Li, X.Y. Ranking of Closeness Centrality for Large-Scale Social Networks; Springer: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
  85. Freeman, L.C. A set of measures of centrality based on betweenness. Sociometry 1977, 40, 35–41. [Google Scholar] [CrossRef]
  86. Rousseau, R.; Zhang, L. Betweenness centrality and q-measures in directed valued networks. Scientometrics 2008, 75, 575–590. [Google Scholar] [CrossRef]
  87. GB/T 50378-2014. Assessment Standard for Green Building; Ministry of Housing and Urban-Rural Development of the People’s Republic of China (MOHURD): Beijing, China, 2014.
  88. Richardson, G.R.A.; Lynes, J.K. Institutional motivations and barriers to the construction of green buildings on campus: A case study of the University of Waterloo, Ontario. Int. J. Sustain. High. Educ. 2007, 8, 339–354. [Google Scholar] [CrossRef]
  89. Zhao, X.B.; Hwang, B.G.; Yu, G.S. Identifying the critical risks in underground rail international construction joint ventures: Case study of Singapore. Int. J. Proj. Manag. 2013, 31, 554–566. [Google Scholar] [CrossRef]
  90. Jamison, A. Turning Engineering Green: Sustainable Development and Engineering Education. Engineering, Development and Philosophy; Springer: Dordrecht, The Netherlands, 2012. [Google Scholar]
  91. GreenBiz Group. Green Building Technique: A Two-Minute Briefing on Key Business Environmental Issues; GreenBiz Group Inc.: Oakland, CA, USA, 2005. [Google Scholar]
  92. Kats, G.; Alevantis, L.; Berman, A.; Mills, E.; Perlman, J. The Costs and Financial Benefits of Green Buildings; Massachusetts Technology Collaborative: Westborough, MA, USA, 2003. [Google Scholar]
  93. Bromilow, F.; Pawsey, M. Life cycle cost of university buildings. Constr. Manag. Econ. 1987, 5, 3–22. [Google Scholar] [CrossRef]
  94. Dochinger, L.S. Interception of airborne particles by tree plantings. J. Environ. Qual. 1980, 9, 265–268. [Google Scholar] [CrossRef]
Figure 1. The relationship of elements from three dimensions.
Figure 1. The relationship of elements from three dimensions.
Sustainability 10 01389 g001
Figure 2. The framework of influential factors for green residential building development (GRBD).
Figure 2. The framework of influential factors for green residential building development (GRBD).
Sustainability 10 01389 g002
Figure 3. Sociogram of influential factors for GRBD.
Figure 3. Sociogram of influential factors for GRBD.
Sustainability 10 01389 g003
Figure 4. Degree centrality sociogram of influential factors for GRBD.
Figure 4. Degree centrality sociogram of influential factors for GRBD.
Sustainability 10 01389 g004
Figure 5. Closeness centrality sociogram of influential factors for GRBD.
Figure 5. Closeness centrality sociogram of influential factors for GRBD.
Sustainability 10 01389 g005
Figure 6. Betweenness centrality sociogram of influential factors for GRBD.
Figure 6. Betweenness centrality sociogram of influential factors for GRBD.
Sustainability 10 01389 g006
Table 1. Cronbach’s alpha scores of indexes of green residential building development (GRBD).
Table 1. Cronbach’s alpha scores of indexes of green residential building development (GRBD).
ElementCronbach’s AlphaCategoryNumber of QuestionsCronbach’s Alpha
Environmental Elements0.707Political Environment60.771
Economic Environment40.946
Social Environment20.770
Juristic Environment30.842
Natural Environment30.887
Technical Environment20.745
Resource Elements0.705Government20.738
Marketing Agency30.847
Bank20.789
Research Institute20.917
Designer50.744
Builder60.773
Supervisor40.974
Supplier20.786
Certificate Authority20.846
Consumer80.814
Local Society20.816
Capacity Elements0.770Developer60.770
Table 2. Rotated component matrix.
Table 2. Rotated component matrix.
Component
12345
I10.8660.2040.0890.2540.080
I20.8420.229−0.0360.2170.246
I70.0730.1240.1400.1510.903
I190.2210.9060.1030.0830.134
I210.8300.2190.2140.177−0.099
I320.2210.710−0.0010.1790.086
I370.1320.909−0.0870.0070.107
I490.095−0.0840.1360.870−0.120
I510.2520.2140.0800.8570.091
I520.2830.1930.0690.7290.129
I580.0750.1550.189−0.0980.889
I600.1030.0960.8100.0470.242
I61-0.040−0.0680.8830.1080.102
I620.163−0.0150.8580.1060.023
Note: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 6 iterations.
Table 3. Measurement indexes of GRBD.
Table 3. Measurement indexes of GRBD.
Primary IndexesSecondary Indexes
GovernmentMandatory Policy for Developing (U1)
Incentive Policy for Developing (U2)
Strictness of Examination and Approval (U3)
LocalLocal Economy Development Level (U4)
Local Cooperation (U5)
TechnologyTechnology Level of GRBs (U6)
Design Level of GRBs (U7)
Technology Application in Design and Construction (U8)
ConsumerConsumers’ Acknowledgement for GRBD (U9)
Consumers’ Income (U10)
Incentive Policy for Purchasing (U11)
DeveloperDevelopment Strategy and Innovation Orientation (U12)
Developer’s Acknowledgement and Positioning for GRBD (U13)
Experience and Ability for GRBD (U14)
Table 4. Relation matrix of influential factors and development phases.
Table 4. Relation matrix of influential factors and development phases.
0: No Relation,
1: Weak Relation,
2: Moderate Relation,
3: Strong Relation
Project Acquiring PhaseProject Preparing PhaseProject Designing PhaseProject Constructing PhaseProject Selling Phase
Mandatory Policy for Developing (U1)2.561.892.111.671.00
Incentive Policy for Developing (U2)1.891.671.591.151.30
Strictness of Examination and Approval (U3)2.482.222.221.891.30
Local Economy Development Level (U4)2.261.741.701.411.70
Local Cooperation (U5)1.891.781.631.481.44
Technology Level of GRBs (U6)1.561.332.372.001.15
Design Level of GRBs (U7)1.371.332.561.781.11
Technology Application in Design and Construction (U8)1.261.152.262.411.26
Consumers’ Acknowledgement for GRBD (U9)1.221.001.441.112.11
Consumers’ Income (U10)1.040.890.850.782.52
Incentive Policy for Purchasing (U11)1.370.890.930.672.19
Development Strategy and Innovation Orientation (U12)2.191.781.931.591.48
Developer’s Acknowledgement and Positioning for GRBD (U13)2.191.741.931.261.63
Experience and Ability for GRBD (U14)1.781.891.931.701.59
Table 5. Adjacency matrix of influential factors in GRBD.
Table 5. Adjacency matrix of influential factors in GRBD.
U1U2U3U4U5U6U7U8U9U10U11U12U13U14
U1-14.5719.6916.7215.5516.0015.5115.4512.029.9610.4617.1816.7016.63
U214.57-15.7913.7112.7112.7312.3712.3010.288.989.1713.9313.6813.61
U319.6915.79-18.1216.9317.3616.8416.8913.2811.1911.5518.6018.0818.17
U416.7213.7118.12-14.6714.6414.1614.2312.1010.7310.8916.0915.8115.69
U515.5512.7116.9314.67-13.7913.3613.4911.119.729.8314.9414.5914.68
U616.0012.7317.3614.6413.79-14.8115.1211.299.289.3815.2414.7015.09
U715.5112.3716.8414.1613.3614.81-14.7311.018.979.0614.7814.3114.68
U815.4512.3016.8914.2313.4915.1214.73-11.289.319.2314.8614.2114.88
U912.0210.2813.2812.1011.1111.2911.0111.28-9.579.2712.1212.0312.08
U109.968.9811.1910.739.729.288.979.319.57-9.0510.4710.5610.51
U1110.469.1711.5510.899.839.389.069.239.279.05-10.6910.7610.54
U1217.1813.9318.6016.0914.9415.2414.7814.8612.1210.4710.69-16.0316.04
U1316.7013.6818.0815.8114.5914.7014.3114.2112.0310.5610.7616.03-15.65
U1416.6313.6118.1715.6914.6815.0914.6814.8812.0810.5110.5416.0415.65-
Table 6. Degree centrality of influential factors for GRBD.
Table 6. Degree centrality of influential factors for GRBD.
FREEMAN’S DEGREE CENTRALITY MEASURES
DegreeNrmDegreeShare
U12Development Strategy and Innovation Orientation13.000100.000.088
U13Developer’s Acknowledgement and Positioning for GRBD13.000100.000.088
U4Local Economy Development Level12.00092.3080.081
U14Experience and Ability for GRBD12.00092.3080.081
U2Incentive Policy for Developing11.00084.6150.074
U1Mandatory Policy for Developing11.00084.6150.074
U8Technology Application in Design and Construction11.00084.6150.074
U6Technology Level of GRBs11.00084.6150.074
U7Design Level of GRBs11.00084.6150.074
U3Strictness of Examination and Approval10.00076.9230.068
U5Local Cooperation10.00076.9230.068
U9Consumers’ Acknowledgement for GRBD10.00076.9230.068
U11Incentive Policy for Purchasing7.00053.8460.047
U10Consumers’ Income6.00046.1540.041
DESCRIPTIVE STATISTICS
DegreeNrmDegreeShare
1Mean10.57181.3190.071
2StdDev1.91714.7430.013
3Sum148.0001138.4621.000
4Variance3.673217.3650.000
5SSQ1616.00095,621.3050.074
6MCSSQ51.4293043.1110.002
7Euc Norm40.200309.2270.272
8Minimum6.00046.1540.041
9Maximum13.000100.0000.088
Network Centralization = 21.79%
Table 7. Closeness centrality of influential factors for GRBD.
Table 7. Closeness centrality of influential factors for GRBD.
CLOSENESS CENTRALITY
FarnessnCloseness
U12Development Strategy and Innovation Orientation13.000100.000
U13Developer’s Acknowledgement and Positioning for GRBD13.000100.000
U4Local Economy Development Level14.00092.857
U14Experience and Ability for GRBD14.00092.857
U1Mandatory Policy for Developing15.00086.667
U2Incentive Policy for Developing15.00086.667
U6Technology Level of GRBs15.00086.667
U7Design Level of GRBs15.00086.667
U8Technology Application in Design and Construction15.00086.667
U3Strictness of Examination and Approval16.00081.250
U5Local Cooperation16.00081.250
U9Consumers’ Acknowledgement for GRBD16.00081.250
U11Incentive Policy for Purchasing19.00068.421
U10Consumers’ Income20.00065.000
DESCRIPTIVE STATISTICS
FarnessnCloseness
1Mean15.42985.444
2StdDev1.9179.642
3Sum216.0001196.219
4Variance3.67392.975
5SSQ3384.000103,511.578
6MCSSQ51.4291301.645
7Euc Norm58.172321.732
8Minimum13.00065.000
9Maximum20.000100.000
Network Centralization = 32.66%
Table 8. Betweenness centrality of influential factors for GRBD.
Table 8. Betweenness centrality of influential factors for GRBD.
BETWEENNESS CENTRALITY
BetweennessnBetweenness
U12Development Strategy and Innovation Orientation2.9953.840
U13Developer’s Acknowledgement and Positioning for GRBD2.9953.840
U4Local Economy Development Level2.7043.467
U14Experience and Ability for GRBD1.9522.503
U9Consumers’ Acknowledgement for GRBD1.5341.966
U1Mandatory Policy for Developing1.4041.800
U2Incentive Policy for Developing1.1341.454
U11Incentive Policy for Purchasing0.6020.772
U6Technology Level of GRBs0.4520.580
U7Design Level of GRBs0.4520.580
U8Technology Application in Design and Construction0.4520.580
U10Consumers’ Income0.1430.183
U3Strictness of Examination and Approval0.0910.117
U5Local Cooperation0.0910.117
DESCRIPTIVE STATISTICS
BetweennessnBetweenness
1Mean1.2141.557
2StdDev1.0361.329
3Sum17.00021.795
4Variance1.0741.766
5SSQ35.68258.648
6MCSSQ15.03924.719
7Euc Norm5.9737.658
8Minimum0.0910.117
9Maximum2.9953.840
Network Centralization Index = 2.46%
Back to TopTop