Search Results (1,350)

Agricultural development is vital for East Africa’s economic growth, yet the region faces significant disparities and systemic barriers. A critical problem exists due to the lack of an integrated quantitative framework to systematically comparing agricultural capacities and facilitate optimal resource allocation, as existing studies often overlook combined internal and external factors. This study proposes a comprehensive multi-criteria decision-making (MCDM) model to assess, categorize, and strategically profile the agricultural development capacity of 18 East African countries. The method employed is an integrated Entropy-MAIRCA model, which objectively weighs six criteria (the food production index, arable land, production fluctuation, food export/import ratios, and the political stability index) and ranks countries by their distance from an ideal development state. The experiment applied this framework to 18 East African nations using official data. The results revealed significant differences, forming four distinct strategic groups: frontier, emerging, trade-dependent, and high risk. The food export index (C4) and production volatility (C3) were identified as the most influential criteria. This model’s contribution is providing a science-based, transparent decision support tool for designing sustainable agricultural policies, aiding investment planning, and promoting regional cooperation, while emphasizing the crucial role of institutional factors. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

The complexity of assessment is a significant problem in designing renewable energy source (RES) products, especially when one wants to take into account their various aspects, e.g., technical, environmental, or social. Hence, the aim of the research is to develop a model supporting the decision-making process of RES product development based on meeting the criteria of quality, environmental impact, and social responsibility (QES). The model was developed in four main stages, implementing multi-criteria decision support methods such as DEMATEL (decision-making trial and evaluation laboratory) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), as well as criteria for social responsibility and environmental impact from the ISO 26000 standard. The model was tested and illustrated using the example of photovoltaic panels (PVs): (i) five prototypes were developed, (ii) 30 PV criteria were identified from the qualitative, environmental, and social groups, (iii) the criteria were reduced to 13 key (strongly intercorrelated) criteria according to DEMATEL, (iv) the PV prototypes were assessed taking into account the importance and fulfilment of their key criteria according to TOPSIS, and (v) a PV ranking was created, where the fifth prototype turned out to be the most advantageous (QES = 0.79). The main advantage of the model is its simple form and transparency of application through a systematic analysis and evaluation of many different criteria, after which a ranking of design solutions is obtained. QES ensures precise decision-making in terms of sustainability of new or already available products on the market, also those belonging to RES. Therefore, QES will find application in various companies, especially those looking for low-cost decision-making support techniques at early stages of product development (design and conceptualization). Full article

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

Supplier selection is a crucial factor for ensuring compliance with the circular economy’s principles. Existing approaches often overlook the integration of circularity and risk assessment in supplier evaluation, limiting their effectiveness in achieving sustainability goals. This paper addresses this gap by applying suitable criteria and proposing a structured decision-making model for circular supplier selection. The model innovatively integrates Multi-Criteria Decision Analysis (MCDA) techniques with risk evaluation, providing a comprehensive framework for assessing suppliers in circular supply chains. By advancing the theoretical understanding of circular supplier selection, this research contributes to both academia and practice, reinforcing the alignment between supply chain decision-making and the Sustainable Development Goal (SDG), particularly Target 12.5. Full article

Football performance results from the dynamic interaction between physical, tactical, technical, and psychological dimensions—each of which also influences player well-being, recovery, and readiness. However, integrated monitoring approaches remain scarce, particularly in youth and sub-elite contexts. This systematic review screened 341 records from PubMed, Scopus, and Web of Science, with 46 studies meeting the inclusion criteria (n = 1763 players; age range: 13.2–28.7 years). Physical external load was reported in 44 studies using GPS-derived metrics such as total distance and high-speed running, while internal load was examined in 36 studies through session-RPE (rate of perceived exertion × duration), heart rate zones, training impulse (TRIMP), and Player Load (PL). A total of 22 studies included well-being indicators capturing fatigue, sleep quality, stress levels, and muscle soreness, through tools such as the Hooper Index (HI), the Total Quality Recovery (TQR) scale, and various Likert-type or composite wellness scores. Tactical behaviours (n = 15) were derived from positional tracking systems, while technical performance (n = 7) was assessed using metrics like pass accuracy and expected goals, typically obtained from Wyscout^® or TRACAB^® (a multi-camera optical tracking system). Only five studies employed multivariate models to examine interactions between performance domains or to predict well-being outcomes. Most remained observational, relying on descriptive analyses and examining each domain in isolation. These findings reveal a fragmented approach to player monitoring and a lack of conceptual integration between physical, psychological, tactical, and technical indicators. Future research should prioritise multidimensional, standardised monitoring frameworks that combine contextual, psychophysiological, and performance data to improve applied decision-making and support player health, particularly in sub-elite and youth populations. Full article

Renovating existing buildings is a key strategy for achieving the EU’s climate targets, as over 75% of the current building stock is energy inefficient. This study evaluates the environmental impacts of three façade renovation scenarios for an office building at the Technical University in Zvolen (Slovakia) using a life cycle assessment (LCA) approach. The aim is to quantify and compare these impacts based on material selection and its influence on sustainable construction. The analysis focuses on key environmental indicators, including global warming potential (GWP), abiotic depletion (ADE, ADF), ozone depletion (ODP), toxicity, acidification (AP), eutrophication potential (EP), and primary energy use (PERT, PENRT). The scenarios vary in the use of insulation materials (glass wool, wood fibre, mineral wool), façade finishes (cladding vs. render), and window types (aluminium vs. wood–aluminium). Uncertainty analysis identified GWP, AP, and ODP as robust decision-making categories, while toxicity-related results showed lower reliability. To support integrated and transparent comparison, a composite environmental index (CEI) was developed, aggregating characterisation, normalisation, and mass-based results into a single score. Scenario C–2, featuring an ETICS system with mineral wool insulation and wood–aluminium windows, achieved the lowest environmental impact across all categories. In contrast, scenarios with traditional cladding and aluminium windows showed significantly higher impacts, particularly in fossil fuel use and ecotoxicity. The findings underscore the decisive role of material selection in sustainable renovation and the need for a multi-criteria, context-sensitive approach aligned with architectural, functional, and regional priorities. Full article

This study investigates the application of the Analytic Hierarchy Process (AHP) as a decision-support mechanism for managing complex sustainability issues in industrial settings, specifically within the framework of circular economy principles. Focusing on a case from the brewery sector, developed under the EU ECOFACT initiative, this research evaluates ten distinct configurations for the must cooling process. These alternatives are assessed using environmental, economic, and technical criteria, drawing on data from life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The findings indicate that selecting an optimal scenario involves balancing trade-offs among electricity and water consumption, operational efficiency, and overall environmental impacts. Notably, Scenario 3 emerges as the most balanced option, consistently demonstrating superior performance across the primary evaluation criteria. The use of AHP in this context proves valuable by introducing structure and transparency to a multifaceted decision-making process where quantitative metrics and sustainability objectives intersect. By integrating empirical industrial data with an established multi-criteria decision approach, this study highlights both the practical utility and existing limitations of conventional AHP, particularly its diminished ability to discriminate between alternatives when their scores are closely aligned. These insights suggest that hybrid or advanced AHP methodologies may be necessary to facilitate more nuanced decision-making for circular economy transitions in industrial environments. Full article

As industrial companies transition from the Industry 4.0 stage to the more human-centric and resilient Industry 5.0 paradigm, there is a growing need for structured assessment tools to prioritize modern technologies. This paper presents an integrated multi-criteria decision analysis (MCDA) approach to support the strategic assessment of technologies from three complementary perspectives: economic, organizational, and technological. The proposed model encompasses six key transformation areas and 22 technologies representing both the Industry 4.0 and 5.0 paradigms. A hybrid approach combining the DEMATEL (Decision-Making Trial and Evaluation Laboratory) and PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) methods is used to identify cause–effect relationships between the transformation areas and to construct technology rankings in each of the assessed perspectives. The results indicate that technologies such as the Internet of Things (IoT), cybersecurity, and supporting IT systems play a central role in the transition process. Among the Industry 5.0 technologies, hyper-personalized manufacturing, smart grids and new materials stand out. Moreover, the economic perspective emerges as the dominant assessment dimension for most technologies. The proposed analytical framework offers both theoretical input and practical decision-making support for companies planning their transformation towards Industry 5.0, enabling a stronger alignment between implemented technologies and long-term strategic goals. Full article

The maritime industry faces growing pressure to reduce greenhouse gas (GHG) emissions, reflected in the progressive adoption of stricter international energy regulations. Wind-Assisted Propulsion Systems (WAPS) offer a promising solution by significantly contributing to decarbonization. This paper presents a versatile simulation and optimization platform that supports the conceptual design of WAPS-equipped vessels and evaluates the viability of such investments. The platform uses a steady-state force equilibrium model to simulate vessel performance along predefined routes under realistic weather conditions, incorporating regulatory frameworks and economic assessments. A multi-objective optimization framework identifies optimal designs across user-defined criteria. To demonstrate its capabilities, the platform is applied to a bulk carrier operating between China and the USA, optimizing for capital expenditure, net present value (NPV), and CO₂ emissions. Results show the platform can effectively balance conflicting objectives, achieving substantial emissions reductions without compromising economic performance. The final optimized design achieved a 12% reduction in CO₂ emissions, a 7% decrease in capital expenditure, and a 6.6 million USD increase in net present value compared to the reference design with sails, demonstrating the platform’s capability to deliver balanced improvements across all objectives. The methodology is adaptable to various ship types, WAPS technologies, and operational profiles, offering a valuable decision-support tool for stakeholders navigating the transition to zero-carbon shipping. Full article

Reservoir group flood control scheduling decision-making faces multiple uncertainties, such as dynamic fluctuations of evaluation indicators and conflicts in weight assignment. This study proposes a risk analysis model for the decision-making process: capturing the temporal uncertainties of flood control indicators (such as reservoir maximum water level and downstream control section flow) through the Long Short-Term Memory (LSTM) network, constructing a feasible weight space including four scenarios (unique fixed value, uniform distribution, etc.), resolving conflicts among the weight results from four methods (Analytic Hierarchy Process (AHP), Entropy Weight, Criteria Importance Through Intercriteria Correlation (CRITIC), Principal Component Analysis (PCA)) using game theory, defining decision-making risk as the probability that the actual safety level fails to reach the evaluation threshold, and quantifying risks based on the First-Order Second-Moment (FOSM) method. Case verification in the cascade reservoirs of the Qiantang River Basin of China shows that the model provides a risk assessment framework integrating multi-source uncertainties for flood control scheduling decisions through probabilistic description of indicator uncertainties (e.g., Zmax1 with μ = 65.3 and σ = 8.5) and definition of weight feasible regions (99% weight distribution covered by the 3σ criterion), filling the methodological gap in risk quantification during the decision-making process in existing research. Full article

Precise evaluation of risk and reliability is crucial for decision making and predicting the outcome of investment in a photovoltaic power system (PVPS) due to its intermittent source. This paper explores different methodologies for risk evaluation and reliability assessment, which can be categorized into qualitative, quantitative, and hybrid qualitative and quantitative (HQQ) approaches. Qualitative methods include failure mode analysis, graphical analysis, and hazard analysis, while quantitative methods include analytical methods, stochastic methods, Bayes’ theorem, reliability optimization, multi-criteria analysis, and data utilization. HQQ methodology combines table-based and visual analysis methods. Currently, reliability assessment techniques such as mean time between failures (MTBF), system average interruption frequency index (SAIFI), and system average interruption duration index (SAIDI) are commonly used to predict PVPS performance. However, alternative methods such as economical metrics like the levelized cost of energy (LCOE) and net present value (NPV) can also be used. Therefore, a risk and reliability approach should be applied together to improve the accuracy of predicting significant aspects in the photovoltaic industry. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

This study identifies the optimal location for an offshore energy island to supply sustainable power to desalination plants along the Red Sea coast. As demand for clean energy in water production grows, integrating renewables into desalination systems becomes increasingly essential. A decision-making framework was developed to assess site feasibility based on renewable energy potential (solar, wind, and wave), marine traffic, site suitability, planned developments, and proximity to desalination facilities. Data was sourced from platforms such as Windguru and RETScreen, and spatial analysis was conducted using Inverse Distance Weighting (IDW) and Multi-Criteria Decision Analysis (MCDA). Results indicate that the central Red Sea region offers the most favorable conditions, combining high renewable resource availability with existing infrastructure. The estimated regional desalination energy demand of 2.1 million kW can be met using available renewable sources. Integrating these sources is expected to reduce local CO₂ emissions by up to 43.17% and global desalination-related emissions by 9.5%. Spatial constraints for offshore installations were also identified, with land-based solar energy proposed as a complementary solution. The study underscores the need for further research into wave energy potential in the Red Sea, due to limited real-time data and the absence of a dedicated wave energy atlas. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article