Analytic Hierarchy Process Analysis Research Articles

Scrap Steel Recycling: A Carbon Emission Reduction Index for China

Accurately assessing carbon emissions from recycling scrap steel is essential for reducing emissions in the steel industry, especially in China, the world’s largest crude steel producer. In this study, a carbon emission reduction index was introduced to evaluate the effectiveness of recycling scrap steel in reducing emissions. The index considers the three processes used in scrap steel recycling: blast furnace ironmaking, converter steelmaking, and electric arc furnace steelmaking. This study developed an evaluation model using fuzzy analytic hierarchy process and iterative cluster analysis to determine the reduction of carbon emission. From a life cycle perspective, this study identified primary factors contributing to emissions, including fuel, raw materials, electric energy, and auxiliary materials. Then, the carbon emission reduction index for scrap recycling was developed by examining the production of one ton of steel and each additional ton of scrap steel, which can provide valuable insights into the environmental impact of scrap recycling. Finally, the study forecasts the future Carbon Emission Reduction Index for steel scrap recycling. The study indicates an increase in the carbon emission reduction index for scrap recycling prior to 2017, followed by a decrease about 11.8% from 2017 to 2018 and increases from 2018 to 2021. Finally, it dropped by 8.7% per cent in 2022. Similarly, the carbon emission reduction index for electric furnace steelmaking increased prior to 2019, then subsequently decreased. It is changing by ten per cent a year. Additionally, the scrap recycling index experienced a significant decrease of 90% in 2015, followed by a gradual increase until 2017 and then a consistent decrease every year thereafter. The index suddenly rose in 2021 and then decreased change for policy reasons. The forecast results suggest a gradual increase in the carbon emission reduction index per ton of steel scrap in the future. In conclusion, the practicable modeling methodology has the ability to assist government organizations and private enterprises in devising efficient green and low-carbon development tactics.

Assessment of the Susceptibility of Urban Flooding Using GIS with an Analytical Hierarchy Process in Hanoi, Vietnam

The incidence of floods is rapidly increasing globally, causing significant property damage and human losses. Moreover, Vietnam ranks as one of the top five countries most severely affected by climate change, with 1/3 of residents facing flood risks. This study presents a model to identify flood susceptibility using the analytic hierarchy process (AHP) in the GIS environment for Hanoi, Vietnam. Nine flood-conditioning factors were selected and used as initial data. The AHP analysis was utilized to determine the priority levels of these factors concerning flood susceptibility and to assess the consistency of the obtained results to develop a flood-susceptibility map. The performance of the model was found to be significant based on the AUC value for the obtained receiver operating characteristic (ROC) curve. The flood-susceptibility map has five levels of flood susceptibility: the area with a very high susceptibility to flooding accounts for less than 1% of the map, high- susceptibility areas for nearly 11%, moderate-susceptibility areas for more than 65%, low- susceptibility areas for about 22%, and very low-susceptibility areas for 2%. Most of Hanoi has a moderate level of flood susceptibility, which is expected to increase with urban expansion due to the impacts of urbanization. Our findings will be valuable for future research involving urban planners, and disaster management authorities and will enable them to make informed decisions aimed at reducing the impact of urban flooding and enhancing the resilience of urban communities.

Driving factors for developing integrated farming: Multi-criteria decision-making analysis

Integrated farming (IF) by temporal and spatial mixing of crops, livestock, fishery, and allied activities in a single farm, is considered a critical multifunctional option for smallholder farmers, who form the backbone of Indian agriculture, to ensure sustainable livelihoods, the productivity of agricultural enterprises, stability of farm income, food and nutritional security. However, the adoption of IF still remains low. To explore the critical factors of IF systems and prioritize them for stakeholders’ decision-making and development of strategies, the current research was undertaken to integrate SWOT (Strengths, Weaknesses, Opportunities, and Threats) - AHP (Analytic Hierarchy Process) analysis. We used a multi-stage sampling to select 60 practicing IF adopters in West Bengal state of eastern India for the collection of pair-wise comparison data on 32 SWOT factors using a close-ended questionnaire and subjected to AHP analysis to understand the quantitative importance of each factor of SWOT. Results of the multi- criteria analysis showed the total priority weight of the opportunity component (0.450) was the highest, followed by strength (0.341), weakness (0.114), and threat components (0.095). Based on the global priority weight of all 32 SWOT factors, sustainable livelihood security” was the key driving factor (0.081) followed by promotion of organic farming (0.072), better risk management (0.063) and incorporation of high-value crops (0.063) of opportunity component. Increased farm production and productivity (0.058) and enhancement in income (0.055) of strength component also played as vital driving factors. There was no factor of weakness and threat components within the first 10 important factors. The insights of this study may help improve extension services to smallholder farmers for prioritizing strategies in the adoption process of IF. Additionally, it can also help policymakers to design targeted policies, remove barriers, foster innovation, and promote sustainable practices, contributing to food security and environmental sustainability.

Boosting large‐scale construction project risk management: Application of the impact of building information modeling, knowledge management, and sustainable practices for optimal productivity

AbstractThis study investigates the relationship between Building Information Modeling (BIM) and knowledge management in large‐scale construction projects, emphasizing risk management, and increasing productivity. This study aims to identify the most important and impactful aspects of modern management techniques in the large‐scale construction sector to improve construction quality; focusing on cost reduction, energy conservation, and pollution reduction during construction. Employing a validated questionnaire from experts and the Analytical Hierarchy Process (AHP), the research prioritizes key factors, elucidating the pivotal role of BIM in understanding project geometry, efficient documentation, design flexibility, and reduced project delays. Ascertained through data analysis employing the pairwise comparisons method of the AHP analysis method, the compatibility of all comparisons and matrices arising from the decision‐making process of experts was below 0.1. The results establish BIM's technical capabilities, economic considerations, and pollution reduction as paramount, contributing significantly to improve large‐scale construction project risk management. The prioritization of the main criteria reveals their significance in the following order: BIM technical capabilities (0.281), BIM economic capabilities (0.231), pollution reduction (0.228), knowledge management capabilities (0.096), BIM time capabilities (0.090), and human resources management capabilities (0.074). BIM can improve risk management for large‐scale construction projects by: using the BIM model to understand the geometry and features of large‐scale construction projects; digitizing and organizing contract documents, project data, plans, work instructions, roles and responsibilities, cost, time, and energy analysis, and other project‐related documents for efficient project management; decreased duplication and project delays; reducing the need for on‐site personnel during implementation and CO2 emissions reduction. According to the results of the BIM model utilization to better understand the geometry and characteristics of large‐scale construction projects, the item was identified as the most significant and influential among the main components.

Study on decision-making for orthodontic treatment plans based on analytic hierarchy process

BackgroundOrthodontics is a common treatment for malocclusion and is essential for improving the oral health and aesthetics of patients. Currently, patients often rely on the clinical expertise and professional knowledge of doctors to select orthodontic programs. However, they lack their own objective and systematic evaluation methods to quantitatively compare different programs. Therefore, there is a need for a more comprehensive and quantitative approach to selecting orthodontic treatment plans, aiming to enhance their scientific validity and effectiveness.MethodsIn this study, a combination of the analytic hierarchy process (AHP) and semantic analysis was used to evaluate and compare different orthodontic treatment options. An AHP model and evaluation matrix were established through thorough research and semantic analysis of patient requirements. This model considered various treatment factors. Expert panels were invited to rate these factors using a 1–9 scale. The optimal solution was determined by ranking and comparing different orthodontic treatment plans using the geometric mean method to calculate the weights of each criterion.ResultsThe research indicates a higher preference for invisible correction compared to other orthodontic solutions, with a weight score that is 0.3923 higher. Factors such as comfort and difficulty of cleaning have been given significant attention.ConclusionThe Analytic Hierarchy Process (AHP) method can be utilized to effectively develop orthodontic treatment plans, making the treatment process more objective, scientific, and personalized. The design of this study offers strong decision support for orthodontic treatment, potentially improving orthodontic treatment outcomes in clinical practice and ultimately enhancing oral health and patients’ quality of life.

The collaborative approaches of the knowledge-based performance management system and lean six sigma to improve contractor productivity and safety performances

Purpose This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities. Design/methodology/approach The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework. Findings The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field. Research limitations/implications The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors. Practical implications This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs. Social implications This paper does not discuss social implications Originality/value This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

Identifying the Optimal Valuation Model for Maritime Data Assets with the Analytic Hierarchy Process (AHP)

Data are becoming the most important factor in the development of the socio-economy, and data can be reevaluated as the owner’s valuable asset, which can increase the owner’s value. Therefore, each company is fiercely competing to secure data. Even in the marine field, maritime data are being produced exponentially, but it is difficult to expect more value creation because data are only stored rather than being used. This study used the analytic hierarchy process (AHP) methodology to select a suitable valuation model necessary to discover new values for maritime data. As a result of AHP analysis of 33 experts based on the stratified factors extracted from previous studies and expert opinions, the market approach (A2) was found to be the most suitable model. In addition, the most important factors to consider when selecting a valuation model were in the order of the characteristics of the maritime data (M1), the features of the maritime data market (M2), and the features of the maritime data valuation model (M3). The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous surface ships (MASSs).

Utilization of Multi-Mission Satellite Altimetry for Wave Energy with Site Suitability Analysis using the Analytic Hierarchy Process

Space technology advancements have enabled the acquisition of marine data that support the research on wave energy as an alternative to reduce fossil fuel dependency and mitigate climate change. Malaysia's ocean renewable energy potential lacks attention from local authorities due to insufficient in-situ data, posing challenges in investigating ocean characteristics, such as wave heights. This study investigated Malaysia's wave energy potential using extensive significant wave height data from multiple altimetry missions. The former assessed site suitability using the Analytical Hierarchy Process (AHP) multicriteria analysis, incorporating marine constraints, namely socioeconomic, physical, and environmental factors. The multicriteria findings were integrated into a Geographical Information System (GIS) to improve the site suitability analysis and generate a localized suitability index for wave energy. Validation of satellite altimeter data with in-situ measurements showed a strong correlation and low RMSE. AHP analysis indicated good consistency in the criteria analysis, with a consistency ratio of 0.045, which falls below the limit of 0.1. The coastal and offshore regions of the Malaysian seas are suitable for harnessing wave energy with energy ranges up to 4.21 kW/m. Therefore, this study provides valuable information to stakeholders and the government to increase their interest in wave energy.

Concept of Social Entrepreneurship in the Development of Creative Economy Tourism in Indonesia (Case Study: Cirendeu Village)

Social entrepreneurship is currently viewed as a solution to address various social issues, including welfare, healthcare, education, poverty alleviation, and community empowerment, among others. This research aims to develop a social entrepreneurship concept suitable for Cirendeu Village. The concept of social entrepreneurship is closely related to the 8th Sustainable Development Goal (SDG), which emphasizes “Decent Work and Economic Growth.” Social entrepreneurship represents a form of business that not only seeks financial profit but also has robust social or environmental objectives. The methods employed in this research include the Analytic Hierarchy Process (AHP) and the Business Model Canvas. Research data were collected from 19 respondents through demographic statements and the distribution of AHP questionnaire matrices. The AHP method serves as a strategy weighting approach and assists in formulating priority strategies for the company. Furthermore, the Business Model Canvas method aims to describe the appropriate business model for the company based on the AHP analysis. The establishment of criteria is a crucial step in the decision-making process. Combining the Process Hierarchy Analysis Method (AHP) with the Business Model Canvas (BMC) can enhance effectiveness, ensuring that AHP criteria align with Business Model Canvas (BMC) elements. The AHP results indicate that the distribution channel is the top priority criterion, with a weight of 0.193. These priority results are then incorporated into the BMC strategy elements, combining both existing and new elements.

Semendo Coffee Development and Partnership Strategy to Enhance Governance for Food Security and Climate Change Mitigation

Semendo coffee is a major income source for agricultural households in the Semendo region. Agroforestry is commonly employed practice at numerous Semendo coffee plantations. Timber and Multipurpose Tree Species widely cultivated to provide shade. This study examines Semendo coffee’s governance-enhancing development and collaboration methods. SWOT (strength, weakness, opportunities, threats) and AHP (analytic hierarchy process) analysis are used to determine the most suitable governance strategies and policies for Semendo coffee development. The data was gathered from key persons who possess expertise and serve as representatives for pertinent stakeholders and commercial entities throughout the Semendo coffee supply chain. SWOT analysis shows that the Strength-Opportunity (SO) strategy could improve Semendo coffee governance. High-quality coffee production, relevant party cooperation, and marketing network expansion are the proposed strategies. The AHP says mutual need drives partnership decision-making the most. Plasma-core partnerships are best for creating a strong sense of mutual need. Integration of the SO strategy with the core-plasma partnership is proposed to improve Semendo coffee governance. Agroforestry in Semendo coffee production can produce fruits and sequester carbon, reducing climate change. Agroforestry-based Semendo coffee management is expected to improve food supply, distribution, and consumption. This development is expected to maintain environmental integrity, notably in climate change mitigation.

Study of the Development Potential of Geospatial-Based PSEL (Waste Processing into Electrical Energy) Areas

Since 1989, most of the waste processing in DKI Jakarta Province has ended at the Bantar Gebang TPST, causing the available capacity to accommodate waste to approach the maximum limit. One alternative processing that has a high level of efficiency in reducing the volume of waste is the PSEL (Waste to Electrical Energy Management) facility. Planning PSEL facilities in urban areas requires consideration from all aspects. While the space utilization in DKI Jakarta is quite dense and there are no practical guidelines in determining the location of PSEL facilities, it is necessary to conduct a comprehensive study, namely a study of the potential development of PSEL areas. The purpose of this study is to obtain the location and spatial pattern of the potential development of the PSEL area and obtain the characteristics of the area with the 'Recommended' category as the location of PSEL facility development. This study uses geospatial analysis with the quantitative Weighted Overlay method, where geospatial modeling is carried out by inputting the weight of each criterion (aspects that have an influence on determining the location of PSEL facilities) obtained from the results of the Fuzzy Analytic Hierarchy Process (FAHP) analysis assisted by a questionnaire tool. The results of the study obtained 2 (two) areas that have High Category status with a total area of 1.52 ha located in Cilincing District and Kelapa Gading District, North Jakarta with the condition that the first area occupies an improper designation zone (Industrial and Warehousing Zone) while the second area has a radius distance of less than 500 m to the nearest settlement and tourist location.

Environmental and economic assessment of hydrogen-powered ferries for inland transportation

This paper investigates the environmental and economic impacts of hydrogen-powered ferries in inland passenger and car transportation using Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA). Three types of electrolysers and two types of fuel cells, focusing on their manufacturing locations, are compared with two Marine Diesel Oils (MDOs) with various sulphur percentages. In addition, this study utilises the Analytical Hierarchy Process (AHP) to weigh and rank the environmental impacts of all power options based on expert opinions. As a result of the combination of LCA and AHP analysis, the most environmentally friendly power option is found to be the Solid Oxide Electrolyser-Solid Oxide Fuel Cell system for the case study, while the worst option is the Proton Exchange Membrane Electrolyser- Proton Exchange Membrane Fuel Cell system. Moreover, MDOs are the most cost-effective power options in all carbon-tax scenarios.

Research on constructing a safety assessment model for the “Environment-Psychology” space in urban villages based on CPTED theory

ABSTRACT This study aimed to develop an “environment-psychological” safety assessment model for urban villages. The objective was to create a comprehensive safety evaluation and identify key influencing factors. To start, establish a spatial environmental safety assessment system for urban villages, drawing on the six principles of Crime Prevention Through Environmental Design (CPTED) theory. Use Analytic Hierarchy Process and Bayesian network analysis for determining weights and scores. The research findings reveal that: 1) The safety of the spatial environment in urban villages is directly induced by factors such as “natural surveillance” and “image maintenance” while being indirectly impacted by factors like “territoriality” and “access control.” Notably, the effects of activity support and target hardening on spatial environmental safety are found to be insubstantial. 2) Z urban village in Xuzhou city has a level IV spatial environmental safety rating. Factors include line-of-sight transparency, environmental hygiene, and road layout. The proposed optimization measures include improving space layout, enhancing surveillance levels, and maintaining a favorable environment for crime prevention. This study aids decision-makers in establishing priority sequences for spatial planning during urban village renewal. The “Environment-Psychology” model enhances the explanatory power of environmental factors on psychological elements and can aid in spatial optimization strategies.

GIS-Based Spatial Analysis Model for Assessing Impact and Cumulative Risk in Road Traffic Accidents via Analytic Hierarchy Process (AHP)—Case Study: Romania

On a global scale, traffic incidents are a leading cause of mortality and material damage. Romania exhibits the highest rate of road traffic fatalities both in the European Union and worldwide, requiring a comprehensive examination of its overall influence on a national level. The current study uses an extensive approach centering on a spatial analysis model based on the Analytic Hierarchy Process (AHP). Employing a series of spatial databases, this model delineates the geographical distribution and characteristics of road accidents to establish both their cumulative national impact and the identification of high-risk areas. The spatial database, containing traffic incident data, is constructed using geolocation techniques and integrated through network analysis to evaluate the impact in relation to distance. The AHP framework is applied in analyzing the impact across five key dimensions: accident severity, occurrence mode, prevailing weather conditions, traffic restrictions, and road markings. This multi-level AHP analysis not only identifies high-risk hotspots but also confirms the effectiveness of the proposed spatial model.

Choice modelling of eCooking adoption by households in Ghana

ABSTRACT The use of electric cooking appliances, also referred to as eCooking devices, presents a practical and sustainable solution for achieving universal access to clean cooking facilities by 2030. A choice modelling analysis on the factors that influence the selection of an eCooker in Ghana has been conducted to facilitate market expansion for organisations seeking to offer clean cooking services. The study utilised Analytical Hierarchy Process (AHP), Fuzzy-TOPSIS, and Data Envelopment Analysis (DEA) methodologies to assess and rank electric cookers based on crucial factors such as kWh consumption rate, cooking efficiency, affordability, time-saving features, ergonomics, space utilisation capabilities, compatibility with cookware types, ease of cleaning and maintenance procedures, accessibility, among others. The rate of consumption was identified as the most critical factor, emphasising the significance of energy efficiency in the decision-making process. The analysis showed that the induction stove was optimal, considering both Fuzzy-TOPSIS and DEA evaluations. Although the induction stove may consume a considerable amount of electricity, its efficiency, performance, and other attributes make it the preferred choice. This study presents a systematic approach to assessing and ranking electric cooking stoves in Ghana, offering valuable insights for both consumers and policymakers.

Enhancing 5G Antenna Manufacturing Efficiency and Reliability through Blockchain and Smart Contract Integration: A Comprehensive AHP Analysis

This study pioneers the enhancement of 5G antenna manufacturing efficiency and reliability by integrating blockchain and smart contract technologies, supported by an in-depth Analytic Hierarchy Process (AHP) analysis. At the heart of our innovation lies the blockchain-based SER-M (B-SER-M) model, which delineates ‘Subject’, ‘Environment’, and ‘Resources’ as crucial factors in the manufacturing process. Our refined AHP analysis reveals ‘Subject’ as the paramount factor, with a pivotal influence weight of 0.465, underscoring its significance in elevating production efficiency and reliability. The integration of blockchain technology facilitates impeccable record-keeping and tracking at each production stage, thereby bolstering data integrity and enhancing traceability. Furthermore, the incorporation of smart contracts streamlines operations by automating processes, enabling the rapid identification and resolution of issues. These technological advancements not only significantly elevate manufacturing efficiency but also markedly improve reliability and quality control across antenna production. The enhanced results of our study demonstrate the formidable potential of integrating cutting-edge technologies in manufacturing, presenting a solid model for sustaining industry competitiveness in an increasingly digital and interconnected realm. Our contributions lay the groundwork for transformative advancements in manufacturing practices, setting a new benchmark for the integration of blockchain and smart contract technologies in enhancing 5G antenna production efficiency and reliability.

Comprehensive Performance Evaluation of an Unmanned Excavator Based on Signal Stability Extraction

The comprehensive performance of unmanned excavators is crucial for the development and optimization of the field of construction machinery. To effectively improve the unmanned excavator to meet the needs of the market, it is imperative to quantify the evaluation method of the comprehensive performance of unmanned excavators. In this study, an evaluation method combining a fuzzy analytic hierarchy process and multivariate image area analysis method is proposed. Firstly, based on the feature extraction of the signal stability of the unmanned excavators, fifteen evaluation indexes were proposed. Then, the case study is used to obtain the scores corresponding to these indexes. The fuzzy analytic hierarchy process is applied to determine the relative weight of the selected evaluation criteria, in which the uncertain and imprecise judgments of decision makers are converted into fuzzy numbers. At the same time, the braking performance of the three types of unmanned excavators was comprehensively evaluated and ranked using the multivariate image area analysis method as an empirical example. Finally, a weight analysis is performed to check the robustness of the ranking results. The results show that the proposed method is effective and feasible. It provides a reference for the performance improvement and efficiency optimization of unmanned excavators.

Spatial assessment of flood vulnerability and waterlogging extent in agricultural lands using RS-GIS and AHP technique-a case study of Patan district Gujarat, India.

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district's land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.

An integrated ecosystem service-based approach for the sustainable management of forested islands in South Korea

Implementing sustainable island forest management policies requires categorizing the islands into groups based on key indicators and establishing a consistent management system. This study proposes a novel framework to support an ecosystem service-based approach to managing forested islands in South Korea. First, a typology of six forested island categories is established based on varying extents of natural vegetation cover (as a percentage of forest cover) and connectivity to the mainland. An analytic hierarchy process (AHP) analysis was conducted with island forest experts to identify priority ecosystem services (ESs) for the sustainable management of each island type. On connected islands, provisioning services (natural resources and natural medicines) are more important than regulatory (erosion control) or supporting services (genetic diversity). On unconnected islands, particularly those with a small proportion of natural vegetation, regulatory services (erosion control) require a greater emphasis on management. Since Type 3 islands require urgent management as connectivity to the mainland makes natural vegetation=sparse island forest ecosystems vulnerable to anthropogenic activities, the land-use scoring method was performed on Jin-do. Comparisons between the AHP-derived expert demand for key island ESs and the spatial distribution of ES supply potential revealed mismatches between supply and demand for erosion control, freshwater supply and habitat provision. The framework developed in this study can help guide policy and decision-making processes and indicate where interventions should be focused to achieve sustainable island management.

Selection of municipal solid waste disposal technology using the Analytic Hierarchy Process and Genetic Algorithm for Gulf Cooperation Council Countries

Municipal Solid Waste (MSW) management represents a significant challenge in rapidly urbanizing regions, notably within the Gulf Cooperation Council (GCC) countries. This study focuses on Kuwait, exemplifying the GCC context, to identify the most suitable MSW disposal methods. We integrate the Analytic Hierarchy Process (AHP) with Genetic Algorithms (GA) for a comprehensive evaluation and optimization of disposal options, considering 13 criteria across economic, environmental, technical, and safety dimensions. Five disposal methods (landfilling, controlled dumpsites, composting, incineration, and refuse-derived fuel [RDF] combustion) are analyzed for their fit to Kuwait's unique environmental and socio-economic conditions. The AHP analysis highlights the paramount importance of "Health & Safety of Employees" (19.8%), "Water Pollution" (16.2%), and "Air Pollution Control" (15.4%) as criteria, underscoring a regional prioritization of health and environmental considerations over cost. Composting and RDF combustion emerge as the preferred disposal methods, with AHP-derived priority percentages of 22.7% and 22.3%, respectively. GA optimization further validates these methods as optimally aligned with the regional waste management framework. Conclusively, composting and RDF combustion are identified as the most feasible MSW disposal options for Kuwait, with potential applicability across the GCC, contingent on alignment with local economic and environmental policies. This research provides a critical foundation for policymakers and stakeholders in crafting sustainable, region-specific waste management strategies.