The transition toward a circular economy (CE) is central to sustainable development and requires technological innovation to assess countries’ readiness for systemic change. However, existing CE assessment frameworks remain largely static, are prone to bias in qualitative evaluations, and are constrained by small sample sizes, limiting statistical efficiency, predictive robustness, and policy relevance. In this study, we develop a Circular Economy Readiness Prediction (CERP) Model that leverages Wasserstein Generative Adversarial Networks (WGANs) and multivariate statistical analysis to predict the transition capacity of EU-27 countries, addressing small sample size limitations and enabling robust testing and clearer insights into CE readiness. Using 12 Eurostat indicators across the CE domains, the model operationalizes the Circular Material Use Rate (CMUR) as a measure of circular performance. The proposed framework achieves strong predictive accuracy (MSE = 0.0367, MAE = 0.1507, RMSE = 0.1915) and reveals how trade integration, material dependency, and greenhouse-gas mitigation jointly shape CE outcomes. Using hierarchical clustering, we classified the EU-27 countries into four categories (Leaders, Fast Followers, Emerging Adopters, and Laggards) providing a differentiated basis for targeted policy and ESG investment strategies. Anchored in the Natural Resource-Based View, Institutional Readiness Theory, and Socio-Technical Transition Theory, the study advances a novel interface between AI-driven modeling and transition governance. The CERP model demonstrates how generative AI can enhance evidence-based decision-making for CE policy design, funding allocation, and monitoring under the European Green Deal, while also contributing to broader insights on data-driven sustainability governance beyond the EU.

PurposeThis study aims to address critical sustainability challenges in modular construction by developing a predictive machine learning (ML) framework to estimate off-cut material waste and associated carbon costs during the construction phase. The objective is to enable environmentally and economically informed planning through a decision-support system that integrates ML and time-series forecasting, supporting engineers and contractors in making early-stage decisions aligned with sustainability goals.Design/methodology/approachA dual-model approach is used. ML algorithms are trained in architectural and material features to predict cut-off waste from modular wall panels, with performance validated using fivefold cross-validation to ensure robustness and generalizability. Concurrently, autoregressive integrated moving average (ARIMA) time-series modeling is applied to forecast greenhouse gas emissions, incorporating a shadow carbon pricing mechanism consistent with Net Zero strategies. The data set comprises 170 modular projects used for model training and validation. Model performance is assessed using mean absolute error, root mean square error, coefficient of determination (R2) and mean absolute percentage error (MAPE).FindingsGradient boosting achieved the highest predictive accuracy, with material type and room count identified as the key predictors of waste. The model also demonstrated consistent performance across folds with strong accuracy for dominant targets. The ARIMA model effectively projected carbon emissions, achieving a strong MAPE score. The integrated framework successfully links material waste and emissions to carbon costs, enabling early-stage sustainability interventions. The resulting decision-support tool operationalizes these insights, allowing practitioners to predict waste, emissions and costs before execution, thereby enhancing material efficiency, cost planning and regulatory compliance.Research limitations/implicationsThis study is limited to modular office buildings in the United Arab Emirates, specifically wall panel installations. Broader applicability to other building types and structural components remains to be explored. Future research should focus on generalizing the framework across diverse contexts and integrating adaptive learning models trained on projects from different regions.Practical implicationsThe proposed framework provides construction professionals with actionable insights for improving material efficiency, forecasting emissions and identifying carbon-related expenditures at the project inception phase. This supports compliance with emerging carbon pricing regulations while improving procurement planning, sustainability integration and waste reduction strategies.Originality/valueThis study presents a novel innovative integration of ML and time-series forecasting to simultaneously estimate material waste, carbon emissions and associated economic costs in modular construction. By embedding predictive analytics within a sustainability framework, it provides a scalable tool for minimizing construction waste and internalizing the environmental costs in modular construction planning.

This paper critically examines the relationship between the Circular Economy (CE), Industry 4.0 (I4.0), and sustainable performance (SP). While prior research commonly portrays CE and I4.0 as mutually reinforcing drivers of sustainability, emerging evidence suggests that their sustainability implications vary across organisational and institutional contexts. Challenging assumptions of universally positive effects, this paper advances a multilevel, capability-based perspective that reconceptualizes the CE-I4.0-SP relationship as conditional rather than inherently synergistic. The paper conceptualizes I4.0 as a set of differentiated capabilities comprising managerial dimensions linked to strategic alignment and resource orchestration, and operational dimensions associated with implementation and process efficiency. The paper argues that the sustainability implications of CE practices depend on how these capabilities are configured across organisational settings. It further introduces a multilevel framework demonstrating how the CE-SP relationship varies across micro (firm), meso (supply chain), and macro (institutional) levels. Based on this framework, the paper develops propositions explaining how capability configurations shape sustainability outcomes across different levels of analysis. By reframing the CE-I4.0-SP nexus through a multilevel capability lens, the paper provides a conceptual foundation for future sustainability research.

Supply chain activities account for up to 50% of global carbon emissions. Effective carbon accounting is critical for efforts to transition to net zero and the decarbonization agenda. However, carbon accounting is challenging due to complexities in reliable data collection and transparency, which may lead to greenwashing and raise concerns about the accountability of emissions. Therefore, to address these challenges, this paper proposes, designs, and implements a novel blockchain-powered smart contract to enhance transparency, traceability, and accountability in carbon accounting across global supply chains. The proposed solution comprises a comprehensive system architecture, sequence diagrams, algorithms, and cost and security analysis using smart contracts, detailing transparent processes for production and consumption-based carbon accounting. The prototype is developed and demonstrated through an illustrative multi-regional supply chain case study to validate the feasibility and functionality of the proposed framework; the case study is intended as a proof-of-concept demonstration rather than a full-scale global deployment. The solution ensures transparency of carbon accounting by effectively managing supply chain emissions. The smart contract codes are made publicly available on GitHub.

Cooling-dominated buildings in hot-arid regions represent a critical frontier for decarbonization. In the United Arab Emirates, high-rise residential towers constructed prior to stringent energy codes exhibit high electricity intensity and long operational lifetimes, making retrofit prioritization both a technical and financial challenge. While previous research has evaluated individual energy efficiency measures, most studies rely on static simulation approaches that assume automatic retrofit adoption and overlook dynamic feedbacks among physical performance, behavioral adaptation, climate evolution, and investment decision rules. This study develops a system dynamics (SD) model to evaluate long-term carbon mitigation pathways for a representative 15-storey residential building in Abu Dhabi over the period 2010-2050. The model integrates physical heat-balance equations (transmission, infiltration, and internal loads), equipment degradation dynamics, behavioral set-point adaptation, and endogenous payback-based investment triggers within a unified feedback-driven structure. Climate projections follow the Shared Socioeconomic Pathways (SSPs) climate scenario, specifically SSP2-4.5 scenario pathway, enabling assessment of how gradual temperature increases compound cooling demand and influence retrofit timing and economic outcomes. The model was calibrated against historical consumption data, achieving an average annual deviation of 0.376% under the business-as-usual case. A seasonal uncertainty analysis was further conducted to evaluate the influence of validation deviations on long-term environmental and economic retrofit outcomes. Seven retrofit scenarios and two integrated packages were simulated, encompassing operational adjustments (thermostat set-point increases), equipment upgrades (LED lighting and high-efficiency chillers), and envelope improvements. Results show that low-cost operational measures yield immediate reductions in cooling demand and bills, while LED and high Coefficient of Performance (COP) chiller replacements provide the strongest standalone techno-economic performance. Envelope retrofits exhibit heterogeneous outcomes, in that, wall insulation proves conditionally feasible, whereas glazing and roof upgrades fail to meet payback thresholds in a fa & ccedil;ade-dominated high-rise context. The superior performance of integrated retrofit packages is further supported under the evaluated uncertainty bounds. The optimal bundle, which combines a 25 degrees C set-point, 7 W LEDs, COP 3.3 chillers, and external wall insulation achieves approximately 18,626 t CO2 cumulative reduction by 2050, with a 5.5-year payback and an average annual return of 16.7%, while maintaining consistent comparative performance across the evaluated seasonal uncertainty ranges. Adding high-performance glazing increases cumulative abatement marginally but more than doubles capital requirements and significantly lowers financial returns. This reveals a critical trade-off between marginal carbon abatement and capital efficiency: maximum emissions reduction does not necessarily coincide with optimal economic performance. Methodologically, the study advances SD application at the building level by embedding thermodynamic relationships, degradation processes, behavioral feedbacks, and investment decision heuristics within a single endogenous modelling framework. By conditioning retrofit adoption on payback thresholds rather than assuming exogenous implementation, the model reflects real-world asset management behavior and improves policy realism. The analysis further shows how climate warming under SSP2-4.5 progressively amplifies cooling demand, strengthening the economic case for early intervention. Practically, the research provides an empirically grounded decision-support tool tailored to high-rise residential buildings in hot-arid climates, with direct applicability to the UAE and comparable regions. The modeling architecture is transferable and scalable framework for other contexts.

The construction industry is a major contributor to resource consumption and construction and demolition waste (CDW), increasing pressure to transition from linear delivery models toward circular economy (CE) practices. Digital technologies are widely promoted as key enablers of this transition. However, empirical understanding of how digital-circular integration is implemented in practice remains limited, particularly across project lifecycles and within rapidly developing contexts. This study addresses this gap by empirically examining digital-circular integration in the United Arab Emirates (UAE) construction industry. Adopting a qualitative research design, the study draws on semi-structured interviews with 18 construction professionals who are involved in digitalization, sustainability, project delivery, and governance. The findings indicate that digital technologies such as building information modeling, digital twins, and data-driven technologies enable circular practices by enhancing material visibility. They also support waste recovery and facilitate lifecycle-oriented decision-making. At the same time, digital-circular integration is contingent on sociotechnical conditions, including leadership commitment, early stakeholder engagement, data governance, and procurement practices. Fragmented digital systems and cost-driven decision-making were found to constrain circular outcomes. The study further identifies intentional, emergent, and hybrid digital-circular integration pathways through which practices evolve across construction project lifecycles. Building on these insights, an empirically grounded roadmap is developed to support practitioners and policymakers in operationalizing digital-circular integration in the construction industry. The study advances a socio-technical understanding of digital-circular integration and provides context-specific empirical insights relevant to the UAE and similar construction markets.

The energy trilemma framework, which balances energy security, environmental sustainability, and energy equity, underpins the UAE’s National Energy Strategy 2050, which seeks to diversify the energy mix and reduce carbon emissions. As part of this strategy, the UAE has pursued peaceful nuclear energy to generate low-carbon electricity and improve performance across all three dimensions of the trilemma. In 2024, the final unit of the Barakah Nuclear Power Plant, a Generation III + APR1400 facility entered commercial operation. While nuclear energy is widely considered a low-carbon source, life cycle assessment (LCA) studies have shown significant variability in emissions, with reported interquartile ranges between 47 and 220 gCO₂-eq/kWh. Moreover, few LCA studies focus on nuclear plants operating in arid or Middle Eastern contexts, creating a gap in region-specific data necessary for evidence-based policymaking. To address this, the study applies a Hybrid LCA to quantify the full direct and indirect carbon emissions associated with Barakah’s 60-year operational life. Results estimate lifecycle emissions at 6.35 gCO₂-eq/kWh, well below typical global averages. A scenario analysis comparing the current 18-month refueling cycle with an alternative 24-month cycle highlights opportunities for operational optimization and further emissions reduction. A sensitivity analysis also examines the influence of temporal boundaries on results. The findings not only demonstrate Barakah’s contribution to the UAE’s decarbonization goals but also provide a transparent benchmark for nuclear performance in similar regional and technological contexts. This study supports the development of targeted, data-driven energy policy and enhances understanding of nuclear energy’s lifecycle sustainability.

Global efforts to mainstream sustainable consumption and production increasingly depend on the capacity of governance systems, firms, and markets to align around systemic transformation. Yet sustainable consumption often remains difficult to operationalize, even where policy ambition and organizational engagement are strong. This study examines this policy–practice gap by conceptualizing firms not only as producers, but also as large-scale consumption actors. Drawing on institutional theory, the study introduces the concept of governance misalignment, defined as the divergence between policy expectations and the economic, institutional, infrastructural, and market conditions shaping firm behavior. Based on qualitative interviews with firms and policymakers in the United Arab Emirates food and clothing sectors, the findings show that sustainable consumption and production outcomes are co-produced through interactions among regulatory design, firm-level decision-making, supply chain capacity, infrastructure readiness, and consumer demand. Three forms of misalignment are identified: regulatory ambiguity, fragmented implementation, and competing institutional pressures. These dynamics constrain firms’ ability to move beyond symbolic compliance and limit the mainstreaming of sustainable practices, particularly where cost pressures, import dependence, weak demand signals, and uneven sectoral governance persist. The study makes three contributions. First, it advances sustainable consumption behavior research by shifting attention from individual consumer choice to systemic consumption processes embedded within firms and supply chains. Second, it develops theoretically grounded propositions explaining how governance misalignment shapes market-level sustainability outcomes. Third, it proposes a governance alignment framework comprising regulatory clarity, economic instruments, infrastructure capacity, and consumer engagement as practical pathways for more impactful and scalable sustainability transitions. By positioning firms as consumption actors, the study advances the field of research in sustainable consumption behavior (SCB) from individual behavior to systemic consumption processes, demonstrating that sustainable consumption depends on alignment across governance structures, firm-level decision-making, and consumer demand. Consequently, the study highlights how governance alignment can support the global mainstreaming of sustainable consumption and production amid accelerating climate, resource, and social challenges.

Sustainable consumption research often examines isolated behaviors, product categories, or consumption stages, limiting understanding of which behavioral drivers are generalizable and which are context specific. This study addresses this limitation by developing and empirically testing an extended Theory of Planned Behavior (TPB) framework across six sustainable consumption scenarios in the UAE: food purchase, food utilization, food disposal, clothing purchase, clothing utilization, and clothing disposal. Conceptually, the study also argues that extensions of established behavioral theories should be framed as context-sensitive extended models rather than generic “extended” models and therefore labels the proposed framework the Context Sensitive Extended TPB (CSE-TPB) model. Data were collected from 1,263 UAE consumers spread across six context-specific surveys and analyzed using partial least squares structural equation modeling (PLS-SEM). The framework incorporated core TPB constructs in addition to personal norms, moral norms, injunctive norms, trust in businesses, awareness of government sustainability goals, satisfaction from sustainable behavior, environmental knowledge, social media influence, product availability, and perceived cost and acceptability. The findings show that the extended framework provides a meaningful explanatory basis across all six contexts, while also revealing important sector- and stage-specific differences. Overall, the findings support context-sensitive theory development and targeted sustainability policy intervention.

This study examines how Circular Economy (CE) practices and Industry 4.0 (I4.0) capabilities influence Sustainable Performance (SP) at the firm-level amid increasing environmental, social, and regulatory pressures. Although prior research suggests that digital technologies can support circular strategies, limited empirical evidence explains how I4.0 capabilities shape the CE–SP relationship at the firm-level across industries. To address this gap, the study investigates the direct, mediating, and moderating roles of operational and managerial I4.0 capabilities within the CE–SP nexus. A sequential mixed-methods design was employed, combining survey data from 138 firms analyzed using partial least squares structural equation modeling​ (PLS-SEM) with 10 semi-structured expert interviews for contextual validation. The findings show that CE practices positively influence economic, social, and environmental performance. Managerial I4.0 capabilities partially mediate the relationship between CE practices and social and environmental performance, whereas operational capabilities show no significant mediating effects. Although CE practices support the development of both operational and managerial digital capabilities, the moderating effects of I4.0 capabilities were limited and only marginally significant across selected sustainability dimensions. The study advances understanding of the CE–I4.0–SP relationship by showing that sustainability outcomes depend primarily on CE practices and managerial rather than purely operational digital capabilities. It also provides practical insights for managers and policymakers seeking to align digital transformation initiatives with CE objectives.

Purpose This study examines how digitalization and Circular Economy (CE) practices interact to support Net-Zero readiness in the United Arab Emirates (UAE) construction sector. The study aims to explore how digital infrastructures operationalize CE practices, shape implementation trade-offs, and interact with governance mechanisms to support Net-Zero transitions across fragmented construction ecosystems.Design/methodology/approach An exploratory qualitative research design was adopted based on 18 semi-structured interviews with experts across the UAE construction industry. Data were analysed using thematic analysis to examine how digital and circular strategies are implemented, governed, and aligned with the UAE's Net-Zero 2050 agenda.Findings The findings demonstrate that digital infrastructures and CE practices are mutually reinforcing mechanisms supporting Net-Zero readiness. Together, they support measurable carbon reduction outcomes and governance-aligned decision-making. Digital technologies support carbon monitoring, resource optimization, and data-driven decision-making, while CE practices support waste reduction, material circularity, and embodied carbon mitigation. However, implementation remains constrained by upfront costs, skills shortages, fragmented data environments, and interoperability challenges. The findings are synthesized into a sustainable, smart, and resilient framework explaining pathways toward Net-Zero construction transitions in the UAE.Originality/value This study contributes empirical evidence on how governance arrangements, organizational capabilities, and digital-circular integration shape Net-Zero readiness in a policy-driven construction context. Unlike predominantly conceptual studies, the paper provides practice-based qualitative insights into digitally enabled circular transitions in the UAE construction sector.

The integration of circular economy (CE) practices with Industry 4.0 (I4.0) technologies in industrial sectors remains limited, primarily due to a lack of practical knowledge about key drivers, barriers, and mitigation strategies. Despite their strong potential to enhance sustainable performance (SP), the adoption of CE–I4.0 faces several challenges across organizational levels. This study aims to empirically investigate the key drivers, barriers, and mitigation strategies for effective CE–I4.0 adoption, focusing on micro, meso, and macro-organizational contexts. Data were collected through an open-ended survey of 287 professionals, including sustainability leaders, managers, executives, and consultants, with 128 responses from micro level organizations, 87 from meso level organizations, and 71 from macro level organizations. The findings reveal level-specific drivers: “Technological innovation” at the micro level, “Regulatory pressure and sustainability standards” at the meso level, and “Regulatory and policy support” at the macro level. Across all levels, financial constraints emerged as the most critical barrier: “High initial investment costs” at the micro level, “High capital investment requirements” at the meso level, and “High upfront and ongoing costs” at the macro level. Mitigation strategies varied accordingly, including financial, skill, and change management at the micro level; education, collaboration, and technology adoption at the meso level; and awareness, stakeholder engagement, and infrastructure development at the macro level. These results underscore the need to tailor interventions to organizational scale while coordinating system-wide actions for enhanced sustainability. The study introduces a CE–I4.0 integration framework that consolidates these insights into an evidence-based roadmap linking drivers, barriers, and strategies across levels, providing actionable guidance for managers and policymakers. By offering a multi-level empirical analysis of CE–I4.0 integration, the study advances theory and practice, supporting coordinated adoption that enhances efficiency, sustainability, and organizational resilience.

The integration of Industry 4.0 (I4.0) technologies with Circular Economy (CE) practices offers a promising pathway to improving Sustainable Performance (SP). This study employs a mixed-methods approach combining a systematic literature review (SLR) with an empirical survey of 161 respondents to identify the key factors shaping this integration. The thematic analysis reveals regulatory and policy influences (40 mentions), operational efficiency (27 mentions), cost reduction (26 mentions), and resource efficiency (26 mentions) as the most prominent drivers. Financial constraints emerged as the dominant barrier (80 mentions), followed by resistance to change (48 mentions) and the absence of regulatory frameworks (27 mentions). A systematic comparison between SLR- and survey-derived factors identifies nine previously unreported drivers. These include green financing, sustainability-oriented cultural shifts, ethical and social responsibility, enhanced resource efficiency, stakeholder engagement and change management, leadership commitment, long-term strategic vision, human-capital development, and technological innovation. The analysis also reveals four novel barriers: lack of awareness and knowledge, data-management challenges, integration complexity, and insufficient regulatory frameworks. Participants most frequently recommended workforce training, pilot-based implementation, stakeholder engagement, financial planning, and structured change management as key strategies for mitigating barriers. Based on these empirical insights, the study proposes a data-driven integration framework wherein I4.0 technologies strengthen enabling conditions and mitigate constraining factors, thereby enhancing CE implementation and improving SP outcomes. The findings offer concrete and actionable guidance for organizations seeking to leverage digital technologies to advance circularity and sustainable operational performance.

Rapid urbanization, resource pressures, and technological transformation in the Gulf region underscore the need for integrated strategies linking sustainability policy with digital innovation. This study develops a novel Integrated Policy–Innovation Framework that connects circular economy (CE) principles with Industry 4.0 (I4.0) technologies such as artificial intelligence, the Internet of Things, and blockchain. The framework operationalizes innovation-driven sustainability governance in the United Arab Emirates (UAE). By aligning digital transformation with sustainability goals, the framework enables data-driven decision-making, predictive analytics, and cross-sector collaboration, supporting a transition toward circular and resilient systems. The study adopts a mixed-method approach, combining bibliometric and critical analyses to examine sustainability and innovation policies across the Gulf Cooperation Council (GCC) region. It also includes a review of UAE national policy documents and sixteen semi-structured interviews with experts and policymakers. Findings indicate that while digital tools enhance decision-making, cross-sector collaboration, and performance monitoring, implementation remains constrained by institutional fragmentation, data governance gaps, and limited policy alignment. The proposed framework integrates technological, institutional, and innovation-system mechanisms to strengthen evidence-based policymaking and accelerate CE–I4.0 adoption. It provides a structured pathway linking policy vision with innovation practice, enabling circular, low-carbon, and resilient systems across sectors. By bridging sustainability policy and digital transformation, the study contributes a governance model supporting the UAE's transition toward long-term environmental resilience and innovation leadership. It also offers transferable insights for other emerging economies pursuing circular–digital transformation.

Purpose This study aims to examine how value engineering (VE) can be aligned with the 3R principles – reduce, reuse and recycle – to advance sustainable construction practices. It empirically investigates the key drivers, challenges, benefits and enabling tools and techniques influencing the adoption of 3R-driven VE across different stages of the construction project lifecycle, thereby bridging the gap between conceptual frameworks and practical implementation. Design/methodology/approach A mixed-methods strategy was adopted, combining a systematic literature review, a structured questionnaire survey of 200 construction professionals from 14 countries and 30 semi-structured expert interviews. The Relative Importance Index (RII) was used to prioritize influencing factors, while thematic analysis of interview data was used to validate, enrich and contextualize the quantitative findings. Findings The findings reveal that supportive government policies, early integration of VE during project planning and structured functional analysis are the most influential drivers of 3R-driven VE adoption. Major challenges include limited awareness of economic and sustainability benefits, shortage of skilled professionals and lack of standardized tools for sustainable material management. Key benefits include reduced construction waste, improved alignment between lean construction and sustainability objectives and enhanced lifecycle performance. Building information modeling-based material tracking and life cycle assessment emerged as the most critical enabling tools. Practical implications The paper offers an environment for practitioners and policymakers to integrate 3R methods into VE, facilitated by digital technologies and incentive-based contracts, thus promoting cost-effective, waste-reducing and resilient building methodologies. Originality/value This study contributes to sustainable construction research by providing a comprehensive empirical assessment of VE implementation grounded in the 3R principles. It advances practical understanding of circular economy integration by synthesizing drivers, challenges, benefits and enabling tools and techniques within a unified, phase-based framework spanning the construction project lifecycle.

PurposeThe study aims to develop an integrated framework to enhance the value engineering (VE) approach in construction by leveraging building information modeling (BIM) and artificial intelligence (AI). The framework focuses on material optimization and sustainable resource management while ensuring quality and cost-effectiveness to attain the circular economy (CE). Design/methodology/approachA systematic literature review, guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, is conducted to examine the applications of VE in construction. A mixed-method approach combines quantitative analysis, including keyword co-occurrence and clustering, with qualitative content analysis. The Delft Ladder approach is employed to structure the integration of VE with BIM and AI technologies, forming the foundation of the novel industrial practice-based framework. FindingsThe study reveals significant potential for enhancing VE through digital transformation. Integrating BIM and AI with VE principles demonstrates improved efficiency in material optimization and reduction of environmental impacts. The proposed Framework promotes closed-loop systems in construction by enabling data-driven decision-making, improving resource efficiency and allowing stakeholders to adopt CE principles throughout the construction lifecycle. Practical implicationsThe framework offers construction professionals pragmatic solutions to mitigate embodied carbon, encourage material reuse and fulfill sustainability objectives. It tackles issues in conventional VE implementation by integrating digital technologies with CE procedures for efficient material management. Originality/valueThis research introduces an innovative framework that uniquely integrates VE principles with BIM and AI functionalities, employing the reduce, reuse and recycle methodology. The framework can enhance value and minimize expenses through optimization and material efficiency to achieve both functionality and cost-effectiveness.

The adoption of circular economy (CE) practices supported by Industry 4.0 (I4.0) technologies remains limited, despite their strong potential to enhance sustainable performance (SP). Organizations continue to face practical challenges in identifying effective strategies, enabling technologies, and implementation conditions. Drawing on 20 semistructured interviews with scholars and practitioners engaged in CE initiatives and I4.0 adoption across diverse sectors globally, this research examines how CE-I4.0 integration is implemented in practice. The findings identify four interrelated strategic themes shaping CE-I4.0 integration: policy and governance, organizational, technological, and social and ethical. These themes capture how regulatory frameworks, organizational practices, digital capabilities, and social considerations collectively influence the implementation of CE-I4.0 initiatives. Within and across these themes, strategies interact dynamically with enabling opportunities and implementation challenges, forming a continuous strategy cycle in which policy frameworks shape organizational practices, guide technological adoption, support social and ethical outcomes, and are refined through feedback from implementation. By conceptualizing CE-I4.0 integration as a dynamic and interaction-driven process, this study advances research on sustainability strategies. It also proposes a strategy cycle nexus framework that provides actionable guidance for managers and policymakers seeking to design and implement effective, digitally enabled CE transitions.

The rapid global adoption of solar photovoltaics brings both environmental benefits and end-of-life sustainability challenges. Current solar panel lifecycle management suffers from low recycling rates, limited traceability, and fragmented stakeholder coordination. This paper proposes a blockchain-enabled Circular Economy (CE) framework that integrates Internet of Things (IoT), Artificial Intelligence (AI), and smart contracts to enhance lifecycle traceability, incentivize recycling, and facilitate peer-to-peer trading of refurbished components. Blockchain ensures secure, transparent asset tracking, while IoT and AI enable predictive maintenance and lifecycle optimization. Smart contracts automate rewards for responsible recycling, aligning stakeholder incentives. A case study involving 975 kg of solar panels demonstrates 3237 kg of verified carbon savings at 83% recycling efficiency, with outcomes securely recorded via Ethereum and Interplanetary File System (IPFS). The framework offers a scalable digital infrastructure for lifecycle sustainability and regulatory compliance through privacy-preserving audits. This research lays the groundwork for integrating carbon credit markets and decentralized governance into the renewable energy circular economy.