Search Results (411)

In recent years, gentrification has frequently been linked to housing inequality and mass evictions and portrayed in a negative light. This study defines gentrification in terms of neighborhood median household income, income diversity, and their level and trajectories over 14 years (2010–2023), and classifies neighborhood gentrification stages using hierarchical clustering. It then adopts a quantitative, data-driven approach to examine the pairwise statistical and geospatial relationships among gentrification stages, executed evictions (2023–2024), and other housing characteristics (rent, socioeconomic status, and demographics) across New York City neighborhoods. The results demonstrate that pre-gentrifying neighborhoods were 39.08 times more likely to have eviction rates 1.5 standard deviations above the mean, 14.19 times more likely to be geospatial eviction hotspots, and three times more likely to experience evictions per neighborhood than fully gentrified neighborhoods. Income-adjusted regression models indicate that gentrification-stage membership explains variation in eviction severity beyond median household income alone, increasing explained variance from 27.9% to 42.6%; late-stage gentrifying and stable low-change neighborhoods showed significantly lower eviction severity than fully gentrified neighborhoods at comparable income levels. Spatially, the Bronx, the most economically disadvantaged borough, contained 11 of 17 eviction-outlier NTAs and 37 of 48 geospatial eviction hotspots, making it the most eviction-concentrated area in the city. This clustering-based classification framework relies only on widely available Census and administrative data and can be applied to other metropolitan areas or adapted to related urban domains such as land-use and zoning analysis. Targeted, actionable recommendations are offered to direct eviction-prevention resources toward the most vulnerable communities. Full article

This study aimed to identify socioenvironmental vulnerability profiles among Mexican households and analyze their association with health expenditure. Data from 86,102 households included in the 2024 National Household Income and Expenditure Survey were analyzed. Socioenvironmental profiles were constructed using housing, basic services, sanitation, household energy, socioeconomic stratum, and overcrowding through factor analysis of mixed data and k-means. Internal validation, stability analyses, algorithm comparisons, and sensitivity analyses supported a three-profile solution representing low, intermediate, and high vulnerability. Health expenditure was examined using survey-weighted descriptive estimates, exploratory nonparametric comparisons, and a survey-adjusted two-part model. The intermediate vulnerability profile showed higher odds of reporting health expenditure than the low vulnerability profile (OR = 1.129, 95% CI: 1.048 to 1.217, p = 0.002), whereas the high vulnerability profile showed no significant difference. Among households with positive expenditure, high vulnerability was associated with lower logarithmic expenditure ($\beta =-0.341$, 95% CI: $-0.490$ to $-0.192$, p < $0.001$), whereas the intermediate profile was not significantly different in the main model. The association for high vulnerability remained significant in sensitivity analyses, while results for the intermediate profile were more sensitive to model specification and income adjustment. Despite several statistically significant associations, effect sizes in the exploratory comparisons were small and the regression models explained a limited proportion of the variability in health expenditure. The findings therefore indicate modest associations between socioenvironmental vulnerability and health expenditure, with household economic resources and other unmeasured health-related factors likely contributing to the observed differences. Full article

Disamenity and amenity facilities pull property values in opposite directions, yet prior studies have treated disamenities as a homogeneous category and have been constrained by data scattered across agencies and by mismatched spatial scales. This study integrates multi-source spatial data, comprising 3426 sub-districts (eup-myeon-dong) nationwide, a 500 m population grid of 376,668 cells, and the actual point locations of individual facilities, into a single spatial framework and applies a multi-method design that combines a spatial Durbin model (SDM), explainable machine learning (XGBoost–SHAP), an environmental-justice analysis, and a micro-siting analysis. Four findings emerge. First, not all disamenities are associated with lower prices: only pollution-emitting facilities with a clear emission signature, namely, sewage treatment plants and incinerators, capitalize robustly as cross-sectional associations, into both housing prices and officially assessed land values, with the effect extending into neighboring sub-districts rather than staying purely local. The other disamenities show no robust total effect on either price; their estimates are small or unstable across specifications. Second, capitalization of pollution-emitting facilities is pronounced in the urban sample and disappears in the rural one, and the coefficient structure differs overall between urban and rural areas (global Chow test, p < 10⁻²¹). Third, micro-siting analysis shows that where a facility is placed (siting) and what it leaves behind in prices (capitalization) are distinct layers, and that capitalization tracks the population a facility keeps nearby rather than its nuisance type: pollution-emitting facilities retain enough nearby housing for their burden to register, whereas funerary facilities sited far from people do not. Fourth, the environmental inequity of pollution exposure runs along income rather than age and concentrates in cities rather than in rural areas: the exposure rate among residents of low-income urban sub-districts (49%) is 2.5 times that of the urban population as a whole (19%), while the greater Seoul metropolitan area remains comparatively insulated. These layered findings were possible only once the dispersed spatial data had been integrated and standardized, and the study demonstrates that fusing and efficiently managing spatial data is a precondition for evidence-based land-use and environmental policy. Full article

Green innovation is an important means of mitigating environmental pollution, improving production efficiency, and promoting high-quality economic development. Using prefecture-level city data and China Family Panel Studies (CFPS) data from 2010 to 2022, this study applies the Super-SBM model to measure urban green innovation efficiency and examines its effects on residents’ consumption levels and household consumption structures. The results show that (1) green innovation efficiency significantly increases consumption among urban residents and urban households, whereas its effects on rural residents and rural households are not statistically significant; (2) green innovation efficiency significantly reduces the share of subsistence-oriented consumption while increasing the shares of housing, development-oriented, and tourism consumption, thereby promoting a shift in household expenditure from basic needs toward development- and enjoyment-oriented consumption; (3) wage income and housing debt constitute two distinct transmission channels. Higher wage income facilitates development- and enjoyment-oriented consumption, whereas housing debt increases the share of housing consumption and exerts a crowding-out effect on development-oriented and tourism consumption; and (4) green innovation efficiency has a more pronounced effect on development-oriented and tourism consumption among young and high-income households. For middle-aged and older households, as well as low-income households, the effects of green innovation are reflected more strongly in an increase in the share of housing consumption. The findings provide important empirical evidence that green innovation contributes to improving residents’ quality of life. Full article

Housing financialization has transformed residential property from a basic social necessity into a speculative asset, intensifying socio-spatial inequalities in the Global South. In Iran, a bank-dominated financial regime and high spatial inequality severely restrict access to housing finance, particularly for low-income households and climate-induced migrants in mid-sized cities. While the spatial consequences of financialization have been extensively studied in major metropolises, neighborhood-scale exclusion mechanisms in secondary cities remain significantly understudied. This study addresses this gap by employing a hybrid methodology that integrates the Bayesian Best–Worst Method (BWM) with Geographic Information Systems (GISs) and Fuzzy Overlay analysis to quantify and spatially visualize barriers to housing finance in Birjand, Iran. Eleven indicators were weighted through probabilistic expert elicitation (n = 10) using Markov Chain Monte Carlo (MCMC) sampling, with excellent convergence confirmed by Gelman–Rubin diagnostics (R-hat ≈ 1.00). Results identify land use as the dominant barrier (posterior mean = 0.256; 95% CrI [0.244, 0.270]), followed by homeownership rate and education level (≈0.132 each). Spatial modeling reveals a pronounced north–south monetary–spatial divide: southern districts concentrate financial services and formal tenure, while northern peripheries, predominantly inhabited by low-income and climate-migrant populations, constitute financial deserts. These findings demonstrate that spatial barriers to housing finance are structurally embedded in land-use classification and asset-based gatekeeping. The study offers a replicable probabilistic–spatial framework for diagnosing financial exclusion in Global South mid-sized cities and provides evidence-based guidance for spatially targeted policy interventions aligned with SDGs 1, 10, and 11. Full article

Background: Allergic respiratory diseases, primarily asthma and allergic rhinitis, affect hundreds of millions worldwide. Their rising prevalence reflects complex gene–environment interactions that the exposome framework organizes into external, internal, and social domains shaping the development and progression of respiratory allergic diseases. Methods: Narrative review based on structured searches in PubMed/MEDLINE and Scopus (2015–2025), supplemented with 11 key references; 42 publications were selected from 1015 records. Results: Air pollution showed consistent but modest associations with asthma (HR 1.04–1.12); household dampness and mold showed larger effect sizes on symptom severity (OR 1.48–1.52); and occupational exposures showed the largest effect sizes across studies (RR 3.2–4.3). Systemic inflammation, oxidative stress, and nasal microbiome alterations were identified as internal mediators linking exposures to allergic phenotypes, though mechanistic pathways remain incompletely defined. Social exposome studies suggested context-dependent patterns: socioeconomic advantage correlated with greater sensitization in some settings, while deprivation could amplify disease progression and mortality risk (HR 1.18–1.32). Conclusions: The exposome offers a valuable integrative framework, but clinical translation is limited by scarce longitudinal multi-exposure studies and underrepresentation of low- and middle-income regions. Priorities include life-course cohorts integrating all three domains and targeted interventions in housing and occupational settings. Full article

Housing is a crucial determinant of population migration. However, the mechanisms through which urban housing influences floating-population re-migration, as well as its role in guiding the efficient spatial allocation of populations, remain underexplored. This study investigated the impact of urban housing status on population re-migration based on the spatial equilibrium theory, and empirically tested this relationship using nearly 370,477 individual migration intentions records from the China Migrants Dynamic Survey (CMDS). The key findings are as follows. First, urban housing status is related to shaping population re-migration intentions. In particular, owner-occupied housing and government-provided low-rent housing are associated with lower re-migration intentions. Second, institutional constraints on migrant populations can vary somewhat depending on household registration status. Rural-registered floating populations may sometimes face somewhat more restrictions in accessing urban housing and public services. By contrast, high-wage areas has less re-migration intentions primarily through labor income gains, leading to heterogeneous housing status effects on migration intentions. Further analysis reveals spatial and individual heterogeneity in how urban housing status shapes population re-mobility. Floating populations residing in first-tier, second-tier, and provincial capital cities prioritize employment opportunities. In comparison, first-generation floating populations, those with local spouses, and individuals engaged in low-risk occupations exhibit stronger demand for stable residence. Full article

Falls among older adults are a major public health concern. Older adults living in low-income housing (LIH) are at higher risk of falls due to disadvantages linked to social determinants of health, yet remain underrepresented in research. This study aimed to assess the feasibility of a program addressing fear of falling and its consequences on activity levels among older adults living in LIH in Côte-des-Neiges (Montreal, QC, Canada). A mixed-methods pilot feasibility study was conducted using Bowen et al.’s framework, drawing on data collected through questionnaires, observation grids, attendance records, and semi-structured interviews. The program consisted of six 90 min sessions designed to enhance participants’ confidence in preventing falls while promoting engagement in activities. Fourteen older adults (mean age = 75.5 years) were recruited. Outcome data on fall-related psychological factors, activity engagement, and knowledge about falls were collected before and after the program. Moreover, older participants’ perceived benefits were assessed following program completion. Post-program interviews with older adults, facilitators, and a community field worker were conducted to further explore program feasibility, including its acceptability, implementation, and integration. The program showed strong acceptability and a high attendance rate (95%). Although no statistically significant pre-post changes were observed in the outcome variables, older participants reported several benefits at post-test, including improved knowledge about fall prevention (100%) and greater confidence in their ability to avoid falls (85%). These findings support the feasibility of implementing this culturally adapted fall prevention program in similar LIH settings and provide valuable insights for its refinement and future research. Further investigation is warranted to examine the program’s feasibility across other LIH settings and linguistic groups, as well as to assess its effectiveness. Full article

Energy poverty poses a critical threat to global sustainable development by undermining household well-being and deepening social inequality. This study draws on data from 17,778 households across six waves of the China Family Panel Studies (CFPS) from 2012 to 2022 to examine the dynamics, determinants, and predictive patterns of household energy poverty in China. Our study also enhances and optimises the four-quadrant classification framework within the Low-Income, High-Cost (LIHC) framework, which jointly evaluates income and energy expenditure using dynamic thresholds. This approach enables us to identify not only households experiencing energy poverty but also those facing heightened vulnerability. In the sample, 7.96% were classified as energy-poor, 29.10% as at risk of energy poverty, 24.14% as at risk of income poverty, and 38.81% as not at risk, indicating that the number of households facing hidden risks far exceeds that of households identified as poor using traditional binary diagnostic methods. Next, we implement a Bayesian-optimised Extreme Gradient Boosting (XGBoost) model to improve predictive accuracy. Thus, the trained model achieved a prediction accuracy of 78%. We employ Shapley Additive exPlanations (SHAP) analysis to interpret the relative importance and interaction of explanatory variables. Our findings reveal three key patterns. First, households at risk of energy insecurity substantially outnumber those already in energy poverty, indicating a large latent vulnerable population that conventional measures often overlook. Second, housing conditions and energy expenditures remain the dominant structural drivers of energy poverty; however, financial pressures related to healthcare, education, and other non-energy expenditures increasingly intensify vulnerability. Third, Bayesian optimisation significantly enhances the model’s capacity to capture nonlinear relationships and complex household heterogeneity. By integrating dynamic measurement with interpretable machine learning, this study advances methodological approaches to energy poverty assessment and provides robust empirical evidence for early-warning systems, differentiated governance strategies, and targeted policy design in the context of China’s energy transition. Full article

Climate change is increasing thermal vulnerability in hot–humid tropical regions, especially among low-income indigenous communities with limited access to mechanical cooling. Yet peer-reviewed evidence on the climate-adaptive performance of Shuar vernacular housing remains scarce. This study addresses that gap by assessing indigenous housing in the Shuar community of Kupiamais, Ecuador (NEC Climate Zone 2, Humid–Hot), through a three-phase mixed-method framework combining territorial and typological analysis, a twelve-criterion bioclimatic evaluation matrix, and an exploratory thermal sensitivity analysis based on dynamic energy simulation. Three typologies were compared: one vernacular dwelling (V1, Shuar jii nee) and two introduced typologies (M2, M3). V1 achieved 66.7% bioclimatic compliance, compared with 62.5% for both introduced typologies, with its advantage concentrated in envelope air permeability, shading of vertical surfaces, and organic roofing. The thermal sensitivity analysis further indicates that the jii nee maintains indicative passive thermal autonomy for most of the simulated annual period without mechanical conditioning, with indoor operative temperatures estimated to remain within the NEC Zone 2 comfort range (18–26 °C); these results derive from an exploratory, uncalibrated dynamic energy simulation of V1 alone and should be interpreted as indicative tendencies rather than validated predictions of absolute indoor conditions. These findings provide empirical evidence of the climate-adaptive value of Shuar vernacular architecture, identify five transferable passive strategies, and propose a low-resource, replicable framework for evaluating indigenous housing in the Global South. The study also clarifies how typological change can erode passive cooling capacity, cultural continuity, and low-resource climate adaptation in settlements. Full article

Improving indoor thermal comfort in high-altitude rural housing remains a persistent challenge for low-income communities in the Peruvian Andes. This study evaluates the thermal performance of a standardized Sumaq Wasi modular dwelling in Langui (Cusco, Peru, 3969 m.a.s.l.) and proposes passive envelope modifications that enhance comfort while preserving economic feasibility. A multi-objective optimization approach combining EnergyPlus simulations with the NSGA-II algorithm was applied to minimize total thermal discomfort ($TD{I}_{total}$), bedroom underheating ($TDI{U}_{bedrooms}$), and 10-year life cycle costs ($LCC$). The calibrated model incorporated field measurements of indoor air temperatures. Global sensitivity analysis using Morris and Sobol methods identified ceiling thermal transmittance as the dominant contributor for $TD{I}_{total}$, and exterior wall solar absorptance as the driver of $TDI{U}_{bedrooms}$. Optimization reduced $TD{I}_{total}$ and $TDI{U}_{bedrooms}$ to 22% and 8% of the base case, requiring additional investments of USD 2347 and USD 1959, respectively, above the base case cost (USD 8100). Cost-neutral strategies, raising exterior wall solar absorptance to 0.9 and increasing the skylight-to-roof ratio (13.1%), reduced bedroom underheating to 30% of the base case and outperformed a scenario with two 400 W electric heaters. These results demonstrate that context-appropriate passive design can substantially improve comfort under severe climatic and financial constraints. Full article

Background: Indoor Environmental Quality (IEQ) in housing plays a critical role in supporting health, comfort, and daily well-being, yet research and practice often address thermal, visual, acoustic, and air quality conditions in isolation. Objective: This systematic review synthesizes findings from peer-reviewed studies to examine how residential IEQ is experienced and shaped through interactions among physical building factors, environmental conditions, occupant behaviors, and socio-economic contexts. Methods: A systematic literature review was conducted following PRISMA guidelines, including 110 peer-reviewed studies published between 2015 and 2025. Data were extracted and coded from 10,838 quotations and corresponding measured environmental parameters, enabling cross-domain thematic synthesis across eight IEQ domains and four analytical themes. Results: The results show persistent perception-to-measurement gaps, particularly in ventilation usability, low-frequency noise, nighttime thermal conditions, and moisture control. Demographic factors, including age, life stage, health sensitivity, and housing tenure, influence how IEQ conditions are perceived. Integrated IEQ assessments indicate that sleep-critical spaces, moisture robustness, and simple, quiet control systems exert disproportionate influence on overall environmental satisfaction. Conclusions: The findings highlight the need to prioritize preventive design strategies addressing moisture, thermal comfort, acoustics, and lighting, while improving usability of environmental controls. Future research should expand longitudinal and cross-context studies, particularly in low-income communities, and strengthen links between IEQ performance and health outcomes. Healthy residential environments require understanding IEQ not only as a technical performance metric but as a spatial and social condition. Full article

Background/Objectives: Food and nutritional insecurity (FNI) is a major social determinant of health that disproportionately affects older adults, with significant implications for their health, nutrition, and well-being. In this context, this scoping review aims to map and synthesize the available scientific evidence on the main determinants of FNI among older adults, considering socioeconomic, health-related, functional, psychosocial, and structural factors. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute methodology and reported following the PRISMA-ScR guidelines. A comprehensive search was performed across eight databases (PubMed/MEDLINE, EMBASE, Scopus, Web of Science, CINAHL, LILACS, ProQuest, and Google Scholar), up to November 2024. Original studies addressing FNI in individuals aged ≥60 years were included. Study selection and data extraction were conducted independently by two reviewers, with disagreements resolved by consensus. Results: Of 5897 records identified, 15 studies met the inclusion criteria. FNI in older adults was described as a multifactorial phenomenon associated with low income, limited education, social isolation, widowhood, chronic diseases, functional limitations, depressive symptoms, and poor housing conditions. Structural determinants, including institutional racism, gaps in social protection systems, and barriers to accessing food assistance programs, were also reported. Considerable heterogeneity in measurement instruments highlights the complexity of assessing FNI in this population. Conclusions: Addressing FNI in older adults requires moving beyond isolated interventions toward integrated, intersectoral strategies that tackle its underlying social and structural drivers. Strengthening social protection systems, reducing access barriers, and promoting equity-oriented policies are essential to ensure adequate nutrition and support healthy and dignified aging. Full article

High rates of pest infestations are frequently reported in low-income homes in the U.S. However, even with access to effective insecticides, most residents are unaware of how to properly control indoor pests. We designed and evaluated the effect of in-home integrated pest management (IPM) education and provision of pest management tool kit on German cockroach (Blattella germanica L.) infestations. Twenty-nine apartments were enrolled based on the presence of German cockroaches. They were divided into two groups: an intervention group (12) and a control group (17). The intervention group received a cockroach prevention and control flyer designed by the researchers, gel bait, a bottle of boric acid dust, sticky traps, and in-person brief education on how to prevent and control cockroaches. The control group did not receive education or pest control materials. The intervention resulted in a significantly higher reduction in cockroach numbers and infestation rates compared to the control. At 6 months, the geometric mean cockroach count per apartment decreased by 99% in the intervention group and 51% in the control group. Cockroaches were no longer detected in 58% and 6% of the intervention and control groups, respectively. However, the IPM education did not result in significant improvement in the sanitation rating. Full article

COVID-19 was a prolonged public-health shock that disrupted mobility, access to services, and household spending. Although the official U.S. poverty rate declined to 11.1%, the Supplemental Poverty Measure rose to 12.9%, suggesting that material hardship persisted unevenly across places. This study asks whether pre-existing livelihood vulnerability and local epidemic burden translated into geographically concentrated consumption losses during 2020–2022. Because sustained consumption loss can erode households’ health-related spending, tracking where spending declines concentrate helps connect local social and environmental conditions to how communities withstand a health crisis. We analyze consumer expenditure, unlike prior research relying on aggregate retail sales, to capture fine-grained economic strains as a proxy for shock-absorption capacity. A Livelihood Vulnerability Index (LVI) was calculated for each U.S. county using 16 socio-economic variables, and counties were classified as high- or low-risk. A multilevel model then examined how socio-economic and COVID-19 factors at county and census tract levels shaped consumption changes. Higher-risk communities experienced greater consumption reductions. At the census tract level, the non-White ratio, vacancy rate, built year, per capita income, education level, and housing value were significant. At the county level, COVID-19 cases and deaths, crowding, public transportation use, and vehicle availability mattered most. These findings support place-targeted strategies that combine public-health response with socio-environmental interventions to reduce disparities rooted in pre-existing vulnerability. Full article