Environmental Infrastructure Research Articles

As Botswana experiences a demographic transition marked by an expanding population of older adults, understanding the determinants of disability among older adults becomes critical for shaping inclusive health and social policies. Disability in later life often stems not only from biological ageing but also from intersecting personal, household, and community-level conditions. Despite increasing attention to ageing in sub-Saharan Africa, few studies have comprehensively assessed the multilevel factors influencing functional limitations in older populations. Drawing on nationally representative microdata from the 2022 Botswana Population and Housing Census, this study employed Generalized Linear Models (specifically Poisson regression) to examine the severity of disability among individuals aged 65 years and above. Disability was measured using a composite count variable derived from three functional domains: mobility, self-care, and cognition. The final analytical sample comprised 47,309 older adults. The model integrated a wide range of covariates across individual, household, and community levels. Model selection was based on goodness-of-fit statistics, including AIC, BIC, and deviance diagnostics. The multilevel Poisson model revealed that age, gender, education, marital status, and employment status were significant individual-level predictors of disability. Older adults aged 65-69 and 70-74 were significantly less likely to experience multiple disabilities compared to those aged 80+, with IRRs of 0.434 and 0.576 respectively. Males had lower disability counts than females (IRR = 0.648), and those with only primary or less education had higher disability rates than those with tertiary education (IRR = 1.207). At the household level, individuals in smaller households (number of rooms) reported significantly higher disability levels. At the community level, urban residence, access to electricity, internet, and transportation services were all associated with reduced disability. Although interaction terms were not explicitly specified, the integrated model structure supported inferences about cross-level interactions between environmental infrastructure and individual vulnerabilities. This study provides a comprehensive analysis of the multifactorial determinants of old-age disability in Botswana. The findings underscore the need for integrated, multisectoral strategies that go beyond healthcare access to include educational equity, age-friendly infrastructure, digital inclusion, and gender-sensitive social protection. Policies must address not only individual risk but also household and community conditions that jointly shape disability outcomes. These insights provide a critical roadmap for building age-inclusive societies as Botswana continues to age.

Changes in the complexation behavior of cadmium with dissolved organic matter in the Huaihe River basin: Environmental drivers, regional differences and socio-economic impacts.

Localized heavy rainfall due to climate change has led to the frequent inundation of sewage treatment facilities, consequently causing significant secondary impacts such as water pollution and industrial paralysis. To address this, this study quantitatively reflected the specific characteristics and importance of environmental facilities to develop a more practical flood vulnerability assessment method. The proposed flood vulnerability index consists of a Hazard Index and a Mitigation Index. The former quantifies the risk level based on 1D/2D coupled inundation simulation results, and considers the importance of facilities and the location of key equipment. The latter evaluates structural and non-structural response capabilities through a checklist. A pilot application to the Gwangju Sewage Treatment Plant confirmed that the developed method can derive a quantitative vulnerability score reflecting both physical inundation threats and the facility’s mitigation capacity. This assessment technique is expected to serve as a practical tool for quantitatively diagnosing facility-specific vulnerabilities, thereby supporting preemptive disaster prevention planning and investment prioritization for critical environmental infrastructure.

This study aims to empirically analyze the partial and simultaneous influence of environmental atmosphere, host friendliness, and location accessibility on tourists’ revisit intention at Homestay Gardu Pandang Selo, Boyolali Regency. Tourism growth in this area continues to increase, yet the low rate of repeat visits indicates a gap between visitor expectations and the quality of experience offered. Employing a quantitative causal-associative method, data were obtained from 96 respondents who had stayed at least once, using purposive sampling. Data analysis was performed with multiple linear regression using IBM SPSS Statistics 25. Classical assumption tests confirmed that the regression model met the requirements of normality, multicollinearity, and heteroscedasticity. The results revealed that environmental atmosphere and location accessibility had a positive and significant effect on revisit intention, while host friendliness showed an insignificant effect. Accessibility emerged as the most dominant factor influencing revisit intention, indicating that physical access and travel convenience play a central role in shaping tourist loyalty. The findings emphasize the importance of environmental management and infrastructure development as strategic priorities for homestay managers. Although host friendliness did not show a significant impact, maintaining a warm and consistent hospitality standard remains essential to sustain visitor satisfaction. Overall, this study contributes theoretically by reinforcing behavioral intention theory in tourism and practically by offering insights for community-based tourism management to strengthen destination competitiveness through repeat visitation.

Additive manufacturing (AM) is revolutionizing space exploration and manufacturing by addressing unique challenges in weight reduction, material optimization, and on‐demand production. This review examines the current advances and future directions of AM for on‐Earth and in‐space applications. The study highlights the role of AM in producing lightweight, high‐performance components for satellites, rockets, and space habitats, leveraging technologies such as powder bed fusion, directed energy deposition, binder jetting, sheet lamination, and material extrusion. Key applications include the development of propulsion systems, structural components, and thermal management devices optimized for the harsh conditions of space. In‐space manufacturing is explored as a pivotal innovation, enabling the on‐demand production of tools, components, and infrastructure in microgravity environments, reducing launch costs and enhancing mission scalability. Challenges such as material defects, anisotropic properties, and residual stresses are discussed alongside strategies for mitigation, including real‐time monitoring and advanced post‐processing techniques. The review also emphasizes the ethical, regulatory, and sustainability considerations of AM for space exploration, addressing issues of resource utilization and environmental impact. Furthermore, it uniquely integrates advancements in artificial intelligence‐driven process control, in situ resource utilization, and sustainability aspects of AM for space missions.

With the growth of digital economies, data quality forms a key factor in enabling use and delivering value. Existing research defines quality through technical benchmarks or provider-led frameworks. Our study shifts the focus to actual users. Thirty-seven distinct data quality dimensions identified through a comprehensive review of the literature provide limited applicability for practitioners seeking actionable guidance. To address the gap, in-depth interviews of senior professionals from 25 organizations were conducted, representing sectors like computer science and technology, finance, environmental, social, and governance, and urban infrastructure. Data are analysed using content analysis methodology, with 2 level coding, supported by NVivo R1 software. Several newer perspectives emerged. Firstly, data quality is not simply about accuracy or completeness, rather it depends on suitability for real-world tasks. Secondly, trust grows with data transparency. Knowing where the data comes from and the nature of data processing matters as much as the data per se. Thirdly, users are open to paying for data, provided the data is clean, reliable, and ready to use. These and other results suggest data users focus on a narrower, more practical set of priorities, considered essential in actual workflows. Rethinking quality from a consumer’s perspective offers a practical path to building credible and accessible data ecosystems. This study is particularly useful for data platform designers, policymakers, and organisations aiming to strengthen data quality and trust in data exchange ecosystems.

In the context of aggravation of the climate agenda and continuing instability in the global energy market, the diversification of the energy structure in favour of innovative and low-carbon technologies and resources, among which bioenergy occupies a special place, is becoming an urgent area of the countries' strategies. According to the Global Bioenergy Statistics Report 2024, biofuels, including bioethanol, biodiesel, biochar and biogas, account for about 9% of global energy production, which is characterised by low greenhouse gas emissions and high energy efficiency. Taking into account the current trends in the development of bioenergy technologies and its potential to address the climate agenda, this study analyses the possibilities of implementing biocarbon projects and provides an economic assessment of alternative options for biomass production with a focus on the production of third-generation biofuels based on seaweed. The object of special attention is the systematisation of technologies and infrastructural conditions for scaling up third-generation bioenergy, including for diversification of the global energy balance and development of a carbon-neutral economy. As one of the key results of the study, recommendations on the formation of institutional and infrastructural environment for achieving commercial feasibility and effective scaling of biocarbon projects have been developed.

Climate change poses significant risks to urban infrastructure and ecosystems, particularly in rapidly urbanizing African cities. Lagos, Nigeria’s center of excellence exemplifies these vulnerabilities due to its fast-paced growth, weak land-use enforcement, and recurrent climate-induced hazards such as floods and erosion. Hence, this study examines the interplay between climate-resilient infrastructure and environmental sustainability. The target populations for the study were the Real Estate Developers in Lagos as well as Officials of the Ministries of Physical planning, Environment and Waterfront Infrastructure development. Drawing on empirical evidence, policy analysis, and secondary literature, the research identifies fifteen major challenges affecting the integration of resilience and sustainability in Lagos, including institutional fragmentation, financial limitations, inadequate enforcement of planning regulations, and loss of natural buffers. The results of the weighted mean score analysis revealed that flooding, weak governance, and poorly regulated urban expansion rank among the most critical barriers. The findings corroborate existing literature that highlights how unplanned urban growth exacerbates ecological fragility in African cities, while also pointing to Lagos unique status as a center of excellence but yet struggling with rapid demographic and infrastructural pressures. The study concludes that without deliberate policy enforcement, sustainable financing mechanisms, and adoption of nature-based solutions, Lagos faces escalating adaptation costs and recurrent infrastructure failures. Recommendations include strengthening institutional capacity, mainstreaming climate risk assessments into infrastructure planning, promoting public-private partnerships, restoring ecological buffers, and enhancing community participation. By addressing these gaps, Lagos State can enhance resilience, safeguard livelihoods, and serve as a model for climate-sensitive urban development in sub-Saharan Africa.

Tourism performance often depends on the joint provision of built (“hard”) and environmental (“green”) infrastructure, yet their combined effects are not well established. Using official data for Saudi Arabia’s 13 regions (2023–2024), this study constructs composite hard and green indices, estimates ordinary least squares models with heteroskedasticity-consistent inference, and probes spatial heterogeneity using geographically weighted regression (exploratory) alongside k-means/hierarchical clustering. Hard infrastructure is the strongest and most consistent correlate of overnight visitors and spending, whereas green infrastructure exhibits non-positive marginal effects over the observed range of hard capacity; a negative, statistically significant Hard × Green interaction indicates diminishing returns to greening as built capacity increases. Clustering differentiates metropolitan hubs from nature-oriented regions, underscoring place-specific policy needs. Practically, results support sequencing prioritizing foundational access and basic accommodation in under-served regions, quality upgrades and public-realm enhancement in mature centers, and targeted green interventions where marginal gains are greatest. Key limitations (cross-sectional design; coarse green metrics) motivate richer environmental indicators and longitudinal data to clarify dynamics and thresholds over time.

In an era of rapid urbanization and environmental degradation, sustainable urban development is imperative to ensure a high quality of life while reducing carbon footprints. This paper presents a comprehensive framework that leverages artificial intelligence to drive data-informed decisions in smart cities, ultimately aiming to create sustainable and carbon-free urban environments. Our approach integrates diverse datasets representative of key urban challenges including energy efficiency, air quality, infrastructure durability, and both residential and industrial energy consumption into a unified predictive modeling platform. Utilizing PyCaret’s low-code machine learning (ML) library, we automated the training, selection, and evaluation of numerous regression models, with a particular focus on ensemble-based methods such as Extra Trees, CatBoost, and LightGBM. Rigorous benchmarking across six publicly available datasets demonstrated near-perfect predictive performance, with R2 values often exceeding 0.99 and minimal error metrics observed in multiple domains. These results highlight the models’ robustness and suitability for high-stakes applications in urban sustainability, ranging from energy optimization to environmental monitoring. The performance benchmarking presented in this paper serves as a practical validation of the proposed AI-driven framework, covering essential smart city domains such as environmental monitoring, infrastructure resilience, and energy efficiency. The study not only underscores the potential of AI in transforming urban infrastructure but also provides a scalable and interpretable framework for real-world deployment. By converting vast, heterogeneous urban data into actionable insights, our work paves the way for smarter, carbon-neutral cities, where predictive analytics serve as the cornerstone of sustainable urban policy and operational excellence.

The management of open fractures in Colombia is influenced by special conditions shaped by the unique medical, social, and infrastructural environment that exists within the many varied regions of the country. These factors make the treatment of open fractures and associated injuries particularly complex.

Autonomous Delivery Vehicles (ADVs) are increasingly used for transporting goods in 5G network-enabled smart factories, with the compute-intensive localization module presenting a significant opportunity for optimization. We propose ACCESS-AV , an energy-efficient Vehicle-to-Infrastructure (V2I) localization framework that leverages existing 5G infrastructure in smart factory environments. By opportunistically accessing the periodically broadcast 5G Synchronization Signal Blocks (SSBs) for localization, ACCESS-AV obviates the need for dedicated Roadside Units (RSUs) or additional onboard sensors to achieve energy efficiency as well as cost reduction. We implement an Angle-of-Arrival (AoA)-based estimation method using the Multiple Signal Classification (MUSIC) algorithm, optimized for resource-constrained ADV platforms through an adaptive communication-computation strategy that dynamically balances energy consumption with localization accuracy based on environmental conditions such as Signal-to-Noise Ratio (SNR) and vehicle velocity. Experimental results demonstrate that ACCESS-AV achieves an average energy reduction of 43.09% compared to non-adaptive systems employing AoA algorithms such as vanilla MUSIC, ESPRIT, and Root-MUSIC. It maintains sub-30 cm localization accuracy while also delivering substantial reductions in infrastructure and operational costs, establishing its viability for sustainable smart factory environments.

Space tourism is becoming a new milestone in space exploration on the horizon of technological development for humanity as a whole, opening commercial prospects and scientific opportunities. However, its development must align with the concept of sustainable development, minimizing environmental and social risks. Baikonur has been one of the main hubs of space tourism in the world since the launch of the first space tourist into orbit. Baikonur plays an important role in launching orbital and suborbital flights, including prospective tourist ones, both in terms of infrastructure and geographical location. This article assesses the compatibility of space tourism development centered on Baikonur with the goals of sustainable development and Kazakhstan’s national “Green Economy” strategy. The article discusses current environmental conditions, regional infrastructure, investment structure, and ongoing sustainability efforts in the Kyzylorda region, as well as their integration into the broader framework of the UN 2030 Agenda and national strategic documents such as “Kazakhstan–2050.”

This article presents a comprehensive architectural framework for implementing secure multi-tenant FinTech platforms that leverage artificial intelligence for real-time fraud detection while maintaining stringent regulatory compliance and data security standards. The proposed architecture addresses the complex challenges of deploying AI-driven financial services across shared infrastructure environments through innovative approaches, including containerized database sharding, attribute-based access control systems, and secure enclave computation technologies. The framework integrates Apache Kafka and Apache Flink streaming platforms to enable high-velocity transaction processing with end-to-end encryption protocols, ensuring data isolation between tenants while supporting cross-tenant analytical capabilities essential for effective machine learning model training and inference. Advanced AI model implementations incorporate ensemble learning techniques for credit risk assessment and deep learning architectures for fraud detection, utilizing dynamic threshold management systems and automated response frameworks to optimize performance across diverse financial scenarios. The architecture's compliance framework addresses Payment Card Industry Data Security Standard, General Data Protection Regulation, and Sarbanes-Oxley Act requirements through comprehensive audit trails, immutable compliance records, and automated policy enforcement mechanisms that adapt dynamically to changing regulatory landscapes across multiple jurisdictions.

The construction of large-scale infrastructure, such as power facilities, requires extensive use of reinforced concrete. The durability degradation of reinforced concrete structures in chloride environments involves multi-physics coupling effects, chloride ion diffusion, rebar corrosion, and concrete damage. Existing models neglect the coupling mechanisms among these processes and the influence of mesoscale structural characteristics. Therefore, this study proposes a diffusive–mechanical coupled phase field by integrating the phase field, chloride ion diffusion, and mechanical equivalence for rebar corrosion, establishing a multi-physics coupling analysis framework at the mesoscale. The model incorporates heterogeneous meso-structure of concrete and constructs a dynamic coupling function between the phase field damage variable and chloride diffusion coefficient, enabling full-process simulation of corrosion-induced cracking under chloride erosion. Numerical results demonstrate that mesoscale heterogeneity significantly affects crack propagation paths, with increased aggregate content delaying the initiation of rebar corrosion. Moreover, the case with corner-positioned rebar exhibits earlier cracking compared to the case with centrally located rebar. Furthermore, larger clear spacing delays delamination failure. Comparisons with the damage mechanics model and experimental data confirm that the proposed model more accurately captures tortuous crack propagation behavior, especially suitable for evaluating the durability of reinforced concrete components in facilities such as transmission tower foundations, substation structures, and marine power facilities. This research provides a highly accurate numerical tool for predicting the service life of reinforced concrete power infrastructure in chloride environments.

BackgroundThe health of school-aged individuals significantly impacts a country’s success and development. Numerous policies and programs have been implemented globally to enhance school health, influenced by various factors. This study aims to identify the factors affecting school health policies and explore ways to improve them.MethodsThis qualitative study was conducted in 2022 using semi-structured interviews. Participants included managers, policymakers, and health workers in schools, selected through purposive sampling. The data collection tool was a semi-structured interview guide developed by the research team. Interviews were conducted both in person and via telephone. The framework analysis method was employed to interpret the interview data.ResultsThe study examined various facets of the issue, including the identification and analysis of beneficiaries, background factors, the policymaking process, and the content of school health policies. Proposed solutions included fostering a culture of improving school health, integrating policies and programs, securing financial resources, engaging relevant bodies and organizations, taking a macro-level view of school health, and ensuring adequate infrastructure and specialized human resources.ConclusionDeveloping need-based policies for students, promoting inter-organizational and inter-departmental cooperation among school health stakeholders, securing financial resources and environmental infrastructure, leveraging technology, and prioritizing school health are crucial. This underscores the importance of utilizing the latest scientific evidence to inform policy content and implementation.

The integration of nanoengineered materials into concrete systems has emerged as a promising strategy for enhancing structural performance and sustainability. This study presents a hybrid experimental-analytical investigation into the use of multilayer graphene as a smart admixture in high-performance concrete. The research combines mechanical testing, microstructural characterization, and a multi-objective optimization model to determine the optimal graphene dosage that maximizes strength gains while minimizing carbon emissions. Concrete specimens incorporating multilayer graphene (ranging from 0.01% to 0.10% by weight of cement) were tested over 7 to 90 days for compressive, tensile, and flexural strengths. Simultaneously, X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray analyses revealed crystallinity enhancement, pore densification, and favorable elemental redistribution due to graphene inclusion. A normalized composite objective function was formulated to balance three maximization targets—compressive, tensile, and flexural strength—and one minimization goal—carbon emission. The highest objective score (Z = 1.047) was achieved at 0.10% graphene dosage, indicating the optimal balance of strength performance and environmental efficiency. This dual-framework study not only confirms graphene’s reinforcing effects experimentally but also validates the 0.10% dosage through mathematical scoring. The outcomes position of multilayer graphene as a powerful additive for high-strength, low-carbon concrete, especially suited for infrastructure in hot and arid environments. The proposed optimization approach provides a scalable pathway for performance-based graphene dosing in future innovative concrete formulations.

This commentary examines Cuba as a case study for understanding digital authoritarianism not as a uniform, monolithic model, but as a set of contextually specific governance arrangements shaped by scarcity, geopolitical isolation, and hybrid state strategies. Drawing on fieldwork conducted between 2016 and 2024, it situates the Cuban experience within comparative debates on authoritarian control of the internet. The analysis shows how Cuba's model blends securitisation with developmental policies, operating within a constrained infrastructural environment and under conditions of external technological and economic exclusion. This peripheral model reveals the need to reconceptualise digital authoritarianism in ways that account for diversity, adaptation, and resource limitations.

This study examines the role of environmental infrastructure in enhancing place affection and residents’ quality of life (QOL) in Lenger, a mountainous tourism destination in southern Kazakhstan. Rooted in Social Exchange Theory, the research investigates how eco-friendly infrastructure, such as green spaces, parks, and sustainable waste management, shapes emotional connections to place and promotes physical and mental well-being, aligning with Sustainable Development Goal 3 (Good Health and Well-being). A structured survey involving 417 residents was conducted, and Structural Equation Modeling (SEM) was employed to analyze the direct and indirect relationships among environmental infrastructure, place affection, and QOL. The findings reveal that environmental infrastructure positively influences both QOL and place affection, with place affection acting as a partial mediator in this relationship. This indicates that infrastructure investments not only improve physical conditions but also foster emotional attachment, which in turn enhances overall well-being. The study emphasizes that emotionally resonant infrastructure is essential for sustainable tourism planning and public health promotion. By focusi ng on the case of Lenger, the research contributes to a growing body of literature highlighting the psychosocial dimensions of tourism development. It offers practical recommendations for policymakers and urban planners to incorporate community - oriented and ecologically sensitive infrastructure in tourism regions. The findings suggest that infr astructure designed to engage residents emotionally and support their environmental values can lead to stronger community bonds, healthier lifestyles, and more sustainable tourism outcomes. By highlighting the psychosocial pathways linking infrastructure development and community well-being, this research offers valuable insights for policymakers, urban planners, and tourism developers aiming to balance ecological sustainability with socio-emotional needs in emerging tourist destinations like Lenger.

Tourism and the environment are closely interlinked in a reciprocal relationship. As environmental concerns become increasingly central to global tourism agendas, assessing the ecological performance of leading destinations is essential. This study aims to evaluate the environmental performance of the world's top 15 tourist destinations and determine their sustainability-based competitiveness. The Environmental Performance Index (EPI), which includes 11 categories, was used as the main data source. To ensure objectivity, the CRITIC method was employed to calculate the weights of the criteria, and the TOPSIS was used to rank the countries based on their environmental performance. The findings reveal that air pollution, fisheries, waste management, and water and sanitation infrastructure are the most influential criteria in distinguishing environmental performance across countries. According to the TOPSIS results, Japan, the United Kingdom, and France ranked highest in environmental performance, while Mexico, Türkiye, and Malaysia ranked lowest. The results highlight significant differences in environmental policy effectiveness and infrastructure quality among the countries.