Search Results (79)

The transition toward integrated urban–rural development represents a complex socio-technical challenge in post-poverty alleviation China. This study examines how the reform of agricultural service systems—especially the rollout of full-process socialization services—reshapes urban–rural integration by embedding new institutional, technological, and organizational structures into rural production. Drawing on staggered provincial pilot programs, we apply a double machine learning framework to assess the causal impact of service reform on the urban–rural income gap, labor reallocation, and agricultural productivity. Results show that agricultural socialization services enhance systemic efficiency by reducing labor bottlenecks, increasing technology diffusion, and fostering large-scale coordination in agricultural operations. These effects are most pronounced in provinces with stronger institutional capacity and higher levels of mechanization. The findings highlight agricultural service reform as a systemic intervention that alters resource allocation logics, drives institutional change, and fosters structural convergence across urban and rural domains. This research contributes to the understanding of agricultural modernization as a systems-engineered solution for regional inequality. Full article

Famed lawyer Clarence Darrow (1857–1938) argued strongly for an early-life public health approach to crime prevention, one that focused on education, poverty reduction, and equity of resources. Due to his defense of marginalized persons and his positions that were often at odds with his legal colleagues and public opinion, he was known as the Big Minority Man. He argued that the assumption of free will—humans as free moral agents—justifies systems of inequity, retributive punishment, and “unadulterated brutality.” Here, the authors revisit Darrow’s views and expand upon them via contemporary research. We examine increasingly louder argumentation—from scholars across multiple disciplines—contending that prescientific notions of willpower, free will, blameworthiness, and moral responsibility, are contributing to social harms. We draw from biopsychosocial perspectives and recent scientific consensus papers calling for the dismantling of folk psychology ideas of willpower and blameworthiness in obesity. We scrutinize how the status quo of the legal system is justified and argue that outdated notions of ‘moral fiber’ need to be addressed at the root. The authors examine recent arguments for one of Darrow’s ideas—a public health quarantine model of public safety and carceral care that considers the ‘causes of the causes’ and risk assessments through a public health lens. In our view, public health needs to vigorously scrutinize the prescientific “normative” underpinnings of the criminal justice system. Full article

This study developed an Energy Quadrilemma Index (EQI) for Latin American countries, analyzing data from six countries from 2014 to 2020. Using the Principal Component Analysis method, this work reduced the dimensionality of 20 indicators grouped into four dimensions: energy security, energy equity, sustainable development, and a new social context axis. The results reveal significant disparities among the countries in the study. For example, Uruguay shows robust indicators, Paraguay exhibits low utilization of the energy it produces, and Chile displays the poorest results in the sustainable development axis. Many countries’ widespread dependence on hydroelectricity makes them vulnerable to water crises. The results show that social, economic, and structural inequalities represent the main barriers to the energy transition, often marginalizing low-income populations. Ensuring a fair and inclusive transition requires implementing targeted policies and solutions adapted to each country’s specific context. Although Costa Rica leads in performance, it faces significant challenges in the field of sustainability. In contrast, Honduras has made some progress with sustainable development but still demonstrates weaknesses in other areas. These results highlight that standardized solutions can exacerbate regional inequalities, demanding approaches more tailored to local needs. This work’s novelty lies in the use of the social context dimension as a feature to assess energy poverty in selected countries. Full article

This study examines the impact of financial inclusion (FI) on reducing poverty and income inequality in Latin America and the Caribbean (LAC), using panel data from 15 countries for the period 2004–2021. System GMM with robust errors was used to address endogeneity issues, and FI was assessed in terms of access to and use of the financial system. The results indicate that increased FI contributes to reducing poverty and income inequality in LAC. While access to financial services plays a crucial role in poverty reduction, the utilization of financial services has a more profound impact on combating income inequality. These results underscore the importance of policies designed to improve financial access and promote the use of financial products and services. It is recommended to expand the banking infrastructure, facilitate the provision of low-cost accounts, and strengthen financial education programs. Full article

Colombia is a megadiverse, multiethnic, and multicultural country with a tremendous socio-ecological systems (SESs) diversity, which faces essential challenges arising from human activities, low levels of sustainable economic development, poverty, and social inequality rates, and the persistence of multiple forms of military, political, and social violence. Understanding the resilience of this complex system is both fundamental and challenging due to the contradictory effects of economic development and regional ecosystem degradation. This research proposes the Socio-Ecological Resilience Integrated Index (SERII) to assess historical changes in socio-ecological resilience in Colombia’s departments (political-administrative units) between 1985–2022. The SERII considers the trade-offs between ecosystems, social systems, and production systems, providing a complete perspective of integrated management with a geographic resolution at the level of general political-administrative units. The results reveal a spatial variation in the SERII, with worse conditions in the Caribbean, the Pacific, and the Colombian Amazon (on the country periphery) and better conditions in departments of the country center. From 1985 to 2022, the SERII experienced a decrease (51.5%), driven by ecosystem degradation, increased extractive activities (illegal and illegal), and the persistence of military, political, and social violence. While the limitations of the proposed indicator are described, the SERII effectively replicates the overall resilience of Colombia’s departments to external shocks and allows for suggesting regional management priorities for the targeted promotion of sustainable development. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article

Background/Objectives: Although the Robin Hood Index (RHI) is increasingly used to quantify geographic health inequality and guide resource redistribution, empirical evidence on whether higher physician density reduces RHI-measured inequality remains limited. This study systematically reviews and meta-analyzes RHI-based research to assess the association between physician distribution and health inequalities. Methods: We conducted a systematic review and meta-analysis of studies using the RHI to evaluate health inequalities, without restrictions on country or publication date. Following PRISMA 2020 guidelines and registered in PROSPERO (CRD42024496486), we searched PubMed, Scopus, and OpenGrey literature, extracted data on physician density and RHI outcomes, and conducted a meta-analysis. Odds ratios (ORs), ln(OR), and 95% confidence intervals (CIs) were calculated, and risk of bias was assessed using the Robvis tool. Results: Seventeen studies covering 720 regions and 1.07 billion individuals were included. Three clusters emerged: physician redistribution (10 studies), poverty–mortality associations (six studies), and systematic reviews (one study). Physician redistribution was strongly associated with increased inequality and policy attention (r = 0.73; p = 0.0038). Meta-analysis of eight redistribution studies yielded a pooled OR of 1.24 (95% CI: 0.54–2.86), consistent in sensitivity analysis (OR = 1.26; 95% CI: 0.56–2.89). Poverty–mortality studies also showed a correlation with the number of variables considered (r = 0.59; p = 0.022). Conclusions: A greater physician supply is associated with increased health inequalities, with statistical support but limited certainty. Methodological heterogeneity in RHI-based studies constrains comparability. Standardized methodologies and broader analytic models are needed to inform research and guide health policy. Full article

The economic downturns in South Africa present a significant threat, with the potential to disrupt the nation’s notable advances in addressing the persistent challenges of high unemployment, widespread poverty and stark inequality. In the absence of substantial and extensive structural transformation, South Africa’s aspirations to achieve its ambitious development goals may remain unattainable. Building on the precedent of a blend of literature review, comprehensive ex post analysis, and applied general equilibrium modelling tailored for ex ante assessments, this paper assesses options and impacts of alternative ambitious developmental interventions. The results indicate that, despite implementing a variety of strategies, there remains a disheartening underperformance in economic indicators. However, ex ante evaluations indicate that with targeted interventions and supportive government policies, the country can achieve economic growth and job creation. Simulation results identify sectors of personal and social service activities, transport, finance, and insurance as having the most formidable potential to significantly reduce unemployment while simultaneously catalysing robust economic growth. These pivotal sectors, nestled within the broader services and industries, are uniquely poised to bolster overall productivity and diminish unemployment, while adeptly absorbing a considerable influx of highly educated and skilled labour. This suggests that South Africa can decisively accelerate its economic progress by embracing a dual-pronged approach: fostering structural shifts towards manufacturing and services, while steadfastly advancing the upskilling of its dynamic workforce. Full article

Background: Adherence to iron supplementation is influenced by systemic barriers, including poor healthcare infrastructure, shortage of healthcare providers, and limited access to antenatal care (ANC) services. These challenges are more pronounced in rural areas. However, evidence on urban–rural disparities in non-adherence to iron supplementation remains limited, particularly in sub-Saharan Africa. This study examined these regional differences, stratified by income levels and national contexts. Method: This analysis utilised Demographic Health Survey (DHS) data conducted between 2015 and 2023 from 26 sub-Saharan African countries, including 287,642 women from urban (n = 91,566) and rural areas (n = 196,076). The outcome of this study was non-adherence to iron supplementation, defined as taking iron supplementation for less than 90 days during pregnancy. This study examines urban–rural differences in non-adherence stratified by country income levels based on World Bank 2022 income classifications and national context. A chi-square test was used to assess urban–rural differences, with a p-value of <0.05 considered statistically significant. Results: Non-adherence was significantly higher in rural areas (68.42%) than in urban areas (51.32%) (p < 0.001), with the disparity more pronounced in low-income countries (LICs). Ethiopia, Madagascar, Uganda, and Burundi were among the countries with the highest rural non-adherence, reflecting severe poverty and limited access to ANC. In contrast, Zimbabwe showed an inverse trend, where rural adherence was higher than urban. Conclusions: Rural sub-Saharan Africa has significantly higher non-adherence to iron supplementation, particularly in LICs, likely driven by systemic barriers such as poor infrastructure and limited access to healthcare. This non-adherence in rural areas undermines efforts to improve pregnancy and birth outcomes across the region. Targeted interventions, like those in Zimbabwe, can help address these inequities and improve maternal health outcomes. Full article

Objective: Low birth weight is a serious public health problem even in developed countries. The objective of this study was to assess the ability of machine learning to predict low birth weight rates in big cities in the USA on an ecological/population level. Study design: The study was based on publicly available data from the Big Cities Health Inventory Data Platform. The collected data related to the 35 largest, most urban cities in the United States from 2010 to 2022. The model-agnostic approach was used to assess and visualize the magnitude and direction of the most influential predictors. Results: The models showed excellent performance with R-squared values of 0.82, 0.81, 0.81, and 0.79, and residual root mean squared error values of 1.06, 0.87, 1.03, 0.99 for KNN, Best subset, Lasso, and XGBoost, respectively. It is noteworthy that the Best subset selection approach had a high RSq and the lowest residual root mean squared error, with only a four-predictor subset. Influential predictors that appeared in three/four models were rate of chlamydia infection, racial segregation, prenatal care, percentage of single-parent families, and poverty. Other important predictors were the rate of violent crimes, life expectancy, mental distress, income inequality, hazardous air quality, prevalence of hypertension, percent of foreign-born citizens, and smoking. This study was limited by the unavailability of data on gestational age. Conclusions: The machine learning algorithms showed excellent performance for the prediction of low birth weight rate in big cities. The identification of influential predictors can help local and state authorities and health policy decision makers to more effectively tackle this important health problem. Full article

This study investigates the role of public health expenditure on human capital development in South Africa to promote economic development. Despite extensive public health investments and economic reforms, persistent socioeconomic challenges such as poverty, unemployment, and inequality impede sustainable economic growth. This study uses an autoregressive distributed lag model, a vector error correction model (VECM), quantile regression, and Granger causality analysis to assess the relationship between fiscal health policies and human development. The findings confirm that government health spending significantly enhances human development in the short and long run, while unemployment and population growth exert adverse effects. VECM variance decomposition results indicate that the influence of public health expenditure remains persistent, though diminishing over time, with growing contributions from unemployment. Quantile regression shows the heterogeneous impact of health spending across different levels of economic development, emphasising its greater effectiveness at higher development stages. Causality analysis reveals a unidirectional relationship from public health expenditure to human development; this shows the need for sustained healthcare investment. The study calls for policies combining health spending with economic strategies to boost productivity, reduce inequality, and promote inclusive growth. Strengthening institutional efficiency and ensuring macroeconomic stability are crucial for maximising long-term human capital to promote sustainable development. Full article

Effectively measuring the income-generating effects of government subsidies is an important issue in assessing agricultural policies and implementing precision poverty alleviation. This study utilizes panel data from the China Family Panel Studies (CFPS) to screen a sample of 13,583 individual residents. We empirically analyze the impact of government subsidies on income inequality among farm households using fixed effects models, quantile regressions, and mediated effects models. Our study found that the following results: (1) The mechanism of subsidy action combines the functions of increasing income and regulating income distribution. Marginal benefits to low-income groups are more obvious. (2) Increasing productive agricultural inputs can reduce the income gap of farmers, which is more significant than the direct subsidy of funds. (3) The incentive effect of subsidies is strongest in the Northeast and weakest in the East. Compared to middle-aged and older farmers, the utility level of the subsidy is stronger in the youth group. Full article

Energy poverty, defined as a households’ limited ability to secure affordable energy, has become a key issue in the energy transition debate due to its impact on well-being, health risks, and social inequality. We investigate its health impacts using two-way fixed effects and high-dimensional fixed effects models, two-stage least squares, and quantify additional medical costs during extreme cold events with grouped fitting regression. We assess the effect of China’s Photovoltaic Poverty Alleviation Program using the Difference-in-Differences-in-Differences technique. Our results show that energy poverty significantly worsens household health deprivation, extreme cold events further increase medical costs in southern regions, while northern energy-poor families reduce healthcare spending to cover heating costs, and the Photovoltaic Poverty Alleviation Program significantly reduces both the medical burden and catastrophic medical expenditure among energy-poor households. These findings underscore the need for energy transition policies that combine targeted subsidies, health support during cold seasons, and wider deployment of modern heating technologies to protect vulnerable families and ensure a fair, resilient energy transition. Full article

Objectives: This scoping review aims to fill research gaps by exploring four guiding research questions. First, we aim to understand the major barriers encountered by U.S. menstruating college students in accessing menstrual health products at their educational institutions, households, and community. Second, we aim to identify social determinants of health (SDoH) that significantly influence and contribute to menstrual health inequities experienced by U.S. college students. Third, we aim to explore the measurement tools that have been used to assess menstrual health inequities experienced by U.S. menstruating college students. Lastly, we aim to determine the lessons learned and recommendations to improve overall menstrual health outcomes in U.S. menstruating college students. Study Design: This scoping review followed the Arksey and O’Malley framework (2005) and incorporated recommendations from the Joanna Briggs Institute (JBI) for the extraction, analysis, and presentation of results. Methods: The four databases searched were PubMed, Embase (Ovid), Web of Science, and Cochrane Library (Medline). Included articles were (1) published between 2000 and 2023, (2) focused on menstruating college students in the United States, (3) addressed menstrual health, menstrual equity, and period poverty in college campuses in the U.S., and/or (4) explored challenges encountered by menstruating college students in accessing menstrual health products. Results: A total of seven studies were retained for assessment. The most frequently cited Healthy People 2030 categories were economic stability (n = 5) and social and community context (n = 5). The most-cited barriers were sorted into the “Financial Constraints and Accessibility” (n = 13) theme category. Conclusions: Our study highlights practical applications and several recommendations for the future design, adoption, implementation, and evaluation of effective evidence-based interventions to address period poverty and subsequent barriers imposed by menstruating college students specific SDoH. More research is needed to further explore the health implications of menstrual health on mental, physical, and socioeconomic outcomes of menstruating individuals, particularly young adults experiencing different challenges as they venture into college and build their careers. Full article

The intersection of digitalization and sustainability is reshaping the tourism industry, with digital platforms playing a transformative role in optimizing travel experiences while simultaneously influencing economic inclusivity, labor dynamics, and environmental responsibility. This paper explores how Industry 4.0 technologies—such as artificial intelligence (AI), big data, blockchain, virtual reality (VR), and the Internet of Things (IoT)—are integrated into digital tourism platforms, assessing their dual impact on sustainability and market structures. The study develops a conceptual framework around five key dimensions: market power and digital dependency, AI-driven automation and workforce transformation, innovation and digital inclusion, sustainability innovations, and data security and governance. While digital platforms enhance personalization, operational efficiency, and eco-conscious travel, they also reinforce economic disparities, monopolization, and regulatory challenges, raising concerns related to SDGs such as SDG 1 (No Poverty), SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), SDG 10 (Reduced Inequalities), SDG 12 (Responsible Consumption, and Production), SDG 13 (Climate Action), and SDG 16 (Peace, Justice, and Strong Institutions). The study highlights the need for equitable governance frameworks to mitigate risks associated with AI-driven monopolization, algorithmic bias, and data privacy violations while ensuring digital accessibility for small and medium-sized enterprises (SMEs). The findings contribute to ongoing discussions on platform economics, digital governance, and sustainable tourism transformation, offering policy and managerial implications for fostering an inclusive and environmentally responsible tourism industry. Full article