Sustainable Decarbonization Under Renewable Energy Penetration: A Hybrid Fixed Effects and Machine Learning Framework for Multi-Country Panel Evidence

Citra

doi:https://doi.org/10.54216/JSDGT.060202

Journal of Sustainable Development and Green Technology | 2026

Green Finance, Renewable Energy Adoption, and Carbon Emissions: Evidence from a Ten-Country Panel Dataset (2010–2022)

The capital mobilisation needed to achieve the energy transition has put green finance on the agenda of climate policy discussions, but it is empirically unclear whether financial flows branded green translate into emissions abatement. Drawing on publicly available World Bank World Development Indicators and Climate Policy Initiative data for ten major economies over 2010–2022, this paper quantifies the relationships among green finance flows, renewable energy share, and per-capita CO2 emissions using pooled ordinary least squares regression, panel correlation analysis, and a cross-sectional multiple regression model. Renewable energy share is a strong negative predictor of per-capita emissions (r = −0.661; p < 0.001; β&circ; = −0.1104), with each percentage-point increase in renewable penetration associated with a reduction of approximately 0.11 metric tons of CO2 per capita. A three-variable cross-sectional model combining renewable energy share, green finance, and GDP per capita accounts for 61% of cross-country variance in emissions in 2022 (R2 = 0.613). There is a positive and heterogeneous relationship between green finance flows and renewable adoption, which is most notable when the financial commitment is combined with strong regulatory frameworks. The country level trajectory analysis shows that all the ten economies have been on the rise in their renewable share between 2010 and 2022, but at a rate varying between 0.37 and 2.88 percentage points annually, and that the institutional environment is a moderating factor. The results have immediate implications on the design of green bond markets, the lending policy of multilateral development banks, and the structures of additionality that support net-zero transition schemes.

groups

Ihtisham ul Haq mail -

Andino Maseleno mail

link https://doi.org/10.54216/JSDGT.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Machine Learning for At-Risk Student Identification in Virtual Learning Environments: A Multi-Classifier Analysis Using the Open University Learning Analytics Dataset

The detection of students who will face academic difficulties or leave their studies during their initial course period provides universities with a brief time frame to develop effective solutions. This research paper conducts a systematic analysis which tests multiple machine learning classifiers on the Open University Learning Analytics Dataset (OULAD) which serves as one of the most widely used public educational datasets that presents data from 32593 students who studied 22 different courses through distance learning. The four classification methods include logistic regression decision tree random forest and gradient boosting which use a feature set that combines student demographic information and virtual learning environment (VLE) clickstream-based engagement data. The primary discovery shows that VLE behavioral characteristics constitute the most important elements for Random Forest which identifies total click volume and active VLE days and typical daily click volume as its top four elements which make up 92.8% of total importance while demographic information has less impact. Random Forest achieves the strongest held-out test performance (AUC = 0.998, F1 = 0.978, accuracy = 98.2%) while Decision Tree shows lower results with AUC = 0.959 which demonstrates how performance losses occur when systems need to be understandable. At-risk students in the two groups present a 75.8% decrease in total VLEclicks which results in an average of 49.0 clicks compared to 203.0 clicks with a t value of 104.0 and a p value less than 0.001. The research describes its complete end-to-end prediction pipeline which includes details about its model evaluation framework and its dataset to enable future researchers to reproduce the study. The results have direct implications for the design of early-alert systems and the ethical deployment of predictive analytics in higher education.

groups

Emad Bashkail mail -

Nesrin Merhi mail

link https://doi.org/10.54216/IJAIET.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2026

Risk-Aware Cyberattack Analytics for Unmanned Aerial Vehicle Communications: A Publication-Ready Gradient- Boosting Framework

Cyberattack detection in unmanned aerial vehicle environments has become an essential requirement for dependable digital operations. Security analytics for these environments should not only separate benign and malicious traffic, but should also provide interpretable evidence that can support timely triage and intervention. This paper presents a risk-aware classification framework for UAV communication security based on a leakage-screened feature design and a gradient-boosting ensemble model. The framework combines multiclass discrimination, probability-based decision logic, and feature-level interpretation within one coherent workflow. The study demonstrates that a carefully designed ensemble approach can provide balanced and operationally meaningful cyberattack recognition while remaining transparent enough for practical cybersecurity management. The results also show that communication-structure variables provide strong discriminatory power and that replay-type activity remains more difficult to separate than benign or denial-of-service behavior. The proposed framework therefore contributes a reproducible analytical design and a managerial reading of cyberattack classification for UAV operations.

groups

Andino Maseleno mail -

Aa Hubur mail

link https://doi.org/10.54216/JCIM.180105

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2026

Multi-Sector Circular Economy Integration and Transition: Evidence from Textile, Construction, Energy, Chemical Manufacturing, and Agri-Food Systems in Uzbekistan

The transition from a linear economic model to a circular economy (CE) marks a profound change, driving toward better resource management and ecological robustness. Although much previous investigation has concentrated on specific industries, this research takes a broader, multi - sector approach. It delves into how circular economy principles are being incorporated across crucial industries such as textiles, construction, energy, chemical production and agriculture in Uzbekistan. Employing a qualitative research methodology, which involved synthesizing existing literature and scrutinizing policies, the study pinpoints the primary forces driving this change, the obstacles encountered and the connections between different sectors that influence the move toward a circular economy. The results highlight that shared circular practices - like industrial symbiosis, optimizing resource use, adopting renewable energy and developing circular business models - are essential for boosting both environmental sustainability and economic viability. Nevertheless, this transition faces hurdles due to inadequate infrastructure, disjointed governance structures and insufficient skilled personnel, especially within developing nations. Examining Uzbekistan specifically, we observe both growing policy dedication and ongoing structural difficulties, underscoring the need for synchronized governance, investment in green initiatives and robust innovation systems. This work adds to the existing body of knowledge by introducing a conceptual framework that spans multiple sectors. This framework illustrates how industrial systems are interconnected and how these connections contribute to achieving sustainable development goals. Moreover, it offers practical policy suggestions aimed at speeding up circular economy adoptions in Uzbekistan and comparable developing countries.

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Muhammad Eid Balbaa mail -

Olim Astanakulov mail -

Habibe Elif Kutlugun mail

link https://doi.org/10.54216/JSDGT.060201

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2026

Geometry-Driven BIM Element Category Recognition from Open IFC Property Records: A Reproducible Engineering Science Study

Accurate object semantics are essential for building information modeling (BIM) workflows to enable interoperability, model checking, quantity take-off, performance analysis, and other downstream engineering applications. However, in practice, Industry Foundation Classes (IFC)-based model exchanges often feature limited or poorly identified semantic tags, particularly during interoperability with authoring and reviewing tools. This research proposes a re-producible, geometry-based learning algorithm for the automatic recognition of BIM element categories based on publicly available IFC-based property data. The empirical analysis is based on 780 object instances from ten BIM categories from a publicly available sample of IFC object records. A rule based parser translates semi-structured BIM text exports into engineering features as bounding box dimensions, coordinates, elevations and object-status. The study compares three supervised machine-learning baselines via stratified five-fold cross-validation: logistic regression, random forest and extra trees. Random forest performed best overall with an accuracy of 0.992, balanced accuracy of 0.971, a weighted F1-score of 0.992, and a macro F1-score of 0.970. The analysis of feature importance shows that bounding-box height, width, length, spatial coordinates and externality related descriptors are the most important features. The results demon-strate significant semantics can be extracted from minimal engineering descriptors without the need for deep learning of meshes. This work provides an interpretable and efficient baseline for BIM enrichment, assessment, and interoperability-focused preprocessing for engineering science use-cases.

groups

Sonia Ahmed mail -

Marek Salamak mail

link https://doi.org/10.54216/IJBES.120203

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2026

A BIM-Linked Mathematical Decision Model for Energy Retrofit Prioritisation in Existing Building Portfolios

Building information modelling is increasingly applied to structure engineering information across the life cycle of built assets, but existing buildings are often underconnected to operational data for retrofit prioritisation. This research proposes a BIM-connected retrofit prioritisation model that converts building-performance information into an engineering information layer for initial screening. The method integrates BIM-aligned feature organisation, transparent machine learning, diagnostic validation, and scenario-driven screening to flag buildings for further assessment by engineers. The paper proposes a workflow for institutions and cities seeking to transition from disparate disclosure records to evidence-based retrofit prioritisation without relying on the immediate availability of digital twins. The results suggest that operational, geometric, and typological features can be used to generate interpretable screening markers that help guide engineering judgement, benchmarking, and incremental retrofit strategies. This research offers a replicable model that supplements, rather than substitutes for, in-depth audit and modelling.

groups

Ashraf Elhendawi mail -

Moustafa Metwally mail

link https://doi.org/10.54216/IJBES.120204

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2026

Trust-Aware Early Detection of Grey-Hole Behaviour in Flying Ad Hoc Wireless Networks: A Data-Driven Study Using Recent FANET Traces

Flying ad hoc networks (FANETs) enable dynamic multi-hop communication in un-manned aerial nodes, but their routing plane is vulnerable to selective forwarding attacks that decrease packet delivery rates while avoiding the sudden effects of denial. This paper proposes a trust-aware routing and detection approach for early detection of grey-holes in ad hoc flying networks. The paper employs an analysis-ready data set based on the public FAN-GHETS24 data set, a new data set for early time-series classification of attacks in FANETs. The Trust-Aware Routing Grey-Hole Detection (TAR-GHD) model uses a com-bination of link quality evidence, route stability, packet consistency and trust dynamics in a lightweight detection layer that can be executed alongside traditional ad hoc routing. A mathematical formulation is given for evidence aggregation, temporal trust evolution, risk assessment and route warning. The empirical study measures the detection of normal, mild, moderate and heavy grey-hole attacks in various node-density, mobility, observation window, and classification settings. The findings demonstrate that trust and packet-loss dynamics offer reliable early indicators of grey-hole attacks, while mobility and route changes make it harder to distinguish normal loss from malicious loss. The best-performed configuration resulted in an F1-score over 0.93 (held-out evaluation), with the most influential features related to packet delivery, forwarding ratio, trust score and drop-rate dynamics. The results highlight lightweight and explainable trust evidence as a viable technique for enhancing the security of wireless ad hoc routing in UAV-assisted applications.

groups

Meinhaj Hussain mail -

Andino Maseleno mail

link https://doi.org/10.54216/IJWAC.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2026

ADML-IDS: An Adaptive Ensemble Machine Learning Framework for Intrusion Detection in Wireless Ad Hoc and Sensor Networks

As wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs) are (DoS) attacks has become a critical security concern in mission-critical wireless (DoS) attacks has become a critical security issue. This paper proposes ADML-IDS, an Adaptive Machine Learning Intrusion Detection System that integrates ensemble of Random Forest, XGBoost and Gradient Boosting classifiers using a Flooding, and Scheduling—as well as normal traffic. Flooding, Scheduling and normal traffic. Experiments are conducted on the open-source WSN-DS dataset, which contains 166,000 network observations using the LEACH hierarchical routing protocol with 23 features obtained from NS-2 simulation. The data preprocessing steps include Min- Max normalisation and Synthetic Minority Over-Sampling Technique (SMOTE) to balance classes, and importance-based feature selection to retain 19 features. A rigorous ten-fold crossvalidation strategy is followed. ADML-IDS achieves an overall accuracy of 99.57%, weighted F1-score of 0.9956 and AUC-ROC of 0.9985. AUC-ROC of 0.9985, outperforming each of the sub-classifiers and five state-of-the-art methods. Scalability experiments demonstrate that the accuracy of detection remains above network size reaches 200 nodes, and with a reasonable computational cost. A formal presentation of the energy-aware network model and ensemble decision rule is tables are also included along with a full description of the algorithm tables.

groups

Ahmed Aziz mail -

Mahmoud Abdel-Salam mail

link https://doi.org/10.54216/IJWAC.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2026

Key-Aware Link Selection for Quantum Wireless Networks: A Data-Driven Study of Satellite-to-Ground QKD Access Links

The application of quantum key distribution, satellite communication and programmable wireless access in future secure wireless networks is anticipated to enhance secure communication infrastructure. But their realisation demands more than just the physical realisation of quantum links. The network controller needs to determine when a quantum-secured wireless link can be used to serve a request, how orbit type and weather conditions impact the volume of usable keys, and whether the secure key rate is high enough to admit a route. In this paper, we introduce Q-SARA, a quantum-secure access and routing admission model for satellite-assisted quantum wireless networks. It assesses candidate QKD access links with secure key rate (SKR), quantum bit error rate (QBER), link loss, contact duration, visibility probability and propagation delay. A smaller, pre-processed dataset is derived from the public Satellite-to-Ground QKD SKR dataset, which contains the calculated key performance indicators for Low Earth Orbit, Medium Earth Orbit and Geostationary satellite-to-ground QKD links using the prepare-and-measure and entanglement-based protocols. The empirical analysis examines 7,200 link-level data and assesses Q-SARA across orbit, protocol, optical ground station, elevation, atmospheric, and service classes. The findings demonstrate that link selection based only on key volume can be misleading when assessing service quality, while the multi-criteria score provides better balance between security, visibility and latency. LEO links have better instantaneous key rates, GEO links have better visibility, and MEO links lie in between and can be exploited when link quality and service are taken into account together. The results suggest that quantum wireless access should be considered as a service admission problem rather than a physical-layer key generation problem.

groups

Khaled Sh. Gaber mail -

S. K. Towfek mail

link https://doi.org/10.54216/IJWAC.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Sustainable Decarbonization Under Renewable Energy Penetration: A Hybrid Fixed Effects and Machine Learning Framework for Multi-Country Panel Evidence

Realizing the carbon reduction capabilities of deploying renewable energy. is core to the constructive plan of effective climate policy in heterogenous national. contexts. Even though there is an accumulating corpus of panel econometric and machine learning. literature dealing with this relationship, methodological inconsistencies and limited geographic scope leave important empirical questions unanswered. This paper put forward a mixed analytical model combining a within-group Fixed Effects. country-clustered standard errors estimator and a Random Forest ensemble. model to measure the combined effect of renewable energy penetration, economic growth, energy consumption and reliance on fossil fuels per capita carbon. emissions. Findings affirm that the growth of renewable energy has a statistically significant impact. strong and economically significant negative impact on carbon intensity, which remains. following the elimination of country-specific unobserved heterogeneity. Economic structure and energy efficiency are shown to be co-dominant determinants, highlighting. that the energy transition is not decoupled of larger structural. transformation. Articulated income-group and regional heterogeneity issues. single-coefficient policy prescriptions, which propose decarbonization. plans have to be aligned to the national development levels. The machine learning complement validates econometric variable rankings and proves. good cross-country generalizability with country-stratified. cross-validation.

groups

Citra Dewi mail

link https://doi.org/10.54216/JSDGT.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new