Accelerating the circular economy transition requires policy arrangements that connect recurring resource-conversion activities, economic incentives, and credible verification across production systems. This study develops a systems policy framework for upcycling by linking consumption-tax incentives to verified circular material flows. Upcycling is conceptualized as a multi-actor material-conversion system integrating interdependent material, fiscal, and information-verification flows. The study combines qualitative comparative policy analysis and framework synthesis. Statutes, official policy documents, government guidance, and international-organization reports are analyzed by the policy functions they perform within the circular material-conversion system, including intervention stage, target activity, support basis, timing and refundability, and verification requirements. The analysis identifies a relative functional gap at the recurring acquisition-input-conversion stage: reviewed instruments commonly support specific products, transactions, capital investment, taxable income, or virgin-material disincentives, rather than verified and repeated resource conversion after commercialization. The proposed framework links incentives to verified acquisition, actual production input, and value-enhancing conversion. It establishes activity-based eligibility criteria and incorporates traceability requirements, safeguards against duplicate claims and excessive fiscal exposure, ex post verification, recapture, and periodic review. The resulting principles can be translated into VAT/GST-linked credits or refunds, separate business tax credits, environmental-tax mechanisms, and other fiscal architectures.
Green development is confronted with the challenge of achieving a win–win outcome for both the economy and the environment. Digital and intelligent transformation offers a critical opportunity for the green development of enterprises. Drawing on panel data of Chinese A-share listed companies from 2015 to 2024, this study adopts a capability–resource synergy framework and employs empirical methods to investigate the complex mechanisms through which digital and intelligent transformation affects green technology innovation. The results indicate that digital and intelligent transformation significantly promotes green technology innovation among Chinese enterprises. Further analysis reveals that increasing government subsidies and enhancing absorptive capacity are two important mechanisms through which digital and intelligent transformation drives green technology innovation. Moreover, absorptive capacity also plays a threshold role. Heterogeneity analysis shows that the positive effect of digital and intelligent transformation on green technology innovation is more pronounced in state-owned enterprises, manufacturing firms, and heavily polluting enterprises. By adopting absorptive capacity as a theoretical lens, this study provides a knowledge-based explanation of how digital and intelligent transformation influences green technological innovation. Importantly, absorptive capacity serves a dual mediating and threshold function, which enhances the explanatory power and ensures a more comprehensive mechanistic analysis.
Digitised community social-work services distribute access across digital, assisted, in-person, and outreach routes. This qualitative case study addresses a service-system problem: support can complete one task while the route to the next service stage and professional contact remains uncertain. Station A in Guangdong, China, was analysed through semi-structured interviews with 20 participants—eight older service users, six family members, three frontline social workers, one station manager, and two community volunteers—together with observation, service and activity records, platform materials, and policy documents. Eight focal service-user accounts formed the comparison. The analysis developed six case-derived access positions: independent digital access, assisted transition, visit-dependent contact, scheduled outreach, entry blockage, and informal fallback. Across these positions, service access depended on the fit among service stage, task requirements, scope of assistance, route convertibility, and professional-contact conditions. Registration assistance enabled entry in one episode, followed by an in-person route for the next service task. Offline routes produced contact conditions ranging from visit-dependent contact to scheduled outreach. The article develops stage–route–contact alignment to explain how support for one task and the route available for the next shape service continuity. The framework links these arrangements to the timing, directness, and continuity of professional contact.
Resource-constrained cities often face aging infrastructure, fragmented governance, ecological risks, and limited public finance. These pressures are acute in historic districts, where heritage protection overlaps with residents’ everyday needs. This study examines a place-based micro-renewal project in Xiangtan, a resource-constrained legacy industrial city in central China, where urban renewal practices have received national-level recognition. It asks how micro-renewal can reconfigure blocked relations within a local urban system and whether these changes exhibit features associated with adaptive governance under fiscal and spatial constraints. The study combines the socio-ecological-technical systems (SETS) framework with Actor-Network Theory (ANT) and draws on institutional archives, semi-structured interviews, and participant observation. The findings identify three mechanisms: informal coordination, pragmatic recombination of institutional and technical resources, and cross-scale feedback between local implementation and higher-level review. These mechanisms helped reconnect fragmented actors and resources and enabled local adjustment during project implementation. The observed patterns of coordination, feedback, and adjustment are consistent with features of adaptive governance, while their contribution to durable adaptive capacity and longer-term urban resilience requires further longitudinal evidence. This study contributes to urban systems research by showing how place-based micro-renewal can reconfigure fragmented institutional, technical, and material relations during implementation in resource-constrained historic districts.
Demand forecasting and inventory management have evolved as parallel bodies of literature, despite their operational interdependence, and the resulting disconnect between prediction and decision has been diagnosed as a standing gap in operational research. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) protocol, this review systematically codes 46 studies published between 2010 and 2023 in journals listed by the Australian Business Deans Council, operationalizing an established prediction–inventory integration hierarchy to evaluate how modern artificial intelligence architectures couple prediction with decision-making. We find that predict-then-optimize (PtO) and end-to-end or joint learning (E2E) architectures co-evolve rather than succeed one another. Crucially, integration level is orthogonal to model class: seven of the seventeen E2E studies use no neural network, so decision-focused coupling does not inherently require the opacity of deep learning. Instead, architecture performance is highly contingent on a domain’s data regime and cost structure rather than algorithmic sophistication alone. We provide a contingency framework equipping academic researchers, analytics practitioners, and supply chain managers with actionable architecture-selection guidance, identifying hybrid PtO–E2E designs, which remain absent within this corpus, as the unresolved structural frontier of the predictive AI era.
