Search Results (539)

Against deglobalization and intensifying geopolitical conflicts, maritime bulk commodity supply chain vulnerability and resilience governance are strategic priorities for 75% of countries. To tackle rising global uncertainty, this study proposes the country-level risk identification, monitoring, and extrapolation (RIME) framework for such supply chains, which aligns with the theoretical demand for macro, end-to-end risk integration beyond the traditional firm-level focus. Based on the “supplier country–shipping route–importing country” spatiotemporal linkage, we construct the first standardized country-level vulnerability index. It overcomes the limitations of existing static and localized assessments by integrating spatiotemporal, multi-source risks across the full physical chain, thereby enabling dynamic, macro-level monitoring and supporting systematic diagnostics and trend tracking of national supply chain security. We also develop an emergent risk simulation technique to quantify the direction and intensity of compound disturbances as well as the system’s dynamic responses. Empirical validation with China’s iron ore imports shows that the index effectively captures risk evolution, while the simulations confirm that sudden disruptions amplify systemic risk. This framework fills national strategic security theoretical gaps and provides governments with dynamic monitoring, quantitative assessment, and policy forecasting tools. Full article

Cultural ecosystem services (CESs) represent a critical link between ecosystems and human well-being and constitute a core foundation for regional sustainable development. The balance between CES supply and demand directly affects the coordination efficiency between ecological conservation and socio-economic development, making it a key prerequisite for ecosystem management, conservation planning, and policy formulation. This study focuses on the circum-Taihu region and integrates multi-source data to assess public perceived demand and spatial supply capacity of CESs. Supply–demand matching relationships are examined across three dimensions, namely, scenic beauty, cultural heritage, and recreation, through the construction of a region-specific CES quantitative indicator system. The impacts of multiple environmental factors on CES supply–demand dynamics are further explored to provide scientific support for coordinated ecological, cultural, and economic sustainability at the regional scale. The findings demonstrate the following: (1) the proposed methodology effectively quantifies CES perception and supply capacity in the circum-Taihu region. Scenic beauty exhibits the highest perception levels, whereas cultural heritage and recreation show lower perception. Cultural heritage displays the strongest supply capacity, whereas scenic beauty and recreation exhibit weaker supply. (2) Significant spatial imbalances exist between CES perception levels and supply capacity across the circum-Taihu region. Areas exhibiting mismatches constitute the largest proportion for cultural heritage CESs, followed by scenic beauty, with recreation displaying the smallest amounts of imbalance. (3) Environmental drivers exert differentiated effects on CES supply–demand relationships. Slope, road network density, and elevation have significant positive effects, whereas the normalized difference vegetation index (NDVI), distance to water bodies, and distance to roads exhibit significant negative effects. Distance to roads imposes the strongest inhibitory influence on CES perception, whereas elevation emerges as the most influential driver of public perceived CES levels. Full article

Investigating the spatio-temporal differentiation patterns and driving factors of ecosystem services (ESs) supply and demand is of great significance for early warning of ecosystem imbalance risks and identifying regional natural resource supply–demand conflicts. This study takes the typical coal-grain overlapping area (CGOA) in Eastern China as the research object, dividing it into mining townships (MT) and non-mining townships (NMT) for comparative analysis. By integrating the InVEST model, ESs supply–demand ratio (ESDR) index, four-quadrant model, and the XGBoost-SHAP algorithm, the study systematically reveals the spatiotemporal differentiation characteristics and driving mechanisms of ESs supply and demand from 2000 to 2020. The results indicated that: (1) grain production (GP) service maintained a continuous supply–demand surplus, with the ESDR of NMT areas surpassing that of MT areas in 2020. The ESDR of water yield (WY) service was significantly influenced by interannual fluctuations in supply, showing deficits in multiple years. The decline in carbon sequestration (CS) service and sharp increase in carbon emissions led to a continuous decrease in the ESDR of CS service, with MT areas facing a higher risk of carbon deficit. (2) The spatial heterogeneity of ESs supply and demand was significant, with GP and CS services exhibiting a typical urban-rural dual spatial structure, and the overall region was dominated by the Type II ESs supply–demand matching (ESDM) pattern. The ESDR of WY service generally decreases from Southeast to Northwest across the region. with the Type IV ESDM pattern dominating in most years. (3) Human activities are the core driving force shaping the supply–demand patterns of ESs. Among these, land use intensity exhibits a nonlinear effect, high population density demonstrates an inhibitory effect, and MT areas are more significantly affected by coal mining subsidence. Natural environmental factors primarily drive WY service. The research findings can provide a scientific reference for the coordinated allocation of regional natural resources and the sustainable development of the human–land system. Full article

This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia. Full article

The study addresses the assessment of environmental sustainability in agriculture, noting that the existing scientific literature has predominantly focused on negative environmental impacts, particularly greenhouse gas emissions from the livestock sector. It argues that a comprehensive evaluation of farming systems should go beyond impact-based metrics and instead compare the demand and supply of natural capital, using appropriate methodologies such as the ecological footprint (EF). Accordingly, the objective of the study is to analyze the environmental sustainability of fattening pig farming systems in Italy by applying the EF to compare a virtuous case-study farm (located in Umbria, 72.4 ha of utilized agricultural area, and 1960 pigs per year) with a representative sample of ninety-four specialized pig-fattening farms drawn from the Italian FADN 2023 database. The results show the following marked differences between the two systems: the case study exhibits a positive ecological balance (EB = +50.1 gha; IEP = +0.69 gha/ha), while the FADN sample displays, on average, a negative ecological balance (EB = −167.6 gha) and a strongly negative sustainability index (IEP = −3.84 gha/ha). These findings indicate that, in a sector characterized by generalized environmental unsustainability, the preservation of natural capital can be achieved not only through low-impact technical solutions, but also by addressing structural factors (e.g., livestock density per unit area and the presence of non-productive land uses). Overall, the study demonstrates that sustainability assessment requires explicitly comparing natural capital demand and supply, rather than merely quantifying emissions. Full article

Ice-and-snow tourism cities face pronounced seasonal fluctuations that place strong pressure on urban accommodation systems. Understanding the spatial distribution, accessibility, and service capacity of hotels is therefore critical for sustainable tourism management in cold-region cities. Taking Harbin, China, as a representative winter tourism destination, this study develops a GIS-based spatial analytical framework to examine the spatial organization and service performance of star-rated hotels. Using data from 553 three-star and above hotels, combined with questionnaire survey data (N = 224), we apply the Nearest Neighbor Index (NNI), Kernel Density Estimation (KDE), and raster-based cost-distance accessibility analysis to identify spatial clustering patterns, accessibility differentiation, and mismatches between hotel supply and peak seasonal demand. We find that available hotel rooms can only meet about 60% of peak-season demand, indicating a severe capacity deficit. The results reveal a clear core–periphery spatial structure of star-rated hotels, significant accessibility disparities among hotel categories, and a pronounced mismatch between accommodation capacity and tourism demand during peak winter seasons. Peripheral areas exhibit limited accessibility and insufficient service capacity, while central districts experience high concentration and pressure. These findings highlight the importance of integrating spatial equity and seasonal demand considerations into accommodation planning and infrastructure optimization, providing policy-relevant insights for sustainable tourism development in cold-region cities. Full article

In recent years, global supply chains have become increasingly vulnerable to geopolitical tensions, pandemics, and energy crises, particularly in resource-constrained regions characterized by weak infrastructure and high transaction costs. Using panel data on A-share listed firms in China’s central and western regions from 2010 to 2022, this study examines the effect of firm-level digital transformation on supply chain resilience. We construct a digital transformation index and employ an instrumental-variable approach based on the interaction between terrain ruggedness and lagged digital transformation to address endogeneity concerns. Empirical results show that the digital transformation of enterprises has significantly enhanced the resistance and recovery capabilities of the supply chain, verifying its effectiveness in resource-constrained environments. Mechanism analyses reveal that this effect operates through increased supply chain diversification—especially customer diversification—and improved supply–demand matching enabled by more accurate demand forecasting and inventory management. Heterogeneity tests indicate that the resilience-enhancing effects are more pronounced among non-state-owned firms, manufacturing enterprises, and firms in less technology-intensive industries. Overall, our findings provide empirical support for transaction cost economics, dynamic capability theory, and the resource-based view, highlighting the strategic role of digital investment in strengthening supply chain resilience in infrastructure-constrained settings and contributing to the aims of Sustainable Development Goal 9. Full article

Growing consumer demand for sustainable food products has amplified the use of “green” marketing claims, yet transnational agri-food supply chains face a critical “perception–reality gap” due to data fragmentation and the absence of independent verification, fostering significant greenwashing risks. This study explores how the synergistic integration of Blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) can bridge this gap. Utilizing a PRISMA-inspired qualitative systemic analysis and scenario modeling, we propose the “Converging Technologies for Sustainable Agri-Food” (CTSAF) model, formalized through a mathematical Green Claim Veracity Index (Vi) and AI-driven anomaly detection algorithms. The analysis evaluates three maturity-level scenarios against expert-calibrated Key Performance Indicators (KPIs). Results demonstrate that while traditional and blockchain-only systems remain vulnerable to the “Oracle Problem”, the integrated CTSAF model (Scenario III) achieves “Very High” performance in data accuracy and audit efficiency. By transforming passive record-keeping into an autonomous governance layer, this framework provides a strategic roadmap for substantiating environmental claims in alignment with the EU Green Claims Directive and the Digital Product Passport framework. Full article

Supply–demand balance in wind–solar dominant energy transition is challenged by the volatility of wind–solar power. Complementarity of wind–solar power has been introduced to suppress this volatility. Although multiple indices have been developed to quantify complementarity, a quantitative index with explicit physical meaning remains lacking. Additionally, complementarity’s temporal stability, which is imperative for wind–solar site selection, is unclear. In this study, these knowledge gaps are closed through developing a Daily Complementarity Index of wind–solar generation (DCI) and a nuanced national assessment of complementarity in China. The results of the comparison of our index with existing indices and site validation confirm the reasonability of the DCI and its improvements in interpretability. The average DCI of China ranges from 0.06 to 0.88, with a pronounced low-DCI zone across the Sichuan Basin and Chongqing municipality, and a high–DCI zone along the Three-North Shelterbelt. Temporally, the complementarity of wind–solar power in China follows a slight increase trend (3.96 × 10⁻⁵ year⁻¹), with evident seasonal characteristics, in which the highest and lowest are 0.37 and 0.17, respectively. This study introduces an effective tool for quantifying complementarity, and these findings can offer valuable reference for China’s renewable energy transition. Full article

This study examines the potential implications of the EU–Mercosur free trade agreement for the Polish horticultural sector, with particular emphasis on sustainability, trade competitiveness, and structural complementarities between the regions. Drawing on production, trade, and demographic data for the EU, Poland, and Mercosur countries, the analysis evaluates the alignment of horticultural supply and demand structures, the degree of intra-industry exchange, and the economic conditions shaping bilateral trade. The research applies the Grubel–Lloyd index and a Poisson Pseudo-Maximum Likelihood (PPML) gravity model to assess the determinants of Poland’s horticultural exports to Mercosur. The results indicate that trade remains predominantly inter-industry, reflecting substantial differences in agricultural specialisation and regulatory frameworks. At the same time, rising income levels in Mercosur, together with selected product-level complementarities, indicate emerging export opportunities for Poland. Poland’s trade with the Southern Common Market remains mainly as inter-industry, with the greatest export potential concentrated in high-value-added processed goods. Divergent sustainability standards, particularly in pesticide use, environmental regulation, and carbon-intensive transport, pose structural challenges that may affect the competitiveness and environmental footprint of expanded trade. Overall, the findings provide evidence that closer integration with Mercosur may support export diversification, but requires careful alignment with the EU’s sustainability objectives to ensure resilient and environmentally responsible development of the horticultural sector. Full article

A well-designed layout for urban parks plays a crucial role in constructing livable cities and enhancing residents’ well-being. The provision of age-friendly park access is fundamental to building an elderly-friendly city. However, previous studies have lacked comprehensive analyses that integrate the distribution of the elderly population, park accessibility, park quality, environmental characteristics, and social equity within a unified framework. Specifically, the supply–demand imbalance mechanism underlying the spatial variations in accessibility has not been adequately addressed. This study employs an improved two-step floating catchment area (2SFCA) method, combined with Lorenz curves and urban park-adapted Gini coefficients, to examine the supply–demand relationship and allocation differences between the elderly population and parks at the neighborhood and community levels. The analysis highlights issues related to equity and accessibility and explores their spatial disparity and influencing factors. The key findings are as follows: (1) The classic 2SFCA model exhibits significant biases in evaluating park supply–demand relationships, accessibility, and equity at a fine-grained scale, indicating the necessity of high-precision modeling. (2) Park accessibility in the Old City of Nanjing follows a dual-ring pattern of high accessibility, contrasted with clustered areas of low accessibility, while accessibility equity shows a central–peripheral gradient. Overall equity is relatively low, with good walking accessibility within only about one-third of communities. (3) Park supply levels, neighborhood construction year, and plot ratios are the primary factors influencing park accessibility for elderly residents. The comprehensive aging index is positively correlated with the equity in park layout, whereas housing prices and neighborhood size do not exhibit a simple linear relationship with park accessibility or equity for elderly residents. These findings provide a comprehensive and realistic perspective for understanding elderly park accessibility and equity, offering decision-making references for enhancing urban livability, managing an aging society, and formulating spatial equity policies in the future. Full article

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China. Full article

This work addresses the problem of demand forecasting in supply chain management, where the consolidation of scattered and heterogeneous data and the lack of precise forecasting methods generate operational inefficiencies, resulting in increased backorders and high inventory costs. To tackle these challenges, we propose a novel Decision Support System that jointly integrates an intelligent processing engine based on Graph Neural Networks (GNNs) for time series forecasting. Our approach lies in explicitly modeling the demand prediction task as a Multivariate Time Series forecasting problem on a causal dependency graph. Specifically, we use a GCN to process a graph where the nodes represent the target demand and key exogenous variables (Consumer Sentiment Index, Consumer Price Index, Personal Income, and Unemployment Rate), and the edges explicitly encode the interdependencies and causal relationships among these economic factors and demand. Unlike previous applications of GNNs in supply chain management, which typically focus on inventory networks or single-factor interactions, our approach uses GCN to dynamically capture the temporal interactions among multiple macroeconomic and internal series on future demand. We compare our method with other machine learning algorithms for demand forecasting. In the experiments conducted, the proposed GCN approach can accurately predict the abrupt changes that appear in demand behavior over time, whereas the other comparison methods tend to excessively smooth these transitions. Full article

Against the global backdrop of urbanization and sustainable development, urban parks—key public spaces for carbon sequestration, heat island mitigation, and public health promotion—have made their service performance a critical metric for evaluating urban human settlement quality. However, traditional evaluations relying on static questionnaires and aggregate indicators often fail to capture the spatiotemporal dynamics of park usage and complex supply–demand relationships. To address this gap, this study developed a three-dimensional dynamic evaluation model (“Vitality Level, Demand Matching, Service Supply”) for 59 urban parks in Gongshu District, Hangzhou, integrating multi-source data (mobile phone signaling, POIs, park vectors, demographic statistics). The model includes nine indicators (e.g., Temporal Activity Difference, Vitality Stability Index) with weights determined via the entropy weight method. Empirical results show: (1) Gongshu’s park service performance presents a “core-periphery” spatial disparity, with high-performance parks concentrated in central areas (e.g., West Lake Culture Square) due to convenient transportation and diverse functions; (2) Performance levels vary significantly between weekdays and weekends, with higher stability on weekdays and more pronounced supply–demand mismatches on weekends; (3) Time-series cross-validation and Monte Carlo simulations confirmed the model’s robustness. This framework shifts park research from “static quantitative description” to “dynamic performance diagnosis,” providing a scientific basis for refined planning and efficient management of parks in high-density cities. Full article

Power management strategies for microgrids are typically designed around quantitative performance metrics such as cost, efficiency, and reliability. While effective in many settings, these approaches often do not fully account for qualitative, human-centric considerations, such as the relative importance or criticality of different loads. This limitation is especially relevant in remote or community-based energy systems, and becomes more pronounced in wind-powered microgrids, where variable generation and limited resources require flexible and context-aware operational decisions. In this work, a qualitative-driven power management framework is proposed that incorporates stakeholder-defined qualitative indices into microgrid energy allocation. A community–importance (CI) index is used to represent qualitative needs as normalized weighting factors, which are then used to guide power redistribution during supply–demand imbalances. The framework is demonstrated using a wind-powered microgrid with heterogeneous load types and is evaluated under different operating scenarios. The results show that the proposed approach supports prioritized and socially informed power allocation while preserving overall system feasibility. Rather than replacing conventional quantitative optimization, the framework acts as a complementary decision-support layer and is particularly well suited for microgrids serving remote or resource-constrained communities where qualitative priorities play an important role in operational planning. Full article