Vanadium is increasingly recognized as a strategic and critical mineral resource on a global scale. Despite possessing the world’s largest reserves, China’s vanadium industry continues to face constraints such as limited industrialization and inadequate deep-processing capabilities. Mapping key industrial linkages is crucial for clarifying upstream-downstream interactions and enhancing the sector’s core competitiveness. In this study, we develop a disaggregated, nested input–output (I–O) model to systematically characterize these interdependencies. Our analysis reveals that, through a “resource-processing-application” global collaborative network, China’s vanadium industry has established an industrial ecosystem centered in the Asia-Pacific region. This ecosystem is defined by multiregional interconnectedness and comprehensive chain coverage, which collectively drive industrial advancement. In terms of industrial positioning, the core dynamic stems from a bidirectional synergy between China’s steel and chemical sectors. Regarding regional collaboration, upstream supply is directly bolstered by Australia’s iron ore mining industry. Downstream market linkages are anchored by the steel industries of Japan and Republic of Korea, which serve as technical collaboration hubs, while an export-oriented network is sustained by construction demand in the Middle East and emerging markets across the Asia-Pacific. Furthermore, the sequence of “material input-refining processing-vanadium steel output” characterizes its transnational circulation pattern.

Amid escalating geopolitical tensions, the global nickel resource trade is facing mounting systemic risks. This study develops a network-based framework that integrates structural exposure risk indicators and structural stress testing based on extinction analysis to assess the vulnerability of the global scrap nickel trade network (GSNTN). Results reveal a dual-risk structure characterized by intensified direct exposure and increasing efficiency imbalance. Four simulation scenarios of cooperation disruptions and policy barriers indicate that, nations exhibiting high dependency and reachability but low constraint tend to act as high-intensity risk. In contrast, highly constrained nodes embedded in cohesive trade clusters are prone to becoming passive vulnerable receptors, forced to absorb concentrated systemic pressure. Notably, some low-trade value intermediary countries act as disruption amplifiers. The findings highlight the vulnerability of the GSNTN, emphasizing that major countries should strengthen cooperation and avoid conflicts to ensure the stable operation of the supply chain.

The accelerating adoption of clean energy technologies, especially electric vehicles (EVs) and energy storage systems (ESS), is rapidly increasing demand for lithium-ion batteries (LIBs), raising critical concerns about lithium supply sustainability. Here, we develop a dynamic material flow analysis framework to quantify global lithium flows and stocks from 1991 to 2021 and to project future lithium supply and demand through 2050 under coupled scenarios of EV and ESS expansion, battery cathode transition, battery lifespan extension, recycling improvement, and EV-to-ESS cascade utilization. Our projections show that faster ESS growth will intensify medium-term lithium pressure, while EV-to-ESS cascade utilization can gradually relieve that pressure by substituting for newly manufactured ESS batteries as larger volumes of retired EV batteries. Advancements in LIB technologies, extended battery lifespans, and enhanced recycling can significantly alleviate supply-demand gaps, but under high-demand scenario they remain insufficient, making additional supply-side responses indispensable, especially faster expansion in mining, refining, and lithium chemical conversion. These findings highlight the need to jointly consider EV and ESS development, recycling, second-life use, and coordination of supply chain expansion in long-term lithium planning.

Geopolitical risks increasingly threaten global supply chain and trade stability. As a strategically vital resource, rare earths hold substantial economic value in bilateral trade and have become a key arena for international competition and negotiation. Amid geopolitical tensions, rare earth trade is especially susceptible to becoming a focal point of conflict. Therefore, this paper examines how geopolitical risk affects the volume of bilateral rare earth trade, with a focus on the underlying mechanisms and its varied impact across different segments of the rare earth industry chain. We collect bilateral trade data for the complete rare earth industry chain, encompassing upstream ores, midstream smelted products, and downstream functional materials, from key trading entities such as China, the United States, Australia, the Netherlands, Germany, Malaysia, Vietnam, Japan, Republic of Korea, and Thailand between 2000 and 2021. Subsequently, a panel regression model is used to evaluate the extent. The findings indicate that geopolitical risk has a negative effect on the scale of bilateral trade in rare earth. Specifically, it inhibits the scale of bilateral trade in rare earth by increasing trade costs and altering bilateral political relations between countries. However, a favorable institutional environment of trading partner countries can mitigate this negative effect. The heterogeneous results show that geopolitical risk has a negative effect on the scale of bilateral trade in upstream and midstream rare earth products. In contrast, it has a promotive effect on the scale of bilateral trade in downstream rare earth products. This is attributed to the greater competitiveness and irreplaceability of downstream products compared to other rare earth products. This research helps countries address the shock of geopolitical risk and establish a more stable rare earth trade system in a complex and constantly changing world.

Under the “One Country, Two Systems, Three Legal Jurisdictions” framework, cross-regional collaboration in the construction industry of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is more complex than other urban agglomerations, and this makes traditional methods relying on manual analysis of influencing factors inefficient and subjective. While existing large language models (LLMs) can meet the needs of intelligent applications, they lack specific domain knowledge. Considering the intelligent advantages of LLMs, this research proposes a lightweight expert agent through providing an optimised low-rank adaptation (LoRA) model SVDSR-LoRA, integrating domain knowledge using the fine-tuning method. Experiments on the 1.5b lightweight base model of qwen2.5 and deepseek-r1 show that the proposed SVDSR-LoRA training method can increase the mid-term convergence speed by 36%-50% compared with the standard LoRA method. A lightweight multi-expert agent influencing factor analysis system is constructed to simulate a collaborative analysis environment with experiments showing the hit rate in high-frequency influencing factors reached 100%. The comprehensive evaluation scored at 4.03/5.0 and demonstrated the capability in revealing the external environmental factors influencing cross-regional collaboration in the construction industry of the GBA systematically, which was significantly better than that of a single model (2.52-3.22/5.0). The proposed SVDSR-LoRA training model and the MAS system establishing pattern can provide a reference for rapid and effective lightweight agent system construction methods for intelligent analysis tasks of cross-regional collaboration in the construction industry of the GBA, as well as other similar LLM-based cross-regional collaboration research

Frequent extreme weather events not only threaten the safety of subway engineering construction but also pose severe challenges to project organizations. Facing continuous external shocks, enhancing organizational resilience (OR) has become a promising way for subway projects to effectively address climate risks. However, the existing theoretical understanding of OR in subway projects remains limited, and there is a lack of empirical evidence explaining how OR can be systematically constructed to cope with extreme weather. To this end, this study employed web crawling and expert interviews to collect multi-stakeholder interview data and diverse historical materials that characterize OR in subway projects under extreme weather events. Based on this, topic modeling techniques and focus group discussions were employed to model and optimize multi-source data, analyzing the structural, distributional, and multi-stakeholder differentiation characteristics of OR in subway projects. The findings show that OR in subway projects represents a collective capability composed of perceptual, planning, coping, adaptive, recovery, and bounce-forward learning capabilities, encompassing 43 dimensions. Among these, planning capability serves as the primary foundation for shaping OR, while bounce-forward learning capability provides critical support for transitioning from reactive response to proactive leapfrogging. Additionally, the five key stakeholders exhibit different patterns of OR, yet each plays a unique role in collaboratively constructing OR. This study not only advances the theoretical understanding of the multidimensional characteristics of OR in subway projects but also provides practical insights for project stakeholders in formulating targeted strategies to enhance resilience.

Emissions trading system has introduced carbon cost in emission-controlled firms that directly increased their product prices and indirectly raised the prices of other goods throughout supply chains, which ultimately drove up the living cost of households. We utilize China National Input-Output Table 2020 and the per capita annual consumption expenditure survey data 2020, both from China statistical yearbook, to model the impact of ETS on living cost of urban and rural households. The results show that the current product price increases in various sectors are relatively small (0.038%–2.947%), with heavy industries experiencing larger price increases compared to light industries. For eight consumption categories, notable price increases are observed in the Housing and Household facilities, articles and services (HFA&S) sectors, both reaching 0.324%. As to households, urban households (0.183%) experience a disproportionately higher cost burden compared to rural households (0.173%), suggesting progressive distributional effects. Moreover, living costs rise more in developed provinces than in less-developed regions. Overall, these regional and urban-rural cost burden disparities become increasingly pronounced under two conditions: higher cost pass-through rates, increased carbon pricing levels. Regarding carbon market expansion, the urban-rural cost burden disparity first increases during initial sectoral coverage growth, then decreases as coverage becomes more comprehensive.

Amid escalating climate pressures and uneven regional development, cross-regional technology cooperation has become essential for coordinated low-carbon transitions. This study proposes a novel “knowledge-equipment-institution” synergy framework to address gaps in understanding technology diffusion mechanisms and their emission reduction impacts within China’s regional diversity. Four regional cooperation alliances are identified, distinguishing between technology frontier and backward provinces. A multi-regional dynamic computable general equilibrium (CGE) model is constructed to assess cooperation outcomes. Two technology diffusion channels are integrated in the model: knowledge spillovers and machine and equipment (M&E) trade. Our findings reveal a fundamental trade-off between short-term efficiency and long-term resilience. Cooperation through M&E trade yields immediate energy saving and GDP growth benefits, but its long-term effectiveness is constrained. In contrast, cooperation via knowledge spillovers, despite modest short-term economic costs, fosters greater long-term adaptive capacity. The effectiveness of cooperation is highly heterogeneous across regions. For instance, the Beijing–Tianjin–Hebei–North-east alliance shows fluctuating carbon productivity, while the Yangtze River Economic Belt experiences productivity losses due to heavy industry lock-in. Ultimately, we find that a win-win dynamic is achievable, where technology frontier provinces accelerate industrial upgrading (tertiary sector share + 0.15%) and backward provinces enhance production efficiency. However, a sustained partnership hinges on the backward provinces’ commitment to enhancing their own absorptive capacity to avoid “free-riding” and low-level equilibrium traps. The findings provide a quantitative basis for designing differentiated and phased regional cooperation strategies to support adaptive climate governance.

Modern engineered systems are composed of multiple components. These components deteriorate over time, resulting in a decrease in the system’s overall performance. Inspecting the health states of all components provides comprehensive information for making optimal maintenance decisions, which, however, may incur high costs and result in prolonged system downtime. This necessitates combining system-level and component-level inspections into an optimal inspection and maintenance (IM) policy design to maximize overall profit. To date, this topic remains challenging and underexplored, which is investigated in this paper. First, we propose a bi-level IM policy where, at each decision epoch, the operator sequentially determines (i) whether to conduct a component-level inspection and (ii) which components to maintain, based on the system-level performance. Next, we consider that components have different deterioration processes and are subject to non-negligible IM duration, which renders the problem both partially observable and non-equidistant in decision timing. We adopt a Partially Observable Semi-Markov Decision Process (POSMDP) to model the decision-making process and compute the POSMDP quantities. To address the curse of dimensionality posed by the exponential growth of the belief space with respect to the number of components and their discrete states, we develop an enhanced Successively Approximated Point-Based Value-Iteration Algorithm (SARSOP). Two improvements make the algorithm scalable and efficient. First, we introduce macro-actions that enable the operator to defer intervention until system-level performance falls below a predetermined threshold, thereby reducing the depth of the SARSOP search tree by aggregating multiple primitive actions in the original POSMDP. Second, we propose a reward-shaping scheme that encourages SARSOP to prioritize exploration of these macro-actions. A comprehensive case study of photovoltaic (PV) panels demonstrates both the superiorities of the proposed bi-level IM policy and the developed solution algorithm.

Tracking time-sensitive space targets, characterized by high uncertainty and highly dynamic motion, requires multi-satellite coordination while accounting for the risk of target loss. This study proposes a mathematical model and a Time-Sensitive Space Multi-Target Observation Scheduling (TSSMTOS) algorithm that considers both tracking rewards and target loss scenarios. First, we establish a generalized real-time multi-satellite scheduling framework for tracking these unpredictable, rapidly evolving space targets. Subsequently, we introduce a Double Deep Q-network for Variable-Number Targets (DDQN-VNT). This approach enables the real-time allocation of dual satellites to each target, guided by heuristic rules, effectively addressing dynamic observation requirements. Experimental results demonstrate that DDQN-VNT outperforms traditional rule-based algorithms, the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), and Adaptive Large Neighborhood Search (ALNS) in complex scenarios, exhibiting superior performance, enhanced efficiency, and robust generalization capabilities.

Industrial classification tasks often face challenges such as class imbalance, noise and non-stationary data distributions. Most feature engineering methods aided by evolutionary algorithms and large language models (LLMs) of-ten rely on the predictive performance of downstream classification metrics, while neglecting the feature distribution structure and the relationship between features and labels under distribution shifts. To address these issues, we propose the Feature Meta-Model Agent (FMM-Agent), a framework that evolves features within a meta-model space defined by statistical information, rather than operating directly on raw data. FMM-Agent enables LLMs to perform operator restructure and refine the chain-of-thought to obtain better feature shaping. We further introduce a unified scoring mechanism to jointly evaluate label relevance and distribution stability, allowing the feature pool to gradually move toward better feature distribution shapes. Experiments conducted on 11 data sets show that FMM-Agent consistently improves the recognition ability of minority classes in terms of balanced accuracy, g-mean, and recall, and its performance is superior to other comparative methods. Ablation studies confirm the necessity of evolutionary restructure and generation mechanism strategies. In addition, experimental results with different LLMs show that although stronger models can produce more stable evolutionary process, the overall performance improvement of FMM-Agent does not depend on a specific model. It is worth noting that although FMM-Agent incurs additional inference time costs due to evolutionary feature generation, it achieves a good balance be-tween computational overhead and performance improvement.

Human–AI collaboration is becoming central to operational prediction, decision-making, and control. As AI systems become embedded in operational workflows, organizations face a recurring risk: decision support may improve while the practical space for human intervention, accountability, and learning becomes too constrained for meaningful contestation. This article develops an operational contestability framework to explain when and why this risk becomes collaboration failure. The key question is not simply whether humans remain formally “in the loop,” but whether AI-enabled operational decision systems preserve practical room for questioning algorithmic representations, reallocating decision authority, and revising collaboration routines before execution makes intervention costly or infeasible. The framework identifies three driving mechanisms: cognitive asymmetry, dynamic delegation, and meta-learning. These mechanisms progressively constrain what can be represented, who can intervene, and how collaboration routines can be revised. It also specifies two amplifying conditions: cognitive coupling and accountability-defensibility. These conditions raise the operational and justificatory costs of deviating from algorithmic baselines. The erosion of operational contestability unfolds through a recurrent process of Encoding, Habituation, and Closure (EHC): representations and authority rules are built into system design, algorithmic outputs become planning baselines, and those baselines become operational commitments under time pressure, interdependence, and accountability exposure. Generative AI intensifies this process by turning recommendations, explanations, and justifications into portable decision artifacts, thereby accelerating habituation and making closure less visible. The framework reframes human–AI collaboration as an operations management problem: governing decision systems so that representation, authority, and learning remain contestable across repeated decision cycles.

Zero-carbon industrial parks are core demonstration carriers for global industrial deep decarbonization and a key research hotspot in climate change and sustainable development. However, three structural issues have long restricted the formation of a globally comparable research paradigm: fragmented accounting benchmarks, imbalanced research priorities, and poor generalizability of findings. This comment systematically analyzes the above deviations: divergent accounting rules across mainstream frameworks weaken cross-study comparability; studies overfocus on energy system transition while neglecting core process-level decarbonization; case-specific conclusions cannot adapt to the transition needs of developing economies. To correct these deviations, this comment proposes a universal minimum consensus benchmark, advocates a rebalanced research agenda, and outlines a differentiated globally adaptable framework with seven priority propositions, to advance the systematic development of the field.