Search Results (1,197)

This research evaluates the suitability of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) for improving financial return predictions across 15 major worldwide stock indices. The proposed method uses graph modeling to represent financial index relationships which enables the detection of symmetric market dependencies including mutual spillover effects and bidirectional influence patterns. The symmetric network structures become most important during financial instability because market interdependencies strengthen at such times. The evaluation process compares these models against XGBoost and Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) traditional forecasting approaches. The results of 30 time-series cross-validation experiments show that GNN models produce lower RMSE and MAE values, especially during financial crises and recovery phases and volatile market periods. The models show reduced advantages when markets remain stable. The research demonstrates that graph-based forecasting models which incorporate symmetry effectively detect complex financial relationships which leads to important implications for investment strategies and financial risk management and global economic forecasting. Full article

Despite numerous global initiatives, such as the Sustainable Development Goals (SDGs) and the implementation of environmental, social, and governance (ESG) metrics aimed at mitigating climate change, promoting social welfare, and addressing a variety of other causes, progress has been significantly slower than expected, particularly in developing economies. Thus, we attempted to link corporate ESG to sustainable development. It was also investigated whether ESG contributes to a reduction in corporate risk. Using panel data and the Generalized Method of Moments (GMM) technique, we examine the relationship between ESG scores and important financial risk indicators such as systematic risk (beta), stock price volatility, unsystematic risk, and the cost of capital (WACC). The findings show that corporations place a disproportionate emphasis on governance (G) rather than environmental (E) and social (S) characteristics. ESG and G governance were also found to be statistically significant predictors of financial risk. This disparity shows that companies may be using high governance scores to conceal underperformance in environmental and social issues, raising worries about greenwashing and superficial compliance. As a result, their contributions to SDGs such as affordable and clean energy (SDG 7), climate action (SDG 13), and reduced inequalities (SDG 10) are minimal. The findings highlight the need for a more open, balanced, and integrated ESG approach, one that not only promotes sustainable development but also improves long-term financial resilience. Full article

Economic stability in emerging market economies is increasingly shaped by the interplay between global financial integration, domestic monetary dynamics, and asset price fluctuations. Yet, early detection of financial market disruptions remains a persistent challenge. This study constructs a Financial Stress Index (FSI) for Pakistan, utilizing monthly data from 2005 to 2024, to capture systemic stress in a globalized context. Using Principal Component Analysis (PCA), the FSI consolidates diverse indicators, including banking sector fragility, exchange market pressure, stock market volatility, money market spread, external debt exposure, and trade finance conditions, into a single, interpretable measure of financial instability. The index is externally validated through comparisons with the U.S. STLFSI4, the Global Economic Policy Uncertainty (EPU) Index, the Geopolitical Risk (GPR) Index, and the OECD Composite Leading Indicator (CLI). The results confirm that Pakistan’s FSI responds meaningfully to both global and domestic shocks. It successfully captures major stress episodes, including the 2008 global financial crisis, the COVID-19 pandemic, and politically driven local disruptions. A key understanding is the index’s ability to distinguish between sudden global contagion and gradually emerging domestic vulnerabilities. Empirical results show that banking sector risk, followed by trade finance constraints and exchange rate volatility, are the leading contributors to systemic stress. Granger causality analysis reveals that financial stress has a significant impact on macroeconomic performance, particularly in terms of GDP growth and trade flows. These findings emphasize the importance of monitoring sector-specific vulnerabilities in an open economy like Pakistan. The FSI offers strong potential as an early warning system to support policy design and strengthen economic resilience. Future modifications may include incorporating real-time market-based metrics indicators to better align the index with global stress patterns. Full article

ESG has emerged as a key non-financial indicator, drawing significant investor focus. Disparities in ESG ratings may skew investor perceptions, potentially endangering stock values and financial market stability. This paper examines the link between ESG rating divergences and stock price crash risk, drawing on data from six Chinese and global ESG rating agencies. Focusing on Shanghai and Shenzhen A-share listed firms, it analyzes information from 2015 to 2022 within the theoretical contexts of information asymmetry and external monitoring. This study finds that ESG rating divergence markedly elevates stock price crash risk, a relationship that persists through a series of robustness checks. Specifically, the mechanisms operate through two key pathways: increased reputational damage risk due to information asymmetry and reduced external monitoring due to weakened external governance. The results of the heterogeneity analysis indicate that ESG rating divergence exacerbates stock price crash risk more significantly for non-state-owned firms, firms with low levels of marketization, and firms in high-pollution industries. This study provides clear actionable strategic paths and policy intervention points for investors to avoid risks, firms to optimize management, and regulators to formulate policies. Full article

The European seabass is a key Mediterranean aquaculture species, vital for sustainably meeting rising global protein demands amid declining wild fish stocks. Genetic analyses have identified the six6 gene as a candidate target of domestication and selective breeding, with two SNPs showing significant genotypic differences between wild and farmed European seabass populations. Further analyses revealed differential six6 expression between larval and juvenile stages, suggesting a potential developmental role. This study explores associations between these SNPs and important aquaculture traits across early developmental stages. Seabass samples were examined at 34 days post-hatching (dph, larval stage) and 71 dph (juvenile stage). We examined associations between specific six6 SNPs and morphological traits using traditional morphometrics, analyzing 20 and 26 characteristics in the larval and juvenile stages, respectively. Shape and size differences were examined with allometric correction. The six6 gene was primarily associated with body length, height, and caudal fin morphology. Notably, homozygous six6 genotype combinations at the studied SNPs were associated with increased body length in a developmental stage-specific manner. Variation in this gene also appeared to influence eye development in juveniles. These findings offer phenotypic evidence supporting previous genetic and expression studies in European seabass, highlighting their potential applications in fisheries and aquaculture. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

Global warming and rapid urban industrialization are profoundly transforming land-use patterns and carbon storage capacity in terrestrial ecosystems. A rigorous analysis of spatiotemporal variations in regional land-use changes and carbon storage dynamics provides critical insights for sustainable land-use planning and ecological security, particularly within the context of achieving carbon peaking and carbon neutrality targets. In this study, the PLUS-InVEST model was coupled with climate change and policy constraints to construct six future scenarios. We analyzed the characteristics of land-use evolution and the spatial and temporal changes in carbon storage in the Sanjiangyuan region from 2000 to 2020. We also predicted the potential impacts of land-use shift on carbon storage. The results show the following: (1) Land-use transitions exerted significant impacts on carbon stock. The Sanjiangyuan region experienced a net carbon stock reduction of 9.9 × 10⁶ t during 2000–2020, with the most pronounced decline (6.1 × 10⁶ t) occurring between 2000 and 2010. (2) Under the same climate scenario, the natural development (ND) scenario exhibited decreasing carbon reserves relative to 2020 baseline levels. Notably, land-use planning scenarios demonstrated spatially heterogeneous impacts, with the ecological protection (EP) scenario consistently maintaining higher carbon stocks compared to the ND scenario. (3) Multivariate driver interactions exerted stronger control over spatial carbon storage patterns than any individual factor. These findings inform targeted land-use management strategies to enhance regional carbon sequestration capacity, promote sustainable development, and support China’s carbon peaking and neutrality objectives. Full article

Finding out the dynamics of soil organic carbon and inorganic carbon is paramount for sustaining terrestrial carbon cycling and climate change mitigation. From the 1980s to 2010s, substantial changes in land use, climate, and agricultural practices have occurred across North China. This study systematically quantified the stratified dynamics of soil carbon stocks (0–100 cm with 20 cm intervals) and their compositional shifts by using the geographically weighted regression kriging model. The model integrated soil sample data from provincial surveys across North China with key environmental covariates (e.g., elevation, precipitation, air temperature, and the vegetation index) to spatially predict and analyze vertical carbon stock changes. The results indicated that soil carbon stocks decreased considerably by 5.86 Gt in the one-meter soil profile from the 1980s to the 2010s. Significant losses in soil inorganic carbon stocks directly contributed to net soil carbon sources. These significant soil inorganic carbon losses of 7.03 Gt, originating primarily from losses of 7.35 Gt in deeper soil layers (20–100 cm), effectively offset increases of 1.17 Gt in soil organic carbon. About two-thirds of regions in North China have been categorized as carbon source regions. These are distributed for the most part in arid and semi-arid areas and the Qinghai–Tibet Plateau. The remaining one-third of regions have been classified as carbon sink regions which are primarily found in the Loess Plateau, the Huang–Huai–Hai Plain, the Middle-lower Yangtze Plain, and the Northeast China Plain. Significant losses in soil inorganic carbon stocks caused by strong carbon sources may undermine global measures aimed at enhancing terrestrial ecosystem carbon sequestration and fixation. Our results highlight the urgent need to account for vulnerable subsurface inorganic carbon pools in regional carbon sequestration strategies and climate models. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha⁻¹) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha⁻¹) and Manipur the lowest (102.8 Mg ha⁻¹). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha⁻¹). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R² = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation. Full article

Promoting social equity is a global issue, and common prosperity is an important goal for human society’s sustainable development. This study is the first to examine climate risks’ impacts on common prosperity from the perspective of corporate employees, providing micro-level evidence for the coordinated development of climate governance and social equity. Employing data from companies listed on the Shanghai and Shenzhen stock exchanges from 2016 to 2023, a fixed-effects model analysis was conducted, and the results showed the following: (1) Climate risks are positively associated with the common prosperity of corporate employees in a significant way, and this effect is mainly achieved through employee guarantees, rather than employee remuneration or employment. (2) Climate risk will increase corporate financing constraints, but it will also force companies to improve their ESG performance. (3) The mechanism tests show that climate risks indirectly promote improvements in employee rights and interests by forcing companies to improve the quality of internal controls and audits. (4) The results of the moderating effect analysis show that corporate size and performance have a positive moderating effect on the relationship between climate risk and the common prosperity of corporate employees. This finding may indicate the transmission path of “climate pressure—governance upgrade—social equity” and suggest that climate governance may be transformed into social value through institutional changes in enterprises. This study breaks through the limitations of traditional research on the financial perspective of the economic consequences of climate risks, incorporates employee welfare into the climate governance assessment framework for the first time, expands the micro research dimension of common prosperity, provides a new paradigm for cross-research on ESG and social equity, and offers recommendations and references for different stakeholders. Full article

Renovating existing buildings is a key strategy for achieving the EU’s climate targets, as over 75% of the current building stock is energy inefficient. This study evaluates the environmental impacts of three façade renovation scenarios for an office building at the Technical University in Zvolen (Slovakia) using a life cycle assessment (LCA) approach. The aim is to quantify and compare these impacts based on material selection and its influence on sustainable construction. The analysis focuses on key environmental indicators, including global warming potential (GWP), abiotic depletion (ADE, ADF), ozone depletion (ODP), toxicity, acidification (AP), eutrophication potential (EP), and primary energy use (PERT, PENRT). The scenarios vary in the use of insulation materials (glass wool, wood fibre, mineral wool), façade finishes (cladding vs. render), and window types (aluminium vs. wood–aluminium). Uncertainty analysis identified GWP, AP, and ODP as robust decision-making categories, while toxicity-related results showed lower reliability. To support integrated and transparent comparison, a composite environmental index (CEI) was developed, aggregating characterisation, normalisation, and mass-based results into a single score. Scenario C–2, featuring an ETICS system with mineral wool insulation and wood–aluminium windows, achieved the lowest environmental impact across all categories. In contrast, scenarios with traditional cladding and aluminium windows showed significantly higher impacts, particularly in fossil fuel use and ecotoxicity. The findings underscore the decisive role of material selection in sustainable renovation and the need for a multi-criteria, context-sensitive approach aligned with architectural, functional, and regional priorities. Full article

Between approximately 725 and 518 Ma, a suite of specialized felsic plutons and granitic stocks were emplaced across the Arabian Shield, many of which are now recognized as highly mineralized prospects enriched in rare earth elements (REEs), rare metals, and radioactive elements bearing mineralizations. The current investigation focused on the radiological and geochemical characterization of naturally occurring radionuclides, specifically ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K, within three strategically selected granitic prospects, namely, J. Tawlah albite granite (TW), J. Hamra (HM), and J. Abu Al Dod alkali feldspar syenite and granites (AD). Concerning the radioactivity levels of the investigated granitic stocks, specifically the activity concentrations of ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K, the measured average values demonstrate significant variability across the TW, HM, and AD stocks. The average ²³⁸U concentrations are 195 (SD = 38.7), 88.66 (SD = 25.6), and 214.3 (SD = 140.8) Bq/kg for TW, HM, and AD granitic stocks, respectively. Corresponding ²²⁶Ra levels are recorded at 172.4 (SD = 34.6), 75.62 (SD = 25.9), and 198.4 (SD = 139.5) Bq/kg. For ²³²Th, the concentrations are markedly elevated in TW at 5453.8 (SD = 2182.9) Bq/kg, compared to 77.16 (SD = 27.02) and 160.2 (SD = 103.8) Bq/kg in HM and AD granitic stocks, respectively. Meanwhile, ⁴⁰K levels are reported at 1670 (SD = 535.9), 2846.2 (SD = 249.9), and 3225 (SD = 222.3) Bq/kg for TW, HM, and AD granitic plutons, respectively. Notably, these values exceed the global average background levels, indicating an anomalous enrichment of the studied granitic occurrences. The mean radiological hazard indices for each granitic unit generally exceed global benchmarks, except for AEDE_out in the HM and AD stocks, which remain below international limits. The geochemical disparities observed are indicative of post-magmatic alteration processes, as substantiated by the interpretation of remote sensing datasets. In light of the significant radiological burden presented by these granitic stocks, it is essential to implement a rigorous precautionary framework for any future mining. These materials must be categorically excluded from uses that entail direct human exposure, especially in residential construction or infrastructure projects. Full article

The depletion of global fish stocks poses a major challenge to sustainable development, particularly in economies where marine resources are critical to livelihoods and food security. In this study, the relationship between economic development and the sustainability of fish stocks is examined using the Environmental Kuznets Curve (EKC). We use panel data from 32 economies between 2002 and 2020 and analyze the fish stock status indicator (EPI_FSS) from the Environmental Performance Index, which captures the proportion of national catches from overfished or collapsed stocks. Using a dynamic panel approach and the generalized method of moments (GMM), we investigate how the human development index (HDI) and other socio-economic factors influence changes in the state of fish stocks. Our results show a statistically significant inverted-U-shaped (∩-shaped) relationship between the HDI and the state of fish stocks, suggesting that the deterioration of fish stocks increases at lower levels of development, but improves beyond a certain threshold. In addition, higher levels of foreign direct investment (FDI), education, and research and development (R&D) spending are associated with better outcomes for fish stocks. These results suggest that while early economic growth may put pressure on marine resources, sustained investment in human capital, innovation, and global integration is critical to promoting long-term marine sustainability. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article