Circular Economy-Based Decision-Making Model for Contractor Selection

Abstract

Increasing environmental pollution has reinforced the necessity of implementing circular economy (CE) as a sustainable approach to reducing resource consumption, waste generation, and carbon emissions. Despite the construction industry’s significant environmental impact in terms of global carbon emissions, water consumption, and biodiversity loss, only 12% of its materials exhibit circular characteristics, necessitating improvements in terms of circularity in construction projects. This study develops a CE-based decision-making model for contractor selection, focusing on off-site construction (OSC), which offers greater circularity potential than conventional construction methods. The decision-making model, established through literature analysis and expert discussions, utilizes the fuzzy analytic hierarchy process (AHP) to evaluate CE criteria and employ the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine contractor suitability. The findings indicate that Material Circularity, Energy Circularity, and Product Circularity are the most influential criteria, with green procurement emerging as the highest-priority factor. The model was validated through a hypothetical case study involving four contractors experienced in sustainable construction. The results demonstrate the model’s capacity to produce sensitive outcomes in terms of decision-making.

1. Introduction

Increasing environmental pollution is making industries look for more sustainable solutions. As global resource consumption increases, the concept of circular economy (CE) has gained prominence, leading to its frequent mention in international standards [1]. The construction industry, due to its traditional structure, has faced challenges in adapting to changes. However, the adoption of technologies developed through the Construction 4.0 revolution, the increasing use of Building Information Modeling (BIM), and the incorporation of green building certifications (LEED, BREEAM, etc.) enhance sustainability in the construction industry. Nevertheless, buildings still consume 40% of the total energy consumed, with construction and demolition waste comprising the largest share of waste types, accounting for 36% [2]. Despite this, the global circularity rate decreased from 9.1% in 2018 to 8.6% in 2020, revealing a widening gap that should have been closed over time [3]. This situation underscores the increasing significance of circular economy (CE) initiatives within the construction industry.

While the applications of CE hold critical significance within the construction industry, the existing body of research addressing its implementation remains limited [4]. Furthermore, a consensus has yet to be established regarding the practical applications of CE in the building and construction industry [5]. Within this context, the present study recognizes off-site construction (OSC) as a promising avenue to enhance circularity in the construction sector. OSC entails the transition of construction processes from on-site locations to controlled manufacturing facilities, offering not only temporal, financial, and sustainability advantages, but also providing an opportunity for the integration of circular practices [6].

A review of studies on the circular economy (CE) emphasizes that selecting the right contractors remains a major challenge in achieving true circularity [7,8]. Research has shown that integrating CE principles with off-site construction (OSC) creates a mutually reinforcing effect, strengthening both approaches while making a significant contribution to sustainability [6]. Therefore, an OSC-focused study will be beneficial in increasing circularity in the construction industry. These construction projects, which involve the prefabrication of components off-site followed by their assembly on-site, differ from traditional construction similarly to sustainability evaluations. Hence, the development of a decision-making model capable of OSC would facilitate strategic decisions encompassing procurement, material choice, energy consumption, and circularity-efficient waste generation [9].

To overcome these challenges, this study proposes a contractor selection model rooted in CE principles for off-site construction projects. By promoting competent circular contractors, this study aims to improve the construction industry’s alignment with the SDGs. The primary impetus driving this study revolves around the potential to enhance efficiency by leveraging off-site construction techniques to amplify the adoption rate of CE within construction projects. This trajectory holds promise for the mitigation of waste generation, the curtailment of energy consumption and, notably, a reduction in raw material utilization in a manner conducive to circularity. Emerging from an exhaustive review of the existing literature, the prominent research gaps and pertinent contributions related to this current research endeavor can be succinctly summarized as follows:

• The adoption of OSC in the construction industry provides substantial benefits aligned with the three pillars of sustainability: economic, social, and environmental sustainability. Implicated in various SDGs, such as SDG 3, 6, 7, 8, 9, 11, 12, 13, 15, and 17, the concept of CE offers a notable opportunity for the advancement of OSC and the applicability of CE principles. However, the development of contractor selection models for OSC based on CE principles remains underexplored [6]. The existing literature primarily encompasses foundational definitions, factors, and strategies [1], the adaptation of technologies to CE principles [10], platform or framework proposals [11,12], and other dimensions of circular economy investigation. Therefore, the selection of contractors who are skilled in implementing CE principles through the proposed model can significantly enhance the adoption of these principles within construction projects [13].
• As robust multi-criteria decision-making (MCDM) methods, the fuzzy analytic hierarchy process (FAHP) and fuzzy TOPSIS techniques were integrated and employed for contractor selection in OSC. The hybrid fuzzy MCDM approach of criteria evaluation and alternative assessment stands as one of the most frequently employed methodologies in contractor selection [14]. While MCDM methods have been widely applied in various selection problems, including supplier selection, to the best of our knowledge, this study represents one of the first attempts to specifically employ a hybrid fuzzy MCDM approach for contractor selection in OSC. By utilizing this hybrid model, decision-makers aiming to achieve CE objectives can enhance the reliability of selection processes and improve circularity-driven decision-making.
• Among the barriers to OSC, “the lack of skilled contractors” ranks in the top three according to the literature [15]. There exists a gap in the literature regarding this issue in studies related to OSC. Contractor selections have been explored in the construction industry for different project types (i.e., infrastructure projects like road works, tunnel projects, superstructures, etc.) [16]. Therefore, this study represents a pioneering and original contribution to the field by addressing contractor selection in OSCs, such as prefabrication, modular construction, and industrialized building systems [17].

Despite the increasing recognition of the importance of CE principles in addressing environmental issues, their practical application in construction remains limited and lacks standardization. OSC offers a promising pathway to enhance circularity through controlled processes and improved sustainability, yet the integration of CE principles into OSC practices is underexplored. A key gap is the lack of decision-making models tailored to the selection of contractors for OSC projects based on CE criteria. Existing studies often focus on general CE frameworks or sustainability strategies, with limited attention paid to the role of contractor capabilities. Although hybrid fuzzy MCDM methods have shown success in various selection contexts, their use in CE-oriented OSC contractor selection is rare. Further complicating the issue is the shortage of skilled contractors in circular practices; this is one of the main barriers to OSC adoption yet it is rarely addressed in detail. Bridging these gaps is essential to supporting strategic and CE-aligned contractor selection in the construction industry.

Considering the gaps identified in the existing literature, this study aims to address these shortcomings by developing a practical and structured multi-criteria decision-making model for the selection of contractors capable of implementing circular economy (CE) principles in off-site construction (OSC) projects. The objectives of the study are as follows: (1) We aim to review previous academic research on contractor selection and identify the key evaluation criteria discussed in the literature. (2) We seek to examine the unique challenges associated with OSC projects and establish appropriate contractor evaluation criteria grounded in CE principles. (3) We aim to determine the relative importance of each criterion, thereby supporting the practical application of CE in the construction industry and contributing to the resolution of a commonly noted gap in the literature. (4) Further, we aim to develop a user-oriented and systematic decision-making model that facilitates the selection of the most suitable contractors for CE-focused OSC projects.

2. Literature Review

In this study, a systematic literature review was conducted on contractor selection and the circular economy within the construction industry. Scopus was selected as the primary database because of its extensive coverage, widespread use in academic research, and reliability in conducting systematic reviews [17]. To bridge the research gap in contractor selection within CE for OSC projects, the following search code was initially developed for CE: (TITLE-ABS-KEY (“circular economy” OR “circularity”) AND (“contractor”)). The following code was developed for OSC projects: ((TITLE-ABS-KEY (“off site construction” OR “Offsite” OR “modular construction” OR “prefabrication”) AND TITLE-ABS-KEY (“contractor”))). This search identified 695 documents published up to February 2024. The titles of the initially gathered articles were examined while considering exclusion criteria such as ‘non-English’ or ‘conference papers’, with a specific focus on ‘construction projects’. Subsequently, the abstracts and keywords of these articles were carefully reviewed. From the full-text assessment, 89 articles were selected for a more detailed analysis to address the topic more precisely. During this phase, any studies that did not specifically concentrate on OSC projects were excluded. Some relevant studies related to CE and OSC that were not retrieved in the initial Scopus search were identified through a snowballing strategy, including publications from Web of Science (WoS), Google Scholar, and other sources. This includes 3 reports from relevant organizations in CE, 2 books in these fields, and various articles. Combining the snowballing method with a systematic literature review, promising benefits are obtained [18]. Ultimately, 86 documents successfully passed the initial screening and underwent a comprehensive examination. These steps are summarized in Figure 1, while the subsequent stages of the study are detailed in Section 3.

In this section, we present a summary of the studies that focus on the relationship between CE and the construction industry, off-site construction practices, and contractor selection for off-site construction projects, respectively.

2.1. Circular Economy and the Construction Industry

The UN Environment Program (2015) highlights the shift towards the “circular economy”, altering waste management by emphasizing recycling. This shift from linear to circular economics signifies ongoing societal, governmental, and sectoral adaptation. Japan, the EU, and China have adopted models for transitioning [19]. The EU’s Green Deal (2020) requires sustainable business models for EU companies. The British Standards Institution’s Guide aids CE implementation [20]. Parallel initiatives within the construction industry aimed at identifying circular solutions to reduce raw material consumption, carbon emissions, and waste. Similarly to the Ellen MacArthur Foundation’s delineation of CE as a regenerative mechanism wherein materials are upheld at their utmost value within a “closed loop”, it is argued in [21] that within the realm of construction, it is imperative to repurpose end-of-life building materials. From their perspective, deconstructing these materials into their fundamental components is essential, allowing them to serve as resource repositories for new architectural projects and ensuring the continuation of a self-sustaining cycle. Various approaches exist for the implementation of CE. It is highlighted in [22] that, despite the increasing recognition of circular economy principles in the U.S. construction sector, challenges such as regulatory inconsistencies, budget constraints, and resistance to change hinder widespread adoption, necessitating multi-stakeholder collaboration and targeted policy interventions to drive systemic transformation. This diversity further highlights the necessity of a model for selecting contractors with a circular focus. In this study, the CS model proposed considers one or more of these approaches. Therefore, comprehensive research on CE practices applicable to the construction industry is presented in

Table 1. Circular conomy implementations.

Philosophical Approaches of Circular Economy	Explanation of the Philosophy	References
Material flow model	The material flow model consists of three interconnected cycling loops, encompassing natural materials, bio-compatible materials, and non-bio-compatible materials.	[23]
3R framework	Established an economic mechanism to replace the concept of “end-of-life” with the reduction, reuse, and recycling of materials in manufacturing, distribution, and consumption processes.	[24]
4R framework	Official EU policy framework for CE, adding “recovery” to the most commonly 3Rs (reduction, reuse, recycling).	[1]
6Rs framework	The R concept is expanded to the 6Rs (with the addition of redesign and remanufacture)	[25]
9Rs framework	Later evolved into the R concept (with the further addition of refurbish, repair, and refuse).	[26]
10Rs framework	Principles of sustainable resource management and waste reduction.	[27,28]
Slowing resource loops	Extends the lifespan of products and slows down the flow of resources by promoting long-lasting goods and product life extension services like repair and remanufacturing.	[1]
Closing resource Loops	Value creation involves recovering and reusing building products from end-of-service-life buildings.	[21]
Narrowing Resource Loops	Reducing resource consumption in products and production processes. Reusing materials requires techniques like recycling. Unlike slowing loops, it does not affect product flow speed or entail service loops like repairs.	[29]
Macro-system	This perspective emphasizes the necessity to adapt the industrial composition and structure of the entire economy.	[1]
Meso-systems	The typical perspective centers on eco-industrial parks as systems.	[30]
Micro-systems	Examine goods, individual businesses, and the necessary steps for enhancement.	[31]
ReSOLVE	The framework applies six circular principles (regenerate, share, optimize, loop, virtualize, and exchange) to improve asset use, extend lifespan, and shift resource use from finite to renewable sources.	[32]
6 CE Principles	It defines a fundamental collection of six CE principles (system thinking, stewardship, transparency, collaboration, innovation, value optimization) to which all organizations are recommended to adhere.	[20]
CE implementation in businesses, organizations, and production systems.	The implementation of sustainable consumer products in businesses, using indicators for restoration, regeneration, resource efficiency, climate, energy, and value preservation.	[19]
CE monitoring framework	The proposed framework categorizes and classifies indicators based on the logic of what and how, encompassing functions, products, components, materials, embodied energy, and a reference scenario.	[33]

.

Circular construction aims to create structures that can adapt to functional changes and serve as resources at the end of life; they are primarily constructed from recycled or reused materials [10]. This practice faces complexities due to traditional construction materials and industry-specific characteristics, making reuse or deconstruction challenging [34]. There is a strong awareness of CE principles, but the absence of standards stemming from construction methods and design strategies complicates the application of generic circularity indicators [35]. Consequently, academic studies have proposed various circularity indicators for circular construction. To enhance reliability and accuracy, Building Circularity Indicators (BCIs) have been developed [36]. These indicators not only find utility in CE models but also aid in identifying circular building components, enhancing end-of-life (EoL) performance through Life Cycle Assessment (LCA) and Material Flow Analysis (MFA), and facilitating the development of CE-LCA models [37]. In [38], it is outlined that in the construction industry, the supply chain is segmented into Forward Logistics (design, manufacturing, construction, operations) and Reverse Logistics (deconstruction, product reuse, waste distribution, material reprocessing) phases from a CE perspective, emphasizing the necessity of horizontal integration. The EMF’s Material Circularity Indicator (MCI) was adapted to construction in [39], which also introduced the Building Circularity Indicator (BCI) as a quantitative assessment method. These enhancements were integrated to create a BIM-based approach in [40]. A total of 30 indicators were identified in [5]; these were categorized into pillars such as Technical Cycle, Biological/Renewable, Recycling Efficiency, Functional Lifetime, Disassembly, Reusability Index, Adaptability, Energy, Emissions, Water, Heritage, and Economy. An analysis of 42 approaches presented in [10] highlighted CE constraints that do not align with reuse and refuse approaches (e.g., steel rusts, wood decomposes, bricks degrade). They stressed the need for an integrated CE perspective and underscored the potential of BIM in tracking materials and component data for efficient design.

However, the precise definition of circularity in construction, perhaps due to the industry’s relatively less mature CE understanding than manufacturing, has not yet been established [10]. The transition will be feasible with informed circular decision-making and increased awareness. Thus, this study will conduct a comprehensive circular assessment for Construction Sustainability (CS), building upon six core dimensions of circularity: functions, products, components, materials, embodied energy, and a reference scenario [33]. This research not only contributes to contractor selection but also supports the adaptation of CE in the construction industry.

2.2. Circular Economy and Its Relationship with Off-Site Construction Projects

The necessity of adhering to environmental standards and implementing measures to reduce environmental impact has prompted individuals involved in construction to embrace off-site construction (OSC) practices. Despite the benefits of OSC in meeting urbanization, housing, and sustainability goals, its adoption has been slow [41], particularly due to the lack of contractor selection tailored to OSC projects. OSC and circular construction (CC) are two interrelated concepts, with one being closely connected to the other. The increasing integration of Building Information Modeling (BIM), especially in conjunction with end-of-life circular economy strategies (e.g., 3R, 6R), is expected to enhance the utilization of OSC, particularly in modular structures [18]. The relationship between the CE and OSC projects is significant because OSC, often referred to as prefabrication or modular construction, inherently aligns with CE principles. OSC involves manufacturing, building components in a controlled factory environment that enables a reduction in waste and more efficient recycling within a CE framework [7]. Modular components, used in OSC, are designed for ease of assembly and disassembly. As a result of recycling and reducing OSC, extending lifespan and minimizing the need for new materials exist compared to traditional construction implications [34].

One of the other key points is longevity for CE. OSC produces durable, high-quality components that extend building lifespans, reducing the need for frequent replacements and resource-intensive renovation. In this respect, that procurement also influences circularity in OSC. Reference [42] addressed the lack of attention to procurement management in OSC. They formulated an MCDM model to choose suitable OSC procurement methods and identified project quality, cost control, and financial arrangements as the most crucial factors, whereas Integrated Project Delivery (IPD) and construction management (CM) methods received the highest benefit scores. The comparison between prefab and traditional buildings in the implementation of seven different CE strategies was addressed in [43]. The results indicate that, despite the common barriers associated with CE strategies, OSC methods should be treated differently from traditional construction due to differences such as lean applicability, lower complexity compared to traditional methods, carbon intensity, technological barriers in disassembling monolithic buildings, and degree of mobility. In the literature, there are supplier selection studies conducted specifically for OSC, recognizing its distinctiveness from traditional construction [44]. In the context of the circular economy, supplier selection aims to ensure that materials and resources are sustainable and suitable for recycling. The supplier selection studies conducted for the development of the CS model in this study will serve as a foundation. However, this study, which primarily addresses the contractor selection problem, will not only contribute to the performance of contractors but also provide a clear understanding, enhancing circularity within the project. Thus, the construction industry can overcome barriers related to its limited progress in circularity.

2.3. Contractor Selection Studies for Construction Projects

Contractor selection is a critical process that directly affects project success because the contractor’s competence is closely related to project performance. Since selecting an inappropriate contractor can increase the risk of project failure, a systematic and comprehensive evaluation approach is of great importance. To address this need, a multidimensional decision-making framework for the contractor prequalification process was developed in [45], emphasizing that contractor qualifications should be compatible with project objectives. This framework includes the evaluation of contractors according to criteria such as reputation, past performance, financial stability, technical expertise, and operational capacity. The Qualifier-1 Program, which supports this process, calculates a total weighted score by combining the scores received by each contractor according to the evaluation criteria [46]. In addition, an information-based model was developed to ensure more systematic and informed decisions, thus making the contractor selection process in construction projects more consistent and objective [47]. Over time, various decision-making methods have been developed for contractor selection, adapting to different project requirements and criteria. The literature highlights that selection processes differ based on project type and objectives. Unlike traditional approaches, off-site construction (OSC) involves designing, producing, and assembling structural elements at a location separate from their final installation site. Therefore, contractors in OSC projects must possess expertise, not only in on-site construction but also in planning, design, production, and modular assembly [42]. The distinct characteristics of OSC align with circular economy (CE) principles, promoting sustainability by optimizing resource use, minimizing waste, and enabling the reuse of structural elements. In this context, the current literature was examined to better understand how contractor selection differs in OSC. Important insights into existing methods are provided in [14] through a comprehensive analysis of contractor selection strategies. In addition,

Table 2. Prior research on selecting contractors.

The Focus of the Study	Project Type	Numbers of Criteria and Contractors	Method for Evaluating Criteria	Method for Evaluating Alternative	Reference
Used AHP and ANP to prioritize competitive contractors	Bridge	48/4	Fuzzy Delphi	AHP and ANP	[48]
Using the OPA for selecting contractors in construction projects, overcoming MCDM limitations.	Construction	20/4	OPA using Delphi and PCA	OPA-based ranking	[49]
Selection of a green contractor for the solar power plant project.	Solar power plant	6/6	SWARA	CRADIS	[50]
Machine-learning-based framework for contractor selection considering sustainability, risk, and safety.	Public	8/15	Machine Learning models	Multi-objective math model	[51]
Performance analysis models for CS with decision support and consistency control.	Construction	8/-	Fuzzy AHP	Fuzzy AHP	[52]
Investigating critical risk factors in selecting joint venture contractors	Infrastructure	32/-	Factor analysis and risk ranking	Expert judgment and survey-based	[53]
Enhancing contractor selection through fuzzy techniques.	Public	8/7	Fuzzy Buckley method	Fuzzy TOPSIS and fuzzy SAW	[54]
Community-responsive model for post-disaster reconstruction using ‘Build Back Better’.	Post-disaster reconstruction	39/4	BBWM	Fuzzy VIKOR	[55]
Systematic model for contractor prequalification.	Green building	25/6	AHP	AHP	[56]
Used historical procurement data, to create a pre-tendering CS analysis model.	Historical residential	3/38	PreCSAM	PreCSAM	[57]
Handles bid evaluation complexities by introducing input-output indices and using DEA cross-efficiency and AHP for decisions.	Construction	7/10	AHP	DEA	[58]
Presents a DM framework to indicate the best-fit contractor for the IPD project.	Highway	13/-	FIS modeling	Fuzzy Toolbox	[59]
Developing a sustainable circular contractor selection model.	Hydropower	36/4	Fuzzy AHP	Fuzzy TOPSIS	[8]
Established a robust framework, focusing on contractor prioritization and various criteria.	Residential	40/3	Standard Deviation	MAUT	[60]
A unique model based on HFSS and LINMAP developed.	Infrastructure	14/3	LINMAP	Similarity Measure	[61]
Indetermination, imprecision, and uncertainty of bid evaluations.	Reconstruction	10/5	HFLTSs	ELECTRE III	[62]
Developing an AHP with PROMETHEE model together.	Construction	7/5	AHP	PROMETHEE	[63]
Developing a CE-based DM model for contractor selection	Off-site Construction	29/4	Fuzzy AHP	Fuzzy TOPSIS	Current study

LINMAP: linear programming techniques for multidimensional analysis of preferences; HFSS: hesitant fuzzy soft set; MAUT: multi-attribute utility theory score; DEA: data envelopment analysis; OPA: ordinal priority approach; BBWM: Bayesian best worst method. SWARA: Stepwise Weight Assessment Ratio Analysis, CRADIS: Compromise Ranking of Alternatives from Distance to Ideal Solution, FIS: fuzzy inference system.

summarizes recent studies addressing contractor selection criteria and methodologies used in different projects.

3. Research Methodology

The methodology used to develop a CE-based decision-making model for contractor selection in off-site construction projects is summarized in Figure 1. Initially, a systematic literature review was conducted, as outlined in Section 2, to gather comprehensive information on the CE principles and OSCs for a comprehensive understanding of how off-site construction projects may use the circular economy. Based on the objectives of the study and the methodological flow, the following research questions were formulated:

RQ1: What are the key selection criteria for identifying circular contractors in construction projects?

RQ2: How can these criteria be validated and refined through focus group discussions (FGDs)?

RQ3: How can the prioritized selection criteria be integrated into a hybrid MCDM model for contractor evaluation in construction projects?

RQ4: How effective is the proposed fuzzy AHP–fuzzy TOPSIS-based model in evaluating and selecting the most appropriate circular contractor?

To address the proposed research questions, the study adopted a multi-stage approach. First, relevant criteria for CE-based contractor selection in OSC projects were identified. These criteria were refined and validated through focus group discussions (FGDs) with industry experts. A questionnaire survey was then conducted to assess the practical significance of the criteria. The data were analyzed using a fuzzy analytic hierarchy process (FAHP) to determine criteria weights. Finally, contractor alternatives were evaluated via fuzzy TOPSIS within a hypothetical case study to demonstrate model applicability.

Figure 1. Comprehensive overview of methodological approach.

Figure 1. Comprehensive overview of methodological approach.

3.1. Validation of CE-Based Contractor Selection Criteria with Focus Group Discussion

This study utilized the focus group discussion (FGD) method for validating the identified criteria from the literature. The primary rationale for choosing FGD over other methods lies in its ability to facilitate discussions among participants with a background in CE and OSC, despite these being distinct subjects that complement each other. FGD, being an exploratory and collaborative technique, engages participants in dynamic discussions guided by a moderator to elicit valuable insights and opinions [64].

As suggested by [65], an effective focus group typically consists of 5 to 20 participants. Rather than following a strict numerical rule, the literature highlights three key principles for ensuring group quality [64]: avoiding groups larger than 20, as they can hinder effective moderation; maintaining at least 5 participants to ensure productive discussion [66]; and prioritizing expert competence over quantity. In accordance with these principles, our study assembled a group of 15 experts with diverse professional roles, including project owners, contractors, designers, and consultants, all of whom possess extensive experience in off-site construction and demonstrate strong familiarity with circular economy (CE) concepts (

Table 3. Participant demographics and expertise in the focus group discussion sessions.

Expert	Degree	Education	Expertise	Stakeholder Type	Experience (in Years)
E1	M.Sc.	Civil Eng.	Project control management	Contractor	TE: 10, ME: 5, OSC: 5, CE: 6
E2	B.Sc.	Civil Eng.	Project control management	Owner	TE: 10, ME: 5, OSC: 5, CE: 5
E3	Ph.D.	Civil Eng.	Project control management	Owner	TE: 15, ME: 6, OSC: 10, CE: 5
E4	M.Sc.	Civil Eng.	Project management	Contractor	TE: 12, ME: 5, OSC: 10, CE: 6
E5	M.Sc.	Architecture	Sustainable construction	Consultant	TE: 10, ME: 5, OSC: 5, CE: 5
E6	Ph.D.	Civil Eng.	Project planning and scheduling	Contractor	TE: 14, ME: 6, OSC: 7, CE: 5
E7	M.Sc.	Geotechnical Eng.	Geotechnical engineering	Designer	TE: 17, ME: 7, OSC: 15, CE: 8
E8	Ph.D.	Civil Eng.	Project management	Owner	TE: 26, ME: 16, OSC: 16, CE: 5
E9	Ph.D.	Civil Eng.	Contract management	Contractor	TE: 10, ME: 5, OSC: 5, CE: 5
E10	Ph.D.	Civil Eng.	Project planning and scheduling	Consultant	TE: 19, ME: 17, OSC: 8, CE: 7
E12	M.Sc.	Civil Eng.	Project management	Owner	TE: 12, ME: 5, OSC: 5, CE: 5
E13	M.Sc.	Civil Eng.	Project management	Sub-Contractor	TE: 22, ME: 10, OSC: 13, CE: 5
E14	Ph.D.	Environmental Eng.	Sustainable construction	Consultant	TE: 25, ME: 15, OSC: 10, CE: 9
E15	M.Sc.	Civil Eng.	Project manager	Sub-contractor	TE: 11, ME: 5, OSC: 10, CE: 5

TE: total experience in the construction industry; ME: managerial experience; OSC: off-site construction experience; CE: circular economy experience.

). The experts were selected based on the following criteria: (1) a minimum of 10 years of experience in the construction industry; (2) at least 5 years of direct involvement in off-site construction projects; (3) at least 5 years of knowledge or application of CE principles; (4) a minimum of 5 years in a managerial role; and (5) at least a bachelor’s degree in engineering or architecture. To ensure a global perspective, participants were drawn from multinational projects, with four experts based in the EU, five in the USA, and six in Turkey.

FGDs were performed in two sessions. In the first FGD session, experts received clear theoretical insights on OSC and CE to familiarize them with the study’s background. Each expert was required to choose the methodology that most effectively addressed CE-based selection issues within the framework. These proposals underwent thorough deliberation among all experts, and the session concluded with the group reaching a consensus on the optimal approach advocated by [33]. The recommended approach delineates six dimensions (functions, products, components, materials, embodied energy, and a reference scenario) to systematically analyze the CE aspects within the context of OSC projects.

In the second session, using these categories, CE-based contractor selection criteria identified from the existing literature were presented to experts to assess the validity of the selection criteria based on the Likert scale. Subsequently, the assessments provided by the experts were scrutinized using descriptive techniques, as also recommended [67]. The results showed that criteria labeled as “Early-stage designer collaboration” were considered unsuitable as they received scores below 3, resulting in their removal from the list of criteria. The underlying reason for this outcome was that the item was perceived as a sub-criterion of other criteria (FC2, FC4, RC4). In this session, participants were also invited to propose additional selection factors. The moderator, also a co-author of this study, collected recommendations from each expert. These suggestions were then discussed, and their relevance was evaluated by the participants. A selection factor was considered valid if all experts agreed on its suitability. In cases where consensus was not achieved due to differing opinions, the moderator made the final decision based on the majority’s perspective.

As a result of the FGD session, an additional selection factor (EC5) was overlooked in the existing literature. Consequently, a total of 28 selection factors were identified and validated.

Table 4. Circular construction contractor selection criteria.

Main CE Indicators	ID	Criteria for Contractor Selection	Descriptions	References
Functional Circularity	FC1	Organization structure and CE model	The function of a contractor includes project management, which can influence circularity by being supported with CE approaches like 10R, 6 CE principles, or circular economy sub-business models.	[27]
	FC2	Social progress	Social factors can impact the functions and production performance of contractors, thereby indirectly affecting circularity.	[68]
	FC3	Procurement method experience in IPD and CM procurement	In a comprehensive study conducted on OSC projects, IPD and CM were selected as the best project delivery systems. This pertains to management strategies and experience aimed at achieving CE objectives.	[42]
	FC4	Onsite circularity	Although on-site tasks are minimized when selecting OSC, they should still be conducted by circularity functions.	[69]
Product Circularity	PC1	Green procurement	The green procurement process, which encourages more efficient use of material resources, prefers environmentally friendly products, and reduces waste, influences circularity in construction projects.	[70]
	PC2	Supply chain circularity	Involved in integrating circular economy principles in product design’s early stages, encouraging consumer involvement in the circular economy, and establishing clear criteria for CE partnerships.	[13,44]
	PC3	Product control for defective manufacture	In OSC projects, distances are planned considering carbon emissions from transportation. Repeated transport due to product errors increases the carbon footprint and lowers the circularity rate.	[7,71]
	PC4	Product-as-a-service models.	Instead of purchasing certain products, the option of leasing or utilizing them as a service is offered, allowing the provider to handle the maintenance, repair, and end-of-life management, thus contributing to the CE.	[21,33]
Componential Circularity	CC1	Usability of components in different projects.	Disassembling components for reuse in different projects extends the product’s lifespan, thereby increasing resource efficiency and contributing to the circular economy.	[7,43]
	CC2	Enhance building structure durability through components.	Increasing the durability of building structures allows structural elements to be used for longer periods, thus reducing construction waste and material inefficiency, resulting in cost savings in the long term.	[18]
	CC3	Prioritization of modular elements.	The use of standardized and modular elements requires a smaller variety of materials and components. This situation facilitates production processes, reduces costs, and enables easy maintenance, repair, and reuse or upgradability of components at the end of their life.	[72]
	CC4	Employing reversible interconnections among components in varied layers	The capability to efficiently and autonomously remove, adapt, reuse, repair, renew, or replace components with abbreviated life spans significantly impacts CE.	[7,18]
	CC5	Having a disassembly manual document for the structure	Even after the end of use, dismantling and recycling of components contain crucial information. Ease of recycling in the CE provides opportunities such as waste reduction, efficient resource utilization, and increased reuse.	[13,73]
Material Circularity	MC1	Resource consumption and integrated utilization rate	When determining the consumption rate of different resource types in a project, consideration is given to how these resources are used together. In addition to calculating mineral and energy consumption expressed as unit GDP, the evaluation also considers the combined and efficient utilization of resources.	[74]
	MC2	Reduction in material waste and the lean production chain	The aim is to reduce waste generated during the construction process and establish an efficient, low-waste production chain. This criterion aims to increase material usage efficiency through waste reduction strategies and the optimization of material flow.	[43,75]
	MC3	Utilization of recycled raw materials	Using engineered wood and recycled-content concrete among other bio-based materials enhances circularity.	[76]
	MC4	Building material passport document for the project	Resource usage in circular construction should be monitored, evaluated, and optimized. Having detailed and accessible information is indispensable for recovering and reusing building components and materials.	[11,35]
	MC5	Assess material toxicology through the Bill of Materials (BoL) and mitigate monstrous hybrids.	Determining material toxicology via the BoL facilitates the preference for more environmentally friendly and healthier materials. This eases the adoption of safer, eco-friendly, and sustainable products, preventing the use of harmful materials and thus contributing to a CE.	[72,77]
Energy Circularity	EC1	Using renewable energy in production	Integrating renewable energy sources into production reduces fossil fuel dependency and carbon footprint, promoting sustainability in CE.	[41,68]
	EC2	Production machinery or plant efficiency	Enhancing machinery and plant efficiency minimizes energy use and waste by adopting energy-saving technologies and optimizing workflows within CE.	[69]
	EC3	Provide sufficient information for LCA	Conducting Life Cycle Assessments (LCAs) helps evaluate a project’s carbon performance, identifying carbon-intensive materials and aiding in the development of carbon reduction strategies to contribute to the CE.	[37,40]
	EC4	Transportational energy	The amount of energy used during the transportation of modular building components to the construction site affects Energy Circularity through transportation. The distance and the type of logistics used significantly impact the amount of energy consumed.	[7]
	EC5	Use of waste organic material	The criterion examined under the biological circularity of the CE contributes to circularity through the transformation or conversion of biogas into energy. Examples of organic waste include food waste, agricultural residues, wood waste, and similar organic materials.	Expert evaluation
Referential Circularity	RC1	Financial resilience	The effective management of the project’s financial resources involves budgeting, allocation of financial resources, and cost control.	[14]
	RC2	Technological capability and innovation readiness	The contractor’s existing technical infrastructure includes innovative tools, digital systems, and technological advancements in business processes (such as the use of BIM, big data analysis, etc.).	[11]
	RC3	Quality assurance	Monitoring and determining quality standards throughout the lifecycle of materials used in circular construction projects, from production to recycling processes, contributes to the longevity of products, increases their recycling potential, and reduces waste, thereby helping achieve the goals of the CE.	[41,70]
	RC4	Experience in construction projects	Internal expertise reduces the risk levels in completed tasks. This enhances efficiency in construction projects, thereby enabling gains in resources, time, and economy.	[41,63]
	RC5	Occupational safety and workforce wellbeing	It concerns the contractor’s ability to uphold occupational health and safety standards, implement procedures, and ensure safe working conditions for the personnel.	[41]

shows the finalized list of CS criteria along with their corresponding references.

To improve conceptual clarity, the criteria listed in Table 4 were reviewed regarding the core CE principles: waste minimization, reuse/recycling, resource efficiency, and lifecycle thinking. Several criteria clearly support waste reduction (e.g., MC2, PC3), while others encourage reuse or recycled content (e.g., MC3, CC1, EC5). Resource efficiency is addressed through EC2 and PC1, and lifecycle thinking is reflected in criteria related to LCA, disassembly documentation, and service models (e.g., EC3, MC4, PC4). This demonstrates that the evaluation framework integrates CE foundations across its structure.

3.2. Application of Hybrid Fuzzy MCDM in Circular Contractor Selection

The application of fuzzy AHP has gained prominence in efforts to address the inherent uncertainties in expert evaluations, especially in CS studies. Recent research highlights the widespread use of fuzzy methodologies in this domain, emphasizing their effectiveness in accommodating imprecise assessments [78]. A comprehensive review of decision-making techniques tailored to contractor selection is provided by the authors in [14], underscoring the integration of fuzzy AHP and fuzzy TOPSIS as pivotal approaches. This is further supported by [79], who argue that traditional AHP and TOPSIS rely heavily on precise expert judgments, necessitating the use of fuzzy logic to account for evaluative ambiguities. Consequently, fuzzy-based methods have been widely adopted across various subfields of construction management. Building on this foundation, the present study employs a hybrid fuzzy multi-criteria analysis, combining fuzzy AHP and fuzzy TOPSIS to identify the most suitable contractor for circularity, as depicted in Figure 1. To enhance reliability in fuzzy AHP and fuzzy TOPSİS applications, fuzzy linguistic variables (

Table 5. Linguistic scales for fuzzy AHP and fuzzy TOPSIS.

	Linguistic Variables	TFN	TFR		Linguistic Variables	TFN
Fuzzy AHP	Equally important	(1, 1, 1)	(1, 1, 1)	Fuzzy TOPSIS	Worst	(0, 0, 1)
	Slightly important	(1/2, 1, 3/2)	(2/3, 1, 2)		Very Poor	(0, 1, 3)
	Moderately important	(1, 3/2, 2)	(1/2, 2/3, 1)		Poor	(1, 3, 5)
	Important	(3/2, 2, 5/2)	(2/5, 1/2, 2/3)		Fair	(3, 5, 7)
	Strongly important	(2, 5/2, 3)	(1/3, 2/5, 1/2)		Good	(5, 7, 9)
	Extremely important	(5/2, 3, 7/2)	(2/7, 1/3, 2/5)		Very Good	(7, 9, 10)
					Excellent	(9, 10, 10)

) were employed instead of traditional numerical scales, offering a more intuitive approach.

3.2.1. Fuzzy AHP Application for MCDM

In the fuzzy AHP application, data collection utilized linguistic variables, which were later converted into triangular fuzzy numbers (TFNs). These TFNs encapsulate the smallest, largest, and most likely values of expert judgments, ensuring simplicity and clarity [80]. Data was gathered through a questionnaire survey administered to 15 experts, following a focus group discussion (FGD). The questionnaire survey was conducted both face-to-face and online, comprising two main sections. In the first section, experts conducted pairwise comparisons among the primary categories influencing selection factors. The second section involved pairwise comparisons of selection factors within specific dimensions, including requirement, ability, and outcome. Each expert completed a total of seven pairwise comparison matrices. To ensure the reliability of expert judgments, the consistency of their evaluations was assessed using the consistency ratio (CR), with a threshold of 0.1. Experts exceeding this limit received feedback to refine their assessments, ensuring high-quality data for analysis. The consistency index (CI) and CR were calculated using the following equations:

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(1)

$$CR={\displaystyle \frac{CI}{RI}}$$

(2)

where $CI$ represents the consistency index, ${\lambda }_{max}$ is the maximum eigenvalue of the comparison matrix, $n$ is the number of criteria in the matrix, and $RI$ denotes the random index.

Once consistent and reliable data sets were obtained, the pairwise evaluations were aggregated to derive group decisions. The linguistic expressions provided by the experts were transformed into triangular fuzzy equivalents based on Table 5. These transformed values were then aggregated using Equation (3) to form the group decision matrix, where K is the total number of respondents:

$$l_{ij}={\left(\prod _{k=1}^K{l}_{ijk}\right)}^{1/K},m_{ij}={\left(\prod _{k=1}^K{m}_{ijk}\right)}^{1/K}, l_{ij}={\left(\prod _{k=1}^K{u}_{ijk}\right)}^{1/K}$$

(3)

The extent analysis method described in [81], one of the most widely adopted fuzzy AHP approaches, was used to analyze the aggregated data, providing normalized weights for each selection factor. These weights were subsequently used as the weight vector in the fuzzy TOPSIS analysis, as shown in Figure 1. To address potential disagreements among experts, all pairwise comparisons were aggregated using the geometric means of TFNs. This method inherently captures judgment variability and integrates uncertainty into the final criteria weights.

3.2.2. Fuzzy TOPSIS Application for MCDM

The TOPSIS method is based on calculating the shortest distance from the Positive Ideal Solution (PIS) and the longest distance from the Negative Ideal Solution (NIS) to determine the most favorable alternative [65]. Due to its simplicity and robustness, this method is widely applied in various fields. To further adapt it to situations involving uncertain data, [82] extended the approach by introducing the fuzzy TOPSIS method. In this method, the weights and ratings are determined using linguistic variables, as outlined in Table 5. Although this analysis can be conducted using real contractor performance data, in this study, it was performed using a hypothetical case to validate the model’s structure and ensure its functionality under controlled conditions. The model can be directly implemented in practice by using actual contractor scores and executing the fuzzy TOPSIS steps accordingly.

During the survey, participants evaluated the decision alternatives based on the selection factors, and the resulting data were analyzed using the fuzzy TOPSIS approach, as detailed below.

In the first step, the importance of weights of the criteria $\widetilde{w_j}$ is determined, representing the calculated weights of each selection factor, as described in Section 3.2.1.

$$W=\left[{\tilde{w}}_1{\tilde{w}}_2--{\tilde{w}}_1--{\tilde{w}}_n\right]$$

(4)

In Step 2, the individual decision matrices from all experts are combined using the following equation:

$${\tilde{x}}_{ij}={\displaystyle \frac{1}{K}}\left[{\tilde{x}}_{ij}^1+{\tilde{x}}_{ij}^2+\dots {\tilde{x}}_{ij}^K\right]$$

(5)

where ${\tilde{x}}_{ij}^K$ represents the decision matrix of the $K^{th}$ decision-maker. This aggregation process generates the overall decision matrix, structured as follows:

$$\tilde{D}=\left[\begin{array}{ccc}{\tilde{x}}_{11}& \dots & {\tilde{x}}_{11}\\ ⋮& \ddots & ⋮\\ {\tilde{x}}_{m1}& \dots & {\tilde{x}}_{mn}\end{array}\right], i=1, 2, \dots m;j=1, 2, \dots n$$

(6)

In Step 4, a linear scale transformation is applied to convert the various criteria scales into a comparable format, resulting in the normalized decision matrix $\tilde{R}$, as computed using Equation (7) below:

$$\tilde{R}={\left[{\tilde{r}}_{ij}\right]}_{m\times n} i=1, 2, \dots m;j=1, 2, \dots n.$$

(7)

$${\tilde{r}}_{ij}\left({\displaystyle \frac{a_{ij}}{c_j^{*}}},{\displaystyle \frac{a_{ij}}{c_j^{*}}},{\displaystyle \frac{a_{ij}}{c_j^{*}}}\right) and c_j^{*}={max}_i{c}_{ij} \left(benefit criteria\right)$$

(8)

Ultimately, the normalized fuzzy decision matrix is multiplied by the criteria weights obtained through the fuzzy AHP method to generate the weighted normalized fuzzy performance matrix.

$$\tilde{V}={\left[{\tilde{v}}_{ij}\right]}_{m\times n} i=1, 2, \dots m;j=1, 2, \dots m;j=1, 2, \dots n \mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} {\tilde{v}}_{ij}={\tilde{r}}_{ij}\left(·\right)W_j$$

(9)

Step 6 involves determining the fuzzy Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS) using the normalized fuzzy numbers, where the minimum and maximum values of these numbers are identified.

$$A^{+}=\left\{{\tilde{v}}_1^{+}, {\tilde{v}}_2^{+}, {\tilde{v}}_3^{+}, \dots {\tilde{v}}_J^{+}\right\}$$

(10)

$$A^{-}=\left\{{\tilde{v}}_1^{-}, {\tilde{v}}_2^{-}, {\tilde{v}}_3^{-}, \dots {\tilde{v}}_J^{-}\right\}$$

(11)

The distance of each alternative from the fuzzy PIS and fuzzy NIS is calculated using the formulas provided, which measure how far each alternative is from both the fuzzy PIS and the fuzzy NIS:

$$d\left({\tilde{v}}_{ij},{\tilde{v}}_j^{+}\right)=\sqrt{{\displaystyle \frac{1}{3}}}\left[{\left({\tilde{v}}_{ija}-{\tilde{v}}_{ja}^{+}\right)}^2+{\left({\tilde{v}}_{ija}-{\tilde{v}}_{jb}^{+}\right)}^2+{\left({\tilde{v}}_{ija}-{\tilde{v}}_{jc}^{+}\right)}^2\right]$$

(12)

$$d\left({\tilde{v}}_{ij},{\tilde{v}}_j^{-}\right)=\sqrt{{\displaystyle \frac{1}{3}}}\left[{\left({\tilde{v}}_{ija}-{\tilde{v}}_{ja}^{-}\right)}^2+{\left({\tilde{v}}_{ija}-{\tilde{v}}_{jb}^{-}\right)}^2+{\left({\tilde{v}}_{ija}-{\tilde{v}}_{jc}^{-}\right)}^2\right]$$

(13)

$$d_i^{+}=\sum _{j=1}^{n}d\left({\tilde{v}}_{ij},{\tilde{v}}_j^{+}\right), i=1, 2, 3, \dots m$$

(14)

$$d_i^{+}=\sum _{j=1}^{n}d\left({\tilde{v}}_{ij},{\tilde{v}}_j^{-}\right), i=1, 2, 3, \dots m$$

(15)

The proximity coefficient (PCi) indicates how close each alternative is to both the fuzzy PIS and fuzzy NIS. It is calculated by comparing the distances from both ideal solutions:

$$P{C}_i={\displaystyle \frac{d_i^{-}}{d_i^{+}-d_i^{-}}}$$

(16)

Overall, this study assessed four alternative contractors based on their adherence to 28 circularity criteria (Table 4).

4. Results

This section presents the results of fuzzy AHP and fuzzy TOPSIS analyses, respectively.

4.1. Fuzzy AHP Analysis Results

The model was developed using a combination of methods, including focus group discussions (FGDs), fuzzy AHP, and fuzzy TOPSIS analysis, to provide a comprehensive framework that aids decision-makers in making informed choices. Before applying the fuzzy AHP method, the consistency of participants responses was assessed, and all responses found to be consistent (

Table 6. Consistency ratios of experts with respect to CE criteria.

Experts	Main Criteria	Functional Circularity	Product Circularity	Componential Circularity	Material Circularity	Energy Circularity	Referential Circularity
E1	0.87	4.18	4.53	4.44	4.35	7.22	7.23
E2	8.97	6.90	2.25	3.67	6.22	1.58	6.78
E3	2.92	7.03	4.41	5.33	7.55	1.89	5.65
E4	8.98	1.23	4.36	1.83	7.93	4.49	6.77
E5	3.81	0.28	5.80	6.03	4.60	2.19	6.01
E6	9.48	4.97	5.37	2.22	7.42	1.73	9.44
E7	3.52	8.47	4.41	8.15	8.44	3.12	7.12
E8	5.43	4.98	0.00	4.92	1.74	1.83	3.58
E9	3.15	1.63	3.97	5.57	8.96	4.17	2.95
E10	7.41	4.99	8.06	7.33	6.93	5.33	7.46
E11	6.02	1.63	2.25	8.93	7.45	8.15	3.88
E12	8.45	5.85	6.14	6.52	9.28	4.02	6.06
E13	2.54	2.19	6.86	4.31	4.23	1.19	5.75
E14	1.29	6.62	6.86	6.96	6.93	5.23	7.35
E15	1.78	6.90	2.25	3.10	6.22	1.58	8.72

). The consistency ratio (CR) for each matrix remained within the acceptable 10% threshold, with no violations observed. These results confirm that the data collected from experts is both reliable and robust, being usable in further analysis.

The key findings of the study are summarized in

Table 7. Ranking of CE-base contractor selection criteria.

Main Criteria and Sub-Criteria	Normalized Weight	Main Ranking	Criteria Ranking	Overall Weight	Rank
Functional Circularity	0.1513	5
FC1	0.2236		4	0.0338	17
FC2	0.2339		3	0.0354	13
FC3	0.2812		1	0.0426	3
FC4	0.2612		2	0.0395	9
Product Circularity	0.1702	3
PC1	0.2630		1	0.0448	1
PC2	0.2586		2	0.0440	2
PC3	0.2372		4	0.0404	6
PC4	0.2413		3	0.0411	4
Componential Circularity	0.1620	4
CC1	0.1817		5	0.0294	24
CC2	0.2066		2	0.0335	19
CC3	0.2163		1	0.0350	15
CC4	0.1904		4	0.0309	23
CC5	0.2049		3	0.0332	20
Material Circularity	0.1928	1
MC1	0.1927		4	0.0372	12
MC2	0.2064		3	0.0398	8
MC3	0.2111		1	0.0407	5
MC4	0.2089		2	0.0403	7
MC5	0.1809		5	0.0349	16
Energy Circularity	0.1790	2
EC1	0.2165		2	0.0388	10
EC2	0.1965		3	0.0352	14
EC3	0.2166		1	0.0388	11
EC4	0.1829		5	0.0327	22
EC5	0.1875		4	0.0336	18
Referential Circularity	0.1447	6
RC1	0.2276		1	0.0329	21
RC2	0.2083		2	0.0301	25
RC3	0.1919		4	0.0278	27
RC4	0.1921		3	0.0278	26
RC5	0.1801		5	0.0261	28

, which outlines the selection factors, their corresponding weights, circularity criteria, and their priorities. This framework enhances the reliability, accuracy, and effectiveness of decision-making, particularly in construction projects. By integrating a diverse set of selection factors and assessing their relative importance, the model supports decision-makers in systematically evaluating and selecting the most suitable contractor.

Moreover, the model emphasizes the prioritization of circular economy (CE) criteria, guiding decision-makers in selecting the most suitable methods based on their specific requirements. The findings also highlight the complexity of choosing the most appropriate circular contractor, necessitating the use of a systematic approach to consider all relevant factors. Neglecting such an approach may raise concerns about the accuracy and validity of the decision, particularly among stakeholders like project owners. Therefore, decision-makers are encouraged to apply the proposed model to ensure a more reliable and effective selection process for circular contractors in construction projects.

According to Table 7, the findings indicate that Material Circularity, Energy Circularity, and Product Circularity were the most critical criteria in the model, with corresponding weights of 0.1928, 0.1790, and 0.1702, respectively. Indicator-based assessments of the criteria revealed that green procurement (PC1) was not only the most significant factor in product circularity but also the most influential factor across the entire model. In the overall evaluation, while Material Circularity (MC) was chosen as the most important main group, it was determined that PC1, PC2, and FC3 emerged as the most critical criteria overall.

4.2. Fuzzy TOPSIS Analysis Results

In this study, four hypothetical contractors were designed by a group of experts, aligning with methodologies previously established in related research [65]. The purpose of this step was to validate the applicability of the model. Although this study did not involve an actual contractor selection process, the evaluations were based on real firms known to the experts. Each expert provided ratings for the criteria based on their knowledge and experience, and these responses were processed by the moderator in a structured Excel sheet, which was then analyzed using the fuzzy TOPSIS method. To achieve this, two experts (E3, E9) provided ratings based on companies they currently work for, while the other two (E12, E15) assessed companies they had previously worked for. All four companies, represented by four experts separately, have prior experience with prefabrication or modular construction in their past projects, demonstrating their capability in off-site construction methodologies. While contractors B and C, both engaged in green building projects, had the opportunity to score higher overall due to their sustainability-oriented practices, it is essential to emphasize that this modeling exercise was designed to test the robustness and applicability of the proposed decision-making framework. Moreover, contractors A, B, and C represent firms operating on an international scale, particularly in the U.S. and Europe, whereas contractor D primarily focuses on transportation projects and is in the early stages of integrating sustainability-driven construction practices. A total of 112 data points were collected for the four companies and evaluated using the fuzzy TOPSIS method. Experts received detailed information about the contractors and evaluated them based on 28 CE criteria using a 7-point Likert scale (Table 5), appropriate for the fuzzy TOPSIS application. Contractors B and C, being involved in green building projects, were given the opportunity to score higher overall. During the evaluation, judgments about the companies were made based on the available information, and a single decision-maker performed the scoring.

Table 8. Assessment of the contractors.

Contractors	CC	Rank
A	0.1416	4
B	0.2991	2
C	0.3347	1
D	0.2246	3

presents the overall performance of the contractors based on their CCi values, calculated using the fuzzy TOPSIS analysis. It also includes the individual CCi values for each indicator.

The results show that contractor C (CCi: 0.2991) was the most circular, followed closely by contractor B (CCi: 0.2991). The other two contractors performed well in terms of RC and FC but were not selected in the overall ranking. In cases where multiple contractors need to be involved in a project, the decision-maker is provided with the flexibility to select more than one alternative based on the key CE criteria, allowing for the consideration of multiple viable options.

5. Discussion

The main criteria identified as critical in the study include Material Circularity (MC), Energy Circularity (EC), and Product Circularity (PC). These results are in line with existing circularity assessment methods in the literature, such as Material Flow Analysis and Life Cycle Assessment [36]. Modular components used in OSC projects extend material lifetime and reduce the need for new resources with their easy assembly and disassembly features [72]. Furthermore, green procurement (PC1) emerged as the most influential factor in the model, supporting the recommendations of [29] to increase circularity through business models and design strategies. Based on these findings, procurement strategies (PC1 and PC2) emerge as key factors in the transition to a circular economy. However, many contractors lack the infrastructure needed to effectively integrate sustainable supply chains. This gap between theoretical relevance and real-world implementation poses a major challenge for the sector. It suggests that systemic reforms may be necessary to support meaningful circular adoption. In addition, the factors weighted highly, such as PC1, EC1, EC3, and MC3, share a common feature: they are all directly linked to emission mitigation during the operational phase of construction projects. This highlights the model’s alignment with current sustainability priorities, particularly the need for cost-effective carbon reduction strategies in building operations, as emphasized in the recent literature [37].

The study also highlights that circularity is a structural challenge beyond just material choices. For example, project delivery methods (FC3) play an important role in determining whether circular outcomes can be achieved. This finding underlines the need for alternative project management approaches, such as Integrated Project Delivery (IPD) and Construction Management (CM), to provide a more appropriate framework for circular construction practices. Successful adoption of Product-as-a-Service models (PC4) and utilization of recycled raw materials (MC3) were also identified as key enablers of circular construction. However, the results indicate a change in contractor selection criteria. While traditional evaluation criteria remain relevant, factors such as occupational safety and workforce wellbeing (RC5) and previous construction experience (RC4) appear to be less important in the context of circularity. This suggests that traditional experience alone is no longer a dominant evaluation factor, but rather the contractor’s ability to integrate circular economy principles into its operations has become a more decisive factor.

The study demonstrates that the inherent nature of OSC projects aligns well with CE principles. Increased integration of BIM and recent CE strategies (e.g., 3R, 6R) are expected to boost OSC adoption [18]. Particularly, the ease of assembly and disassembly of modular components enhances reuse potential, contributing to circularity. The comparative analysis of traditional and modular construction presented in [43] confirms the potential of OSC to promote circularity. The findings suggest that selecting the right contractor directly impacts project circularity and sustainability objectives. OSC’s alignment with Material Circularity facilitates effective waste recovery and reduces the carbon footprint of projects [13]. Furthermore, supply chain management is a critical factor influencing circularity in OSC projects. It is emphasized in [42] that strategies like Integrated Project Delivery (IPD) and Construction Management (CM) improve decision-making processes in OSC projects. Moreover, the comparisons in [43] comparisons reveal that OSC has lower carbon intensity and reduced complexity compared to traditional construction methods. The need to reassess supplier selection for OSC from a circular perspective is highlighted in [44], emphasizing its critical role in achieving project sustainability objectives. In support of these findings, the case study results showed that contractors with higher CE alignment—particularly contractors A and B—also performed better on OSC-relevant criteria such as project delivery methods (FC3), modular design (PC3), and component circularity (CC). In contrast, contractor D, with limited OSC and international experience, received the lowest scores. These observations show a parallel with previous empirical studies suggesting that modular and prefabricated construction methods—key components of OSC—can lead to up to 87% waste reduction, more than 15% lower embodied carbon, and 60–68% less energy consumption compared to traditional construction [7]. This consistency reinforces the view that firms that are strong in CE are also more capable of delivering circular outcomes in OSC contexts. Although they are derived based on a hypothetical scenario, these results support the leverage effect discussed in the literature [7,43], indicating that contractors with experience in OSC are likely to hold a competitive advantage in CE-based evaluations. However, further empirical studies involving real-world contractor assessments are needed to validate and generalize these findings.

The model demonstrates how decisions guided by CE principles redefine waste as a resource and promote the more efficient use of materials and resources in off-site construction (OSC) projects. However, its applicability extends beyond OSC projects and provides a versatile framework for broader applications. Given the significant impact of CE, this study focuses specifically on OSC projects and provides a practical perspective from which CE can be effectively addressed. A significant gap in the literature lies in the practical application of CE, particularly in contractor selection. This study fills this gap by incorporating CE-based criteria into the selection process. It also adds value by providing a structured implementation roadmap, not only at the project initiation stage but also throughout the project lifecycle. The findings from the hypothetical case study suggest that aligning contractor selection with CE criteria can significantly improve circularity performance. This suggests that a shift from traditional selection methods to CE-compliant criteria can promote more sustainable contractor practices, thus reinforcing the idea that the sector can accelerate its transition to a circular and resource-efficient model.

The construction industry traditionally follows a linear economic cycle where materials are used and discarded [83]. Achieving circularity requires a fundamental shift towards CE principles. While economic concerns are often cited as an obstacle to this transition, this study focuses on demonstrating how CE can be implemented in practice. As a result, economic factors are not treated as the primary selection criterion but are included under financial resilience (RC1). The lower weight assigned to this factor suggests that circular decisions may lead to cost increases, but other considerations take precedence. In addition, strategies such as ‘product-as-a-service’ models (PC4) not only contribute to cost savings, but also enhance resource efficiency, reinforcing circularity in construction projects. By integrating environmental, economic and social dimensions, this approach is aligned with the three pillars of sustainability. Thus, this study differentiates itself from broader sustainable construction research by maintaining a specific CE-focused perspective. Furthermore, country-level policies provide crucial insights into the practical challenges and opportunities of implementing CE in construction. The EU, through the Green Deal and the British Standards Institution’s guidance, actively promotes circularity in the sector [20]. Countries like The Netherlands have set ambitious goals, such as achieving a fully circular construction economy by 2050 [19]. In contrast, Japan focuses on industrial efficiency and material reuse [19], while the U.S. still faces fragmented CE adoption due to regulatory inconsistencies and financial constraints [22]. These differences underline the need for flexible and context-aware contractor selection models that align with national regulatory maturity and strategic goals.

The study can be improved by making the contractor selection process iterative and integrating agile methods with continuous improvement mechanisms. Reference [84] showed that the Scrum methodology can be applied in construction projects in the design phase and also in the construction phase. Similarly, Scrum’s sprint logic can ensure that contractor compliance with CE criteria is monitored throughout the project through periodic performance evaluation. Sprint-based progress, especially in the production and delivery processes of prefabricated modules, can prevent material waste by providing a more flexible and optimized supply chain management. In addition, sustainability performance can be continuously measured during the project process and improvement sprints can be created, so that contractors can be effectively guided in achieving CE goals by receiving faster feedback. Given the challenge of performance-based assessment of criteria, a web-based model with improved accessibility could be developed. To address the common issue of data loss, data gathered by selected contractors can be integrated with BIM. This integration would contribute to enhancing time, cost, and quality management and enhance other project success factors such as integration, scope, human resources, communication, and risk management. It is anticipated that the smart systems and AI integration offered by Industry 5.0 will further enhance the effectiveness of circularity strategies in OSC projects.

6. Conclusions

This study underscores the necessity of integrating CE principles into contractor selection for off-site construction (OSC) projects to enhance circularity in the construction industry. By leveraging a fuzzy AHP and fuzzy TOPSIS-based decision-making model, this research provides a structured approach for evaluating contractor suitability based on key circularity criteria. The findings demonstrate that contractor selection significantly impacts the adoption of CE principles, emphasizing the need for systematic decision-making to improve sustainability outcomes. Also, CE is not merely a waste management process but a comprehensive approach encompassing all phases of a project, from design to closure. Therefore, if a truly circular construction industry is to be established, selecting contractors that comply with CE requirements will not only enhance motivation within the sector but also improve the circularity of projects and, consequently, the entire construction industry. Over time, this increased circularity can enable the construction industry to integrate with other industries, fostering a broader CE ecosystem.

The proposed model includes selection factors, their respective weights, circularity criteria, and their priorities. This framework is designed to facilitate more reliable, accurate, and effective decision-making across various contexts, particularly in construction projects. By incorporating a broad range of selection factors and assessing their relative importance, the model enables decision-makers to comprehensively evaluate and choose the most suitable contractor. Moreover, the model highlights the prioritization of CE criteria, assisting decision-makers in selecting the most appropriate methods tailored to their specific requirements. The results further demonstrate that choosing the most suitable circular contractor is inherently complex and requires a systematic approach to account for all relevant factors. Failing to adopt such an approach could lead to questions regarding the accuracy and validity of the decision, particularly from stakeholders such as project owners. Therefore, decision-makers are advised to utilize the proposed decision model to ensure a more reliable and effective selection process for circular contractors in construction projects.

While this study applied the proposed model to four hypothetical contractors, it is important to acknowledge that the outcomes were shaped by the characteristics of these selected firms. Contractor D, being a firm primarily focused on transportation projects and not an internationally operating construction company, exhibited relatively lower circularity performance compared to the others. However, this does not imply that local contractors inherently fail to meet CE requirements. On the contrary, the model’s evaluation criteria are designed to provide unbiased results, independently of a contractor’s scale or market reach. Moreover, the nature of a company’s past project types was not considered a direct criterion for circularity assessment, as the evaluation focused on broader sustainability indicators rather than specific sectoral experience. As the number of evaluated contractors increases, the model’s accuracy in distinguishing circularity performance among different firms will improve. Future implementations of this model could benefit from expanding the number of evaluated contractors to refine the decision-making process further. Additionally, if the model is integrated into a digital tool, it could serve as a decision-support system that not only ranks contractors based on CE compliance but also provides a roadmap to enhance their alignment with circularity principles. Such a tool would enable alternative contractors to understand their performance across the six main CE categories and identify key areas for improvement, ultimately fostering a more circular construction industry.

In summary, this study highlights the importance of CE criteria in contractor selection while underlining the necessity of integrated approaches in project management. The adoption of innovative methods, facilitated by Industry 5.0’s AI and smart systems, is expected to enhance circularity in construction projects and minimize their environmental impact. As [40] emphasized, implementing reduction-, reuse-, and recycling-focused strategies in contractor selection will increase CE’s effectiveness in the construction sector. By integrating BIM, the model can help prevent data loss in projects, improving not only time, cost, and quality management but also other key success factors such as integration, scope, human resources, communication, and risk management. This, in turn, will enhance the accessibility and usability of CE through technology, paving the way for a more sustainable future in the construction industry. For this reason, future research could focus on developing a digital tool to enhance both the accessibility and usability of the model. This could be achieved by designing a prequalification checklist that clearly outlines contractor capabilities with respect to specific CE principles such as a reduction in material waste and the lean production chain (MC2) or the prioritization of modular elements (CC3). In addition, establishing measurable performance indicators under each criterion would provide an objective evaluation framework and strengthen conceptual coherence. In this way, not only would the specific requirements of CE in the construction industry be clearly defined, but the model, having been transformed into a functional tool, could be effectively adopted within the sector.