Published
2025-11-18
Section
Research Articles
License
Copyright (c) 2025 Ali Adel, Nidhal Jasm Mohammed Ali, Wissam Anwar Mohammed Hassan Ali, Huda Yousif Khattab, Faris Abdul Kareem Khazal

This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal adopts the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which means that anyone can reuse and redistribute the materials for non-commercial purposes as long as you follow the license terms and the original source is properly cited.
Author(s) shall retain the copyright of their work and grant the Journal/Publisher rights for the first publication with the work concurrently licensed since 2023 Vol.8 No.2.
Under this license, author(s) will allow third parties to download, reuse, reprint, modify, distribute and/or copy the content under the condition that the authors are given credit. No permission is required from the authors or the publisher.
This broad license intends to facilitate free access, as well as the unrestricted use of original works of all types. This ensures that the published work is freely and openly available in perpetuity.
By providing open access, the following benefits are brought about:
- Higher Visibility, Availability and Citations-free and unlimited accessibility of the publication over the internet without any restrictions increases citation of the article.
- Ease of search-publications are easily searchable in search engines and indexing databases.
- Rapid Publication – accepted papers are immediately published online.
- Available for free download immediately after publication at https://esp.as-pub.com/index.php/ESP

Copyright Statement
1.The authors certify that the submitted manuscripts are original works, do not infringe the rights of others, are free from academic misconduct and confidentiality issues, and that there are no disputes over the authorship scheme of the collaborative articles. In case of infringement, academic misconduct and confidentiality issues, as well as disputes over the authorship scheme, all responsibilities will be borne by the authors.
2. The author agrees to grant the Editorial Office of Environment and Social Psychology a licence to use the reproduction right, distribution right, information network dissemination right, performance right, translation right, and compilation right of the submitted manuscript, including the work as a whole, as well as the diagrams, tables, abstracts, and any other parts that can be extracted from the work and used in accordance with the characteristics of the journal. The Editorial Board of Environment and Social Psychology has the right to use and sub-licence the above mentioned works for wide dissemination in print, electronic and online versions, and, in accordance with the characteristics of the periodical, for the period of legal protection of the property right of the copyright in the work, and for the territorial scope of the work throughout the world.
3. The authors are entitled to the copyright of their works under the relevant laws of Singapore, provided that they do not exercise their rights in a manner prejudicial to the interests of the Journal.
About Licence
Environment and Social Psychology is an open access journal and all published work is available under the Creative Commons Licence, Authors shall retain copyright of their work and grant the journal/publisher the right of first publication, and their work shall be licensed under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Under this licence, the author grants permission to third parties to download, reuse, reprint, modify, distribute and/or copy the content with attribution to the author. No permission from the author or publisher is required.
This broad licence is intended to facilitate free access to and unrestricted use of original works of all kinds. This ensures that published works remain free and accessible in perpetuity. Submitted manuscripts, once accepted, are immediately available to the public and permanently accessible free of charge on the journal’s official website (https://esp.as-pub.com/index.php/ESP). Allowing users to read, download, copy, print, search for or link to the full text of the article, or use it for other legal purposes. However, the use of the work must retain the author's signature, be limited to non-commercial purposes, and not be interpretative.
Click to download <Agreement on the Licence for the Use of Copyright on Environmental and Social Psychology>.
How to Cite
AI-Powered Analytics for Climate Data Management and Policy Implementation
Ali Adel
Al-Turath University, Baghdad 10013, Iraq
Nidhal Jasm Mohammed Ali
Al-Mansour University College, Baghdad 10067, Iraq
Wissam Anwar Mohammed Hassan Ali
Al-Mamoon University College, Baghdad 10012, Iraq
Huda Yousif Khattab
Al-Rafidain University College, Baghdad 10064, Iraq
Faris Abdul Kareem Khazal
Madenat Alelem University College, Baghdad 10006, Iraq
DOI: https://doi.org/10.59429/esp.v10i11.4000
Keywords: AI-powered analytics; climate data management; deep learning; CNN-RNN hybrid model; climate forecasting; predictive modeling; climate policy
Abstract
The rising intricacy and volume of climate data are major challenges within climate modelling, forecasting, and policy implementation. Conventional statistical methods can lag behind in identifying nonlinear relationships, seasonal variations, and long-term climate patterns. The analysis of big climate data utility can greatly be increased through AI-enabled analytics behind it, where this work aimed to evaluate the effectiveness of various deep learning models in climate data application. A hybrid CNN-RNN model to simultaneously examine spatial and temporal climate data was created, with better performance than traditional prediction models and the ability to decrease both prediction errors and uncertainty. Its high-resolution predictions covered multiple sources of data, including satellite imagery, weather stations and historical climate data. The model validation metrics confirmed test-retest reliability was high with the Hybrid CNN-RNN performing the lowest R² and highest RMSE amongst the models tested. AI-Rank-Recognizance: The models demonstrated a possibility of using AI-analytic technologies to improve climate prognosis, analyze relationships between data units of the climate, and formulate adaptive legislative measures. With its progress, the fields of model interpretability, computational efficiency, and real-time deployment still face challenges. Future work around explainable AI, real-time climate tracking, and the incorporation of socioeconomic factors will help to take SDG projections to the next level. Utilizing AI for climate analysis, this research provides insights that may support sustainability planning and inform evidence-based discussions around climate-related policies.
References
[1]. 1.Amnuaylojaroen T. Advancements and challenges of artificial intelligence in climate modeling for sustainable urban planning. Frontiers in Artificial Intelligence. 2025;8.
[2]. 2.Cho H, Ackom E. Artificial Intelligence (AI)-driven approach to climate action and sustainable development. Nature Communications. 2025;16.
[3]. 3.Codyre P, Murphy P, Fionnagáin D, O’Farrell J, Tessema Y, Spillane C, et al. Measuring climate resilience in low- and middle-income countries using advanced analytical techniques and satellite data: a systematic review. Frontiers in Climate. 2025.
[4]. 4.4. Ekeh AH, Apeh CE, Odionu CS, Austin-Gabriel B. Leveraging machine learning for environmental policy innovation: Advances in Data Analytics to address urban and ecological challenges. Gulf Journal of Advance Business Research. 2025.
[5]. 5.Larosa F, Hoyas S, Conejero H, García-Martínez J, Nerini F, Vinuesa R. Large language models in climate and sustainability policy: limits and opportunities. Environmental Research Letters. 2025;20.
[6]. 6.Ukoba K, Onisuru O, Jen T, Madyira D, Olatunji KO. Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome. Environmental Science and Pollution Research International. 2025;32:10705-24.
[7]. 7.Zhou Z, Tang W, Li M, Cao W, Yuan Z. A Novel Hybrid Intelligent SOPDEL Model with Comprehensive Data Preprocessing for Long-Time-Series Climate Prediction. Remote Sensing [Internet]. 2023; 15(7).
[8]. 8.Alqerafi ANKNM. Using AI to Help Reduce the Effect of Global Warming. Power System Technology. 2024;48(1).
[9]. 9.Kumar T, Sandeep, U., Nagasri, T., Kumar, P., & Swathi, K. . Leveraging Artificial Intelligence to Address Climate Change. International Journal of Innovative Science and Research Technology (IJISRT). 2024;9(8).
[10]. 10.Shuford J. Interdisciplinary Perspectives: Fusing Artificial Intelligence with Environmental Science for Sustainable Solutions. Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023. 2024.
[11]. 11.Srivastava A, Maity R. Assessing the Potential of AI–ML in Urban Climate Change Adaptation and Sustainable Development. Sustainability [Internet]. 2023; 15(23).
[12]. 12.Shahbazi Z, Jalali R, Shahbazi Z. AI-Driven Sentiment Analysis for Discovering Climate Change Impacts. Smart Cities. 2025.
[13]. 13.Rojek I, Mikołajewski D, Andryszczyk M, Bednarek T, Tyburek K. Leveraging Machine Learning in Next-Generation Climate Change Adaptation Efforts by Increasing Renewable Energy Integration and Efficiency. Energies. 2025.
[14]. 14.Zhou F, Shi Y, Zhao P, Gu Z, Li Y. Dynamic climate graph network and adaptive climate action strategy for climate risk assessment and low-carbon policy responses. Frontiers in Environmental Science. 2025.
[15]. 15.Nieves V, Ruescas A, Sauzède R. AI for Marine, Ocean and Climate Change Monitoring. Remote Sensing [Internet]. 2024; 16(1).
[16]. 16.Khan H, Alfwzan W, Latif R, Alzabut J, Thinakaran R. AI-Based Deep Learning of the Water Cycle System and Its Effects on Climate Change. Fractal and Fractional. 2025.
[17]. 17.Peddagolla N, Muniganti P. Implementing Artificial Intelligence for Hydroinformatics and Enhanced Climate Forecasting to promote Sustainable Decision Making and Improve Ecological Understanding. Journal of Information Systems Engineering and Management. 2025.
[18]. 18.Subramanian A, Palanichamy N, Ng K-W, Aneja S. Climate Change Analysis in Malaysia Using Machine Learning. Journal of Informatics and Web Engineering. 2025.
[19]. 19.Talha M, Nejadhashemi A, Moller K. Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review. Heliyon. 2025;11.
[20]. 20.Tian L, Zhang Z, He Z, Yuan C, Xie Y, Zhang K, et al. Predicting Energy-Based CO2 Emissions in the United States Using Machine Learning: A Path Toward Mitigating Climate Change. Sustainability. 2025.
[21]. 21.Tasha S. A Review of Artificial Intelligence Applications in Climate Change Mitigation. International Journal of Environment and Climate Change. 2025.
[22]. 22.Giannopoulos M, Tsagkatakis G, Tsakalides P. Higher-Order Convolutional Neural Networks for Essential Climate Variables Forecasting. Remote Sensing [Internet]. 2024; 16(11).
[23]. 23.Ladi T, Jabalameli S, Sharifi A. Applications of machine learning and deep learning methods for climate change mitigation and adaptation. Environment and Planning B: Urban Analytics and City Science. 2022;49(4):1314-30.
[24]. 24.Akomea-Frimpong I, Dzagli JRAD, Eluerkeh K, Bonsu FB, Opoku-Brafi S, Gyimah S, et al. A systematic review of artificial intelligence in managing climate risks of PPP infrastructure projects. Engineering, Construction and Architectural Management. 2023;ahead-of-print(ahead-of-print).
[25]. 25.Barrie I, Adegbite AO, Osholake SF, Alesinloye TS, Bello AB. Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions. World Journal of Advanced Research and Reviews. 2024.
[26]. 26.Kayusi F, Chavula P, Lungu G, Mambwe H. AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges. LatIA. 2025.
[27]. 27.Economou T LG, Tzyrkalli A, Constantinidou K, Lelieveld J. A data integration framework for spatial interpolation of temperature observations using climate model data. PeerJ. 2023;11.
[28]. 28.Bilal M, Ali G, Iqbal MW, Anwar M, Malik MSA, Kadir RA. Auto-Prep: Efficient and Automated Data Preprocessing Pipeline. IEEE Access. 2022;10:107764-84.
[29]. 29.Jiang Q, Li S, editors. Artificial Intelligence Algorithms in Statistical Analysis. 2024 International Conference on Data Science and Network Security (ICDSNS); 2024 26-27 July 2024.
[30]. 30.Le AT, Shakiba M, Ardekani I, Abdulla WH. Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network. Applied Sciences [Internet]. 2024; 14(19).
[31]. 31.Zha W, Zhang J, Dan Y, Li Y. A novel wind power prediction method of the lower upper bound evaluation based on GRU. Transactions of the Institute of Measurement and Control. 2024;47(3):599-609.
[32]. 32.Yang Y, Sun W, Zou M, Qiao S, Li Q. Multi-model seasonal prediction of global surface temperature based on partial regression correction method. Frontiers in Environmental Science. 2022;10.
[33]. 33.Yamamoto H, Kondoh J, Kodaira D. Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation. Energies [Internet]. 2022; 15(15).
[34]. 34.Nooni IK, Ogou FK, Chaibou AA, Nakoty FM, Gnitou GT, Lu J. Evaluating CMIP6 Historical Mean Precipitation over Africa and the Arabian Peninsula against Satellite-Based Observation. Atmosphere [Internet]. 2023; 14(3).
[35]. 35.Schneider T, Leung LR, Wills RCJ. Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence. Atmos Chem Phys. 2024;24(12):7041-62.
[36]. 36.Allen AEA, Tkatchenko A. Machine learning of material properties: Predictive and interpretable multilinear models. Science Advances.8(18):eabm7185.
[37]. 37.Kimpson T, Paxton EA, Chantry M, Palmer T. Climate-change modelling at reduced floating-point precision with stochastic rounding. Quarterly Journal of the Royal Meteorological Society. 2023;149(752):843-55.
[38]. 38.Cheong S-M, Sankaran K, Bastani H. Artificial intelligence for climate change adaptation. WIREs Data Mining and Knowledge Discovery. 2022;12(5):e1459.
[39]. 39.Pei X, Wu J, Xue J, Zhao J, Liu C, Tian Y. Assessment of Cities’ Adaptation to Climate Change and Its Relationship with Urbanization in China. Sustainability [Internet]. 2022; 14(4).
[40]. 40.Satish M, Prakash, Babu SM, Kumar PP, Devi S, Reddy KP, editors. Artificial Intelligence (AI) and the Prediction of Climate Change Impacts. 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA); 2023 7-8 Oct. 2023.
[41]. 41.Islam F. Artificial Intelligence-powered Carbon Market Intelligence and Blockchain-enabled Governance for Climate-responsive Urban Infrastructure in the Global South. Journal of Engineering Research and Reports. 2025.
[42]. 42.Maideen AAK, Mohammed S, Basha N, Basha VAA. Effective Utilisation of AI to Improve Global Warming Mitigation Strategies through Predictive Climate Modelling. International Journal of Data Informatics and Intelligent Computing. 2024.
[43]. 43.Koç FŞ, Savaş P. The use of artificially intelligent chatbots in English language learning: A systematic meta-synthesis study of articles published between 2010 and 2024. ReCALL. 2025;37(1):4-21.






