Search Results (510)

Urban parks are central to public health and equity, yet less is known about how park travel distance, park “attractor” types, and time-of-day visitation rhythms co-evolved through and after the COVID-19 pandemic. Using anonymized smartphone mobility traces for public parks in Las Vegas, USA (2018–2022), we construct weekly origin–destination flows between census block groups (CBGs) and parks and link origins to socio-economic indicators. We first estimate visitor-weighted mean travel distance with a segmented time-series model that allows pandemic-related breakpoints. Results show that average park-trip distance (≈8.4 km pre-pandemic), including a substantial share of long-distance trips (≈52% of visits), contracted sharply at the onset of COVID-19, and that both travel radii and seasonal excursion peaks only partially rebounded by 2022. Next, cross-sectional OLS/WLS models (R² ≈ 0.08–0.14) indicate persistent socio-spatial disparities: CBGs with higher educational attainment and larger shares of Black and Hispanic residents are consistently associated with shorter park-trip distances, suggesting constrained recreational mobility for socially disadvantaged groups. We then identify a stable two-type park typology—local versus regional attractors—using clustering on origin diversity and long-distance share (silhouette ≈ 0.46–0.52); this typology is strongly related to visitation volume and temporal usage profiles. Finally, mixed-effects models of evening and late-night visit shares show that regional attractors sustain higher nighttime activity than local parks, even as citywide evening/late-night visitation dipped during the mid-pandemic period and only partly recovered thereafter. Overall, our findings reveal a durable post-pandemic re-scaling of park use toward more proximate, CBG-embedded patterns layered on enduring inequities in access to distant, destination-oriented parks. These insights offer actionable evidence for equitable park planning, targeted investment in high-need areas, and time-sensitive management strategies that account for daytime versus nighttime use. Full article

This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications. Full article

In the macro context of promoting sustainable development and achieving net zero emissions, the role of green technology innovation, renewable energy utilization and environmental policy is crucial. However, there is still a lack of consistent empirical evidence regarding the combined emission reduction effect of these three factors in OECD countries. This study aims to empirically examine the combined impact of green technology innovation (GTI), renewable energy consumption (REC), and environmental taxes (ETAX) on carbon dioxide emissions. We expect that the former two will effectively reduce emissions, while the effect of environmental taxes depends on their design. Based on the panel data of 35 OECD economies from 1990 to 2019, this study adopts the augmented mean group (AMG) as the main estimation method, and uses the common correlation mean group (CCEMG) for the robustness test. To control potential endogenous issues, the difference generalized method of moments (GMM) is also employed for estimation. The causal relationship between variables is tested using the Dumitrescu–Herlin method. The results show that, as expected, GTI and REC have a significant negative impact on carbon dioxide reduction. However, ETAX is positively correlated with carbon emissions and does not have statistical significance, which deviates from the ideal policy effect and suggests that there may be efficiency bottlenecks in the current tax design. The causality test further reveals that there is a significant two-way causal relationship between CO₂ emissions and GTI, REC, ETAX, GDP, and fossil fuel consumption (FEC). Therefore, it is recommended that OECD countries give priority to expanding investment in green technologies and renewable energy infrastructure and re-evaluate and optimize environmental tax policies to effectively promote the transition to a low-carbon economy. Full article

Despite intensified global efforts to accelerate the renewable energy (RE) transition, the influence of artificial intelligence (AI) and energy security risk (ESR) on RE adoption remains underexplored in the United States. This study examines the nonlinear and time-varying effects of AI, ESR, financial development (FD), and economic growth (GDP) on RE consumption from 1990Q1 to 2020Q4. Annual data were converted to quarterly frequency using the quadratic match sum method, and the Wavelet Cross Quantile Regression (WCQR) technique was employed to capture dynamic relationships across quantiles and time scales. The results show that AI and FD consistently stimulate RE adoption, while ESR shifts from a negative short-term influence to a positive long-term effect. Similarly, GDP initially reduces RE consumption but becomes supportive over longer horizons. This study offers new contributions by providing the first empirical evidence on the role of AI in shaping the U.S. renewable energy transition and by jointly examining technological, financial development, and energy security determinants within a unified framework. Policy implications suggest prioritizing investment in AI-based grid and storage systems, expanding green financing tools to lower capital barriers, and adopting long-term energy security strategies to sustain progress toward a low-carbon energy system. Full article

Even though many nations invest heavily in education to boost economic growth and development, evidence continues to show that university graduates struggle to find employment and eke out a living, obscuring our understanding of the true value of education. Comprehending the experiences of university graduates can assist in determining the support needed for their personal and economic growth. However, research in Zimbabwe on the experiences of graduates after university education is limited. This study explores the experiences of 14 university graduates, drawn from one university in Zimbabwe, who were in their fifth year after graduating. Data were generated from semi-structured interviews and analysed through the lens of credentialism theory. Findings revealed a significant discrepancy between education and employment wherein the value of education is being re-evaluated by people, with many questioning the assumption that higher education guarantees job opportunities. The disconnect highlights the need for policymakers to reassess the way education is delivered and aligned with labour market needs. The study proposes that universities should draw insights from industry leaders when crafting their curriculum and determining enrolment figures. The research extends scholarship on the interplay between education and employment, thereby guiding educational policy formulation in developing countries. Full article

Distributed generation (DG) is one of the types of generation with great potential in the world and fits into the incentive schemes used worldwide. In addition, biogas as a fuel for DG presents itself as an interesting option, both from an economic and a sustainability point of view. Thus, this article addresses the modeling of the investment evaluation process in biogas-based DG projects in Brazil, using the Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) techniques, intending to define a standard model. Although the study is applied to the Brazilian scenario, the proposed investment evaluation model can be applied to any scenario as long as the specificities of each location or country analyzed are considered. The results show that the model supports decisions on new investments in the sector and highlights the process sequence and main decision points for quality analysis. In addition, the model highlights the need to know the regulations and incentives for DG using renewable energy sources (RES) in the country, shows the available technologies, the process for producing electricity using biogas, the times for collecting operational data, estimating revenues, and investment evaluation methodologies, and promotes the growth of knowledge about RES. Finally, this approach supports sustainable development by using renewable resources efficiently, reducing waste, and lowering greenhouse gas emissions. It also helps investors and policymakers make decisions about low-carbon energy systems. Full article

Guo Moruo’s “Rebirth of the Goddesses” is among the landmark works of modern Chinese poetry. Its myth-rewriting amounts to an act of “disenchantment” carried out amid the ruins of “enchantment”. Yet this heroic undertaking is caught in a triple dialectical vortex: in order to disenchant, it must appeal to the primordial “energies” of myth (nature, life, imagination); in order for disenchantment to be effective, it strategically “uses enchantment” (by requisitioning textual canons and ritual authority); and in the end—because of the intensity of rewriting and the depth of political and spiritual investment—it becomes itself a new layer of enchantment (a cycle of re-enchantment). This exposes the core dilemma of China’s modernity project: to build a new order on the ruins of tradition is necessarily a tragic enterprise of rupture and continuity, of disenchantment and re-enchantment at once. Full article

Urinary tract infections (UTIs) are linked to adverse pregnancy outcomes, yet epidemiological data on gestational UTIs in Kenya are limited. This study assessed diagnostic and management practices in Kisumu County to inform diagnostic and antimicrobial stewardship. A hospital-based retrospective study was conducted from February 2020 to February 2021 among 416 records of pregnant women at Chulaimbo and Nyahera Sub-County Hospitals. Socio-demographic, laboratory, and clinical history data were collected using structured forms and analysed in STATA 16.0. Statistical methods included chi-square, multivariate logistic regression, and Spearman’s rank correlation (p ≤ 0.05). Dipstick-based presumptive proportion of UTIs was 57.9% (241/416). Only 1.4% (6/416) had microbiological confirmation despite infections being recorded. The mean maternal age was 23.92 years, parity two, mean antenatal visits two, and mean haemoglobin 10.73 ± 1.8 g/dL. The first antenatal care attendance occurred at varying gestational ages in 56% (233/416). Antibiotics prescribed were 60% from WHO ‘Access’ group and 40% from ‘Watch’ group. Gestational UTI’s in Kisumu County were frequently managed without confirmatory diagnosis, increasing antimicrobial resistance risk. Strengthening management requires better laboratory capacity, sustained financial investment, improved antibiotic access, and adherence to WHO AWaRe guidelines to protect maternal and neonatal health. Full article

In the area of the European Union (EU) energy policy, among the entities involved in the process of financing investments in renewable energy sources (RESs), the European Investment Bank (EIB) plays a particularly important role. Therefore, the aim of the research was to identify the relationship between the EIB’s financing of RES projects and the level of energy transition, measured by the share of RES in gross final energy consumption (RE). The goal was achieved using quantitative methods and a two-way fixed-effects panel model FE (country and year), based on data from EIB, Eurostat, World Bank, OECD, EDGAR, and Our World in Data for 2012–2023. As a result of the research, it was determined that the scale of EIB financing alone does not translate into short-term growth of the RE in the examined sample (EU countries). Indeed, the effectiveness of funding depends on the regulatory and institutional context; the grid’s ability to absorb new capacities (throughput, storage, demand flexibility); and from the time horizon (delayed materialization of effects). Increasing the efficiency of converting euros into RE percentage points requires better targeting (power + grid), simplification of procedures and good financial assembly with the right allocation of risks. Full article

The siting of renewable energy systems (RESs) in regions vulnerable to multiple climate hazards presents a critical challenge for sustainable infrastructure planning. Traditional approaches, primarily driven by static assessments of solar and wind potential, often neglect the compounded risks posed by floods, droughts, and windstorms, resulting in investments that are operationally vulnerable and economically unsustainable. This study proposes a novel spatio-temporal artificial intelligence (AI) framework for multi-objective RES deployment that integrates satellite-derived resource maps, high-resolution hazard data, and dynamic climate time series into a unified optimization pipeline. The methodology employs a gated recurrent unit (GRU)-based encoder to capture temporal hazard dynamics, combined with a multi-objective evolutionary algorithm (NSGA-II) to balance energy yield and resilience. A case study in South Africa’s Vhembe District demonstrates the framework’s effectiveness: the proposed model reduces the average hazard exposure by 31.6% while preserving 96.4% of the baseline energy output. Attention-based saliency analysis reveals that flood and windstorm hazards are the dominant drivers of site exclusion. Compared to conventional siting methods, the proposed framework achieves superior trade-offs between performance and risk, ensuring alignment with South Africa’s Just Energy Transition and Climate Adaptation strategies. The results confirm the value of spatio-temporal embeddings and hazard-aware multi-objective optimization in guiding resilient, data-driven energy infrastructure development. This model offers direct benefits to energy planners, climate adaptation agencies, and policymakers seeking to implement resilient, data-driven renewable energy strategies in hazard-prone regions. Full article

This article analyzes how entrepreneurship education contributes to the development of economic awareness, supports innovation, and promotes the principles of sustainable development. The competencies acquired through this approach can lead to increased interest in renewable energy sources (RES) as both an economically viable and environmentally friendly solution. The research was empirical in nature and included both quantitative and qualitative components. In-depth interviews and surveys were used to identify factors that facilitate or hinder the integration of RES-related issues with entrepreneurship education. The main research goal of the article was to understand how entrepreneurship education influences the perception of renewable energy sources as an attractive and profitable investment and how it can support the development of social entrepreneurship and innovation in the renewable energy sector. The authors conducted an extensive literature review to identify research gaps in existing analyses. A lack of comprehensive empirical studies was identified that would comprehensively demonstrate how entrepreneurship education shapes attitudes and behaviors that foster interest in and implementation of RES technologies. Based on the obtained results, recommendations for educational systems were formulated, indicating the need to incorporate elements of entrepreneurial education into the process of shaping economic and social awareness. This approach can significantly contribute to increasing interest in renewable energy sources and thus strengthening the concept of sustainable development. Full article

A techno-economic-environmental assessment tool was tailored to a fish sector case study. The fish sector, combined with two renewable components (wind and hydro), was analysed, and sensitivity analyses were carried out to integrate other renewables in a specifically developed optimization model (i.e., HY4RES-AHS). The model used an evolutionary method and resulted in the following conclusions: Scenario 2 excels financially, with the highest IRR (42%), shortest payback (4 years), and lowest investment (EUR 14,500), though it suffers from high energy losses (27.4%) due to a limited grid feed-in (120 kW). Scenario 4 is the most sustainable, with the highest SSR (97.8%) and SCR (63.4%) and lowest grid emissions (12.83 t CO₂ eq.), supported by 600 kW PV and strong biomass use, but it has the lowest NPV (EUR 2241) and longest payback (25 years). Scenario 3 offers the best overall balance, achieving the highest NPV (EUR 741,293), solid IRR (20%), low energy losses (2.8%), and strong SSR (94%). Scenarios 5 and 7 prohibit grid feed-in, resulting in the highest energy losses (46.7% and 48.4%) and poor sustainability. Scenario 6 is financially strong (NPV EUR 602,280) but lacks biomass and biogas, reducing system resilience and autonomy. In summary, Scenario 2 is cost-efficient, Scenario 4 leads to sustainability, and Scenario 3 exhibits balanced performance. Full article

Companies face multi-criteria problems every day, such as prioritizing projects, investments, and suppliers. In this respect, the literature offers countless methods, some of which provide partial and flawed solutions. Therefore, identifying and replacing a flawed method with a more efficient one is fundamental. However, this replacement is not straightforward because each method has a different evaluation structure. More specifically, in this study, the Mapping method uses scoring evaluations, whereas the Analytic Hierarchy Process (AHP) uses pairwise comparisons. In other words, scoring evaluations are incompatible with pairwise comparison evaluations. This incompatibility prevents one method from being replaced by another without loss of information. This is a significant gap because the re-evaluation process is expensive, time-consuming, and may even be impossible if the experts are no longer available. This study presents a novel approach to automatically substitute prioritization methods without loss of information. The approach was applied to a real-world case involving forty-four Brazilian companies. The specific case shows the prioritization of four projects evaluated by scores, combined with three new projects evaluated by pairwise comparisons. The application of the approach offers specific and general contributions. For example, substituting prioritization methods without loss of information, such as the Mapping method with AHP. Flexibility in choosing the evaluation method that offers greater psychological comfort to the experts. Obtaining transitive pairwise comparison matrices independently of the number of new projects evaluated. Full article

The increasing integration of renewable energy sources (RES) in Spain is leading to substantial amounts of surplus electricity, presenting a strategic opportunity for green hydrogen production as a key enabler of energy storage and decarbonisation. This study quantifies this untapped potential for 2024. Based on the difference between installed renewable capacity and actual generation, an economically viable surplus of 18,419 GWh was identified within an optimal 10-h operating window. The hydrogen production potential was modelled for three electrolysis technologies—Alkaline (AEL), Proton Exchange Membrane (PEM) and Anion Exchange Membrane (AEM)—using total energy consumption values of 57.40, 65.55 and 59.95 MWh/t H₂, respectively, including auxiliary systems. The estimated annual hydrogen production ranges from 280,999 t (PEM) to 320,897 t (AEL), with AEM yielding an intermediate value of 307,247 t. The analysis reveals a strong regional concentration, with more than 63% of the potential located in Castile and León, Andalusia, Castile-La Mancha and Extremadura. While this range represents an upper technical limit, it highlights the significant opportunity to valorise surplus renewable energy, contingent on targeted investment and a supportive regulatory framework. Full article

Background/Objectives: Breast cancer is the most prevalent neoplasm in women. Improved screening and systemic therapies have allowed more patients to choose breast-conserving surgery over mastectomy. However, preserving glandular tissue while achieving negative margins remains difficult. Traditional intraoperative margin assessment techniques like frozen section analysis, cavity shave margins, intraoperative ultrasonography, and specimen radiography aim to reduce positive margins and re-excision rates but face several limitations, including time consumption, interpretive challenges, and operator dependency. Our aim was to critically evaluate both conventional and emerging intraoperative margin assessment techniques in breast-conserving surgery, highlighting their clinical utility, limitations, and potential to reduce re-excision rates and improve patient outcomes. Methods: We assessed PubMed and Google Scholar databases using search terms such as specimen radiography, intraoperative ultrasonography, mass spectrometry, optical coherence tomography, artificial intelligence, and others. Studies were selected based on relevance, language, and completeness, and refined through author consensus. Conclusions: Conventional techniques have demonstrated value in reducing re-excisions and preserving cosmetic outcomes. Emerging tools like MarginProbe, fluorescence imaging, mass spectrometry (MasSpec Pen, iKnife), OCT, and AI-enhanced imaging show promise in offering real-time feedback and higher diagnostic accuracy. However, high costs, training needs, and data variability limit their widespread adoption. Investment in standardised protocols and multicentre trials is essential. Integration of imaging, spectroscopy, and AI may offer the most robust framework for improving surgical outcomes and quality of life for breast cancer patients. Full article