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All Journal International Journal of Smart Systems International Journal of Technology and Modeling
Gan, Keemo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Climate Change Mitigation: Applications of Advanced Modeling Techniques Gan, Keemo; Huang, Chung
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.159

Abstract

Climate change poses one of the most pressing challenges to global sustainability, necessitating comprehensive mitigation strategies informed by robust scientific analysis. This article examines the role of advanced modeling techniques in enhancing climate change mitigation efforts across multiple scales and sectors. We explore recent developments in integrated assessment models, machine learning algorithms, and high-resolution climate simulations that enable more accurate projections of future climate scenarios and their socioeconomic impacts. The study discusses how these sophisticated computational approaches facilitate the evaluation of mitigation pathways, including renewable energy transitions, carbon capture technologies, and nature-based solutions. Particular attention is given to the integration of uncertainty quantification methods and the coupling of physical climate models with economic and land-use models to support evidence-based policy decisions. Case studies demonstrate the application of ensemble modeling techniques, deep learning frameworks, and scenario analysis in identifying cost-effective mitigation strategies at regional and global levels. Results indicate that advanced modeling approaches significantly improve the accuracy of emission reduction projections and enhance our understanding of feedback mechanisms within the climate system. The article also addresses current limitations in data availability, computational constraints, and the challenges of downscaling global projections to local contexts. We conclude that continued refinement of modeling techniques, combined with improved interdisciplinary collaboration and stakeholder engagement, is essential for designing effective climate mitigation policies that can achieve the goals outlined in international climate agreements.
Artificial Intelligence of Things-Based Smart System Architecture for Sustainable Industrial Transformation Gan, Keemo; Huang, Chung
International Journal of Smart Systems Vol. 1 No. 4 (2023): November
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i4.93

Abstract

The integration of Artificial Intelligence of Things (AIoT) has become a strategic enabler for sustainable industrial transformation by combining intelligent data processing, connected sensing, autonomous decision-making, and real-time system optimization. This article proposes an AIoT-based smart system architecture designed to support sustainable industrial operations through the integration of Internet of Things devices, edge computing, cloud platforms, artificial intelligence models, and decision-support mechanisms. The proposed architecture emphasizes four main layers: data acquisition, intelligent processing, system integration, and sustainability-oriented decision support. By enabling predictive maintenance, energy optimization, resource efficiency, production monitoring, and adaptive process control, the architecture provides a foundation for industries seeking to improve operational performance while reducing environmental impact. The study also discusses key implementation challenges, including data interoperability, cybersecurity risks, infrastructure readiness, model explainability, and organizational capability. Furthermore, the proposed framework highlights the role of AIoT in supporting Industry 4.0 and Industry 5.0 transitions by balancing automation, human-centered intelligence, and sustainable value creation. The findings suggest that AIoT-based smart systems can serve as a transformative approach for achieving more resilient, efficient, and environmentally responsible industrial ecosystems. This article contributes to the development of sustainable industrial digitalization by offering a conceptual architecture that can be adapted across manufacturing, energy, logistics, and process industries.
Co-Authors Huang, Chung