Search Results (16,248)

Extreme climate events have emerged as a critical threat to the economic resilience and environmental sustainability of urban systems. As a central pillar of the low-carbon transition, improvements in carbon emission efficiency (CEE) are increasingly recognized as a potential pathway to mitigate the occurrence and intensity of such events. Drawing on a balanced panel dataset of 163 cities from 2006 to 2022, this study integrates an Extreme Gradient Boosting (XGBoost) model augmented with SHAP (Shapley Additive Explanations) analysis and a Geographically and Temporally Weighted Regression (GTWR) framework to examine the nonlinear and spatially heterogeneous effects of CEE on the Climate Physical Risk Index (CPRI). The results reveal a distinct two-stage dynamic pattern, in which CEE initially exacerbates and subsequently mitigates climate risk, indicating a nonlinear transition from short-term intensification to long-term alleviation. This relationship shows clear differences across city levels and climate types. The strongest effects appear in peripheral cities and in areas with extreme rainfall dominance (ERD). Spatial analysis based on GTWR also shows a clear north–south pattern. The effect of CEE in reducing risk becomes stronger from the south to the north. Based on these results, the study suggests different land-use policy strategies for different city types and climate conditions. The results give actionable insights for designing targeted carbon governance policies. These policies aim to deal with the growing challenges caused by extreme climate events under ongoing climate change. Full article

This paper explores how higher education institutions (HEIs) in the Gulf Cooperation Council (GCC) are advancing the Sustainable Development Goals (SDGs) amid rapidly evolving national development agendas. This study reviews publicly available institutional documents and global SDG ranking data to identify patterns of SDG integration: through academic programs, research, and community engagement. The data shows active engagement of the universities in the region linked with varying SDGs. The analysis also reveals that sustainability initiatives in Gulf universities are not purely educational or environmental undertakings; rather, they function as strategic instruments aligned with national visions, international positioning and soft power objectives. Accordingly, this study assesses institutional commitment to the SDGs as expressed through, and made visible by, publicly available reporting, rather than the effectiveness or real-world impact of that engagement, which the available data cannot establish. Guided by theoretical perspectives, the paper argues that SDG engagement remains largely shaped by global ranking frameworks and policy imperatives. While the GCC higher education sector is increasingly embedded in the global sustainability discourse, meaningful localization of SDG practices and data transparency remain limited. By drawing attention to these dynamics, the study contributes to the literature on higher education and sustainable development in the Arab Gulf, emphasizing the need for context-sensitive frameworks and stronger regional collaboration to advance the 2030 Agenda. It calls for strengthened collaboration, capacity development, and tailored policy approaches to fully harness the transformative potential of the SDGs. Future research should explore the sociopolitical drivers of SDG adoption to deepen understanding of HEIs’ contributions to sustainable development in the region. Full article

Traditional olive groves are widely recognised as providers of landscape, environmental and cultural public goods in Mediterranean rural areas, but their long-term economic viability remains uncertain. This study assesses the income gap between traditional, intensive and super-high-density (SHD) olive-growing systems in a representative hill olive-growing area in Tuscany (central Italy), characterised by physical and structural conditions typical of traditional Mediterranean systems. Using a discounted cash-flow framework, the analysis compares long-term financial performance through standard investment appraisal indicators and uses the Equivalent Annual Value (EAV) as a policy-relevant benchmark for calibrating support. The results reveal a clear structural divergence: while intensive and SHD systems achieve higher profitability and faster capital recovery, the traditional system exhibits a persistent income disadvantage under market conditions. The estimated EAV gap amounts to approximately 950 €/ha relative to the intensive system and 3104 €/ha relative to the SHD system—values that represent the additional annual support required to preserve traditional olive groves and prevent abandonment. These values can also be interpreted as the annual private opportunity cost of maintaining traditional olive landscapes rather than converting them to more financially competitive systems. Break-even analysis further shows that the traditional system requires an oil price of at least 9.6 €/kg to achieve economic viability without public support, compared to 6.97 €/kg and 4.13 €/kg for the intensive and SHD systems, respectively. The findings highlight a structural misalignment between private profitability and social value, suggesting that the conservation of traditional olive landscapes cannot rely on market mechanisms alone and requires targeted, evidence-based policy instruments. Full article

Freshwater aquaculture in China has expanded rapidly in recent decades, raising growing concerns about its environmental sustainability. However, the relationship between industrial agglomeration and environmental efficiency in freshwater aquaculture remains insufficiently understood. Using panel data from 18 major freshwater aquaculture provinces in China from 2009 to 2023, this study investigates the nonlinear effects of industrial agglomeration on environmental efficiency. Environmental efficiency is evaluated using a Global Super-SBM model incorporating undesirable outputs, while industrial agglomeration is measured by the location quotient index. A two-way fixed-effects model is employed for empirical estimation. The results reveal a significant inverted U-shaped relationship between industrial agglomeration and environmental efficiency, with a turning point at an agglomeration level of 2.519. Moderate agglomeration improves environmental efficiency through economies of scale and technology diffusion, whereas excessive agglomeration generates crowding effects that reduce efficiency. Further mechanism analysis shows that technology diffusion, proxied by the number of trained fishermen, plays a significant mediating role in this relationship. This study provides new empirical evidence on the nonlinear environmental effects of industrial agglomeration in freshwater aquaculture and offers policy implications for optimizing industrial spatial layout and developing differentiated environmental regulations to support the green and sustainable development of the sector. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

LIFE REVIVE aims to restore ecological status and ecosystem services in the Lima and Vouga river basins (NW Iberian Peninsula), where hydromorphological alteration and hydropower-driven flow regulation are major causes of water bodies failing to reach Good Ecological Status under the EU WFD. The project targets key pressures such as longitudinal fragmentation by weirs and dams, artificial flow regimes, degradation of spawning substrates, and the spread of invasive aquatic plants, which strongly affect fish communities, including sea lamprey, salmonids, and other diadromous species. Technically, the project combines barrier removal or eco-adaptation, nature-like fish passes, and spawning-habitat renaturalisation with optimized environmental flow regimes (EFR) downstream of important hydropower systems, explicitly accounting for present and future hydroclimatic scenarios. Multi-scale ecohydrological modelling (species distribution models, habitat suitability models, GLM/GAM approaches) will quantify fish–flow–habitat relationships and support the definition of operational EFR guidelines that balance ecological requirements with hydropower and agricultural constraints through joint work with the main Portuguese hydropower operator, EDP. Impact evaluation is structured around a rigorous BACI monitoring design in intervention and control tributaries, using standard WFD biological indices for fish and aquatic/riparian vegetation, hydromorphological indices (HQA, HMS, RHS), and project-specific Key Performance Indicators for water quality, biodiversity, and habitat. Expected outcomes include the restoration of at least 51 km of rivers towards free-flowing conditions, reduced hydromorphological pressure in more than 20 km of heavily modified river stretches, and measurable increases in the distribution and abundance of fish species and native vegetation. A strong communication and capacity-building programme underpins public engagement, while a decision matrix for barrier prioritization, technical workshops, and pilot replications in additional basins (e.g., Alva, Mouro, Deva, and Tea in Galicia) are designed to maximize transferability, policy uptake, and long-term sustainability of the solutions beyond the project lifetime. Full article

The synergistic transition toward digital transformation and green development has been widely regarded as a core pathway to achieving sustainable development in knowledge production. Using balanced panel data from 39 economies covering 2018–2024, this study employed a two-way fixed-effects model to examine the associations of the energy efficiency of digital infrastructure and the energy structure with carbon intensity (CI). The findings showed that: (1) Reductions in Power Usage Effectiveness (PUE) values were significantly associated with higher macro-level CI (coefficient = −2.1564, p < 0.05), which is consistent with the possibility of a rebound effect in the digital sector. Further, time-series discontinuity tests further suggested that the surge in AI computing power, especially in 2023–2024, may have coincided with a structural shift in this relationship (Chow test, p < 0.05). (2) A Panel Threshold Regression (PTR) identified an optimal renewable energy threshold at 59.82%. Crucially, the carbon rebound effect remained highly significant across both high and low green power regimes, demonstrating that supply-side energy transition alone cannot fully absorb the exponential carbon footprint of digital expansion. Furthermore, Instrumental Variable (IV-2SLS) and Placebo Break Tests confirmed the strict validity of these findings. (3) The emission-reduction benefits related to digital knowledge spillovers appeared to be subject to time lags and a possible energy lock in effect, while current environmental policies and carbon pricing mechanisms appear to impose insufficient constraints. This study provides a crucial quantitative framework for monitoring and evaluating the environmental sustainability of the ICT sector. By highlighting the limitations of pure supply-side greening and the necessity of absolute carbon caps, our findings offer integrated policy approaches to align the exponential growth of Generative AI with global sustainable development goals. Full article

The rapid expansion of solar photovoltaic (PV) deployment has led to increasing concerns regarding end-of-life module management and the sustainability of material supply chains, where waste volumes are projected to reach 3.3–5.6 million tons by 2045. This study evaluates the environmental and economic impact of advanced photovoltaic recycling in Germany, focusing on high-value material recovery from crystalline silicon modules. A Full Recovery of End-of-Life Photovoltaics (FRELP) pathway is developed, integrating light-pulse delamination and molten salt etching, and a comparative life cycle assessment and economic assessment framework is applied. The results indicate that advanced recycling achieves high recovery rates for silicon, silver, aluminum, copper and low-iron glass, yielding around €1174.88 per ton of panels recycled. Economic analysis shows that manufacturing PV modules from recycled materials reduces costs by approximately 60–77% compared to virgin material production, mainly due to avoided energy-intensive upstream processes. From an environmental perspective, the recycling-based pathway yields net benefits across impact categories, as avoided impacts from primary material extraction outweigh additional burdens associated with recycling. Overall, PV recycling in Europe is shown to be environmentally and economically favorable; however, technological maturity and policy constraints remain key barriers to large-scale implementation and a holistic overall recycling process, indicating the need for targeted policy support. Full article

The impact of air pollution on human health has been widely studied in recent decades. Recent findings show that even low levels of air pollution can be harmful to our health, causing disease and early death. However, these studies are very limited in the central region of Türkiye. Therefore, this study focused on the association between the daily variations in air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) and hospital admissions due to respiratory, cardiovascular, and total (non-accidental) causes in the Sivas province. Daily average concentrations of air pollutants were obtained from two air quality (AQ) monitoring stations, and daily meteorological (air temperature and relative humidity) data were obtained from one meteorological station in Sivas province to determine the effects of air pollution on hospital admissions. It was found to be a significant relationship between air pollution and respiratory hospital admissions in the province. The results of the study showed the relative magnitudes of the risks of cardiovascular diseases and hospital admissions related to air pollutants were as follows: The highest association of each pollutant with cardiovascular diseases was observed for PM₁₀ at lag 4 (ER = 1.74%; 95% CI = 0.95–3.19%), PM_2.5 at lag 2 (ER = 5.12%; 95% CI = 1.39–19.0%), NO₂ at lag 8 (ER = 4.89%; 95% CI = 0.08–288.8%) and SO₂ at lag 5 (ER = 1.21%; 95% CI = 1.10–1.32%). It was seen that short-term exposure to air pollution in Sivas between 2016 and 2019 was positively associated with increasing respiratory hospital admissions. As the first air pollution study to use the generalized linear model (GLM) method in hospital admissions in Sivas, these findings may have implications for local environmental policies and help to combat air pollution. Full article

This study applies a Long Short-Term Memory (LSTM) model to predict key air pollutants, i.e., sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM_2.5), as well as the Air Quality Index (AQI) across South Africa using satellite-derived observations. The analysis focuses on comparing original pollutant fields with model-generated predictions for two consecutive days, highlighting both spatial patterns and predictive performance. Results reveal a persistent and intense pollution hotspot over the Mpumalanga Highveld, driven by coal-fired power generation and industrial activities. Elevated pollutant concentrations in this region translate into AQI levels ranging from Unhealthy to Very Unhealthy, while most other parts of the country remain within the Good category. Spatial comparison between original and predicted fields shows strong agreement, with only minor deviations in areas characterized by steep emission gradients and localized plumes. Quantitative evaluation using RMSE (0.020390) and MSE (0.000416) confirms the high accuracy of the predictive model, with error values remaining extremely low across all pollutants and AQI outputs. PM_2.5 exhibits the smallest errors (MSE = 4.230169 × 10⁻⁶), while slightly higher values for SO₂ (MSE = 2.628 × 10⁻⁴) and NO₂ (MSE = 1.39541 × 10⁻⁴) reflect the difficulty of capturing sharp spatial transitions associated with point-source emissions. Despite these localized discrepancies, the model demonstrates robust skill in replicating both pollutant magnitudes and AQI classifications. Overall, the findings indicate that machine-learning approaches offer a reliable, high-resolution tool for air-quality prediction in South Africa and have strong potential for supporting operational forecasting, exposure assessment, and environmental policy development. Full article

Residential buildings must now be designed and retrofitted as adaptive climate–health–work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. This review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is a hypothesis-generating, decision-support and benchmark-development framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance. Full article

Across the European Union, deposit–refund systems (DRSs) are increasingly seen as a crucial response to mounting waste management challenges, including low recycling rates and rising plastic pollution. By attaching a refundable deposit to beverage containers, these systems incentivise civilians to return packaging, thereby significantly reducing litter and improving the quality of recyclable materials. In the context of ambitious EU circular economy and waste reduction targets, DRSs play a key role in addressing resource inefficiency and environmental degradation. The empirical results obtained from a comprehensive panel regression analysis, which accounts for heterogeneity in policy effectiveness, reveal that the effectiveness of a DRS is not uniform across the packaging spectrum in the EU. The results indicate that the presence of a DRS and higher deposit values have a statistically significant and positive effect on packaging waste recycling rates, especially for plastic packaging waste, and that DRSs play a major role in improving and increasing the separate collection of packaging waste, ensuring good quality of the aforementioned fractions. In addition, some structural factors, such as population density and tourism intensity, have a strong impact on recycling efficiency. The findings highlight the importance of implementing deposit systems to achieve circular economy objectives and provide an empirical basis for improving waste management policies in the EU, thereby strengthening evidence-based decision-making. Full article

Introduction: Conjunctival and eyelid lesions encompass a broad spectrum of conditions, ranging from benign entities to malignant neoplasms, and may significantly impact ocular health and quality of life. Their occurrence is influenced by environmental exposure, demographic characteristics, and access to healthcare services. In public health settings, delayed diagnosis and limited access to specialized care may contribute to disease progression. Objective: To characterize the clinical and epidemiological profile of conjunctival and eyelid lesions in patients treated at a referral ophthalmology service within the Brazilian Unified Health System (SUS). Methods: This retrospective observational study reviewed medical records of patients diagnosed with conjunctival and/or eyelid lesions during ophthalmologic screening campaigns. Clinical and demographic variables were analyzed using descriptive statistics to assess prevalence and distribution patterns. Results: A total of 66 patients were included. Conjunctival involvement predominated (78.8%), with the nasal region being the most frequently affected location (62.1%). Pterygium was the most prevalent diagnosis (31.8%), followed by racial melanosis (15.2%) and melanocytic nevus (12.1%). No statistically significant differences were observed according to sex or lesion laterality. However, a significant association was identified between lesion type and anatomical location. Conclusions: Conjunctival and eyelid lesions in this population were predominantly benign and associated with demographic and environmental factors. These findings highlight the importance of structured screening strategies and early diagnosis to improve clinical outcomes and support public health policies aimed at reducing the burden of ocular disease within the SUS. Full article

The launch of sixth-generation (6G) mobile networks is expected to introduce significant variability in Quality of Service (QoS), driven by environmental conditions, traffic heterogeneity, device diversity, and network slicing policies. Existing clustering-based QoS analysis methods rely primarily on using only KPI variables, such as latency, throughput, jitter and packet loss datasets, and classical geometric validity metrics, providing limited insight into the stability, predictive capability, and operational relevance of discovered clusters. To address these limitations, this study proposes a context-aware QoS modelling framework and a unified network-centric cluster evaluation protocol. A dataset comprising 2345 observations is constructed by integrating QoS indicators with contextual and operational variables, including weather conditions, time of day, geographic region, traffic type, device class, and slice identity. Four clustering paradigms, k-means, DBSCAN, spectral clustering, and Deep Embedded Clustering (DEC), are evaluated using both classical metrics and three proposed evaluation measures: Contextual Cluster Stability (CCS), QoS-Regime Predictive Consistency (QPC), and Slice-Level Reliability Separation (SLRS). The results demonstrate that classical clustering metrics alone are insufficient for assessing QoS regime quality. While DEC achieves strong structural performance in latent space, all methods exhibit near-zero predictive consistency and weak reliability separation. These findings reveal a consistent divergence between structural clustering quality and operational usefulness, indicating that unsupervised clustering alone is insufficient for QoS prediction and reliability-aware decision-making. The proposed framework provides a foundation for evaluating clustering methods in context-sensitive network environments and highlights the need for integrating temporal modelling and reliability-aware learning in future 6G network optimisation systems. Full article

This study investigates the relationship between energy consumption and ecological sustainability in South Africa over the period 1990–2023, with a particular focus on the roles of renewable energy consumption, non-renewable energy consumption, and economic growth. Ecological sustainability is proxied by the Load Capacity Factor (LCF), a comprehensive measure that captures the balance between biocapacity and environmental pressure. The study employs the Nonlinear Autoregressive Distributed Lag (NARDL) model to capture both short-run and long-run asymmetric effects, decomposing renewable energy consumption into positive and negative shocks to identify nonlinear dynamics. Descriptive statistics reveal moderate stability in the LCF, increasing adoption of renewable energy, sustained economic growth, and persistent dependence on fossil fuels. Unit root tests confirm mixed integration orders, justifying the use of the NARDL framework. Empirical results indicate that positive shocks in renewable energy consumption significantly enhance ecological sustainability, while negative shocks reduce the LCF, highlighting the asymmetric impact of renewable energy. Non-renewable energy consumption exhibits a statistically significant long-run association with ecological sustainability, reflecting South Africa’s continued structural dependence on fossil-fuel-based energy systems during the study period. Granger causality tests show that renewable energy and non-renewable energy consumption are key drivers of ecological sustainability, whereas economic growth and environmental conditions exhibit bidirectional feedback. The findings provide evidence for the strategic importance of promoting renewable energy adoption, reducing fossil fuel reliance, and integrating sustainability considerations into economic planning. Policy recommendations emphasize investment in renewable energy infrastructure, incentives for green energy adoption, and the integration of environmental objectives into economic development strategies to enhance South Africa’s ecological resilience. Full article