PROPHETIC ECOLOGICAL ETHICS: INTEGRATING HADITH TRADITIONS INTO CLIMATE CHANGE MITIGATION IN THE NUSANTARA
Brunei Darussalam Brunei Darussalam 
Turkey 
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Climate Change Mitigation, Hadith traditions, Prophetic Ethics
Abstract
Climate change poses severe ecological and socio-cultural challenges in the Nusantara, where mitigation efforts often overlook locally embedded ethical systems. Religious traditions, particularly within Islamic contexts, remain underutilized despite their strong influence on environmental behavior. This study aims to examine how Prophetic ecological ethics derived from Hadith traditions can be integrated into climate change mitigation strategies in the Nusantara. A qualitative interdisciplinary design was employed, combining hermeneutic analysis of selected Hadith texts with contextual examination of environmental data and community practices. The study analyzed 120 Hadith narrations categorized into ecological themes and aligned them with regional sustainability indicators. Findings reveal that themes such as water conservation and moderation in consumption significantly correlate with improved environmental behaviors, especially when reinforced by community participation and institutional support. Variations across ecological domains indicate stronger influence at the individual behavioral level compared to structural environmental challenges. The study concludes that Hadith-based ecological ethics provide a practical and culturally resonant framework that complements scientific and policy-driven approaches to climate mitigation. Integration of religious ethics enhances both engagement and sustainability outcomes when embedded within local socio-cultural contexts.
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References
Abul Hasanaath, A., Luqman, H., Katib, R., & Anwar, S. (2025). FSBI: Deepfake detection with frequency enhanced self-blended images. Image and Vision Computing, 154, 105418. https://doi.org/https://doi.org/10.1016/j.imavis.2025.105418
Alazwari, S., Jamal Alsamri, M. O., Alamgeer, M., Alabdan, R., Alzahrani, I., Rizwanullah, M., & Elneil Osman, A. (2024). Artificial rabbits optimization with transfer learning based deepfake detection model for biometric applications. Ain Shams Engineering Journal, 15(12), 103057. https://doi.org/https://doi.org/10.1016/j.asej.2024.103057
Aldrees, A., Abuzinadah, N., Umer, M., AlHammadi, D. A., Alsubai, S., & Alharthi, R. (2025). Deepfake detection using optimized VGG16-based framework enhanced with LIME for secure digital content. Image and Vision Computing, 162, 105696. https://doi.org/https://doi.org/10.1016/j.imavis.2025.105696
Alkurdi, D. A., Cevik, M., & Akgundogdu, A. (2024). Advancing Deepfake Detection Using Xception Architecture: A Robust Approach for Safeguarding against Fabricated News on Social Media. Computers, Materials and Continua, 81(3), 4285–4305. https://doi.org/https://doi.org/10.32604/cmc.2024.057029
Alsolai, H., Mahmood, K., Alshuhail, A., Ben Miled, A., Alqahtani, M., Alshareef, A., Alallah, F. S., & Alghamdi, B. M. (2025). Guardian-AI: A novel deep learning based deepfake detection model in images. Alexandria Engineering Journal, 126, 507–514. https://doi.org/https://doi.org/10.1016/j.aej.2025.04.095
Bendiab, G., Haiouni, H., Moulas, I., & Shiaeles, S. (2025). Deepfakes in digital media forensics: Generation, AI-based detection and challenges. Journal of Information Security and Applications, 88, 103935. https://doi.org/https://doi.org/10.1016/j.jisa.2024.103935
Chen, B., Liu, X., Xia, Z., & Zhao, G. (2023). Privacy-preserving DeepFake face image detection. Digital Signal Processing, 143, 104233. https://doi.org/https://doi.org/10.1016/j.dsp.2023.104233
Coppolino, L., Cristiano, G. M., D’Antonio, S., Giglio, J., Mazzeo, G., & Romano, L. (2025). A Blockchain Solution for Decentralized Content Verification and its Application to Deepfake Detection and Fintech Credit Scoring. Blockchain: Research and Applications, 100406. https://doi.org/https://doi.org/10.1016/j.bcra.2025.100406
Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. Computers in Human Behavior Reports, 16, 100538. https://doi.org/https://doi.org/10.1016/j.chbr.2024.100538
Eutamene, H. B., Hamidouche, W., Keita, M., Taleb-Ahmed, A., & Hadid, A. (2025). Integrating perceptual quality analysis and caption-based features for robust deepfake video detection. Computers and Electrical Engineering, 128, 110699. https://doi.org/https://doi.org/10.1016/j.compeleceng.2025.110699
Gao, Q., Zhang, B., Wu, J., Luo, W., Teng, Z., & Fan, J. (2025). Leveraging facial landmarks improves generalization ability for deepfake detection. Pattern Recognition, 164, 111528. https://doi.org/https://doi.org/10.1016/j.patcog.2025.111528
Garg, D., & Gill, R. (2025). Unmasking Deepfakes: A Review of Current Datasets, Tools, and Detection Features. Procedia Computer Science, 259, 1737–1748. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.129
Guo, S., Li, Q., Gao, M., Zhu, X., & Rida, I. (2025). Generalizable deepfake detection via Spatial Kernel Selection and Halo Attention Network. Image and Vision Computing, 160, 105582. https://doi.org/https://doi.org/10.1016/j.imavis.2025.105582
Han, R., Wang, X., Bai, N., Hou, J., Zhang, W., & Li, J. (2025). HSFF-Net: Hierarchical spectral-feature fusion network for deepfake detection and localization. Neural Networks, 192, 107967. https://doi.org/https://doi.org/10.1016/j.neunet.2025.107967
Hynek, N., Gavurova, B., & Kubak, M. (2025). Risks and benefits of artificial intelligence deepfakes: Systematic review and comparison of public attitudes in seven European Countries. Journal of Innovation & Knowledge, 10(5), 100782. https://doi.org/https://doi.org/10.1016/j.jik.2025.100782
Irfan, M. T., Arora, B., Sandotra, N., & Raza, A. A. (2025). On Machine Learning and Deep Learning based Deepfake Generation and Detection. Procedia Computer Science, 259, 1927–1936. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.148
Javed, M., Zhang, Z., Dahri, F. H., Laghari, A. A., Kraj?ík, M., & Almadhor, A. (2025). Real-Time Deepfake Detection via Gaze and Blink Patterns: A Transformer Framework. Computers, Materials and Continua, 85(1), 1457–1493. https://doi.org/https://doi.org/10.32604/cmc.2025.062954
Kumar, A., Singh, D., Jain, R., Jain, D. K., Gan, C., & Zhao, X. (2025). Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach. Information Fusion, 118, 102993. https://doi.org/https://doi.org/10.1016/j.inffus.2025.102993
Kumar, B. A., Misra, N. K., Pathak, N., Ahmadpour, S.-S., Krishnamoorthy, M., Shukla, D. K., Patidar, M., & Hakimi, M. (2025). Hybrid CMNV2: DeepFake faces classification and recognition using deep learning methods. Results in Engineering, 28, 107513. https://doi.org/https://doi.org/10.1016/j.rineng.2025.107513
Lai, Z., Zhang, Y., Li, D., & Ni, J. (2025). Leveraging High-Frequency Diversified Augmentation for general deepfake detection. Journal of Information Security and Applications, 89, 103994. https://doi.org/https://doi.org/10.1016/j.jisa.2025.103994
Liu, B., Liu, B., Ding, M., & Zhu, T. (2024). MeST-Former: Motion-enhanced Spatiotemporal Transformer for generalizable Deepfake detection. Neurocomputing, 610, 128588. https://doi.org/https://doi.org/10.1016/j.neucom.2024.128588
Liu, R., Zhang, J., & Li, H. (2025). Hierarchical multi-source cues fusion for mono-to-binaural based Audio Deepfake Detection. Information Fusion, 120, 103097. https://doi.org/https://doi.org/10.1016/j.inffus.2025.103097
Loovens, J., & Tinmaz, H. (2025). A systematic literature review of deepfakes in forensic science. Forensic Imaging, 43, 200647. https://doi.org/https://doi.org/10.1016/j.fri.2025.200647
Matli, W. (2024). Extending the theory of information poverty to deepfake technology. International Journal of Information Management Data Insights, 4(2), 100286. https://doi.org/https://doi.org/10.1016/j.jjimei.2024.100286
Momin, M. D. S., Sufian, A., Barman, D., Leo, M., Distante, C., & Damer, N. (2025). Explainable deepfake detection across different modalities: An overview of methods and challenges. Image and Vision Computing, 163, 105738. https://doi.org/https://doi.org/10.1016/j.imavis.2025.105738
N, A. D., & Simon, P. (2025). DeepGuardNet: A Novel CNN Architecture for DeepFake Image Detection. Procedia Computer Science, 258, 811–818. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.313
Peng, C., Chen, Y., Liu, D., Wang, N., & Gao, X. (2025). FairForensics: mitigating attribute bias in deepfake detection by integrating texture and attribute features. Neural Networks, 192, 107899. https://doi.org/https://doi.org/10.1016/j.neunet.2025.107899
Pintelas, E., & Livieris, I. E. (2025). Convolutional neural network framework for deepfake detection: A diffusion-based approach. Computer Vision and Image Understanding, 257, 104375. https://doi.org/https://doi.org/10.1016/j.cviu.2025.104375
Qiu, X., Miao, X., Wan, F., Duan, H., Shah, T., Ojha, V., Long, Y., & Ranjan, R. (2025). D2Fusion: Dual-domain fusion with feature superposition for Deepfake detection. Information Fusion, 120, 103087. https://doi.org/https://doi.org/10.1016/j.inffus.2025.103087
Rabhi, M., Bakiras, S., & Di Pietro, R. (2024). Audio-deepfake detection: Adversarial attacks and countermeasures. Expert Systems with Applications, 250, 123941. https://doi.org/https://doi.org/10.1016/j.eswa.2024.123941
Sagar, N. K., & Arukonda, S. (2025). A Novel CNN-LSTM Approach for Robust Deepfake Detection. Procedia Computer Science, 258, 1844–1855. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.436
Saif, S., Tehseen, S., & Ali, S. S. (2024). Fake news or real? Detecting deepfake videos using geometric facial structure and graph neural network. Technological Forecasting and Social Change, 205, 123471. https://doi.org/https://doi.org/10.1016/j.techfore.2024.123471
Sharma, R., & Dwivedi, R. (2025). Unmasking deepfakes: Eye blink pattern analysis using a hybrid LSTM and MLP-CNN model. Image and Vision Computing, 154, 105370. https://doi.org/https://doi.org/10.1016/j.imavis.2024.105370
Sharma, S., & Selwal, A. (2025). Improved Deepfake Detection with Optimized Preprocessing for Low-Quality Images. Procedia Computer Science, 258, 507–516. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.286
Siddiqui, F., Yang, J., Xiao, S., & Fahad, M. (2025). Diffusion model in modern detection: Advancing Deepfake techniques. Knowledge-Based Systems, 325, 113922. https://doi.org/https://doi.org/10.1016/j.knosys.2025.113922
Somoray, K., & Miller, D. J. (2023). Providing detection strategies to improve human detection of deepfakes: An experimental study. Computers in Human Behavior, 149, 107917. https://doi.org/https://doi.org/10.1016/j.chb.2023.107917
Soto-Sanfiel, M. T., Angulo-Brunet, A., & Saha, S. (2025). Deepfakes as narratives: Psychological processes explaining their reception. Computers in Human Behavior, 165, 108518. https://doi.org/https://doi.org/10.1016/j.chb.2024.108518
Taleby Ahvanooey, M., Mazurczyk, W., Wang, Z., & Zhao, J. (2025). A novel framework for assessing determinant risk factors on cyber (dis)trust behaviors of netizens in deepfakes. Engineering Applications of Artificial Intelligence, 159, 111319. https://doi.org/https://doi.org/10.1016/j.engappai.2025.111319
Thirumaleshwari Devi, B., & Rajasekaran, R. (2025). Deepfake Video Detection Using Ada-Boosting on the DFDC Dataset. Procedia Computer Science, 258, 1091–1101. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.344
Wahab, A. (2025). Futures of Deepfake and society: Myths, metaphors, and future implications for a trustworthy digital future. Futures, 173, 103672. https://doi.org/https://doi.org/10.1016/j.futures.2025.103672
Wang, X., Song, W., Hao, C., & Liu, F. (2025). Deepfake Detection Method Based on Spatio-Temporal Information Fusion. Computers, Materials and Continua, 83(2), 3351–3368. https://doi.org/https://doi.org/10.32604/cmc.2025.062922
Wazid, M., Mishra, A. K., Mohd, N., & Das, A. K. (2024). A Secure Deepfake Mitigation Framework: Architecture, Issues, Challenges, and Societal Impact. Cyber Security and Applications, 2, 100040. https://doi.org/https://doi.org/10.1016/j.csa.2024.100040
Zou, Z., Peng, D., Zhao, Y., Tian, Z., & Wang, S. (2025). TTP-AP: Test-time projection of augmented prototypes for generalized deepfake detection. Knowledge-Based Systems, 330, 114710. https://doi.org/https://doi.org/10.1016/j.knosys.2025.114710
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Copyright (c) 2026 Nadiah Ismail, Khalid Mahmud, Hale Y?lmaz
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