ESTIDAMAA (E_ISSN: 3078-428X) is an academic journal dedicated to publishing research in the field of sustainability. Published by Peninsula Publishing Press in Dubai, ESTIDAMAA serves as a platform for researchers, academics, and professionals to explore innovative solutions that address environmental, social, and economic challenges. The journal welcomes original research, reviews, and case studies on topics such as sustainable development, renewable energy, climate change, green technologies, and sustainable urban planning. ESTIDAMAA aims to contribute to the global dialogue on creating a sustainable future by promoting interdisciplinary and impactful research.
Explainable Artificial Intelligence Framework for Sustainable Energy Management and Data-Driven Decision Making
In today's world with the considerable rise in the energy consumption of Energy Systems, the necessity to intelligent and sustainable management of energy has surfaced as a key requirement for energy efficiency and effective data-driven decision making. In recent years, AI and ML techniques have proven to be a promising way to predict energy consumption patterns, yet most of the existing solutions are black-box models, which lack interpretability. This limitation reduces trust in the users and lowers the likelihood of implementing AI solutions in the energy sector that promote sustainability. This study suggests an Explainable Artificial Intelligence Framework for Sustainable Energy Management and Data-Driven Decision Making to offer correct and clear energy analysis, combining machine learning based prediction with Explainable AI techniques. The proposed framework compares the performance of three models of machine learning namely Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) with an energy consumption dataset that features environmental and operational parameters. The performance of the models implemented are assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R²) of regression. Two methods of explaining the contribution of features and individual prediction results, namely, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) are embedded to explain the individual prediction outcomes and feature contribution in an enhanced transparency of the model. The results of the experiments confirm that the developed models have a good prediction performance, with the XGBoost model being the most successful approach for forecasting. Moreover, the explainability analysis reveals the most influential factors on energy consumption and gives valuable insights for optimization of sustainable energy. The proposed framework aims to balance predictive accuracy with interpretability of the models by converting traditional AI-based energy prediction models into transparent decision support models. The developed approach can help the energy manager and stakeholders to understand the consumption pattern, optimize the operational of the system and contribute in taking the initiatives for sustainable consumption.
Explainable Artificial Intelligence (XAI)
Copyright (c) 2026 Musaria Karim Mahmood, Mohammad Abdullah Abbas https://creativecommons.org/licenses/by/4.0
Generative AI Data Centers and the Net-Zero Challenge: A Multi-Dimensional Assessment of Energy Demand and Carbon Emissions
As Generative Artificial Intelligence (AI) is booming, demands for large-scale data center infrastructure are skyrocketing and electricity usage, carbon emissions and global Net-Zero goals are a growing concern. This study introduces a lightweight Python-based simulation framework to evaluate the long-term environmental sustainability of Generative AI data centers across four key development pathways: Baseline, High Growth, Efficiency, and Green Transition. The proposed framework combines publicly available energy and environmental data sets to provide an estimate of annual electricity demand, carbon emissions, renewable energy contribution, Power Usage Effectiveness (PUE), cumulative emissions, and the Net-Zero Gap Index (NZGI) from 2025 to 2050. Results from simulations show that significant differences in environmental performance exist between the scenarios investigated. The least sustainable development pathway is the High Growth scenario which has the highest demand for electricity (3000 TWh), carbon emissions (540 MtCO₂e), and cumulative carbon emissions (13,234.70 MtCO₂e) by 2050. The Green Transition scenario both reduces electricity demand (1350 TWh) via optimising operational performance in the system, and provides an energy mix with the lowest annual carbon emissions (64 MtCO₂e) and highest level of renewable energy (90%) to meet future climate targets, with the smallest Net-Zero Gap Index (1.88), showing greatest alignment with future climate targets. The results show the potential benefits of optimizing the use of energy in data center operations, and how quickly renewable energy can be deployed to maximize potential environmental impacts of Generative AI infrastructure. The proposed framework is transparent, computationally efficient and reproducible, and can offer decision support tools for researchers, policymakers and industry stakeholders to follow sustainable AI development paths towards Net-Zero.
Copyright (c) 2026 Tirus Muya, Kassem Hamze https://creativecommons.org/licenses/by/4.0
Applying f-CIMAS for Expert-Based Criteria Weighting in Sustainable Landfill Site Selection
In developing countries like Libya, where unplanned waste disposal jeopardizes ecological integrity and impedes the advancement of cleaner industry, choosing sustainable landfill sites is a crucial concern. In order to choose landfill sites in Misurata, Libya, this study suggests an integrated multi-stage methodological framework for expert-based criteria weighting. Eleven geographical and environmental criteria were identified via a thorough examination of the literature. A standardized Likert-scale questionnaire was used by a panel of six domain experts to assess these criteria. Prior to any mathematical modeling, inter-expert agreement was statistically verified using Kendall's Coefficient of Concordance (W = 0.5496), which confirmed moderate-to-good consensus. The criteria were then screened and narrowed down to six high-impact characteristics using the Relative Importance Index (RII) and Standard Deviation (SD): environmental effects, population density, land cover, land use compatibility, soil type, and groundwater depth. In order to reflect the inherent ambiguity of professional judgment, the Fuzzy Criteria Interaction Management and Assessment System (f-CIMAS) was subsequently implemented, utilizing Triangular Fuzzy Numbers and expert weights generated from experience. The findings indicate that land cover (16.91%), land use compatibility (16.62%), soil type and groundwater depth (15.75% each), and environmental effects and population density share the greatest priority weight (17.49% each). This research demonstrates the use of f-CIMAS to landfill site selection, providing a rigorous, impartial framework for decision-making that supports Libya's objectives for cleaner production and sustainable waste infrastructure.
Copyright (c) 2026 Zakaria Maafa, Ibrahim Badi https://creativecommons.org/licenses/by/4.0
Zoogeomorphological Influences on Wildlife Conservation and Management: A Systematic Review and Case Study of Maasai Mara National Reserve, Kenya
Zoogeomorphology, which is the mutual effect of biological activity and landforms, provides a significant yet underused framework for evidence-based wildlife conservation and management. This paper seeks to review international literature on the importance of zoogeomorphological processes toward biodiversity conservation in savanna ecosystems with a focused case study at Maasai Mara National Reserve (MMNR), Kenya. The Maasai Mara happens to be one among many other species-rich savanna landscapes in the world under increasing pressures from climate variability, land-use change, and human activities that create challenges for effective conservation planning. A structured search protocol was used to carry out this review which revealed 86 studies as relevant documentation on how fauna create landforms through processes like trampling, grazing, digging, burrowing, dunging, and wallowing among others influencing soils and hydrology vegetation structure habitat availability as well as species interactions. Evidence has been presented here regarding large mammals playing the role of ecosystem engineers creating heterogeneity in habitats resource distribution as well as population dynamics over different scales. The case study from Maasai Mara brings out these interactions practically by showing how activities of wildlife and livestock around water points floodplains migration corridors significantly demarcate landscape structure ecological viability. Results indicated extensive documentation on zoogeomorphological effects yet confirmed that such events were almost entirely absent from formal integration into conservation planning monitoring frameworks or any regulatory instruments. The study also suggested that management strategies based on insights from zoogeomorphology could enhance ecosystem resilience improve habitat connectivity and foster adaptive conservation under new environmental conditions. It highlighted the imperative need for incorporating landform–biota interactions into wildlife management practices to achieve greater long-term sustainability of savanna protected areas within Kenya and beyond.
Copyright (c) 2026 Fredrick Kayusi, Petros Chavula, Collins Ochumbe https://creativecommons.org/licenses/by/4.0
Integration Value Engineering and Quality of Maintenance and its Impact on Product Quality Improvement: Field Research at Electrical and Electronic Industry Companies in Baghdad
This research aims to study the impact of integrating value engineering (VE) and quality maintenance (QM) on improving product quality in manufacturing industries. Value engineering focuses on improving product functionality while reducing costs, while quality maintenance ensures that equipment and procedures are optimized for the purpose of preventing failures and defects. Combining these two variables is expected to improve operational efficiency, reduce equipment downtime, and enhance overall reliability and product quality, of electrical and electronic industry companies' products. in Baghdad. A questionnaire was used to determine the opinions of a sample of technicians of size (280) technician in research field companies. The research reached a number of conclusions, the most important of which were: The integration effect of both value engineering technology and quality of maintenance on product quality, has been determined. They enhance the quality of the companies' products in the research field.
Copyright (c) 2026 Wid A. Abdul-Jabbar, Qasim N. Alwan, Maad M. Mijwi, Ali S.Abed Al Sailawi https://creativecommons.org/licenses/by/4.0
Temporal Trend Analysis of Water Quality Index for Sustainable Water Resource Management
This study aimed to explore the dynamics of water quality indices (WQIs) and their relationship with various physicochemical parameters, addressing a critical gap in understanding adaptive management's role in water quality enhancement. Utilizing a comprehensive dataset of 29,159 records and 24 variables, including key numeric parameters such as alkalinity, ammonia, BOD, chloride, and dissolved oxygen, we analyzed water quality trends over several years. The study employed robust statistical methods to assess seasonal and annual variations in WQI and its correlation with physicochemical factors. Results showed a significant upward trend in WQI scores and dissolved oxygen levels, indicating an overall improvement in water quality. The implementation of adaptive management strategies, including pollution mitigation and regulatory measures, was linked to these positive trends. Seasonal fluctuations were observed, with peak WQI improvements during late spring and autumn, correlating with increased precipitation and runoff events. Specifically, dissolved oxygen concentrations exhibited a statistically significant increase (p < 0.05), reinforcing the effectiveness of adaptive interventions. Our findings suggest that adaptive management strategies, which integrate real-time data and account for climatic variability, are effective in enhancing water quality. These results indicate that targeted adaptive interventions can significantly improve aquatic health and resource resilience. Policymakers and water managers are encouraged to adopt these strategies, emphasizing continuous monitoring and adaptive responses to environmental challenges. Despite limitations such as reliance on historical data, the study provides a strong foundation for future research to build upon. The insights offered here contribute valuable guidance for the development of sustainable, adaptive water quality management practices.
Copyright (c) 2025 Fredrick Kayusi, Enio Deneko https://creativecommons.org/licenses/by/4.0
Strengthening Africa's systems for climate change resilience: The role of Climate Information Systems (CIS)
This study examines the role of Climate Information Services (CIS) in strengthening Africa’s resilience to climate change amid increasing climate variability and extreme events. The objective is to assess the current state, effectiveness, and challenges of CIS across the continent and to identify pathways for enhancing their contribution to climate-resilient development. Methodologically, the study adopts a mixed qualitative approach that combines a critical review of scholarly and policy literature, systematic document analysis, stakeholder consultations, and comparative case studies from East, West, and Southern Africa, complemented by thematic analysis to synthesise key patterns and insights. The results show that CIS adoption has increased across Africa, particularly in agriculture, where access to timely and tailored climate information has contributed to improved decision-making, higher crop yields, and reduced climate-related economic losses. However, the findings also reveal uneven coverage and utilisation of CIS, driven by persistent constraints such as limited observational infrastructure, weak institutional capacity, low literacy levels, digital divides, and pronounced gender disparities that restrict access for women and rural communities. The discussion highlights that innovations such as downscaled climate products, mobile-based dissemination, digital platforms, and public–private partnerships can significantly enhance the relevance and reach of CIS when coupled with strong user engagement and co-production of knowledge. In conclusion, CIS are a critical tool for building climate resilience in Africa, but their transformative potential will only be realised through sustained investment in data systems, inclusive and gender-responsive service design, and stronger integration into national and regional development and adaptation frameworks.
Copyright (c) 2025 Petros Chavula, Fredrick Kayusi, Srinivas Kasulla, S J Malik, Harshit Mishra https://creativecommons.org/licenses/by/4.0
A robot design that helps increase agricultural yields while providing psychological comfort to the use
The population-driven, urbanization-driven, and labor shortage-driven global demand for food increasingly requires technological innovation to meet such a need. Agricultural robots are among the most recent technological innovations aimed at enhancing productivity, efficiency, and sustainability while improving the state of psychological and physical health of their human operators. The current article reviews studies reporting on the design and control of those robotic systems that set an objective not only to increase yields but also to increase psychological comfort through approachable and collaborative Human–Robot Interaction (HRI). Agroautomation technological drivers are discussed in this paper. HRC is presented as a framework for adaptive user-centered system design with necessary factors regarding cognitive ergonomics levels of autonomy and interfaces for cooperation—such as gesture- and electromyography-based control in unstructured environments. Analysis regarding design considerations, challenges in autonomy, and nonverbal communication methods relevant to achieving safe, efficient, and psychologically supportive interaction between humans and robots is provided. With advanced robotics, artificial intelligence, and Internet of Things (IoT) technologies, collaborative robotic systems have the potential to transform modern agriculture- increasing productivity while improving human well-being. It is this techno logically intensified wave that grows fields of hope.
Copyright (c) 2025 Noah Mohammed Saleh, Raed A. Hasan, Mehdi Qahraman Fakhruldin, Ali Mohammed Saleh, Muhaiman faleh hamad, Ahmed H. Ahmed, Muqdad Daham Jasim, Hamza Omar Sadeeq https://creativecommons.org/licenses/by/4.0
Mechanically-Informed Design of Flexible Solar Cells: Coupled Deformation–Efficiency Modeling
The Flexible solar cells are the most potential type of solar cells to enable wearable, foldable, and conformable energy harvesting systems. Therefore, mechanical fragility and strain-induced performance loss remain scientifically challenging limitations for their large-scale deployment. A new mechanically informed design framework is developed in this work that clearly couples deformation mechanics with a photovoltaic efficiency model capable of predicting strain-driven degradation as well as accounting explicitly for it. A typical architecture of perovskite-based flexible solar cells is analyzed using integrated finite element simulations together with a strain-dependent drift-diffusion model. The mechanical module solves stress/strain distributions due to bending/stretching/cyclic loads while the electrical module contains constitutive relations between local strains and bandgap shifts; mobility changes (variation) in carrier transport; recombination dynamics(parameters). Direct importation (feeding) deformation fields into an electronic solver allows mapping at what scales(load scale or device scale) does charge transport & power conversion efficiency get affected by load. The results find optimum substrate modulus, active-layer thickness, and kirigami-inspired geometric design that reduce peak strain and increase fatigue life (mechanically resilient photovoltaic architecture) to provide clear guidelines. This work proposes a framework to fill the critical gap between material mechanics and solar-cell physics by framing an approach pathway for next-generation flexible solar cells with high durability, stability, and energy performance through a predicted design.
Copyright (c) 2025 Mehdi Qahraman Fakhruldin, Raed A. Hasan, Khalil F. Yasin, Ali Mohammed Saleh, Muqdad Daham Jasim, Noah Mohammed Saleh, Omar K. Ahmed https://creativecommons.org/licenses/by/4.0
Assessing Wind and Solar Resource Potential in the Kurdistan Region of Iraq for On-Site Oilfield Electrification
This study clearly illustrates that oilfields in Kurdistan are capable of eliminating the use of diesel by integrating hybrid solar-wind systems. The resource assessment performed for major fields indicates steady high values of solar irradiance (average between 5.4 and 5.9 kWh/m²/day) with moderate wind speeds appropriate to be integrated with turbines. Simulation outcomes from a GIS-based site appraisal, Weibull-oriented wind characterization, and HOMER Pro optimization affirmed that hybrids cut operational dependency on diesel generators drastically — fuel consumption drops by fifty-eight to sixty-seven percent in all locations where hybrids are installed. Some of the reasons these configurations have competitive levelized costs of energy between 0.045 and 0.068 USD/kWh which is well below the costs from electricity generation based on current diesel systems include lower net present cost improved system performance due to battery supported autonomy increased reliability as well as emission reduction which translates tens of thousands. It notes that hybridization not only yields economic results but also falls in line with the broader energy transition agenda of Iraq whereby it enables cleaner, decentralized, and more resilient power systems for remote oilfields. These results equally affirm renewable-driven hybrid microgrids as an economically compelling pathway and technically robust support for sustainable oilfield electrification in Kurdistan Region.
Copyright (c) 2025 Raed A. Hasan, Omar K. Ahmed, Khalil F. Yasin, Noah Mohammed Saleh, Ali Mohammed Saleh, Ahmed Hasan Ahmed, Muqdad Daham Jasim, Mehdi Qahraman Fakhruldin https://creativecommons.org/licenses/by/4.0
Harnessing climate information systems and artificial intelligence (ai) innovations in enhancing resilience to climate change in Africa
The ocean-climate relationship determines a unique tropical architecture which produces increased seasonal and temporal variation of wind, waves, currents and thermal stratification. The objective of the is to identify how Climate Information Systems (CIS) and emerging artificial intelligence (AI) technologies can benefit Africa's climate change resilience, with a focus on Kenya's exposed coastal economies. The study employed mixed-methods approach on the selected population stratified random sampling was used to select 225 individuals who were administered structured questionnaires, interviews and Focused Group Discussion. Quantitative and qualitative methods were both used to study data such as Statistical packages and regression analysis. The results were that CIS has a high level of awareness of 40.9% "Very Aware," and 41.8% of the respondents use AI occasionally. Regression analysis reveals a high positive relationship (R = 0.789) of CIS and AI with resilience explaining 61.9% of variance, highlighting indicators such as CIS awareness (coefficient = 0.441) and use of AI (coefficient = 0.358) as excellent predictors, whereas bottlenecks such as lack of infrastructure (36.0%) and awareness (28.4%) hinder effective implementation. The study recommends that mounting of gas analyzer sensors on smartphone to convert physical changes into electrical signals that can be interpreted by AI algorithm systems and automatically send collected data to various stations. The stakeholders should raise awareness and education on CIS and AI, invest in necessary infrastructure, and leverage partnership among the government, academia, and the private sector to mitigate challenges and develop resilience to climate change in Kenya's coastal economies.
Copyright (c) 2025 Fredrick Kayusi https://creativecommons.org/licenses/by/4.0
AI-Augmented Scrum: A Unified, Explainable Framework for Agile Software Development
In an era characterized by accelerated software delivery, enterprises are increasingly required to develop high-quality software products at scale while reducing costs and meeting tighter delivery schedules, yet traditional Scrum-based Agile frameworks, despite enabling iterative and flexible development cycles, often struggle to effectively address critical challenges such as accurate backlog prioritization, reliable sprint capacity forecasting, and comprehensive user story quality assurance, challenges that become even more significant in large-scale, distributed, and data-intensive environments where planning accuracy, transparency, and explain ability are paramount, and to overcome these gaps, this study proposes AI-Augmented Scrum, a unified, explainable, and enterprise-ready engineering framework inspired by Google-scale practices that seamlessly integrates artificial intelligence (AI) into the Agile development lifecycle through three tightly coupled modules, the first being AI-powered backlog prioritization which enhances the traditional Weighted Shortest Job First (WSJF) technique by leveraging hybrid machine learning models and heuristic-driven estimations of business value, time criticality, and risk reduction to enable objective, data-driven, and auditable prioritization decisions, the second being probabilistic sprint capacity forecasting which combines Monte Carlo simulations with Bayesian bootstrapping to generate high-confidence and uncertainty-aware predictions of team velocity and sprint capacity, and the third being an AI Coach for user story quality which employs advanced natural language processing (NLP) and semantic analysis to identify ambiguity, detect missing acceptance criteria, flag oversized tasks, and uncover hidden dependencies, thereby improving backlog completeness and reducing sprint failure rates, and to validate the framework, experiments were conducted using real-world datasets extracted from Jira and GitHub Projects alongside synthetic product backlogs containing over 120 user stories and historical sprint velocity data across ten sprints, and the results demonstrate that AI-Augmented Scrum achieves 97% prioritization accuracy, surpasses manual WSJF methods by 32%, delivers 95% sprint forecast reliability with a 37% improvement over traditional approaches, and enhances user story quality by 96%, offering a scalable, transparent, and adaptive pipeline applicable to diverse domains such as healthcare, fintech, IoT, and AI-driven product innovation.
Copyright (c) 2025 Yasmin Makki Mohialden, Nadia Mahmood Hussien, Saba Abdulbaqi Salman, Ahmed Hussein Ali https://creativecommons.org/licenses/by/4.0
Analytical assessment of green AI architectures for advancing sustainable resource use in circular economies
This We present a concise analytical write-up focusing on green AI architectures and sustainable resource use in circular economies. Two chenges in the transition to a circular economy are insufficient resource efficiency and sustainability assessment of decisions affecting resource use. AI-enabled solutions can contribute positively but often with energy and emissions costs that undermine their ecological value. The analytical lens examines green AI architectures that avoid, minimize or counterbalance adverse energy, data, and life-cycle sustainability impacts. The idea behind the circular economy is to use as few resources as possible and keep and recover as many materials as possible in socio-technical systems. It works by using closed-loop systems, product designs that make it easy to take things apart, and service models that make products last longer. The end goal is to separate economic growth from the use and depletion of natural resources. Encouraging the effective use of resources makes it easier to put circular economy ideas into action and speeds up the shift to fully circular production systems. The main goal of resource circularity is to close the material loop by efficiently getting valuable resources back through processes like extraction, collection, recovery, recycling, reproduction, and remanufacturing. Even though AI doesn't directly help, its impact on these processes is both big and life-changing.
Copyright (c) 2025 Abdulazeez AlSajri https://creativecommons.org/licenses/by/4.0
New Additions to the Sediments of the Tigris River from Al-Dora Bridge to Al-Mada'in for the Period 2015-2024
Tigris River is a fundamental freshwater body in Iraq; however, it is being put under more and more pressure due to urbanization, agricultural drainage, industrial effluents, as well as the climatic changes. The present study examines the dynamics of the Al-Dora BridgeAl-Mada in reach sediment during 20152024 in terms of physical, chemical and emergent contaminants. Sediment grain size, mineralogy, organic matter and heavy metals (Pb, Cd, Cr) were determined in 15 locations by laser diffraction, XRD, and ICP-MS, and maps produced using GIS. The findings indicate complete prevalence of fines (0.080.25 mm) with organic concentration between 5 to 12 percent. The urban sectors had heavy metal concentrations of more than the WHO limits with a range of Pb 0.02-0.03 mg/kg and Cd 0.002-0.003 mg/kg. Reported in 2018, microplastic concentrations were 50-150 particles/kg, mostly in and around urbanized regions. Spatial patterns have shown that agricultural areas were more enriched with organism and urban areas more contaminated. The results imply that the current river management approach requires certain refinements that could incorporate an effective approach to pollution control, sediment tracking, and land-use management to maintain the integrity of the ecological functioning of Tigris River.
Copyright (c) 2025 Rasha Abdulmajeed Jawad https://creativecommons.org/licenses/by/4.0
A Cloud based Smart Wireless Sensors using Secure Android Multi-Agent System
This paper aims to present a comprehensive proposal for the implementation of smart wireless sensor networks (WSNs) to monitor patients using smartphone devices. Smartphones offer three key capabilities that can be effectively leveraged in healthcare settings: first, the array of sensors integrated within smartphones; second, the wide range of communication technologies (i.e. WiFi, Bluetooth, IrDA, RF, etc.) which enable multiple pathways for transmitting data; and third, location services that can assist healthcare providers in tracking and locating patients. A major contribution of this paper is the conceptualization of a global healthcare information repository in the cloud, accessible via the internet. This repository would impose intelligent constraints on the WSN environment and serve as a valuable source of information for future research undertaken in the healthcare domain. I have formatted it as an academic paper introduction, tightened the writing in places, used more formal/technical language, and highlighted the key ideas and contributions. Please let me know if you would like me to modify or expand on any part of the rewrite.
Copyright (c) 2025 Mustafa M. Akawee, Raed A. Hasan, Enas F. Aziz, Modammed Ariff Ameedeen, Kassem Danach, Kassem Hamze https://creativecommons.org/licenses/by/4.0
AI in Smart Cities: A Review of Urban Data Processing, Prediction, and Optimization Techniques
Rapid advancement in the information and communication technologies ICs has recently provided new techniques for processing and analyzing the current data sources in smart cities. Various technologies for distributing cities to build them more livable, efficient, intelligent, sustainable and resilient. Intelligent and efficient data processing models for machine vision smart cities applications include intelligent detecting systems to monitor vehicle speeds and flow and pedestrian movements in roads and walkways. Smart and efficient storage and retrieval models for processing massive video data in the cloud for vehicle and traffic law monitoring in video surveillance and transportation systems are also discussed. Imaging and recognition-based models for analyzing the currently prevalent image data in cities are also reviewed, including deep learning-based fire recognition systems for smart fire prevention and manhole cover detection systems in the roadway and street monitoring. There is also a noteworthy review of prediction models for flow data in smart cities to anticipate traffic congestion and pedestrian movement trends. In addition, there are optimization models applied in smart cities, including intelligent routing models and path planning for vehicle navigation in logistics/distribution and delivery systems [1]. The above family of models are mainly adapted to smart mobility and pedestrian application domains. However, there are also various researchers working on data processing models in other urban domains, either independently or in an integrated manner with transportation. Numerous smart city platforms in the markets for data management and integration with diverse applications have also been reviewed. It is promising to deliver a relatively comprehensive review of urban data processing models with various techniques for different application domains. Smart cities have been subjected to rapid growth and therefore have become a major trend in the development of smart environments. Smart cities aim to improve the livability of urban settlements, enhance sustainability, and reduce operational costs. The smart city concept is expected to be of enormous benefit and it has been embraced by major cities around the world. However, the vision value of smart cities can only be fully realized through the successful integration of cutting-edge technologies and promising urban management models.
Copyright (c) 2025 Ismael khaleel Khlewee, Kassem Hamze, Tirus Muya https://creativecommons.org/licenses/by/4.0
Natural Language Processing-Driven Communication for Improved Citizen Alignment in Smart Cities
This study aims to explore the integration of smart buildings (SBs) into smart cities (SCs) and assess their potential to improve urban performance. It uses AI tools, such as OpenAI’s ChatGPT and Google Bard, to evaluate 26 SB service criteria across five SC domains: energy, mobility, water, and waste management. The study employs an AI-driven methodology to quantify the cost-effectiveness, resilience, and sustainability of SCs. The framework involves five processes: (1) scraping smart city announcements from the web, (2) typescript pre-processing, (3) using a latent Dirichlet allocation model for strategic topics, (4) measuring intertopic similarities with Hellinger distance, and (5) assessing communication trends in real-world urban environments. The study deconstructs top-down discourses of smart cities to uncover political programs aimed at aligning citizens and improving urban outcomes. Insights gained from the analysis reveal the impacts of SB services on SC domains, identifying areas for further integration and enhancement. This study presents a novel AI-driven approach to evaluate the integration of SBs into SCs, providing a comprehensive assessment of their performance. The findings offer valuable insights into the potential benefits and challenges of incorporating smart buildings into urban environments, highlighting opportunities for improved cost-effectiveness, resilience, and sustainability.
Copyright (c) 2025 Weiwei Jiang, Mohammed Almaayah, Rami Shehab https://creativecommons.org/licenses/by/4.0
Federated Learning for Smart and Sustainable Forest Fire Detection in Green Internet of Things
With the exponential rise in the adoption of the Internet of Things (IoT), sensors have become an essential part of smart systems, enabling real-time monitoring and control in applications such as energy management, security, and safety. Among these, early fire detection is a critical application to prevent devastating consequences. This paper introduces a novel Federated Learning (FL) framework designed for the rapid detection of forest fires within smart and sustainable environments using the Green Internet of Things (GIoT). The proposed framework integrates distributed learning across multiple edge devices to detect fire incidents without compromising data privacy. It leverages a modified DenseNet121 architecture enhanced with a soft attention mechanism, capable of accurately classifying fire and no-fire scenarios even under challenging weather conditions. The dataset was augmented to simulate fog and haze, ensuring model robustness in real-world environments. The experimental results demonstrate that the proposed system achieves outstanding performance with a training accuracy of 97.8% and a validation accuracy of 97.06%, confirming its effectiveness and scalability in edge-enabled fire detection systems.
Copyright (c) 2025 Aitizaz Ali, Umar Islam https://creativecommons.org/licenses/by/4.0
Mapping the Intersection of Artificial Intelligence and the Sustainable Development Goals: A Bibliometric and Scientometric Analysis (2003–2025)
This study presents a comprehensive bibliometric analysis of global research at the intersection of Artificial Intelligence (AI) and the Sustainable Development Goals (SDGs) from 2023 to 2025, based on 1,349 Scopus-indexed documents. The findings reveal a rapidly emerging yet structurally diverse field characterized by high thematic dispersion, moderate growth, and early citation impact. India leads in research output, while countries like Malaysia and Saudi Arabia exhibit high international collaboration rates. Conceptual mapping identifies two dominant clusters: one focused on applied sustainability domains and another on foundational AI methods and education. Despite growing momentum, challenges remain in metadata consistency, ethical integration, and translation of knowledge into actionable outcomes. This work provides a critical reference point for advancing AI-SDG scholarship toward greater coherence, inclusivity, and global policy relevance.
Copyright (c) 2025 Abdelaziz I. Hammouri, Amiza Amir https://creativecommons.org/licenses/by/4.0
Evaluating Educational Strategies Focused on Achieving Sustainable Development Goals in Diverse Global Contexts
This paper examines educational strategies focused on achieving the United Nations Sustainable Development Goals (SDGs) across diverse global contexts. Education plays a pivotal role in promoting sustainable development, yet its alignment with SDGs remains a complex challenge due to varying cultural, economic, and institutional contexts. This study explores different educational approaches, including curriculum integration, experiential learning, teacher training, and the use of technology, to advance sustainability. Through case studies from both developed and developing countries, as well as marginalized communities, the paper evaluates the effectiveness of these strategies and the barriers faced in their implementation. It also discusses the importance of measuring the impact of educational initiatives on SDG progress, using key performance indicators and impact assessment tools. The findings highlight the need for tailored educational models that account for regional differences, as well as the critical role of policy, governance, and strategic partnerships in overcoming challenges. The paper concludes with actionable recommendations to enhance the integration of sustainability into education systems worldwide, ensuring a long-term impact on achieving SDGs for future generations.
Copyright (c) 2024 Ahmed Abdel Qader Ismail Alnajem, Yitong Niu, Amer Ibrahim https://creativecommons.org/licenses/by/4.0
Transitioning Emerging Economies Toward Sustainability by Implementing Targeted Policy Interventions
Transitioning emerging economies towards sustainable development is critical to addressing global environmental, social and economic challenges. This study examines the role of targeted policy interventions in accelerating sustainable development in developing countries. Analyzing case studies and data from various sectors, the study identifies key drivers of renewable energy adoption, circular economic inclusion, adaptation, and sustainable agricultural practices permanent The importance of the study to adapt policies to local conditions, apply economic incentives, and foster international cooperation. Furthermore, it highlights the challenges posed by political instability, resource scarcity and lack of technical capacity, providing strategic recommendations to mitigate these barriers Findings highlight the potential of targeted interventions to change development trajectories, achieve the Sustainable Development Goals (SDGs) and ensure long-term resilience plant in emerging economies. This research contributes to the discourse on sustainability by providing actionable insights for policy makers, stakeholders and international organizations committed to promoting sustainable development in the world all of the above.
Copyright (c) 2024 Amer Ibrahim https://creativecommons.org/licenses/by/4.0
Advancing Urban Sustainability Through Public-Private Collaboration to Create Environmentally Conscious Infrastructure
The study dealt with the role of artificial intelligence applications in developing educational and learning strategies in higher education, by enhancing administrative work, increasing the efficiency of scientific and cognitive capabilities, and development of scientific research, in an optimal educational environment, Besides, it revealed the possible barriers accompanying the use of electronic machines. The study observed that using Artificial Intelligence devices at high levels of advancement and high sources of expenditure was their effective role for the advancement of higher education institutions and even improving labor productivity for the purpose of making the whole process of learning transactions more effective, so there is an urgent need to structure the awareness of stakeholders in education regarding the importance of using these devices to develop teaching and learning strategies and not to make challenges in front of fearing human resources. It is worth mentioning that the descriptive analytical method has been used in this study, which indicated the role of artificial intelligence and its applications in human capital development in higher education institutions.
Copyright (c) 2024 Rui Wang, Zhaoju Jia, Chaofan Ji, Haiyang Wang, Jianlin Yao, Yuqing Yang https://creativecommons.org/licenses/by/4.0
Advancing Energy Efficiency with Smart Grids and IoT-Based Solutions for a Sustainable Future
Increasing global energy demand, coupled with environmental concerns and limited resources has created an urgent need for sustainable and efficient energy management Traditional energy systems , characterized by its inefficiency and reliance on fossil fuels, is unable to meet the growing need for sustainable energy. This research addresses these challenges by investigating the integration of smart grids and Internet of Things (IoT) technologies to increase energy efficiency, reduce carbon emissions, and enable energy systems types have been improved upon Determine the integration of assembly, energy savings, system reliability, and impact on renewable evaluation capabilities.The findings suggest that combined implementation of these technologies can lead to a 20-25% reduction in energy consumption, a 30-35% reduction in carbon emissions and a 40% improvement in grid reliability in Furthermore, the integration of renewable energy reaches 50-60%, where IoT-enabled -Consumer awareness increases by 60% as device monitoring drives These results highlight the potential of smart grids and IoT-based solutions in solving critical energy challenges and paving the way to emphasize a sustainable energy future. The review concludes with a call for continued innovation, management and policy support to enable wider global adoption of this transformative technology.
Copyright (c) 2024 Dima Haider Rasheed, Sagar B. Tambe https://creativecommons.org/licenses/by/4.0
The Role of Green Technologies in Mitigating Carbon Footprints in Industrial Sectors
Due to energy-intensive processes, dependence on fossil fuels and poor waste management, industry is among the largest contributors to global carbon emissions This environment this growing environmental problem highlights the urgent need for sustainable solutions to reduce carbon emissions. This study examines the role of green technologies in addressing these challenges, focusing on renewable energy systems, carbon capture and storage (CCS), circular economic practices, and digital products on the other hand. The main objective is to assess how these technologies can reduce greenhouse gas emissions and increase energy efficiency and potentially increase the industrial sustainability of resources activities in the field. Findings show that integrating green technologies can lead to a 25% reduction in energy consumption, 85% efficiency in carbon capture, and greenhouse gas emissions has decreased by 50% in a decade Furthermore, sustainable materials and energy efficiency solutions promise to reduce waste and improve the relative quality of infrastructure use Display results. Despite constraints such as high start-up costs and technical barriers, the study highlights the significant potential of green innovation to transform technology It provides a call to action for businesses , policy makers and consumers to embrace this technology as a means to achieve global sustainability goals ends.
Copyright (c) 2024 Israa Ibraheem Al Barazanchi, Dima Haider Rasheed https://creativecommons.org/licenses/by/4.0
Assessing the Impact of Circular Economy Practices on Global Waste Management Systems
The enormous rate of urbanization, industrialization and unreasonable consumption positions severe pressure on global waste management systems as well as the exceedingly fast pace of pile-up of rubbish threatening a broke ecological balance. This research investigates the Circular economy (CE) practices as a constructive strategy to solve these problems. However, the issue is that our current linear waste management systems are extremely inefficient, having an overreliance on the use of landfilling and incineration for disposing of waste, leading to resource depletion and greenhouse gas emissions. This research aims to assess the effects of CE practices on waste reduction, resource efficiency and landfill diversion with a view towards understanding regional variability and the obstacles(s) and opportunities to adoption. The study adopts a mixed-methods perspective, combining qualitative data from stakeholder interviews and case studies with quantitative information concerning waste management performance. Highlights show that the CE practices decrease waste generation by 25–40%, boost recycling rates of up to 50–65%, and divert as much as 70–80% of waste away from landfills. The research still points to challenges including economic costs, gaps in policy and technology especially in the developing world. The results are closely aligned with global benchmarks and affirm the efficacy of CE practices. Finally the paper provides policy implications aimed at policymakers, industries and researchers observing that to promote faster adoption of CE practices there is an urgent need for coherent policies, technological innovation and public awareness. Combined, these results highlight how CE principles can transform waste management systems and support the pursuit of global sustainability goals.
Copyright (c) 2024 Noor Al-Huda K. Hussein, Xin Huang, Xiang Li, Jianling Yao https://creativecommons.org/licenses/by/4.0
Integrating Renewable Energy Systems into Urban Planning for Sustainable Cities
The 21st century has seen a race of urbanization, one that is rapidly increasing energy demands and leading to environmental degradation as well as climate change due to the dependency on fossil fuels. While the shift to urban sustainability is a major solution to this issue, how renewable energy systems may fit into well-orchestrated urban planning remains an open question. Our goal is to explore approaches, tools and governance frameworks for cities to transition towards renewable energy given the optimal economic, social,, and environmental impact. A summary of key findings states, "As this paper demonstrates, the integrated use of renewable energy significantly curtails greenhouse gases, achieving 1.2 million tons of CO₂ reductions per year and providing renewables for a full 35% of urban energy demand." It also points to economic advantages, such as $200 million in annual energy savings and 5,000 direct jobs created in renewable energy. Social equity is also a part of the study, with 10,000 households receiving clean energy. These findings constellate around the relevance of the inclusion of renewables in urban planning as an alternative solution to climate change but also a driver of urban resilience and sustainable development more generally. We urge all stakeholders to implement inclusive policies, invest in innovative technologies and mobilize communities in order to accelerate the transition towards renewable energy systems for cities
Copyright (c) 2024 Noor Al-Huda K. Hussein, Ola Mohammed Basel https://creativecommons.org/licenses/by/4.0
A Review on the Impact of Fly Ash on the Resistance of Ultra-High Performance Concrete to Acid and Sulfate Attacks
A huge amount of fly ash (FA) was generated every year and dumped in landfills in large quantities and leading to environmental pollution issues. The use of FA in normal concrete and ultra-high performance concrete (UHPC) can reduce pollution in both cases, namely, reduce the accumulation of FA as waste material in landfills and reduce the cement production that generates around 7% of total carbon dioxide (CO2) emissions. This study is a comprehensive review to find out the effect of FA on some durability properties of UHPC, namely acid and sulfate attacks. The results indicate that the use of FA has a positive effect on the increase in the resistance of UHPC samples against acid and sulfate attacks. It is recommended to use nano-FA in the treatment of corrosion and carbonation of UHPC.
Copyright (c) 2024 Asal M. Hamad , Hussein M. Issa, Rodhan Abdullah Salih, Jafar Keighobadi, Azil Bahari Alias , Raed A. Hasan, Noah Mohammed Saleh, Ali Mohammed Saleh , Hussein M. Hamada , Idan I. Ghdhban https://creativecommons.org/licenses/by/4.0
Features and functional components in the use of artificial intelligence in human capital development (higher education as a model)
The study addressed the role of artificial intelligence applications in enhancing educational and learning strategies in higher education, by improving administrative functions; raising the efficiency of scientific and cognitive capabilities, and developing scientific research, in an improved educational environment, in addition to revealing the potential obstacles that accompany the process of using electronic machines. The study concluded that artificial intelligence has an effective role in enhancing and improving the performance of higher education institutions, which made the learning process more efficient, so there is an urgent need to raise awareness among stakeholders in education about the importance of using artificial intelligence applications in teaching and learning strategies, and not to make challenges an obstacle in employing human resources. The study relied on the descriptive analytical method to reveal the role of artificial intelligence and its applications in developing human capital in higher education institutions.
Copyright (c) 2024 Abaker Abdelbanant Adam Ibrahim https://creativecommons.org/licenses/by/4.0
Resilience and Adaptation to Climate Change: Community-Based Strategies in Coastal Regions
Coastal communities are highly vulnerable to climate change impacts, including sea level rise, extreme weather events, and ecological degradation These challenges pose significant threats to the livelihoods, infrastructure and well-being of coastal communities , especially in developing areas with limited resources to grow The effectiveness of -based optimization strategies (CBA) is investigated. The crisis statement focuses on the urgent need for sustainable and adaptable adaptation solutions that can mitigate the negative impacts of climate change, and enable communities to take ownership of these efforts. Traditional top-down approaches have generally proven inadequate to address the unique socio-cultural and environmental dynamics of coastal areas. The objectives of this study are threefold: to identify and analyze key CBA strategies that have been implemented in coastal areas, to assess their effectiveness in reducing vulnerability and increasing resilience, and to explore the role of local knowledge, resources and governance play a part in the development of a successful change strategy. The results show significant improvements in several key areas. Income increased by 25%, and 35% of households adopted climate-resilient lifestyles. Economic losses from disasters were reduced by 45% and key infrastructure was strengthened by 80% against climate risk. The study found a 28% reduction in climate-related diseases, a 90% increase in water quality and the restoration of 600 hectares of biodiversity, leading to a loss of biodiversity above 20%. In addition, 75% of the community participated actively in decision-making, and 65% of the reform projects integrated traditional knowledge. These findings suggest that CBA strategies not only effectively reduce the immediate risks of climate change, but also build long-term resilience by empowering communities to lead the adaptation process. The study highlights the importance of scaling up these initiatives to increase coastal resilience globally.
Copyright (c) 2023 Yitong Niu, Vugar Abdullayev, Abuzarova Vusala Alyar, Asgarov Taleh Kamran https://creativecommons.org/licenses/by/4.0
Green Technologies and Their Role in Mitigating Climate Change: A Comparative Study Across Developing Nations
Climate change poses major challenges around the world, with developing countries particularly vulnerable due to lack of resources and services. Green technologies—such as renewable energy, sustainable agriculture, and green materials—offer promising solutions to mitigate climate change impacts but the adoption of these technologies in developing countries are still uneven, driven by political, economic, and technological factors. This study seeks to examine the role of green technologies in climate change mitigation in four developing countries—India, Brazil, South Africa, and Kenya—through comparative analysis. The problem statement focuses on the inequitable adoption of green technologies in developing countries, where financial constraints, lack of technical expertise, and inconsistent government policies prevent widespread implementation And as a result comes to be considered which shows that green technologies have contributed significantly in reducing carbon emissions in these countries. For example, India’s solar power system installed 62 GW of solar capacity, reducing emissions by 150 million metric tons per year. Brazil’s biofuel industry, which produces 35 billion liters of ethanol a year, has reduced its reliance on fossil fuels for transportation. The Kenya geothermal sector generated 863 MW of electricity, reducing the country’s dependence on fossil fuels by 45%. However, the study also highlights barriers such as inadequate funding, inadequate infrastructure and social opposition, which limit the potential of all these technologies.