Search Results (508)

The article presents the results of an empirical study of the localisation processes of the Sustainable Development Goals (SDGs) in 102 regions of Russia and Kazakhstan for the period 2015–2024. Based on the author’s methodology for constructing the SDG Localisation Index (SDGLI) using the method of the main components, a quantitative assessment of regional progress was carried out according to 14 SDGs. Cluster analysis has identified four sustainable types of regions that differ in the structure and dynamics of sustainable development. Using eco-metric tools (panel regressions with fixed effects, spatial models, difference-in-differences method), key factors of interregional differentiation were identified, including economic, social, institutional and spatial determinants. Particular attention is paid to assessing the effect of adopting regional sustainable development strategies. A decomposition of interregional inequality was carried out, which made it possible to quantify the contribution of various groups of factors. The results of the study contribute to the theory of regional economics and can be used to improve regional policies in the field of sustainable development. Full article

This paper examines how artificial intelligence can support sustainability assessment in cultural industries, using national film industries as a test case. The Film Industry Sustainability Index (FISI) is introduced as a composite indicator covering cultural diversity, economic resilience, and Sustainable Development Goal (SDG) alignment for 42 national economies from 2005 to 2023. Knowledge-Enhanced Mamba (KE-Mamba), a selective state-space forecasting model, is then proposed to combine annual panel indicators with country-level film-industry knowledge graph (KG) embeddings and large language model (LLM)-derived screenplay-oriented narrative proxies from film synopses. To reduce factual errors in title-level narrative scoring, the LLM is anchored to verified United Nations Educational, Scientific and Cultural Organization (UNESCO) records and the European Audiovisual Observatory’s LUMIERE film-admissions database using rank-one model editing (ROME). On the 2020–2023 held-out test period, KE-Mamba achieves a composite FISI mean absolute error (MAE) of 0.0389, a mean absolute percentage error (MAPE) of 5.61%, and an $R^2$ of 0.934, outperforming autoregressive integrated moving average (ARIMA), tree-based, long short-term memory (LSTM), and base Mamba baselines. Additional robustness checks using a pre-pandemic split, two-way fixed-effects panel regression, alternative FISI weighting schemes, KG embedding ablations, and human validation of LLM narrative scores support the reliability of the proposed framework. Policy simulations are interpreted as model-based projected associations rather than causal estimates. The results show that knowledge-enhanced sequence models can provide transparent forecasting support for sustainable cultural-industry policy. Full article

Islamic banking is increasingly expected to align Sharia-based intermediation with sustainability objectives, yet empirical evidence remains limited on how sustainability disclosure links financing structures with bank performance. This study examines whether Islamic Sustainable Banking (ISB) functions as a mediating mechanism between profit-sharing financing, debt-based financing, and financial performance in Islamic banks in Indonesia and Malaysia. ISB is measured using an Islamic Sustainable Banking Disclosure Index that integrates Maqasid al-Shariah principles with SDG-oriented disclosure indicators. Using panel data from 23 Islamic banks over 2018–2023 and applying partial least squares structural equation modeling, mediation analysis, PLS-MGA, and permutation tests, the study finds that both profit-sharing and debt-based financing are negatively associated with ISB disclosure, while ISB is positively associated with net profit margin but not return on assets. The mediation results indicate statistically significant negative indirect associations through ISB, suggesting that sustainability disclosure operates as a conditional transmission mechanism rather than an automatic performance driver within the specified PLS-SEM model. Cross-country tests reveal significant differences between Indonesia and Malaysia, particularly in the associations between financing structures and profitability. The study contributes to Islamic sustainable finance by clarifying how Maqasid-oriented disclosure connects financing composition, governance capacity, and profitability, while offering practical implications for bank managers, regulators, and policymakers seeking to integrate sustainability into Islamic banking governance and financing decisions. Full article

The transformation of the labour market is a significant challenge for sustainable regional development, especially in countries with pronounced socioeconomic inequalities. This study aims to analyse the spatial disparities in the Bulgarian labour market at the regional level (NUTS 3, districts) for the period of 2021–2024, combining descriptive spatial analysis, multidimensional cluster analysis, and Local Indicator of Spatial Association (Local Moran’s I-LISA). The analysis is based on three key indicators: employment rates, unemployment rates, and the relative wage index, calculated against the national average. The results of the descriptive analysis reveal clear spatial imbalances, with low labour income persisting in many districts despite relatively high employment rates. The cluster analysis identifies four types of labour markets with specific socioeconomic profiles, confirming that the spatial disparities are a result of specific combinations of indicators and economic specialisation at the regional level. The LISA analysis further reveals the existence of spatially stable cores of vulnerability and local spatial anomalies that highlight the importance of regional context and spatial proximity. The results indicate the need for a spatially differentiated approach in regional policies in line with the objectives of SDG8, SDG10, the European Pillar of Social Rights, and the EU’s cohesion policy. Full article

Climate change threatens ecosystem stability in arid Central Asia, yet regional vegetation responses remain poorly resolved at the operational scale of land-use policy. We integrated long-term meteorological records (2000–2024) from Kazhydromet with Landsat surface-reflectance imagery for four epochs (2010, 2015, 2020, 2025) across the five administrative regions of Southern Kazakhstan (≈710,000 km²). After cross-sensor harmonization of Landsat 5 TM and Landsat 8 OLI, dense vegetation cover (NDVI > 0.4) increased modestly across all regions, with the cumulative area growing from 9.09 to 9.60 million hectares (+5.6%) and a transient 2020 minimum linked to the 2018–2020 drought. Per-region OLS trend slopes were not statistically significant at p < 0.05, given the four-epoch sampling (n = 4). A composite Biodiversity–Climate Sensitivity Index (BCSI), constructed from four normalized components (temperature trend, precipitation deficit, NDVI trend, and the coefficient of variation of dense-vegetation cover as a biodiversity–vulnerability proxy), identifies the lower Syr Darya floodplain and former Aral Sea margins as the most sensitive territories and the Northern Tien Shan as the most resilient. The framework provides an operational evidence base for climate-adaptive conservation aligned with SDG 13 and SDG 15. Full article

Achieving the Sustainable Development Goals (SDGs) by 2030 remains highly uneven across countries, while the structural factors associated with this heterogeneity are still insufficiently understood. This study aims to classify 154 countries according to their full 17-dimensional SDG achievement profiles and to identify the structural indicators statistically associated with the observed typological differences. A two-stage analytical approach was applied. First, k-means cluster analysis based on the scores of all 17 SDGs was used to identify homogeneous groups of countries. Second, canonical discriminant analysis was performed for 64 countries with complete data for 17 indicators selected from international sources according to the “one indicator–one goal” principle. The cluster analysis identified three typologically homogeneous groups of countries that broadly correspond to differences in development level but are not reducible to them. The discriminant model achieved apparent classification accuracy of 90.63% (p < 0.0001), while the first canonical function explained 90.3% of the between-group variation. LOO cross-validation yielded an accuracy of 71.43%, confirming that the model retains meaningful discriminatory power beyond the estimation sample, while the difference between apparent and cross-validated accuracy reflects the constraints of a small sample relative to the number of predictors. The strongest differentiating indicators were the proportion of the urban population living in slums, the Global Peace Index, access to sanitation, and poverty. Overall, the results show that SDG achievement profiles constitute an independent analytical characteristic of countries and that typological differences are primarily associated with basic human development and institutional stability. Full article

The Loskop Dam is a major reservoir on the upper Olifants River in South Africa. Many human activities in the upper river catchment are causing contamination in the river including heavy metals. Although several studies have investigated water pollution in the river system, limited information exists regarding the spatial distribution and ecological risks of heavy metals in the Loskop Dam and their ecological implications. Seasonal heavy metal concentrations and ecological risks associated with heavy metal contamination in the dam were assessed. Though most of the heavy metal concentrations were below detection levels in the water, the concentrations were substantially higher in the sediments, with higher concentrations mainly recorded during winter than summer. Chromium and nickel concentrations in the sediments exceeded the permissible guideline values. Furthermore, contamination factor, enrichment factor and geoaccumulation index were used to determine the extent of chemical pollution, and ecological risk index was used to assess the potential ecological risks. The contamination indices found the sediments to be moderately to highly contaminated by Cr, Pb and Zn. However, the ecological risk values were low, indicating a low ecological risk of contamination posed by heavy metals in the dam. During winter, Cd had the highest ecological risk and during summer, the ecological risk was dominated by Pb, but the values indicated a low contamination (ER <40) and the potential ecological risk index values were also low (RI < 150). Nonetheless, effective conservation strategies are needed to prevent further degradation of the river system. Furthermore, the study reinforces the importance of addressing metal pollution and conservation of freshwater ecosystems, which aligns with the United Nations Sustainable Development Goal (SDG) 6, particularly in enhancing water accessibility and responsible sanitation management. Full article

Sustainability monitoring has mainly focused on measuring where countries stand today, rather than anticipating where they are headed. This study develops an AI-based forecasting framework to predict national sustainability outcomes and identify countries whose actual paths deviate from predictions. Using 749 World Development Indicators across 184 countries and regions from 2003 to 2022, a Temporal Fusion Transformer (TFT) is developed using data from 2003 to 2017 (training and validation) and evaluated on a held-out 2018 to 2022 test period, with calibrated prediction intervals constructed retrospectively over the test period. Assuming that historical development patterns remain informative over the forecast horizon, the model achieves mean absolute errors of 1.10 for the Sustainable Development Goals Index (SDGI, 0 to 100 scale) and 0.008 for the Human Development Index (HDI, 0 to 1 scale), reducing error by at least 19 percent for SDGI and 60 percent for HDI relative to linear trend and XGBoost baselines. Of 184 countries and regions, 115 (62 percent) are classified as on-track for both indices. Among the rest, 35 show positive SDGI deviations, mostly developing nations in Sub-Saharan Africa and South Asia that are exceeding their forecast trajectories, while 23 show negative HDI deviations concentrated among nations affected by conflict and economic disruption. We find this asymmetric pattern is consistent with a decoupling between goal-level and capability-level sustainability, in which policy-driven SDG indicators can advance while foundational human development in health and income stalls. Our model identifies economic indicators as the dominant predictors of HDI (7 of the top 10), while SDGI prediction draws on a more balanced mix of economic, social, environmental, and institutional indicators. We also find that better governance is associated with lower prediction error for both SDGI (p = 0.004) and HDI (p < 0.001), suggesting that countries and regions with stronger institutions follow more predictable sustainability trajectories. Full article

This study investigates how sustainable development indicators are shaped in the context of Kazakhstan. The focus is on the interrelationships between economic growth, dependence on the mining sector, and foreign direct investment. In addition, the analysis pays special attention to the impact of the principles of the “green” taxonomy and changes in the ESG direction on these processes. Using annual time-series data, the analysis employs the augmented Dickey–Fuller unit root test, Johansen cointegration methods, and long-run estimation methods, namely, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS). The analysis showed that there is a long-term relationship between GDP, the level of mineral extraction, foreign direct investment and the SDG index. According to the results, economic growth and foreign investment contribute to improving sustainable development indicators, and this effect is statistically confirmed. Conversely, a significant share of the mining sector appears to be linked to an increase in the environmental burden associated with resource dependence, which has a negative impact in the long term. The absence of significant short-term causal relationships suggests that sustainable development indicators evolve through gradual structural and institutional changes rather than short-term fluctuations. These findings suggest that the sustainability of economic growth is influenced by its structural composition, with investment-led diversification and modernization enhancing playing a crucial role in achieving sustainable development goals, while the expansion of the mining sector may hinder this. The study highlights the need and importance of aligning economic policies with the principles of the “green taxonomy”, improving institutional frameworks, and promoting environmentally sustainable investments to support long-term sustainable development trajectories. Full article

Low-carbon and smart agriculture talent cultivation requires structural conditions that vary widely across countries. This study develops the Agricultural Talent Cultivation Readiness Index (ATCRI) as a proxy-based structural diagnostic tool for approximating the multi-dimensional enabling conditions and bottlenecks that shape whether SDG-linked agricultural education transformation can be operationalized at scale. ATCRI covers 160 countries across four interdependent dimensions: Education and Research, Digital/Energy/Enabling Infrastructure, Green Transition Pressure, and Innovation/Institutional Capacity. Results indicate a highly uneven global distribution: high transition pressure does not automatically translate into high readiness, with 17 countries exhibiting a pressure–capacity mismatch. China ranks 21st globally, showing a hybrid profile in which education and innovation capacity are strong while digital delivery infrastructure remains a relative bottleneck. Survey evidence from Chinese crop science students is consistent with this interpretation, revealing elevated practice-oriented reform demand where macro-level structural gaps are sharpest. ATCRI is intended as a diagnostic framework for identifying structural bottlenecks, not as a definitive measure of educational quality or reform outcomes. Full article

Global land degradation metrics often rely on trend-based categories that miss cumulative severity, frequently misclassifying degraded areas as stable. To overcome this, we developed a Land Degradation Index (LDI) to assess degradation across Brazil on a 500 m grid for 2001 and 2021. The LDI integrates land-cover change legacy (deforestation age), ecosystem functioning (Gross Primary Productivity), and soil condition (Soil Organic Carbon) into a six-level gradient ranging from conserved to highly degraded. Results reveal that between 2001 and 2021, Brazil lost 50.5 million hectares of conserved land, while intermediate and severe degradation expanded by 53.5 million hectares. Conservation remained concentrated in the Amazon and Pantanal, whereas degradation intensified across the Atlantic Forest, Cerrado, and Caatinga, particularly along agricultural frontiers. Furthermore, while Indigenous Lands and Quilombola Territories act as vital conservation cores, the LDI reveals intensified degradation in their immediate surroundings, highlighting the intersection of biophysical decline, land conflicts, and socio-environmental vulnerability. The proposed index advances beyond conventional indicators, such as SDG 15.3.1, by incorporating both the intensity and variation of degradation processes into a unified analytical framework, providing a robust, reproducible framework to support Land Degradation Neutrality (LDN) targets, inform public policies, and guide inclusive territorial planning. Full article

This exploratory study assessed the vehicle- and route-dependent response of five multipoint injection passenger vehicles to two commercial fuel additives marketed as octane-related gasoline additives under real-world high-altitude driving conditions in Quito, Ecuador. The tests were conducted on one urban route and one rural/peripheral route using base gasoline with a nominal octane index of RON 85, RON 85 gasoline with Additive A, and RON 85 gasoline with Additive B. Fuel economy and CO₂-related indicators were obtained through the OBD-II port using the Torque Pro application; therefore, the reported values were interpreted as electronic control unit-based estimates rather than direct gravimetric fuel consumption or laboratory emissions measurements. The revised analysis used OBD-derived trip-average fuel economy as the primary response variable. The mixed-effects model showed a significant effect of route on fuel economy (p < 0.001) and a significant fuel condition × route interaction (p = 0.0089), while the main effect of fuel condition was not statistically significant (p = 0.0699). Additive B increased the mean OBD-derived trip-average fuel economy on the urban route from 11.56 to 12.60 km·L⁻¹, but reduced it on the rural route from 13.46 to 12.65 km·L⁻¹. At the vehicle level, the previously extreme Vehicle 3 response was revised to a more plausible increase from 11.03 to 13.64 km·L⁻¹ (+23.68%) when trip-average fuel economy was used. Since the actual RON/MON values and physicochemical properties of the final fuel blends were not experimentally measured, the observed responses cannot be attributed exclusively to octane number enhancement. Overall, the findings indicate that commercial additive performance was vehicle- and route-dependent rather than universally beneficial. This field-based assessment supports evidence-informed decision-making for sustainable mobility and aligns with SDG 16 and SDG 17 through transparent technical evaluation and academic collaboration. Full article

Despite extensive global progress monitoring under the 2030 Agenda, existing Sustainable Development Goal (SDG) assessment frameworks remain structurally blind to within-country distributional disparities. This study addresses this gap by developing a methodologically transparent composite SDG index for multi-level governance assessment, applying it to 218 Nomenclature of Territorial Units for Statistics (NUTS 2) regions across the European Union over the period 2015–2022 (1744 region-year observations). In this context, the term “region-year observations” refers strictly to the balanced panel data structure, which is calculated by observing 218 distinct sub-national regions continuously over an 8-year period (218 regions × 8 years The index aggregates four dimensions—social, economic, educational, and institutional—using min-max normalization. The analysis yields three main results: (1) Spatial econometric analysis reveals strong, persistent positive spatial autocorrelation, with high-performing clusters concentrated in Northern and Western Europe and lagging clusters in Eastern and Southern peripheries. (2) A spatial error model identifies institutional governance quality as a consistent statistical predictor of sub-national SDG performance. The significance of the spatial error parameter (λ = 0.497) suggests that unobservable institutional and geographical common shocks systematically link neighboring regions. (3) Cluster analysis further distinguishes four regional archetypes: Disadvantaged, Leaders, Educated, and Transitional. These findings underscore the need for spatially aware SDG monitoring infrastructure and investment in institutional capacity as prerequisites for equitable governance, as integrating spatial dependencies is crucial to prevent national averages from masking severe regional developmental traps. Full article

Quantitative sustainability assessments in the EU rarely differentiate between the roles of governments, businesses, and the population, making it difficult to empirically test theories of socio-technical transitions, stakeholder governance, and convergence/club convergence. To address this gap, the study constructs four stakeholder-specific indices: the Government Sustainability Index (GSI), Environmental Sustainability Index (ESI), Population Sustainability Index (PSI), and Business Sustainability Index (BSI) alongside a Composite Sustainability Index (CSI). The indices are built from harmonised Eurostat, European Environment Agency, and SDG Index data using min–max normalisation, covering all 27 EU Member States over the period of 2015–2024 (270 country–year observations). The empirical analysis applies K-means clustering, compound annual growth rates (CAGRs), and correlation analysis, complemented by a robustness module testing alternative weighting schemes, z-score normalisation, and ±10% indicator perturbations. The results identify four relatively stable sustainability tiers with limited inter-tier mobility, an S-curve-type relationship between initial performance levels and subsequent growth, a consistent hierarchy of stakeholder response speeds (ESI > GSI > PSI), and a structural slowdown after 2019. These patterns are robust across alternative specifications and imply that EU sustainability transitions follow multiple, tier-structured trajectories shaped by institutional lock-in rather than converging toward a single equilibrium. The framework offers a basis for tier-differentiated and stakeholder-sensitive policy strategies. Full article

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article