Search Results (933)

The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling technologies under uncertainty. Ten key evaluation criteria—encompassing environmental, economic, and technological dimensions—were identified through expert consultation and literature synthesis. The T-Spherical Fuzzy DEMATEL method was first applied to analyze the causal interdependencies among criteria and determine their relative weights, revealing that environmental drivers such as energy consumption, greenhouse gas emissions, and waste generation exert the most systemic influence. Subsequently, six recycling alternatives were assessed and ranked using the CoCoSo method enhanced by Einstein-based aggregation, which captured the complex interactions present in the experts’ evaluations and assessments. Results indicate that Direct Recycling is the most favorable option, followed by the Hydrometallurgical and Bioleaching methods, while Pyrometallurgical Recycling ranked lowest due to its high energy demands and environmental burden. The proposed hybrid model effectively handles linguistic uncertainty, expert variability, and interdependent evaluation structures, offering a robust decision-support tool for sustainable technology selection in the circular battery economy. The framework is adaptable to other domains requiring structured expert-based evaluations under fuzzy environments. Full article

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

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

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. Full article

This study proposes a fuzzy logic-based approach to better manage supply chain uncertainty and improve decision-making flexibility. The developed framework categorizes supply chain activities into procurement, production, distribution and reverse logistics and integrates Lean, Agile, Resilient, and Green (LARG) KPIs within a hierarchical structure. The tool was implemented using Microsoft Excel^TM to enhance usability for practitioners. To test its applicability, the model was applied to a real case study. The results show that lean and resilient practices are consistently well-established across all supply chain phases, while agility and green practices vary significantly depending on the operational area—particularly between internal function (i.e., production and reverse logistics) and external ones (i.e., procurement and distribution). These findings help to better understand how the LARG capabilities are distributed across the different operational areas of the supply chain and offer practical guidance for managers seeking targeted performance improvement. Although the numerical results are context-specific, the framework’s adaptability makes it suitable for diverse supply chain environments. 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

The increasing urgency of the energy transition, particularly in ecologically sensitive regions, demands spatially informed planning tools to guide renewable energy development. This study presents a Multi-Criteria Decision-Making (MCDM) approach to assess the suitability of the Gargano district in southern Italy for the implementation of Renewable Energy Communities. The analysis combines expert-based weighting and the Weighted Linear Combination method to evaluate seven key criteria grouped into environmental, socioeconomic, and technical dimensions. The resulting suitability scores, calculated at the municipal scale, highlight spatial disparities across the district, revealing that areas with the highest potential for Renewable Energy Community (REC) deployment are largely situated at the boundaries of the Gargano National Park. These zones benefit from stronger infrastructure, higher energy demand, and fewer environmental constraints, particularly with regard to wind energy initiatives. Conversely, municipalities within the park exhibit lower suitability, constrained by strict landscape regulations and lower population density. The findings provide valuable insights for regional planners and policymakers, supporting the adoption of targeted, environmentally compatible strategies for the advancement of citizen-led renewable energy initiatives in complex territorial contexts. Full article

Modern manufacturing systems operate in a global and competitive environment, where sustainability has become a critical driver for performance. Performance measurement, as a method for monitoring enterprise processes, plays a central role in aligning operational efficiency with sustainable development goals. Recently, a number of different frameworks, systems, and methods have been proposed for small and medium enterprises. Key performance indicators (KPIs) are known to be powerful tools which provide accurate information regarding bottlenecks and weak spots in companies. The purpose of the current study is to develop an advanced KPI selection/prioritization model and apply it in practice. The initial set of KPIs are obtained based on a literature review. The expert’s knowledge, outlier methods, and optimization of the enterprise analysis model (EAM) are utilized for reducing the initial set of KPIs. A fuzzy analytical hierarchy process (AHP) is implemented for prioritization of the criteria. Five different MCDM (multi-criteria decision-making) algorithms are implemented for prioritization of the KPIs. The recently introduced RADAR method is extended to the fuzzy RADAR method, providing a flexible approach for handling uncertainties. An analysis and comparison of the rankings obtained by utilizing five MCDM algorithms is performed. The prioritized KPIs provide valuable input for improving KPIs with the highest impact in particular small and medium-sized enterprises (SMEs) when implementing sustainability-aligned performance metrics. Full article

This paper presents a multi-criteria decision-making (MCDM) approach for optimizing a microgrid system to achieve Plus-Energy Building (PEB) performance at the University of Coimbra’s Electrical Engineering Department. Using Python 3.12.8, Rhino 7, and PVsyst 8.0.1, simulations considered architectural and visual constraints, with economic feasibility assessed through a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The system is projected to generate approximately 1 GWh annually, with a 98% probability of exceeding 1076 MWh based on Gaussian estimation. Consumption is estimated at 460 MWh, while a 3.8 MWh battery ensures up to 72 h of autonomy. Rooftop panels and green parking arrays, fixed at 13.5° and 59°, minimize visual impact while contributing a surplus of +160% energy injection (or a net surplus of +60% energy after self-consumption). Assuming a battery cost of EUR 200/kWh, each hour of energy storage for the building requires 61 kWh of extra capacity with a cost of 12,200 (EUR/hr.storage). Recognizing environmental variability, these figures represent cross-validated probabilistic estimates derived from both PVsyst and Monte Carlo simulation using Python, reinforcing confidence in system feasibility. A holistic photovoltaic optimization strategy balances technical, economic, and architectural factors, demonstrating the potential of PEBs as a sustainable energy solution for academic institutions. Full article

Alternative fuels obtained from renewable sources, providing low greenhouse gas emissions and high energy efficiency, offer significant advantages in terms of sustainability. In addition, the wide applicability of these fuel types in sectors such as housing, transportation, and industry creates significant opportunities in terms of reducing dependence on fossil fuels. Alternative fuels should be evaluated not only according to their environmental contributions but also based on multi-dimensional criteria such as economic cost, technical suitability, sustainability level, fuel properties, infrastructure requirements, and social acceptance. In this context, a comparative analysis of alternative fuel types in terms of various basic parameters is no longer optional, but a necessity. These parameters generally include symmetrical relationships such as balanced trade-offs between economic and environmental dimensions or mutual effects between technical and social criteria. However, they also show variability and uncertainty depending on the fuel type. Therefore, Spherical Fuzzy Multi-Criteria Decision Making (SF-MCDM) methods, which can effectively represent symmetry in membership and hesitation degrees, have been used to achieve more realistic and reliable results in uncertain decision environments. The proposed model provides a systematic and flexible evaluation structure that helps decision makers determine the most appropriate alternative fuel options and contributes to the formation of sustainable energy policies. Full article

Driven by global climate change and sustainable development, the coordinated development of multiple industries based on photovoltaic energy in the “Desert-Gobi-Wilderness” region has become the key to achieving sustainable development, as well as transforming and upgrading the energy structure. However, the site selection decision for “Photovoltaic +” multi-industry integration, which takes into account economic, social and ecological benefits in a complex ecological environment, is still a key difficulty that restricts the feasibility and scalability of the project. This study first identified and systematically analyzed six “PV +” multi-industry integrations suitable for development in China, including “PV + sand control”, “PV + agriculture”, “PV + agriculture + tourism”, “PV + animal husbandry”, “PV + animal husbandry + tourism”, and “PV + tourism”. Then, a site selection decision framework for “PV +” multi-industry integration consists of three parts. Part 1 establishes a multi-dimensional suitability assessment system that takes into account heterogeneous data from multiple sources. Part 2 uses an integration method based on BWM-CRITIC-TODIM for priority ranking analysis, which first uses a Geographic Information System (GIS) to carry out suitability simulation for the entire region of China—identifying six alternative regions—then uses the interactive and multi-criteria decision-making (MCDM) method to prioritize the alternative areas. Part 3 carries out further sensitivity analyses, scenario analyses, and comparative analyses to verify the dynamics and scientific nature of the site selection decision framework. Finally, this study identifies regions of high suitability for development corresponding to the six multi-industry integrations. The framework is designed to help decision stakeholders achieve precise site selection and benefit optimization for “PV +” multi-industry integration and provides a replicable planning tool for achieving industrial synergy and sustainable development in the “Desert-Gobi-Wilderness” region driven by green energy. Full article

Intermodal transport (IT), which implies the combination of several different types of transport to achieve a more efficient and economical movement of goods, is of increasing importance in modern supply chains. In the conditions of globalization, growth of trade flows and increasingly pronounced requirements for sustainability, effective planning and management of intermodal routes have become crucial, which is why their evaluation and ranking are essential for making strategic and operational decisions. Accordingly, this paper aims to identify the most favorable alternative for developing intermodal transport. Deciding on the choice of the most important intermodal route requires consideration of a large number of criteria, often of a mutually conflicting nature, which places this problem in the domain of multi-criteria decision-making (MCDM). Accordingly, this paper develops a hybrid decision-making model in a fuzzy environment, which combines fuzzy DELPHI (FDELPHI), fuzzy factor relationship (FFARE), and fuzzy axial-distance-based aggregated measurement (FADAM) methods. The model enables the identification and evaluation of relevant criteria, as well as the ranking of defined variants under the requirements and attitudes of various stakeholders. The practical application and effectiveness of the developed model were demonstrated and confirmed by a case study for Bosnia and Herzegovina (B&H). The sensitivity analysis showed that even with changes in the weights of the criteria or the elimination of the most important criteria, the solution remains consistent and reliable. This indicates the robustness of the model and suggests that changes in the parameters do not lead to significant changes in the final results. This confirms the validity of the proposed model and increases confidence in its applicability in practice. Full article

In this study, the authors present the results of a systematic literature review on applications of the technique for order of preference by similarity to ideal solution (TOPSIS) in power systems. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach was used in the identification of publications used in this research. The SCOPUS database was utilized to locate the publication, and a total of 78 articles published between 2014 and 2024 were included in the review. A bibliometric analysis was performed, and reports were given on the annual number of publications and the top 10 cited journals. The main themes emerging from the content review of the publications were types of TOPSIS approaches, calculation of weights in multi-criteria decision-making (MCDM) problems, energy markets applications, renewable energy technologies assessment, heating and cooling systems combined with power systems, power system operation strategies, power system stability assessment, power system operations planning, and other power systems applications. Research trends and developments in the area were analyzed to identify the existing gaps. Proposed future research areas were identified based on trends and gaps presented. Full article

Suppliers nowadays can be directly approached in business-to-business (B2B) E-commerce websites. This makes the product and service information of certain suppliers accessible in online reviews. Therefore, online reviews have become important for B2B supplier evaluation and selection. Recently, the sustainability of food packaging has attracted increasing attention from companies and consumers. This study developed a novel multi-criteria decision making (MCDM) method called Percentage Assessment with Synergistic Comparisons And Aggregated Ranks (PASCAAR) to support the selection of sustainable food package suppliers based on online review information in B2B E-commerce websites. Such a method used three different percentage comparisons between alternatives and the minimal options, and then aggregates the comparisons with their ranks. This study confirmed the effectiveness of PASCAAR by applying it to a case study to select the supplier of sustainable food packages (i.e., biodegradable food containers) from six candidates in the B2B E-commerce website by considering multi-dimensional online review information and their own product properties. Using PASCAAR, this study obtained the outcome that the third candidate is the most suitable one, as quantitative results indicate this supplier has the highest PASCAAR score. Based on the results, this study further conducted thorough sensitivity tests to validate the results. It can be found that, compared with the classical MCDM methods in measuring the performance of alternatives and aggregating evaluation scores, the PASCAAR method can have more robust and informative results. This study also developed a PASCAAR Solver to enable easy implementation of this method. This study contributes to the existing literature by providing new ranking and aggregation ideas in MCDM and can offer practitioners a more informative and highly actionable method for supplier selection and decision support system development by utilizing online review information. Full article

Cement production significantly contributes to global warming, resource depletion, environmental degradation, and environmental pollution. Identifying sustainable alternatives is critical but requires balancing multiple, often conflicting, factors. The objective of this study is to determine the most preferred cement alternative produced in South Africa using an integrated life cycle assessment (LCA) and multi-criteria decision-making (MCDM) framework. The LCA quantified the environmental impacts of seven different cement-type alternatives across 18 midpoint impact categories. The LCA results showed that slag cement-based CEM III/A achieved a 50% reduction in global warming potential (GWP) compared to traditional CEM I (0.57 vs. 0.99 kg CO₂ eq. This study also used the entropy-weighted, COPRAS and ARAS methodologies to evaluate and rank cement types based on their environmental impacts and weighted sustainability criteria and the results showed that fly ash-based CEM II/B-V demonstrated the highest overall sustainability, with utility scores of 100.00 (COPRAS) and 0.7257 (ARAS) in MCDM ranking. These results highlight that fly ash-based cement offers substantial environmental benefits over traditional CEM I, particularly in reducing greenhouse gas emissions and resource consumption. The integrated LCA–MCDM method enables robust prioritization by linking quantitative environmental impacts with objective ranking criteria. Although this analysis focuses on South African cement formulations, the methodology and findings are applicable to other regions with similar production profiles and SCM availability. The framework offers a practical tool for policymakers and industry to support environmentally responsible decision-making in cement production. Full article