Circular economy readiness in Jordan's construction sector expert evidence and structural modeling from a resource economics perspective

1 Introduction

The building and construction sector continues to contribute significantly to global resource extraction, energy consumption, and waste production. Buildings and infrastructure generate large-scale greenhouse gas emissions, land-use change, and material consumption. They also produce considerable quantities of construction and demolition (C&D) waste, which is either landfilled or handled inefficiently (Circle Economy, 2023, 2024; UNEP, 2022; Chen et al., 2022). From an environmental and resource economics perspective, this sector therefore generates substantial negative externalities and exerts pressure on scarce natural resources, which calls for more efficient allocation and use of materials and better management of C&D waste. The circular economy (CE) is recognized globally as a lever for change in the building and construction sector. It aims to replace the conventional linear “take, make, dispose” model with strategies for waste elimination, extended use of products and materials, and environmental restoration (Geissdoerfer et al., 2017; Ghisellini et al., 2016; World Green Building Council, 2023).

Adopting the CE approach in construction entails looking at the whole lifecycle of a project: providing flexible spaces and structures for easy dismantling; employing modular and off-site construction; reusing parts and materials; preventing C&D waste generation; and managing waste effectively. The sector may also look into alternative business models (take-back schemes, service models).In economic terms, the firms are typically at the low end of the hierarchy of the so called “R-strategies”—market and coordination failures keep higher value re-use, re-manufacturing, and service models from taking off.

Jordan is experiencing a variety of environment-related challenges (Al-Btoush et al., 2026b). The country has one of the lowest water availability per capita on earth, imports most of its building materials and is experiencing rapid housing and infrastructure growth. The fact that construction activities are largely dependent on imported resources and resources are relatively scarce makes the economic benefits of increasing resource productivity and decreasing the construction-related C&D waste even more significant. In the case of water, Abu-Ghunmi et al. (2016) demonstrated the opportunity costs of using an end-of-pipe wastewater model and showed how circular options can produce large financial and environmental savings. Ghazo (2025) applied an EFQM-based tool to 449 private-sector organizations and found that organizations whose business practices are aligned with CE tend to be more sustainable overall. Abualbasal et al. (2025) developed a structural equation model for Jordanian SMEs which highlights the roles of skills and competencies, intra- and inter-organizational learning, and corporate social responsibility (CSR) in fostering innovation that leads to CE practices and sustainable performance. Similarly, Vasudevan et al. (2025) showed that CE activities promote flexibility and resilience in entrepreneurial firms. These studies underline the economic relevance of CE in terms of cost savings, competitiveness, and resilience, and motivate a sector-specific investigation of CE readiness in construction (Al-Btoush et al., 2026a).

Whereas there is indeed CE literature in the case of Jordan, researchers tend to look at it from a cross-sectoral perspective, base their analyses on generic CE constructs, and ignore the construction sector. However, the latter exhibits a number of peculiar characteristics that distinguish it from others, such as the project-based work model, fragmented supply chains, large volumes of C&D waste, and dependence on building regulations and public procurement. CE studies for the building and construction sector show that most companies still operate at low CE maturity levels and simply implement a few circular strategies without giving much consideration to innovations in design or business model (Cossar et al., 2025; Giorgi et al., 2022; Guerra and Leite, 2021; Shooshtarian et al., 2022). Shen et al. (2026) developed a meso-level circularity framework for pavements in transport infrastructure and, by employing expert judgement and spherical fuzzy AHP, found a moderate level of circularity (56.9%), while also pointing out that circular system design is of high importance but is neglected in practice.

At a macro level, factors that influence transitions to CE have been discussed as well. Hoang et al. (2026) used provincial panel data in Vietnam and found that income, education, and population density correlate with better CE performance, while poverty and a declining workforce strongly limit the potential for CE. The findings highlight the significance of both the structural context and the capabilities of organizations.

So far, no empirical research has been conducted to evaluate CE readiness in Jordan's construction sector. There is no sector-specific evidence on how construction experts view current CE-related practices, where they identify the main drivers and barriers, how they evaluate internal capabilities and the policy environment, or how these factors are structurally related to perceived CE implementation and future expectations. This paper fills these gaps by gathering expert opinions from 150 professionals in the construction industry in Jordan and conducting a structural analysis of CE readiness.

The objectives are to: Create a construction-specific measurement model of CE implementation, internal capabilities, and CE outlook for Jordan.

Investigate, through structural equation modeling, the relations between perceived drivers, barriers, internal capabilities, policy/procurement support, and perceived CE implementation.

Explore how experts' perception of CE implementation determines their views on the future significance and optimism of the sector toward CE.

From an environmental and resource economics perspective, CE implementation in construction affects resource productivity and the internalization of negative externalities from C&D waste. In our model, barriers reflect market and regulatory failures, while drivers and policy/procurement support act as economic instruments that change incentives for circular practices.

From a resource economics perspective, CE implementation in construction is about improving material productivity under scarcity and internalizing negative externalities from C&D waste. In our model, barriers represent market and regulatory failures (e.g., missing markets for secondary materials, rigid codes, information gaps), while drivers and policy/procurement are economic instruments that change relative incentives for circular practices at the firm and project level.

This study expands the research on CE-in-construction in three principal ways. First, it creates a CE readiness model specific to the construction industry that is empirically tested in a resource-constrained environment. This model combines implementation practices, internal capabilities, and future outlook into one latent structure. Second, it specifies and tests a structural pathway through which perceived motivators have a double effect: they directly influence CE implementation and indirectly work via organizational capabilities, while obstacles and policy/procurement support are explicitly modeled. Third, it illustrates policy and managerial levers, especially regarding capability development, public procurement, and standards, which can facilitate the transition from linear to circular practices in Jordan's construction sector and in similar emerging markets.

In resource-economics terms, we quantify how incentives (drivers/policy) and constraints (barriers/capabilities) relate to perceived resource productivity and C&D waste externalities.

2 Literature review

2.1 Circular economy in construction

The CE-in-construction literature documents numerous potential circular strategies throughout the building lifecycle (Adams et al., 2017; Benachio et al., 2020; Çimen, 2021; Norouzi et al., 2021; Eberhardt et al., 2022; Pomponi and Moncaster, 2017; Kirchherr et al., 2018; Rabayah et al., 2025). For example, at the design stage, these might include design for adaptability and disassembly, modular and off-site construction, and optimization of material quantities through BIM and life-cycle assessment (LCA) (Bassam and Al-Btoush, 2026). The construction phase may involve implementing strategies such as waste minimization, on-site segregation, reuse of components and temporary works, and efficient use of energy and water. In the use phase, adaptive reuse and retrofitting will give a longer life to the building, while at the end-of-life, selective demolition or deconstruction allows for high-value reuse and material recycling.

On the other hand, the empirical evidence indicates that actual implementation is more often than not at a relatively low-value recycling level near the end-of-life stage (Potting et al., 2017; Guerra and Leite, 2021; Giorgi et al., 2022). In fact, most companies are still concentrating on simply recycling their C&D waste (e.g., scrap metals, some crushed concrete) instead of systematically adopting higher-priority R-strategies such as refuse, reduce, reuse, repair, refurbish, remanufacture, and repurpose (Potting et al., 2017; Guerra and Leite, 2021; Giorgi et al., 2022). Cossar et al. (2025) reported that in Aotearoa New Zealand, the majority of building and construction companies have a “beginner” stage of CE maturity; they implement a small number of circular strategies only, and service-based models, material passports, and remanufacturing are among the least adopted.

2.2 Meso-level frameworks and indicators

In order to link macro policies with micro projects, some researchers have suggested the development of meso-level sectoral circularity assessment frameworks. Shen et al. (2026) conceptualized a three-layer model for pavements that encompasses six indicators (CE system design, material, cost, environmental, social, and technical), 17 standards, and 44 factors; they employed spherical fuzzy AHP to obtain indicator weights and a sectoral circularity index. Their study revealed that CE system design is very important and yet not sufficient under implementation, while environmental and technical aspects have a relatively higher level of implementation.

In the construction industry, Cossar et al. (2025) conducted an exploratory sequential mixed-method study. At first, they documented CE strategies throughout lifecycle stages via literature review and expert interviews; subsequently, they surveyed 213 professionals in order to measure CE maturity and implementation levels. Their method is a good illustration of the benefit that can be gained by triangulating the qualitative expert insights with the quantitative survey data for the development of sector-specific CE indicators.

2.3 Organizational determinants and CE outcomes

On the basis of a case-level analysis, Abualbasal et al. (2025) created a structural equation model for Jordanian SMEs, showing that skills and competencies, intra- and inter-organizational learning, and CSR have a major effect on innovation, which subsequently propels CE practices and leads to sustainable performance. By deploying an EFQM-based tool, Ghazo (2025) proved that companies with stronger CE alignment registered higher organizational sustainability scores. Vasudevan et al. (2025) demonstrated that CE practices facilitate organizational flexibility and resilience in entrepreneurial companies in Jordan.

Therefore, these investigations portray the adoption of CE to be highly reliant on internal capabilities and innovation as mediators, thereby accentuating the role of organizational factors (Al-Btoush M. et al., 2026). A more extensive study by Hoang et al. (2026) illustrated that socio-economic factors at the provincial level determine CE performance. In their study, education and income were identified as enablers, while poverty stood out as a deterrent.

In light of the various aspects, the literature concludes that sector-specific CE frameworks, organizational capabilities, and policy and market signals are the key facilitators that jointly determine CE readiness. Recent work in developing construction markets similarly highlights the importance of institutional capacity, stakeholder engagement, and reverse logistics maturity for circular and sustainable construction outcomes (Khan et al., 2026; Khan and Khan, 2026). This research capitalizes on the above findings and focuses on the Jordanian construction industry, gathers expert testimonies, and employs a structural modeling method.

2.4 Theoretical framework and hypotheses

We assume that perceived external conditions (drivers, barriers, and policy/procurement support) and internal capabilities jointly shape CE implementation, which in turn shapes CE outlook. Based on this logic, we test:

• H1: Drivers → CE implementation (positive).
• H2: Barriers → CE implementation (negative).
• H3: Policy/procurement support → CE implementation (positive).
• H4: Internal capabilities → CE implementation (positive).
• H5: Drivers → Internal capabilities (positive).
• H6: CE implementation → CE outlook (positive).

Figure 1 shows the conceptual framework and hypothesized paths.

3 Methodology

3.1 Study design

We carried out an expert-based quantitative cross-sectional study design, which is a typical approach of CE-in-construction that uses professional judgement to characterize sectoral circularity (Cossar et al., 2025; Shen et al., 2026). This paper formulates and validates a structural equation model (SEM) that merges:

The measurement model of the three latent variables: CE implementation (CEI), internal capabilities (INTCAP), and CE outlook (OUTLOOK).

The structural model where CEI is influenced by perceived drivers (DRIV), barriers (BARR), policy/procurement support (POLICY), and INTCAP, thus OUTLOOK is influenced by CEI.

3.2 Sample and data collection

Data were collected from 150 construction professionals in the Jordanian construction sector. Respondents included:

• - Project managers and site managers
• - Design and structural engineers
• - Sustainability/environmental managers
• - Contracts/commercial managers
• - Consultants and developer representatives
• - Waste and recycling managers

The demographic profile of the 150 participants, detailing their specific roles, age distribution, and years of experience, is provided in Appendix Table A0.

Eligibility criteria: Three years of experience in a professional capacity in construction or related fields at a minimum.

Being directly involved with or having good knowledge of project delivery, materials and waste management, or environmental/sustainability processes.

This threshold was selected to ensure that all respondents had meaningful practical involvement in construction projects, while still allowing inclusion of a broad range of mid- to senior-level practitioners.

Participants were recruited through professional networks, industry associations, and online platforms. An online questionnaire was distributed, and only the complete and eligible participant responses were used for the analysis.

This corresponds to a purposive, non-probability sampling strategy aimed at practitioners with relevant experience rather than a statistically representative random sample of all construction professionals. As such, the results should be interpreted as expert-based sectoral insights rather than population estimates.

In total, 225 invitations were distributed through these channels, and 150 complete and eligible responses were obtained, corresponding to a response rate of about 67%. The final sample thus consisted of construction professionals who met the eligibility criteria and represented a mix of project and site managers, design and structural engineers, sustainability/environmental managers, contracts and commercial managers, consultants and developer representatives, and waste and recycling managers.

This study involved an anonymous, minimal-risk survey of construction professionals about work-related practices and perceptions. In line with institutional guidelines, formal ethics committee approval was not required. Participation was voluntary, no personal identifiers were collected, and completion of the online questionnaire was taken as informed consent.

3.3 Measures

The survey tool recorded demographic data (age, role, years of experience) as well as ten main Likert-type scale items (1–5 scale; 1 = very low/strongly disagree, 5 = very high/strongly agree), which were based on the key aspects of CE in construction:

Perceived implementation of CE in the construction sector (CEI) latent:

Question 1: current CE/resource-efficient practices (e.g., reuse, material optimization, careful planning)

Question 2: the extent to which CE concepts are incorporated at the design and planning stage (e.g., design for adaptability/disassembly, modular/off-site construction, design to minimize waste)

Question 3: the standard of C&D waste management (segregation, reuse, recycling, proper disposal)

Determinants of CE implementation: Drivers (DRIV) Question 4: the extent of CE drivers (cost savings, client/donor requirements, regulations, reputation)

Barriers (BARR) Question 5: the severity of CE barriers (codes, costs, lack of markets, cultural resistance, lack of knowledge)

Internal capabilities (INTCAP) latent: Question 6: level of internal organizational support (skills, management commitment, internal processes, training)

Question 7: the degree of a supply chain collaboration for CE (with designers, suppliers, recyclers, and clients)

Policy/procurement support (POLICY) Question 8: The degree to which current regulations, standards, and public procurement facilitate CE

CE outlook (OUTLOOK) latent: Question 9: the importance of CE for Jordanian construction in the next 5–10 years, as perceived by respondents

Question 10: the level of overall optimism about CE development in Jordanian construction over the next 5–10 years

The wording and content of the items were mainly based on international CE-in-construction frameworks (e.g., et al., 2025; Shen et al., 2026) and Jordanian CE organizational models (Ghazo, 2025; Abualbasal et al., 2025). Two academic experts and three practitioners reviewed the questionnaire, and it was pilot-tested with ten experts to validate and ensure its clarity.

In the structural model, drivers (Q4), barriers (Q5), and policy/procurement support (Q8) are therefore treated as single-item observed predictors, and their measurement error is not modeled explicitly.

3.4 Data analysis

All analyses were conducted in Python using dedicated libraries for structural equation modeling and reliability analysis, employing maximum likelihood estimation and standard goodness-of-fit indices.

Data analysis was done mainly in two stages (Bagozzi and Yi, 1988; Hair et al., 2019; Abualbasal et al., 2025; Tarawneh, 2026):

Measurement model (CFA): the adequacy of the three latent constructs (CEI, INTCAP, OUTLOOK) and their indicators was assessed using a confirmatory factor analysis. The reliability of the constructs was checked by Cronbach's alpha and composite reliability, the convergent validity through the average variance extracted (AVE), and the discriminant validity through the Fornell, Larcker criterion and the HTMT ratios (Fornell and Larcker, 1981; Henseler et al., 2015) (refer to Appendix Tables A1, A2).

Structural model (SEM): maximum likelihood was used to estimate the structural relationships proposed in the hypotheses. CEI was regressed on DRIV, BARR, POLICY, and INTCAP, while OUTLOOK was regressed on CEI. The model fit was checked using χ²/df, CFI, TLI, NFI, GFI, AGFI, and RMSEA (Bentler, 1990; Hu and Bentler, 1998). As a diagnostic for multicollinearity among the predictors of CE implementation, we estimated variance inflation factors (VIFs) using an auxiliary multiple regression; all VIF values were below three, indicating no serious collinearity concerns. For the indirect effect of drivers on CE implementation via internal capabilities, we used nonparametric bootstrap estimation with 5,000 resamples and bias-corrected 95% confidence intervals. The standardized path coefficients and the p-values were given. We have tested to rule out the effect of a common method bias using Harman's single-factor test where one unrotated factor was extracted from all ten measurement items, and the proportion of variance explained by the first factor was examined.

R² values for CE implementation and CE outlook are given below to show explained variance from the structural model. Initially, we settled on the baseline SEM, and after looking at the modification indices, we went for an alternative specification where perceived drivers also predict internal capabilities (Q4 → INTCAP); this one was kept based on a better fit and theoretical plausibility (refer to Section 4.2 and Appendix Table A4). This additional Q4 → INTCAP path was not hypothesized a priori and is therefore treated as an exploratory, hypothesis-generating refinement of the model. PCA-based proxy loadings with bootstrap SEs (Appendix Table A3) were applied solely as an intermediate sensitivity check when the CFA software was limited; all main measurements and structural inferences in the main text are based on the SEM results.

4 Results

4.1 Measurement model

The confirmatory factor analysis revealed that the measurement model is a good fit for the dataset and that the latent variables demonstrate strong loadings and internal consistency. All the indicators had a high and significant standardized factor loading for CE implementation (CEI), Q1 = 0.92, Q2 = 0.82, Q3 = 0.92; for internal capabilities (INTCAP), Q6 = 0.86, Q7 = 0.79; for CE outlook (OUTLOOK), Q9 = 0.85, Q10 = 0.87. Composite reliability (CR) values were above 0.80 and average variance extracted (AVE) values were above 0.60 for all constructs, which supported the convergent validity of the constructs.

Discriminant validity was demonstrated as the square roots of AVE were greater than the inter-construct correlations and HTMT ratios were < 0.85. Refer to Table 1 for normalized factor loadings, z-values, p-values, and reliability/validity indices. Harman's single-factor test revealed that the first unrotated factor accounted for 47.6% of the total variance, which is less than the 50% threshold that is commonly used as a heuristic for serious common method bias. Discriminant validity was confirmed through the Fornell, Larcker criterion and HTMT ratios (see Appendix Table A1).

Table 1. Standardized factor loadings and reliability (measurement model).

Loadings presented are standardized estimates of the CFA/SEM. Q1, Q6, Q9 were set to 1.0 in the unstandardized solution; all standardized loadings reported are significant at p < 0.001.

Reliability/validity: CEI, Cronbach's α > 0.80, CR > 0.85, AVE > 0.60; INTCAP, α > 0.70, CR > 0.80, AVE > 0.60; OUTLOOK, α > 0.70, CR > 0.80, AVE > 0.60.

Descriptive statistics (means and standard deviations) for the ten survey items are reported in Table 2.

Table 2. Descriptive statistics for survey items (N = 150).

4.1.1 Descriptive profile of CE readiness

Figure 2 gives an overview of the average scores of the six CE-readiness dimensions that were identified based on the survey items: CE implementation (Q1, Q2, Q3), internal capabilities (Q6, Q7), CE outlook (Q9, Q10), drivers (Q4), barriers (Q5, reverse-coded so that higher values correspond to fewer perceived barriers), and policy/procurement support (Q8). The profile reflects that experts view CE drivers and CE outlook as fairly strong, CE implementation as moderate, internal capabilities as mixed (low internal support but stronger supply-chain collaboration), and policy/procurement support as relatively weak. The dimension “low barriers” scores lower than drivers, thus, the authors suggest that the perceived regulatory, market, and knowledge barriers are still a major constraint to CE implementation in Jordanian construction.

4.2 Structural model

We conducted a structural equation modeling that estimated the relationship between perceived drivers (Q4), barriers (Q5), policy/procurement support (Q8), and internal capabilities (INTCAP: Q6, Q7) for circular-economy implementation (CEI: Q1, Q2, Q3) and CEI to CE outlook (OUTLOOK: Q9, Q10). The comparison of the models showed that the alternative version of the four-factor model in which perceived drivers also predict internal capabilities (Q4 → INTCAP) fits the data better than the original baseline model.

The selected model (drivers → INTCAP → CEI → OUTLOOK) had an excellent fit: X²(28) = 36.28, p = 0.136; CFI = 0.989; TLI = 0.984; RMSEA = 0.044; SRMR = 0.029 (see Table 3). For the sake of openness, the original specification's fit is also reported in Appendix Table A4 X²(36) = 47.98, p = 0.087; CFI = 0.985; RMSEA = 0.047.

Table 3. Model fit indices for the CFA/SEM: χ² (df), p value, CFI, TLI, NFI, GFI, AGFI, and RMSEA (N = 150).

Appendix Table A4 presents the original fit and MIs, and Appendix Tables A1, A3 include validity and sensitivity checks. Given that the alternative model increases the fit and is theoretically sound, we show its estimates below.

The preferred model's standardized factor loadings and structural coefficients are presented in Table 4. The main findings are:

Table 4. Standardized structural path coefficients (β), standard errors, z-values, and p-values for determinants of CE implementation (CEI) and CE outlook (OUTLOOK) (N = 150).

Measurement: all indicators have good loadings on their respective factors (standardized loadings 0.837–0.924; p < 0.001).

Determinants of CE implementation (CEI): perceived drivers are directly and positively associated with CEI (β = 0.342, SE = 0.063, p < 0.001); perceived barriers have a significant negative effect (β = −0.318, SE = 0.062, p < 0.001); policy/procurement support has a moderate positive impact (β = 0.125, SE = 0.059, p = 0.036). Internal capabilities (INTCAP) can be used to predict CEI on their own (β = 0.356, SE = 0.067, p < 0.001).

Drivers → Capabilities: perceived drivers also predict internal capabilities (INTCAP ~ Q4: β = 0.238, SE = 0.090, p = 0.008), signifying that drivers not only impact implementation directly but also indirectly by enhancing organizational capabilities.

Outlook: CEI is an important predictor of experts”CE outlook (OUTLOOK ~ CEI: β = 0.735, SE = 0.047, p < 0.001).

Overall, the model explains approximately 0.57 of the variance in CE implementation (CEI), 0.54 of the variance in CE outlook (OUTLOOK), and 0.06 of the variance in internal capabilities (INTCAP).

The model accounts for a substantial share of the variance in the endogenous constructs (CEI R² = 0.57; OUTLOOK R² = 0.54). VIFs for drivers, barriers, policy/procurement support, and internal capabilities were all below three, supporting the absence of problematic multicollinearity.

4.3 Robustness and diagnostics

We looked at modification indices and alternative specifications; the top modification indices gave a reason to test the drivers → INTCAP path (as reported above).

Convergent validity (indicator loadings, CR, AVE) as well as common-method checks (Harman's single-factor test) confirm the measurement model; the details and the original model diagnostics are in the Appendix Table A4 .

4.4 Interpretation

The results confirm the pivotal role of organizational capabilities in the origination of external drivers to practice: drivers increase INTCAP which is the capacity leading to CE implementation; drivers also have a direct effect on implementation. Perceived barriers are the main negative factor, and policy/procurement interventions only play a slight positive role. Lastly, CE implementation significantly influences experts' optimistic views about future overall industry progress.

4.5 Limitations

These results are based on cross-sectional expert perceptions; causal claims should be treated as model-consistent associations. Longitudinal or project-level validation would strengthen causal inference.

Figure 1 illustrates the full SEM: measurement loadings (λ) on the left and standardized structural paths (β) between predictors, CEI, and OUTLOOK on the right.

CE implementation and CE outlook. CEI → OUTLOOK: β = 0.735, p < 0.001. This big, very significant coefficient shows that the extent to which experts perceive CE implementation influences their CE outlook very much. Those experts who see the present CE practices, design stage integration and waste management in a much better way are far more likely to view CE as the next 5 years, 10 years' main issue and to have believe the sectors will progress.

5 Discussion

This research contributes to the academic field of CE-in-construction in three closely interwoven manners. Firstly, it comes up with a systematic, empirically tested, sector-specific measurement model that captures CE implementation (design-stage integration, resource-efficient practices, and C&D waste management), internal organizational capabilities, and CE outlook as a single latent variable structure. Secondly, it clearly defines and empirically tests a model of the influence of perceived drivers on CE implementation by both the direct effects and the mediating role of capabilities. At the same time, barriers and policy/procurement support are perceived as separate elements, thus breaking down traditional CE transition narratives, which do not fully recognize the role of these different influences as the authors have done here. Thirdly, the model makes it possible to derive contextually relevant but also quite broadly applicable policy and management tools for switching from linear to circular forms of conduct in project-based industries with an emphasis on capability development, standardization, and public procurement sectors of the construction industry in Jordan.

The structural results also pinpoint expected theoretical roles of drivers, barriers, capabilities, and policy in CE transitions with greater precision. The fact that the effect sizes of drivers and internal capabilities are close, and the considerably negative effect of barriers, means that CE adoption in construction is not a reaction to just external incentives but is quite dependent on internal capacity and systemic barriers. This helps a capability-centered perspective of CE transitions whereby organizational learning, managerial commitment, and supply-chain collaboration are equally important as market and regulatory signals. The very strong link from CE implementation to CE outlook indicates a positive-feedback mechanism: what is seen through implementation raises expectations and optimism, which may lead to further adoption through imitation and client demand.

Third, the model gives intervention prioritization advice on empirical basis. The R² results (CEI R² = 0.57; OUTLOOK R² = 0.54) reveal that more than 50% of the variation in the perception of CE implementation and CE outlook is accounted for by the selected set of determinants, which suggests significant leverage points for the policy and management to act upon.

5.1 Generalizability and boundary conditions

The study is primarily focused on the Jordan economy, which is a water-stressed, import dependent and fast-growing urban construction market, but the theoretical model developed may also be applied in other emerging and resource constrained construction markets. The dominance of internal capabilities and the limiting impact of barriers will be typical situations in environments with disjointed supply chains, absence of well-developed markets for secondary materials, and changing regulatory frameworks. The external validity is however restricted in scope, as the evidence is perceived and cross-sectional, and because the size of the effects is subject to the influence of national institutional factors (such as the level of enforcement, scale of procurement, maturity of markets) will affect the size of the effects. Hence, the audience should interpret the structural links as representing mechanisms rather than exact causal figures; it is advised to repeat the study in other countries and also to compare it with objective project-level circularity metrics. While we do not have directly comparable CE readiness indices for countries such as Morocco, Lebanon, or Egypt, these economies share similar features of resource constraints, import dependence, and fragmented construction supply chains. We therefore expect that the structural pattern observed here—strong roles for internal capabilities and barriers and a more modest role for current policy support—may also characterize CE transitions in construction sectors across the wider MENA region, a proposition that future comparative studies could test explicitly.

5.2 Implications for researchers and practitioners

Researchers are thus stimulated by the fact that the measurement model and SEM provide an understandable template for cross-national comparison and longitudinal study; future research should consider perceptual readiness together with project-level circularity metrics and explore mediation and reciprocal dynamics over time. On the other hand, the findings lead practitioners and policymakers to the view that it is sensible to shift resources for capability building and market-making interventions, as such interventions would bring larger returns than isolated regulatory pronouncements alone.

5.3 Managerial and policy implications

The structural findings of this study have several policy implications for the environmental and resource economics of the construction sector in Jordan and other emerging markets. First, the comparable effects of drivers and internal capabilities, coupled with the significant negative effect of barriers, point to the fact that CE adoption in construction is not only a reaction to external incentives but is also influenced by the capacity of firms to respond to these incentives. This implies that policy instruments which only change prices or regulations, and do not tackle capability constraints and coordination failures, may have a limited effect. Second, perceived barriers are a combination of market failures, regulatory inflexibility, and lack of knowledge. Policies that lower transaction costs, change building codes, and facilitate information infrastructure (e.g., material passports) are required. Finally, the state can be a lead customer by including clear CE-related criteria in public tenders and piloting circular projects in public works.

Based on these findings, the following consolidated levers are recommended.

5.4 Strengthen firm capabilities (short-to-medium term)

Targeted training and certification: create accredited CE training modules for project managers, engineers, and procurement officers covering design for disassembly, material passports, and reuse logistics.

Internal governance: provide management with KPI incentives tied to material-use reduction and waste diversion; CE criteria for contracts and quality assurance checklists should be integrated.

Supply-chain facilitation: support industry platforms that connect demolition contractors, recyclers, and suppliers of secondary materials to lower transaction costs.

5.5 Reduce systemic barriers (medium term)

Market creation: introduce various tiers of incentives (such as tax credits, accelerated depreciation, or material subsidies).

Standards and codes: modify construction regulations and product specifications to permit use of certified secondary materials and establish deconstruction standards that facilitate component recovery.

Information infrastructure: require or reward implementation of basic material passports and common data environments (CDEs) at the project level to enhance traceability and reuse feasibility.

5.6 Align procurement and regulatory signals (short-term to long-term)

Green public procurement: add clear CE assessment criteria and scoring in public tenders (e.g., awarding points for designs which demonstrate design-for-disassembly or quantified waste-minimization plans).

Pilot procurement windows: create fast-track tenders for demonstration projects that bundle policy incentives with procurement preferences to showcase scalable circular approaches.

5.7 Demonstration and scaling (short term)

High-visibility pilots: support demonstration projects that showcase circular economy (CE) integration at the design, procurement, and construction stages and that make material flows public through monitoring and reporting.

Knowledge transfer: impose requirements on evaluation reports and open datasets from pilot projects to help the industry learn quickly and reduce technical/market uncertainties.

6 Limitations and future research

There are several limitations of this research that can serve as grounds for future work.

Firstly, the assessment was made on the basis of expert opinions and not physical data at the project level (e.g., measured C&D waste, material circularity metrics). Although relying on expert opinions is a common practice in early-stage CE assessments and meso-level frameworks (Cossar et al., 2025; Shen et al., 2026), it can be biased depending on the influence of the expert's experience, their organizational position, or even optimism/pessimism bias. Expert perceptions may also differ from project-level outcomes (e.g., measured waste diversion rates or secondary-material uptake) and may be influenced by firm interests or limited information. Therefore, the findings should be interpreted as perceived readiness and associations rather than objective performance or causal effects. The following investigation may consider combining the perception-based approaches with the use of the objective indicators, for instance, the measured waste diversion rates, the use of secondary materials, or project-level material circularity indices.

Secondly, the current research employs the cross-sectional design, i.e., one that records the CE readiness at a single moment. The significant correlation between perceived implementation and outlook indicates the existence of dynamic feedback; still, these dynamics cannot be revealed at all through cross-sectional data. Hence, the longitudinal studies, which take the same experts, firms, or projects over time, will allow one to verify whether the changes in CE practices trigger the changes in attitudes, capabilities, and policy landscapes that are maintained over time.

Thirdly, the survey consists of ten items and is equipped with a relatively parsimonious measurement model that focuses on ensuring feasibility and response quality among busy professionals. Though the latent constructs feature strong psychometric properties, they inevitably simplify the complexity of CE in construction. Moreover, the determinants DRIV, BARR, and POLICY are each captured by a single item, which can introduce measurement error and attenuate or bias their estimated effects. The future paper could broaden the instrument to include a wider range of circular strategies (e.g., service-based models, material passports, deconstruction practices), probably relying more directly on the detailed strategy lists and indicators that were developed by Cossar et al. (2025) and (Shen et al., 2026).

Fourthly, the sample was diverse in the roles and experience of the people involved, but the people were all construction experts from Jordan. This means that the results cannot be generalized to other national contexts and regulatory environments. The comparative studies across countries in the Middle East and North Africa, and even larger ones, will allow for testing whether the structural relationships identified here (e.g., the relative importance of internal capabilities vs. policy support) are universal or just the characteristics of the specific contexts where they emerge.

Last but not least, the policy/procurement support, drivers, and barriers in the structural model are perceived to be exogenous. In particular, the post-hoc Q4 → INTCAP pathway identified via modification indices should be cross-validated on independent samples before being considered a stable feature of the CE readiness structure. However, in truth, such constructs are actually the co-evolution of these components; policies can reinforce or weaken drivers, and the successful CE pilots can lead to changes in barriers that are noticed by the participants. The future modeling work can examine the more complex, bidirectional or multi-level structures, combine the firm-level and policy-level dynamics, and relate the expert perceptions with the macro indicators such as CE policies, green public procurement penetration, and C&D waste statistics.

7 Conclusion

This paper adds to the pool of literature on circular economy transitions in the building sector by outlining the first industry-specific structural assessment of CE readiness for Jordan. Based on inputs from 150 local experts and a solid SEM framework, we find that the CE rollout in building works is highly linked to the internal organizational capabilities, the perceived facilitators and inhibitors, and even the existing policy and procurement backing, although the latter are playing a relatively minor role. Besides, the observed CE rollout is a major predictor of the experts' views and hopes concerning the CE's coming into the sector. After combining globally recognized CE-in-construction frameworks with locally developed organizational CE studies this research provides a theoretically and empirically supported commitment to CE-readiness in a resource-strained construction environment.

Interpreted through an environmental and resource economics lens, the findings show how market and policy drivers, systemic barriers, and firm-level capabilities jointly determine the degree to which construction activities in Jordan internalize environmental externalities from C&D waste and improve material productivity. This provides sector evidence to inform procurement and standards as instruments for resource efficiency. Perceived barriers reflect missing or weak markets for secondary materials, regulatory and information failures, and coordination problems in fragmented supply chains, while drivers and public procurement support function as economic instruments that can rebalance incentives toward circular investment and practices.

The findings highlight the necessity for a two-pronged approach: reinforcing the core competencies and inter-firm cooperation of construction companies, and at the same time, changing the codes, standards, and public procurement to better match the circular environmental goals. In practical policy terms, this implies prioritizing capacity-building, clearer CE-related standards for secondary materials and deconstruction, and green public procurement criteria that reward circular design and waste minimization. Together, these measures support progress toward SDGs on sustainable economic growth (SDG 8), sustainable cities and infrastructure (SDG 11 and SDG 9), responsible consumption and production (SDG 12), and climate action (SDG 13) in a resource-scarce context such as Jordan. As the construction sector in Jordan evolves its projects from linear to circular practices, such data-driven insights can be a great support to the policymakers, clients, and firms in coming up with solutions that will allow them to completely tap the circularity potential of the built environment.

7.1 Concluding remark

To sum up, this research has re-oriented the circular economy (CE) transition in the construction sector to be an internally organizationally rooted, externally systemically constrained process. The crucial quickening of the pace significantly depends on the synchronized activities of a firm's capabilities, market instruments, standards, and procurement, which, when done together, can convert a mere visible implementation into a full-fledged sectoral momentum.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frevc.2026.1882785/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Bjorn Birgisson, University of Georgia, United States

Reviewed by: Hilal Khan, National University of Sciences and Technology (NUST), Pakistan

Muhammad Abdul Rehman, Royal Commission for Jubail and Yanbu, Saudi Arabia