Sustainable Resource Management Research Articles

Advancements in phosphorus species profiling and bioavailability assessment with implications for phosphorus sustainability.

Advancements in phosphorus species profiling and bioavailability assessment with implications for phosphorus sustainability.

Health-related dimensions of fishers for sustainable commercial fisheries in the Atlantic Gulf of Guinea: Ecological and social assessments.

Health-related dimensions of fishers for sustainable commercial fisheries in the Atlantic Gulf of Guinea: Ecological and social assessments.

Repurposing decommissioned wind turbine blades: A circular economy approach to sustainable resource management and infrastructure innovation

Repurposing decommissioned wind turbine blades: A circular economy approach to sustainable resource management and infrastructure innovation

AI Solutions for Rice Plant Wellness

Abstract: AI solutions for rice plant wellness leverage advanced technologies to monitor and enhance the health of rice crops. By employing deep learning algorithms and image processing techniques, these solutions can accurately identify early signs of diseases, pests, and nutrient deficiencies. This real-time monitoring allows farmers to make informed decisions, optimizing their crop management practices. Additionally, AI-driven predictive analytics can forecast potential threats to rice health, enabling proactive measures that can significantly reduce crop losses and improve overall yield quality. By analyzing vast datasets, including weather patterns, soil health, and historical crop performance, AI can recommend precise interventions, such as irrigation schedules and fertilizer applications. This not only enhances the efficiency of resource use but also promotes sustainable agricultural practices. Ultimately, AI solutions for rice plant wellness contribute to food security and the resilience of farming communities in the face of climate change and other challenges.The integration of AI into rice farming not only enhances productivity but also supports the sustainable management of resources. AI technologies are revolutionizing rice farming by integrating advanced tools that optimize various aspects of agricultural practices. For instance, AI-powered drones and imaging systems are employed to monitor rice fields, detecting early signs of pests, diseases, and nutrient deficiencies. This real-time data allows farmers to respond promptly, reducing reliance on pesticides and ensuring healthier crops.

Geospatial intelligence and multi-criteria analysis for mapping groundwater potential zones and sustainable resource management in Wadi Qena Basin, Eastern Desert, Egypt

Groundwater is a rare and valuable resource in arid and hyperarid areas. Over the past few decades, population growth, urbanization, and agricultural activities—particularly in developing countries like Egypt—have greatly increased the demand for water supplies. The purpose of this study is to apply a multi-criteria analytical hierarchy process (AHP) in conjunction with remote sensing and geographic information systems methodologies to identify potential zones for groundwater recharge in Wadi Qena, Eastern Desert of Egypt. This valley is considered as one of the most potential valleys for government-led land reclamation and development initiatives. Using several data sources (e.g., Landsat-8 Enhanced Thematic Mapper Plus and Shuttle Radar Topography Mission), as well as all available geologic and hydrogeological data, thematic maps were prepared and combined based on 15 spatial criteria. Using AHP-based specialized knowledge and expert judgment, the study assigned weights to spatial criteria layers and classified Wadi Qena catchment area into five zones: very high (11.75%), high (29.30%), moderate (8.86%), low (47.55%), and very low (2.50%), with the southwestern part having a very high recharge and storage capacity. The model's accuracy was confirmed by comparing the obtained groundwater potential map with the available borehole data and daily productivity from the groundwater aquifers within the valley (e.g., ROC curve with AUC = 0.94). The results demonstrate that the integration of AHP, remote sensing, and GIS techniques is effective for precise groundwater resource assessment, planning, and monitoring in arid regions like Wadi Qena. These insights can assist decision-makers in water-scarce areas in making informed decisions regarding the conservation and sustainable management of groundwater resources.

Species Diversity of Trees with Traditional Medicinal Uses: A Core-Buffer Zone Comparison in a Biosphere Reserve

Giam Siak Kecil-Bukit Batu, a UNESCO Biosphere Reserve, uses a zoned management approach, including core, buffer, and transition zones. The core zone prioritizes biodiversity conservation, buffer zones mitigate human impacts, and transition zones focus on sustainable development. The diversity and density of medicinal tree species in the core and buffer zones of this reserve were investigated. It was hypothesized that the core zone would support greater diversity and density of medicinal trees than the buffer zone. Standardized plot sampling was conducted using 1-ha plots, each subdivided into 25 subplots (20 m × 20 m). Trees with a diameter at breast height exceeding 10 cm were inventoried, measured, and identified, with voucher specimens collected for herbarium confirmation. A total of 52 medicinal tree species from 27 families and 36 genera were recorded. Species richness was higher in the buffer zone (39 species) than in the core zone (29 species). These findings underscore the ecological significance of buffer zones in conserving medicinal tree diversity and providing sustainable resources for local communities. The observed species richness in the buffer zone challenges the conventional assumption that core zones harbor higher biodiversity, highlighting the need for further research on sustainable resource management in buffer zones.

Green AI for Sustainable Employee Attrition Prediction: Balancing Energy Efficiency and Predictive Accuracy

This study investigates the application of Green Artificial Intelligence (AI) principles to employee attrition prediction models, aiming to reduce computational energy consumption while maintaining comparable predictive accuracy to conventional approaches. The research addresses a critical gap at the intersection of sustainable AI and human resource management. A mixed factorial design was employed, evaluating six machine learning algorithms (Logistic Regression, Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbors, Decision Tree) in both conventional and Green AI-optimized versions, across three feature selection methods. Experiments were conducted on the IBM HR Analytics Employee Attrition & Performance dataset (N=1,470), with energy consumption (kWh/Wh) measured using OpenZmeter, CodeCarbon, and CarbonTracker, alongside standard performance metrics (Accuracy, F1-score, AUC-ROC). Results indicate that Green AI models achieved a significant average energy reduction of 44.8% during training and 35.6% during inference compared to conventional models. Crucially, these substantial energy savings were realized with only minimal and statistically non-significant differences in predictive performance (e.g., F1-score: Green AI M=0.62 vs. Conventional M=0.64, p=.07). Specific Green AI strategies, including model parameter optimization and feature selection, effectively reduced computational load while preserving performance. Optimized XGBoost models notably demonstrated a strong balance of high accuracy and reduced energy consumption. This research provides compelling empirical evidence that sustainable AI practices are feasible and effective in HR analytics, offering a viable pathway for organizations to mitigate their carbon footprint and operational costs without compromising the quality of predictive insights.

Innovations in Remote Sensing Techniques for Monitoring Agricultural Crops and Environmental Stress Conditions: A Review

Latest remote sensing techniques developed for monitoring agricultural crops and environmental stress conditions. Remote sensing technologies have become essential tools for monitoring agricultural crops and detecting environmental stress conditions. Recent innovations, including hyperspectral imaging, thermal sensing, microwave systems, and data fusion techniques, have significantly improved the accuracy of detecting crop health, soil moisture, nutrient deficiencies, drought, heat stress, soil degradation, and water resource availability. CubeSats and UAVs improve spatial and temporal resolution for real-time agricultural monitoring. Integrating deep learning and artificial intelligence has further advanced predictive modelling capabilities, enhancing crop yield estimation and stress detection accuracy. Despite these advancements, various challenges persist, including data quality issues, sensor calibration errors, atmospheric interference, high computational requirements, and limited accessibility for small-scale farmers. Economic barriers and ethical concerns related to data security, ownership, and privacy also hinder the widespread adoption of remote sensing technologies. Addressing these challenges requires developing cost-effective sensors, improving data fusion techniques, optimizing AI-driven models, and promoting inclusive access to advanced technologies. Future research should focus on enhancing multi-source data integration, establishing standardized protocols for ethical data management, and improving predictive models for better agricultural decision-making. Incorporating cloud computing and high-performance data analytics will also play a critical role in making remote sensing applications more efficient and accessible. Recent innovations in remote sensing technologies and their applications in agricultural monitoring while identifying existing limitations and providing recommendations for future research. Effective utilization of these technologies can significantly enhance crop monitoring, environmental stress assessment, and precision agriculture practices, contributing to improved food security and sustainable resource management.

Using big data to address global environmental challenges

Global policy frameworks such as the UN Sustainable Development Goals (SDGs) or the Kunming-Montreal Global Biodiversity Framework (KMGBF) as well as numerous EU policies related to species and habitat conservation (e.g. Nature Restoration Law, Birds Directive, Habitats Directive, Water Framework Directive, Marine Strategy Framework Directive), ecosystem services (e.g. Pollinators Initiative, Land Use Land Use Cover and Forestry Regulation, proposed Forest Monitoring Regulation) and the sustainable management of natural resources (e.g. Common Fisheries Policy, Common Agricultural Policy) highlight the urgent need to monitor changes in biodiversity, ecosystems and the natural environment. However, tracking progress towards the ambitious policy goals is challenging and requires a minimum set of measurements that are consistent across scales and regions for deriving indicators that capture the major dimensions of change. Delivering such information is supported by the development of essential variables for climate (ECVs), oceans (EOVs), biodiversity (EBVs) and geodiversity (EGVs) which can be used to characterize and monitor changes on our planet. This can advance science and inform policy. Our knowledge, management and governance of the Earth system ultimately depends on diverse measuring tools and multiple data types, including remote sensing and in-situ data collection with field samples and experiments. For monitoring of biodiversity and ecosystems, EBVs can provide consistent knowledge about multiple dimensions of biodiversity change across space and time. For such variables, diverse data types are required, including structured in-situ observations, citizen science data, and time-series data collected through cutting-edge methods. These cutting-edge methods span DNA-based techniques like eDNA metabarcoding; digital sensors such as cameras, acoustic devices, and GPS tags; and remote sensing technologies, including satellites, drones, airplanes, and weather radars. In the era of “big data”, the vast and often unstructured datasets cannot easily be downloaded or analyzed without advanced, high-throughput processing pipelines. Transforming such data into actionable insights therefore involves applying FAIR (Findable, Accessible, Interoperable, Reusable) principles, integrating heterogeneous data from multiple sensors, testing the robustness and transferability of models and metric calculations, developing automated and transparent processing workflows, leveraging parallel or distributed computing, and employing cloud-based virtual research environments to streamline the analyses. These processed and standardized biodiversity data can be utilized for a variety of applications, including the construction of data cubes for spatiotemporal analysis, the building of models and tools for biodiversity and ecosystem change analysis, and simulations and scenarios using Digital Twins and other forecasting tools. Additionally, artificial intelligence (AI), particularly deep learning, has emerged as a powerful tool for analyzing big and complex datasets, such as imagery from satellites and unmanned aerial vehicles (UAVs), wildlife and insect camera images, acoustic recordings, and LiDAR point clouds. The integration of remote sensing and in situ observations, harmonized data and models, and the use of automatic recorders with AI algorithms will substantially advance biodiversity and ecosystem monitoring. This can provide improved support for species and habitat conservation policies and land use management, and enable science-driven strategies to address global environmental challenges.

Chromosome-level genome assembly of scalloped spiny lobster Panulirus homarus homarus

Lobsters, aquatic organisms of significant economic value, hold an important position in the global aquaculture and fisheries industries. However, due to overfishing and ecological change, the populations of certain lobster species have declined dramatically, prompting conservation efforts in various countries. However, limited genomics research has restricted our capacity to conserve and exploit lobster germplasm resources. Here, we present a chromosome-level reference genome for Panulirus homarus homarus constructed using PacBio long-read sequencing and Hi-C data. The genome assembly size was 2.61 Gb, with a contig N50 of 5.43 Mb, and a scaffold N50 of 36.69 Mb. The assembled sequences were anchored to 73 chromosomes, covering 96.05% of the total genome. A total of 25,580 protein-coding genes were predicted, and 99.98% of the genes were functionally annotated using various protein databases. The high-quality genome assembly provides a valuable resource for studying the biology and evolutionary history of P. h. homarus, and could facilitate sustainable resource management, aquaculture, and conservation of the species.

Driving Efficiency and Sustainability: Integrating Industry 4.0 and Sustainable Human Resource Management Practices in the Logistics Service Sector

This study investigates the impact of Industry 4.0 technologies, including IoT, big data, and smart factories, on logistics sector performance, along with the moderating role of sustainable Human Resource Management (HRM) practices. Data from 552 respondents in Pakistan’s logistics industry were analyzed using structural equation modeling. The results demonstrate that Industry 4.0 implementations positively influence logistics service sector performance, while sustainable HRM practices significantly mediate between Industry 4.0 and performance. Specifically, integrating technologies from Industry 4.0 facilitates operational optimization, cost reduction, and enhanced efficiency in goods delivery. This study underscores the importance of strategic alignment between technological advancements and sustainable HRM practices in fostering logistics service resilience and competitiveness. By leveraging Industry 4.0 innovations and prioritizing sustainable HRM practices, logistics companies can effectively navigate the evolving market landscape and achieve long-term success. Moreover, this study offers a novel roadmap for logistics companies, showing how the synergy between sustainable HRM practices and Industry 4.0 can pave the way for resilience and success in an ever-evolving market landscape.

Sustainable Human Resource Management and Career Quality in Public Utilities: Evidence from Jordan’s Electricity Sector

This study investigates the impact of human resource management (HRM) practices—specifically planning, recruitment, training, and motivation—on dimensions of career quality (job security, promotion equity, and participatory decision-making) among employees of the Jordan Electricity Distribution Company (JEDCO). Utilizing a quantitative cross-sectional survey design, data were collected from 173 employees, allowing for an in-depth exploration of their perceptions and experiences regarding HRM practices. The findings reveal that both training and motivation significantly enhance career quality, with employees who receive advanced training reporting a stronger sense of job security and an increased likelihood to participate in decision-making processes. In contrast, the effects of recruitment and planning practices were found to be marginal due to perceived biases and strategies that fail to adequately address the long-term needs of the workforce. Despite moderate overall career quality scores, key areas for improvement were identified, particularly in job security and employee involvement. This study offers actionable recommendations for JEDCO, such as implementing AI-driven recruitment tools to mitigate nepotism and developing gamified training modules to enhance skill development. Furthermore, it underscores the importance of integrating HRM reforms into Jordan’s National Energy Strategy, thereby supporting Sustainable Development Goal 8. This research represents the first empirical examination linking HRM practices to career quality in Jordan’s energy sector, offering a framework applicable to public utilities in emerging economies (e.g., Lebanon’s EDL). This research extends Social Exchange Theory into non-Western hierarchical contexts, demonstrating how bureaucratic inertia and tribal affiliations weaken reciprocity dynamics—a novel boundary condition contrasting Western-centric SET models.

Toward Resilient Implementation of Land Degradation Neutrality via Systemic Approaches

Land Degradation Neutrality (LDN) is an ambitious initiative by the United Nations Convention to Combat Desertification (UNCCD) to tackle land degradation. Inspired by the “no net loss” concept, LDN seeks to counterbalance unavoidable land degradation—primarily driven by food systems—through targeted regenerative actions at multiple scales—such as regenerative agriculture or grazing practices that simultaneously support production and preserve land fertility. The objective is to ensure that degradation does not surpass the 2015 baseline. While the UNCCD’s Science–Policy Interface provides guidance and the LDN Target Setting Programme has led many countries to define baselines using agreed indicators (soil organic carbon, land use change, and primary productivity), concrete intervention strategies often remain poorly defined. Moreover, the voluntary nature of LDN has limited its effectiveness. A key shortcoming is the lack of integrated planning. LDN should function as a “Plan of Plans”—a coordinating framework to align policies across sectors and scales, reconciling conflicting agendas in areas such as food, energy, and water. To this end, we advocate for a systemic approach to uncover synergies, manage trade-offs, and guide decision-making in complex socio-ecological landscapes. Land degradation is intricately linked to issues such as food insecurity, land acquisitions, and transboundary water stress. Although LDN is implemented at the national level, its success also depends on accounting for global dynamics—particularly “LDN leaks”, where land degradation is outsourced through international trade in food and raw materials. In an increasingly complex world shaped by globalization, resource depletion, and unpredictable system dynamics, effective responses demand an integrated socio-ecological management approach. LDN is not simply a strategy to address desertification. It offers a comprehensive framework for sustainable resource management, enabling the balancing of trade-offs and the promotion of long-term resilience.

Smart water leak detection using wireless sensor networks

This study explores the use of remote sensor systems to detect leaks in underground water pipelines, aiming to tackle the problem of water loss in distribution networks. Identifying and preventing such leaks is essential for the sustainable management of natural water resources. To address this challenge and improve leak detection, the authors designed a remote system that utilizes flexible wireless sensors to identify leaks while minimizing energy consumption. This system also evaluates the time and cost efficiency of smart water leakage detection (SWLD) in pipelines, monitors water levels in storage tanks, and automatically activates controls when water levels drop below a set threshold. The proposed approach is built around two main components. The first utilizes GSM (Global System for Mobile Communications) technology to send SMS alerts to the owner. Core elements of this system include sensors, a GSM module, an Arduino board, and a switch for device control. The second component features an Android-based application that allows for remote management of the system. By enabling early leak detection, the system aims to lower the costs and time involved in pipeline maintenance, enhance operational efficiency, and reduce long-term repair efforts.

Sustainable human resources management in small-medium enterprises in developing countries: a systematic literature review

PurposeThis study aims to examine the crucial role of implementing sustainability strategies in the workplace. Scholars have devoted scant attention to exploring the positive outcomes of the “social” dimension of sustainability within Human Resources Management (HRM) policies in developing countries.Design/methodology/approachThis study performs a systematic literature review of articles published from 2014 to 2024, on Web of Science® and Scopus databases, coupled with a bibliometric study that visualised, through VOSviewer software, the link between the “social” dimension and HRM. In total, 35 articles were selected and analysed.FindingsThe results reveal that organisational support could allow employees to reach their highest performance in firms and society simultaneously. The study provides new insights by proposing theoretical approaches to advance the sustainable HRM literature.Research limitations/implicationsThe study demonstrates a significant evolution of theories applied to understand sustainable HRM. Organisations have a dual objective: Support employee well-being and optimise their performance. We showed that organisational support is an indispensable lever for organisational success.Practical implicationsThe results suggest that businesses can promote sustainability among their staff by adopting sustainable HRM. Firms can thus demonstrate their commitment to sustainability while enhancing their credibility and competitive advantage.Originality/valueThis article focuses on Sustainable Human Resources Management (HRM) in SME in developing countries by mobilising a systematic literature review.

MaxEnt Modeling of the Impacts of Human Activities and Climate Change on the Potential Distribution of Plantago in China.

Human activities exert both beneficial and detrimental impacts on the ecosystem. In recent years, greenhouse gas emissions have significantly increased due to global climate change, causing profound alterations in ecosystem distribution and productivity. The synergistic interplay between climatic shifts and anthropogenic activities is intensifying ecological transformations and disturbances, and accelerating biodiversity depletion. The Plantago genus (Plantaginaceae family) includes 14 herbaceous species among China's flora. This study was conducted to elucidate the spatial distribution of Plantago species patterns across China and evaluate their differential responses to impending climate change and human interventions. In this study, we projected the potential distributions of Plantago species under three climate scenarios (SSP126, SSP245, and SSP585) across current and future temporal intervals (2021-2040, 2041-2060, 2061-2080, and 2081-2100) using the MaxEnt model integrated with ArcGIS V10.8 spatial analysis. A spatial trend analyses was also conducted to assess habitat suitability dynamics by incorporating anthropogenic influence parameters. The model validation yielded AUC values exceeding 0.9, demonstrating excellent model performance and predictive reliability. Precipitation variability and anthropogenic pressure emerged as the most predominant determinants shaping Plantago distributions. Centroid migration analyses further indicated the progressive northward displacement of optimal habitats under the projected climate scenarios. These findings significantly advance our understanding of Plantago species' adaptive responses to environmental changes. This study also offers an invaluable scientific foundation for sustainable resource management and ecological conservation strategies.

Human Capital to Implement Corporate Sustainability Business Strategies for Common Good

The International Financial Reporting Standards (IFRS, 2023) guidelines have indicated the importance of holistic organisational sustainability values (profit, people, and planet) and the required human capital to implement sustainability business strategies to achieve sustainable development goals (SDGs). This empirical research using the strategic choice and sustainable human resource management resource-based theories explores the role of high-performance sustainable work practices (HPSWPs) with sustainability characteristics to shape the required human capital to implement simultaneous environmental, social, and governance (ESG) corporate sustainability business strategies aligned with the organisational sustainability orientation of firms. A total of 203 senior managers from Australian companies participated in this study. The participants completed survey questionnaires, which encompass the holistic organisational sustainability orientation, corporate sustainability business strategy, and high-performance sustainable work practices. The mediation study findings revealed that the social consciousness, stakeholder compassion, ethics of care for wellbeing, and pro-environment characteristics of high-performance sustainable work practices fully mediate the implementation of ESG corporate sustainability business strategies that are aligned with the holistic organisational sustainability orientation. This exploratory research extends the operational strategic choice theory from the sustainable human resource management resource-based perspective in highlighting the role of high-performance sustainable work practices in implementing the choice of environmental, social, and governance (financial) business strategies. Furthermore, the practical implications include improving the quality of voluntary sustainability disclosure by companies in alignment with the IFRS guidelines on management approaches relating to human resource practices to shape the required human capital with sustainability characteristics for corporate sustainability. Future empirical research directions in operationalising simultaneous ESG corporate sustainability business strategies using high-performance sustainable work practices aligned with the holistic sustainability orientation of firms are discussed.

Assessing the Implementation of Forest Tenure Reforms in Kenya

This paper delves into the intricacies of forest tenure reform (FTR) in Kenya, highlighting its outcomes shaped by institutions responsible for reforms implementation. Embracing participatory forest management (PFM), these reforms have catalyzed institutional changes, notably the establishment of the Kenya Forest Service (KFS) and the formation of Community Forest Associations (CFAs). However, challenges persist, including capacity building, leadership effectiveness, budget allocations for collaborative activities, and recognition of customary authority. The study, conducted in 2017, explored the state institutional contexts of forest tenure reform, using a fixed questionnaire applied across all counties involving 5-9 respondents from state implementers of forest tenure reform in each county for a total of 26 respondents. Findings focus on Leadership, customary tenure system and collaborative practice. Key findings emphasize the need for holistic approaches at both National and Sub-national levels, considering social, economic, and political factors to ensure equitable and sustainable forest resource management. Recommendations include targeted capacity-building initiatives, enhanced information exchange, and improved coordination between national and subnational governance levels. Addressing customary authority recognition and fostering local-level engagement are crucial steps towards effective forest tenure reforms in Kenya.

Balancing Growth and Sustainability: Can Green Innovation Curb the Ecological Impact of Resource-Rich Economies?

The global economy faces a critical challenge: balancing economic survival through natural resource utilization with the imperative of long-term environmental sustainability. Green innovation presents a viable solution, yet its effectiveness hinges on establishing well-structured legislative frameworks. This study, covering the period 1996 to 2022, examines the moderating effect of green innovation on the relationship between natural resource rents and ecological footprint while also considering the roles of globalization, financial development, and energy transition in the ten most resource-abundant countries. Utilizing the augmented mean group (AMG) estimator, the findings indicate that natural resource rents significantly contribute to ecological footprint, reinforcing concerns about resource-driven environmental degradation. However, green innovation mitigates these adverse effects, promoting sustainable resource management in alignment with SDG 12 (Responsible Consumption and Production). Additionally, renewable energy and globalization positively influence environmental conditions, reinforcing the drive toward clean and affordable energy (SDG7), while economic growth, financial development, and non-renewable energy exacerbate environmental harm. Furthermore, foreign direct investment (FDI) increases ecological footprint, reinforcing the Pollution Haven Hypothesis for resource-rich economies. Rigorous robustness checks using CCEMG, FMOLS, and DOLS methodologies, along with country-specific analyses, affirm the empirical validity of these results. In light of these conclusions, the paper advocates for legislative reforms to enhance sustainability and optimize resource utilization, ensuring a balanced approach to economic development and environmental preservation.

Towards sustainability and circular economy: conceptual design model for the water-energy-food nexus in Ferdaws Village, Western Desert, Egypt

Sustainable management of natural resources is a pressing global challenge that requires the prevention of loss and waste. Egypt is facing an acute water crisis due to insufficient water supplies throughout the country. To address this issue, it is essential to implement a range of solutions to improve the situation. This study aims to alleviate the stress on multiple resources and environmental impacts in one of Egypt's earliest reclaimed areas (Wadi El-Natrun, Western Desert), by applying systematic modeling to achieve sustainable development objectives and mitigate resource stress. To promote a more sustainable and resilient future, a pilot model ecovillage has been designed for a community of three thousand inhabitants, promoting integrated resource management and considering the Water-Energy-Food (WEF) nexus concept. The ecovillage design incorporates collaborative engagement of groundwater reservoirs for water supply, agricultural production for food demands, power generation, and community sensitivity. Evaluating key aspects related to water supply, power generation, agricultural production, and environmental impact within the proposed pilot design aims to provide insights into the complex interactions among these systems. A four-step methodology for evaluating and implementing the nexus at a local level, which includes identifying linkages, mapping interconnections, focusing on core foundations, and applying the findings to improve project execution was investigated. An understanding of the WEF nexus, demonstrating how such frameworks can enhance the management of scarce resources was provided. The “TORAY DS2” design is employed to analyze model parameters and quantify their values. The study’s findings aim to support decision-makers and stakeholders in developing effective strategies to navigate the complexities of the WEF Nexus, promoting optimal resource management. These findings are promising, indicating a low environmental risk while aligning with both the WEF Nexus and the Sustainable Development Goals.