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Article

Risk Assessment and Mitigation Strategies in Green Building Construction Projects: A Global Empirical Study

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Department of Civil Engineering and Management, School of Engineering, The University of Manchester, Manchester M13 9PL, UK
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Department of Building and Real Estate, Faculty of Construction and Environment, Hong Kong Polytechnic University, Hung Hom, Hong Kong
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School of Natural and Built Environment, Queen’s University Belfast, Belfast BT7 1NN, UK
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Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia
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NICMAR Institute of Construction Management and Research, Delhi-NCR, Bahadurgarh 124507, Haryana, India
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Department of Networking and Communications, School of Computing, SRM Institute of Science & Technology, Kattakulathur 603203, Tamil Nadu, India
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Authors to whom correspondence should be addressed.
Buildings 2025, 15(19), 3485; https://doi.org/10.3390/buildings15193485
Submission received: 6 September 2025 / Revised: 19 September 2025 / Accepted: 19 September 2025 / Published: 26 September 2025
(This article belongs to the Special Issue Digital Technologies, AI and BIM in Construction)

Abstract

The construction industry significantly impacts environmental degradation, making sustainable practices like green building construction projects (GBCPs) essential. Although GBCPs offer substantial benefits, they also come with unique risks related to their sustainable nature and common construction challenges. Research on GBCP risks is often fragmented, lacks proper classification, and misses a global perspective, with insufficient focus on empirical assessment and risk mitigation strategies. This study addresses these gaps by systematically identifying risks associated with GBCPs, empirically assessing them using data from global experts, and proposing mitigation strategies. Utilising reliability tests, descriptive statistics, one-sample t-tests, hypothesis testing, and correlation analysis, 42 risk factors were determined and assigned to nine groups: legal and regulatory, technical, financial, material-related, design, schedule and planning, communication and awareness, performance and operational, and environmental. Green product certification and re-evaluation charges, client finance difficulties, the high cost of green materials and equipment, the absence of qualified project teams, and additional expenditures for green building design and construction are the top five concerns. The study also identifies 45 mitigation strategies, enhancing understanding of GBCP risks and guiding stakeholders in effective risk management and sustainable construction practices.

1. Introduction

Huge-scale infrastructure projects and residential constructions are both included in the construction industry [1] and are essential to a nation’s growth since they provide a number of benefits [2]. They help the economy grow, create jobs, and build new roads and bridges, among other things. The construction industry is also a big reason why the environment is getting worse. It changes the weather, uses much energy, and pollutes the air [3]. Construction activities significantly contribute to greenhouse gas emissions, particularly carbon dioxide (CO2), which is a primary driver of global climate change [4]. Also, earlier studies show that the construction industry is responsible for about 35% of all CO2 emissions and almost 50% of all landfill waste [5]. The construction industry also has a significant effect on how non-renewable energy sources are used, which makes the environment worse and uses up more resources. Tröger et al. [6] say that the construction industry uses 40% of raw materials, 32% of both renewable and non-renewable resources, more than 15% of water resources, and almost half of the world’s energy production. The dust and other things that come from construction work can also make the air quality worse for people who live and work near the sites.
People all over the world are becoming more worried about the building industry’s big environmental problems. To be better for the environment and more focused on sustainability, we need to change how we do things [7]. The construction industry can be better for the environment if it uses building technologies that use less energy and release fewer greenhouse gases. People are starting to think that green buildings are a good way to lessen the damage that building does to the environment [6,8]. In the last few years, there have been a lot more projects to build green buildings. People are paying more attention to buildings that are good for the environment, as this shows [9]. There are three main benefits of green building construction projects (GBCPs): they are suitable for people, the economy, and the environment. Ferrante et al. [4] and Li et al. [10] say that GBCPs are about using less energy, making less waste, and using materials that are good for the environment. He et al. [11] also noted that people are healthier and feel better when they live or work in green buildings instead of regular ones. This supports what the US Green Building Council (USGBC, 2007) said about GBCPs making people happier and healthier. GBCPs also help people save money by making it cheaper to run and maintain a building [12]. Despite the clear environmental, social, and economic benefits of GBCPs, the complexity of implementing these projects introduces numerous risks that are not yet fully understood. Existing studies have identified various risk factors, but they are often fragmented, limited to specific locations, or focused on particular project types, making it difficult to gain a comprehensive global understanding.
There are many benefits to green building construction projects (GBCPs) over regular construction projects, but there are also more problems and risks [9]. The main reason is that we need to meet our usual goals of building high-quality buildings on time and within budget while also meeting our sustainability goals [13]. GBCPs are also more dangerous now because they use new technologies, eco-friendly materials, and more advanced designs [10,14]. In the construction business, problems like bad quality, missed deadlines, and spending too much money are always happening. But GBCPs are also in danger because they are suitable for the environment [15]. For example, using eco-friendly technology in GBCPs could make building harder, which could cause delays and higher costs [16]. Eco-friendly materials can be harder to buy and cost more because they cost more up front, the supply chain is limited, and it is hard to make sure the quality is good [17]. Also, the need for experts might mean that there are not enough qualified professionals and project teams, which could lead to problems with the project and lower quality. Not knowing or not following some rules and laws about sustainability can make things even more dangerous. This can lead to not following the rules and a bad reputation or legal status [18].
Several risk factors linked to GBCPs have been found in earlier research. Table 1 displays the quantity of green building (GB) risks identified in each study, along with the study’s location, subject matter, and methodology. This fragmentation and lack of systematic classification across geographic contexts highlight the need for a comprehensive review and empirical assessment of GBCP risks from a global perspective, which forms the focus of the current study.
But more closely, to see that there are some problems with the current set of materials. Because the risk factors are not grouped or put in the correct order, it is hard to understand the risks that come with GBCPs fully. Moreover, many of the existing studies are limited to specific geographic locations, failing to address the global context and the potential variations in risks across different regions. Therefore, by carefully examining the dangers connected to GBCPs from a worldwide viewpoint, this work seeks to close these gaps. The following are the study’s particular goals:
  • To identify a thorough list of risk variables linked to GBCP by conducting a systematic literature review.
  • To empirically assess the identified risk factors by collecting data from GBCP stakeholders worldwide, enabling a global perspective on the significance and impact of these risks.
  • To offer strategies for GBCP risk reduction, ultimately aiming to promote the successful worldwide implementation of sustainable building practices.

2. Research Methodology

There are four primary phases in the study’s framework, shown in Figure 1. Finding pertinent articles and defining research objectives are the focus of stage 1. Choosing the Scopus database for article retrieval and establishing the research objectives are the first steps in the procedure. Inclusion and exclusion criteria are used to filter the search results once specific GBCP-related keywords are used to search the database. This stage’s last step is to evaluate the retrieved articles’ complete texts, abstracts, and titles to see if they are pertinent to the research. In stage 2, a questionnaire survey is used to gather empirical data. Potential responders are identified, and the questionnaire is created using the risk factors found in the literature research. To reach a wide range of responses, the questionnaire is then distributed via a number of platforms, such as LinkedIn and ResearchGate.
Stage 3 involves the analysis of the collected data using various techniques. Demographic details of the respondents are compiled, and descriptive statistics such as mean, relative significance index (RSI), and standard deviation are calculated. Also, tests like Cronbach’s alpha and hypothesis testing are used to make sure the information is strong. People also use correlation analysis to see how different variables are related to each other. The end of stage 4 is when the results are discussed. GBCPs find and talk about important risk factors. The study also looks at how large and small businesses, as well as developed and developing countries, see risk differently. The results are used to figure out the best ways to lower risk, which can help GBCPs handle risk better.

2.1. Stage 1: Identification of Relevant Sources and Risk Factors

2.1.1. PRISMA Protocol for Articles Retrieval

A systematic literature review was performed utilising a PRISMA protocol through Scopus, a database esteemed for its extensive coverage and rigorous indexing criteria, to pinpoint studies concentrating on risk identification in green building construction projects (GBCPs) [30]. The search string used a combination of words, such as “green construction” OR “green building” OR “sustainable construction” AND “risks” OR “barriers” OR “challenges.” The first search found 2965 documents. To obtain better results, they were then sorted by language and subject area. This process reduced the number of records to 369, which were then checked for titles and abstracts to find the ones that were most useful. The screening process found 81 documents that were thought to be helpful for the research topic. After that, a careful reading of all 81 articles was carried out, and 35 articles that did not fit the study’s goals were left out. This left us with 46 articles to look at in more detail. To perform a comprehensive evaluation, the expanding technique articles required the scrutiny of reference lists from the chosen papers to uncover supplementary relevant studies. This process yielded nine more articles, bringing the total number of relevant articles to 55. These 55 articles formed the basis for the systematic literature review, providing valuable insights into the risks associated with GBCPs and laying the groundwork for the subsequent empirical assessment and risk mitigation strategy development.

2.1.2. GBCP Risk Factors

Forty-two unique risk variables were identified after a careful review of the 55 publications; these are categorised into nine groups and shown in Table 2. The rationale for this grouping was based on the nature and source of each risk factor, aiming to cluster risks with similar origins or impacts to facilitate clearer analysis and management. In cases where a risk could potentially belong to more than one category, it was classified based on the primary aspect it affects. For example, delays caused by the unavailability of eco-friendly materials could be considered both a supply (technical/material) risk and a scheduling risk. In such cases, the risk was classified under technical/material risks, while its impact on scheduling was noted, ensuring overlapping influences were acknowledged without duplicating entries. These groups include risks related to technology, money, materials, equipment, and technical issues; design; scheduling and planning; legal and regulatory issues; communication and awareness; performance and operations; and environmental issues.
Liu et al. [31] said that technical risks in GBCPs can happen when suppliers, project teams, contractors, and subcontractors have technical problems. Maqbool et al. [32] said that building and designing green buildings is risky from a technical point of view. GBCPs are often more expensive than regular buildings because they use green materials and equipment, which could put the company in financial trouble [7,32]. Li et al. [14] say that inflation, not having enough money or resources from clients, and wrong estimates of payback periods or ROI can all make financial risks worse.
GBCPS need to have and be able to choose eco-friendly materials [33]. The same is true for green technology and equipment; if you do not have them, you might worry about technology, equipment, and materials. Abujder Ochoa et al. [34] and Dedasht et al. [35] say that these risks are mainly about the performance, quality, standards, and lack of green technologies, equipment, and materials used in GBCPs. According to Mercogliano et al. [36] and Anagnostopoulos et al. [37], design risk in GBCPs means that there could be problems and unknowns during the design process. These problems could include design data that is wrong or missing, design changes that happen too often, and not properly incorporating sustainability principles into green building designs. Using a schedule and planning risks to find problems and match them with solutions can help the project stay on track. According to Cabral-Ramírez et al. [38], construction projects, even green buildings, often fall behind schedule. There are many reasons why this can happen, such as deadlines that are too strict or not enough supplies and machinery [39].
Regulatory and legal risk includes getting permits that are only for Great Britain, following the right laws and rules, and dealing with any legal problems that may come up [40]. Poor communication, strained relationships with stakeholders, unclear responsibilities for stakeholders, and a lack of public awareness about the importance of green building are all examples of communication and awareness risks that can lead to lower project performance, conflicts, and disputes [41]. According to Fitriawijaya and Taysheng [42], there are risks associated with the operation, maintenance, and productivity of personnel, equipment, and technology in GBCPs. Events that occur during construction and noncompliance with sustainable construction certification standards significantly elevate performance and operational risk [43]. Environmental risks are possible problems that could happen at the GB building site or with the weather, such as unexpected bad site conditions, problems getting land, and the effects of extreme weather events [44]. By explicitly grouping risks and noting overlapping effects, this classification provides a structured framework for analysing GBCP risks while acknowledging the interconnections among different categories.
Table 2. Identified risk factors in GBCP.
Table 2. Identified risk factors in GBCP.
No.Risk CategoryFactorCodeReference
R1TechnicalLimited availability and dependability of subcontractors for green buildingF-01[42,44]
Lack of an experienced and competent project crewF-02[44,45]
Reliability and accessibility issues with green building subcontractorsF-03[46,47]
Lack of suppliers of environmentally friendly items and materialsF-04[30,48]
R2FinancialLack of resources and funding for the clientF-05[49]
Price fluctuations and inflation for labour and green building suppliesF-06[50,51]
Additional expenses for green building design and constructionF-07[52,53]
Expensive green equipment and materials F-08[53,54]
Extra expenses for reassessing and certifying eco-friendly goods and productsF-09[55,56]
Inaccurate payback term or ROI (return on investment) prediction for the projectF-10[57,58]
Lack of market demandF-11[59,60]
R3Material, Equipment, and TechnologyApproved green technology, techniques, and materials are scarce and lacking.F-12[61,62]
Unconfirmed quality of new eco-friendly technology, equipment, materials, and productsF-13[62,63]
New green technology, equipment, materials, and products with inadequate or inaccurate green specificationsF-14[54,58]
Insufficient utilisation of eco-friendly resources, equipment, and technologyF-15[32,35]
R4Design RisksInsufficient and inaccurate design dataF-16[64,65]
Frequent design changes and variationsF-17[66,67]
Insufficient incorporation of sustainability into green building designF-18[68,69]
R5Schedule and PlanningThe green construction process’s delayF-19[70,71]
Unreasonably strict timeline for environmentally friendly buildingF-20[72,73]
Not obtaining supplies or equipment in the allotted periodF-21[74,75]
In sustainable building, a poorly defined scope and an ambiguous role distributionF-22[69,70,76,77]
R6Regulatory and LegalComplex green building approval processes, codes, and restrictionsF-23[78,79]
Modifications to municipal laws and policiesF-24[80,81]
Modifying the rules and certification procedure for green buildingsF-25[82,83]
The project parties’ contractual duties and responsibilities are not adequately defined.F-26[84,85]
Uncertain terms and conditions in green building contractsF-27[78,80,86]
In construction, litigation, court cases, and prosecutions for failing to meet client expectationsF-28[79,87]
R7Communication and AwarenessProject team members’ poor cooperation, communication, and information sharingF-29[88,89]
Weak collaboration among supply chain partners, the project team, and the clientF-30[90,91]
Complex stakeholder composition and requirementsF-31[92]
Stakeholders’ unclear obligations in obtaining green certificationF-32[92,93]
Insufficient public awareness and knowledge F-33[94,95]
R8Performance and OperationalLow labour and equipment productivity F-34[88]
Insufficient GB upkeepF-35[64,65,66]
Difficulties in operating green solutions F-36[69,71,74]
Not fulfilling the certification requirements for sustainable constructionF-37[77,96]
Injuries and accidents during construction F-38[97,98]
R9EnvironmentalUnexpectedly unfavourable site conditions and inadequate construction site investigation F-39[99,100]
There is a strong need to protect the working environment at green construction sites.F-40[91,101]
Uncertainty in purchasing landF-41[98,102]
Changes in the weatherF-42[103,104]

2.2. Stage 2: Data Collection

2.2.1. Questionnaire Development

After the risk factor list was finished, a questionnaire survey was conducted using the Qualtrics web platform to look at the risk variables found in GBCPs. The survey has two main parts.
The first part is meant to get relevant background information from the people taking part. The questions in this part ask about the respondents’ job title, where they work, how long they have been in the building industry, what their highest level of education is, and how big their company is. In numerous studies concerning construction project management, this background information is integrated into the questionnaire development process [101,103].
The second part of the questionnaire has three questions. In the first part, people are asked to rate how important they think 42 risk factors related to GBCPs are on a five-point Likert scale, with 1 being “very low relevance” and 5 being “extremely high importance.” This scale allows for a consistent and measurable assessment of the importance of the risk variables. In the second question, respondents can talk about any other GBCP-related issues that were not brought up in the first question. Any potential hazards not included on the established list may still be documented and considered during the investigation of this open-ended question. The last question is open-ended and asks people to suggest ways to make the GBCP risks less harmful. The poll aims to obtain helpful advice and ideas from professionals in academia and business on how to handle and lower the risks that come with GBCPs by asking this question. The combination of open-ended and closed-ended questions allows for the collection of both quantitative and qualitative data, which leads to a better understanding of the risks and possible ways to reduce them in GBCPs.

2.2.2. Questionnaire Administration

A pilot test is required before the final dissemination to ensure the questionnaire’s validity and reliability [105]. A group of academics and industry professionals reviewed the instrument to check clarity, consistency, and content validity, after which refinements were made. The survey was aimed at researchers and professionals who were very knowledgeable about the construction industry, since their input was expected to improve the quality and dependability of the data collected.
To make sure the questionnaires were sent out correctly, a multi-pronged approach was used. We made a list of professionals and experts in the construction industry based on what they knew and how helpful they would be to the research question. Everyone on the list received an email invitation that was only for them. The email told them what the study was about, asked them to fill out the questionnaire survey, and stressed how important it was for them to take part. The email had a link to the online survey, and people who hadn’t filled out the questionnaire by the deadline received polite reminder emails that stressed how vital their answers were to the study in order to get more people to respond.
Second, the survey was able to reach more people in the construction industry by using professional networks, data from academic journals, and educational platforms. The questionnaire was distributed to authors of papers concerning the research topic and disseminated among 20 LinkedIn groups associated with the construction industry. It was also put on the academic website “ResearchGate. In order to increase the sample size, the snowballing sampling strategy was used, which encouraged survey participants to forward the link to their peers and industry contacts [106]. Consequently, 74 responses were received, of which 55 valid responses were retained after excluding 19 incomplete or inconsistent submissions. This represents a valid response rate of 74.3%. Inclusion criteria required participants to have a minimum of five years of professional or academic experience in construction. Responses were excluded if demographic information or more than 20% of survey items were missing. Given that it satisfies the central limit theorem’s minimal requirement of 30, the sample size of 55 is enough [107,108].

2.2.3. Demographics of the Respondents

Figure 2 displays the survey respondents’ demographic information. The data shows that more than 42 percent of respondents have 10–15 years of relevant experience. Furthermore, 22% have more than 15 years of expertise, and about 36% have 5 to 10 years. According to the professional role perspective, academicians, which include professors and researchers, make up the largest group of responders (40 percent). This suggests that many people are working in the building industry in academic and research capacities. With 21.8%, 14.6%, and 12.8% of respondents, project managers, architects, and engineers rank second in importance. Smaller percentages of respondents were in other roles that were also recorded by the poll, such as consultants, construction workers, and quantity surveyors. The profile analysis indicates that the construction industry attracts professionals from diverse roles, with a strong representation in managerial, engineering, research, and academic positions.
Geographically, the US (7.28%), the UK (14.54%), and China (30.90%) accounted for the majority of responders. Additionally, some responders are from Saudi Arabia, Malaysia, Australia, India, and other nations. In terms of education, almost half (45.5%) had doctorates. A bachelor’s degree was held by 20%, a master’s degree by 25.5%, and a college degree by just 7.3%. Regarding company size, the majority (65.5%) worked for large companies with over 250 employees. Approximately 16% were in medium-sized companies, 11% in small companies, and 7% in micro companies. The diverse respondent profile lends credibility to the compiled perspectives.

2.3. Stage 3: Data Analysis Techniques

Data collected from respondents were analysed using IBM SPSS Statistics 26. A reliability analysis was conducted to evaluate data integrity and identify any items that did not significantly contribute to the overall reliability of the dataset [109]. Data reliability was assessed using Cronbach’s alpha test, with values greater than 0.7 deemed appropriate for exploratory research [110]. Specifically, α ≥ 0.7 is acceptable, α ≥ 0.8 indicates good reliability, and α ≥ 0.9 is considered excellent [111]. According to [112], descriptive statistical analysis sheds light on data attributes, including mean, standard deviation, median, and mode. The sample mean’s departure from the population mean was evaluated using the one-sample t-test with a significance level of 0.05 and a test value of 3 [106,113].
The Mann–Whitney U test and Spearman rank correlation were used for hypothesis testing and correlation analysis, respectively. The Mann–Whitney U test, a nonparametric statistical method, is employed to detect significant differences in data distributions [114]. The null hypothesis in hypothesis testing posited no considerable disparity between academicians and project managers concerning the perceived importance of risk factors. The correlation analysis null hypothesis posited that there was no significant relationship between these two professional roles and the perceived relevance ratings of risk factors. The studies focused on academics and project managers due to their considerable representation among the respondents. Finally, we employed the interrater agreement (IRA) method from [115] to determine whether the respondents’ assessments of the significance of the risk factors were consistent or divergent. We used average within-group variance (aWG), a common statistic in construction research [116], to find out how much the respondents agreed with each other.

2.4. Stage 4: Discussion of Findings

Stage 4 involves a discussion of the study’s findings. We identify and examine the principal risk factors that significantly influence GBCPs. The research investigates the differing perceptions of risk between large and small enterprises, as well as between developed and developing nations. This comparative analysis offers an extensive comprehension of the ways different factors affect the perception of risk. The report also tells you the best ways to lower these risks, which is helpful for making GBCPs stronger. Companies can better prepare for and deal with possible problems if they know these essential things. This will make construction operations more stable.
All methods were carried out following the relevant guidelines and regulations of the University of Manchester. Before data collection, ethical approval for this research was obtained using the University of Manchester’s Ethics Decision Tool, which determined the appropriate level of review based on the nature of the study. Also, informed consent was obtained from all participants involved in the study, and, where necessary, from their legal guardians.

3. Research Findings

3.1. Reliability Analysis

Table 3 presents the results of the Cronbach’s alpha reliability test conducted on the survey data. The purpose of this analysis is to evaluate the internal consistency and reliability of the responses. As shown, the Cronbach’s alpha value for the 42 risk factor importance items is 0.921, which falls into the ‘excellent’ category (α ≥ 0.9), demonstrating very strong internal consistency. A high Cronbach’s alpha indicates that the 42 risk factors as a set effectively measure a unified underlying construct. In this case, the high alpha suggests that all the risk factor importance ratings reliably measure the same latent concept of “risk factor importance.”

3.2. Descriptive Statistics

The percentage of participants who responded, the mean, the standard deviation (SD), the rank, and the relative significance index (RSI) are among the descriptive data shown in Table 4. The distribution of participant opinions is recorded and assessed using a five-point Likert scale: 3 indicates medium importance, 4–5 indicates great importance, while 1–2 indicates low relevance. This distribution pattern helps determine how much participants agree or disagree on the significance of risk variables. Remarkably, F-34 (low labour and equipment productivity) exhibits a comparatively consistent view of its significance, with roughly one-third of individuals representing each importance level. However, a significant majority (60%) assign “high importance” to F-08 (high cost of green material and equipment), highlighting its critical role in project considerations. Just 5.45% of those surveyed said it was of “low importance”.
RSI is used to rank the risk factors [117]. A higher ranking is the outcome of a higher RSI, which denotes greater relevance. Each factor’s ranking can alternatively be ascertained by its mean value. Although global sensitivity analysis has been used in some related research to investigate the robustness of input factors, the RSI approach was selected in this study because it is widely applied in construction risk management research and offers a straightforward, interpretable method for ranking risk factors based on survey data [118,119]. RSI directly translates respondents’ Likert-scale assessments into normalised values, facilitating easier comparison across multiple factors. Given the sample size and the exploratory nature of this study, RSI was considered the most suitable and practical method to establish the hierarchy of risk importance. Out of the 42 risk factors, F-08 (high cost of green material and equipment) is by far the most highly scored, with the highest RSI of 0.85 and the highest mean score of 3.75. Participants view this risk factor as the most important, as evidenced by all of these indicators. The standard deviation also varies from 0.80 to 1.33, demonstrating varied levels of agreement or disagreement among individuals about these risk variables. The high RSI value of 0.85 for F-08 (high cost of green materials and equipment) indicates that stakeholders consider cost management a critical challenge in GBCPs. This suggests that project managers should prioritise budgeting and material sourcing strategies to mitigate financial risks in sustainable construction projects. Conversely, factors with lower RSI values (e.g., F-42) were perceived as less critical, implying that they may require less immediate attention in risk mitigation planning.

3.3. One-Sample t-Test

The average importance rating of each of the 42 risk factors was compared to a test value of 3, which is the midpoint of the 1–5 rating scale, using a one-sample t-test on the survey data (see Table 5). The objective was to ascertain which risk factors were regarded as considerably more or less significant than a moderate level of importance.
According to Gan et al. [120] the results showed that 27 risk factors had p-values higher than 0.05. This means that their mean ratings were not very different from the test value of 3. The respondents perceived these factors as possessing an average, moderate level of significance. However, 15 factors were found to have p-values below 0.05, meaning their mean ratings diverged significantly from the scale midpoint [121]. Among these 15 factors, 13 had mean ratings higher than 3, including F-01 and F-02. This suggests these factors were perceived as significant risks in GBCP. On the other hand, two factors, F-41 and F-42, had means significantly lower than 3, implying respondents viewed them as lower-importance risks. The analysis demonstrates the value of statistical testing, which determines the risk factors that diverge considerably from a neutral level of importance according to the survey ratings. The one-sample t-test shows that F-01 and F-02 are significantly above the neutral level (p < 0.05), confirming that stakeholders perceive these as high-priority risks. Practically, this indicates that proactive measures, such as enhanced supervision, careful material selection, and risk monitoring, should be implemented to ensure the success of green building initiatives.

3.4. Hypothesis Testing and Correlation Analysis

Similarities and discrepancies in academicians’ and project managers’ perceptions of the significance of the 42 risk factors were found by hypothesis testing and correlation analysis, as seen in Table 6. For 35 factors, no statistically significant differences or connections were discovered using the Spearman correlation or Mann–Whitney U test. Both groups had similar views on the importance of these risks, as evidenced by p-values over 0.05.
However, the remaining seven components (F-03, F-08, F-16, F-20, F-29, F-30, and F-32) exhibited statistically significant differences and associations. p-values below 0.05 indicate that respondents in different roles held varying opinions. Academicians and project managers may have differing views on the significance of these seven specific hazards, as indicated by the negative correlation coefficient value, which also demonstrates a negative association between the two professional jobs and the perceived importance of these risk variables. Significant differences observed for F-03, F-08, and F-32 suggest that academicians and project managers perceive certain risks differently. In practice, this highlights the need for alignment sessions or workshops to reconcile perspectives and ensure that critical risks are addressed consistently in GBCP planning and implementation.

3.5. Additional Risk Factors

Participants were invited to rank the significance of the identified risk factors and to suggest any additional risks they considered relevant but were not included in the initial list. This open-ended approach enabled the capture of overlooked or previously unrecognised risks. In total, a further fifty-one criteria were mentioned. However, 32 of these had already been included in the predefined list, demonstrating the extensive coverage of the initial risk identification. After removing the duplicates, 19 unique risk factors emerged from the responses. As shown in Table S1, these encompassed risks related to aspects like safety, technology, training, and project planning.

3.6. Identified Risk Mitigation Strategies

In the last section of the questionnaire survey, participants were asked to describe the mitigation techniques intended to address risk factors in GBCP. Thirteen of the 45 mitigation options that were proposed in light of the findings were brought up frequently. These mitigating techniques are shown in Table 7, along with the frequency and percentage that go with them. Based on the analysis of the survey responses, a framework summarising the top risk mitigation strategies for GBCPs is presented in Figure 3. This flowchart illustrates the hierarchical prioritisation of strategies and links them to the corresponding risk categories, facilitating practical implementation in real-world projects. The parts that follow go over the best mitigating techniques.

4. Discussion of the Result

4.1. Critical Risk Factors

A cut-off point of 65% (i.e., 3.25) is employed in this study to determine the criticality of the factors [122]. Based on this, 16 of the 42 risk factors are considered critical. Figure 4a displays the radar chart, reflecting the assessment visually. The radar chart visually distinguishes critical from non-critical risk factors. The red line represents the mean importance values for each factor, while the blue line indicates the threshold of 3.25 used to determine criticality. Factors F-08, F-05, F-02, F-07, and F-09 exceed the threshold, confirming them as the top five critical risks in green building projects. Factors below the threshold are considered less significant but still relevant for comprehensive risk management.
The most significant risk element is F-08, which is the high price of environmentally friendly supplies and machinery. This element is crucial since it affects a project’s financial viability, which could result in a financial burden and long payback periods. According to Xu et al. [123,124], these high costs may make stakeholders less likely to adopt sustainable practices. Next is F-05, which is the client’s lack of money and resources. This part is significant because it shows how important it is for customers to have enough money and resources for the project to be successful. Shortages that stop the project lifecycle can cause delays and higher capital costs [125]. Third on the list is F-02, which is the lack of a knowledgeable and experienced project team. This part is important because new or untested green products and technologies need special knowledge. The team’s experience is necessary for turning sustainable design into workable solutions. Without it, quality problems, implementation problems, and sustainability goals could be at risk [126]. The fourth most important factor (F-07) pays for the higher costs of designing and building green buildings. Adding green features like renewable energy may make it harder to make money and stick to budgets. F-09, the extra cost of green products and material certification and re-evaluation, comes in fifth. The need to obtain sustainability certifications creates this risk, which could lead to unanticipated costs for retesting non-compliant products. It may result in delays, overspending, problems allocating resources, and maybe unhappy clients [127].

4.2. Risk Perception Between Different Groups

4.2.1. Developed and Developing Nations in Comparison

Table 2 presents the mean values, rankings, and importance ratings for each risk factor as evaluated by respondents from both developed and developing countries. The corresponding radar charts are illustrated in Figure 4b,c. Comparison of radar charts between developed and developing countries highlights differences in perceived risk importance. Respondents from developing countries rate F-08 (high cost of green materials and equipment) as the highest-risk factor, whereas developed countries prioritise F-02 (lack of skilled and experienced project teams). The total number of critical risks is slightly higher in developed countries (19) than in developing countries (18), indicating subtle variations in risk perception across regions.
In terms of distinctions, the first one is found in the mean value of the perceived importance overall for respondents from industrialised and developing nations. In particular, the average for the former group is 3.13, but the average for the latter group is marginally higher at 3.24. Second, the group of industrialised countries has somewhat more critical risk factors than the group of developing countries, with 19 and 18, respectively. Furthermore, the two groups differ in the most critical risk factor. In developing countries, F-08, ‘High cost of green materials and equipment,’ is considered the most significant, while in developed countries, F-02, ‘Lack of skilled and experienced project teams,’ holds the highest mean value, with values of 3.78 and 3.76, respectively.
Eleven consistent risk variables are found to be crucial in both industrialised and developing nations, based on commonalities. For instance, in both groups, F-23, “Intricate approval procedures, codes, and regulations for green buildings,” gets the identical ranking of 12.

4.2.2. Large-Size Versus Non-Large-Size Company

Each risk factor’s mean value, rating, and perceived significance among respondents from large and non-large businesses (micro, small, and medium) are shown in Table S3. Figure 4d,e provide a graphic representation of the outcome. Radar charts for large and non-large companies reveal differences in risk prioritisation. Large companies identify more critical risks (22) and rank F-02 as the most significant, reflecting an emphasis on technical expertise. Non-large companies recognise fewer critical risks (11) and consider F-08 as the top concern, highlighting financial constraints as a key risk factor. Shared critical risks, such as F-03 (technical complexity), suggest certain challenges are universally recognised, irrespective of company size.
Regarding differences, large companies had a somewhat higher overall risk importance rating than non-large companies. This suggests that large companies may perceive greater levels of risk in green building projects overall. Additionally, the number of identified critical risks was higher among large companies at 22 versus only 11 for non-large companies. The wider breadth of critical risks perceived by large companies indicates they may take a more holistic risk management approach across diverse factors.
Additionally, the most critical risk differed between the groups—large companies ranked the absence of skilled teams (F-02) as the top priority. In contrast, non-large companies viewed the high cost of green materials (F-08) as the most critical. This points to large companies’ greater focus on risk mitigation through expertise, while smaller companies prioritise managing financial risks.
Regarding similarities, 9 risk factors were commonly viewed as critical by both large and non-large companies. For instance, the technical complexity of green construction (F-03) was ranked high by both groups. This suggests that certain risks, like technical challenges, are universally recognised across company sizes.

4.3. Top Risk Mitigation Strategies

As indicated in Table 7, 45 risk mitigation strategies were identified from the questionnaire data. The top 5 strategies, with their frequency of occurrence of at least 3, are discussed in this section. A framework summarising these strategies and linking them to the corresponding risk categories is presented in Figure 3, providing a visual guide for practical implementation in GBCPs.
1. Operating with a competent project team: This strategy, involving qualified professionals who meet certification requirements, was the most frequently discussed mitigation measure. It ensures the effective implementation of sustainable construction [128,129]. However, practical challenges include the limited availability of certified professionals in certain regions and higher recruitment costs, which may hinder implementation.
2. Ongoing education and training for stakeholders: Frequently suggested by respondents, this strategy emphasises continuous learning for project practitioners. Zgheib et al. [130] also highlight the critical importance of education and training facilities. Challenges include the need for sustained investment, scheduling flexibility, and participant engagement, which can be resource-intensive.
3. Careful project planning and design: Suggested four times, meticulous planning helps reduce uncertainty and align project activities. Comparing with prior studies, Cabral-Shan et al. [131] also reported that proactive planning improves project efficiency and minimises risks. Challenges include potential increases in upfront project duration and coordination efforts across multiple teams.
4. Active involvement of project stakeholders: Also mentioned four times, engaging stakeholders throughout the project enhances communication and alignment. This aligns with findings in the literature [131], which emphasise the positive impact of stakeholder engagement on project success. Implementation challenges may arise from conflicting stakeholder interests, which can lead to delays or disputes.
5. Sustainable material selection and project-specific risk assessment: Reported three times, this strategy involves choosing environmentally friendly materials and integrating project-specific factors into risk assessments. Previous studies, such as Job et al. [132], also highlight the importance of sustainable material selection in enhancing environmental and operational performance. Challenges include high costs, supply chain limitations, and ensuring consistent material quality.
Overall, these top five strategies provide a prioritised roadmap for risk mitigation in GBCPs, while acknowledging real-world implementation constraints.

5. Implications and Significance

The importance of this study lies in its thorough methodology for identifying and evaluating risk factors in GBCPs from a global standpoint. This study consolidates data from GBCP specialists globally, offering a more comprehensive understanding of the associated risks than prior research, which typically focuses on particular regions or types of green building initiatives. This study differs from previous research as it emphasises both risk assessment and mitigation while gathering data globally. It fills in gaps in the current literature and sets the stage for more research and real-world uses in the field of green building construction. The following parts explain what the study means in terms of theory and practice.

5.1. Theoretical Implications

This research contributes to the existing body of knowledge by conducting a comprehensive and methodical examination of the risk factors associated with GBCPs. The identification and categorisation of 42 unique risk factors into nine distinct categories provides a more organised and sophisticated understanding of the risks linked to GBCPs. Future research may enhance this classification, allowing researchers to examine the relationships among various risk categories and create more customised risk management frameworks.
The research fills a void in the literature by employing a global perspective to evaluate the potential disparities in the perceived importance of risk factors across various geographies and occupational roles. The results show that both academics and project managers have a common understanding of the risks associated with GBCPs and do not have very different opinions on the matter. This information can help researchers plan their next steps and get industry and academia to work together more to solve the problems that GBCPs face. The study emphasises the imperative for further context-specific research on GBCPs, as the relevance of particular risk factors may differ according to the project’s location, scale, and nature. This necessitates augmented research into the particular challenges and opportunities that GBCPs present in various contexts, thereby enabling the formulation of more customised risk management strategies.

5.2. Practical Implications

This paper is significant for project managers, designers, contractors, and lawmakers, among others, because it talks about GBCPs. The findings provide essential guidance for decision-making and resource allocation in green construction projects by identifying and prioritising the most significant risk factors. Project managers can use this information to make better and more focused plans for managing risks. These plans should deal with substantial problems like the rising costs of design and construction, the high costs of green tools and materials, and the difficulties of getting certified for sustainable building.
The people who took the survey said they were having problems, and the suggested ways to lower risk are good ways to fix them. For instance, GBCPs can be sure that they are financially stable by doing thorough feasibility studies and cost–benefit analyses. Strong relationships with manufacturers and suppliers can also help make green materials and equipment easier to find and less expensive. People who work in this field can change these plans to help GBCPs do better. The study’s results also show how important it is to think about sustainability from the beginning of the design process and to give project teams a lot of training and guidance on how to build sustainably and meet certification standards. These insights can help improve training programmes and design standards for GBCPs, which will encourage a more holistic approach to building sustainably.

6. Conclusions

This study systematically reviewed risks in green building construction projects (GBCPs) and identified 42 risk indicators, classified into nine categories: financial, technical, design, schedule and planning, material/equipment/technology, regulatory/legal, communication/awareness, performance/operational, and environmental hazards. Based on a global expert survey, 16 crucial risks were confirmed. The top five risks were as follows: (1) high cost of green materials and equipment (Mean = 3.75, SD = 0.84), (2) lack of client funding and resources (Mean = 3.69, SD = 1.07), (3) lack of knowledgeable and experienced project team (Mean = 3.67, SD = 0.88), (4) additional design and construction costs (Mean = 3.64, SD = 1.01), and (5) certification and re-evaluation costs of green products and materials (Mean = 3.64, SD = 0.80). Respondents also highlighted 19 additional risks, particularly related to safety, technology, training, and project planning.
In terms of mitigation, the analysis emphasised five effective strategies: (1) hiring certified and experienced project teams, (2) providing continuous stakeholder education and training, (3) conducting comprehensive project planning and design, (4) ensuring active stakeholder involvement, and (5) selecting sustainable and eco-friendly materials. These strategies directly address the most critical risks and provide a structured approach for managing challenges in GBCPs.
The study’s strengths include its global perspective and comprehensive risk classification. However, its limitations lie in the small sample size and limited data processing techniques, which may affect the generalizability of the findings. In addition, as the survey was disseminated primarily through convenience channels such as LinkedIn and ResearchGate, there is a potential risk of sampling bias, with an overrepresentation of respondents active on academic and professional online platforms. This limitation should be considered when interpreting the results. Future research should focus on expanding the dataset, conducting in-depth regional case studies, and applying advanced statistical techniques such as PLS-SEM to better capture interactions across risk categories. Future studies may extend this analysis using sensitivity analysis techniques to validate and complement RSI-based findings. Overall, this study contributes valuable insights into risk identification, prioritisation, and mitigation in GBCPs. By providing a clear understanding of critical risks and strategies, it supports informed decision-making and fosters the wider adoption of sustainable construction practices worldwide.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/buildings15193485/s1, Table S1: Additional risk factors; Table S2: Risk perception of respondents from developed countries and developing countries and Table S3: Risk perception of respondents from Non-large size company and Large size company.

Author Contributions

Conceptualization, S.R.M.; Methodology, S.R.M., R.T. and A.-M.Y.; Software, R.T., A.-M.Y. and T.H.; Validation, A.K.S.; Formal analysis, S.R.M., R.T. and A.-M.Y.; Investigation, A.-M.Y. and A.K.S.; Data curation, S.R.M., R.T., A.-M.Y. and T.H.; Writing—original draft, S.R.M., R.T., A.-M.Y., T.H., F.E., M.A. and A.K.S.; Writing—review and editing, S.R.M., F.E., M.A., A.K.S. and M.S.C.; Visualization, T.H.; Supervision, M.A. and M.S.C.; Project administration, F.E., M.A. and M.S.C.; Funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All methods were carried out in accordance with the relevant guidelines and regulations of the University of Manchester. Prior to data collection, ethical approval for this research was obtained using the University of Manchester’s Ethics Decision Tool, which determined the appropriate level of review based on the nature of the study. The research involved human participants through only surveys and did not include any clinical, biological, or medical interventions.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study, and, where necessary, from their legal guardians.

Data Availability Statement

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare that they have no competing interests.

References

  1. Wang, Y.; Zhao, N.; Yin, X.; Wu, C.; Chen, M.; Jiao, Y.; Yue, T. Global future population exposure to heatwaves. Environ. Int. 2023, 178, 108049. [Google Scholar] [CrossRef]
  2. Amir, M.; Deshmukh, R.G.; Khalid, H.M.; Said, Z.; Raza, A.; Muyeen, S.; Nizami, A.-S.; Elavarasan, R.M.; Saidur, R.; Sopian, K. Energy storage technologies: An integrated survey of developments, global economical/environmental effects, optimal scheduling model, and sustainable adaption policies. J. Energy Storage 2023, 72, 108694. [Google Scholar] [CrossRef]
  3. Adhikari, S.; Halden, R.U. Opportunities and limits of wastewater-based epidemiology for tracking global health and attainment of UN sustainable development goals. Environ. Int. 2022, 163, 107217. [Google Scholar] [CrossRef]
  4. Ferrante, T.; Maestosi, P.C.; Villani, T.; Romagnoli, F. A Portfolio of Building Solutions Supporting Positive Energy District Transition: Assessing the Impact of Green Building Certifications. Sustainability 2025, 17, 400. [Google Scholar] [CrossRef]
  5. Kazemi, A.; Mehrani, S.; Homayoun, S. Risk in Sustainability Reporting: Designing a DEMATEL-Based Model for Enhanced Transparency and Accountability. Sustainability 2025, 17, 549. [Google Scholar] [CrossRef]
  6. Tröger, D.; Araneda, A.A.B.; Busnelli, R.; Yajnes, M.; Williams, F.; Braun, A.C. Exploring eco-industrial development in the global south: Recognizing informal waste-picking as urban-industrial symbiosis? Clean. Waste Syst. 2023, 5, 100096. [Google Scholar] [CrossRef]
  7. Settembre-Blundo, D.; González-Sánchez, R.; Medina-Salgado, S.; García-Muiña, F.E. Flexibility and Resilience in Corporate Decision Making: A New Sustainability-Based Risk Management System in Uncertain Times. Glob. J. Flex. Syst. Manag. 2021, 22, 107–132. [Google Scholar] [CrossRef]
  8. Zhang, Y. Application of intensive construction technology in the grand Paris express project: A review. Front. Struct. Civ. Eng. 2025, 19, 488–501. [Google Scholar] [CrossRef]
  9. David, L.O.; Nwulu, N.; Aigbavboa, C.; Adepoju, O. Towards global water security: The role of cleaner production. Clean. Eng. Technol. 2023, 17, 100695. [Google Scholar] [CrossRef]
  10. Liu, Y.; Li, Y.-L.; Min, Y.-T.; Chen, S.; Yang, W.; Gu, J.-T.; Feng, W.-J.; Li, Y.; Hong, C.; Du, J.; et al. Fukushima Contaminated Water Risk Factor: Global Implications. Environ. Sci. Technol. 2025, 59, 3703–3712. [Google Scholar] [CrossRef]
  11. He, W.; Zhang, Y.; Kong, D.; Li, S.; Wu, Z.; Zhang, L.; Liu, P. Promoting green-building development in sustainable development strategy: A multi-player quantum game approach. Expert Syst. Appl. 2024, 240, 122218. [Google Scholar] [CrossRef]
  12. Lian, N.; Ji, W.; Chen, J. Research on the Safety Risk Analysis Framework and Control System for Multi-Type New Energy Storage Technologies. Energies 2025, 18, 798. [Google Scholar] [CrossRef]
  13. Khaing, M.M.; Yin, S. Lifecycle Management of Hydrogen Pipelines: Design, Maintenance, and Rehabilitation Strategies for Canada’s Clean Energy Transition. Energies 2025, 18, 240. [Google Scholar] [CrossRef]
  14. Li, L.; Wang, S.; Zeng, S.; Ma, H.; Zheng, R. Unveiling the social responsibility factors in new infrastructure construction. Eng. Constr. Arch. Manag. 2023, 32, 2272–2298. [Google Scholar] [CrossRef]
  15. Omrany, H.; Al-Obaidi, K.M.; Ghaffarianhoseini, A.; Chang, R.D.; Park, C.; Rahimian, F. Digital twin technology for education, training and learning in construction industry: Implications for research and practice. Eng. Constr. Arch. Manag. 2025, ahead of print. [Google Scholar] [CrossRef]
  16. Charette-Castonguay, A.; Gautam, D.; Shrestha, B.; Ojha, H.C.; Sharma, B.K.; Upadhayaya, M.; Rana, S.; Shrestha, R.; Chaudhary, L.B.; Kandel, B.; et al. Development of a zoonotic influenza distribution assessment and ranking system (ZIDAR): Technical application in Nepal to support cross-sectoral risk-based surveillance. One Health 2025, 20, 100975. [Google Scholar] [CrossRef]
  17. Pal, A.; Lin, J.J.; Hsieh, S.H.; Golparvar-Fard, M. Automated vision-based construction progress monitoring in built environment through digital twin. Dev. Built Environ. 2023, 16, 100247. [Google Scholar] [CrossRef]
  18. Kineber, A.F.; Singh, A.K.; Fazeli, A.; Mohandes, S.R.; Cheung, C.; Arashpour, M.; Ejohwomu, O.; Zayed, T. Modelling the relationship between digital twins implementation barriers and sustainability pillars: Insights from building and construction sector. Sustain. Cities Soc. 2023, 99, 104930. [Google Scholar] [CrossRef]
  19. Waqar, A.; Gultom, M.H.; Qureshi, A.H.; Tanjung, L.E.; Almujibah, H.R. Complexities to the deployment of cloud computing for sustainability of small construction projects: Evidence from Pakistan. Ain Shams Eng. J. 2023, 14, 102559. [Google Scholar] [CrossRef]
  20. Azadnia, A.H.; McDaid, C.; Andwari, A.M.; Hosseini, S.E. Green hydrogen supply chain risk analysis: A european hard-to-abate sectors perspective. Renew. Sustain. Energy Rev. 2023, 182, 113371. [Google Scholar] [CrossRef]
  21. Aziz, K.M.A.; Daoud, A.O.; Singh, A.K.; Alhusban, M. Integrating digital mapping technologies in urban development: Advancing sustainable and resilient infrastructure for SDG 9 achievement—A systematic review. Alex. Eng. J. 2025, 116, 512–524. [Google Scholar] [CrossRef]
  22. Sun, Y.; Zhou, Z.; Li, Q.; He, H. A novel risk assessment method for advanced and environmentally friendly construction technologies integrating RBM and I-OPA. Alex. Eng. J. 2025, 113, 648–660. [Google Scholar] [CrossRef]
  23. Sagan, J.; Mach, A. Construction waste management: Impact on society and strategies for reduction. J. Clean. Prod. 2025, 486, 136369. [Google Scholar] [CrossRef]
  24. Pons-Valladares, O.; Casanovas-Rubio, M.d.M.; Armengou, J.; de la Fuente, A. Approach for sustainability assessment for footbridge construction technologies: Application to the first world D-shape 3D-Printed fiber-reinforced mortar footbridge in Madrid. J. Clean. Prod. 2023, 394, 136369. [Google Scholar] [CrossRef]
  25. Scown, M.W.; Dunn, F.E.; Dekker, S.C.; van Vuuren, D.P.; Karabil, S.; Sutanudjaja, E.H.; Santos, M.J.; Minderhoud, P.S.; Garmestani, A.S.; Middelkoop, H. Global change scenarios in coastal river deltas and their sustainable development implications. Glob. Environ. Change 2023, 82, 102736. [Google Scholar] [CrossRef]
  26. Liu, Y.; Zhao, J.; Zhang, Q.B. A modular automated modelling framework for cut-and-cover excavations in mixed ground conditions. Tunn. Undergr. Space Technol. 2025, 158, 106384. [Google Scholar] [CrossRef]
  27. Abdelalim, A.M.; Salem, M.; Sabah, R.A.; Said, S.O.; ElShafei, H.M.; Badawy, M.G. Optimizing claim management process groups to enhance construction project success. Int. J. Constr. Manag. 2025, 25, 1583–1595. [Google Scholar] [CrossRef]
  28. Torres, A.; Simoni, M.U.; Keiding, J.K.; Müller, D.B.; zu Ermgassen, S.O.; Liu, J.; Jaeger, J.A.; Winter, M.; Lambin, E.F. Sustainability of the global sand system in the Anthropocene. One Earth 2021, 4, 639–650. [Google Scholar] [CrossRef]
  29. Adabre, M.A.; Chan, A.P.C.; Edwards, D.J.; Adinyira, E. Assessing critical risk factors (CRFs) to sustainable housing: The perspective of a sub-Saharan African country. J. Build. Eng. 2021, 41, 102385. [Google Scholar] [CrossRef]
  30. Ogbu, A.D.; Eyo-Udo, N.L.; Adeyinka, M.A.; Ozowe, W.; Ikevuje, A.H. A conceptual procurement model for sustainability and climate change mitigation in the oil, gas, and energy sectors. World J. Adv. Res. Rev. 2023, 20, 1935–1952. [Google Scholar] [CrossRef]
  31. Liu, Y.; Zhang, Y.; Ma, N.; Li, Q. Risk Perception of the ‘Belt and Road’ Countries Based on Global Media Data GDELT. In Procedia Computer Science; Elsevier: Amsterdam, The Netherlands, 2023; pp. 330–337. [Google Scholar] [CrossRef]
  32. Maqbool, R.; Bhuvaneswaran, M.; Rashid, Y.; Altuwaim, A.; Ashfaq, S. A Decision Approach for Analysing the Role of Modern Methods, Project Management and Integrated Approaches in Environmentally Sustainable Construction Projects. KSCE J. Civ. Eng. 2023, 27, 3175–3191. [Google Scholar] [CrossRef]
  33. Afework, A.; Tamene, A.; Gashaw, M. Magnitude of self-reported non-fatal work-related injuries and associated factors among construction workers in Aleta Wondo, Sidama, Ethiopia. Sci. Rep. 2025, 15, 4339. [Google Scholar] [CrossRef]
  34. Ochoa, W.A.A.; Neto, A.I.; Junior, P.C.V.; Calabokis, O.P.; Ballesteros-Ballesteros, V. The Theory of Complexity and Sustainable Urban Development: A Systematic Literature Review. Sustainability 2024, 17, 3. [Google Scholar] [CrossRef]
  35. Dedasht, G.; Zin, R.M.; Ferwati, M.S.; Abdullahi, M.M.; Keyvanfar, A.; McCaffer, R. DEMATEL-ANP risk assessment in oil and gas construction projects. Sustainability 2017, 9, 1420. [Google Scholar] [CrossRef]
  36. Mercogliano, M.; Spatari, G.; Noviello, C.; Di Serafino, F.; Mormile, M.E.; Granvillano, G.; Iagnemma, A.; Mimmo, R.; Schenone, I.; Raso, E.; et al. Building evidences in Public Health Emergency Preparedness (‘BePHEP’ Project)—A systematic review. Int. J. Equity Health 2025, 24, 41. [Google Scholar] [CrossRef] [PubMed]
  37. Anagnostopoulos, L.; Vasileiadis, S.; Kourentis, L.; Bogogiannidou, Z.; Voulgaridi, I.; Nichols, G.; Kalala, F.; Speletas, M.; Hadjichristodoulou, C.; Mouchtouri, V.A. Scoping review of infectious disease prevention, mitigation and management in passenger ships and at ports: Mapping the literature to develop comprehensive and effective public health measures. Trop. Med. Health 2025, 53, 3. [Google Scholar] [CrossRef] [PubMed]
  38. Cabral-Ramírez, M.; Niño-Barrero, Y.; DiBella, J. Lessons from the Implementation of the Sendai Framework for Disaster Risk Reduction from Latin America and the Caribbean. Int. J. Disaster Risk Sci. 2025, 16, 72–83. [Google Scholar] [CrossRef]
  39. Dugbartey, A.N. Systemic financial risks in an era of geopolitical tensions, climate change, and technological disruptions: Predictive analytics, stress testing and crisis response strategies. Int. J. Sci. Res. Arch. 2025, 14, 1428–1448. [Google Scholar] [CrossRef]
  40. Oso, O.B.; Alli, O.I.; Babarinde, A.O.; Ibeh, A.I. Advanced financial modeling in healthcare investments: A framework for optimizing sustainability and impact. Gulf J. Adv. Bus. Res. 2025, 3, 561–589. [Google Scholar] [CrossRef]
  41. Acs, S.; Leite, J.C.; Sanyé-Mengual, E.; Caivano, A.; Catarino, R.; Druon, J.-N.; Di Marcantonio, F.; De Jong, B.; Guerrero, I.; Gurría, P.; et al. Towards sustainable food systems: Developing a monitoring framework for the EU. Front. Sustain. Food Syst. 2024, 8, 1502081. [Google Scholar] [CrossRef]
  42. Fitriawijaya, A.; Taysheng, J. Empowering Digital Twin Through BIM—Blockchain for Carbon Disclosure of Certified Green Buildings. Comput. Des. Appl. 2024, 22, 180–202. [Google Scholar] [CrossRef]
  43. Amin, F.A.; Patriadi, A.; Sajiyo, S. Identification and Mitigation of Risk Factors in the Implementation of the Probolinggo-Banyuwangi Toll Road Project Package 2. J. World Sci. 2025, 4, 1844–1854. [Google Scholar] [CrossRef]
  44. Ding, G.K.C. Sustainable construction—The role of environmental assessment tools. J. Environ. Manag. 2008, 86, 451–464. [Google Scholar] [CrossRef] [PubMed]
  45. Faraji, F. Integrating Smart Polymers, Digital Twins, and AI for Corrosion Mitigation and Structural Health Monitoring in Large-Scale Infrastructure: A Case Study on the Golden Gate Bridge Ra. Available online: https://www.researchgate.net/publication/389317352 (accessed on 5 September 2025).
  46. Judijanto, L.; Hindarto, D.; Wahjono, S.I.; Djunarto, A. Edge of Enterprise Architecture in Addressing Cyber Security Threats and Business Risks. Int. J. Softw. Eng. Comput. Sci. 2023, 3, 386–396. [Google Scholar] [CrossRef]
  47. Calik, I.; Koc, K.; Şahin, O. Life Cycle Risk Management for Improving Labor Productivity in Construction Projects in Türkiye. Buildings 2025, 15, 484. [Google Scholar] [CrossRef]
  48. Singla, H.K.; Phadtare, M. Risk management practices in construction projects: A qualitative exploration of MSMEs in India. J. Adv. Manag. Res. 2025, ahead of print. [Google Scholar] [CrossRef]
  49. Fathalizadeh, A.; Hosseini, M.R.; Vaezzadeh, S.S.; Edwards, D.J.; Martek, I.; Shooshtarian, S. Barriers to sustainable construction project management: The case of Iran. Smart Sustain. Built Environ. 2022, 11, 717–739. [Google Scholar] [CrossRef]
  50. Isang, I.W.; Ebiloma, D.O.; Ukpong, E. Stakeholders’ engagement for advancing a sustainable Nigerian construction industry: A sustainable development goal-driven approach. Smart Sustain. Built Environ. 2025, ahead of print. [Google Scholar] [CrossRef]
  51. Willar, D.; Waney, E.V.Y.; Pangemanan, D.D.G.; Mait, R.E.G. Sustainable construction practices in the execution of infrastructure projects: The extent of implementation. Smart Sustain. Built Environ. 2021, 10, 106–124. [Google Scholar] [CrossRef]
  52. Deng, B.; Lv, X.; Du, Y.; Li, X.; Yin, Y. Critical risk factors for construction supply chain in China: A fuzzy synthetic evaluation analysis. Eng. Constr. Archit. Manag. 2023, 32, 483–506. [Google Scholar] [CrossRef]
  53. Javid, D. Sustainable Finance and RegTech: Building Resilience in Financial Security and Energy Policy. ResearchGate. 2025. Available online: https://www.researchgate.net/publication/388615846_Sustainable_Finance_and_RegTech_Building_Resilience_in_Financial_Security_and_Energy_Policy?channel=doi&linkId=679ee13c52b58d39f2639d47&showFulltext=true (accessed on 5 September 2025).
  54. Giri, O.P.; Sainju, P.R.; Htet, A. Evaluating occupational health and safety practices in an airport construction project in Nepal. Built Environ. Proj. Asset Manag. 2024, 15, 149–164. [Google Scholar] [CrossRef]
  55. Islam, H. Nexus of economic, social, and environmental factors on sustainable development goals: The moderating role of technological advancement and green innovation. Innov. Green Dev. 2025, 4, 100183. [Google Scholar] [CrossRef]
  56. Talebi, S.; Wu, S.; Elghaish, F.; McIlwaine, S. Guest editorial: Industry 4.0 and the future of infrastructure operation and maintenance. Int. J. Build. Pathol. Adapt. 2025, 43, 1–3. [Google Scholar] [CrossRef]
  57. Yu, R.; Mu, Q. Implementation progress of Nature-based Solutions in China: A global comparative review. Nat.-Based Solut. 2023, 4, 100075. [Google Scholar] [CrossRef]
  58. Baayenda, G.; Mberu, M.; Dodson, S.; Zongo, K.; Syonguvi, J.; Ngondi, J.; Zecarias, A. Eritrea’s blueprint for trachoma elimination: A home-grown model for sustainable impact. Int. J. Infect. Dis. 2025, 152, 107814. [Google Scholar] [CrossRef]
  59. Pandey, P.; Huidobro, G.; Lopes, L.F.; Ganteaume, A.; Ascoli, D.; Colaco, C.; Xanthopoulos, G.; Giannaros, T.M.; Gazzard, R.; Boustras, G.; et al. A global outlook on increasing wildfire risk: Current policy situation and future pathways. Trees For. People 2023, 14, 100431. [Google Scholar] [CrossRef]
  60. Nabawy, M.; Ofori, G.; Morcos, M.; Egbu, C. Risk identification framework in construction of Egyptian mega housing projects. Ain Shams Eng. J. 2021, 12, 2047–2056. [Google Scholar] [CrossRef]
  61. Deveci, M.; Varouchakis, E.A.; Brito-Parada, P.R.; Mishra, A.R.; Rani, P.; Bolgkoranou, M.; Galetakis, M. Evaluation of risks impeding sustainable mining using Fermatean fuzzy score function based SWARA method. Appl. Soft Comput. 2023, 139, 110220. [Google Scholar] [CrossRef]
  62. Caldeira, D.; Dores, H.; Franco, F.; Baptista, S.B.; Cabral, S.; Cachulo, M.D.C.; Peixeiro, A.; Rodrigues, R.; Santos, M.; Timóteo, A.T.; et al. Global warming and heat wave risks for cardiovascular diseases: A position paper from the Portuguese Society of Cardiology. Rev. Port. Cardiol. 2023, 42, 1017–1024. [Google Scholar] [CrossRef]
  63. Rafindadi, A.D.; Mikić, M.; Kovačić, I.; Cekić, Z. Global Perception of Sustainable Construction Project Risks. Procedia Soc. Behav. Sci. 2014, 119, 456–465. [Google Scholar] [CrossRef]
  64. Pihl, D. The Role of Objects in Decision-Making Processes the Case of an Energy Renovation. In Proceedings of the 9th Nordic Conference on Construction Economics and Organization, Göteborg, Sweden, 13–14 June 2017. [Google Scholar]
  65. Yuan, J.; Li, W.; Guo, J.; Zhao, X.; Skibniewski, M.J. Social risk factors of transportation PPP projects in China: A sustainable development perspective. Int. J. Environ. Res. Public Health 2018, 15, 1323. [Google Scholar] [CrossRef]
  66. Wibowo, M.A.; Handayani, N.U.; Mustikasari, A. Factors for implementing green supply chain management in the construction industry. J. Ind. Eng. Manag. 2018, 11, 651–679. [Google Scholar] [CrossRef]
  67. Ogunbiyi, O. Implementation of the Lean Approach in Sustainable Construction: A Conceptual Framework. Ph.D. Thesis, University of Central Lancashire, Preston, UK, 2014. [Google Scholar]
  68. Dharmaguptha, U.G. Weerasinghe. In Development of a Framework to Assess Sustainability of Building Projects; Library and Archives Canada = Bibliothèque et Archives Canada: Ottawa, ON, Canada, 2013. [Google Scholar]
  69. Jouan, P.; Hallot, P. Digital twin: Research framework to support preventive conservation policies. ISPRS Int. J. Geoinf. 2020, 9, 228. [Google Scholar] [CrossRef]
  70. Krzemień, A.; Sánchez, A.S.; Fernández, P.R.; Zimmermann, K.; Coto, F.G. Towards sustainability in underground coal mine closure contexts: A methodology proposal for environmental risk management. J. Clean. Prod. 2016, 139, 1044–1056. [Google Scholar] [CrossRef]
  71. Ahmed, A.; Othman, E. Managing Stakeholders’ Needs and Expectations in the Architectural Design Process: A Knowledge Management Approach. 2014. Available online: https://www.researchgate.net/publication/271906814 (accessed on 5 September 2025).
  72. Nath, N.D.; Chaspari, T.; Professor, A.; Behzadan, A.H. Automated ergonomic risk monitoring using body-mounted sensors and machine learning. Adv. Eng. Inform. 2018, 38, 514–526. [Google Scholar] [CrossRef]
  73. Jagannathan, R.; Patel, S.A.; Ali, M.K.; Narayan, K.M.V. Global Updates on Cardiovascular Disease Mortality Trends and Attribution of Traditional Risk Factors. Curr. Diabetes Rep. 2019, 19, 44. [Google Scholar] [CrossRef] [PubMed]
  74. Yuan, J.; Chan, A.P.C.; Xiong, W.; Skibniewski, M.J.; Li, Q. Perception of Residual Value Risk in Public Private Partnership Projects: Critical Review. J. Manag. Eng. 2015, 31, 04014041. [Google Scholar] [CrossRef]
  75. Sepasgozar, S.M.E.; Hui, F.K.P.; Shirowzhan, S.; Foroozanfar, M.; Yang, L.; Aye, L. Lean practices using building information modeling (Bim) and digital twinning for sustainable construction. Sustainability 2021, 13, 161. [Google Scholar] [CrossRef]
  76. Yaseen, Z.M.; Ali, Z.H.; Salih, S.Q.; Al-Ansari, N. Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability 2020, 12, 1514. [Google Scholar] [CrossRef]
  77. Kara, M.E.; Fırat, S.Ü.O.; Ghadge, A. A data mining-based framework for supply chain risk management. Comput. Ind. Eng. 2020, 139, 105570. [Google Scholar] [CrossRef]
  78. Krystosik, A.; Njoroge, G.; Odhiambo, L.; Forsyth, J.E.; Mutuku, F.; LaBeaud, A.D. Solid Wastes Provide Breeding Sites, Burrows, and Food for Biological Disease Vectors, and Urban Zoonotic Reservoirs: A Call to Action for Solutions-Based Research. Front. Public Health 2020, 7, 405. [Google Scholar] [CrossRef] [PubMed]
  79. Ng, A.W. From sustainability accounting to a green financing system: Institutional legitimacy and market heterogeneity in a global financial centre. J. Clean. Prod. 2018, 195, 585–592. [Google Scholar] [CrossRef]
  80. Gunduz, M.; Almuajebh, M. Critical success factors for sustainable construction project management. Sustainability 2020, 12, 1990. [Google Scholar] [CrossRef]
  81. Chatterjee, K.; Zavadskas, E.K.; Tamošaitiene, J.; Adhikary, K.; Kar, S. A hybrid MCDM technique for risk management in construction projects. Symmetry 2018, 10, 46. [Google Scholar] [CrossRef]
  82. Babatunde, S.O.; Perera, S.; Adeniyi, O. Identification of critical risk factors in public-private partnership project phases in developing countries: A case of Nigeria. Benchmarking 2018, 26, 355–2019. [Google Scholar] [CrossRef]
  83. Ika, L.A.; Donnelly, J. Success conditions for international development capacity building projects. Int. J. Proj. Manag. 2017, 35, 44–63. [Google Scholar] [CrossRef]
  84. Rosa, L.V.; França, J.E.M.; Haddad, A.N.; Carvalho, P.V.R. A resilience engineering approach for sustainable safety in green construction. J. Sustain. Dev. Energy Water Environ. Syst. 2017, 5, 480–495. [Google Scholar] [CrossRef]
  85. Zou, P.X.W.; Alam, M.; Sanjayan, J.G.; Wilson, J.L. Managing Risks in Complex Building Retrofit Projects for Energy and Water Efficiency. 2016. Available online: https://www.researchgate.net/publication/308886672 (accessed on 5 September 2025).
  86. Elseknidy, M.; Al-Mhdawi, M.K.S.; Qazi, A.; Ojiako, U.; Mahammedi, C.; Pour Rahimian, F. Developing a sustainability-driven risk management framework for green building projects: A literature review. J. Clean. Prod. 2025, 519, 145891. [Google Scholar] [CrossRef]
  87. Sun, C.; Man, Q.; Wang, Y. Study on BIM-based construction project cost and schedule risk early warning. J. Intell. Fuzzy Syst. 2015, 29, 469–477. [Google Scholar] [CrossRef]
  88. Iqbal, S.; Choudhry, R.M.; Holschemacher, K.; Ali, A.; Tamošaitienė, J. Risk management in construction projects. Technol. Econ. Dev. Econ. 2015, 21, 65–78. [Google Scholar] [CrossRef]
  89. Riley, L.; Guthold, R.; Cowan, M.; Savin, S.; Bhatti, L.; Armstrong, T.; Bonita, R. The world health organization STEPwise approach to noncommunicable disease risk-factor surveillance: Methods, challenges, and opportunities. Am. J. Public. Health 2016, 106, 74–78. [Google Scholar] [CrossRef]
  90. Valipour, A.; Yahaya, N.; Noor, N.M.; Antuchevičienė, J.; Tamošaitienė, J. Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: An Iranian case study. J. Civ. Eng. Manag. 2017, 23, 524–532. [Google Scholar] [CrossRef]
  91. Park, H.; Kim, J.D. Transition towards green banking: Role of financial regulators and financial institutions. Asian J. Sustain. Soc. Responsib. 2020, 5, 5. [Google Scholar] [CrossRef]
  92. Levin, A.; Tonelli, M.; Bonventre, J.; Coresh, J.; Donner, J.-A.; Fogo, A.B.; Fox, C.S.; Gansevoort, R.T.; Heerspink, H.J.L.; Jardine, M. Global Kidney Health 2017 and beyond: A roadmap for closing gaps in care, research, and policy. Lancet 2017, 390, 1888–1917. [Google Scholar] [CrossRef] [PubMed]
  93. Zhao, H.; Li, N. Risk evaluation of a uhv power transmission construction project based on a cloud model and fce method for sustainability. Sustainability 2015, 7, 2885–2914. [Google Scholar] [CrossRef]
  94. Armenia, S.; Dangelico, R.M.; Nonino, F.; Pompei, A. Sustainable project management: A conceptualization-oriented review and a framework proposal for future studies. Sustainability 2019, 11, 2664. [Google Scholar] [CrossRef]
  95. Shaikh, P.H.; Nor, N.B.M.; Nallagownden, P.; Elamvazuthi, I.; Ibrahim, T. A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renew. Sustain. Energy Rev. 2014, 34, 409–429. [Google Scholar] [CrossRef]
  96. Chew, M.Y.L.; Conejos, S.; Asmone, A.S. Developing a research framework for the green maintainability of buildings. Facilities 2017, 35, 39–63. [Google Scholar] [CrossRef]
  97. Etinay, N.; Egbu, C.; Murray, V. Building Urban Resilience for Disaster Risk Management and Disaster Risk Reduction. In Procedia Engineering; Elsevier: Amsterdam, The Netherlands, 2018; Volume 212, pp. 575–582. [Google Scholar] [CrossRef]
  98. Ngacho, C.; Das, D. A performance evaluation framework of development projects: An empirical study of Constituency Development Fund (CDF) construction projects in Kenya. Int. J. Proj. Manag. 2014, 32, 492–507. [Google Scholar] [CrossRef]
  99. Rosa, L.V.; Haddad, A.N.; de Carvalho, P.V.R. Assessing risk in sustainable construction using the Functional Resonance Analysis Method (FRAM). Cogn. Technol. Work 2015, 17, 559–573. [Google Scholar] [CrossRef]
  100. Chan, A.P.C.; Nwaogu, J.M.; Naslund, J.A. Mental Ill-Health Risk Factors in the Construction Industry: Systematic Review. J. Constr. Eng. Manag. 2020, 146, 04020004. [Google Scholar] [CrossRef]
  101. Zolfani, S.H.; Pourhossein, M.; Yazdani, M.; Zavadskas, E.K. Evaluating construction projects of hotels based on environmental sustainability with MCDM framework. Alex. Eng. J. 2018, 57, 357–365. [Google Scholar] [CrossRef]
  102. Guo, B.H.W.; Yiu, T.W. Developing Leading Indicators to Monitor the Safety Conditions of Construction Projects. J. Manag. Eng. 2016, 32, 04015016. [Google Scholar] [CrossRef]
  103. Sanchez, A.X.; Lehtiranta, L.; Hampson, K.D.; Kenley, R. Evaluation framework for green procurement in road construction. Smart Sustain. Built Environ. 2014, 3, 153–169. [Google Scholar] [CrossRef]
  104. Van Der Beek, A.J.; Dennerlein, J.; Huysmans, M.; Mathiassen, S.; Burdorf, A.; van Mechelen, W.; van Dieen, J.; Frings-Dresen, M.; Holtermann, A.; Janwantanakul, P.; et al. A research framework for the development and implementation of interventions preventing work-related musculoskeletal disorders. Scand. J. Work. Env. Health 2017, 43, 526–539. [Google Scholar] [CrossRef]
  105. Shrivastava, S.V.; Rathod, U. Categorization of risk factors for distributed agile projects. Inf. Softw. Technol. 2015, 58, 373–387. [Google Scholar] [CrossRef]
  106. Hogan, D.R.; Stevens, G.A.; Hosseinpoor, A.R.; Boerma, T. Monitoring universal health coverage within the Sustainable Development Goals: Development and baseline data for an index of essential health services. Lancet Glob. Health 2018, 6, e152–e168. [Google Scholar] [CrossRef] [PubMed]
  107. Dziadosz, A.; Rejment, M. Risk Analysis in Construction Project—Chosen Methods. In Procedia Engineering; Elsevier: Amsterdam, The Netherlands, 2015; pp. 258–265. [Google Scholar] [CrossRef]
  108. Ihuah, P.W.; Kakulu, I.I.; Eaton, D. A review of Critical Project Management Success Factors (CPMSF) for sustainable social housing in Nigeria. Int. J. Sustain. Built Environ. 2014, 3, 62–71. [Google Scholar] [CrossRef]
  109. Khanzadi, M.; Sheikhkhoshkar, M.; Banihashemi, S. BIM applications toward key performance indicators of construction projects in Iran. Int. J. Constr. Manag. 2020, 20, 305–320. [Google Scholar] [CrossRef]
  110. Marzouk, M.; Azab, S.; Metawie, M. BIM-based approach for optimizing life cycle costs of sustainable buildings. J. Clean. Prod. 2018, 188, 226–2018. [Google Scholar] [CrossRef]
  111. Ahern, J.; Cilliers, S.; Niemelä, J. The concept of ecosystem services in adaptive urban planning and design: A framework for supporting innovation. Landsc. Urban. Plan. 2014, 125, 254–259. [Google Scholar] [CrossRef]
  112. Shortall, R.; Davidsdottir, B.; Axelsson, G. Geothermal energy for sustainable development: A review of sustainability impacts and assessment frameworks. Renew. Sustain. Energy Rev. 2015, 44, 391–406. [Google Scholar] [CrossRef]
  113. Akadiri, P.O.; Chinyio, E.A.; Olomolaiye, P.O. Design of a sustainable building: A conceptual framework for implementing sustainability in the building sector. Buildings 2012, 2, 126–152. [Google Scholar] [CrossRef]
  114. Shurrab, J.; Hussain, M.; Khan, M. Green and sustainable practices in the construction industry: A confirmatory factor analysis approach. Eng. Constr. Archit. Manag. 2019, 26, 1063–1086. [Google Scholar] [CrossRef]
  115. Ngacho, C.; Das, D. A performance evaluation framework of construction projects: Insights from literature. Int. J. Proj. Organ. Manag. 2015, 7, 151. [Google Scholar] [CrossRef]
  116. Memon, A.H.; Rahman, I.A.; Zainun, N.Y.; Karim, A.T.A. Web-based Risk Assessment Technique for Time and Cost Overrun (WRATTCO)—A Framework. Procedia Soc. Behav. Sci. 2014, 129, 178–185. [Google Scholar] [CrossRef]
  117. Zimmermann, M.; Keiler, M. International Frameworks for Disaster Risk Reduction: Useful Guidance for Sustainable Mountain Development. Mt. Res. Dev. 2015, 35, 195–202. [Google Scholar] [CrossRef]
  118. Guray, T.S.; Kismet, B. VR and AR in construction management research: Bibliometric and descriptive analyses. Smart Sustain. Built Environ. 2022, 12, 635–659. [Google Scholar] [CrossRef]
  119. Bibri, S.E. A methodological framework for futures studies: Integrating normative backcasting approaches and descriptive case study design for strategic data-driven smart sustainable city planning. Energy Inform. 2020, 3, 31. [Google Scholar] [CrossRef]
  120. Gan, X.; Zuo, J.; Ye, K.; Skitmore, M.; Xiong, B. Why sustainable construction? Why not? An owner’s perspective. Habitat Int. 2015, 47, 61–68. [Google Scholar] [CrossRef]
  121. Malaysia, U.K.; Ogunde, A.O.; Olaolu, O.; Afolabi, A.; Owolabi, J.; Ojelabi, R. Challenges Confronting Construction Project Management System for Sustainable Construction in Developing Countries: Professionals Perspectives (a Case Study of Nigeria). 2017. Available online: http://spaj.ukm.my/jsb/index.php/jbp/index (accessed on 5 September 2025).
  122. Nduka, D.O.; Ogunsanmi, O.E. Stakeholders Perception of Factors Determining the Adoptability of Green Building Practices in Construction Projects in Nigeria. 2015. Available online: https://www.iiste.org/Journals/index.php/JEES/article/view/19479 (accessed on 5 September 2025).
  123. Xu, Y.; Chan, A.P.C.; Xia, B.; Qian, Q.K.; Liu, Y.; Peng, Y. Critical risk factors affecting the implementation of PPP waste-to-energy projects in China. Appl. Energy 2015, 158, 403–411. [Google Scholar] [CrossRef]
  124. Ameyaw, E.E.; Chan, A.P.C. Risk ranking and analysis in PPP water supply infrastructure projects. Facilities 2015, 33, 428–453. [Google Scholar] [CrossRef]
  125. Bröchner, J.; Haugen, T.; Lindkvist, C. Shaping tomorrow’s facilities management. Facilities 2019, 37, 366–380. [Google Scholar] [CrossRef]
  126. Hwang, B.G.; Zhao, X.; Tan, L.L.G. Green building projects: Schedule performance, influential factors and solutions. Eng. Constr. Archit. Manag. 2015, 22, 327–346. [Google Scholar] [CrossRef]
  127. Ouache, R.; Chhipi-Shrestha, G.; Hewage, K.; Sadiq, R. An integrated fire risk management framework for smart-green multi-unit residential buildings: Assessment of combustibility, extinguishing strategies, and impact prediction. J. Build. Eng. 2025, 109, 112975. [Google Scholar] [CrossRef]
  128. Luo, L.Z.; Mao, C.; Shen, L.Y.; Li, Z.D. Risk factors affecting practitioners’ attitudes toward the implementation of an industrialized building system a case study from China. Eng. Constr. Archit. Manag. 2015, 22, 622–643. [Google Scholar] [CrossRef]
  129. Whitcraft, A.K.; Becker-Reshef, I.; Justice, C.O.; Gifford, L.; Kavvada, A.; Jarvis, I. No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework. Remote Sens. Environ. 2019, 235, 111470. [Google Scholar] [CrossRef]
  130. Zgheib, R.; Conchon, E.; Bastide, R. Engineering IoT healthcare applications: Towards a semantic data driven sustainable architecture. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Springer: Berlin/Heidelberg, Germany, 2017; pp. 407–418. [Google Scholar] [CrossRef]
  131. Shan, M.; Hwang, B.G.; Zhu, L. A global review of sustainable construction project financing: Policies, practices, and research efforts. Sustainability 2017, 9, 2347. [Google Scholar] [CrossRef]
  132. Job, H.; Becken, S.; Lane, B. Protected Areas in a neoliberal world and the role of tourism in supporting conservation and sustainable development: An assessment of strategic planning, zoning, impact monitoring, and tourism management at natural World Heritage Sites. J. Sustain. Tour. 2017, 25, 1697–1718. [Google Scholar] [CrossRef]
Figure 1. Framework of the study.
Figure 1. Framework of the study.
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Figure 2. Demographic information of the respondents: (a) Professional role; (b) Experience; (c) Level of education; (d) Countries of the respondents; (e) Company size.
Figure 2. Demographic information of the respondents: (a) Professional role; (b) Experience; (c) Level of education; (d) Countries of the respondents; (e) Company size.
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Figure 3. Top Risk Mitigation Strategies and Associated Risk Categories in GBCPs.
Figure 3. Top Risk Mitigation Strategies and Associated Risk Categories in GBCPs.
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Figure 4. Radar Chart Representing: (a) critical and non-critical risk factors; (b) developed company; (c) developing company; (d) Non-large size company; (e) Large size company.
Figure 4. Radar Chart Representing: (a) critical and non-critical risk factors; (b) developed company; (c) developing company; (d) Non-large size company; (e) Large size company.
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Table 1. Summary of studies identifying GB risk factors.
Table 1. Summary of studies identifying GB risk factors.
Research FocusMethodologyStudy LocationNo. of GB Risks IdentifiedReference
Risk identification and evaluation for green residential construction projectsLiterature review and surveySingapore42[19]
Risk assessment and identification in commercial green building projectsLiterature review, interviews, and surveySingapore29[19]
Risk identification and provision of mitigation measures for green retrofit projectsSurvey and interviewSingapore20[20]
Creation of a Model for Risk Assessment in Green Building InitiativesSurveySingapore28[21]
Risk assessment in green building initiatives from a sustainability perspectiveLiterature review and surveyChina19[22]
Identification and assessment of risks in environmentally friendly construction projectsLiterature review and surveyUnited Arab Emirates30[23]
Conducting risk assessment for green retrofit projects and developing a system to address potential hazardsSurveySri Lanka10[24]
Determining the financial incentives and risk factors associated with green building constructionSurveyMalaysia10[25]
Risk assessment in the supply chain for green buildings and creation of management plansSurveyAustralia40[10]
Analysing the interconnections between risks in green construction projects, considering the various stages of the project, and the different risk factors involvedLiterature Review and Survey-22[26]
They are identifying construction dangers and their impact on costs in LEED-certified projects.Survey-13[27]
Recognising material-related hazards in environmentally friendly structuresLiterature review and survey-25[28]
Analysing stakeholder-related risks through the development of a Social Network Analysis (SNA) modelCase study-42[29]
Table 3. Results of a data reliability test.
Table 3. Results of a data reliability test.
ScaleCronbach’s AlphaNo. of Items
A ranking of 42 risk factors’ importance0.92142
Table 4. Analysis using descriptive statistics.
Table 4. Analysis using descriptive statistics.
Percentage of Participants’ RespondentsRSIMeanSDRank
FactorsMinimal Significance (%) (Extremely Low + Low)Moderately Important (%) (Medium)Elevated Significance (%) (High + Extremely High)
F-085.45 34.55 60.00 0.85 3.750.841
F-0514.55 21.82 63.64 0.84 3.691.072
F-0210.91 27.27 61.82 0.84 3.670.883
F-0714.55 30.91 54.55 0.83 3.641.014
F-099.09 29.09 61.82 0.81 3.640.85
F-0314.55 29.09 56.36 0.80 3.561.036
F-0610.91 43.64 45.45 0.79 3.531.097
F-049.09 45.45 45.45 0.78 3.490.848
F-0112.73 41.82 45.45 0.78 3.450.949
F-1020.00 30.91 49.09 0.77 3.41.0510
F-2321.82 25.45 52.73 0.77 3.380.9711
F-2923.64 21.82 54.55 0.76 3.361.0812
F-2221.82 30.91 47.27 0.75 3.351.0413
F-1221.82 36.36 41.82 0.73 3.271.0614
F-3327.27 27.27 45.45 0.73 3.271.2215
F-1629.09 29.09 41.82 0.73 3.251.1716
F-2429.09 23.64 47.27 0.72 3.221.0817
F-1127.27 38.18 34.55 0.72 3.21.0418
F-1825.45 32.73 41.82 0.71 3.21.1519
F-2729.09 29.09 41.82 0.71 3.21.0620
F-3029.09 30.91 40.00 0.70 3.21.0321
F-3229.09 30.91 40.00 0.70 3.18122
F-3723.64 38.18 38.18 0.70 3.181.0223
F-1727.27 36.36 36.36 0.70 3.161.0924
F-3123.64 41.82 34.55 0.70 3.150.8925
F-1329.09 36.36 34.55 0.69 3.091.0926
F-3430.91 34.55 34.55 0.68 3.071.0027
F-3529.09 30.91 40.00 0.68 3.070.9628
F-1934.55 29.09 36.36 0.68 3.051.1329
F-2638.18 21.82 40.00 0.68 3.041.1430
F-1425.45 43.64 30.91 0.67 3.021.0631
F-1530.91 32.73 36.36 0.67 3.021.1132
F-3841.82 14.55 43.64 0.67 3.021.3333
F-3930.91 38.18 30.91 0.67 3.021.1634
F-2529.09 43.64 27.27 0.67 3.000.8635
F-2834.55 29.09 36.36 0.66 3.001.1636
F-3632.73 38.18 29.09 0.65 2.930.8637
F-2134.55 38.18 27.27 0.64 2.911.0138
F-4040.00 30.91 29.09 0.63 2.821.0639
F-2041.82 30.91 27.27 0.62 2.801.1340
F-4149.09 27.27 23.64 0.61 2.651.2541
F-4247.27 23.64 29.09 0.58 2.651.1742
Table 5. Result of one-sample t-test.
Table 5. Result of one-sample t-test.
FactorstMean Difference95%
The Difference’s Confidence Interval
Significance
(2-Tailed)
LowerHigher
F-013.5890.45 0.20 0.71 0.001
F-025.6500.67 0.43 0.91 0.000
F-034.0500.56 0.28 0.84 0.000
F-044.3550.49 0.26 0.72 0.000
F-054.7920.69 0.40 0.98 0.000
F-063.6000.53 0.23 0.82 0.001
F-074.6880.64 0.36 0.91 0.000
F-086.5530.75 0.52 0.97 0.000
F-095.8850.64 0.42 0.85 0.000
F-102.8330.40 0.12 0.68 0.006
F-111.4210.20 −0.08 0.48 0.161
F-121.9040.27 −0.01 0.56 0.062
F-130.6170.09 −0.20 0.39 0.540
F-140.1270.02 −0.27 0.31 0.900
F-150.1210.02 −0.28 0.32 0.904
F-161.6080.26 −0.06 0.57 0.114
F-171.1190.16 −0.13 0.46 0.268
F-181.2950.20 −0.11 0.51 0.201
F-190.3580.06 −0.25 0.36 0.722
F-20−1.314−0.20 −0.51 0.11 0.194
F-21−0.671−0.09 −0.36 0.18 0.505
F-222.4630.35 0.06 0.63 0.017
F-232.9140.38 0.12 0.64 0.005
F-241.4940.22 −0.07 0.51 0.141
F-250.0000.00 −0.23 0.23 1.000
F-260.2370.04 −0.27 0.34 0.814
F-271.3980.20 −0.09 0.49 0.168
F-280.0000.00 −0.31 0.31 1.000
F-292.5020.36 0.07 0.66 0.015
F-301.4460.20 −0.08 0.48 0.154
F-311.2110.15 −0.10 0.39 0.231
F-321.3460.18 −0.09 0.45 0.184
F-331.6520.27 −0.06 0.60 0.104
F-340.5410.07 −0.20 0.34 0.591
F-350.5620.07 −0.19 0.33 0.576
F-36−0.629−0.07 −0.30 0.16 0.532
F-371.3220.18 −0.09 0.46 0.192
F-380.1020.02 −0.34 0.38 0.919
F-390.1160.02 −0.30 0.33 0.908
F-40−1.277−0.18 −0.47 0.10 0.207
F-41−2.049−0.35 −0.68 −0.01 0.045
F-42−2.182−0.35 −0.66 −0.03 0.033
Table 6. Result of hypothesis testing and correlation analysis.
Table 6. Result of hypothesis testing and correlation analysis.
Independent VariableDependent VariableMean RankHypothesis Test Statisticp-Value (2-Sided)Correlation Coefficientp-Value (2-Sided)
F-01Academic15.9898.500.2010.2230.206
Project manager20.29
F-02Academic16.41108.000.3530.1620.36
Project manager19.50
F-03Academic20.3270.000.018−0.412 *0.015
Project manager12.33
F-04Academic18.57108.500.374−0.1550.383
Project manager15.54
F-05Academic18.48110.500.407−0.1440.416
Project manager15.71
F-06Academic18.93100.500.224−0.2110.23
Project manager14.88
F-07Academic19.8680.000.052−0.339 *0.05
Project manager13.17
F-08Academic20.6163.500.009−0.455 **0.007
Project manager11.79
F-09Academic19.0997.000.162−0.2430.165
Project manager14.58
F-10Academic18.27115.000.523−0.1110.532
Project manager16.08
F-11Academic17.23126.000.8230.0390.827
Project manager18.00
F-12Academic17.45131.000.970.0070.971
Project manager17.58
F-13Academic17.25126.500.8380.0360.842
Project manager17.96
F-14Academic19.0797.500.196−0.2250.201
Project manager14.63
F-15Academic17.32128.000.880.0260.883
Project manager17.83
F-16Academic20.1673.500.031−0.376 *0.028
Project manager12.63
F-17Academic17.95122.000.709−0.0650.715
Project manager16.67
F-18Academic18.20116.500.563−0.1010.572
Project manager16.21
F-19Academic19.4389.500.113−0.2760.115
Project manager13.96
F-20Academic20.4567.000.015−0.425 *0.012
Project manager12.08
F-21Academic19.3092.500.138−0.2580.141
Project manager14.21
F-22Academic18.32114.000.499−0.1180.507
Project manager16.00
F-23Academic17.18125.000.790.0460.794
Project manager18.08
F-24Academic17.66128.500.897−0.0230.899
Project manager17.21
F-25Academic18.55109.000.381−0.1530.389
Project manager15.58
F-26Academic19.1496.000.179−0.2340.183
Project manager14.50
F-27Academician19.3292.000.134−0.2610.136
Project manager14.17
F-28Academician17.68128.000.882−0.0260.884
Project manager17.17
F-29Academician19.8979.500.046−0.348 *0.044
Project manager13.13
F-30Academician20.6463.000.009−0.452 **0.007
Project manager11.75
F-31Academician17.64129.000.909−0.020.911
Project manager17.25
F-32Academician20.8059.500.007−0.474 **0.005
Project manager11.46
F-33Academician17.09123.000.7380.0580.744
Project manager18.25
F-34Academician18.16117.500.584−0.0950.592
Project manager16.29
F-35Academician17.34128.500.8950.0230.897
Project manager17.79
F-36Academician19.0997.000.18−0.2340.184
Project manager14.58
F-37Academician19.5287.500.09−0.2950.091
Project manager13.79
F-38Academician19.4888.500.108−0.280.109
Project manager13.88
F-39Academician19.7083.500.071−0.3140.071
Project manager13.46
F-40Academician18.50110.000.414−0.1420.422
Project manager15.67
F-41Academician19.4190.000.119−0.2720.12
Project manager14.00
F-42Academician17.00121.000.6830.0710.689
Project manager18.42
Note: Spearman’s correlation coefficients marked with * indicates significance at the 0.05 level (two-tailed), and ** indicates significance at the 0.01 level (two-tailed).
Table 7. Risk Mitigation Strategies.
Table 7. Risk Mitigation Strategies.
No.Mitigation MeasuresFrequencyPercentage
1Collaborating with a skilled group of experts who meet certification requirements57.9%
2Offering project practitioners and stakeholders ongoing education, training, and knowledge-sharing programmes46.3%
3Choosing naive and untrustworthy designs and construction technical solutions should be avoided in favour of careful project planning and design.46.3%
4Increasing project stakeholders’ and end users’ involvement in green building construction projects46.3%
5Choose sustainable, long-lasting, and environmentally friendly building materials.34.8%
6Analyse the project’s distinctive features, including its location, design, materials, and technologies, as part of a thorough risk assessment to find possible hazards and their possible effects.34.8%
7Improved oversight and management23.2%
8Strong Supply Chain Administration23.2%
9Making certain that the necessary permissions, approvals, and regulatory compliance are carefully examined and accepted23.2%
10Establish Clear Communication and Enhance Communication by fostering a collaborative workplace with a well-defined goal.23.2%
11Collaborating with seasoned and reliable individuals, including contractors who have won contracts in the past and have a history of successfully finishing projects23.2%
12The government ought to offer financial incentives for the construction of green buildings.23.2%
13Raising awareness of the GBCP culture through formal education and professional associations23.2%
14Making use of prefabrication methods for materials in a controlled setting11.6%
15Putting into practice a responsible waste management plan and policy11.6%
16Integrated Design Process (IDP) and Integrated Project Delivery (IPD) methodologies11.6%
17Enhancing productivity, driving demand, and ensuring customer security11.6%
18Enforce stringent quality control procedures11.6%
19Adopt agile project management practices11.6%
20Optimise the allocation of tasks and responsibilities11.6%
21Conduct thorough technology assessments11.6%
22Implement approaches that balance social, economic, and environmental factors throughout the entire project lifecycle11.6%
23Recognise the differences in risks between traditional construction projects and those associated with green building development11.6%
24Perform Life Cycle Cost Analysis11.6%
25Conduct safety constructability studies at each phase of the design process to mitigate safety risks11.6%
26Offer a payment guarantee for the developer’s project11.6%
27Putting in place a system that charges for testing green materials11.6%
28Improving the construction industry’s digital transformation11.6%
29Sharing, registering, and publicising lessons learned from ongoing initiatives to ensure knowledge management for upcoming ones11.6%
30knowing the company from the viewpoint of the customer.11.6%
31The use of BIM11.6%
32Compile a list of the consequences of identified hazards discussed during field surveys and Focus Group Discussions (FGDs) to obtain more reliable data11.6%
33Localising the standards for green buildings11.6%
34Better development of capacity11.6%
35Including green building practices in routine construction procedures and finished goods11.6%
36An organised strategy for handling conflicting green building certification programmes11.6%
37Identify the project’s highest-risk areas through vulnerability assessments and implement targeted improvements to mitigate potential negative impacts11.6%
38Involve specialists or experts with specific expertise in green building11.6%
39Establish measurable objectives or outcomes for the construction of the project11.6%
40Efficiently communicating the economic benefits of sustainability in green building initiatives11.6%
41Increase the amount of required supervision11.6%
42Make sure that there is adequate fund turnover and sensible fund allocation.11.6%
43Including clear and binding contractual provisions11.6%
44Developing strategies or actions that are specifically tailored to address the hazards11.6%
45Making use of financial resources and regulatory frameworks to advance sustainable behaviours11.6%
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Mohandes, S.R.; Taiwo, R.; Yussif, A.-M.; Han, T.; Elghaish, F.; Arashpour, M.; Singh, A.K.; Christo, M.S. Risk Assessment and Mitigation Strategies in Green Building Construction Projects: A Global Empirical Study. Buildings 2025, 15, 3485. https://doi.org/10.3390/buildings15193485

AMA Style

Mohandes SR, Taiwo R, Yussif A-M, Han T, Elghaish F, Arashpour M, Singh AK, Christo MS. Risk Assessment and Mitigation Strategies in Green Building Construction Projects: A Global Empirical Study. Buildings. 2025; 15(19):3485. https://doi.org/10.3390/buildings15193485

Chicago/Turabian Style

Mohandes, Saeed Reza, Ridwan Taiwo, Abdul-Mugis Yussif, Tong Han, Faris Elghaish, Mehrdad Arashpour, Atul Kumar Singh, and Mary Subaja Christo. 2025. "Risk Assessment and Mitigation Strategies in Green Building Construction Projects: A Global Empirical Study" Buildings 15, no. 19: 3485. https://doi.org/10.3390/buildings15193485

APA Style

Mohandes, S. R., Taiwo, R., Yussif, A.-M., Han, T., Elghaish, F., Arashpour, M., Singh, A. K., & Christo, M. S. (2025). Risk Assessment and Mitigation Strategies in Green Building Construction Projects: A Global Empirical Study. Buildings, 15(19), 3485. https://doi.org/10.3390/buildings15193485

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