Search Results (1,592)

Background: Studies outside Japan have linked sugar-sweetened beverage (SSB) intake with weight gain; however, evidence in Japanese adults is scarce, and no study has examined beverage-derived energy in relation to anthropometric indices and handgrip strength. Methods: The participants were employees of Fujita Health University aged 20–39 years (n = 76; male n = 35, average age: 29.97 ± 4.67 years; female n = 41, average age: 27.29 ± 4.53 years). Energy from beverage intake was assessed via the Brief Beverage Intake Questionnaire-15, and energy from alcoholic drinks, milk, SSBs, and total beverages was calculated. The associations of energy from different beverages with nutrient intake, BMI, skeletal muscle mass index (SMI), and handgrip strength were analyzed via ordinary least squares (OLS) regression; quantile regression (QR) and the generalized additive model (GAM) were used for sensitivity analyses. Results: Increased SSB intake was associated with increased BMI (standardized β = 0.35, 95% CI 0.12–0.58, p(OLS) < 0.001; p(QR) = 0.23; p(GAM) < 0.001) and was nonlinearly associated with increased SMI (standardized β = 0.21, 95% CI 0.043–0.37, p(OLS) = 0.02; p(QR) = 0.11; p(GAM) = 0.02), even after adjustment for total energy intake. Modest milk intake was linked to higher protein intake and a higher SMI without a higher BMI (standardized β = 0.18, 95% CI 0.020–0.35, p(OLS) = 0.03; p(QR) = 0.39; p(GAM) = 0.03). Conclusions: A positive association was found between SSB intake and both BMI and SMI and between MILK intake and SMI. Clarification in larger, diverse Japanese populations will be necessary. Full article

(1) Background. The rapid development of the digital economy provides a new perspective for enhancing industrial chain resilience. This study examines how manufacturing firms’ digitalization affects their industrial chain resilience, drawing on resource dependence and dynamic capability theories, and explores spillover effects on upstream and downstream enterprises. (2) Data and Methods. Using panel data from Chinese listed manufacturing firms (2011–2023), we employ ordinary least squares (OLS) models to analyze the relationship, its mechanisms, and heterogeneity. We further match firms with their suppliers and customers to identify spillover effects. (3) Results. Digitalization significantly improves resilience, particularly by enhancing supply–demand matching and competitive capabilities. Effects are stronger for small, labor-intensive, and high-environment, social and governance (ESG) firms. Bargaining power and governance capability are key channels. Spillover effects are heterogeneous, with a stronger impact on downstream customers. (4) Discussion. The positive impact of digitalization varies by firm characteristics, and spillovers differ across the chain. These findings offer precise insights and policy implications for leveraging digitalization to strengthen industrial chain resilience. Full article

Understanding how global climate drivers manifest at regional scales is critical for designing targeted adaptation strategies, particularly in vulnerable mountainous countries. This study provides an integrated assessment of atmospheric CO₂ concentrations, surface temperature, and precipitation trends at both global and Armenian levels from the early 20th century to 2024. Using long-term observational datasets and ordinary least squares regression models with HAC-robust errors, this study quantifies the magnitude and statistical significance of historical climate shifts. Results confirm pronounced global warming (+0.021 °C/year) alongside a moderate rise in global precipitation (+1.13 mm/year). Armenia, however, exhibits substantially accelerated warming (+0.052 °C/year) coupled with a non-significant and spatially heterogeneous precipitation trend, including notable declines in humid regions. CO₂ emissions per capita strongly predict temperature change both globally (0.59 °C/ton) and, even more prominently, in Armenia (1.33 °C/ton), indicating heightened regional climate sensitivity. These findings align closely with Armenia’s Fourth National Communication to the UNFCCC, reinforcing the robustness of the analysis. By revealing how global climate forcings translate into region-specific outcomes—and by discussing the emerging thermal contribution of digital infrastructure—this study underscores the urgency of localized climate adaptation, water resource planning, and agricultural resilience measures. Full article

This study empirically investigates the relationship between Environmental, Social, and Governance (ESG) scores and the financial performance of Mexican companies traded at Bolsa Mexicana de Valores (BMV), based on firm value and profitability. The study used a quantitative method of correlational research. Using data from the Refinitiv, the study analyzes 103 companies operating in 37 different industries listed on the BMV over five years (2019–2023), excluding financial institutions. Ordinary least squares (OLS) regressions revealed a statistically significant, positive correlation between ESG scores associated with higher return on assets (ROA) and market value measured by Tobin’s Q). Stakeholder theory serves as the theoretical foundation, as ESG initiatives may enhance long-term value for stakeholders. The study found that ESG efforts contribute positively to ROA and Tobin’s Q of public companies in Mexico. This study focuses exclusively on Mexican companies, expanding the existing literature. Corporate decision makers and investors can gain insights into ESG’s role in Mexican companies’ financial strategy and stakeholder value creation. Full article

Aquaculture digitalization is increasingly regarded as a crucial pathway to improving productivity, sustainability, and resilience in the fisheries sector. Policy instruments intended to foster this digital transformation—such as substantial subsidies and stringent regulatory mandates—often face constraints stemming from fiscal limitations, administrative burdens, and implementation inefficiencies. Behavioral interventions (nudges) represent a potentially effective and less resource-intensive alternative, yet their capacity—individually or in conjunction with moderate subsidies and regulatory measures—to foster aquaculture digitalization remains empirically underexplored. Drawing on survey data from 254 fish farmers in the lower Yangtze River region and employing a combination of principal component analysis (PCA), ordinary least squares (OLS) regression, Propensity Score Matching (PSM), and Gradient Boosted Trees (GBT) techniques, this study finds that: (1) Social nudging has a robust and consistent positive effect on digital transformation; (2) The effects of subsidies and regulations are heterogeneous and context-dependent; (3) The negative interactions between nudging and constraints, as well as between nudging and subsidies, are context-dependent and tend to inhibit digital transformation; (4) Policy effects display marked heterogeneity across different contexts, particularly with respect to sales channels, external pressures, producers’ transformation capabilities, and the scale of aquaculture operations. These findings deepen the understanding of how behavioral and structural policies interact in agricultural digitalization, emphasizing that effective policy should combine financial and regulatory measures with efforts to strengthen farmers’ digital awareness and behavioral adaptability. Full article

Urbanization has significantly affected the availability and quality of urban green and blue spaces (UGBSs), which may affect mental health. In the United States, rates of anxiety and depression continue to rise, particularly in urban regions. This study examined the relationship between UGBS exposure and mental health, measured by Frequent Mental Distress (FMD), across major cities in the contiguous United States (CONUS) from 2015 to 2017. UGBS exposure was estimated using remote sensing and GIS, and its association with FMD was assessed using Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR). The analyses also included smoking, binge drinking, median income, and educational attainment as covariates. OLS results indicated statistically significant but spatially uniform associations, whereas GWR revealed considerable spatial variation in UGBS and covariate effects across cities. Median income and educational attainment consistently showed inverse relationships with FMD, while smoking showed direct relationships across all years. Binge drinking exhibited both direct and inverse relationships. Additionally, both green space and blue space showed different relationships with FMD depending on location and year. The beneficial effect of UGBS on FMD was not observed in every instance. These findings help clarify the relationship between environmental, behavioral, and socioeconomic factors and mental health in urban settings, providing information that may support informed urban planning and public health decisions. Full article

This study uses the Contingent Valuation Method (CVM), a quantitative approach, with interval regression and Ordinary Least Squares (OLS) models to assess the non-use values of the Zhongjieshan and Liuheng Marine Ranches. The aim of the study is to quantify the monetary value of non-market benefits, examine socioeconomic influences on stakeholders’ Willingness to Pay (WTP), and provide a basis for ecological compensation mechanisms. Zhongjieshan’s annual non-use value is estimated at 28.99–30.81 million CNY (Chinese Yuan) (median WTP 74.33–78.99 CNY per person), while Liuheng’s value is higher at 108–111 million CNY (median WTP 150.20–153.89 CNY per person), suggesting greater ecological and recreational potential at Liuheng. The results show robust model performance, with minimal WTP differences. WTP for Liuheng is primarily influenced by income and environmental awareness, while Zhongjieshan shows a distance-decay effect. Visitor profiles reveal that Zhongjieshan attracts younger, moderately educated visitors, while Liuheng draws more highly educated, economically diverse groups. These findings suggest that Zhongjieshan should prioritize community-based co-management, while Liuheng should focus on high-quality, technology-driven ecological leisure development. The study also emphasizes the need for targeted awareness campaigns and supports the creation of diversified ecological compensation mechanisms beyond government funding. Full article

Cultural heritage digitization (CHD) has become a strategic priority in European cultural and digital policies, driving efforts to enhance accessibility, preservation, and economic engagement. As digital technologies reshape the cultural sector, CHD increasingly intersects with the digital economy, fostering new forms of value creation. Despite this, empirical research on the socioeconomic drivers of CHD remains limited, with existing studies focused mainly on conceptual discussions, expert-based assessments, or institutional case studies. This study systematically analyzes the socioeconomic drivers shaping CHD across Europe using large-scale data from ENUMERATE and Eurostat. An econometric approach combining Ordinary Least Squares (OLS) and Generalized Additive Models (GAMs) is employed to capture both linear and non-linear relationships. The findings show that CHD is shaped by a complex interplay of economic capacity, digital infrastructure, institutional strategy, and societal demand, rather than by targeted funding initiatives alone. By bridging conceptual discussions and systematic econometric analysis, the study provides a robust empirical framework for understanding the external conditions that influence CHD and offers evidence-based insights to support more targeted digital transformation strategies in the European cultural sector. Full article

This study investigates how economic development interacts with sustainability performance in the European Union, focusing on the structural and technological factors that shape progress in the green transition. Using Eurostat data for 27 EU member states over the period 2015–2023, the analysis employs panel econometric models (Pooled Ordinary Least Squares, Fixed Effects, and Random Effects) to explore how circular economy performance, innovation capacity, human capital, and renewable energy use influence environmental and economic outcomes across member states. The results show that R&D intensity and skilled human resources are key drivers of sustainability. Higher levels of circular material use and resource productivity contribute to long-term competitiveness. In contrast, uneven progress in renewable energy deployment points to persistent regional disparities and possible structural constraints that limit convergence. Northern and Western Europe record the strongest advances in innovation and environmental efficiency, whereas Southern and Eastern regions remain affected by industrial legacies and lower absorptive capacity. The findings highlight that, in the short term, renewable energy expansion may involve adjustment costs and potential trade-offs with economic competitiveness in less technologically developed economies. This study provides new comparative evidence on the differentiated pathways of the green transition across the EU. Policy implications suggest the need to reinforce R&D investment, expand circular manufacturing, and support an inclusive technological transition consistent with the European Green Deal and the United Nations 2030 Agenda. Full article

Anti-Poverty Programmes (APPs) are closely linked to rural livelihoods, yet comparative evidence on how participants and non-participants differ in livelihood-capital composition and income-generation patterns remains limited in ecologically fragile pastoral regions. This study draws on a cross-sectional household survey conducted in Northern Tibet in July 2020, covering 696 households—including 225 APP participants and 471 non-participants. Using the Sustainable Livelihoods Framework and the entropy weight method, we construct multidimensional livelihood-capital indices (human, social, natural, physical, and financial capital) and compare the two groups. We further apply Ordinary Least Squares (OLS) regressions to examine factors associated with per capita net income. The results reveal substantial heterogeneity in livelihood capital and income across both groups. APP participants exhibit higher human-capital scores, largely driven by a higher share of skills training, whereas they show disadvantages in physical and financial capital relative to non-participants. Natural capital shows no statistically significant difference between the two groups under the local grassland contracting regime. Significant differences are observed and identified in certain dimensions of social capital. Regression results suggest that income is positively associated with skills training, contracted grassland endowment, and fixed assets, with skills training showing the strongest association. For participants, herd size and labour capacity are not statistically significant correlates of income; for non-participants, larger herds and greater labour capacity are associated with lower income. Taken together, the findings indicate that APP participation is associated with stronger capability-related capital (notably training) alongside persistent constraints in productive assets and financial capacity. Policy implications include improving the relevance and quality of training, strengthening cooperative governance and market linkages, and designing complementary packages that connect skills, inclusive finance, and productive asset accumulation. Given the cross-sectional design and administratively targeted certification of programme participation, the results should be interpreted as context-specific associations rather than strict causal effects. Full article

This study investigates the determinants of financial and market-based sustainability among listed Information Technology (IT) firms in Central and Eastern Europe (CEE) between 2018 and 2024. Drawing on Agency Theory, Stakeholder Theory, Resource-Based View Theory, Dynamic Capabilities Theory and Legitimacy Theory, it examines how leverage, profitability, growth and earnings quality shape firm performance and valuation outcomes. Using a balanced panel of 266 firm-year observations from Poland, Romania, Hungary and Croatia, the analysis applies fixed-effects Ordinary Least Squares (OLS) regressions with heteroscedasticity-robust (HC3) standard errors. The results reveal that lower leverage significantly enhances return on equity, confirming agency-based governance effects, while revenue growth and earnings per share (EPS) are strong positive predictors of profitability. On the contrary, rapid growth increases Stock Price Volatility, reflecting a risk–return trade-off typical of emerging technology markets. Market valuation ratios (P/E) show weak sensitivity to fundamentals, suggesting that investor confidence in CEE IT firms remains partially institutionally constrained. Overall, the findings emphasise that sustainable performance in transitional economies depends more on internal capability deployment and governance discipline than on market perception, highlighting the maturity gap between operational excellence and valuation transparency in the regional IT sector. Full article

Accurate mapping of soil organic matter (SOM) is essential for soil management. Remote sensing (RS) provides broad spatial coverage, while visible and near-infrared (VisNIR) laboratory spectroscopy enables accurate point-scale SOM prediction. Conventional data methods for fusing RS and VisNIR data often rely on principal components (PCs) extracted from VisNIR data that have an indirect relationship to SOM and employ ordinary kriging (OK) for their spatialization, resulting in limited accuracy. This study introduces an enhanced fusion method using partial least squares regression (PLSR) to extract supervised latent variables (LVs) related to SOM and residual kriging (RK) for spatialization. Two fusion strategies (four variants)—RS + first i PCs/LVs and RS + ith PC/LV—were evaluated in the contrasting agricultural regions of Da’an City (n = 100) and Fengqiu County (n = 117), China. Laboratory-measured soil spectra (400–2400 nm) were integrated with many temporal combinations of Landsat 8 imagery. The results demonstrate that LVs exhibit stronger correlations with SOM than PCs. For example, in Da’an, LV6 (r = 0.36) substantially outperformed PC6 (r = 0.02), while in Fengqiu, LV3 (r = 0.40) outperformed PC3 (r = −0.05). RK also dramatically improved their spatialization over OK, as demonstrated in Da’an where the R² for LV2 increased from 0.21 to 0.50. More importantly, in SOM prediction performance, all four fusion variants improved accuracy over RS alone, and the LV-based fusion achieved superior results. In terms of mean performance, RS + first i LVs achieved the highest R² (0.39), lowest RMSE (5.76 g/kg), and minimal variability (SD of R² = 0.06; SD of RMSE = 0.28 g/kg) in Da’an, outperforming the PC-based fusion (R² = 0.37, SD = 0.09; RMSE = 5.85 g/kg, SD = 0.42 g/kg). In Fengqiu, two fusion strategies demonstrated comparable performance. Regarding peak performance, the PC-based fusion in Da’an achieved a maximum R² of 0.57 (RMSE = 4.82 g/kg), while the LV-based fusion delivered comparable results (R² = 0.55, RMSE = 4.94 g/kg); both surpassed the RS-only method (R² = 0.54 and RMSE = 4.98 g/kg). In Fengqiu, however, the LV-based fusion demonstrated superiority, reaching the highest R² of 0.40, compared to 0.38 for the PC-based fusion and 0.35 for RS alone. Furthermore, across different temporal scenarios, the LV-based fusion also exhibited greater stability, particularly in Da’an, where the RS + first i LVs method yielded the lowest standard deviation in R² (0.06 vs. 0.09 for PC-based fusion). In summary, integrating LV-derived variables with RS data enhances the accuracy and temporal stability of SOM predictions, making it a preferable approach for practical SOM mapping. Full article

The adaptation strategies of species to local environments are reflected in phenotypic variations, which could be expressed as trait patterns across the community level. Here, we compiled a dataset of small mammal traits to evaluate the classic ecological rules and to assess predictions related to drought resistance. In June 2017, July 2023, and May–June 2024, a field survey was conducted in Baima Snow Mountain, southwest China, using standardized methods to capture small mammals. Traits potentially corresponding to variations in temperature, productivity, and water availability were measured in the field or calculated in the laboratory. We applied ordinary least squares (OLS) linear regressions to determine the community-level trait variations along the gradients of environmental factors influenced by rain-shadow effects of the mountain system. Results showed that (1) body size decreased with increasing temperature, aligning well with conventional prediction; (2) the proportion of appendage size attributable to allometry decreased with temperature but increased slightly with productivity, thereby violating Allen’s rule while being partly consistent with the resource rule; (3) the renal features did not support the expected negative association concerning water availability but its converse, which may be explained by microhabitat conditions and broad-scale zoogeographic influences within the local community. We conclude that community-level phenotypic variations in small mammals result from complex influences, including climate, productivity, habitat characteristics, and adaptive strategies operating at both micro and macro scales. Full article

This study investigates how digital lifestyles and cognitive fatigue influence sustainable behaviour and energy awareness among Generation Z. Drawing on environmental psychology and social science perspectives, it explores behavioural and cognitive mechanisms linking digital overexposure with pro-environmental engagement. A cross-sectional survey conducted among 683 Polish secondary-school students examined the relationships between digital activity, fatigue, self-regulation, and sustainability practices such as waste segregation, reuse, and consumption moderation. The results show that higher digital fatigue and problematic online use are negatively associated with sustainability engagement, supporting the view that cognitive overload reduces individuals’ capacity for mindful, sustainability-oriented action. Using k-means clustering and robust regression analyses based on ordinary least squares (OLS), this study identifies distinct sustainability behaviour profiles among Generation Z and examines how digital fatigue and problematic online use predict lower engagement in pro-environmental practices. Importantly, educational level moderated this effect, suggesting that energy and sustainability literacy can buffer the adverse consequences of digital exhaustion. The findings contribute to the growing field of digital sustainability and highlight the need to integrate digital well-being and environmental education into youth and social policy frameworks. Full article

Industrial heritage, as a vital carrier of industrial civilization, is a key resource for advancing regional sustainable development. Understanding its spatial distribution and influencing factors is essential for effective conservation and revitalization. This study examines 207 industrial heritage sites in Hebei Province, one of the birthplaces of modern industry in China. By integrating multiple spatial analytical methods, it explores the spatial patterns and influencing factors of industrial heritage. A progressive analytical framework combining GeoDetector, Ordinary Least Squares, and Geographically Weighted Regression models was established to interpret formation mechanisms from factor identification to global and local heterogeneity. Results show that industrial heritage in Hebei forms high-density clusters along the eastern coast and southwestern hinterland, with lower densities in the north and central regions. The spatial centroid shifted from the center to the northeast, then to the southwest, and finally returned to the center. The distribution is shaped by the synergistic interaction of multiple factors: railway networks exert the strongest influence, natural conditions provide fundamental constraints, cultural factors play a reinforcing role, and historical development and policy orientation act as regulatory forces. Region-specific strategies are proposed to guide the conservation and sustainable transformation of industrial heritage in old industrial cities. Full article