Analytic Hierarchy Process Model Research Articles

Groundwater potential mapping in semi-arid region of Northern Nigeria by integrating analytic hierarchy process and GIS

Groundwater resource management in drylands, characterized by climate variability and population growth, is difficult. Exploration and exploitation of groundwater, due to inadequate surface water is very costly. This study employed the analytic hierarchy process (AHP) and GIS to identify groundwater potential (GWP) areas in a semi-arid region of Nigeria. Land-use-land-cover, drainage density, slope, rainfall, static water level, soil, lithology, and aquifer were selected for GWP analysis. Parameter weights were determined using AHP and ranked based on their contribution to GWP by experts. The parameters were then integrated using the weighted overlay tool in ArcGIS 10.5 to produce a GWP map of the study area. Borehole yield data from 245 wells were collected to determine the model accuracy and model validation. Results classified the study area into very high GWP (1.9%), high GWP (8.8%), moderate GWP (62%), low GWP (20.70%) and very low GWP (6.6%). Areas with better GWP include Gurun, Dugol, Zago, Kumbo, Dukku, Doguwa, Riruwai, Dambazau, etc. Moderate GWP areas are widely spread across the state while areas with poor GWP include Sharawa, Kadewa, Koya, Dagar, Jigilawa, Galwanga, Yanganau, Kunchi, Tofa, Tsanyawa, etc. Validation of the AHP model with borehole yield data shows a correlation coefficient of 71.3% giving a good prediction. AHP and GIS can be used to successfully map GWP areas which could serve as an exploration guide for sustainable management of groundwater resources in semi-arid areas.

Leveraging Industry 4.0 for marketing strategies in the medical device industry of emerging economies.

This research investigates the impact of the technologies through an integrated Fuzzy Analytic Hierarchy Process (F-AHP) and VIKOR model to determine their viability as a marketing strategy for business growth in the Prosthetics and Orthotics (P&O) medical device industry. Based on expert data, the study evaluates the integration of Industry 4.0 technologies and ranks them by priority for marketing performance: "Artificial Intelligence & Machine Learning" (0.3295), "Internet of Things" (0.4325), "Virtual Reality & Augmented Reality" (0.5000), and "Big Data" (0.5704). The findings confirm that the proposed model effectively Leverage the industry 4.0 technologies to enhance marketing strategies, using Pakistan as a case study. This research serves as a blueprint for addressing the integration of next-generation marketing tools, technological advancements, and business growth challenges in the P&O industry. It contributes to the growing body of knowledge on the role of technology in improving healthcare marketing and business performance in emerging markets. Future research should explore the broader impact of Industry 4.0 technologies on healthcare marketing, focusing on strategic decision-making, efficient marketing resource allocation, and performance measurement to ensure sustained success in the P&O medical device sector. The study concludes that prioritizing Industry 4.0 technologies, however, limitations such as sample size, data collection constraints, and model complexity may Leverage the generalizability of the results. Future research should address these limitations by expanding the scope of expert involvement and exploring the application of this model in other emerging economies, to further validate the findings and assess the broader implications for healthcare marketing strategies globally.

Prioritizing sustainability of renewable energy projects in Morocco

Purpose The purpose of the study is to examine the electricity generation potential of different renewable energy sources in Morocco, as well as to propose a novel decision support model to prioritize renewable energy alternatives. The aim is to provide recommendations that support Morocco’s energy transition strategy and assist policymakers in devising future sustainable energy policies. Design/methodology/approach The paper integrates the analytic hierarchy process with expert feedback to prioritize five renewable energy alternatives – solar photovoltaics (PV), concentrated solar panels (CSP), wind, hydro and biomass – specific to the Moroccan context. The model uses 14 sub-criteria clustered into four main categories: economic, technological, socio-political and environmental. The criteria were weighted based on inputs from local energy sector experts to capture the distinct priorities and contextual specificities of Morocco’s renewable energy landscape. Findings The study’s findings emphasize the dominance of economic criteria among Moroccan experts. Solar PV emerged as the most suitable option due to its cost-effectiveness and alignment with Morocco’s policies to support solar energy, followed by hydropower and wind. CSP and biomass ranked lowest due to high costs and limited viability. Research limitations/implications This study highlighted the need for quality data to support informed decision-making, as well as for a centralized database to facilitate access to consistent information on renewable energy projects. Practical implications It is recommended that policymakers prioritize mature and cost-effective technologies by establishing supportive regulatory frameworks and expanding investments in hybrid renewable energy infrastructures. Originality/value In addition to introducing the Analytic Hierarchy Process model for the first time within the Moroccan context, to the best of the authors’ knowledge, this research draws recommendations to assist Moroccan policymakers in devising future sustainable policies for the energy sector.

Capabilities toward adoption of outcome-based contracts

PurposeManufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.Design/methodology/approachOur study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).FindingsThe findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.Research limitations/implicationsWe quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.Originality/valueExisting OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

Performance assessment of public transport networks: an AHP-ANP approach

Multi-criteria decision-making (MCDM) methods have been extensively employed by researchers in the public transport (PT) sector. While many studies utilize a single MCDM method, employing multiple MCDM methods can achieve more robust results. Furthermore, integrated MCDM methods have the potential to address the weaknesses and limitations inherent in single MCDM method. In this study, we address the limitations of single MCDM methods by integrating the analytic hierarchy process (AHP) with the analytic network process (ANP) to evaluate public transport networks performance. Based on the established public transport criteria matrix AHP model, we identify interdependencies among criteria. The AHP-ANP method is then applied to assess public transport networks in three case study cities in Australia. The sensitivity analysis conducted on both the AHP and integrated AHP-ANP approaches highlights the influence of criteria interdependency. This research contributes to mitigating the limitations of AHP method and applies AHP-ANP model to provide decision-makers with insights to enhance PT network performance and achieve smart PT network management.

Key Indicators in Analyzing the Sustainability of Afghanistan's Natural Resources

The research investigates the factors influencing the sustainability of Afghanistan's natural resources, aiming to guide the country towards sustainable development and poverty alleviation. Utilizing the Analytic Hierarchy Process (AHP) and strategic planning models, the study identifies and prioritizes key factors such as organizational structure and management, and economic, social, cultural, and ecological aspects. The findings reveal that organizational and managerial structure is the most critical factor, with a weight of 38%. The research emphasizes the necessity of integrated and systematic management of natural resources, which could improve economic and social well-being. It also highlights the importance of strategic planning, monitoring, and evaluation to ensure effective resource management. The study proposes a model for sustainable natural resource management that includes community empowerment, knowledge integration, economic viability, and enhanced communication among stakeholders. The research concludes that a comprehensive understanding of all influencing factors and their interactions is essential for designing a model for sustainable natural resource management.

Research on Monitoring Illegal Wildlife Trade Based on Data-Driven Models

This paper aims to evaluate client selection and project alignment in wildlife conservation projects using the Analytic Hierarchy Process (AHP) and TOPSIS models. The World Wide Fund for Nature (WWF), China Wildlife Conservation Association (CWCA), and the National Forestry and Grassland Administration are selected as candidate clients. Quantitative analysis uses three indicators: resources, power, and interest. The results indicate that WWF is the most suitable client regarding comprehensive resources, power, and interest. Additionally, the paper analyzes the cost-benefit and organizational development contributions of WWF implementing the "Electronic ID" project, highlighting its potential to enhance WWF's international influence and reputation. The study demonstrates that leveraging technology to track and protect wildlife can curb illegal wildlife trade and promote biodiversity conservation.

A Novel Artificial General Intelligence Security Evaluation Scheme Based on an Analytic Hierarchy Process Model with a Generic Algorithm

The rapid development of Artificial General Intelligence (AGI) in recent years has provided many new opportunities and challenges for human social production. However, recent evaluation methods have some problems with regard to consistency, subjectivity and comprehensiveness. In order to solve the above problems, in this paper, we propose an Artificial General Intelligence Security Evaluation scheme (AGISE), which is based on analytic hierarchy process (AHP) technology with a genetic algorithm, to comprehensively evaluate the AGI security based on multiple security risk styles and complex indicators. Firstly, our AGISE combines AHP technology with a genetic algorithm to realize reliable, consistent and objective evaluation for AGI security. Secondly, in our AGISE, we propose implementing more effective AGI security evaluation classification and indicator settings. Finally, we demonstrate the effectiveness of our AGISE through experiments.

Multi-Criteria Analysis in Circular Economy Principles: Using AHP Model for Risk Assessment in Sustainable Whisky Production

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk factors commonly observed in the whisky industry through the development of an analytical hierarchy process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3), and economical (4) risks. The analytical hierarchy process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices.

Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm.

Groundwater potential mapping and its sustainable management using AHP and FR models in the Jedeb watershed, Upper Blue Nile Basin, Ethiopia

ABSTRACT Groundwater potential mapping and its sustainable development are important aspects of the Jedeb watershed due to an increase in groundwater demand and the local population. This study aimed to delineate potential groundwater zones in the Jedeb watershed, using the analytical hierarchy process (AHP) and frequency ratio (FR) model. Nine thematic layers, including lithology, geomorphology, slope, drainage density, land cover and land use, lineament density, rainfall, soil, and static water level, were considered. The thematic layers obtained were integrated into a Geographic Information Systems (GIS) environment to generate the groundwater potential map. Four classes of groundwater potential have been identified: low, moderate, high, and very high. The low classes represent 12.8% and 7.02%; moderate 25.35% and 34.84%; high 38.8% and 33.11%; and very high 23.05% and 25.03%, for the AHP and FR models, respectively. The result map was also validated by using receiver operating characteristic (ROC) curve from which result accuracy was obtained is 85.6% and 88.3% for the AHP method and FR method, respectively. The study's validation verified that the FR model produced more efficacious results than the AHP model. This study, will be helpful in providing planners, and decision-makers with rapid decision-making when it comes to managing groundwater resources.

Development of a fuzzy analytic hierarchy process model, sensitivity analysis, and addressing barriers to additive manufacturing implementation in small- and medium-sized enterprises (SMEs)

Additive manufacturing (AM) is gaining popularity worldwide due to its potential to revolutionize manufacturing processes, improve product customization, and reduce waste. Nevertheless, small- and medium-sized enterprises (SMEs) encounter major challenges when implementing AM technologies. The objective of this research is to utilize the fuzzy analytic hierarchy process (FAHP) to identify and prioritize the critical challenges that impede the implementation of AM for SMEs. The study employs a two-stage methodology. First, a thorough literature review and expert discussions identify 15 major barriers to AM adoption. Second, a systematic model is proposed to establish a ranking of these barriers, which is based on the FAHP. The results are validated using sensitivity analysis to ensure the robustness of the findings. Additionally, potential solutions to overcome these barriers are discussed. The research indicates that the two most significant barriers for SMEs are the high costs of equipment and the scarcity of skilled designers. The model that has been presented is a valuable resource for SME managers and practitioners, as it allows them to make strategic and informed decisions that facilitate the efficient implementation of AM. This research is groundbreaking in its comprehensive analysis of the prioritization and ranking of AM implementation barriers, with a particular emphasis on SMEs. It offers essential insights for future research and practical applications.

AI empowered academia: a fuzzy prioritization framework for academic challenges

Purpose The rapid expansion of artificial intelligence (AI) is progressively reshaping the dynamics of human interaction, communication, lifestyle, education and professional endeavors. The purpose of the study is to comprehend and address the barriers which are impeding the implementation of Generative AI Technologies, such as ChatGPT in the educational landscape. Design/methodology/approach The study used the Fuzzy analytic hierarchy process (AHP) model to analyze the responses gathered from 149 academicians belonging to the northern states of India. Findings The study established that the three most important criteria that influence the adoption of generative AI in the education sector are Risk of Academic Integrity, Risk of biased outcomes and Erosion of Critical Thinking. Research limitations/implications The present study was confined to Fuzzy AHP to extract the critical criteria influencing the decision-making. Various other techniques such as PF-Delphi and PF-CoCoSo can be used further. The results provide significant inputs for future research to understand the effect of adoption of Generative AI in different contexts including both opportunities and the challenges faced by them. Practical implications The study will be beneficial to various stakeholders including students, educators, society and policymakers as the study will highlight the importance of AI tools, introduce the various challenges associated with and explain the use of these tools as productivity-enhancing tools. Originality/value To the best of the author’s knowledge, the present study is a novice as the use of AI in Academia is unexplored and the major criteria influencing the choices have yet been undiscovered.

Flood vulnerability map of the Bagmati River basin, Nepal: a comparative approach of the analytical hierarchy process and frequency ratio model

AbstractThe analytical hierarchy process (AHP) and frequency ratio model (FR), along with the integration of GIS, have proven to be successful approaches for assessing flood-prone areas. However, in Nepal flood vulnerability mapping based on GIS decision analysis is limited. Thus, this study focused on comparing the data-driven FR method and expert knowledge-based AHP technique in a GIS environment to prepare a flood vulnerability map for the Bagmati River basin, helping to explore the gap in flood vulnerability mapping methodologies and approaches. By combining all class-weighted contributing factors, like elevation, precipitation, flow accumulation, drainage density, soil, distance from the river, land use land cover, normalized difference vegetative index, slope and topographic wetness index, the study evaluated the efficiency of FR and AHP in assessing flood vulnerability maps. An inventory map of floods containing 107 flood points was created. Subsequently, the flood vulnerability maps generated using FR and AHP models revealed that 9.30% and 11.36% of regions were in highly vulnerable areas, respectively. Receiver operating characteristics validated the model outcomes, indicating that the FR model’s accuracy of 91% outperformed the AHP model’s 84% accuracy. The study findings will assist decision-makers in enacting sustainable management techniques to reduce future damage in the Bagmati basin.

Evaluation of groundwater quality potential zones using AHP and WIOA models in Shopian District, Jammu and Kashmir, India: A GIS

Groundwater is an important natural resource stored in aquifers beneath the earth's surface. The hilly district of Shopian in Jammu and Kashmir serves as a study area due to its present and future groundwater needs. This study aims to assess the groundwater potential zones (GWPZ) in Shopian District using the analytical hierarchy process (AHP) and weighted index overlay analysis (WIOA). The AHP approach attempted to assign weights to specific groundwater recharge characteristics. The various thematic maps, including rainfall, land cover and usage, slope, drainage density, geomorphology, soil, and geology, were integrated using overlay analysis to create a comprehensive GWPZ map. Geographic information system (GIS) and remote sensing (RS) tools were utilized to indicate the potential groundwater existence in the studied area. Four zones were created from the GWPZ: very poor, poor, good, and very good. The analysis showed that the very poor GWPZ is around 35.12 sq km, the poor zone is roughly 166.89 sq km, the excellent zone is approximately 340 sq km, and the very good zone is approximately 66.13 sq km. The present study reveals significant perspectives for organizing and effectively restocking groundwater locations within the district. It intends to safeguard the sustainability of groundwater resources for future generations by promoting their successful development and management.

GIS-based AHP and MCDA Modeling for Cropland Suitability Analysis: A Bibliometric Analysis

The ' Land Suitability Analysis ' is a useful management method for ensuring that agricultural lands are utilized sustainably and planned based on their potential. Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) for cropland suitability analysis have seen substantial contributions from researchers worldwide. This combination assesses and maps the suitability of land for different crops by utilizing the multi-criteria decision analysis (MCDA) strengths of AHP and the spatial analytic capabilities of GIS. This Bibliometric analysis involves examining publications to identify patterns and trends, such as the most prolific authors & Countries, influential journals, and highly cited papers. It helps in understanding the development and current state of a research field. Using Biblioshiny software, the researchers obtained 183 publications of 687 authors and 319 different institutions using the bibliographic information from the Scopus database. The bibliometric analysis uses the following subcategories: Country, Authors, Publication Sources, Annual Scientific Production, and keywords. By examining the outcomes of bibliometric analysis, methodology, and applications, it was discovered that AHP and MCDA are the most often utilized techniques in this respect. Also, the findings indicated a rising number of publications and a growing interest in the subject, especially in recent years. Over the previous 23 years, the overall trend of publications in this field grew gradually at an annual growth rate of 21.81%. Asian nations, especially China, India, and Iran, have had the biggest influence on the nation's scientific output in the discipline. During this period, India and Iran had the most research papers published. In addition, "GIS," "Land Suitability," and "AHP" are the top three most often used terms. Future trends in this subject are predicted by the current keywords: "GIS," "Land Suitability," "AHP," and "Remote Sensing." Moreover, this exhaustive investigation provides a basis for comprehending the present status and future direction of GIS-based cropland suitability research. These discoveries offer valuable insights for future modeling and research endeavors on the subject and aid in identifying research gaps in the existing literature.

Key Indicators in Analyzing the Sustainability of Afghanistan's Natural Resources

This study examines the factors affecting the sustainability of Afghanistan's natural resources, aiming to provide a framework for sustainable development and poverty alleviation. Using the Analytic Hierarchy Process (AHP) and strategic planning models, key factors such as organizational structure, management, economic, social, cultural, and ecological aspects were identified and prioritized. The findings indicate that organizational and managerial structure is the most critical factor, accounting for 38% of the overall importance. The research underscores the necessity for integrated and systematic management of natural resources to enhance economic and social well-being. It also emphasizes strategic planning, monitoring, and evaluation as essential components for effective resource management. A proposed model for sustainable natural resource management includes community empowerment, knowledge integration, economic viability, and improved communication among stakeholders. The study suggests that Afghanistan can develop its natural resources sustainably by leveraging strengths and opportunities while addressing weaknesses and threats. A comprehensive understanding of the various influencing factors and their interactions is vital for designing an effective model for sustainable management. This research contributes to the discourse on sustainable development in Afghanistan, providing actionable insights for policymakers and stakeholders in the natural resource sector.

Assessment of Spatial Variation in Terrain Parameters Impacting Surface and Groundwater Quality for Sustainable Geo-Environmental Management

Soil and water conservation measures crucial for quality enhancement should focus on terrain-specific challenges. Evaluating groundwater resources from wells in the area is essential to ascertain their appropriateness for different applications. In semiarid tropical regions, the risk of inland salinity can escalate under extreme conditions like droughts and reduced monsoonal rainfall. During droughts, the groundwater table declines, leading to deterioration in groundwater quality, making it unsuitable for consumption, industrial processes, and arboriculture. In this scenario, analysing the spatial variation in water quality parameters becomes crucial for safeguarding environmental geology and effectively managing the geo-environment of impacted regions. Unfavourable geo-environmental conditions can be mitigated by reducing surface and groundwater pollution, sheet erosion, landslides, and land subsidence. Examining the variation in groundwater quality across both spatial and temporal dimensions is necessary to recommend treatments that make groundwater suitable for various uses, including potable purposes. The spatial as well as temporal variations of different water quality parameters, determined through a composite water quality index, can inform land use alterations, resource exploitation without unacceptable consequences, and artificial recharge measures that do not pollute the geo-environment. Enhancing the sustainability of the geo-environment can be achieved by investigating and prioritizing conservation measures and practices. Employing temporal remote sensing alongside related datasets facilitates the assessment of delineated watersheds within the region through the Analytical Hierarchy Process (AHP) Model. This approach is essential for prioritizing watersheds and formulating strategic action plans to sustain a balanced geo-environment.

Analytic Hierarchy Process (AHP)-Based Evaluation of Extremely-Low-Frequency Magnetic Field Contribution Rates

The extremely-low-frequency (ELF) magnetic fields of submarines serve as key characteristics for target detection, with their formation mechanisms being complex and diverse. Effectively manipulating a submarine to reduce its magnetic signature is crucial for enhancing its magnetic stealth capabilities. However, current research on the impact of various causative factors is insufficient. This study proposes a contribution rate assessment method based on the Analytic Hierarchy Process (AHP) model, aiming to provide a theoretical basis for effective manipulation. Initially, a thorough analysis of the threat causes of a submarine’s ELF magnetic fields is conducted, and a corresponding hierarchical threat structure model is established. Subsequently, magnetic field signal characteristics generated by different causes are obtained through simulation, and threat matrices and characteristic matrices are constructed. Finally, the contribution rates of different causative magnetic fields to the total magnetic field are calculated, and the simulation results validate the effectiveness of the method. At the stern detection line, the contribution rate of the wake magnetic field is the highest, reaching 0.7649. Along the radial detection line, the contribution rate of the shaft frequency magnetic field is the highest and gradually decays, eventually falling below the wake magnetic field at 150 m and remaining at an approximately 0.5 contribution rate. This study calculates the contribution rates under different operational conditions and detection scenarios, laying a technical foundation for research on the comprehensive active control strategies of submarine ELF magnetic fields in different scenarios.

Current and emerging waste-to-energy technologies: A comparative study with multi-criteria decision analysis

In response to the rise in waste crisis and the possibility of energy utilization from waste, there has been increasing interest in waste-to-energy (WtE) conversion technologies, which requires intense scientific attention. There are diverse WtE technologies that apply to different waste types and require multidisciplinary decision support. The paper applies a Multi-criteria Decision Analysis (MCDA) tool to compare their economic, technological, socio-cultural, and environmental aspects to help identify the most promising choice. The comparison used in this study concerns four widely used technologies: Incineration (INC), Anaerobic Digestion (AD), Gasification (GAS), and Pyrolysis (PYR), and one emerging WtE conversion technology, Hydro-thermal Carbonization (HTC). The Comparison criteria are divided into four main criteria and fifteen sub-criteria. The Analytical Hierarchy Process (AHP) model was implemented using ’SuperDecisions’ software to make pairwise comparisons of identified criteria and to rank the WtE technology alternatives. Thirty-two international studies were shortlisted to gather data and provide input into the AHP model. The results show that the environmental factors are prioritized with a priority vector of 0.56. Further, the study concludes that the most suitable WtE technology, based on chosen parameters, is AD, followed by HTC, INC, and PYR with the priority vectors of 0.348, 0.201, 0.162, and 0.148, respectively, provided applicability. The emerging technology, HTC, is found to be the second most suitable technology. Further, the results represent the hierarchy structure arranged so that the main components are divided into sub-components with alternatives at the structure’s base, and the ’SuperDecisions’ model based on this hierarchy can be used in the future to find suitable WtE technology for a specific city with the necessary input for identified main and sub-criteria. This research not only provides a structured comparison of WtE technologies but also offers a scalable AHP framework that can be adapted for specific municipal contexts in future studies. By addressing the diverse needs of decision-makers across different regions, our model contributes to a more nuanced understanding of WtE technology selection and lays the groundwork for incorporating local policies and regulations in subsequent research phases.