Abstract: In the new stage of development, promoting the integration of “industry-innovation- talent chains” is an important measure to achieve low-carbon transformation and development in cities. This study analyzes the internal logic of the integration of “industry-innovation-talent chains” from the perspective of triple helix theory. Based on panel data from 281 cities from 2003 to 2021 in China, the composite system synergy model is used to measure the degree of the integration of “three chains”, and identify the comprehensive impact and mechanism path of the integration of “three chains” on urban carbon productivity. The results show that the integration of “three chains” significantly improves urban carbon productivity, and this conclusion still holds true after a series of robustness tests. The heterogeneity analysis results indicate that the integration of “three chains” has a more positive impact on carbon productivity in low-carbon pilot cities, new energy demonstration cities, non resource based cities, and innovative cities. Mechanism testing shows that the integration of “three chains” indirectly improves urban carbon productivity by enhancing industrial agglomeration, promoting technological innovation and optimizing energy structure. The research has expanded the application boundaries of the integration of “three chains” and provided policy insights for improving urban carbon productivity through the integration of “three chains”.

Abstract: We study the strategic release timing of frontier AI systems by competing firms. Each firm develops a model whose quality improves with development time, but faces incentives to release early to capture first-mover advantages. Premature release imposes safety externalities on society that firms do not fully internalize. We characterize the symmetric Nash equilibrium in a preemption game and show that equilibrium release occurs strictly before the social optimum. We analyze four policy interventions: (i) minimum quality standards, which can implement the first-best; (ii) mandatory release delays, which paradoxically reduce deployed model quality by shifting preemption to the announcement stage, where quality locks in before the mandated waiting period; (iii) voluntary safety commitments, which can sustain cooperative outcomes when observable and credible; and (iv) Pigouvian safety taxes, which partially correct the externality but cannot eliminate the preemption distortion alone. Our results speak to ongoing policy debates about frontier AI regulation and generalize to other technologies with safety externalities and first-mover advantages.

Abstract: This systematic review examines the relationships between population dynamics, demographic transition, and the capacity of global agricultural systems to sustain food security for a world population projected to approach ten billion by mid-century. Drawing upon peer-reviewed literature, intergovernmental datasets, and quantitative modelling studies from 2000 to 2026, the review synthesises evidence across five thematic areas: regional patterns of demographic growth and transition; the dietary and logistical consequences of accelerating urbanisation; the conceptual foundations and limitations of per capita food availability modelling; compound pressures on agricultural systems arising from land degradation, freshwater depletion, climate change, and biodiversity loss; and the technological, equity, and governance dimensions of feasible policy responses. The findings demonstrate that aggregate global food production is, in principle, expandable to meet projected demand, but that chronic food insecurity reflects structural inequalities in access, distribution, and governance rather than absolute productive insufficiency. Urbanisation amplifies resource-intensive dietary demand whilst simultaneously creating opportunities for more efficient food distribution. Effective responses require the integration of supply-side intensification, demand-side dietary management, waste reduction, and equitable governance reform. Future research priorities include subnational demographic-agricultural modelling, comparative governance analysis, and investigation of the food system implications of population ageing.

Abstract: The metaverse is often presented as a technological breakthrough driven by developments in virtual reality, blockchain, and artificial intelligence. While these technologies are essential, they do not by themselves generate sustainable economic value. This article argues that media content is the true engine of value creation in the metaverse economy. It is media that attracts users, sustains their engagement, and enables monetization within immersive virtual environments. Drawing on the concept of the attention economy, the metaverse can be understood as a media-centered economic system in which value emerges from users’ time, interaction, and participation rather than from technical infrastructure alone. Economic activity in virtual worlds depends on how long users stay, how deeply they engage, and how actively they participate. Media content-such as games, virtual concerts, immersive storytelling, social spaces, and user-generated experiences- plays a central role in this process. These forms of content function simultaneously as cultural expressions and as economic inputs. By capturing attention and encouraging prolonged engagement, media content creates demand for virtual goods, digital ownership, advertising, and platform-based services. The more compelling the content, the greater the opportunities for economic exchange. In addition, participatory media transforms users from passive consumers into active contributors who create content, build communities, and exchange digital assets. Based on a conceptual review of existing literature, this article demonstrates that media content serves as the foundational infrastructure of value creation in the metaverse. Understanding this role is essential for assessing the metaverse’s long-term economic sustainability and potential.

Abstract: As a critical form of social network in which firms are embedded, supply chain networks profoundly shape firms’ strategic choices and the diversification of their innovation pathways. Drawing on a social network analysis framework, this study constructs firm-level supply chain networks using data on Chinese A-share listed companies from 2008 to 2023 and examines the impact of supply chain network position on corporate green technological innovation and its underlying mechanisms. The results show that both network centrality and structural hole advantages in supply chain networks significantly promote firms’ green technological innovation. Mechanism tests further indicate that advantageous supply chain network positions foster green technological innovation through three channels: enhancing firms’ absorptive capacity, increasing green investor attention, and strengthening supply chain stability. Heterogeneity analyses reveal that the promoting effect of supply chain network position is more pronounced in heavily polluting and highly competitive industries, as well as among state-owned enterprises and firms with higher asset specificity, stronger managerial green cognition, and lower financing constraints. Further analyses show that supply chain network position advantages significantly improve both environmental and economic performance by promoting green technological innovation, suggesting that such network advantages can be transformed into sustainable competitive advantages for firms.

Abstract: This study examines how artificial intelligence (AI) leadership shapes the relationship between technological integration, human capabilities, and innovation activity within organizations, addressing the broader question of whether innovation outcomes are driven by human capital or by AI-enabled efficiency gains. Using a structured survey of 3,079 respondents across industries and regions, the study applies exploratory and confirmatory factor analysis and structural equation modelling to test the proposed relationships. The results indicate that AI leadership has a strong positive effect on in-novation activity while simultaneously exerting a significant negative effect on human capabilities, with no statistically significant relationship between human capabilities and innovation activity. The findings do not support a mediation mechanism, sug-gesting that innovation outcomes can emerge independently of human capability en-gagement. This pattern indicates a structural misalignment in the innovation produc-tion process, where technological inputs are efficiently translated into outputs while human capital remains underutilized. From an economic perspective, the results indi-cate a shift toward efficiency-driven innovation systems, where productivity gains are achieved primarily through AI-enabled processes. The study contributes to the litera-ture by challenging assumptions of human–AI complementarity and by highlighting the importance of leadership in shaping the allocation and integration of technological and human resources.

Abstract: Small farms play a vital role in the rural sector of the European Union, contributing to food production, preserving landscape features, protecting biodiversity, providing public goods, and maintaining social cohesion in rural and remote regions. Nevertheless, the sector has undergone rapid structural transformation in recent decades, accompanied by adverse demographic developments that threaten the long-term sustainability of small-scale agriculture. Understanding how farms respond to evolving economic conditions and policy frameworks is therefore essential for designing effective agricultural policies. This study presents the Stochastic Recursive Model for Multiannual impact Assessment in Agriculture (STREMMiAA). This newly developed simulation model endogenously captures structural, and land-use change dynamics in arable farming systems. The model integrates a farm-level recursive linear programming framework with an ARIMA stochastic process, allowing the dynamic representation of farmers’ adaptive behavior under uncertainty. A post-solution module incorporating means-based environmental indicators enables assessment of environmental performance at the farm level. The model is empirically applied to a representative sample of arable farms in Karditsa (NUTS-3 region), Greece, under three alternative scenarios: (i) Business-as-Usual (BAU), (ii) Common Agricultural Policy (CAP) post-2020, and (iii) CAP post-2020 combined with a scenario of Long War of Attrition (LWA). Simulation results reveal a consistent decline in the share of small farms (<30 ha) and a corresponding concentration of land in larger farms (≥50 ha), indicating limited long-term viability for smaller farms. Land-use projections show an expansion of food crop areas and a decline in industrial crops areas. Environmentally, reductions in water, fertilizer and pesticide use under new CAP-related scenarios (ii and iii) suggest potential benefits for nitrate-polluted areas such as Karditsa.

Abstract: This study advances understanding of industrial symbiosis (IS) by developing a configurational typology of circular ecosystems based on resource-flow patterns. Drawing on data from 68 documented IS ecosystems across 48 countries, the paper applies cluster analysis to five flow-intensity dimensions—material, energy, water, logistics, and knowledge—to identify archetypal configurations. Four stable configurations emerged: low-flow, material-dominant, energy–knowledge, and water-oriented systems.Multinomial logistic regression indicates that sectoral composition and regional context significantly predict configuration membership, while performance analyses confirm that coordinated, contextually aligned ecosystems achieve superior environmental outcomes. Energy–knowledge and water-oriented configurations exhibit strong ecological performance, whereas material-dominant ecosystems show efficiency gains under heavy-industry conditions. Cox’s survival analysis further shows that balanced, coordinated systems achieve greater resilience and longevity. Robustness checks across alternative clustering methods, operationalisations, and sub-samples confirm the stability of these patterns. Results are directionally stable; magnitudes vary slightly across specifications. By applying configurational theory to IS, this paper demonstrates that circular, innovation-oriented ecosystem effectiveness depends on the fit between internal flow structures and contextual environments, thereby moving beyond universal models and offering an empirically grounded framework for policy and design in circular-economy practice and sustainable development.

Abstract: This study investigates the dynamic interplay between financial inclusion, inequality, and systemic risk across European economies from 2000 to 2035 using a stochastic diffusion framework. By integrating drift–diffusion modeling, Monte Carlo simulations, network transmission analysis, and scenario-based forecasting, we examine how financial, labor, and technological factors shape both the level and volatility of inequality. Empirical results indicate that higher financial inclusion consistently reduces mean inequality, dampens volatility, and enhances crisis resilience, while network centrality amplifies the propagation of shocks in high-volatility regions. Forecast scenarios highlight the asymmetric risk structure, showing that proactive digital finance and labor inclusion policies significantly mitigate extreme inequality outcomes. The study provides actionable policy insights for European financial integration, emphasizing that multi-pronged strategies combining digital finance, labor upskilling, and redistribution can stabilize inequality, enhance welfare, and reduce systemic risk.

Abstract: Does Geographic Economy matter in forestalling Crimes? This study tried to answer this question by analyzing the spatial impact of education and employment on violence within the state of West Bengal. This research aims to create a Crime Severity Index to compare with the Crime Rate for the districts of West Bengal. To make the index more justified, the weights are generated based on the individual crime’s respective imprisonment years and the likelihood of that incident. To identify the determinants of crime at a micro-level we considered a panel data structure of 19 cross-sections for 5 time points (from 2000 to 2020). The rank correlation coefficient among different crimes indicates the pattern of crimes has changed over time. A high & positive Moran’s I score suggests that crime rates and the degree of violence are closely linked across different locations, especially at the border regions within the state. The spatial panel analysis outcome shows that factors like higher education among females, stable job placements, and unit of police stations have a substantial influence on regional crimes as well as the severity level over space.

Abstract: Existing global indices of digital development – the Global Innovation Index (GII), the Network Readiness Index (NRI), and the ICT Development Index (IDI) – measure innovation potential, network readiness, and connectivity coverage, respectively, yet none captures the speed at which economies absorb and convert technologies into economic output. This paper introduces the Technology Metabolism Index (TMI), a parsimonious composite indicator comprising seven openly available sub-indicators from World Bank WDI and UN DESA, structured into three components: Readiness (R), Absorption (A), and Output (O). Grounded in cybernetic feedback-loop theory (Ashby, Beer, Forrester), TMI measures the velocity of technological signal propagation through the R→A→O cycle. A pilot calculation for 10 economies – spanning leaders (Korea, Singapore, Estonia), major economies (USA, EU-5, Japan, China), and developing economies (Uzbekistan, Brazil, Nigeria) – reveals three diagnostic metabolic patterns: "metabolic gap" (Uzbekistan: R >> A >> O ≈ 0), "balanced weakness" (Brazil: R ≈ A > O), and "systemic deficit" (Nigeria: R ≈ A ≈ O ≈ 0). Robustness analysis based on weight differentiation across three scenarios confirms rank stability for all 10 economies without exception. An open-source software implementation (TME_INDEX_CALCULATOR, registered certificate DGU 61047) and a four-sheet Excel model ensure full reproducibility. The TMI fills an unoccupied measurement niche in the global digital monitoring ecosystem and offers policymakers a diagnostic tool with arithmetically verifiable targets for accelerating technology metabolism.

Abstract: The prevalence of communicable diseases (also known as transmissible or infectious diseases) has been a significant health issue for sustainable development, hindering economic progress globally. Communicable diseases may affect economic development both directly (via the immediate effects of ill health on productive activities) and indirectly (through the effects of disease on intellectual capacity, mortality, morbidity, and fertility). This study examined the impact of communicable diseases on the economic growth of the SADC region between 2011 and 2024. The study utilised the two-step System-Generalised Method of Moments (GMM) to analyse the data. HIV prevalence and TB incidence served as proxies for communicable diseases, and GDP per capita served as a proxy for economic growth. The study took a comprehensive analytical approach by including variables such as current health expenditure and government expenditure on education as control variables. The findings reveal a significant positive correlation between HIV prevalence, TB incidence and economic growth of the SADC nations, and this is attributed to the fact that the economic growth of most SADC countries does not immediately translate to better healthcare access, as the poor often remain highly vulnerable. Current health expenditure and government expenditure on education negatively affected GDP per capita. This study therefore recommends harmonising the cross-border strategies, strengthening regional surveillance and integrating health into economic planning to ensure a productive workforce within the SADC region.

Abstract: Housing affordability is commonly analysed through price-to-income ratios and housing cost burdens. However, housing systems also function as mechanisms that distribute financial and stability risks across households through tenure structures and institutional arrangements. This study examines housing affordability as part of a broader process of risk allocation within housing systems. Using survey evidence from Slovenia—a high-homeownership system shaped by post-socialist privatization and a limited rental sector—the analysis investigates how households perceive housing affordability and how these perceptions influence expectations regarding future housing market developments. The results show that lower perceived housing affordability is associated with stronger expectations of future housing price growth. Expectations regarding housing purchase price growth are also strongly linked to expectations of future rent increases, suggesting that households interpret both dynamics as part of broader housing market pressure. Regional differences in perceived housing affordability are marginally significant, indicating spatial variation in housing conditions. Respondents who recently participated in the housing market report significantly different expectations regarding future housing price developments. These findings highlight the importance of understanding housing affordability as a perception-based mechanism embedded within broader housing systems.

Abstract: This paper develops an industrial-organization theory of AI routing, modeling how a dual-role platform allocates user queries between an in-house model and an outside expert. We formulate this as a delegated allocation problem featuring endogenous quality investment and data feedback. The expert sets access prices and initial quality, while the platform routes queries by difficulty. Early traffic routed to the expert enhances its future quality through learning-by-serving. In equilibrium, routing follows a cutoff rule. The platform's self-preferencing acts as a tax on outside expertise, raising routing thresholds, reducing outside demand, and compressing both current quality investment and future learning gains. Decentralized routing introduces three inefficiency wedges compared to a dynamic first best: an access-markup wedge, a bias wedge, and a data-feedback wedge. The third is unique to AI routing because traffic allocation directly dictates learning opportunities. Consequently, neutrality and access-pricing remedies are complementary but insufficient together, as platforms fail to internalize the future value of outside learning. This model provides a tractable framework for analyzing AI gateways and router governance.

Abstract: Based on the principal-agent theory, this paper constructs a n-level principal-agent model with multi-branches at the chain terminal, introduces the improved Fehr-Schmidt fairness preference theory, and establishes a n-level incentive framework covering the horizontal and vertical fairness preferences of terminal agents. By treating intermediate agents as a unified whole through the "black-box" approach, this paper focuses on analyzing the influence mechanism of terminal agents' fairness preferences on effort level, incentive contract design and the operational efficiency of the entire principal-agent chain. The research results confirm that incorporating fairness preferences into the incentive mechanism design of green supply chain principal-agent relationships can effectively stimulate the production and operation enthusiasm of terminal agents, improve their effort levels; at the same time, it can significantly reduce agency costs in the principal-agent chain, optimize resource allocation efficiency, and thereby enhance green production efficiency. The conclusions and model design of this study provide a new theoretical path and practical reference for the collaborative operation and sustainable development of actual green supply chains with terminal multi-branch principal-agent structures.

Abstract: This study examines whether the effects of fiscal revenue and expenditure on grain production in China are nonlinear. Using a balanced panel of 31 provinces from 2007 to 2021, we analyze major revenue-side and expenditure-side fiscal instruments, including the cultivated land occupation tax, value-added tax, agricultural insurance subsidies, agricultural loan interest subsidies, rural minimum living security subsidies, education expenditure, and transportation infrastructure expenditure. To identify regime-dependent marginal effects, we employ panel kink and double-kink regression models with endogenously estimated kink points. The results show that fiscal effects are intensity-dependent rather than constant. The cultivated land occupation tax exhibits a kinked relationship with grain production, with a stronger land-protection effect beyond a certain policy intensity. Agricultural insurance subsidies display a double-kink pattern, with the strongest positive effect concentrated in an intermediate subsidy range. Rural minimum living security subsidies are positively associated with grain production at lower levels, but their marginal effect weakens and may become negative after the kink point. Overall, the findings suggest that fiscal policy effectiveness depends not only on policy direction but also on policy intensity, implying that grain-support policies should be calibrated with attention to nonlinear threshold effects.

Abstract: In this paper we study competition between AI providers when users cannot fully verify model quality. Many AI services are not well described as standard search goods, and they are not pure experience goods either. Price, interface quality, and latency are usually observable, but reliability, hallucination risk, and the downstream cost of error are often only imperfectly observable even after use. Building on the emerging view that algorithmic advice can exhibit credence-good features, we embed that insight in a static industrial-organization model of vertical quality differentiation, costly certification, and limited user comprehension. Two firms choose whether to offer a low-quality or high-quality model. High-quality AI reduces error risk, but it is slower and costlier. A high-quality firm can purchase credible certification or disclosure, yet only a subset of users can interpret it. We characterize the pure-strategy equilibrium set, show how low-quality pooling can arise even when superior technology exists, and identify a quality-trap region in which the unique market equilibrium is low-quality pooling although an allocation with one high-quality provider is welfare superior. We then analyze policy. Standardized certification works through the demand side by increasing the fraction of users who can reward quality; minimum quality standards work directly but bluntly; liability shifts firms' cost incentives and weakly shrinks the region in which a low-quality industry outcome can be sustained. Contrary to a common rhetorical move in AI policy debates, these instruments are not interchangeable. The model also clarifies that our framework is a certification model rather than a full Grossman-Milgrom unraveling game: the key distortion comes from limited user comprehension of costly, truthful quality communication. The results offer a tractable industrial-organization foundation for current debates over hallucinations, model evaluation, AI documentation, and governance.

Abstract: Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China's scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models-centralized, distributed and battery-swapping, and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China's large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide.

Abstract: This study examines whether international food price dynamics provide a reliable signal of undernourishment and human development outcomes relevant to the attainment of SDG 2 (Zero Hunger) by 2030. We apply wavelet coherence analysis to the FAO Food Price Index and the prevalence of undernourishment (SDG Indicator 2.1.1) over 2001–2023, testing statistical significance against an AR(1) red-noise null hypothesis. Hybrid ARIMA–Random Forest models generate probabilistic price forecasts through 2030. Despite strong raw coherence (R² ≈ 0.77), only 7.8% of time–frequency cells achieve statistical significance, indicating that apparent co-movement largely reflects autocorrelation rather than substantive dependence. Where significant coherence emerges, it concentrates at medium-run horizons (3–6 years), consistent with undernourishment as a habitual dietary adequacy measure linked to sustained affordability pressures affecting health, productivity, and human capital formation. Rolling correlation analysis reveals a regime shift around 2012—from negative to positive correlation—coinciding with a slowdown in progress toward reducing hunger. Price forecasts exhibit rapidly widening confidence intervals (by ±131 index points by 2030), underscoring fundamental limits to predictability. These findings caution against mechanistic inferences from global price indices to hunger and human development outcomes, redirecting policy emphasis toward domestic transmission channels and nutrition-sensitive safety nets.

Abstract: The Phillips curve remains central to monetary policy, yet its functional form has been intensely debated following the 2021–2023 inflation surge. This paper offers novel empirical evidence by providing the first symmetric comparison of regime-dependent nonlinearities in the inflation–slack relationship between the United States and the Euro Area, using identical threshold and smooth-transition frameworks on quarterly data extending through 2025Q4, the most recent available. Core PCE inflation (US) and core HICP excluding energy, food, alcohol, and tobacco (Euro Area) are modeled as functions of unemployment and output gaps, with controls for oil shocks and inflation expectations. TAR/SETAR and LSTAR estimations uncover statistically significant steepening in tight labor-market regimes. In the US, the slope more than doubles when the unemployment gap falls below –0.61 percentage points. In the Euro Area, a comparable kink emerges near zero (–0.048 pp), with smoother transitions reflecting greater wage and price rigidities. Post-2019 subsamples exhibit amplified nonlinearity, consistent with supply-shock transmission in high-pressure conditions. Extensive robustness checks affirm these findings. The results establish a state-dependent sacrifice ratio, with sharply higher disinflation costs in tight regimes, and highlight substantial risks of monetary policy miscalibration in future high-pressure episodes.