Search Results (1,747)

Dominant factors of changes in natural habitat quality in arid municipal areas of western China over recent decades remain quantitatively unverified, due to limitations in traditional attribution methods that depend on linear causality or low-dimensional interactions. We generated 30 m resolution habitat quality indexes (HQI) of Yinchuan Municipality of western China from 2000 to 2020 using the InVEST model, and then quantified the relative importance of anthropogenic and climatic variables to HQI changes by Random Forest modeling after eliminating cross-variable collinearity, taking, a typical arid Results indicate that a relatively high-level (HQI > 0.6) of the habitat quality predominated in natural habitats covering forests, shrublands, grasslands, and wetlands within Yinchuan Municipality since 2000, accounting for 76.95% of total natural habitat area. Moreover, this relatively high-level remained stable, with 93.32% of all natural habitat types over the past two decades, though the 4.78% of total natural habitat area with improvement slightly outnumbered the 1.90% of area with decline. The municipality-wide mean HQI of the natural habitats fluctuated from 0.65 in 2000 to 0.66 in 2020, which was primarily shaped by anthropogenic factors without a statistically significant monotonic trend, with population density, gross domestic product, and land use dynamic degree accounting for 31.79%–43.46%, 18.95%–27.96%, and 10.66%–18.85% of the fluctuation, respectively. Climatic factors contributed far less to the fluctuation, with the relative importance of individual variables falling in the range of annual precipitation (9.37%–14.77%), mean annual temperature (4.10%–9.61%) and annual solar radiation (3.28%–9.60%). These findings confirm the dominant role of persistent influences from anthropogenic activities rather than their temporal intensification in maintaining the stable high-level habitat quality of Yinchuan Municipality since 2000, and provide direct targeted guidance for formulating habitat quality improvement strategies that coordinate urban construction and ecological protection in arid western China. Full article

Purpose: To characterize ophthalmology research output relative to disease burden and national economic capacity after accounting for population size. Methods: We analyzed Scopus publications, Global Burden of Disease disability-adjusted life years (DALYs), and World Bank economic and population data from 2011 to 2021. Negative binomial and Gamma models evaluated publication volume and positive citation-weighted impact, adjusting for DALYs, gross domestic product (GDP) per capita, population, disease category, and publication year. Results: Refractive disorders accounted for 45.4% of DALYs and 23.2% of publication volume; cataract, 44.7% and 29.8%; glaucoma, 5.2% and 30.6%; and age-related macular degeneration, 3.8% and 15.2%. High-income countries accounted for 11.8% of burden, 67.2% of publication volume, and 74.2% of citation-weighted impact; lower-middle-income countries accounted for 49.1%, 7.9%, and 6.8%, respectively. DALYs were not independently associated with publication volume (adjusted count ratio [aCR], 0.99; 95% confidence interval [CI], 0.98–1.01; p = 0.278), whereas GDP per capita (aCR, 4.47) and population (aCR, 13.99) were strongly associated (both p < 0.001). For positive citation-weighted impact, DALYs had a small association (adjusted ratio of means [aRM], 1.05), whereas GDP per capita (aRM, 2.86) and population (aRM, 4.96) were strongly associated (all p < 0.001). Conclusions: After accounting for population size, absolute disease burden was not independently associated with publication volume, whereas national economic capacity remained strongly associated with both outcomes. These patterns should inform research and service-delivery priorities. Full article

Planetary health recognizes that human health, well-being, and sustainable development depend fundamentally on the integrity of natural ecosystems that regulate climate, maintain biodiversity, secure food and water resources, and protect populations from environmental risks. As environmental degradation increasingly threatens this life-support system, there is a growing need for economic indicators that explicitly recognize the value of natural capital alongside conventional measures of development. Gross Ecosystem Product (GEP) has emerged as comprehensive metric for quantifying the monetary value of ecosystem services and informing evidence-based policy. This study estimates India’s GEP for the period 2011–2012 to 2020–2021 using an ecosystem accounting framework aligned with the System of Environmental Economic Accounting (SEEA). The analysis integrates provisioning services (timber, non-timber forest products, fuelwood, and fisheries), regulating services (carbon sequestration), and cultural services (nature-based tourism) using market price, replacement cost, and benefit transfer methods based on secondary data from national and published sources. India’s total GEP is estimated at US$207.05 billion, highlighting the substantial but under-recognized contribution of ecosystems to the national economy and societal well-being. Regulating services dominate GDP, with carbon sequestration alone accounting for US$166.73 billion, followed by provisioning services, while cultural services contribute a relatively smaller share. These findings reveal a significant gap between ecosystem contributions and their representation in conventional metrics such as Gross Domestic Product (GDP), underscoring the limitations of growth-centric development frameworks. By integrating ecosystem values into national accounting, GEP provides a practical tool for advancing planetary health, strengthening climate resilience, conserving biodiversity, supporting ecosystem-based management, and guiding policies that align economic development with ecological sustainability and long-term human well-being. Full article

This study develops a multidimensional, indicator-based framework for assessing whether the expansion of research and development (R&D) resources in Türkiye is accompanied by broadly distributed innovation capacity, digital market integration, and ecosystem dynamism. The analysis uses the latest available official TurkStat tables, whose reference periods span 2015–2025, and treats them as a diagnostic profile rather than a longitudinal or causal dataset. Gross domestic R&D expenditure reached TRY 651.8 billion in 2024, and the business enterprise sector accounted for 64.8% of the total. R&D expenditure was geographically concentrated: İstanbul and Ankara together represented 61.2% of national expenditure. During 2022–2024, product innovation rates rose from 21.6% among small enterprises to 45.1% among large enterprises, while information and communication and scientific R&D activities each recorded a product specialisation ratio of 2.19. In 2025, 93.6% of enterprises had fixed-line internet access, but only 8.6% had a connection of at least 1 Gbit/s; in 2024, 12.4% made web sales and 27.8% of web sellers sold abroad. The proposed Strategic Innovation and R&D Capacity Index (SIRDCI) is therefore presented as a transparent conceptual diagnostic. A partial firm-size proxy is demonstrated only with indicators available at a common firm-size level; ecosystem dynamism remains a national contextual dimension because it cannot be disaggregated by firm size. The descriptive results reveal a positive firm-size gradient, marked sectoral and regional concentration, and a gap between digital access and commercialisation. These patterns do not establish causal mechanisms; instead, they identify areas that may warrant further capability-oriented policy attention and evaluation. Full article

Various explanatory factors for perceived happiness have been identified in cross-country comparisons, but their relative importance and causal relationships remain unknown. Associations and causality were studied between 17 societal, economic, and religious variables contributing to happiness scores on the World Happiness Report (WHR) in 147 countries. In a correlation analysis, the strongest association with national happiness scores was observed for the median gross domestic product (GDP)/capita (r squared 0.69), followed by the mean GDP/capita (0.65), tertiary education (0.42), corruption (−0.41), democracy index (0.35), religious non-affiliation (0.31), and consanguinity (−0.25). Bayesian statistical analysis indicated strong causal cascades originating from democracy (a positive effect) and, to a lesser degree, corruption (a negative effect) during the years 1945–1985 to subsequent national happiness levels, with a delay of several decades. The results were confirmed to have good reproducibility with Bootstrap resampling, indicating robust findings. Although the median GDP/capita had the strongest association with happiness in the cross-sectional analysis, it was not identified as a primary causal factor in the Bayesian analysis, in which high levels of democracy and low levels of corruption were entangled with median GDP/capita as apparent prerequisites for perceived happiness. Our results suggest that the median GDP should be used as an indicator of the economic prosperity of citizens instead of the currently established mean GDP when studying the links between economy, democracy, and well-being across countries. Full article

Provincial cold-chain investment decisions must reconcile food-loss prevention, digital visibility, logistics capability and the environmental burden of freight-intensive growth. This study develops a sustainability-oriented readiness–intensity framework for 31 provincial-level regions in mainland China. The baseline model uses 14 auditable public-data criteria and combines Criteria Importance Through Intercriteria Correlation (CRITIC) with the standard Combined Compromise Solution (CoCoSo) algorithm. Because the observations combine 2024 statistics, a 2023 digital-finance index and the cumulative 2020–2025 cold-chain-base list, the design is described as an asynchronous cross-sectional snapshot rather than a single-year panel. Municipal sewage and green-space variables are interpreted as regional enabling capacity, not direct cold-chain environmental performance; road freight turnover relative to gross domestic product is treated as a cost-type freight-intensity transition-pressure proxy. A separate diagnostic replaces the earlier inverse-size term with logistics residuals conditional on agri-food output. Shandong, Guangdong, Jiangsu, Henan and Zhejiang form the leading demonstration-readiness group. Equal-weight CoCoSo closely matches the CRITIC result (Spearman ρ = 0.996), while TOPSIS and VIKOR retain the broad ordering but expose local method sensitivity. Dropping either digital criterion, removing the three indirect green proxies, winsorizing the normalization range, varying the CoCoSo compromise parameter and substituting 2022 digital data do not alter the leading pattern. Under an assumed 5% indicator-error perturbation, Shandong and Guangdong remain within ranks 1–2, whereas the ordering of several adjacent provinces is less secure. The framework supports sequenced investment packages rather than a deterministic league table and distinguishes demonstration-ready, scale-led, intensity-led and coverage-building contexts. Full article

The dual transition, green and digital, has become the strategic axis of the European Union, but quantitative evidence on the extent to which the adoption of digital technologies and artificial intelligence effectively supports the decoupling of economic growth from material consumption remains fragmented. Our study examines this relationship for the 27 Member States of the European Union over the period 2015–2024, using a balanced panel constructed entirely from verifiable official secondary data, without any interpolated values. The dependent variable is resource productivity, expressed as the ratio between gross domestic product and domestic material consumption. The results of the panel data analysis show that the variables are first-order integrated and cointegrated. The fixed-effects model indicates a positive and significant effect of digital adoption on resource productivity, with human capital being the most robust determinant, and research intensity also exerting a positive effect. An analysis of the recent series of AI adoption (2021, 2023, 2024) highlights a positive and significant association with resource productivity. The findings support the idea that digitalization and AI can be complementary to sustainability objectives, but not a substitute for investment in human capital and institutional quality. The study features a two-component architecture: a balanced panel (2015–2024) for digital adoption and an exploratory, repeated cross-sectional analysis for the recent series on artificial intelligence adoption. Full article

Tourism plays an important role in the global economy, contributing massively to the gross domestic product (GDP) and creating numerous jobs. The introduction of emerging technologies can accelerate the sector’s growth through personalization, improved user experience and better operational efficiency. The present work investigates the impact of visible light communications (VLC) in the tourism and hospitality industry based on the analysis of the recent literature published in the last decade, with the scope of improving tourist experience, operational efficiency and sustainability. Additionally, this work aims to critically evaluate the advantages and disadvantages of implementing VLC technology in the tourism and hospitality industry. For these purposes, this study presents a narrative review of the recent academic literature. The findings indicate that VLC technology can be used in a wide range of tourism-related applications, including contactless hotel services, indoor positioning and navigation, secure communications, accessibility solutions for visually impaired individuals and energy-efficient lighting infrastructure. In addition, this review demonstrates VLC’s potential to support the development of smart and sustainable tourism destinations through the integration of user-centered communication and illumination infrastructure. Finally, this work also identifies several challenges that affect large-scale deployment, including implementation costs, line-of-sight dependency and ongoing standardization issues. Full article

Achieving environmental sustainability, particularly the targets outlined in Sustainable Development Goal 13 (Climate Action), is a critical global imperative. This investigation analyzes the heterogeneous effects of artificial intelligence (AI), tourism intensity, tourism expenditure, the Gross Domestic Product (GDP) share contributed by tourism, natural resource rents, and environmental policy stringency on the ecological footprints of advanced countries from 2000 to 2022. Using a robust analytical framework featuring advanced econometric methods, specifically the Method of Moments Quantile Regression (MMQR) and an innovative machine learning approach—Panel Quantile-on-Quantile Kernel-Based Regularized Least Squares (PQQKRLS)—the research elucidates complex, nonlinear interdependencies. Key empirical results show that AI adoption significantly mitigates ecological footprints across all quantile distributions. Conversely, heightened tourism intensity and increased tourism expenditure are associated with greater environmental degradation. The analysis further indicates a U-shaped tourism–ecological footprint relationship, suggesting that tourism’s economic contribution may initially reduce ecological pressure but may increase it again beyond a certain expansion threshold. These conclusions underscore the necessity for advanced nations to adopt synergistic policy frameworks that strategically leverage AI technologies, promote sustainable tourism practices, and reinforce rigorous environmental governance to advance climate action and ecological sustainability. Full article

Background/Objective: In the multifactorial etiology of asthma, environmental factors, including sun exposure, measured either by sunshine hours (SHs) or global solar surface irradiance (GSSI), are critical. The objective of this study is to estimate the association between sun exposure and the prevalence of asthma in Spanish schoolchildren (6–7 years old) and adolescents (13–14 years old) from six geographic centers in Spain (A Coruña, Bilbao, Cantabria, Cartagena, Pamplona and Salamanca) included in the Global Asthma Network (GAN) study during 2016–2019. Methods: Measurements of SHs and GSSI were obtained from the Spanish “Agencia Estatal de Meteorología”. A semi-individual cross-sectional design was used, and multilevel logistic regression models were employed, with adjustments for age, sex, temperature, relative humidity and gross domestic product, taking the center as the reference level. Permutation tests were performed to account for the low number of centers. Results: Increases in both SHs and GSSI were associated with a lower prevalence of “asthma ever” in the 6–7 and 13–14 years age groups. An increase of 100 annual sunshine hours was associated with a lower prevalence of “asthma ever” among schoolchildren (adjusted odds ratio [aOR] = 0.82; 95% confidence interval [CI], 0.79–0.85); from the crude model, permutation p = 0.08. In adolescents, the corresponding estimates were aOR = 0.90 (95% CI, 0.87–0.94); from the crude model, permutation p = 0.02. An increase of 1 kWh m⁻² day⁻¹ in GSSI was associated with a lower prevalence of “asthma ever” among schoolchildren (aOR = 0.15; 95% CI, 0.10–0.23); from the crude model, permutation p = 0.25. In adolescents, the corresponding estimates were aOR = 0.37 (95% CI, 0.25–0.54); from the crude model, permutation p = 0.01. Conclusions: The results of this study suggest that sun exposure, measured either as SHs or GSSI, may be protective against asthma in adolescents. In contrast, the association between sun exposure and the prevalence of “asthma ever” in schoolchildren was not significant, although it followed a similar trend. Confirmation of these findings in other geographical settings could improve our understanding of the role of sun exposure in asthma prevention and control. Full article

This article forecasts electricity demand over two-month, one-year, and two-year horizons using 20 years of open-access data from the COES system operator. The proposed approach applies machine learning (ML) algorithms with exogenous variables and optimized LGBMRegressor hyperparameters to reduce forecasting error. Its performance is compared with mathematical statistical models (MSMs), including SARIMAX, ARIMA, and ARIMA with cross-validation. The MSM-based approaches produced lower performance metrics than the ML-based techniques and required longer computational execution times. The implementation was carried out in Google Colab Pro using Python 3.12 and libraries such as skforecast, taking advantage of the available high RAM capacity to reduce the computational time of the two forecasting techniques analyzed. For the two-month forecasting horizon, the lowest mean absolute error (MAE) was achieved with the LGBMRegressor algorithm including exogenous variables and optimized hyperparameters, with a value of 98.69 MW, whereas ARIMA with cross-validation yielded an error of 207.93 MW. These results indicate that the use of ML algorithms for electricity demand forecasting reduces forecasting errors and requires less computational execution time. Therefore, only ML was used for the one-year and two-year forecasting horizons. Based on this result, a one-year forecast was obtained with the LGBMRegressor algorithm, yielding an MAE of 111.77 MW, while the two-year forecast produced an MAE of 96.344 MW. This work incorporated socioeconomic exogenous variables, such as quarterly GDP, population, and access to electricity, which improved the medium-term forecasting model. The resulting forecasts may be useful for both the operation and planning of the electric power system (EPS). Full article

Saudi Arabia’s cereal imports are a cornerstone of its food security policy, driven by an arid climate, limited water resources, and persistently high domestic demand. This study employed the ARDL cointegration framework on data from 1992 to 2022 to explore the dynamic relationships among cereal imports (IMP), Gross Domestic Product (GDP), cereal production (PRO), and agricultural water use (WA). Descriptive analysis revealed a clear upward trend for cereal imports and GDP, contrasting with a downward trend for PRO and WA during the study period. This pattern reflects high import dependency, economic diversification, and water scarcity policies. The ARDL results confirmed a long-run equilibrium relationship, where GDP and agricultural water use showed a positive, significant relationship with cereal imports, while cereal production had a significant negative long-term impact on imports. Short-run dynamics were complex, suggesting that lagged increases in GDP and water use were associated with reduced imports, while cereal production had a positive impact on imports, which is attributed to the country’s low cereal self-sufficiency ratio and structural import dependence. The conclusion underscores the constraints of limited water resources, leading to greater reliance on cereal imports. The study recommends water-efficient agricultural strategies and adjusted import policies to enhance food security. Full article

This study examines whether the accumulated stock of private credit provides early-warning information for subsequent deterioration in banking-sector asset quality. It combines annual Passport banking indicators with World Development Indicators for 58 countries over 2010–2024; the preferred sample contains 746 country–year observations. A second-order dynamic fixed-effects model links log(1 + NPL), where NPL denotes the non-performing loan ratio, to lagged private credit to gross domestic product (GDP), real credit growth, lending rates, bank capital, GDP growth, inflation, and unemployment. Its preferred credit-depth coefficient is 0.00377, implying that a 10-percentage-point increase is associated with approximately 0.15 percentage points more NPLs one year later at the sample median. To operationalize early-warning calibration without claiming a universal cutoff, the paper reports the sample credit-depth quartiles and estimates a country fixed-effects linear probability model using the European Banking Authority’s 5% gross-NPL supervisory trigger. In that alternative outcome, a 10-percentage-point increase in credit depth is associated with a 2.78-percentage-point higher conditional probability of NPLs reaching 5% or more (p = 0.002). On a strictly common 609-observation sample, the credit-depth coefficients at one-, two-, and three-year horizons are 0.00501, 0.00960, and 0.01266. Lending rates and unemployment are positive, whereas annual credit growth and capital ratios are not robust predictors. Pooled interactions do not reject equal slopes across broad country partitions. System generalized method of moments (GMM) passes conventional tests but violates a persistence-bound credibility check. The evidence supports an early-warning interpretation, not a causal claim. Full article

This study proposes a two-variable fractional calculus–based modeling framework for analyzing gross domestic product (GDP) growth rates using key macroeconomic indicators of G8 countries and Türkiye over the period of 1998–2022. The economic variables considered include exports, imports, inflation, foreign direct investment, and unemployment rates, with data obtained from the World Bank. Caputo-type fractional derivatives combined with the least squares method are employed to construct two-variable economic models, where GDP growth rate is treated as the dependent variable. Multiple combinations of economic factors are systematically examined to evaluate their modeling performance. The accuracy of the proposed models is assessed using the Mean Absolute Percentage Error (MAPE). The results indicate that the export–inflation combination yields the lowest modeling error for Japan, while the inflation–unemployment combination produces the highest error for Russia. Overall, the findings demonstrate that fractional calculus–based bivariate models provide an effective and flexible framework for capturing nonlinear and memory-dependent dynamics in economic growth analysis. Full article

Quantifying water and soil resource (WSR) carrying capacity is crucial for sustainable regional development. However, humid hilly regions like Hunan Province remain understudied, facing ecological vulnerability despite abundant water resources. This study constructs a comprehensive evaluation index system based on the Production–Living–Ecological Space (PLES) framework and applies an improved entropy weight technique for order preference by similarity to ideal solution (TOPSIS) method; a coupling coordination model among production, living, and ecological space subsystems; and an obstacle degree model to annual-scale data from 2011 to 2022 for Hunan’s 14 prefecture-level cities. The results show the following: (1) The overall WSR carrying capacity index increased from 0.28 in 2011 to 0.52 in 2022, indicating a continuous improvement in carrying capacity during the study period. Spatially, the carrying capacity is higher in central and eastern regions and lower in western and southern areas, with most cities falling into low, relatively low, or medium categories. (2) The coupling coordination degree among the three PLES subsystems improved but remained in a transitional stage (0.30–0.45), indicating unbalanced development. (3) The top five obstacle factors are water yield modulus, length of water supply pipelines, ecological water use rate, gross domestic product (GDP) per capita, and per capita water resources—with the ecological water use rate exceeding 19% in every year of the study period. These findings highlight the need to coordinate production, living, and ecological subsystems to improve WSR carrying capacity. Targeted strategies, including efficient water use, optimized resource allocation, and ecological protection, are recommended for sustainable water–soil resource management in Hunan Province. Full article