Search Results (812)

Climate change is one of the most pressing global environmental challenges, driven by the accumulation of greenhouse gases in the atmosphere. Industrial processes, particularly plastic injection molding, are major contributors due to their high energy demand, raw material use, and waste generation. This study quantifies the carbon footprint of plastic injection molding operations and identifies emission hotspots to support alignment with sustainability objectives. A greenhouse gas inventory was developed for the production processes of Petka Mold Industry in Adana, Türkiye, covering 1 January–31 December 2023. The assessment followed the ISO 14064-1:2019 standard and included emissions from direct fuel consumption, purchased electricity, refrigerant leaks, company vehicles, employee commuting, business travel, purchased goods, and waste transportation. Carbon dioxide, methane, and nitrous oxide were calculated in carbon dioxide equivalent units. This research represents the first comprehensive carbon footprint study in the plastic mold sector integrating all categories (Categories 1–6). In addition, uncertainty and materiality analyses were applied to ensure robustness and transparency, an approach rarely adopted in similar industrial contexts. While most previous studies are limited to Categories 1–3, this work expands the boundaries to all categories, offering a pioneering model for industrial applications. The total corporate GHG emissions for 2023 were calculated as 3922.75 metric tons of CO₂e. Among the categories, purchased raw materials and end-of-life product stages were the most significant contributors, whereas transport and auxiliary services had smaller shares. The results provide a reliable baseline for developing action plans and pursuing emission reduction targets. By combining full category coverage with rigorous assessment tools, this study contributes methodological novelty to corporate carbon accounting and establishes a foundation for future progress toward carbon neutrality. Full article

In alignment with the United Nations’ Sustainable Development Goals (SDGs) and the global pursuit of net-zero emissions, higher education institutions (HEIs) are increasingly expected to demonstrate robust climate accountability and effective decarbonization strategies. This three-year longitudinal study presents a comprehensive assessment of greenhouse gas (GHG) emissions at a higher education institution, employing the ISO 14064-1:2018 framework to strengthen inventory design, boundary delineation, and data governance protocols. Findings indicate that purchased electricity constitutes the largest share; however, fugitive refrigerant leakage and Scope 3 activities—particularly commuting and business travel—represent substantial and often underestimated components of the institution’s carbon footprint. Methodological refinements, including the incorporation of updated emission factors coefficients and enhanced data verification, have revealed the sensitivity of GHG inventories to both policy reforms and behavioral changes, as well as institutional policy reforms. The study also demonstrates that targeted refrigerant management and low-carbon mobility initiatives can generate measurable mitigation effects, even under conditions of expanding campus activity. Beyond the institution-specific results, this research proposes a replicable framework that integrates ISO 14064-1 compliance with data quality assurance and digital verification tools. This framework provides HEIs globally with a structured pathway to enhance reporting credibility, develop evidence-based mitigation strategies, and accelerate progress toward carbon neutrality. These insights underline the strategic role of universities in advancing sector-wide climate leadership and contributing to sustainable development transitions. Full article

Background/Rationale: Transnational caregiving may be influenced by religious beliefs and cultural traditions that frame elder care as both a moral and religious obligation. While migration alters caregiving dynamics, religious teachings and cultural expectations remain central in guiding transnational caregiving practices. This study examines how Christian Nigerians who have immigrated to Canada navigate caregiving responsibilities within a transnational context, integrating their religion, cultural values, and the practical realities of crossing borders. Methods: This study employed a predominantly qualitative narrative approach, drawing on in-depth interviews with Nigerian Christian immigrants (N = 10) providing transnational care. Data collection involved a pre-interview survey and semi-structured interviews, providing the opportunity for participants to share their lived experiences. Thematic analysis was used to identify recurring themes related to the role of religion and culture in caregiving, ensuring a comprehensive exploration of participants’ perspectives. Findings: Caregiving is shaped by religious duty and cultural obligation, reinforced by biblical teachings and cultural values. Participants view elder care as a moral responsibility, tied to spiritual rewards and familial duty. Despite migration demands, family-based care remains preferred over institutional care, with social stigma attached to neglecting elders. Conclusions: Religion and culture remain integral to transnational caregiving practices, sustaining caregiving responsibilities despite migration-related realities. While religious teachings provide moral guidance and emotional support, cultural expectations reinforce caregiving as a collective and intergenerational duty. Policies and resources are needed that support transnational caregivers, ensuring they can fulfill their caregiving roles while adapting to new sociocultural environments. Policymakers should prioritize the implementation of policies and programs to support transnational caregivers, including family reunification measures, caregiving-related travel provisions, culturally tailored eldercare services, diaspora–local collaborations, organized caregiver support groups, and financial mechanisms such as tax incentives for remittances dedicated to elder care. Full article

Boyacá, a department in the Colombian Andes, is recognized for its rich natural, cultural, and historical heritage, positioning it as an emerging tourism destination with strong potential for sustainable development. Its portfolio encompasses ecological, cultural, adventure, and rural modalities that contribute to local economies and community well-being. This study employs a mixed-methods design combining documentary analysis with fifty semi-structured interviews conducted across five provinces, engaging stakeholders such as hotel managers, travel agencies, restaurateurs, agroecological farmers, museum directors, café entrepreneurs, and ecotourism operators. The findings reveal that innovation, particularly through infrastructure, modernization, digital adoption, and service professionalization—is critical to strengthening competitiveness. However, persistent challenges include stakeholder fragmentation and the absence of a coordinated promotional strategy, both of which limit visibility and shared value generation. To address these challenges, the study proposes integrated innovation pathways, including thematic circuits (heritage, independence, and coffee routes), personalized tourism experiences, and the use of digital and experiential marketing tools. These approaches highlight the need for collaborative governance involving public, private, and community actors. The article contributes empirical evidence to the debate on tourism innovation and sustainability in emerging regions, underscoring Boyacá’s paradox: global recognition coexists with fragmented governance. It argues that innovation-driven strategies, aligned with sustainability principles, are essential for consolidating Boyacá as a competitive and regenerative tourism destination. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

This study presents an optimisation algorithm to solve a collaborative vehicle routing problem with time windows. The algorithm was developed and tested on a real-world case study to investigate the potential for a shared-fleet operation involving public organisations, specifically, the Isle of Wight Council (IWC) and the National Health Service (NHS). The aim was to evaluate whether collaborative use of public-sector vehicles could reduce total fleet size, operational costs, and vehicle-kilometres travelled, while maintaining existing service levels. The study develops a two-stage optimisation algorithm that incorporates real-world constraints such as vehicle capacity, time windows, and pre-assigned mandatory stops. The first stage maximises the number of assignable collaborative tasks across fleets, while the second stage minimises the total travel cost conditional on this maximum assignment. Using historical data and a novel optimisation algorithm, vehicle movements were modelled to evaluate benefits in terms of cost savings, reduced CO₂ emissions and vehicle usage. The case study results generated by the algorithm suggested that considerable improvements could be made by integrating patient diagnostic collection rounds into the existing IWC minibus routes: (a 10.6% reduction in CO₂ emissions (644 kg/month) and vehicle kilometres (2300 km/month), a 20.2% reduction in working hours (219 h/month), and a 17.8% saving in cost (GBP (£) 3596/month) leading to IWC gaining a potential additional revenue of GBP (£) 54,829 annually while reducing costs by 22.4% for the NHS. The findings highlighted the potential benefits of shared fleet collaborations between public sector organisations, offering a model for similar collaborations in other public sector contexts. Full article

Achieving climate protection goals in the transport sector requires the adoption of innovative mobility solutions and new vehicle concepts. In Germany, commercial transport accounts for one-quarter of the total car mileage. Many of these trips are comparatively short, often involve a single passenger, and require the transport of only small or lightweight goods—yet they are typically carried out by car. Substituting cars with small and light electric vehicles (LEVs) wherever feasible could make commercial transport more efficient and environmentally friendly. LEVs combine a favorable weight-to-payload ratio with the high efficiency of electric drivetrains. This study estimates the share of car trips in commercial transport in Germany that could theoretically be substituted by LEVs. The analysis is based on a comparison of trip characteristics from a national travel survey with the technical capabilities of selected LEV categories. Our results indicate that up to 73% of commercial car trips and 44% of mileage could theoretically be covered by LEVs, with particularly high potential for trips in commercial passenger transport. Although limitations in range and payload restrict the universal applicability of LEVs, the findings reveal substantial opportunities to make commercial transport cleaner and more sustainable. These insights highlight the relevance of LEVs for sustainable commercial transport and offer a data-driven basis for further discussion of their potential and for guiding targeted policy and planning. Full article

With accelerated urbanization and the pursuit of the “dual carbon” goals, shared bicycles have re-emerged as a green travel option. This study focuses on the central urban area of Nanjing and develops a carbon emissions reduction (CER) estimation model for shared bicycles. By analyzing spatio-temporal dimensions, it systematically assesses carbon reduction benefits and highlights the synergy with metro-connected travel. Key findings are as follows: (1) shared bicycles primarily support short-distance commuting, with a daily cycling pattern exhibiting a bi-modal distribution and a pronounced peak period demand; (2) cycling trips concentrate in densely populated and commercially vibrant zones, with a spatial pattern of central aggregation and multi-point diffusion; (3) each kilometer cycled by a shared bicycle reduces carbon emissions by about 96.19 g, with daily reductions of around 42.72 t and annual reductions up to 15,591.04 t; (4) the CER benefits of bicycle–metro integration are especially pronounced, contributing nearly 45.00% during peak periods; and (5) factors such as travel mode shifts, metro station layouts, and the development of electric vehicles continue to influence the CER benefits of shared bicycles. This work provides scientific evidence to inform urban green travel policies and transportation infrastructure optimization in cities. Full article

As a key element of the sharing economy, ride-sharing plays a vital role in promoting sustainable urban mobility by optimizing vehicle utilization rates, lowering carbon emissions, and alleviating traffic congestion. Despite its cost-efficiency and sustainability benefits, ride-sharing adoption remains limited in the post-pandemic period due to behavioral changes and safety concerns. Accordingly, using survey data from 425 commuters in Hefei, concerns about COVID-19 and satisfaction with ride-sharing services were integrated into the theory of planned behavior framework. Structural equation modeling was applied to examine the relationship between ride-sharing intention and actual usage behaviors. The results indicated that ride-sharing intention was significantly positively affected by subjective norms (β = 0.428 ***), service satisfaction (β = 0.315 ***), and perceived behavioral control (β = 0.162 *), but significantly negatively affected by concerns about COVID-19 (β = −0.183 **). Concerns about COVID-19 significantly negatively affected travelers’ actual ride-sharing behaviors (β = −0.2 **). Furthermore, ride-sharing intention was identified as a significant positive predictor of travelers’ behaviors: specifically, their likelihood of accepting a ride-sharing order (β = 0.395 ***). These findings offer transport authorities evidence-based strategies for designing targeted interventions during health crises, particularly through reinforcing social norms, improving service quality, and implementing transparent health protocols to ensure both user safety and sustainability. Full article

Background. Length of stay, operationalized here as nights per booking (NPB), is a first-order driver of yield, labor planning, and environmental pressure. The COVID-19 pandemic and the rise of long-stay remote workers (often labeled “slomads”, a slow-travel subset of digital nomads) plausibly altered stay-length distributions, yet national, booking-weighted evidence for the United States remains scarce. Purpose. This study quantifies COVID-19 pandemic-era and post-pandemic shifts in U.S. Airbnb stay lengths, and identifies whether higher averages reflect (i) more long stays or (ii) longer long stays. Methods. Using every U.S. Airbnb reservation created between 1 January 2019 and 31 December 2024 (collapsed to booking-count weights), the analysis combines: weighted descriptive statistics; parametric density fitting (Gamma, log-normal, Poisson–lognormal); weighted negative-binomial regression with month effects; a two-part (logit + NB) model for ≥28-night stays; and a monthly SARIMA$(0,1,1){(0,1,1)}_{12}$ with COVID-19 pandemic-phase indicators. Results. Mean NPB rose from 3.68 pre-COVID-19 to 4.36 during restrictions and then stabilized near 4.07 post-2021 (≈10% above 2019); the booking-weighted median shifted permanently from 2 to 3 nights. A two-parameter log-normal fits best by wide AIC/BIC margins, consistent with a heavy-tailed distribution. Negative-binomial estimates imply post-vaccine bookings are 6.5% shorter than restriction-era bookings, while pre-pandemic bookings are 16% shorter. In a two-part (threshold) model at 28 nights, the booking share of month-plus stays rose from 1.43% (pre) to 2.72% (restriction) and settled at 2.04% (post), whereas the conditional mean among long stays was in the mid-to-high 50 s (≈55–60 nights) and varied modestly across phases. Hence, a higher average NPB is driven primarily by a greater prevalence of month-plus bookings. A seasonal ARIMA model with pandemic-phase dummies improves fit over a dummy-free specification (likelihood-ratio = 8.39, df = 2, p = 0.015), indicating a structural level shift rather than higher-order dynamics. Contributions. The paper provides national-scale, booking-weighted evidence that U.S. short-term-rental stays became durably longer and more heavy-tailed after 2020, filling a gap in the tourism and revenue-management literature. Implications. Heavy-tailed pricing and inventory policies, and explicit regime indicators in forecasting, are recommended for practitioners; destination policy should reflect the larger month-plus segment. Full article

The travel accommodation sector within the sharing economy relies heavily on user-generated reviews. Drawing on data from insideairbnb.com for the Porto district from 2016 to 2020, this study examines the influence of online reviews from the standpoint of the sentiment expressed on accommodation prices, alongside other determinants such as locational attributes. The primary objective is to assess a broad set of factors affecting listing prices, with a particular focus on the degree and nature of sentiment expressed in online reviews. The dataset, comprising more than 250,000 reviews, was enriched with spatial and geographical variables, including key amenities, accessibility to public services, host characteristics, and locational indicators. A hedonic spatial regression model was employed to account for spatial dependencies. The findings reveal that sentiments expressed in user reviews exert a stronger influence on pricing than purely quantitative review metrics. Furthermore, host and listing characteristics, as well as geographical factors, play a substantial role in determining prices. The main contribution and novelty of this study lies in the joint analysis of sentiment and geographical attributes as drivers of accommodation pricing. Another contribution of this paper lies in the analysis of a broad geographical area encompassing both a historic city that is popular among European destinations and predominantly rural regions. Full article

Urban parking spaces are key city resources that directly affect how easily people get around and the quality of their daily travel. Accurately predicting future parking space availability can improve the efficiency of using parking spaces. For instance, it can enhance smart parking applications like shared parking and EV charging scheduling. However, because parking behavior is dynamic and constantly changing, it is challenging to predict parking space availability over the medium-to-long term. This paper proposes a Scale-Fusion Transformer model (SFFormer) to address dynamic changes in parking spaces availability caused by complex parking behaviors, as well as challenges in medium-to-long-term prediction modeling. The three key innovations are as follows: (1) a scale-fusion module integrating short-term and long-term parking trends, (2) an adaptive data compression mechanism for multi-scale prediction tasks, and (3) a Transformer-encoder-based time pattern capturing architecture, which is adaptable to diverse parking lots and long-term prediction scenarios. Experiments on real parking datasets demonstrate that the SFFormer model significantly outperforms state-of-the-art models such as iTransformer, PatchTST, DLinear, and Autoformer. Full article

Background/Objectives: Meningococcal disease (MD) remains a significant public health concern worldwide. In Serbia, mandatory immunization against MD with the meningococcal polysaccharide vaccine (MenAC) for high-risk groups and international travelers was introduced in 2006. Since 2017, the polysaccharide vaccine has been replaced with the quadrivalent meningococcal conjugate vaccine (MenACWY). The aim of this study was to analyze long-term trends in incidence, age-specific patterns, seasonality, and lethality of invasive meningococcal disease (IMD) in the Autonomous Province of Vojvodina (AP Vojvodina), Serbia, over a 28-year period. Methods: A descriptive study analyzed all reported cases of IMD in AP Vojvodina, from 1997 to 2024. Data were obtained from the regional communicable disease surveillance system, based on mandatory hospital reporting and case classification according to national and WHO guidelines. Temporal, demographic, and clinical characteristics, along with disease outcomes, were analyzed. Results: From 1997 to 2024, 175 IMD cases were reported in AP Vojvodina. The annual incidence peaked in 1997 (1.24/100,000), with smaller surges in 2003 and 2005. Since 2006, coinciding with the introduction of immunization against MD, a sustained decline has been observed, with incidence rarely exceeding 0.30/100,000. A slight resurgence occurred in 2023–2024, with 13 cases reported. From 1997 to 2024, IMD in AP Vojvodina exhibited a clear seasonal pattern, with most cases occurring in winter and early spring, peaking in January (17%), March (12%), and February (11%), and the fewest cases occuring in the summer months. Throughout the study period, the highest IMD incidence rates were consistently observed among infants <1 year of age and children aged 1–4 years, with peaks of up to 22.9/100,000 and 16.0/100,000, respectively. Incidence was much lower in older age groups, especially adults. After a 2006 peak, rates declined across all ages, with a slight resurgence in 2023–2024 among children and adolescents. Children aged 1–4 years made up the largest share of IMD cases, peaking in January–March (45.1%). Half of the infant cases were recorded in October–November, while cases in older children, adolescents, and adults were fewer and showed varied monthly patterns, with small peaks in winter and early spring. During the 28-year study period, the highest IMD mortality rate was observed among infants <1 year of age (0.59 per 100,000 population), followed by children aged 1–4 years (0.32 per 100,000). Mortality rates declined progressively with increasing age, with the lowest rate recorded among individuals aged ≥40 years (0.01 per 100,000). Of the 175 IMD cases reported in AP Vojvodina (1997–2024), 21 were fatal (case fatality rate [CFR] = 12.0%). The CFR of IMD varied across age groups. The highest CFR was observed among individuals aged ≥40 years (21.4%), followed by the 5–9 years (17.4%) and <1 year (16.7%) age groups. None of the patients had been vaccinated against MD. Fatal outcomes were more common in children aged 1–4 years and among rural residents, though differences were not statistically significant (p > 0.05). Most deaths (57.1%) occurred in the first quarter of the year. A strong association was found between clinical form and outcome, with meningococcal sepsis being significantly more frequently associated with fatality than meningitis (p = 0.0002). Deaths were sporadic over time, with most occurring within 1–2 days of notification. All confirmed fatal cases were due to serogroup B. Conclusions: MD remains a rare yet serious public health threat in AP Vojvodina. Mortality rates indicate that the public health impact of this disease is greatest among the youngest age groups; however, the risk of death, i.e., disease severity, does not appear to be age dependent. The recent rise in cases, high fatality among sepsis patients, and absence of prior vaccination among all IMD cases highlight the need for enhanced surveillance, physician education, and consideration of introducing both MenACWY and MenB vaccines for high-risk groups. Full article

Combining public transportation (PT) with Bike-Sharing Systems (BSSs) offers a pathway toward the sustainable development of urban mobility. These systems can reduce fuel consumption, air pollution, and street congestion, especially during peak hours. Moreover, PT and BSS are frequently used by individuals without access to private vehicles, including low-income groups and students. Whereas increasing PT network infrastructure is constrained by issues such as high capital costs and limited street space (which inhibits mass transit options like BRT or trams), BSS can be used as an adaptable and affordable solution to fill these gaps. In particular, BSS can facilitate the “first-mile–last-mile” legs of PT journeys. However, many transit agencies still rely on traditional joint service planning and overlook BSS as a critical mode in integrated travel chains. This paper proposes that PT and BSS be considered as a unified network and introduces a framework to assess whether access to this integrated system is equitably distributed across urban areas. The framework estimates demand for travel using public mobility options and supply at the level of Traffic Analysis Zones (TAZs), treating PT and BSS as complementary modes. Spatial accessibility analysis is employed to examine connectivity using factors that affect access to both PT and BSS. The proposed approach is tested by taking Tehran as the focus of the case analysis. The results identify the most accessible areas and highlight those that require improved PT-BSS integration. These findings provide policy-relevant suggestions to promote equity and efficiency in urban transport planning. The outcomes reveal that central TAZs in Tehran receive the highest level of PT-BSS integration, while the western and southern TAZs are in urgent need of adjustment to ensure better distribution of integrated public transportation and bike-sharing services. Full article