Environmental and Earth Sciences

Abstract: Rapid urbanization has dramatically transformed land use land cover (LULC) patterns and ecosystem services in megacities of the Global South, with major implications for carbon storage and climate regulation. This study investigates LULC dynamics, associated carbon storage changes, and future urban growth in the Dhaka Metropolitan Area from 1994 to 2044. Multi-temporal Landsat imagery was used to classify six LULC categories for 1994, 2004, 2014, 2019, and 2024. Carbon storage was quantified using the InVEST model by integrating LULC maps with literature-based carbon density values representing four carbon pools. Future LULC scenarios for 2034 and 2044 were simulated using a Cellular Automata–Artificial Neural Network (CA-ANN) model within the MOLUSCE framework, incorporating six biophysical and infrastructural driving factors. Model validation produced an overall accuracy of 70.13% with satisfactory kappa indices. Results reveal that built-up expansion occurred mainly at the expense of vegetation and water bodies, causing nearly a 40% decline in total carbon storage between 1994 and 2024, with water bodies contributing the largest share of carbon loss. Process-based analysis identified dominant transformation clusters driven by urban reclamation and hydrological loss, alongside vegetation clearance and land degradation. Future projections indicate continued urban growth and further carbon depletion by 2044. These findings highlight the urgent need to conserve wetlands and urban green spaces and to integrate carbon-sensitive strategies into sustainable urban planning.

Abstract: The proliferation of water hyacinth (Eichhornia crassipes), an invasive aquatic macro-phyte, poses significant environmental challenges to water bodies globally. This study investigates the valorization of this problematic biomass into a sustainable adsorbent for dye removal from aqueous solutions. Water hyacinth was subjected to chemical activa-tion using sulfuric acid at varying concentrations to evaluate its adsorption efficacy for methylene blue (cationic dye) and Ponceau S (anionic azo dye). The performance was benchmarked against conventional adsorbents including rubber and wood. Spectro-photometric analysis revealed that water hyacinth activated with concentrated sulfuric acid achieved removal efficiencies exceeding 90% for methylene blue and 99.4% for Ponceau S, substantially outperforming both rubber (77%) and wood (9%). Elazab et al. reported 90% methylene blue removal using cobalt nanoparticle-enhanced water hya-cinth carbon, whereas our unmodified, diluted acid-activated biomass achieved 98% removal without metal nanoparticles. Notably, diluted acid activation demonstrated comparable efficacy with removal rates of 98% and 82% for methylene blue and Ponceau S, respectively. These findings underscore the dual benefit of mitigating water hyacinth invasion while providing an eco-friendly, cost-effective solution for textile wastewater treatment. The comparable performance of diluted acid activation suggests a more sus-tainable approach that minimizes environmental impact while maintaining high ad-sorption capacity.

Abstract: Soybean biodiesel plays a pivotal role in local bioeconomy strategies and global transport decarbonization. However, carbon footprint assessments often rely on national default values or exclude soil organic carbon (SOC) dynamics. This study provides a comprehensive cradle-to-wheel Life Cycle Assessment (LCA), unifying a six-crop-season agricultural inventory (2019/20–2024/25) in Córdoba province with primary industrial data from four local biodiesel processing plants. At the farm gate, the weighted average carbon footprint of soybean was 135.1 kg CO₂eq t⁻¹ excluding soil dynamics and 331.4 kg CO₂eq t⁻¹ (+145.3%) when incorporating Tier 2 SOC changes. For 1 MJ of soybean biodiesel burned in engines, total life cycle GHG emissions ranged from 14.36 to 21.16 g CO₂eq MJ⁻¹ for local combustion in Córdoba and from 18.02 to 24.82 g CO₂eq MJ⁻¹ when exported to Europe. These figures represent a 74% to 81% GHG emission reduction compared to the European Union fossil fuel benchmark (94 g CO₂eq MJ⁻¹). Agricultural production and transport accounted for 23%–42% of total emissions, oil extraction for 7%–11%, and biodiesel industrialization for 47%–70%. Results demonstrate that regional, small-scale biodiesel production in Argentina achieves environmental performances highly competitive with large agro-export facilities, fulfilling strict international RED II/III sustainability criteria.

Abstract: It may be argued that forest monitoring based only on land-cover conversion can miss functional decline inside areas that remain spectrally forest-like. This study presents a label-free remote-sensing framework for mapping candidate hidden forest degradation from annual satellite data. The method combines physically interpretable Sentinel-2 vegetation, moisture and burn-sensitive indices with Landsat land surface temperature and year-to-year AlphaEarth embedding drift. These signals are normalised and fused into an annual hidden functional degradation score, then separated into two pathways: candidate spectral cover-loss/conversion pixels and candidate hidden degradation within pixels that still satisfy relaxed forest-like spectral conditions. Dynamic World probabilities are excluded from model construction and used only afterwards as a public land-cover transition reference, which avoids direct label leakage while retaining the limitation that both Dynamic World and part of the model input use Sentinel-2 information. It should be noted that, across six annual transitions from 2019 to 2025, AlphaEarth drift and the combined score separated Dynamic World transition pixels from stable-forest reference pixels with AUC values of 0.948–0.999 and 0.939–0.999, respectively. At the top 2% threshold for 2024–2025, F1 reached 0.937 for AlphaEarth drift and 0.916 for the combined score. These validation results apply to the full-domain score layers rather than to direct confirmation of the hidden stable-forest pathway itself. The combined score does not always outperform AlphaEarth drift on F1, but it provides a more physically interpretable candidate early-warning layer for forest-condition assessment and follow-up validation.

Abstract: Warm season heat may be associated with infant mortality, but temperature definitions and county social context can influence estimates. We linked county mortality, daily maximum temperatures, and American Community Survey estimates in an exploratory ecological study. Complete samples comprised 1,483 overall county years during 2019–2024, 1,721 White county years and 963 Black county years during 2017–2024. Poisson mean models included county and year effects, live birth offsets, and county clustered uncertainty. Per 10 additional May–September days at or above 85°F, simultaneous adjustment for poverty, education, and Black population share yielded rate ratios of 1.0126 (95% CI, 1.0025–1.0227) for overall mortality, 1.0152 (95% CI, 1.0038–1.0267) for White mortality, and 1.0088 (95% CI, 0.9926–1.0252) for Black mortality. The overall 80°F association also persisted (rate ratio, 1.0135; 95% CI, 1.0018–1.0254). Formal Black–White comparisons were inconclusive. More warm days were associated with higher overall and White mortality after measured contextual adjustment. Exploratory cutoff selection and annual exposure aggregation limit interpretation of a minimum harmful temperature.

Abstract: Cultural tourism drives rural revitalization in China, yet traditional villages with comparable heritage resources differ sharply in the number of visitors they attract. Focusing on the single spatial variable of openness, this study examines nine comb-shaped Cantonese villages in the Pearl River Delta, classifies their layouts into typical-comb, curved-comb and mesh-comb types, and builds a three-tier openness evaluation system of ten AHP-weighted indicators covering external accessibility, internal connectivity and public-space configuration. External transportation accessibility dominates the weighting (0.653). Openness correlates strongly with annual tourist volume (r = 0.826, p < 0.01) and explains 63.7% of its variation (adjusted R² = 0.637), whereas layout type alone is not significant (p = 0.238). Mesh-comb villages attain the highest openness (0.683), and typical- and curved-comb villages the lowest. An interval correction coefficient [0.64, 1.285] converts point prediction into interval prediction, absorbing non-spatial influences. For Huitong Village, renovation raised openness from 0.576 to 0.675, and the observed 800,000 visitors fell within the predicted interval, a deviation of only 0.6%. These findings establish openness as a quantifiable predictor of cultural-tourism attractiveness and yield a “limited porosity” strategy for suitability evaluation, scheme comparison and management decisions.

Abstract: The goal of this study is to model the rate of change between spring and winter in groundwater quality for irrigation in coastal wells in the municipality of Guasave, Sinaloa, Mexico. In 58 well-water samples (29 sampled in spring and 29 in winter), the following parameters were measured: pH, electrical conductivity (E.C.), total dissolved solids, temperature, salinity, static water level, Na⁺, Ca²⁺, Mg²⁺, K⁺, Cl^–, SO₄^2–, CO₃^2–, and HCO^3–. The original sodium adsorption ratio (SARor), corrected (SARco), and adjusted (SARad) were calculated. For spring and winter and using Pearson and Spearman (Sr) coefficients, ordinary and repeated measures correlations were calculated. To identify differences between seasons, paired tests and Piper diagrams were used. The rate of quality change was modeled using multiple non-linear regressions. All models were rigorously validated. For the quality change rate, three predictive quadratic polynomial models were obtained. These three models presented the following significant values for Sr: 0.653 (E.C. vs. SARor), 0.575 (E.C. vs. SARco), and 0.470 (E.C. vs. SARad). Observed and predicted quality were numerically classified based on E.C. vs. SARor, SARco, and SARad. The information from these models can be applied to prevent soil contamination caused by irrigation with saline and sodic water, potentially boosting agricultural yields in ‘‘the agricultural heart of Mexico.’’

Abstract: One potential solution to reduce urban emissions in Johannesburg is to transition to electric vehicles (EVs). However, research support is needed to understand the behavioral, socio-demographic, and infrastructural barriers that may hinder the adoption of EVs, hence this study. The results showed that younger respondents had lower trust in EVs (t(388) = -2.045, p = 0.042), and postgraduates were significantly more aware of environmental benefits (F(6, 383) = 4.365, p < 0.001). Lower-income earners reported higher operational (t(388) = 2.682, p = 0.009) and financial barriers (t(388) = 3.805, p < 0.001). Awareness of environmental benefits strongly predicted perceptions of vehicle performance (B = 0.371, β = 0.398, p < 0.001) and adoption enablers (B = 0.481, β = 0.421, p < 0.001). Ultimately, awareness of environmental benefits (B = -0.153, t = -2.617, p = 0.009) was crucial, yet it did not subdue concerns about operational barriers (Interaction: B = 0.047, p = 0.477), indicating that operational efficiency primarily influenced willingness to adopt. In conclusion, this study showed that in some emerging market contexts, EV transition might not be solely and significantly supported by environmental sensitization but rather coupled with addressing pressing operational limitations like charging station availability.

Abstract: As cities emerge as the primary drivers of global consumption-based emissions, the need for greenhouse gas (GHG) accounting methods that model multisectoral urban interactions has become paramount. In this context, consumption-based (CB) urban GHG emission inventories have arisen as a useful tool for political decision-makers. This systematic literature review was carried out across three databases, covering al-most 20 years of publications strictly related to the elaboration of CB GHG emission inventories, reaching a total of 100 publications. More than a third of the articles ex-plored Life-Cycle Assessment (LCA), 34% used Environmentally-Extended In-put-Output Analysis (EEIOA), with only 10% delving into hybrid methodologies. A case study comparison was performed for cities that applied different methodologies, in terms of carbon footprint. The strengths and weaknesses of each framework were also assessed. The results showed that both EEIOA and LCA were useful to model complex carbon flows and accounted for the majority of emissions associated to urban activity. EEIOA excelled for its operational simplicity and speed in the calculation of carbon footprint when input-output matrices are available, but it was not as suitable for policy-making. LCA required a larger volume of information and its processing was more time-consuming, but it was also capable of identifying hotspots. Hybrid approaches obtained carbon footprint results similar to those of EEIOA or LCA, while covering certain weaknesses of these standalone methodologies.

Abstract: Microplastic (MP) pollution represents a significant global environmental threat due to its widespread presence in aquatic ecosystems. However, research on MP contamination in Mexican rivers is currently limited. The objective of this study was to assess the spatial and temporal distribution of MPs in surface water at 18 sites along the Guanajuato River basin, including both influent and effluent from a wastewater treatment plant (WWTP) located in the middle basin. The presence of MPs in the feces of the American white pelican (Pelecanus erythrorhynchos) and the neotropic cormorant (Nannopterum brasilianum), two species residing in the lower basin, was also investigated. Surface water analyses indicated that mean MP concentrations were significantly higher during the rainy season (804.9 ± 784.4 particles L⁻¹) than during the dry season (446.8 ± 375.5 particles L⁻¹). The WWTP achieved over 94% removal efficiency for MPs. Fibers and fragments comprised 51.5% and 48% of the MPs, respectively. The most common polymer types were poly-propylene–polyethylene terephthalate (PP–PET, 23.4%) and polycarbonate (PC, 17.2%). MPs were highly prevalent in the feces of both pelicans (95.6%) and cormorants (94.8%). The morphologies and polymer types identified in feces samples were consistent with those found in surface water. These results highlight the extent of MP contamination in the urban sections of the Guanajuato River and underscore potential risks to local aquatic bird species.

Abstract: Forests and trees play a critical role in the hydrological cycle by providing water-related ecosystem services (ES). However, they are also water consumers, highlighting the importance of considering water in forest and landscape restoration (FLR), which is often overlooked. This study investigates how to better integrate the forest-water nexus in policies, plans, and strategies under the African Forest Landscape Restoration Initiative (AFR100) to ensure effective and sustainable FLR without compromising water security and the livelihoods of local communities. To achieve this goal, the “Water-smart Forest and Landscape Restoration (W-FLR)” Tool was developed and pilot-tested in Ethiopia, Benin, and Madagascar, and replicated in Togo, using a six-step approach and cross-case analysis, involving international, national and local experts. The W-FLR Tool enabled the development and validation of W-FLR Roadmaps in each country, identifying priority actions related to policy mainstreaming, capacity development, and on-the-ground implementation of FLR. The case studies demonstrated the relevance of the Tool, its adaptability to different contexts and replicability, building on participatory approaches. The Tool can be used to position water-smart FLR actions as cost-effective and long-term investments. Thus, the W-FLR Tool can support practitioners, policymakers, and scientists to adopt a water-centric lens in FLR.

Abstract: Environmental noise and air pollutants may share emission sources, but their temporal association can vary among urban settings. This exploratory study examined noise, carbon monoxide (CO), PM₂.₅ and PM₁₀ at residential, industrial and airport locations in Selangor, Malaysia, during a monitoring campaign spanning November 2025 to March 2026. One AirSync unit per site recorded measurements at one-minute intervals using factory-calibrated PM, CO and noise sensing components. Hourly median data were summarised using Spearman rank correlations, alongside descriptive temporal profiles and daily particulate concentration comparisons. Noise–pollutant associations were weak at all sites (|ρ| ≤ 0.246). The largest coefficient was for CO and noise at the airport (ρ = 0.246). PM₂.₅ and PM₁₀ outputs were strongly coupled (ρ ≥ 0.999). At the industrial site, sensor-derived daily means exceeded the numerical Malaysian 24-hour thresholds on 99 of 127 valid days for PM₂.₅ and 84 of 127 days for PM₁₀. These findings provide limited support for a simple contemporaneous noise-only indicator under the conditions examined. Interpretation remains constrained by one site per category, measurement uncertainty, discrete CO outputs and the absence of time-series-adjusted inference or independent predictive validation.

Abstract: Dissective analysis of predictive skill for medium range ensemble streamflow forecasting is carried out for 43 headwater locations in the US. To assess the component-specific contributions of the full HEFS, 20-yr hindcasting experiments were performed for the wettest 3-month period for each location. The results show that, when all ranges of observed flow are considered, hydrologic modeling, the use of NWP precipitation forecast and its post processing, streamflow assimilation and streamflow post processing contribute on average 6, 43, 31 and 20% to the collective predictive skill in reference to climatology, respectively. The contributions vary greatly from location to location and with the magnitude of the verifying observed flow in reflection of the predictability of precipitation and streamflow. When only high flows are considered, the use of NWP precipitation forecast and its post processing is by far the largest contributor. For several locations, including some with significant snowmelt, the predictive skill is noticeably poor due to deficient or very challenging hydrologic modeling relative to climatology. Overall, the results indicate that, for holistic improvement of medium range ensemble streamflow forecasting, efforts should be directed at improving hydrologic modeling particularly at skill-challenged locations in addition to improving future forcings particularly for large events.

Abstract:

Pesticides and antibiotics frequently co-occur in aquatic environments, yet their combined effects on aquatic life remain poorly understood. To address this gap, we evaluated whether antibiotic co-exposure alters pesticide toxicity in juvenile zebrafish (Danio rerio). We tested five pesticides, including chlorantraniliprole (CAP), chlorpyrifos (CPF), buprofezin (BPF), difenoconazole (DFZ), and boscalid (BO), both alone and in mixtures with three common antibiotics: rifampicin (RIF), ampicillin (AMP), and tetracycline (TET). Adding RIF significantly potentiated CAP toxicity, lowering its 96-hour LC₅₀ from 61.85 to 40.72 mg/L, yielding an LC₅₀ ratio (LCR) of 1.52. Notably, this potentiation was fully abolished by the co-addition of a second antibiotic (AMP or TET). For CPF, only the ternary mixture with RIF and AMP produced a slight but significant reduction in toxicity (LCR = 0.95). For BPF, only co-exposure with TET significantly potentiated toxicity (LCR = 1.18). In contrast, both DFZ and BO showed significantly reduced toxicity when combined with RIF or AMP, with LCRs ranging from 0.80 to 0.91. These varied responses reveal that antibiotic-pesticide interactions are highly chemical-specific rather than a generalized consequence of mixture exposure. Collectively, these findings underscore that single-compound toxicity data alone are insufficient for assessing pesticide hazards in multi-contaminant environments.

Abstract: Continuous monitoring stations are the sensing layer of urban air-quality management, yet their records are often used to infer pollution “drivers” without controlling for weather. We ask what predicts fine particulate matter (PM_2.5) in Navi Mumbai, a coastal Indian city, using screened daily data from five stations for 2021–2025, hourly data from two, and ERA5 reanalysis weather checked against Mumbai surface and radiosonde observations. Vector autoregressions with exogenous weather and calendar terms were estimated without imputation, together with hourly models, a stagnation index and out-of-sample forecasts. Lagged co-pollutants add little to daily prediction: equivalence tests bound six of seven effects within ±5% per standard deviation, while lagged ozone, an oxidant signal, raises PM_2.5 by 4%. Omitting weather, or using PM10, manufactures co-pollutant “drivers”. Rainfall and boundary-layer height lower same-day PM_2.5 by 10–24% per standard deviation, and date effects shared by all stations explain 78% of the variance, three-quarters of it weather. A stagnation index raises the frequency of days above the national standard two- to fourfold and, combined with persistence, warns as skillfully as an extreme gradient boosting (XGBoost) forecaster, which itself does not beat persistence. Monitoring data support citywide, meteorology-triggered warnings better than single-pollutant controls inferred from comovement.

Abstract: Motor-manual tree felling by chainsaws remains a widespread but hazardous forestry practice. This operation consists of executing a directional notch followed by a felling cut that releases a hinge. The hinge geometry (defined by its height, width, and alignment relative to the directional notch) is a critical determinant of safe felling, controlling the direction and stability of the falling tree. How the hinge's geometry might affect these aspects has not yet been studied. This study evaluates variations in hinge flexibility and bending stiffness using wooden square beams of European beech (Fagus sylvatica) and silver fir (Abies alba) in a rig reproducing the classical felling cuts, varying hinge geometry combinations. Two geometric parameters, back-cut height (S) and hinge width (C) were examined. Mechanical testing was performed using a universal testing machine, and load–deformation curves were smoothed with a Savitzky–Golay filter; the Critical Bending Angle and Yield Point were determined by using the 0.02° offset method and absorbed energy by Simpson integration. Results reveal different mechanical behavior between the two dimensions: hinge width primarily determines yield force, whereas back-cut height governs flexibility, showing a strong negative correlation with the critical bending angle. Significant species-specific differences were observed for both response variables. Hinge toughness, expressed as absorbed energy per unit hinge cross-section, was found to be independent of back-cut height and about 1.8 times higher in beech (0.39 kJ·m⁻²) than in fir (0.22 kJ·m⁻²).

Abstract: This study analyses groundwater quality in the Rhône–Mediterranean–Corsica (RMC) basin using 27,741 samples collected from 7,625 sampling points and 224 groundwater bodies, covering a 34-year period (1987–2021). The objective is to quantify, using statistical and machine learning methods, the respective contributions of temporal variability, spatial variability, and lithology to the hydrochemical and microbiological composition of aquifers. The results reveal a marked dichotomy between microbiological and physico-chemical parameters. Faecal contamination (E. coli, enterococci) is dominated by a strong temporal component (33–36% of variance), whereas spatial structuring by lithology remains very limited (< 11%). Conversely, major physico-chemical parameters (calcium, conductivity, sulphates, etc.) exhibit near-zero temporal variability (<7%) and a spatial structuring strongly controlled by lithology, with gains in explained variance of up to 20% through lithological grouping. Linear discriminant analysis achieves an overall classification rate of 56.2%, but with wide disparities: the crystalline basement is perfectly discriminated (76%), while alluvial formations are not discriminated at all (0%), as their chemical signature is more strongly influenced by anthropogenic pressures than by lithology. Machine learning methods, particularly k-nearest neighbours (k-NN), significantly improve performance (76.2% vs. 56.2% for LDA), confirming the existence of nonlinear structures in the data. Unsupervised validation via K-means confirms the reality of the lithological signal. From an operational standpoint, these findings justify a differentiated approach to groundwater management: a geological approach for chemical parameters, and an approach based on land use and agropastoral practices for bacteriological parameters. The proposed methodology – combining PCA, HCA, nested ANOVA, LDA and machine learning – provides a transferable analytical framework applicable to other major European river basins.

Abstract: Water resources in the semi-arid agricultural landscapes of Northern Kazakhstan are under intense stress from the climatic, ecological, socioeconomic issues, which increase the human–wildlife confrontations. Within this project the Extended Livelihood Vulnerability Index (LVI) was developed to assess farm holdings’ water-related vulnerability and climate change adaptive capacity for the Saiga Antelope (Saiga tatarica) recovery. The framework integrates 13 components and 62 indicators covering climate and natural hazards, human–wildlife conflict, water resources issues, natural and land resources, agriculture, infrastructure, socioeconomic conditions, and livelihood strategies. The assessment combines hydroclimatic data from 1961–2024, field surveys, questionnaires, official statistics, spatial information, and saiga migration data from Naurzum and Auliekol Districts of Kostanay Region. Overall LVI values were 0.485 in Naurzum and 0.431 in Auliekol. Natural Resources availability indexes showed the highest component score (1.000 in both districts), followed by Human–Wildlife Conflict (0.862 and 0.770), next Climate Change and Natural Disasters (0.741 in both). Climate analysis revealed significant increase by warming (0.28 °C per decade) without a significant long-term precipitation trend. The project findings demonstrate that water resources availability stress is a cross-cutting dimension of rural vulnerability, while saiga recovery creates an additional pathway of socio-ecological complexities y through competition for water, pasture, and agricultural lands.

Abstract: Abstract Humans are continuously exposed to lysosomotropic xenobiotics, including cationic amphiphilic drugs, per- and polyfluoroalkyl substances (PFAS), redox-active heavy metals, and airborne particulates, through occupational, dietary, and environmental routes. Lysosomes are acidic organelles central to cellular degradation, nutrient sensing, and regulated cell death, and the stability of the lysosomal membrane is essential for cell viability, yet it is a preferential target for accumulation of these agents. This review synthesizes current understanding of the molecular mechanisms driving lysosomal membrane permeabilization (LMP) and lysosomal membrane rupture (LMR) following xenobiotic exposure, and their downstream consequences for human tissue health. We review lysosomal injury, namely intralysosomal ion trapping, metal‑catalyzed Fenton chemistry, and lipid peroxidation, which converges to generate oxidative membrane pores linked to respiratory, hepatic, renal, and neurodegenerative toxicity in exposed populations. We also examine cellular membrane‑repair systems, particularly the Endosomal Sorting Complex Required for Transport (ESCRT)-III machinery and ER–lysosome lipid transfer pathways, that act to restore lysosomal integrity and limit tissue injury. Failure of these protective mechanisms initiates distinct regulated cell death programs, including apoptosis, ferroptosis, and pyroptosis, that underlie chronic disease following sustained environmental exposure. Finally, we discuss the dual role of LMP in human health: both as a mediator of environmental toxicant‑induced injury and, more briefly, as an exploitable vulnerability in disease contexts such as drug-resistant cancer. By integrating findings from emerging non‑mammalian sentinel models and advanced imaging modalities, this review provides a framework for understanding how environmental lysosomal membrane dynamics translate into human health outcomes.

Abstract: This article constructs and evaluates a composite sustainability index aligned with the 5P framework (People, Planet, Prosperity, Peace, and Partnerships) to compare Colombia and Mexico with 14 selected countries and territories in the Middle East and its regional environment, using information available for 2020–2023. The design is quantitative, documentary, and comparative and integrates eight international indicators. The main specification is estimated for 16 comparable cases, after verifying coverage, harmonizing directionality, controlling extreme values, and normalizing the variables on a scale from 0 to 100. Uniform weighting by dimension constitutes the baseline scenario, while an expanded database of 32 countries is used exclusively for exploratory KMO and Bartlett diagnostics and to estimate alternative weights through principal component analysis. The countries with the best aggregate performance are Colombia, Israel, Mexico, and Kuwait, although their results are not homogeneous across the evaluated dimensions. The nonparametric Spearman and Kendall coefficients, together with the leave-one-out tests and the Monte Carlo simulation, confirm that the classification is generally stable, although changes occur when assumptions incorporated into the model are modified. Likewise, the comparison with the SDG Index yields moderate convergence results (Spearman ρ = 0.507; Kendall τ = 0.371; Pearson r = 0.550). Under these conditions, the index is useful for identifying performance groups, characterizing profiles, and detecting gaps across countries; it also constitutes a transparent and replicable tool whose results should be interpreted as an analytical approximation rather than as a definitive classification or a measure that fully captures the sustainability of a nation.