Search Results (459)

Food and nutrition production play a pivotal role in China’s transition toward a nutrition-sensitive food system. Alongside rapid urbanization and dietary shifts, substantial transformations have occurred in food production patterns. This study investigates inequality in China’s food and nutrition sectors from 1991 to 2021 by employing the Theil index and Gini coefficient, analyzing its drivers from both regional and categorical perspectives. The findings reveal significant disparities in food production concentration across different categories, with notable shifts over the study period. Land-intensive agricultural products—including cereals, oil crops, sugar crops, pulses, roots, tubers, and livestock—exhibited increasing inequality, as indicated by rising Gini coefficients and Theil indices, suggesting greater spatial concentration. In contrast, labor-intensive categories such as fruits and aquatic products showed declining inequality, reflecting broader distribution. Notably, inequality within specific food types (e.g., wheat, beet, and rapeseed production) exceeded disparities among broader food categories. Nutrition inequality, measured by both indices, also increased between 1991 and 2021. However, variations across different nutrients were relatively minor, as diversified nutrition sources mitigated inequality within food categories. Geospatial analysis further highlighted distinct patterns: cereals were the primary contributors to disparities in energy, protein, and mineral supply; oil crops and livestock products drove fat inequality; while vegetables and fruits predominantly influenced vitamin inequality. These findings offer critical insights for optimizing China’s food and nutrition distribution strategies, supporting more equitable and sustainable food system development. Full article

Rural tourism agglomerations are increasingly viewed as catalysts for diversified regional growth, integrated rural revitalization, and improved farmer prosperity. However, most studies focus on urban and developed regions, leaving spatial patterns and evolutionary mechanisms in underdeveloped rural areas poorly understood. This study takes Jilin Province, an economically lagging region, as an example, measuring rural tourism agglomeration using spatial analysis methods including the Gini coefficient, nearest-neighbor index, Ripley’s K function, kernel density, and buffer analysis. Results show that agglomeration is significant and strengthening over time, with clear regional variations. All types of rural tourism products exhibit an “increase followed by decrease” pattern across spatial scales, evolving from isolated “nodes” to continuous “areas”. Agglomeration is subject to triple constraints from natural, economic, and social dimensions. This study suggests that high-quality rural tourism development should leverage point–axis spillover from flagship scenic areas, promote surface expansion of characteristic villages and towns, and strengthen network connectivity through roads and talent-information channels. Full article

This study develops a four-dimensional value-assessment framework encompassing economic, innovation, social, and cultural dimensions to evaluate the multidimensional performance of family firms in China. Drawing on the entropy weighting method, we construct a composite value index for 251 A-share listed family firms from 2014 to 2023 and apply spatial statistical techniques—including Dagum Gini coefficients, Theil indices, and coefficients of variation—to examine temporal evolution and regional disparities. We further estimate explanatory panel models with firm and year fixed effects (Hausman test favoring FE) to identify the firm-level determinants of composite value. Leverage exhibits a significantly negative association with value, while firm size and innovation capacity are positively related; no significant moderating effect of technology-intensive industry is found. A robustness check using equal weights (0.25 for each dimension) yields an almost perfect correlation (0.9999) with the entropy-weighted index, confirming that the dominance of the innovation dimension in the weighting scheme does not materially affect the overall conclusions. The results highlight the importance of integrating multidimensional value perspectives into both academic research and policy design to promote balanced, inclusive, and sustainable development trajectories for family enterprises. Full article

Urban spatial justice is a critical issue in the context of rapid urbanization. Improving public well-being depends on the efficient use of park green space (PGS) resources. This study evaluates the spatial distribution equity and social equity of PGS in Jinan City, Shandong Province, China, with the aim of optimizing their spatial layout, mitigating poor accessibility due to uneven spatial distribution, and improving the quality of life for all inhabitants. Firstly, based on Sustainable Development Goal 11 (SDG11), we constructed an urban sustainable development index system to quantify residents’ demand levels. The supply level was measured through three dimensions: quantity, quality, and accessibility of PGS utilizing multi-source geospatial data. A coupling coordination degree model (CCDM) was employed to analyze the supply-demand equilibrium. Secondly, Lorenz curves and Gini coefficients were utilized to evaluate the equity of PGS resource distribution to disadvantaged populations. Finally, a k-means clustering algorithm found the best sites for additional parks in low-accessibility regions. The results show that southern areas—that is; those south of the Yellow River—showed greater supply-demand equilibrium than northern ones. With a Gini index for PGS services aimed at vulnerable populations of 0.35, the citywide social level distribution appeared to be relatively balanced. This paper suggests an evaluation technique to support fair resource allocation, establishing a dual-perspective evaluation framework (spatial and social equality) and giving a scientific basis for PGS planning in Jinan. Full article

The reliability of decision-making algorithms within soft set theory is fundamentally constrained by their underlying membership functions. Traditional binary approaches overlook the implicit connections between the attributes a candidate possesses and those it lacks—connections that can be inferred from the wider candidate pool. To address this core challenge, this paper puts forward the Relational Membership Value Calculation (RMVC), an algorithmic framework whose core is a fine-grained relational membership function. Our approach moves beyond binary logic to capture these nuanced interrelationships. We provide a rigorous theoretical analysis of the proposed algorithm, including its computational complexity and robustness, which is validated through a comprehensive sensitivity analysis. Crucially, a comparative analysis using the Gini Index quantitatively demonstrates that our method provides significantly higher granularity and discriminatory power on a representative case study. The RMVC is implemented as an open-source Python program, providing a foundational tool to enhance the reasoning capabilities of AI-driven decision support and expert systems. Full article

This paper investigates how Sustainable Development Goals SDG 3 (Health and Well-being) and SDG 10 (Reducing Inequality) interacted during the period 2009–2021 within the context of income disparities in the European Union and Ukraine. The central assumption is that lowering income inequality improves overall population health. The research proposes a conceptual model with four main elements: classifying countries according to their Gini index along with their performance on SDG 3 and SDG 10; analyzing how income inequality and progress on SDG 10 influence health outcomes (SDG 3); categorizing countries based on the strength of links between inequality measures and well-being indicators; and interpreting these results in the context of Ukraine’s European integration aspirations. Methodologically, cluster analysis, correlation and regression models, and semantic differentiation are applied. The findings show that a reduction in income inequality positively affects health and well-being. Nonetheless, Ukraine continues to face considerable structural and institutional hurdles. From a governance standpoint, the study highlights the need for cohesive policies that integrate economic, health, and social dimensions. Effective public management should coordinate national reforms to match EU healthcare and social policy standards. Strengthening institutions, ensuring fair access to healthcare services, and adopting inclusive policy instruments remain crucial to advancing both SDG 3 and SDG 10 targets, as well as supporting Ukraine’s broader integration with the European Union. Full article

Green space (GS) equity is a crucial component of environmental justice. From the perspective of environmental justice, this study focuses on the equity of GS across sub-districts with varying GDP levels in Guangzhou, quantitatively assessing and comparing GS equity in areas with different development statuses. However, existing research still lacks sufficient exploration of the relationship between micro-scale socioeconomic indicators and GS equity. To address this gap, this study investigates the inequality of GS availability across neighborhoods during the rapid urbanization process in Guangzhou’s central urban area from 2000 to 2020. Key indicators for measuring GS availability—including GS area, per capita GS area, and NDVI—were selected and calculated for each sub-district in 2000 and 2020. This approach reveals spatial disparities in GS distribution between the two years. Subsequently, the Theil index and Gini index were employed to assess the degree of inequality in GS. Using GS area data and NDVI data, this study analyzes per capita GS area and NDVI values across sub-districts with different development levels in Guangzhou’s central urban area. Statistical methods such as the Theil index were then applied to evaluate the equity of these indicators. The findings indicate that between 2000 and 2020, Guangzhou experienced significant urbanization, a notable decline in total GS area, a marked improvement in NDVI values, and a substantial improvement in GS equity. There is a conflict between the supply of green resources and the demand for high-density economic/population centers. This research provides scientific evidence for urban planners and policymakers to promote the equitable distribution and sustainable development of GS. Full article

Equity in urban greenery is essential to improving residents’ well-being and contributing to environmental justice. Research on equity in street-scale urban greenery remains limited, but this study addresses it by employing the green view index (GVI), a widely recognized indicator for assessing green space quality from a pedestrian perspective, using semantic segmentation methods and Baidu Street View (BSV) images to quantify street-level greenery. Through spatial clustering and hot spot analysis, the visibility and spatial distribution of street greenery in Hangzhou’s central urban area were examined. Furthermore, the Lorenz curve, Gini coefficient, and location entropy were applied to evaluate disparities in green visibility across urban spaces. The results show that the average GVI at the sample point level, road level, and district level in the study area are 0.167, 0.142, and 0.177, respectively. Meanwhile, the spatial heterogeneity of the GVI is highly pronounced, with distinct clustering characteristics. The Gini coefficient of street greenery visibility is 0.384, indicating a moderate level of inequality in the distribution of greenery resources. Notably, a higher GVI does not necessarily correspond to better internal greenery equity, highlighting disparities in the distribution of urban greenery. This study offers a more precise and refined quantification of urban greenery equity, providing critical insights for addressing spatial disparities and informing urban planning strategies aimed at promoting equitable green infrastructure. Full article

Introduction: Respiratory infections cause serious complications responsible for a significant number of deaths in Brazil. In addition, the causes of death can be influenced by social and economic inequalities in Brazilian regions. Objective: To analyze the epidemiological profile and the influence of demographic and socioeconomic factors on deaths from respiratory infections in the adult population between 2014 and 2023 in Brazil. Methods: This was an analytical ecological study using data from the Death Information System. Death incidences were calculated. Multinomial logistic regressions and correlation tests were used to analyze the influence of socioeconomic factors on deaths. Results: There were high incidences of deaths from unspecified pneumonia, unconfirmed tuberculosis and complicated influenza. Deaths from pneumonia and the Gini index were positively correlated, considering the variables black ethnicity (R = 0.894), age over 90 (R = 0.869) and no schooling (R = 0.818) before the pandemic. The odds ratio of death from tuberculosis and influenza in the 70–79 age group (OR = 3.97) and black ethnicity (OR = 1.24), respectively, were higher in the pandemic and post-pandemic periods compared to the previous period. Conclusions: Deaths from respiratory infections were mainly influenced by demographic variables and socioeconomic inequalities in Brazil. Full article

Exploring the regional disparities in and spatiotemporal evolution of the new energy vehicle promotion level (NEVPL) is essential for facilitating low-carbon and environmentally sustainable development. This study employs a multidimensional index system to assess the NEVPL across 31 Chinese provinces from 2017 to 2023, utilizing data on NEV ownership, annual NEV sales, the number of public charging piles, and the vehicle-to-pile ratio. The NEVPL scores were estimated using the Entropy-TOPSIS method. Spatial patterns and the mechanisms of regional disparities were examined using a suite of spatial analytical techniques, including the standard deviation ellipse (SDE), gravity center analysis, Dagum Gini coefficient decomposition, and kernel density estimation. The results reveal three principal findings. First, NEVPL exhibited a sustained upward trend nationwide. The eastern region consistently maintained a leading position, the central and western regions demonstrated steady growth, and the northeastern region remained underdeveloped, leading to an expanding regional gap. Second, spatial distribution transitioned from an early dispersed pattern to a structure characterized by both agglomeration and differentiation. The promotional center gradually shifted toward the southeast, and high-value regions became increasingly isolated, forming island-like clusters. Third, spatial inequality was mainly driven by inter-regional differences, which contributed to over 70 percent of the total variance. The rising intra-regional disparity within the eastern region emerged as the predominant factor widening the national gap. These findings offer empirical evidence to support the refinement of new energy vehicle (NEV) policy frameworks and the promotion of balanced regional development. Full article

Objectives: Depressive symptoms are highly prevalent among individuals with chronic kidney disease (CKD). This study explores their associated factors and gender differences among middle-aged and older CKD patients in China. Methods: Based on the health ecology model (HEM), this study utilized the 2018 cross-sectional data from the China Health and Retirement Longitudinal Study (CHARLS) to examine gender differences in CKD patients across demographic groups. A multivariate logistic regression identified factors associated with depressive symptoms and gender differences among middle-aged and older patients with CKD in China. Additionally, a random forest model was constructed to rank the importance of key predictors based on the Gini index. Results: Among 1422 CKD patients, 50.35% reported depressive symptoms (42.97% of males and 59.56% of females). Factors significantly associated with higher depressive symptoms included female gender, rural residence, poor self-reported health, sleep duration < 7 h, and limitations in Activities of Daily Living (ADLs) and Instrumental Activities of Daily Living (IADLs). The association of smoking and ADLs on depressive symptoms in CKD patients varied considerably between genders. Self-reported health and life satisfaction were the two variables most strongly associated with depressive symptoms among CKD patients. Conclusions: The study shows that female CKD patients have a higher prevalence of depressive symptoms than males. Several factors are significantly associated with depressive symptoms in patients with CKD. These findings provide valuable insights that potentially inform the development of targeted prevention and management strategies for depressive symptoms in middle-aged and older CKD patients in China. Full article

The rapid development of the global transportation industry has led to increased carbon dioxide emissions, intensifying the pressure to reduce these emissions. On the basis of constructing a global carbon emission analysis framework for the transportation industry, this study used panel data on carbon emissions from the transportation industry in 140 countries or regions for a long-term time series from 1971 to 2018. The standard deviation ellipse, Gini coefficient, and Moran’s I index were used to characterize the spatial patterns of carbon emissions in the global transportation industry. The factors influencing carbon emissions from the global transportation industry were analyzed using quantile regression. The main findings are as follows: (1) From the distribution pattern, the total carbon emissions from the global transportation industry showed a significant upward trend, and the spatial polarization characteristics were particularly significant. (2) The Gini coefficient of global carbon emissions from the transportation industry showed a significant downward trend, characterizing a more balanced spatial distribution. (3) From the perspective of correlation patterns, the spatial distribution of carbon emissions from the global transportation industry was positively correlated. (4) Regarding influencing factors, population size had a significant role in promoting carbon emissions from the transportation industry, and the difference was not apparent. The influence of affluence on carbon emissions was basically in line with the characteristics of the Kuznets curve, technological advances had a significant negative influence on carbon emissions, and participation in the global value chain had a significant influence on carbon emissions from countries or regions with high carbon emissions. In conclusion, it is necessary to enhance international cooperation on carbon emission management in the global transportation industry and adopt differentiated policy measures. For instance, we should accelerate the construction of a multimodal transport system, increase the promotion and support for new energy heavy-duty trucks, implement policies such as priority road rights for new energy heavy-duty trucks and reduce tolls on expressways, and deepen the integration of transportation and energy. Full article

The Pareto distribution has been widely used to model income distribution and inequality. The tail index and the Gini index are typically computed by iteration using Maximum Likelihood and are usually interpreted in terms of the Lorenz curve. We derive an alternative method by considering a truncated Pareto distribution and deriving a simple closed-form approximation for the tail index and the Gini coefficient in terms of the mean absolute deviation and weighted quartile differences. The obtained expressions can be used for any Pareto distribution, even without a finite mean or variance. These expressions are resistant to outliers and have a simple geometric and “economic” interpretation in terms of the quantile function and quartiles. Extensive simulations demonstrate that the proposed approximate values for the tail index and the Gini coefficient are within a few percent relative error of the exact values, even for a moderate number of data points. Our paper offers practical and computationally simple methods to analyze a class of models with Pareto distributions. The proposed methodology can be extended to many other distributions used in econometrics and related fields. Full article

The presence of Active Galaxy Nuclei (AGN) can affect the morphological classification of galaxies. This work aims to determine how the contribution of AGN affects the most-used morphological parameters down to the redshift of z ∼ 2 in COSMOS-like conditions. We use a sample of >2000 local non-active galaxies, with a well-known visual morphological classification, and add an AGN as an unresolved component that contributes to the total galaxy flux with 5–75%. We moved all the galaxies to lower magnitudes (higher redshifts) to map the conditions in the COSMOS field, and we measured six morphological parameters. The greatest impact on morphology occurs when considering the combined effect of magnitude, redshift, and AGN, with spiral galaxies being the most affected. In general, all the concentration parameters change significantly if the AGN contribution is >25% and the magnitude > 23. We find that the GINI coefficient is the most stable in terms of AGN and magnitude/redshift, followed by the moment of light (M20), Conselice–Bershady (CCON), and finally the Abraham (CABR) concentration indexes. We find that, when using morphological parameters, the combination of CABR, CCON, and asymmetry is the most effective in classifying active galaxies at high-redshift, followed by a combination of CABR and GINI. Full article

This article introduces the Index of Distributive and Developmental Outlook (IDDO), a composite indicator designed to jointly assess economic performance and income inequality across EU-27 Member States. While GDP per capita is widely used to evaluate national prosperity, and the Gini coefficient captures income distribution, their separate use often obscures the interaction between growth and equity—an essential dimension of sustainable development. To address this gap, the IDDO integrates normalized values of both indicators using arithmetic and geometric means. The study applies the IDDO to a longitudinal dataset covering the years 2005, 2014, and 2024, allowing for comparative and temporal analysis. Based on IDDO scores, countries are classified into four development types: balanced development, growth with inequality, equity with stagnation, and dual vulnerability. Results show that while some Member States, such as Luxembourg, Czechia, and Slovenia, maintain consistently high IDDO levels, others—including Bulgaria, Romania, and Latvia—exhibit persistent challenges in aligning growth with equitable outcomes. The findings underscore the need for cohesion policies that prioritize not only economic convergence but also distributive fairness. The IDDO provides a practical and adaptable tool for diagnosing development patterns, benchmarking performance, and informing policy design within the EU framework. Full article