Search Results (285)

This study uses data of A-share listed companies in Shanghai and Shenzhen from 2020 to 2024. We manually collect green procurement lists from official government procurement websites and match them with firm samples. Employing the two-way fixed effects model and the Bootstrap method, this paper empirically examines the impact of green public procurement on corporate carbon reduction. The results show that green public procurement significantly improves firms’ carbon reduction performance. Mechanism analysis indicates that AI adoption and government green subsidies further strengthen this effect. Heterogeneity tests reveal that the impact is more pronounced for state-owned enterprises, high-tech firms and enterprises in regions with advanced digital economies. Accordingly, we propose suggestions including strengthening the driving role of green procurement, promoting coordination between green procurement and digital technology, optimising the allocation of green funds, and implementing targeted differentiated incentives. This research helps clarify the internal mechanism of green public procurement on carbon emission reduction performance and provides references for improving relevant practices in carbon emission reduction. Full article

AI-enabled cyber–physical systems (CPSs) are increasingly deployed in public governance contexts where they sense human populations, infer classifications or risks, and trigger interventions that can shape liberty, equality, and access to essential services. In these deployments, governance failures often arise not only from model error but from systems-level interactions across data generation, model updates, organizational practices, and downstream actuation. This paper introduces a Risk–Rights–Rules (3R) architecture that treats fundamental rights and legal rules as enforceable constraints on the sensing–inference–actuation loop, rather than as external ethical aspirations. Building on established risk-management baselines and safety engineering practice, we specify a testable assurance object, a structured 3R assurance case, that links rights claims to explicit assumptions, measurable evidence, and accountable control points across the lifecycle. The approach is designed to reduce “legitimacy drift” in stochastic decision pipelines by making uncertainty, demographic error, contestability, and procurement leverage auditable at the system level. The result is a governance blueprint for high-consequence public-sector AI deployments for governance failures, which is both technically robust and institutionally defensible. Full article

This paper develops a theoretical model to analyze the protective effect and nonlinear mechanism of preferential rules of origin (ROOs) on regional supply chains amid global value chain restructuring and rising regional supply chain security demands. Supported by numerical simulations and a triple difference-in-differences (DDD) empirical approach based on the China–ASEAN Free Trade Agreement (CAFTA), the findings reveal a nonlinear, inverted U-shaped relationship between ROO stringency and supply chain stability—exhibiting a typical Laffer curve characteristic. Moderate restrictions significantly promote intra-regional intermediate goods procurement and stabilize regional supply chain layout, while excessively stringent rules raise enterprise compliance costs and restrain integration. These findings carry important implications for regional economic resilience and sustainable development. While our empirical analysis focuses on economic resilience (measured through regional procurement stability), we discuss how well-designed ROO may also support broader sustainability goals, including contributions to SDG 8 (Decent Work and Economic Growth) and SDG 17 (Partnerships for the Goals) through more stable and inclusive regional production networks. The study highlights the need for careful calibration of ROO stringency to balance protective effects with compliance costs in pursuit of both resilient and sustainable regional trade governance. Full article

Background/Objectives: Technology transfer (TT) has been identified as a global health priority due to its impact on improving access to vaccines, including for pandemic influenza preparedness and response through bilateral and multilateral mechanisms. This study aimed to (1) characterize examples of influenza vaccine TT (IVTT) and (2) identify key lessons learned that may inform future activities relevant for next-generation influenza vaccine technologies. Methods: Using a contingent effectiveness model, a convergent mixed-methods study was conducted with vaccine manufacturers and global experts to capture quantitative survey data on IVTT activities and enablers and qualitative data on successes, challenges, and opportunities for IVTT through interviews, complemented by secondary data from peer-reviewed and grey literature to characterize additional IVTT observations. Results: This study included 24 participants, including 14 representatives from 13 vaccine manufacturers and 10 experts. Interviews were conducted with representatives from eight manufacturers and seven experts. Eighteen IVTT observations were identified through the surveys and interviews, of which 15 IVTT transfers were completed and 13 resulted in an approved vaccine. Secondary data provided additional evidence on eight IVTT recipients and one supplier, expanding the range of institutional and programmatic contexts assessed. Shorter IVTT completion and vaccine approval timelines were observed in association with prior TT experience and private management structures for manufacturers, for pre-pandemic/pandemic influenza vaccines versus seasonal influenza vaccines, and among bilateral transfer mechanisms (versus multilateral mechanisms) and fill/finish transfer methods. Manufacturers also described spillover benefits, including the use of IVTT-related know-how for the development of COVID-19 and routine vaccines. Both manufacturers and experts generally agreed on a list of 17 enablers for successful IVTT and ranked government commitment to vaccine production and procurement as the top enabler. Findings from the literature-based observations were consistent with primary data and included additional public sector recipient experiences, evidence of widespread human capital development, and a commentary on the importance of the demand environment. Conclusions: Assessed IVTT activities across primary and secondary data sources yielded commercial and spillover benefits as described in the contingent effectiveness model and provided a triangulated analysis of IVTT experiences across manufacturers, experts, and documented cases. Participants agreed that effective technology transfer is contingent upon a host of determinants. Using a systematic application of the contingent effectiveness model to IVTT, this study provided an exploratory analysis of past activities among vaccine manufacturers and experts. While certain nuances for influenza were identified, the lessons learned from this study may be applicable for other TT activities, including those to support pandemic preparedness. The contingent effectiveness model is a useful tool to inform and evaluate future TT activities. Full article

As China rapidly transitions to a moderately aging society, the sustainable delivery of public elderly care services has emerged as a critical legal and governance challenge. Government procurement has become a pivotal mechanism through which the state engages both social and market actors in providing elderly care services. However, the sustainability of this service delivery mechanism remains constrained by fragmented legal norms, unstable fiscal guarantees, inconsistent service standards, weak supervision, and regional inequalities. This article examines how legal regulation can support the sustainable delivery of government-procured public elderly care services in China. Based on qualitative, desk-based legal and policy analysis, it reviews the evolution of China’s national and local regulatory framework, assesses the current system from the perspectives of institutional, fiscal, social, and governance sustainability, and identifies key legal and institutional dilemmas, arguing that China should construct a hierarchical legal framework, establish stable fiscal guarantee rules, develop unified service standards, strengthen whole-process supervision, and improve legal mechanisms for regional coordination. These reforms would enhance the rule-of-law foundation of government-procured elderly care services and provide a reference for other aging societies seeking sustainable public service delivery models. Full article

Public procurement systems are prone to risks such as collusion, contractual concentration, and irregular subcontracting, which undermine transparency and accountability. Traditional fiscal oversight approaches remain largely retrospective, limiting their ability to anticipate irregularities and prevent potential losses. Addressing the gap between theoretical machine learning models and real-world institutional deployment, this study introduces an applied system innovation that integrates two complementary approaches at a national scale: a Contractual Network Model (Mallas Contractuales) and a Predictive Risk Model for Contractors. The first component uses graph-based analytics, employing an Entity–Link–Property schema to represent relationships among entities, contractors, and contracts, thereby enabling the detection of structural patterns associated with collusive or anomalous behavior. The second component implements supervised machine learning models, trained on more than 16 million contracts and 2.6 million contractors from sources such as SECOP, RUES, DIAN, and national sanction registries. Models, including Random Forests and Gradient Boosted Trees, were optimized via cross-validated hyperparameter search and evaluated on a separate hold-out set using ROC AUC and Gini metrics, achieving strong discriminatory performance under the available retrospective validation setting while maintaining operational interpretability. Both approaches were deployed in a modular architecture that integrated Databricks, i2 Analyst’s Notebook, and Power BI dashboards, providing interactive visualizations and risk scores at multiple levels. Together, these systems demonstrate how the convergence of graph analytics and predictive modeling enables proactive fiscal auditing, strengthens institutional capacity, and offers a replicable framework for public sector accountability. Full article

This paper investigates how sustainable public procurement (SPP) is operationalised in school catering in the Metropolitan City of Rome and how it reshapes market conditions affecting the participation of small and medium-sized enterprises (SMEs) in the region. While SPP is widely framed as a lever for sustainability and local development, its concrete effects on SME inclusion and supply-chain organisation remain underexplored. Drawing on procurement document analysis and supply-chain reconstruction in the Metropolitan City of Rome, the study examines how sustainability criteria—such as organic quotas, traceability requirements and quality standards—are translated into operational requirements. The findings show that SPP goes beyond the simple performative addition of sustainability requirements to existing markets and actively reorganises market coordination structures and supply-chain relations. Procurement shapes not only what is sourced, but also how logistics, continuity of supply, and coordination are organised across the agri-food chain. SME participation emerges as conditional and capability-dependent rather than automatically enabled by sustainability-oriented procurement. In fragmented agri-food systems, smaller firms often participate indirectly through intermediaries or larger catering operators rather than through direct access to contracts. Rather than interpreting these dynamics as a simple exclusion of SMEs, the paper argues that SPP operates as a form of selective and asymmetrical market-shaping, redistributing participation opportunities unevenly across actors depending on their organisational and coordination capacities. The paper contributes to the literature by conceptualising procurement as a governance instrument whose effects depend on the interaction between procurement architecture, sustainability requirements, and the structural characteristics of the supply base. More broadly, it highlights the importance of aligning sustainability objectives with existing supply-chain capacities and territorial market structures when designing procurement policies. Full article

Sustainability targets in contemporary construction projects are increasingly defined through embodied carbon limits, circular material obligations, waste diversion benchmarks, and energy performance requirements. However, a persistent gap remains between the establishment of these commitments during policy and design stages and their effective verification throughout project delivery and post-handover operation. Although Building Information Modelling (BIM), digital twins, and associated digital monitoring systems are widely discussed in sustainable construction research, their collective role in enabling continuous sustainability compliance assurance within construction management remains insufficiently synthesised. This study addresses this gap through a PRISMA-guided systematic review and structured comparative thematic synthesis of 117 peer-reviewed studies published between 2016 and 2026. A structured analytical coding matrix, MMAT-informed methodological quality appraisal, and descriptive evidence mapping were used to evaluate dominant digital technologies, sustainability compliance domains, lifecycle verification gaps, and study validation approaches. The findings indicate that current research remains concentrated around BIM-enabled design modelling and isolated operational analytics, with comparatively limited attention to integrated multi-stage sustainability verification during procurement, construction, commissioning, and operation. Four recurring sustainability compliance domains requiring stronger construction management control are identified, including embodied carbon verification, material reuse traceability, waste diversion monitoring, and energy performance validation. In response, the study proposes a Digital Sustainability Compliance Framework that conceptually integrates sustainability targets, PMBOK-aligned project control functions, BIM information models, digital twins, sensor systems, and centralised construction data platforms within a continuous lifecycle verification architecture. The study repositions digital technologies as governance-oriented infrastructures for more transparent, auditable, and continuously monitored sustainability compliance assurance while highlighting the need for future empirical validation. Full article

Background: Clinical artificial intelligence (AI) is rapidly moving from retrospective model development into prospective evaluation, implementation, and routine care. Existing reviews have addressed specific aspects of this transition, including monitoring, drift, implementation, governance, and human–AI interaction; however, these bodies of work remain methodologically and conceptually fragmented across different review traditions. Methods: We conducted a scoping review of review-level and review-oriented literature. We searched MEDLINE, Embase, Scopus, and Web of Science Core Collection from database inception to 28 February 2026. We charted review characteristics and conducted an inductive thematic synthesis of extracted review-level findings, while distinguishing operational, deployment-proximal, methodological, and conceptual/governance-oriented evidence. Results: We included 25 review-level publications spanning systematic, scoping, methodological, narrative, and governance-oriented reviews. Three major themes emerged. First, clinically important risks were consistently framed as socio-technical rather than purely algorithmic: trustworthiness depended not only on technical performance, but also on fairness, transparency, workflow fit, human oversight, and organisational readiness. Second, the included review literature consistently recommended post-deployment monitoring but showed limited operational maturity; monitoring methods, action thresholds, fairness surveillance, and corrective responses were weakly standardised, and mature evidence from activated systems in routine care remained sparse. Third, trustworthy implementation was increasingly framed as a lifecycle governance challenge extending beyond procurement and initial validation to include local validation, subgroup auditing, drift detection, controlled updating, incident response, and, where necessary, rollback or retirement. Discussion: The review literature suggests a persistent normative–operational gap, meaning that recommendations about what trustworthy clinical AI should require have advanced faster than evidence on how monitoring, updating, and governance are implemented in routine care. The strongest unresolved challenge is therefore not principal generation alone, but the translation of monitoring and governance expectations into actionable operational systems. Conclusions: Post-development trustworthiness in clinical AI should be understood as a lifecycle property, not a one-time technical achievement. Future work should prioritise stronger operational evidence, clearer reporting of deployment-proximal and post-deployment evaluation, methodological standardisation of monitoring metrics and thresholds, implementation research on feasible governance models, and evaluation frameworks for assessing post-deployment safety, fairness, accountability, and sustainability. Full article

This study examines the transformative role of digital and artificial intelligence (AI)-enabled public procurement systems in enhancing governance efficiency within smart city environments, with a specific focus on Yerevan, Armenia. As urban administrations increasingly adopt data-driven governance models and digital infrastructures, public procurement remains a critical yet underexplored domain for innovation in transition economies. Despite ongoing e-government reforms in Armenia, procurement systems continue to face challenges related to procedural inefficiencies, limited transparency, and institutional constraints. To address these challenges, the paper develops a Governance Efficiency Framework that integrates digitalization, AI capabilities, and multi-criteria decision-making principles to assess and optimize public procurement processes in urban settings. The framework incorporates key dimensions such as transparency, operational efficiency, accountability, and data integration, enabling a comprehensive evaluation of procurement performance. The empirical application of the framework to the case of Yerevan provides insights into the structural and technological determinants of procurement efficiency in a transition economy context. The findings indicate that while digitalization has contributed to improvements in transparency, significant limitations remain in efficiency and system integration. A scenario-based analysis further suggests that AI-enabled analytics, process automation, and digital procurement platforms have the potential to reduce administrative delays, enhance transparency, and support more strategic and evidence-based decision-making under assumed implementation conditions. By bridging the fields of public procurement, digital governance, and smart city research, this study contributes both theoretically and practically. It offers a structured and adaptable framework for policymakers and urban administrators seeking to modernize procurement systems and strengthen governance efficiency in evolving digital environments. Full article

Material selection in architectural design necessitates a multifaceted evaluation of economic, technical, esthetic, and cultural variables. Beyond fundamental requirements such as cost, structural integrity, and transparency, architects must synthesize subjective attributes, including warmth and formality, with objective constraints like multifunctionality and cultural heritage. Despite the strategic impact of material choice on project performance, empirical research systematically categorizing these governing criteria remains sparse. Furthermore, existing methodologies often overlook the psychophysical principles of human perception essential for construction material evaluation. Thus, this study identifies the fundamental factors influencing material selection and establishes a hierarchical framework to prioritize their relative significance within the design process. The research employs a weighted Analytic Hierarchy Process integrated with the Weber–Fechner law (W-AHP) to structure and quantify selection criteria. By incorporating perceptual scaling principles into the AHP framework, the methodology accounts for variations in judgment sensitivity across different evaluation scales. A hierarchical decision model was developed to categorize criteria and sub-criteria, followed by pairwise comparisons to derive priority weights. Results reveal a distinct priority hierarchy among the identified criteria and confirm that judgment sensitivity varies significantly across evaluation scales. The W-AHP method produced differentiated weightings that accurately reflect the psychological intensity of professional decision-making, offering a structured mechanism to balance functional performance with complex design intentions. This study contributes to the field of construction management by introducing the W-AHP method as a novel decision-support tool. The integration of Weber–Fechner perceptual principles enhances weight differentiation and addresses the inherent subjectivity of architectural evaluation, providing a transparent methodology to justify material procurement within a rigorous engineering management context. Full article

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement. Full article

Climate finance has become a major means of fostering sustainability in the construction industry, which encounters higher pressures to mitigate its environmental footprint without sacrificing economic viability. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study employs a hybrid approach, integrating a systematic literature review (SLR) and bibliometric analysis, to provide a comprehensive overview of the role and mechanisms of climate finance for sustainable practices in the construction industry. From 2019 to 2025, 176 papers were identified in the Scopus (73) and Web of Science (103) databases. The SLR enables both systematic collection and qualitative analysis of financial instruments, policy frameworks, and sustainability performance metrics, and bibliometric analysis provides a report of publication behavior, geographic distribution, and thematic network. Findings suggest intense clustering of research in countries, with India, China, and the United States as key focus areas, and that construction firms predominantly accessed climate finance on instruments including green bonds, sustainability-linked loans, public–private partnerships, and multilateral climate funds. Sustainability performance is commonly assessed using indicators such as carbon emissions, energy efficiency, lifecycle costs, and environmental, social, and governance (ESG) metrics. The findings also highlight the critical role of public policies, such as green procurement, carbon pricing, and fiscal incentives, in enabling sustainable construction practices. From a theoretical perspective, this study contributes to the understanding of how financial mechanisms, policy frameworks, and sustainability metrics interact to drive sectoral transformation. Future research should focus on standardizing sustainability metrics, evaluating financing impacts, and expanding studies in emerging economies. Full article

Scope-3 emissions from construction supply chains (CSCs) account for the majority of the construction sector’s greenhouse gas (GHG) footprint. However, procurement-driven decarbonization (PDD) remains constrained by persistent data quality (DQ) deficits, including boundary divergence, limited verification, incomplete information, and fragmented interoperability. This PRISMA-guided systematic literature review (SLR) synthesizes 68 studies to examine how governance mechanisms (GMs) and digital technologies (DTs) can be co-designed within procurement workflows to improve the reliability of carbon data. By integrating quantitative matrix-based analysis, qualitative thematic coding, and a governance–technology pairing logic, the review identifies a division of labor across DQ dimensions. Standard-based governance and boundary rules strengthen completeness, consistency, and interpretability. At the same time, DTs enhance accessibility and timeliness and provide targeted improvements in accuracy and logical coherence when embedded within structured schemas. Assurance emerges as the most reliable mechanism for accuracy, information-management standards for timeliness, and early stakeholder involvement for accessibility. These insights translate into procurement-oriented measures, including European Standard (EN)-aligned scope definitions; ISO 14083-aligned logistics accounting; Industry Foundation Classes (IFC)/Level of Information Need (LOIN)-based information requirements; selective assurance; uncertainty-aware disclosure; and integrated digital measurement, reporting, and verification (MRV) systems combining Environmental Product Declaration (EPD) platforms, Artificial Intelligence (AI) validation, and blockchain. Collectively, these measures enable comparable, verifiable data and support scalable decarbonization. Full article

Proof-of-Work (PoW) cryptocurrency mining is conventionally characterised as an energy competition, yet this paper provides evidence that the primary competitive margin has shifted from electricity procurement to semiconductor acquisition. Using Bitcoin (BTC) and Bitcoin Cash (BCH)—two SHA-256 networks sharing identical hardware but differing in scale and governance—as a natural comparative setting, we apply the Autoregressive Distributed Lag (ARDL) bounds testing approach to 112 weekly observations (January 2019–March 2021). Mining reward exhibits near-unity long-run elasticity with respect to both hash rate and energy consumption (0.773–1.009), confirming miners’ price-taking behaviour. Critically, the shutdown threshold—an efficiency-based cost floor derived from ASIC hardware generations—dominates all cost-side regressors with elasticities of $-1.941$ to $-2.156$, substantially exceeding electricity price effects in both magnitude and significance. VAR analysis provides evidence consistent with a centralisation paradox: rising chip efficiency Granger-predicts increased mining pool concentration for BTC (${\chi }^2=33.64$, $p<0.001$) via a revenue-redistribution mechanism, while electricity costs carry no equivalent structural consequence. Zivot–Andrews tests confirm that China’s 2021 mining ban produced a significant transient disruption but no permanent structural break in BTC’s hash rate trajectory, consistent with the geographic mobility of capital-intensive hardware. These findings imply that standard energy-price policies address the wrong margin; effective governance of PoW sustainability requires redirecting regulatory attention toward the semiconductor supply chain—a conclusion with direct relevance to SDG 7 and SDG 13. Full article