Climate Change Mitigation: Applications of Advanced Modeling Techniques
DOI:
https://doi.org/10.63876/ijtm.v4i3.159Keywords:
Climate change mitigation, Advanced modeling techniques, Integrated assessment models, Machine Learning, Emission reduction pathways, Climate policy optimizationAbstract
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.
Downloads
References
E. N. Bañares, M. S. Mehboob, A. R. Khan, and J. C. Cacal, “Projecting hydrological response to climate change and urbanization using WEAP model: A case study for the main watersheds of Bicol River Basin, Philippines,” Journal of Hydrology: Regional Studies, vol. 54, p. 101846, Aug. 2024, doi: https://doi.org/10.1016/j.ejrh.2024.101846.
T. Akiyama et al., “Motives for food choice among young students amid the rapid urbanization in Lao PDR,” Food and Humanity, vol. 3, p. 100397, Dec. 2024, doi: https://doi.org/10.1016/j.foohum.2024.100397.
H. Li, G. Du, G. M. Qamri, and S. Li, “Green innovation and natural resource efficiency: The role of environmental regulations and resource endowment in Chinese cities,” Journal of Environmental Management, vol. 370, p. 122338, Nov. 2024, doi: https://doi.org/10.1016/j.jenvman.2024.122338.
K. Gupta and C.-N. Lee, “A Vehicle Congestion Prediction Approach for Smart City Traffic Management,” Procedia Computer Science, vol. 260, pp. 675–682, 2025, doi: https://doi.org/10.1016/j.procs.2025.03.246.
Saluky and Yoni Marine, “A Review: Application of AIOT in Smart Cities in Industry 4.0 and Society 5.0,” ijss, vol. 1, no. 1, pp. 1–4, Feb. 2023, doi: https://doi.org/10.63876/ijss.v1i1.1.
Saluky, Y. Marine, A. Zaeni, A. Yuliati, O. R. Riyanto, and N. Bahiyah, “Pothole Detection on Urban Roads Using YOLOv8,” in 2023 10th International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia: IEEE, Sept. 2023, pp. 1–6. doi: https://doi.org/10.1109/ICISS59129.2023.10291192.
A. A. Dar and A. J. Shaik, “Spatiotemporal clustering and multivariate forecasting of air quality index across Indian cities using machine learning and deep learning models,” Franklin Open, vol. 13, p. 100435, Dec. 2025, doi: https://doi.org/10.1016/j.fraope.2025.100435.
U. U. Rehman, “The future role of artificial intelligence in energy management systems for smart cities: A systematic literature review of trends, gaps, and future direction,” Sustainable Computing: Informatics and Systems, vol. 49, p. 101249, Jan. 2026, doi: https://doi.org/10.1016/j.suscom.2025.101249.
A. Chahal et al., “Predictive analytics technique based on hybrid sampling to manage unbalanced data in smart cities,” Heliyon, vol. 10, no. 24, p. e39275, Dec. 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e39275.
Z. Jing, Y. Luo, X. Li, and X. Xu, “A multi-dimensional city data embedding model for improving predictive analytics and urban operations,” IMDS, vol. 122, no. 10, pp. 2199–2216, Nov. 2022, doi: https://doi.org/10.1108/IMDS-01-2022-0020.
S. N. Khonina, N. V. Golovastikov, and N. L. Kazanskiy, “Photonic sensors for the internet of things (IoT) and smart cities,” Sensors and Actuators A: Physical, vol. 398, p. 117317, Feb. 2026, doi: https://doi.org/10.1016/j.sna.2025.117317.
E. Dritsas and M. Trigka, “Big data and Internet of Things applications in smart cities: Recent advances, challenges, and critical issues,” Internet of Things, vol. 34, p. 101770, Nov. 2025, doi: https://doi.org/10.1016/j.iot.2025.101770.
I. Nassra and J. V. Capella, “Hybrid big data optimization based energy-efficient and AI-powered green architecture toward smart cities and 5G-IoT applications,” Journal of Electronic Science and Technology, vol. 23, no. 4, p. 100328, Dec. 2025, doi: https://doi.org/10.1016/j.jnlest.2025.100328.
Y. Lee and E. Ng, “An adaptable framework for resilient subtropical low-income housing under future climate and predicted lifestyles: a Hong Kong case study,” Energy and Buildings, vol. 348, p. 116412, Dec. 2025, doi: https://doi.org/10.1016/j.enbuild.2025.116412.
T. Dent, R. Comunian, and S. Kim, “Entrepreneurial capability? Understanding the resources needed for sustainable cultural and creative entrepreneurship in cities. A case study of Enschede, The Netherlands,” City, Culture and Society, vol. 43, p. 100672, Dec. 2025, doi: https://doi.org/10.1016/j.ccs.2025.100672.
Z. R. M. A. Kaiser and D. Boakye Boadu, “Urban governance and sustainable city concept: Do the individual characteristics of city leaders and cities’ contextual factors affect the adoption of sustainable city policies?,” Cities, vol. 170, p. 106626, Mar. 2026, doi: https://doi.org/10.1016/j.cities.2025.106626.
C. Sharma, S. Choudhary, and S. S. Mishra, “Unfolding the relationship between data privacy and security, users’ trust and satisfaction in smart cities using the IS model,” Transforming Government: People, Process and Policy, vol. 19, no. 4, pp. 775–794, Nov. 2025, doi: https://doi.org/10.1108/TG-06-2025-0190.
A. Alnuaim, “Intrusion Detection and Security Attacks Mitigation in Smart Cities with Integration of Human-Computer Interaction,” CMC, vol. 86, no. 1, pp. 1–33, 2026, doi: https://doi.org/10.32604/cmc.2025.069110.
A. Wulan, R. Rahman, and Desvi, “Security Analysis of the VoIP (Voice Over Internet Protocol) System,” ijss, vol. 1, no. 3, pp. 105–116, Aug. 2023, doi: https://doi.org/10.63876/ijss.v1i3.69.
S. Praharaj, “Command and control governance in the 100 smart cities mission in India: Urban innovation or utopias?,” Applied Geography, vol. 184, p. 103766, Nov. 2025, doi: https://doi.org/10.1016/j.apgeog.2025.103766.
Y. Bozkurt, A. Rossmann, Z. Pervez, and N. Ramzan, “Assessing data governance models for smart cities: Benchmarking data governance models on the basis of European urban requirements,” Sustainable Cities and Society, vol. 130, p. 106528, July 2025, doi: https://doi.org/10.1016/j.scs.2025.106528.
J. Ali and I.-L. Popa, “Dombi Power Aggregation-Based Decision Framework for Smart City Initiative Prioritization under t-Arbicular Fuzzy Environment,” CMES, vol. 145, no. 1, pp. 857–889, 2025, doi: https://doi.org/10.32604/cmes.2025.064604.
A. S. Omar and R. El-Shatshat, “A survey on model-based and data-driven operational optimization approaches for integrated energy systems in smart cities,” Electric Power Systems Research, vol. 249, p. 111946, Dec. 2025, doi: https://doi.org/10.1016/j.epsr.2025.111946.
H. Wang, J. Zhou, F. Li, A. Razzaq, and X. Yang, “Digital governance and environmental Sustainability: The impact of smart cities on corporate carbon footprints,” Journal of Environmental Management, vol. 395, p. 127824, Dec. 2025, doi: https://doi.org/10.1016/j.jenvman.2025.127824.
S. Kaynak, B. Kaynak, O. Mermer, and I. Demir, “City-scale digital twin framework for flood impact analysis: Integrating urban infrastructure and real-time data analytics,” Urban Climate, vol. 64, p. 102640, Dec. 2025, doi: https://doi.org/10.1016/j.uclim.2025.102640.
N. Fatemi and J. Fattahi, “Adaptable semantic interoperability in heterogeneous smart grids using Large Language Models,” Energy Reports, vol. 14, pp. 5774–5789, Dec. 2025, doi: https://doi.org/10.1016/j.egyr.2025.12.011.
F. Loia, C. Perillo, and G. Gravili, “Unleashing the power of digital twin and big data as a new frontier for smart mobility: An ecosystem perspective,” Big Data Research, vol. 43, p. 100576, Feb. 2026, doi: https://doi.org/10.1016/j.bdr.2025.100576.
D. Yedilkhan, S. Saleshova, G. Omarova, B. Akhmetzhanov, Z. Sarsenova, and B. Amirgaliyev, “AI-Driven Urban Analytics Using IoT-Enabled Mobile Sensor Networks for Environmental Monitoring,” Procedia Computer Science, vol. 272, pp. 619–624, 2025, doi: https://doi.org/10.1016/j.procs.2025.10.257.
X. Zheng, D. Lin, Z. Hu, Y. You, and C. Zhu, “Integration design for inverted inward-turning inlet and waverider airframe with boundary-layer removal effect,” Aerospace Science and Technology, vol. 165, p. 110500, Oct. 2025, doi: https://doi.org/10.1016/j.ast.2025.110500.
