Search Results (4,413)

Understanding the spatial organization of the new materials industry chain is essential for promoting sustainable industrial development. However, existing research rarely examines it as an integrated intercity network spanning multiple segments and specialized sub-sectors. To address this gap, this study constructs the New Materials City Association Network (NM-CityNet) using firm-level cross-regional equity investment data for 294 Chinese cities from 2010 to 2024. NM-CityNet includes two dimensions: segment networks (upstream, midstream, downstream) and sub-sector networks (advanced basic materials, critical strategic materials, and frontier new materials). A chain-lock model is applied, combined with social network analysis and the quadratic assignment procedure. Location quotients are integrated with weighted degree to capture specialized division-of-labour patterns. Using these methods, this study reveals the regional distribution, network structure, specialization patterns, and formation mechanisms of NM-CityNet. Results show that: (1) upstream core cities cluster in eastern China, midstream activities diffuse toward central and western regions, and downstream activities concentrate along the south-eastern coast; (2) NM-CityNet remains sparse and shows clear community structures, while different segments form differentiated spatial organization mechanisms; (3) sub-sectors exhibit clear specialization, with critical strategic materials showing broader spatial coverage; (4) drivers are heterogeneous: administrative proximity promotes link formation; government S&T financial-support differences are positively associated with link formation, although this association may partly reflect selective investment effects; economic and transport disparities inhibit link formation; innovation differences matter only in the midstream segment; and resource-endowment differences matter upstream and downstream. Full article

This study investigates the transmission of global risk sentiment and U.S. monetary conditions across emerging equity markets. Using Multiple Wavelet Coherence (MWC) and Quantile-on-Quantile Regression (QQR) over January 2016–December 2025, the analysis examines time–frequency co-movements and asymmetric linkages between emerging market equity indices, the CBOE Volatility Index (VIX), and the U.S. Treasury yield spread (T10Y3M). The results reveal substantial heterogeneity across markets. China, Russia, Turkey, Mexico, Egypt, and South Africa exhibit stronger long-run synchronization with external financial conditions. Saudi Arabia and Nigeria display more episodic exposure to external shocks. India, Brazil, Indonesia, and the United Arab Emirates represent intermediate cases characterized by recurrent but less persistent linkages. The findings suggest that global risk sentiment and U.S. monetary conditions affect emerging markets differently across investment horizons and periods of financial stress. The robustness analysis indicates that synchronization patterns became fragmented following the tightening cycle and rising geopolitical tensions after 2022, with less uniform spillover transmission across regions. The analysis highlights the importance of nonlinear and time-varying mechanisms in shaping financial spillovers across emerging equity markets. Full article

The Sustainable Development Goals (SDGs) Prosperity Cluster (SDGs 7–11) represents a multidimensional framework linking economic growth, social inclusion, environmental sustainability, and resilient development. This review critically examines the interconnections among Affordable and Clean Energy (SDG 7), Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), Reduced Inequalities (SDG 10), and Sustainable Cities and Communities (SDG 11), with the aim of exploring how these goals collectively contribute to sustainable prosperity. Adopting a structured literature review methodology informed by PRISMA principles, the study synthesizes peer-reviewed and gray literature collected from major academic databases and institutional sources. The findings indicate that progress toward the prosperity-oriented SDGs remains uneven across regions due to disparities in governance quality, technological capacity, infrastructure development, and social inclusion. Renewable energy transitions, digital innovation, circular economy initiatives, green infrastructure, and sustainable urban planning emerge as critical drivers of long-term prosperity, while inequality, weak institutional coordination, inadequate human-capital investment, and uneven access to technology remain major barriers. The review further demonstrates that progress in one SDG strongly influences outcomes in others, emphasizing the importance of integrated and policy-coherent approaches rather than isolated sectoral actions. Conceptually, the paper advances the understanding of the “Prosperity Cluster” by positioning dynamism, equity, and environmental stewardship as mutually reinforcing dimensions of sustainable development. The study concludes that achieving sustainable prosperity requires governance systems capable of balancing economic competitiveness with environmental responsibility and social justice. Greater international cooperation, inclusive policymaking, and investment in resilient infrastructure and human capital are essential to ensure that prosperity benefits present and future generations without leaving vulnerable populations behind. Full article

Urban expansion has reshaped land-use patterns, altered the provision of ecosystem services, and brought challenges to regional sustainable development. However, studies on urban agglomerations with uneven development remain insufficient. This study takes the core development area of the Central Plains Urban Agglomeration as the study area and explores changes in ecosystem services during multidimensional urbanization from 2000 to 2020. Using the CASA and InVEST models, three ecosystem services, namely net primary productivity (NPP), water yield (WY), and soil conservation (SC), were quantified. Spatial associations and local heterogeneity were analyzed using the bivariate Moran’s I. The results show that regional urbanization exhibited a Zhengzhou-centered monocentric pattern, with rapid growth in GDP density and significant expansion of urban land. The responses of ecosystem services to urbanization showed divergent trends, with NPP increasing slightly, while WY and SC decreased. NPP and SC showed a synergistic effect, whereas WY had trade-off relationships with both services. Due to uneven regional development, urbanization indicators and ecosystem services showed evident spatially heterogeneous relationships. This study provides evidence for ecological conservation, ecosystem-service management, and sustainable spatial governance in developing urban agglomerations where rapid growth and ecological constraints coexist. Full article

Based on China’s provincial panel data from 2000 to 2022, this paper constructs a measurement model of the spatial misallocation of fossil energy and investigates its impact on regional economic development, its transmission mechanism, as well as the moderating effect of new energy development. The results show that: (1) Spatial misallocation of fossil energy significantly hinders economic development, and this conclusion is robust under a variety of robustness checks; (2) The inhibitory effect of fossil energy spatial misallocation on economic development is most pronounced in the central region, regions with insufficient energy allocation, and in the context of coal misallocation; (3) New energy development not only exerts a positive driving effect on economic development, but also weakens the negative impact of fossil energy misallocation; after crossing a critical threshold, its effect shifts from inhibition to promotion; (4) Fixed-asset investment, industrial structure, and energy efficiency play negative mediating roles. The negative indirect effects of these three variables superimpose on the negative direct effect of fossil energy spatial misallocation, further strengthening the impediment to economic development. This study provides a basis for optimizing fossil energy allocation and promoting the coordinated development of traditional and new energy sources. Full article

Improving cultivated land use efficiency (CLUE) is a crucial pathway to solve China’s food security challenges. In recent years, the development of digital inclusive finance (DIF) has transformed the economic effects of input and output in the cultivated land utilization process. Employing Yangtze River Delta as the experimental area, which represents one of China’s most densely populated regions with the most acute arable land scarcity per capita, this study utilized a super-efficiency slacks-based measure (SBM) model that incorporated undesirable outputs to measure CLUE using a balanced panel data of 25 prefecture-level cities in the Yangtze River Delta from 2011 to 2023. Additionally, this study employed panel regression analysis combined with a mediation effect model to examine the impact of DIF on CLUE. The findings are as follows. (1) CLUE exhibits an upward trend throughout the study period, with significant improvements noted in the northwest regions, and evolves into a spatial distribution of “high in the northwest and low in the south.” (2) DIF effectively enhances CLUE, supported by extensive robustness tests. (3) Heterogeneity analysis reveals that the effect of DIF on CLUE varies systematically across contexts: it peaks in regions undergoing medium-level urbanization, remains most potent under extremely low credit availability, and is optimized by moderate bank outlet coverage. (4) Mechanism analysis demonstrates that the DIF positively influences CLUE by increasing fixed-asset investment in the primary industry. Based on these results, this study provides targeted policy recommendations for digital inclusive finance to serve the high-quality development of cultivated land utilization. Full article

This article develops and empirically supports a multi-level model for integrating sustainable practices in the hospitality industry, using the Zhetysu region of Kazakhstan as a regional case. The theoretical basis of the study is formed by the concepts of sustainable development, ESG principles and the Triple Bottom Line framework, which are integrated into a macro-, meso- and micro-level structure of sustainability management. The empirical analysis uses regional statistical data on the hotel sector for 2022–2025, including service volume, employment, wages, accommodation capacity, bed-days, investments and environmental protection expenditures. On this basis, a system of sustainability indices was constructed to assess economic, social and environmental dynamics. The results show that the Composite Sustainability Index increased from 0.00 in 2022 to 0.66 in 2025, indicating positive but uneven progress. Social indicators demonstrated the most stable improvement, while economic sustainability remained constrained by low capacity utilisation and unstable labour productivity. Environmental indicators were the weakest component, reflecting fragmented and inconsistent green practices. The novelty of the study lies in linking ESG and Triple Bottom Line principles with measurable regional indicators and a multi-level governance model. The proposed framework and roadmap can support regional authorities, tourism organisations and hospitality enterprises in coordinating sustainability initiatives and monitoring their implementation. Full article

The primary objective of this article is to examine the organizational, economic, and sustainability-related implications of implementing artificial intelligence (AI) systems in small and medium-sized enterprises (SMEs) in Poland. The study combines a survey of 112 SMEs in the Kuyavian–Pomeranian region, including 70 AI-using firms, with 13 in-depth interviews with managers. The quantitative analysis applies logit models to identify determinants of perceived AI effects on internal processes: working time and workload reduction, automation, cost effects, and creativity. The qualitative component explains how AI is adopted and embedded in business practice. The results show that AI adoption in SMEs is increasingly common but remains uneven and mostly operational. The strongest effects concern workload reduction and time efficiency, particularly in service firms and where AI is used intensively. Advanced AI adoption increases the probability of perceiving workload and cost-related effects. However, these effects should not be interpreted simply as direct cost reduction. Rather, AI improves productivity and work capacity while creating new costs related to paid tools, data preparation, integration, output verification, and governance. The interviews show that AI implementation follows a staged path: from curiosity-driven experimentation, through cognitive work augmentation, to workflow integration and, in selected cases, AI-enabled business model innovation. The transition from ad hoc use to strategic implementation depends less on firm size alone and more on process maturity, capabilities, and data readiness. Barriers also change with maturity: early-stage firms face a lack of knowledge, time, and clear use cases, whereas advanced users encounter data quality, hallucinations, security, integration, and governance problems. The study finds that sustainability considerations, particularly environmental impacts and ESG-related implications of AI, remain largely unperceived in SME decision-making. Entrepreneurs primarily interpret sustainability through the lenses of organizational resilience, long-term competitiveness, adaptability, and responsible digital transformation rather than through formal environmental metrics. The findings suggest that SME managers should implement AI gradually, link adoption to measurable process-level outcomes, and invest in AI literacy and governance. They should also integrate responsible AI principles into organizational strategy to support sustainable digital transformation. The study contributes to the literature by showing that AI adoption in SMEs should be understood not only as a productivity-enhancing process but also as a broader organizational transition shaping long-term sustainability and resilience. Full article

Land use/land cover (LULC) is one of the core driving factors affecting terrestrial ecosystem carbon storage and exacerbating global warming. As an area with the most intense land–sea interactions, China’s coastal zone has experienced drastic LULC transition and carbon storage fluctuations during the rapid urbanization process. Based on the InVEST model, this study analyzes the spatiotemporal dynamics of LULC and carbon storage (CS) in China’s coastal regions from 2000 to 2024, and simulated multi-scenario carbon storage trajectories for 2050 integrating the SSP-RCP scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, the XGBoost-SHAP and generalized additive models (GAMs) were introduced to deeply analyze the nonlinear characteristics and temporal heterogeneity of the driving mechanisms of CS evolution. The results show the following: (1) During the study period, the LULC structure of the coastal region was dominated by cropland and forestland consistently accounting for over 85%, but exhibited a competitive pattern characterized by the continuous expansion of built-up land severely squeezing ecological spaces. (2) The total regional CS showed an overall phased downward trend, accompanied by increasing fragmentation of high carbon sink areas. Notably, as the core carbon pool, the reduction in forest area was the dominant factor causing regional net carbon losses. (3) CS remained relatively stable under SSP1-2.6, representing a sustainable development pathway with low greenhouse gas emissions. In contrast, SSP2-4.5, SSP3-7.0, and SSP5-8.5 exhibited more pronounced declines in carbon storage by 2050, indicating that SSP1-2.6 is the most favorable pathway for maintaining long-term carbon storage stability in China’s coastal regions. (4) The driving mechanism of CS has undergone a profound shift from being dominated by natural ecological baselines to human activities. Land use intensity (LUI) has emerged as the strongest predictor in the model, and the nonlinear impacts of human activities have grown increasingly complex over time. This study highlights the complex impacts of high-intensity human disturbances on the coastal carbon cycle, providing a scientific basis for formulating differentiated carbon management strategies and adaptive spatial land-use planning oriented toward the “Dual Carbon” goals. Full article

Examining how artificial intelligence industry agglomeration (AIIA) affects carbon and pollution reduction is crucial for China’s agricultural sustainability. Existing research mainly examines the effect of artificial intelligence (AI) on the reduction of single pollutants while overlooking how industry agglomeration influences the marginal cost of coordinated abatement, a key issue for the agricultural resource–environment–economy system. Using panel data for 30 Chinese provinces from 2016 to 2024, this study constructs a marginal cost-based indicator of agricultural pollution–carbon reduction synergy (APCRS) and examines the effect of AIIA. The full-sample results reveal that AIIA has a U-shaped relationship with APCRS. Technological progress partially mediates this relationship. Agricultural socialized services and rural industrial integration buffer the initial negative association, whereas agricultural labor productivity strengthens the curvature of the estimated nonlinear pattern. The effect of AIIA also varies with external conditions and is more pronounced in regions with higher levels of marketization and industrialization while remaining significantly U-shaped across grain strategic zones. This dynamic process is more likely to emerge when public innovation investment and rural household income exceed critical thresholds. These findings provide new evidence for understanding how AI-driven agglomeration can support green agricultural transformation. Full article

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability. Full article

The growing penetration of renewable generation changes both producers’ marginal-cost and electricity-market price formation. This paper develops a mean field game model to examine how heterogeneous generators adjust marginal generation costs through renewable-oriented investment and how these decisions feed back into bid-stack clearing. Each generator controls the drift of its marginal cost, while the clearing price is determined by a demand-dependent quantile of the population cost distribution. The model leads to a coupled system with a non-local payoff. Simulations show that cost-reduction investment shifts the marginal-cost distribution toward lower-cost regions, but the widening distribution indicates heterogeneous effects. Generators below and close to the clearing margin have stronger incentives to reduce costs, whereas high-cost generators far above the margin face weaker incentives. These results suggest that market competition can support renewable-oriented cost reduction, but complementary policies may be needed for high-cost generators. Full article

Thorium (Th) is increasingly considered a promising fertile material for sustainable nuclear energy—which is not fissile itself, but convertible to fissile ²³³U—particularly as a by-product of rare earth element (REE) processing. This study develops a parametric techno-economic assessment (TEA) framework synthesizing published data from China, Russia, the USA, India, and Europe to establish the methodological foundation for evaluating thorium recovery economics from monazite ores and REE tailings under Central Asian conditions. Monazite typically contains 4–12% ThO₂, while tailings contain 0.1–3%, making secondary resources attractive for future recovery strategies. Particular attention is given to integration with uranium tailings and the application of advanced materials such as nanocomposite sorbents and carbon-based electrodes. Reported production costs of ThO₂ range from 50 to 500 USD/kg depending on process scale, feedstock quality, and co-production of REEs. The reviewed studies consistently show that coupling thorium recovery with REE processing improves economic feasibility. Modern approaches, including hybrid technologies and electrosorption systems, may reduce operational costs and improve process efficiency. Despite challenges related to capital investment, market uncertainty, and radioactive waste management, thorium continues to attract growing interest as a potential component of future nuclear fuel cycles and advanced reactor systems, including small modular reactors. To the best of the authors’ knowledge, this is the first parametric TEA framework structured around Central Asian conditions, combining literature-derived regional data, scenario-based process economics, and Monte Carlo sensitivity analysis within a single discounted cash flow structure. Full article

To clarify the internal mechanism through which the scale of higher education influences regional economic development, this work constructed an operational framework of education, talents, science and technology, and industry. Based on the 2023 data of 31 provincial administrative regions in China, covering 178 national high-tech industrial development zones, an empirical analysis was conducted using descriptive statistics and the Bootstrap mediating-effect test. The findings indicate that the expansion of higher education scale can enhance the level of talent supply, promote the agglomeration of scientific and technological innovation resources, drive the development of industrial scale, and thereby significantly boost economic growth. Among these pathways, the scale of the undergraduate and postgraduate student population exerts a complete mediating effect, while research and development investment and the number of enterprises in high-tech zones demonstrate a partial mediating effect. Notably, a striking contrast emerges between regular undergraduate institutions and double-first-class universities. The former exhibit significant positive mediating effects, whereas the latter’s economic driving effect remains largely unrealized. Furthermore, the uneven distribution of high-quality educational resources, particularly the spatial polarization of double-first-class universities, coupled with a mismatch between talent cultivation and industrial demands, and the “spatial isolation” of achievements, all restricted the radiating effect of higher education on regional economies. Therefore, it is necessary to implement a regionally differentiated layout of higher education, optimize the allocation mechanism of scientific and technological innovation resources, strengthen industry–university–research collaboration, and give full play to the effect of industrial agglomeration. Full article

Background: Polyendocrine metabolic ovarian syndrome (PMOS/PCOS) is the most common hormonal disorder in women of reproductive age and is linked to infertility as well as long-term metabolic and psychological problems. In the Gulf Cooperation Council (GCC) region, rising obesity, dietary changes, and sedentary lifestyles may be increasing its burden. However, prevalence estimates remain highly inconsistent due to differences in diagnostic criteria and measurement methods rather than true variation in disease rates. Objective: This study aimed to describe the situation by systematically pooling available evidence on the prevalence of PMOS among women in GCC countries and by summarizing the range of clinical features reported across included studies. Methods: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. We searched five major bibliographic databases (PubMed, Scopus, Web of Science, Cochrane Library, and Embase) and the Google Scholar search engine for observational studies published up to 1 June 2026. Studies were eligible if they reported PMOS prevalence and related clinical features among women of reproductive age residing in GCC countries. After removing duplicates and screening 570 initially identified records, 25 studies met our inclusion criteria; 24 were included in the quantitative meta-analysis after excluding one high-risk study. Risk of bias was appraised using the Joanna Briggs Institute Checklist for Prevalence Studies. A random-effects meta-analysis using the DerSimonian-Laird method, combined with the Freeman-Tukey double arcsine transformation, was used to estimate the pooled prevalence. Heterogeneity was quantified using the I² statistic and Cochran’s Q test. Subgroup analyses explored differences by country, diagnostic method, study setting, and publication period. Meta-regression was used to identify study-level factors that explained between-study variability. Results: Across 24 studies involving 77,890 women, the pooled prevalence of PMOS was 17.59% (95% CI: 12.98–23.40%). Country-level estimates ranged from 6.56% in Oman to 23.0% in Saudi Arabia. Heterogeneity across all analyses was extremely high (I² = 99.6%), and meta-regression identified the diagnostic tool as the single most important source of variation, explaining 42.7% of between-study variance. Studies using structured clinical criteria (Rotterdam or NIH) yielded prevalence estimates around 13–14%, while those relying on self-report or physician diagnosis without standardized criteria reported considerably higher figures (20–37%). Common clinical features included menstrual irregularity (up to 100% of PMOS cases in clinical cohorts), hirsutism (5–100%), acne and oily skin (17–74%), and obesity (17–73%). Awareness of PMOS among women in the region was highly variable, ranging from under 3% to nearly 100%. Conclusions: PMOS is a significant public health concern across the GCC region. The markedly higher pooled prevalence combined with high rates of obesity and metabolic risk in this population calls for urgent, coordinated action. Standardizing diagnostic practices, investing in population-level screening, and developing culturally tailored awareness programs are essential steps toward reducing the clinical and social burden of PMOS. Full article