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.

Abrupt transitions that are associated with global crisis events can lead to systemic collapse. Previous models for investigating early warning signals for time series–based oil systems focused on the fluctuation characteristics of single points and ignored the dynamic process in the oil series, which is characterized both by historical changes in the oil series and by its nonlinear fluctuations coupled with related variables. In this study, an early warning model that combines a deep learning model, a Markov regime-switching model and a reconstructed hybrid network, including a self-dynamic network and a relationship-dynamic network, is proposed. West Texas Intermediate (WTI) crude oil and natural gas futures daily prices are selected as the sample data. Abrupt transitions are identified, and their characteristics that are influenced by typical events are investigated. The dynamic features can be measured through the structures of the hybrid network; the in-degrees, out-degrees, and weighted degrees of the nodes follow power-law distributions. Early warning signals for abrupt transitions can be effectively captured via the deep learning model. Importantly, the deep learning-based early warning model integrated with the hybrid network outperforms that integrated with the self-dynamic network, with the average accuracies of both models on the training and testing sets exceeding 90

Analyzing the evolution of heterogeneous consumer preferences for electric vehicles (EVs) is crucial for accurately identifying consumer demands and market development stages. Based on model-level monthly sales data from the Chinese automotive market between 2017 and 2023, this study employs a random coefficient discrete choice model with aggregated data to examine the evolution of revealed consumer preferences for EVs across multiple dimensions, including price, energy type, and vehicle specifications. Counterfactual analysis is also conducted to disentangle the impacts of fiscal incentives and technological progress on EV adoption. The results indicate that consumers prioritize cost-effectiveness and fuel economy. EV preferences have risen rapidly over the past seven years, showing a trend toward premiumization. Battery electric vehicles provide a more stable consumer utility. Notably, fiscal incentives effectively stimulate consumers’ purchase intentions, while technological progress serves as the fundamental driving force behind the long-term growth of the EV market.

The production process of China’s vanadium industry is of critical importance for understanding efficiency, cost formation, and emission outcomes in strategic mineral sectors. This study constructs a Material-Product (M-P) chain framework to characterize the staged production process of China’s vanadium industry and systematically examines physical flows, production costs, carbon emissions, emission intensities, and vertical intra-industry linkages across five stages. To capture production efficiency dynamics, unit-time production is introduced as the core shock variable, reconstructing the M-P chain shock-response framework from an efficiency-oriented perspective and avoiding the confounding effects of conventional scale-based indicators. The results reveal a pronounced “high-cost, medium-efficiency, and high-emission” pattern with strong stage heterogeneity and vertical transmission effects. The fourth stage exhibits the highest production efficiency but also the strongest carbon intensity response, highlighting an urgent efficiency-environment trade-off. The second stage functions as a central hub, where production rhythm fluctuations exert amplified influences on both upstream and downstream stages. The third stage, characterized by high costs and low unit-time output, emerges as a key constraint on economic sustainability, while the first and fifth stages contribute relatively little to overall system performance.

As the global energy transition accelerates, the low-carbon benefits realized downstream in new energy supply chains do not mean that upstream resource extraction is environmentally benign. The local ecological costs induced by lithium extraction therefore require renewed scrutiny. Existing studies have largely focused on single regions or single environmental effects, making it difficult to identify the general patterns and context-dependent mechanisms underlying the impacts of lithium development. Methodologically, this study develops an integrated evaluation framework that combines ecosystem service value, development economic performance, and a synergy deviation index to characterize economic-environmental relationships across different lithium development systems. This framework is integrated with XGBoost-SHAP and covariance-based structural equation modeling to examine model-attribution patterns, nonlinear response intervals, and structural pathway associations. This design allows ecological costs, development gains, spatial imbalance, and pathway heterogeneity to be assessed under a unified analytical structure. Empirically, the results show that lithium development is generally accompanied by a pronounced spatial coupling between mining expansion and increasing economic-environmental imbalance. This imbalance does not increase linearly with development intensity but exhibits clear landscape-specific differentiation. In high-intensity development systems, conflict tends to be spatially concentrated; in systems where mining is superimposed on broader development activities, conflict tends to diffuse along networked corridors; and in ecologically sensitive systems, severe imbalance may emerge even at relatively small development scales. Further analysis indicates that resource type alone does not determine the magnitude of upstream costs; ecological baseline and development organization strongly shape how disturbance propagates. These findings suggest that the assessments of critical-mineral sustainability should consider not only extraction intensity and downstream decarbonization benefits but also upstream environmental costs across different development systems. The study provides methodological support and empirical evidence for lithium governance and the sustainability assessment of critical mineral supply chains.

Operationalizing the planetary boundaries (PB) framework at a regional scale is a critical sustainability challenge. This study addresses this gap within the Yangtze River Economic Belt (YREB), a globally significant nexus of urban, agricultural, and ecological systems. By integrating 30 m land use data with socioeconomic statistics, we developed an improved ecosystem service value ( V_E ) calculation method and coupled ecological carrying capacity (EC) with ecological footprint (EF) to establish an ecological boundary (EB) model. Our findings reveal three key insights: (1) Total ESV increased by 92.4

Aluminum, as a strategic mineral resource, plays a critical role in the development and strategic maneuvers of major nations. Owing to the uneven distribution of aluminum mines, countries seek to expand their national mineral resource entitlements through various economic mechanisms. Shareholding relationships reveal intricate economic ties between nations, offering a novel analytical angle for examining national mineral resource dependence and economic influence. This paper is based on shareholding relationships and constructs a global aluminum resource bipartite shareholding complex network from a listed mining company shareholding perspective, uncovers shareholders and their national affiliations, and discusses the impact of shareholding relationships on the global aluminum resource entitlements structure and community distributions. Our findings indicate that domestic shareholders hold the most of aluminum resource entitlements. Over 90 % of aluminum resource entitlements are concentrated in ten countries, with Australia and Indonesia holding the highest entitlements ranks. Aluminum resource entitlements are influenced by factors such as resource endowment, technological level, and economic activity. Investment in foreign shareholdings can increase the resource entitlements of countries with insufficient resource endowments. The United States, for example, has elevated its aluminum resource entitlements ranking through extensive foreign investments. Our research extends the theory of resource entitlements and provides significant academic contributions and practical guidance for ensuring resource supply security and promoting effective international resource management.

This study employs multiscale decomposition and causal inference to investigate the multiscale causal effects between futures in different segments of China's steel industry chain from September 2019 to December 2023. The decision tree model is used to analyse the heterogeneity of causal effects under external events. The results indicate that upstream iron ore and downstream rebar futures are affected by futures in other segments; simultaneously, their short-term price fluctuations greatly impact the steel industry chain. The short-term price impacts between futures show significant causal effects, including upstream futures affected by downstream rebar and hot rolled coil futures and midstream futures affected by downstream wire rod and rebar futures. However, downstream futures are affected by the short- and medium-term causal effects of upstream iron ore and midstream ferrosilicon futures. External events such as coronavirus disease 2019 and financial policy make these causal effects heterogeneous, especially the short-term causal effects of wire rod and iron ore futures, thereby impacting the price fluctuations of futures in other segments. The results help market participants enhance their intrinsic understanding of multiscale price fluctuations and make more accurate decisions under the impact of external events.

Understanding multi-scale spatiotemporal dynamics of urban carbon emissions is critical for crafting targeted decarbonization strategies. However, existing studies predominantly examine emissions at singular scales, overlooking cross-scale interactions and causal spatial dependencies. This study proposes a hierarchical analytical framework integrating urban agglomeration, county, and 500m grid levels to dissect carbon emission patterns across China's Yangtze River economic belt (YREB) from 2010 to 2020. Leveraging convergent advances in satellite-derived NPP-VIIRS-like nighttime light data and provincial energy inventories, we develop an ensemble approach combining geographical convergent cross mapping (GCCM) with multi-scale geographically weighted regression (MGWR) to unravel causal mechanisms and scale-dependent drivers. Our findings reveal three insights: (1) Emission trajectories exhibit strong path dependency, with the Yangtze River delta agglomeration contributing 60.4 % of total YREB emissions through 2020, while emerging hotspots demonstrate spatial decoupling from traditional economic cores; (2) Causal analysis identifies technology-intensity and tertiary sector growth as dominant mitigation factors, contrasting with persistent carbon lock-in effects from legacy infrastructure; (3) MGWR exposes paradoxical regional dynamics where urbanization drives emission reductions in advanced economies yet accelerates emissions in developing regions. The framework advances spatial econometrics by reconciling Simpson's paradox in cross-scale analysis while providing actionable intelligence for tiered carbon governance. This contribution establishes a replicable paradigm for transboundary emission management in mega-economic corridors globally.

This study examines the impact of international crude oil prices on national sub-price indices following external shocks. It analyzes the heterogeneous transmission mechanisms of these shocks across diverse national price index networks. To achieve this, we employ Granger causality tests as the filter to construct impulse response networks. This approach helps unveil the duration, magnitude, and pathways of impact on sub-price indices in five countries: China, the US, Russia, Germany, and the UK. Our findings suggest that the impact of crude oil price changes on national sub-price indices is most pronounced within 1-2 months, and more persistent on the Producer Price Index (PPI) than the Consumer Price Index (CPI). Identifying specific sub-price indices affected by shocks shows that China and the US are more significantly impacted. Moreover, identifying the transmission paths of crude oil price changes within a country's internal price system underscores the significance of the CPI of transportation. This study of price transmission within countries offers key insights for managing economic shocks at the microeconomic level.

Tungsten, as a key mineral resource, suffers from value loss in developing countries due to technological defects. It is necessary to strengthen regional cooperation to prevent this phenomenon. The Belt and Road Initiative(BRI) provides a platform to promote tungsten resources cooperation, thereby generating trade dependence network between countries. Exploring key countries in the tungsten industry chain trade dependence network is of great significance for mitigating trade risks in the future. To this end, this paper construct tungsten industry chain trade dependence multilayer network model to explore key countries' role. Results show that (1) the BRI has made remarkable impact on global trade pattern and intensified the trade dependence between countries; (2) China, Vietnam, and Indonesia have played important roles in the single-layer upstream, midstream, and downstream trade dependence network; (3) Remarkably, Thailand and Singapore, as countries with limited tungsten resources, are critical countries in both multilayer trade dependence networks and their own industrial chains over past eleven years, differing from single-layer networks. Our findings provide a holistic picture of key countries ranging from upstream, midstream, downstream and even the whole tungsten industry chain, attempting to draw attention from the perspective of trade dependence in cooperative organizations, namely BRI and provide policy implications for future risk management.

This paper investigates both the direct and indirect pathways through which climate change risks influence the fossil energy stock market in China, focusing on the mediating effect of investor attention and the moderation effect of the crude oil market. Utilizing China’s daily climate risk data and energy stock return data from September 4, 2017, to June 30, 2022, we employ the partial linear function coefficient model, zero-inflated negative binomial regression, and the Bootstrap technique to unravel these complex relationships. Our analysis reveals pivotal findings: (1) Climate transition risk has a U-shaped nonlinear direct effect on fossil energy stock returns, with a critical inflection point identified at 0.3364. This risk also positively mediates the return rate by shaping investor attention. Notably, exceeding a risk threshold of 0.3 intensifies the adverse impact of oil price volatility on returns. (2) Climate physical risk does not exert a discernible direct effect or mediation on the return rate of fossil energy stocks. These insights offer a comprehensive perspective on the complex interplay between climate change risk and China’s fossil energy stock market, shedding light on the nonlinear dynamics that govern these relationships. From a practical perspective, these findings underscore the need for policymakers to design risk mitigation strategies tailored to transition risks, while investors should remain vigilant to oil price volatility when climate risk exceeds critical thresholds.

As the global economy continues to evolve and human society’s resource demands grow, the strategic significance of mineral resources becomes increasingly pronounced. The competition for mineral resources extends beyond primary minerals and encompasses the entire industrial chain. This article, from the perspective of the industrial chain, defined the boundaries of the critical mineral resource industrial chain as the whole industry chain of exploration and mining-smelting and processing (transportation)-production-industrial utilization-recycling and reuse. We defined the availability of critical mineral resources in terms of each link of the industrial chain, emphasizing the critical roles played by different segments of the industrial chain. Building upon existing research methods on mineral resource availability, we identified 20 factors that influence the availability. These factors were categorized into six dimensions: geological, economic, technological, geopolitical, regulatory, and social aspects. Furthermore, we defined the industrial chain links that they directly affect, and constructed the indicator system of the availability of critical mineral resources from the perspective of different links. In the process of the systematic review, we found that research on critical mineral resource availability from the industrial chain perspective still has shortcomings in terms of its implication, secondary supply, multi-link nexus, and model development and utilization. We proposed four major frontier directions: (1) The new connotation of critical mineral resource availability from the perspective of industrial chain. (2) The novel construction of the relationship between primary resource and secondary resource availability under the background of resource challenges. (3) The coupling relationship of multi-link availability of the whole industrial chain of critical mineral resources. (4) The construction of the availability database and research model of the whole industrial chain of critical mineral resources.

Driven by emerging technologies such as batteries in electric vehicles (EVs), global lithium market undergoes significant changes. To explore market instability of global lithium resources subjected to EV development, lithium exploration, and carbon constraints, a model of global lithium markets-electric vehicle development-carbon constraints (GL-EV-CC) is established based on a four-dimensional difference equation feedback system. Accordingly, a detailed analysis strategy by virtue of bifurcation and chaotic theory is investigated, with which some sudden changes and critical points can be obtained to provide some implications in face of the challenges of future market changes. Results show that lithium demand for EVs will evolve from an equilibrium state to a periodic state and finally to a chaotic state with the continuous change of the degree of investment in Research and Development (R&D) of EVs. When the degree of exploration is less than 0.15, attention should be given to the resource exhausted crisis. When the degree of carbon constraint is high, market instability of lithium demand should be fairly concerned, while when the degree of carbon constraint is low, market instability of lithium supply should be focused. Unlike previous studies, chaotic dynamic analysis is employed in this study, and thereby not only a broader sensitivity analysis but also complex dynamic behaviors such as chaotic behavior at the moment of equilibrium can be explored, in which feasible policies can be proposed to control some policy parameters to be within a reasonable range.

Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.

The key to resource security is to make sure supply safety. Due to the difference of resource endowment, however, the distribution of global production and consumption is not matched, so that the supply shock can spread within the multi-layer formed by global supply chains. Therefore, understanding how supply shock spreads within the multi-layer network is critical for policy makers to prevent the propagation of resources crisis. From the perspective of global steel product chain, we constructed a supply shock propagation model based on a multi-layer network and simulated the propagation effect using the steel chain trading data. Results show that: (1) No matter how large the impact strength, only a few countries such as Russia, Ukraine, Brazil and Australia, once the supply shortage of steel upstream products occurs, can affect the downstream products of most countries in the world. (2) If the country's risk resistance is extremely weak, the supply shock propagation in the pig iron network is dominated by trade relations, while crude steel network is dominated by production relations, and steel product network is dominated by the coupling relationship between trade and production. (3) Faced with the risk of complete interruption of exports from different risk sources, the critical points of infection thresholds in importing countries are different. (4) The propagation path of supply risk in the multi-layer network of steel trade presents a multiple hub radial network structure with key countries as the core. These findings help policy makers adjust trade policies in a timely and scientific manner.

This study investigates integration dynamics between the Chinese stock market and major developed counterparts—Australia, Germany, Japan, the UK, and the US—focusing on portfolio diversification. Using a comprehensive analytical approach from 2012 to 2022, encompassing events like the Belt and Road Initiative, the Shanghai market crash, US-China trade tensions, and the COVID-19 pandemic, the research employs descriptive statistics, unit root tests, cointegration analysis, and VECM-based Granger Causality Tests. Findings indicate modest integration, endorsing diversified portfolios for developed country investors due to higher returns in China with acceptable risk. Unit root analysis confirms cointegration with developed indices, indicating relatively low integration. Granger Causality Tests reveal bidirectional causality, emphasizing mutual influence. Notably, no causal link exists between the US and China, possibly due to regulatory disparities and the trade war. The study enhances understanding of Chinese stock market dynamics, supporting global economic intertwining and urging further openness of China's domestic shares for economic growth.

Tungsten, as an important strategic resource, is increasingly crucial for the industrial development of various countries (regions). From the perspective of the tungsten value chain, this paper uses complex network to construct global embedded tungsten value flow networks (GETVFNs) through value flow relationships in trade to analyze their structural evolution and analyzes the influence mechanism of GETVFNs through temporal exponential random graph model (TERGM). The results show that (1) GETVFNs have the significant characteristic of disassortativity. (2) China had the most import trading partners. Germany had the highest total unit value of imported embedded tungsten products. (3) Transitivity, connectivity, and preference attachment effects have significant impacts on the evolution of GETVFNs; when both countries (regions) are neighboring, are closer geographically, sign the same regional trade agreement, and have a common language, they are more likely to generate an embedded tungsten value flow relationship. (4) The technological level of tungsten mill products and cemented carbide (downstream products) significantly affects the evolution of the global embedded tungsten value flows. Understanding the structural evolution and influencing factors of GETVFNs can help countries (regions) optimize their industrial structures to obtain higher economic benefits, avoid trade risks, and promote the rational development of international tungsten trade.