Search Results (657)

The misallocation of renewable (RE) and non-renewable energy (NRE) resources may lead to the inefficiency of economic development, thereby hindering the achievement of sustainable development goals. Basing data on 282 Chinese cities during 2005–2021, a relative factor price distortion coefficient was employed to estimate the degree and direction of resource misallocation (RM) for RE, NRE, capital, and labor at both the aggregate city level and across four disaggregated city categories. Output gaps and efficiency losses are further quantified by incorporating RM analysis into the economic growth accounting framework, revealing significant heterogeneity in RM across cities. Findings show that (1) RE and labor misallocation exceed those of NRE and capital at the city level. RE misallocation is dominant in energy misallocation. There exists an underallocation of RE, NRE, and labor, while capital is overallocated. (2) Renewable energy input and output (RE-IO) cities exhibit the highest overall RM (32.1%), whereas renewable energy input (RE-Input) cities possess the lowest ones (21.2%). Four city types demonstrate an underallocation of RE and an overallocation of capital. (3) Both output gaps and efficiency losses are on the rise. Output changes sources are transferred from the variations in factor inputs to those in total factor productivity (TFP). The contribution from the RM changes is limited. The results provide a reference for reducing RM and achieving energy transition. Full article

Although there are theoretically expected associations between problematic smartphone use (PSU) and materialism, there is a lack of research that examines these associations using a longitudinal design, focusing on both within-person and between-person effects. Clarifying this relationship may inform interventions for these related conditions. Accordingly, data from three annual waves collected from a substantial group of Chinese adolescents (N = 3029, M_age = 12.26 ± 2.36, male: 50.00%) were used to assess within-person and between-person effects in the association between PSU and materialism. Traditional cross-lagged panel models were utilized to analyze the data, which consistently showed reciprocal positive associations between PSU and materialism across all waves. In contrast, the random intercept cross-lagged panel model revealed that PSU and materialism exhibited reciprocal associations over time at the between-person level. However, no significant cross-lagged linkage was observed between PSU and materialism at the within-person level. These findings enhance our understanding of the temporal dynamic relationship between PSU and materialism and underscore the necessity to disaggregate within-person and between-person effects to elucidate the nature of the longitudinal associations between PSU and materialism. The study also has implications for theoretical and practical understanding. Full article

Background: The increasing integration of Environmental, Social, and Governance (ESG) factors into financial decision-making has prompted debate over their impact on corporate credit risk. While many studies suggest that ESG performance may enhance firms’ resilience, empirical evidence remains mixed due to data inconsistency and methodological heterogeneity and differences in time horizons over which ESG effects materialise. Methods: The study investigates the relationship between ESG performance and credit risk using a panel of European firms from 2020 to 2024, a phase highly characterised by substantial macroeconomic shocks. The Altman Z-score serves as a proxy for default risk, while ESG data are sourced from Refinitiv Eikon. Four fixed-effects panel regressions are estimated: a baseline model using aggregate ESG scores, an extended model with financial controls, and disaggregated and sector-specific models. Results: The findings indicate that ESG scores—either aggregated or by pillar—show limited statistical significance in explaining variations in the Z-score. In contrast, financial variables such as solvency, liquidity, and cash flow ratios display strong, positive, and significant effects on credit stability. Some heterogeneous sectoral effects emerge: social factors are positive in technology, while governance has a negative impact in basic materials. Conclusions: ESG initiatives may not yield immediate improvements in default risk metrics, particularly over short and crisis-dominated periods, but could enhance financial resilience over time. Combining ESG information with traditional financial ratios remains essential; the results underscore the importance of consistent and high-quality ESG disclosure to reduce measurement error and enhance comparability across firms. Full article

The connectivity demands of high-performance computing (HPC), artificial intelligence (AI) and data centers are driving the development of a new generation of multimode optical components. This paper discusses the vertical cavity surface emitting laser (VCSEL) bandwidth and noise performance needed to support 106 Gbd line rates with PAM4 modulation for 200 Gbps per lane multimode optical links. A −3 dB bandwidth greater than 35 GHz and a RIN of less than −152 dB/Hz are demonstrated. No uncorrectable errors were observed over 50 m of OM4 fiber, demonstrating good link stability. VCSEL device performance and the associated wear-out life are presented. Leveraging good device reliability and low power consumption of VCSEL-based links, a novel VCSEL near-packaged optics (NPO) concept is proposed for optical interconnects in AI scale-up network applications. Optical interconnects allow for longer reaches, compared to copper interconnects, which facilitate larger AI clusters with network disaggregation. The proposed VCSEL NPO can achieve an energy efficiency of ~1 pJ/bit, which is the highest among optical interconnects. Full article

Trinidad and Tobago (T&T) has regional influence in the Caribbean, with a diversified economy focused on energy, manufacturing, and finance compared to the tourism-related economies of most of the other Caribbean islands. Notwithstanding, this has not translated into equitable opportunities for all, specifically for people with disabilities. A lack of disaggregated employment data thwarts effective policy planning for people with disabilities. This research sought to examine the barriers to their employment in T&T. Underpinned by the social model of disability, a concurrent mixed-methods approach was employed. Emanating from interviews with people with disabilities and key informants, challenges to employment access, employer perceptions, and apathy emerged as key themes, together with the underlying issues of a lack of legislation and inequitable access to mainstream education. The survey findings indicated that 64% of employers had never employed people with disabilities, 57% expressed neutrality regarding future employment of such individuals, and 69% had not introduced workplace accommodations. A key recommendation of the study is the establishment of an employer resource centre that assists employers in creating and maintaining inclusive workplace accommodations for people with disabilities. This study is the first in Trinidad and Tobago to examine these research objectives from multiple perspectives. Full article

Non-intrusive load monitoring (NILM), as a key technology in smart-grid advanced metering infrastructure, aims to disaggregate mains power from smart meters into individual load-level power consumption. Traditional NILM methods require centralizing sensitive measurement data from users, which poses significant privacy risks. Federated learning (FL) enables collaborative training without centralized measurement data, effectively preserving privacy. However, FL-based NILM systems face serious threats from attacks such as model inversion and parameter poisoning, and rely heavily on the availability of a central server, whose failure may compromise measurement robustness. This paper proposes a hybrid FL framework that dynamically switches between centralized FL (CFL) and decentralized FL (DFL) modes, enhancing measurement privacy and system robustness simultaneously. In CFL mode, layer-sensitive pruning and robust parameter aggregation methods are developed to defend against model inversion and parameter poisoning attacks; even with 30% malicious clients, the proposed defense limits the increases in key error metrics to under 15.4%. In DFL mode, a graph attention network (GAT)-based dynamic topology adapts to mitigate topology poisoning attacks, achieving an approximately 17.2% reduction in MAE after an attack and rapidly restoring model performance. Extensive evaluations using public datasets demonstrate that the proposed framework significantly enhances the robustness of smart-grid measurements and effectively safeguards measurement privacy. Full article

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. Full article

The significance of access to energy sources for fostering innovation is increasing. Regions should, however, base their competitiveness not merely on innovation, but also on social cohesion and ecological ambitions. In this context, the objective of this article is to evaluate the sustainable competitiveness potential of Polish regions from the perspective of energy-related factors, as well as to identify the trends and the disparities observed over the past decade. The study employs a multidimensional comparative analysis (MCA), operationalized through the development of a Synthetic Measure of Potential (SMP) constructed from ten disaggregated indicators encompassing resource-related, economic, environmental, and social dimensions of energy. This approach is complemented by a cluster analysis using Ward’s method to identify patterns and groupings within the data. The empirical results demonstrate that sustainable competitiveness potential with regard to energy factors has generally increased, although it was not a linear process. The most favorable trend was observed for the generation of energy from renewable sources. An interesting side effect of transformation was observed in the energy balance. Further, despite the significant decrease in industrial electricity consumption per unit of gross value added, the energy poverty level increased. The study offers several practical implications for advancing the green transformation, emphasizing the uneven regional impacts of this process and underscoring the necessity of a coordinated policy framework to support the energy transition. Full article

Background: Tanzania is undergoing a rapid nutrition and epidemiological transition that has shifted dietary patterns and lifestyles toward more Westernised models, contributing to an increase in diet-related non-communicable diseases (NCDs), including obesity. Youth aged 15–35 years are particularly vulnerable to these shifts. Objectives: The objective of this scoping review was to map the available evidence on youth obesity in Tanzania, focusing on (1) data gaps in epidemiological reporting; (2) the ongoing nutrition transition; and (3) existing food system and health-related policies targeting youth. Methods: A targeted search was conducted in PubMed, Scopus, and the grey literature. The PCC (Population/Concept/Context) framework guided the study selection, focusing on youth and general young adults aged 15–35 years in Tanzania. Eligible studies published between 2000 and June 2025 were included. Results: The search yielded 247 peer-reviewed articles, of which 35 met the inclusion criteria. The findings reveal substantial gaps in epidemiological reporting, particularly limited regional data and inconsistent age disaggregation, which often obscures youth-specific patterns. Evidence on nutrition and lifestyle transitions is limited and fragmented, while available policies addressing obesity and related risk factors are broad in scope and rarely tailored to the youth population. Conclusions: This review demonstrates that evidence on obesity among Tanzanian youth is scarce, unevenly reported, and insufficiently specific to this age group. Clear gaps exist in epidemiological surveillance, research on nutrition transition, and youth-focused policy design. Strengthening age-specific monitoring systems, generating context-specific evidence, and developing targeted, measurable, and actionable strategies for youth could enhance Tanzania’s efforts to curb the rising burden of obesity and related NCDs. Full article

This study presents an integrated framework for lifecycle assessment (LCA) and lifecycle costing (LCC) of buildings and districts that combines machine learning-based temporal disaggregation, physics-based simulation, and holistic environmental evaluation. The methodology addresses a key limitation of conventional LCA practice: the reliance on temporally aggregated energy data, which obscures daily and seasonal variability affecting environmental and economic indicators. A hierarchical disaggregation algorithm was used to reconstruct hourly electricity profiles from monthly totals and was coupled with the INTEMA building energy performance simulator and the VERIFY LCA/LCC platform. The disaggregation algorithm was validated on an office building in Cardiff, UK, supported by cross-validation across multiple UK office buildings, and achieved strong agreement with measured hourly consumption (R² = 0.81, RMSE = 3.71 kWh). In the Cardiff case, the reconstructed hourly profiles reproduced lifecycle greenhouse gas emissions and costs within 0.5% of the reference hourly measurement approach, compared with deviations of 44.1% and 2.9% under conventional monthly aggregation. The complete hybrid framework was then applied to a district in Massagno, Switzerland, encompassing eight buildings with heterogeneous typologies, for which only aggregated energy data were available (monthly for the office building and annual for the others). Over a 20-year horizon, total emissions reached 9429 tCO₂-eq and primary energy demand approached 226 GWh, equivalent to 41 kgCO₂-eq·m⁻²·yr⁻¹. The results illustrate the framework’s applicability to multi-building systems and its ability to support LCA and LCC in contexts with limited temporal data availability. Full article

Multimodal Large Language Models (MLLMs) offer powerful capabilities for processing and generating text, image, and audio data, enabling real-time intelligence in diverse applications. Deploying MLLM services at the edge can reduce transmission latency and enhance responsiveness, but it also introduces significant challenges due to the high computational demands of these models and the heterogeneity of edge devices. In this paper, we propose DistMLLM, a profit-oriented framework that enables efficient MLLM service deployment in heterogeneous edge environments. DistMLLM disaggregates multimodal tasks into encoding and inference stages, assigning them to different devices based on capability. To optimize task allocation under uncertain device conditions and competing provider interests, it employs a multi-agent bandit algorithm that jointly learns and schedules encoder and inference tasks. Extensive simulations demonstrate that DistMLLM consistently achieves higher long-term profit and lower regret than strong baselines, offering a scalable and adaptive solution for edge-based MLLM services. Full article

This review explores the intersection of gender, geography, and sustainability by examining the role of women in the blue economy across Europe’s Outermost Regions (ORs). Despite growing recognition of the blue economy’s role in sustainable development, there is limited understanding of how women participate in these sectors at the geographic periphery of the European Union. Using publicly available data from Eurostat, INSEE, ISTAC, and other national portals, we analyze employment patterns through a gender lens, supported by qualitative insights from case studies in regions such as the Azores, Réunion, and Guadeloupe. Due to the scarcity of disaggregated blue economy data, general labor force participation is used as a proxy, highlighting both opportunities and visibility gaps. Theoretically grounded in feminist political ecology and intersectionality, the review identifies key barriers, including data invisibility, occupational segregation, and structural inequalities, as well as resilience enablers such as women-led enterprises and policy interventions. We conclude with targeted recommendations for research, policy, and practice to support inclusive blue economies in ORs, emphasizing the need for better data systems and gender-sensitive coastal development strategies. Full article

To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balance between load shifting, load management and power dispatch. In this regard, this research study aims to investigate the efficiency of various machine learning models for whole-house energy consumption prediction and appliance-level load disaggregation using Non-Intrusive Load Monitoring (NILM). The primary objective is to determine which model offers the most accurate forecasts for both individual appliance consumption patterns and the total amount of energy used by the household. The empirical study presents comparative performance analysis of machine learning models, i.e., Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting and Support Vector Regressor (SVR) for load forecasting and load disaggregation. This research is conducted on PRECON: Pakistan Residential Electricity Dataset consisting of 42 Pakistani households. The dataset was recorded originally as one minute per sample, but the proposed study aggregated it to hourly samples to evaluate models’ alignment with the typical sampling rate of smart meters in Pakistan. It enables the models to more accurately depict implementation scenarios in real-world settings. The statistical measures MAE, MSE, RMSE and R² have been employed for performance evaluation. The proposed Random Forest algorithm out-performs all other employed models, with the lowest error values (MAE: 0.1316, MSE: 0.0367, RMSE: 0.1916) and the highest R² score of 0.9865. Furthermore, for detecting appliance events from aggregate power data, ensemble models such as Random Forest performed better than other models for ON/OFF prediction. To evaluate the suitability of machine learning models for real-time, appliance-level energy forecasting using Non-Intrusive Load Monitoring (NILM), this study presents a novel evaluation framework that combines learning speed and edge adaptability with conventional performance metrics (e.g., R², MAE). This paper introduces a NILM-based approach for load forecasting and appliance-level ON/OFF prediction, representing its capacity to improve residential energy efficiency and encourage sustainable energy consumption, while emphasizing operational metrics for implementation in embedded smart grid systems—an area mainly neglected in prior NILM-based research articles. The results provide useful information for improving demand-side energy management, facilitating more effective load disaggregation, and maximizing the energy efficiency and responsiveness of smart grids. Full article

Background/Objectives: Europe’s demographic shift is putting increasing pressure on long-term care (LTC) systems and raising concerns about the sustainability of LTC financing. In this paper, we analyse Austria’s LTC system, particularly its universal long-term-care allowance (LTCA), and aim to project LTCA expenditure under different future scenarios. Methods: We use a dynamic microsimulation model to project LTCA expenditure under four scenarios up to the year 2080. Combining LTCA statistics with pooled data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we estimate care needs and prevalence rates across all seven care allowance levels. This enables us to project both public spending and individual lifetime costs, disaggregated by sex and education. Results: Although total LTCA expenditure is projected to rise due to population ageing, scenario comparisons show that compositional shifts—such as higher educational attainment, which is linked to lower care needs and gains in healthy life expectancy accompanying mortality improvements—can significantly mitigate cost growth. The projected total expenditure increases range from 29% in a scenario where increasing life expectancy—as assumed in official population projections—is neglected to 185% in a scenario accounting for rising life expectancy but no future health gains. The findings also highlight the impact of longevity and education on the distribution of individual lifetime costs. Conclusions: Beyond its policy implications for LTC planning, this study demonstrates the advantages of dynamic microsimulation in capturing individual-level heterogeneity, offering a significant improvement over traditional macrosimulation approaches. Full article

A course was created at a large private R1 university in the northeast U.S. to explore Diversity, Equity, Inclusion, and Accessibility (DEIA) in STEM in response to and to fulfill a university-wide DEIA requirement for undergraduates. To assess the effectiveness of the course, open-response pre- and post-tests were designed that measured students’ understanding of barriers to learning and participation across four underutilized groups in STEM: (1) women, (2) racial minorities, (3) people with disabilities, and (4) people raised in lower socioeconomic households. Written responses on the first and last day of class were analyzed for 69 unique students in three successive cohorts (Fall 2022, 2023, and 2024) and disaggregated by student-reported demographic data. A common codebook was developed that could be broadly applied to all four underutilized groups with overarching categories of individual/self; cultural/societal; and institutional/educational/career, with codes and subcodes specific to each category. Additionally, codes distinct to each underutilized group also emerged. As intended, students on average cited more total and unique barrier codes in the post-test than in the pre-test, confirming that the course had deepened their understanding of the multifaceted challenges and opportunities within educational systems and the broader culture that impact STEM inclusivity. When exploring STEM barriers for women, women reported more unique codes in the pre-test than men, but men showed higher gains from pre- to post-test. Similarly, White and Asian students showed greater gains than racial minority students when identifying STEM barriers for racial minorities. Students without disabilities reported a doubling in unique STEM barrier codes in the post-test. In these three groups, codes related to academic and workplace discrimination were commonly cited. Students who reported being from a low socioeconomic household were limited in this study, though these individuals included more unique codes in their pre-test responses on average. Students in this group commonly cited barriers related to access to opportunity. In general, we found that STEM students acquired significant understanding of barriers to STEM participation in the four underutilized groups of focus after completing a dedicated DEIA course. Additionally, learning gains were often greater in the majority (or privileged) demographic. Full article