Search Results (232)

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

In the European Union, decarbonization has progressed unevenly across sectors and member states. This study examines sectoral CO₂ trajectories in the EU-27 during 2000–2022 using a harmonized annual panel built primarily from the European Commission’s Energy Statistical Country Datasheets and complemented with EDGAR/JRC sectoral emissions data. The empirical strategy combines descriptive analysis with OLS, fixed-effects, log-linear, and exploratory difference-in-differences specifications to assess conditional associations among per capita CO₂ emissions, the renewable energy share, GDP per capita, and the carbon price. EU-wide CO₂ emissions declined by 26.4% over the study period, with the largest contraction in the energy sector, while transport emissions remained comparatively stable. Across specifications, renewable energy share is consistently associated with lower emissions, although its magnitude weakens after controlling for time-invariant country heterogeneity. Carbon price is negatively associated with emissions in the baseline and log-linear models. In contrast, the exploratory DiD interaction is not statistically informative in the main treatment specification and yields negligible effect sizes in regional split models. The sign reversal in GDP between the pooled and within-country models indicates that cross-country differences and within-country dynamics should not be treated as equivalent. Overall, the findings support a heterogeneous and multi-speed decarbonization pattern and suggest that carbon pricing is better understood as part of a broader policy mix rather than as a stand-alone causal driver. Full article

This study presents a comprehensive regional analysis of the COVID-19 pandemic’s impact on tourism in Slovakia during 2018–2024, employing rigorous statistical methods to quantify sectoral transformations. Based on extensive data on visitor arrivals, revenues, and accommodation facility utilisation across eight NUTS III regions, the analysis identifies four distinct regional tourism clusters characterised by differentiated recovery trajectories. Paired t-tests confirmed statistically significant changes in international tourist arrival indices across seven regions (p < 0.05), validating fundamental structural reorientation in tourism demand. The findings reveal pronounced heterogeneity in recovery patterns: while the Bratislava Region and the Žilina Region achieved substantial revenue growth (46.04% and 146.54%, respectively), domestically oriented regions (Banská Bystrica, Košice, Nitra, Prešov, and Trenčín) demonstrated minimal recovery (8.19% aggregate growth). Critical findings include the persistence of passive tourism dominance (94.09% of national revenues), declining international competitiveness from traditional Western European source markets, and compensatory expansion from emerging markets (USA +398.73%, Oman +234.68%, and Poland +226.55%). The ANOVA analysis revealed no statistically significant differences between regional indices in 2024 (p = 0.362), indicating market stabilisation despite differentiated trajectories. The study emphasises the necessity of regionally calibrated sustainable strategic interventions to diversify experiential tourism, activate the domestic market, and enhance technological infrastructure to build sectoral resilience against future exogenous shocks. Full article

This study investigates whether external terms of trade (TOT) and mining-sector GDP in Peru share a stable long-run relationship. Although mining has played a central role in the country’s growth trajectory, its performance remains highly exposed to international price cycles, raising questions about its structural sustainability under persistent external shocks. Using quarterly data for 2001–2024, the analysis applies Johansen cointegration techniques and estimates a bivariate Vector Error Correction Model (VECM) to evaluate long-run co-movement and short-run adjustment dynamics. The results identify a single cointegrating relationship in which mining GDP acts as the primary adjustment variable, gradually correcting deviations from long-run equilibrium, while short-run TOT shocks do not exert direct contemporaneous effects on mining growth. The estimated speed of adjustment is low, suggesting a prolonged convergence process consistent with the capital-intensive and rigid structure of the mining sector. Robustness exercises—including estimation with heteroskedasticity and autocorrelation consistent (HAC) standard errors and an extended specification incorporating gross fixed capital formation—confirm the stability of the long-run relationship. These findings indicate that the structural sustainability of mining output depends on the interaction between external price dynamics and the sector’s capacity to adjust to persistent international shocks. The study concludes that, in the Peruvian case, structural sustainability in the mining sector is not determined solely by global price trends, but is also conditioned by domestic productive and institutional factors that govern the speed of adjustment in the presence of sustained external volatility. Full article

Urban depopulation has become an increasingly visible phenomenon worldwide, affecting cities of different sizes and economic structures. This article develops a typology of U.S. depopulating cities beyond the Rust Belt’s iconic industrial cities, which dominate academic literature, to include a wider range of shrinking settlements in the shadows. The analysis is based on a dataset of U.S. census places constructed from decennial census population data (1990–2020) combined with employment structure indicators and spatial classification variables identifying metropolitan position and industrial specialization. Using 1990–2020 population change and three explanatory dimensions—city size, industrial heritage, and peripheral location—the analysis identified 1082 places that lost at least 10% of their population. Logistic regression showed manufacturing and mining reliance, small size, and remoteness as significant predictors of depopulation. Based on these factors, settlements are divided into seven types, from large urban centers to small peripheral towns with fewer than 5000 people. The overwhelming predominance of small towns (97%) in the sample highlights their distinct development challenges and questions the narrative of decline focused solely on larger industrial cities. By situating American trajectories within the broader shrinking cities discourse, the findings demonstrate the value of typology as a methodological tool for identifying intra-group heterogeneity, capturing regional differences, and establishing a more reliable basis for comparative urban studies. Ultimately, the study shows that urban decline in the United States is not exclusively a Rust Belt phenomenon, but a multidimensional process encompassing different scales, sectors, and geographies. Full article

As debates intensify over establishing culture as a standalone Sustainable Development Goal (SDG) beyond 2030, this paper studies the policy implications of such a shift and its consequences for the future of global development frameworks. While acknowledging growing calls for a standalone cultural SDG—often framed as SDG18—this study cautions that isolating culture as a separate goal risks reinforcing sectoral silos and undermining its crosscutting relevance in sustainable development. Instead, the paper argues that cultural sustainability is more effectively advanced through systematic mainstreaming across the existing SDGs, ensuring balanced integration alongside economic, environmental, and social dimensions. Using qualitative and quantitative content analysis supported by NVivo, the research examines how culture is represented in SDG implementation reports, policy briefs, and Voluntary National Reviews (VNRs). The findings reveal persistent patterns of marginalization, thematic narrowness, and regional inconsistency in the treatment of culture, indicating structural limitations in SDG implementation rather than a lack of cultural relevance. This reinforces the fact that culture needs to be more visible within the SDG framework; however, the question remains: how? By comparing the two dominant policy trajectories—advocacy for a standalone cultural SDG and the mainstreaming of culture across the existing SDGs—this paper identifies pathways and a set of policy-oriented recommendations to strengthen cultural integration without further fragmenting the sustainability agenda. Full article

This study investigates how firms in high-emission sectors progress along the low-carbon transition by analysing the joint dynamics of Management Quality (MQ) and Carbon Performance (CP) using probabilistic modelling and explainable machine-learning methods. Digitalisation is conceptualised as the increasing use of data-driven and algorithmic tools in corporate governance, sustainability monitoring, and regulatory oversight, enabling a more granular assessment of corporate transition pathways across multiple time horizons. Using annual Transition Pathway Initiative data for 175 firms over the period 2018–2025, we apply discrete-time Markov chains to capture state persistence and directional mobility in MQ and CP, while Hidden Markov Models uncover latent performance regimes shaping firms’ transition trajectories across three decarbonisation horizons (2028, 2035, and 2050). To enhance interpretability and policy relevance, CatBoost-based feature importance analysis identifies governance, emissions-related, and sector-specific drivers of transitions between states. The results indicate a steady and highly persistent improvement in Management Quality, reflecting cumulative consolidation of governance structures, while Carbon Performance evolves more slowly and heterogeneously, with only moderate convergence emerging toward the 2050 horizon. Latent-regime estimates reveal a gradual shift from volatile, low-performance pathways toward more stable transition regimes over time. From a policy perspective, the findings suggest that governance improvements alone are insufficient to ensure timely emission reductions, highlighting the need for digitally enabled, sector-specific regulatory incentives and enforcement mechanisms targeting realised Carbon Performance. Full article

This study develops an integrated analytical framework to assess Russia’s green hydrogen demand potential and cost-competitiveness pathways across the steel production and road transport sectors. Using bottom-up sectoral analysis, we estimate Russia’s theoretical hydrogen demand potential at approximately 18.2 Mt/year. Three policy scenarios model demand realization trajectories under differentiated support regimes, calibrated to European alternative fuel vehicle diffusion patterns and Russian statistical data. A learning curve framework projects green hydrogen costs as an endogenous function of cumulative production, with learning rates of 5% and 10.1% representing conservative and optimistic technology development pathways. Results indicate that under realistic policy support and 10.1% learning rates, hydrogen costs decline from USD 15/kg to USD 7.23/kg by 2050, reaching the USD 10/kg competitiveness threshold by approximately 2035. However, Russia’s costs remain 2–4 times higher than global optimal-location projections due to scale disadvantages and infrastructure constraints. Policy recommendations emphasize front-loaded support mechanisms, export market integration with EAEU partners, and electrolyzer technology localization to accelerate learning effects and achieve cost competitiveness within mid-term planning horizons. Full article

Accurate monitoring of aboveground biomass (AGB) is essential for forest carbon accounting and climate change mitigation, yet signal saturation and the treatment of forest landscapes as biophysically homogeneous entities remain significant barriers to high-fidelity mapping. This study implements an ecologically integrated model that leverages forest-type specific (coniferous vs. broadleaf) to enhance regional AGB retrieval. By refining established data fusion techniques with structural and compositional parameters, this approach seeks to mitigate systematic biases often found in generic regional assessments. Compared with 360 geo-referenced subplots, our stratified Support Vector Regression (SVR) model significantly outperformed non-classified counterparts, achieving an R² of 0.76 and a reduced RMSE of 18.48 Mg/ha. This refined precision enabled a nuanced time-series analysis (2013–2020), revealing that while regional AGB increased from 157.13 to 192.23 Mg/ha, this trajectory was punctuated by a distinct sub-regional growth plateau between 2016 and 2018. By correlating these trends with disturbance data, we identified a 11.27% biomass decline in southwestern sectors linked to a tripling of burned area, pinpointing intensified fire regimes as the primary driver overriding recovery-driven carbon gains. These findings demonstrate that harmonizing multi-sensor signals with functional forest differentiation provides the necessary sensitivity to track carbon resilience, offering a scalable and robust tool for operational forest management and global carbon cycle research. Full article

Coordinated mitigation of greenhouse gases (GHGs) and air pollutants (APs) offers an effective strategy to address climate and air quality challenges, yet systematic evaluations in medium-sized industrial cities remain limited, despite their coal-dependent energy systems and emission-intensive manufacturing that disproportionately shape national emission trajectories. Thus, this study focuses on Weifang, a representative industrial city in Shandong Province, developing a high-resolution, multi-pollutant inventory and applying quantitative synergy indices to characterize emission patterns, sectoral contributions, and hotspot regions. In 2023, Weifang’s total emissions comprised 114.54 million metric tons (Mt) CO₂, 121.91 thousand metric tons (kt) CH₄, and 27.67 kt N₂O, alongside major APs including CO (662.99 kt), TSP (154.44 kt), and NOx (100.83 kt). Industrial sources and electricity-heat production contributed over 80% of CO₂ and SO₂, while agriculture dominated CH₄ (59.5%) and N₂O (40.5%). Mobile sources accounted for 66.6% of NOx, over 20% of VOCs, and 61.4% of CO. Spatially, suburban areas produced over 65% of total emissions due to heavy industry and agriculture, whereas the urban core exhibited higher intensities but lower total contributions. Bivariate and integrated synergy indices revealed stronger SO₂-NOx-CO₂ synergies in the urban core, while suburban emissions were more heterogeneous and spatially dispersed. Synergy analysis indicated strong SO₂-CO₂ co-variation from shared industrial sources but weak NOx-CO₂ correlations due to divergent origins. Hotspot mapping identified industrial parks, power plants, steel zones, and suburban agriculture as priority control areas. These findings demonstrate that source-specific measures are critical to maximizing co-benefits. The proposed methodological framework offers transferable insights for evaluating emission synergies in other industrial cities. Full article

In a rapidly changing professional landscape marked by digitization, socio-economic transformations, and post-pandemic upheavals, understanding career trajectories has become an interdisciplinary concern. This study presents a systematic bibliometric review of 135 peer-reviewed articles published between 1990 and 2025 and extracted from the Scopus database. Using VOSviewer and Bibliometrix, the analysis maps the intellectual structure, thematic evolution, and methodological trends in career path research. The results reveal a high concentration of studies in Anglo-Saxon contexts, with a predominance of the education, health, and hospitality sectors. Key populations include students, women, and recent graduates, while seniors, informal workers, and non-Western contexts remain underrepresented. This field is conceptually diverse, structured around protean and borderless career models, and increasingly interested in themes such as sustainability, digital transformation, and gender inequality. Cross-sectional quantitative approaches dominate methodologies, while longitudinal and mixed designs are rare. Thematic mapping reveals four key clusters: sociodemographic factors, professional development, labor market dynamics and identity formation. Citation analysis reveals key contributions to career theory, social capital and organizational support. This review reveals gaps in geographic coverage, theoretical integration and methodological pluralism. It calls for a more inclusive, contextualized and interdisciplinary approach to better understand the complexity of contemporary careers. Full article

Administrative strategies are essential for ensuring efficiency and effectiveness in public institutions, particularly in the context of low- and middle-income countries where governance challenges and resource constraints persist. This study analyzes the relationship between organizational commitment and administrative management in a local public financial institution in Peru. Drawing on Meyer and Allen’s three-component model of commitment (affective, continuance, and normative) and classical administrative theory (planning, organizing, directing, and controlling), the research explores how psychosocial drivers influence perceptions of administrative practices. A cross-sectional, quantitative, non-experimental design was applied, surveying 31 employees using validated Likert-scale questionnaires. Fieldwork was conducted from January to June 2024. Non-parametric correlation analysis revealed a strong and statistically significant positive association between organizational commitment and administrative management (Spearman’s rho = 0.661, p < 0.01). Normative commitment was the most influential dimension, underscoring the role of loyalty and ethical obligation in sustaining perceived administrative management. These findings highlight the importance of strengthening human capital and organizational commitment as part of administrative strategies for institutional development. The study contributes to debates on governance and public sector reform by emphasizing how organizational dynamics in local institutions can shape broader trajectories of economic growth and development in emerging contexts. Full article

Platinum group metals (PGMs)—comprising platinum (Pt), palladium (Pd), rhodium (Rh), iridium (Ir), ruthenium (Ru), and osmium (Os)—are indispensable strategic materials for key industries, including automotive manufacturing, petrochemical engineering, and the new energy sector. Given the uneven global distribution of primary PGM reserves and the widening supply–demand gap, recovering PGMs from secondary sources—primarily metallurgical by-products and spent catalysts—has become a strategic priority. synergistic smelting, leveraging “multi-feedstock complementarity” and “multi-technology coupling,” offers an efficient approach to overcoming challenges associated with secondary resources, such as low grades, complex matrices, and refractory separation. This paper systematically reviews the technological evolution of synergistic smelting for PGMs recovery, focusing on three aspects: the characteristics and processing bottlenecks of PGMs-bearing secondary resources, the development trajectory of traditional metallurgical technologies, and innovative breakthroughs in microwave-assisted synergistic smelting. A comparative analysis between traditional and microwave-based technologies is conducted across four dimensions: resource adaptability, technical performance, environmental sustainability, and industrial maturity. Finally, the core challenges currently confronting microwave-assisted synergistic smelting and future directions for industrial demonstration are elaborated on. This study serves as a comprehensive reference for the efficient and sustainable recovery of PGMs, with significant implications for the circular economy and strategic resource security. Full article

Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand Cat Optimization (SCO), is proposed to enhance fault identification and predictive maintenance capabilities. The model utilized multivariate sensor data from cyber-physical and IoT-enabled robotic platforms to learn operational patterns and predict failures with enhanced reliability. The At-GRU provides deeper temporal feature extraction, thereby improving classification performance. The robustness of the proposed model is validated through analysis of a benchmark dataset for industrial robots, and the results demonstrate that the proposed model exhibits impressive predictive capacity, surpassing other prediction methods and predictive maintenance approaches. Additionally, the performance evaluation indicates a lower computational cost due to the lightweight gating architecture of GRU, combined with attention. The robotic motion is further optimized by the SCO algorithm, which reduces energy usage, execution delay, and trajectory deviations while ensuring smooth operation. Overall, the proposed work offers an intelligent and scalable solution for next-generation industrial automation systems. Furthermore, the proposed model demonstrates the real-world applicability and significant benefits of incorporating hybrid artificial intelligence models into real-time robot control applications for smart manufacturing environments. Full article

Advancing Integrated Water Resources Management (IWRM) is essential for integrating land and water strategies and ensuring access to safe and secure water services. Yet, assessing the quality of IWRM implementation remains a persistent challenge for policy and practice. This study presents the first systematic review of 375 empirical articles to consolidate evidence on how enablers and obstacles shape IWRM’s effectiveness in advancing Sustainable Territorial Development (S-TD). Following PRISMA guidelines and combining bibliometric and qualitative coding procedures, we identify ten categories of enablers and eleven categories of obstacles. Results show that institutional strengthening, stakeholder participation, and technological innovation are the most frequent enablers, while fragmentation, coordination challenges, and financial limitations are the most prevalent obstacles. Beyond frequency patterns, this review highlights that outcomes depend on the configurations and interactions of these factors, which condition IWRM’s capacity to steer sustainable development trajectories in the territory. By comparing enablers and obstacles across nexus sectors (food, energy, land) and geographic scales (sub-basin, basin, transboundary, urban, national), we delineate scale- and sector-sensitive pathways linking IWRM to S-TD. To support further research, we provide an open-access dataset as a unique resource for replication, comparative analysis, and policy design, enabling evidence-based decision-making toward sustainability and resilience across diverse geographical and institutional contexts. Full article