TY  - JOUR
T1  - Artificial Intelligence Enabled Climate Change Communication: The Role of ClimateGPT
TT  - Yapay Zekâ Destekli İklim Değişikliği İletişimi: ClimateGPT'nin Rolü
AU  - Zeydan, İlknur
PY  - 2025
DA  - April
Y2  - 2025
DO  - 10.17680/erciyesiletisim.1580090
JF  - Erciyes İletişim Dergisi
JO  - JEC
PB  - Erciyes Üniversitesi
WT  - DergiPark
SN  - 1308-3198
SP  - 93
EP  - 108
VL  - 0
IS  - Special Issue of the 1st Environment and Communication: Climate Change and Sustainability Symposium
LA  - en
AB  - Climate change is one of the most important environmental problems humanity faces. To combat climate change, it is necessary first to understand the concept of climate change correctly and to know its negative effects and solution methods. Unfortunately, there is a problem in climate change communication between scientists and the public. Artificial intelligence (AI) can overcome this problem. The use of artificial intelligence in climate-related communication contributes to the increase in the effectiveness of communication. This study aims to explain the basic concepts of climate change to the public and students with the help of AI. For this purpose, ClimateGPT, an AI tool developed by Erasmus AI company, was used. ClimateGPT answered a series of questions about climate change, greenhouse effect and greenhouse gases, climate change effects, mitigation and adaptation measures, and finally climate communication. As a result of the study, ClimateGPT has been shown to produce mostly accurate and clear answers that everyone can understand. The AI-generated information can be used to educate the public about climate change. Therefore, this study contributes to the United Nations Sustainable Development Goal number 13: Climate Action.
KW  - Artificial Intelligence
KW  - Climate Change
KW  - ClimateGPT
KW  - Communication.
N2  - İklim değişikliği insanlığın karşı karşıya olduğu en önemli çevre sorunlarından biridir. İklim değişikliğiylemücadele edebilmek için öncelikle iklim değişikliği kavramını doğru anlamak, olumsuz etkilerini veçözüm yöntemlerini bilmek gerekmektedir. Ne yazık ki bilim insanları ile toplum arasında iklim değişikliğiiletişiminde bir sorun bulunmaktadır. Yapay zekâ bu sorunun üstesinden gelebilir. Yapay zekanın iklimleilgili iletişimde kullanılması iletişimin etkinliğinin artmasına katkı sağlamaktadır. Bu çalışma iklimdeğişikliğinin temel kavramlarını Yapay zekâ yardımıyla topluma ve öğrencilere anlatmayı amaçlamaktadır. Bu amaçla Erasmus AI firması tarafından geliştirilen bir yapay zekâ aracı olan ClimateGPT kullanılmıştır. ClimateGPT, iklim değişikliği, sera etkisi ve sera gazları, iklim değişikliğinin etkileri, azaltım ve uyum önlemleri ve son olarak iklim iletişimi konularında bir dizi soruyu yanıtladı. Çalışmanın sonucunda, ClimateGPT’nin herkesin anlayabileceği, büyük ölçüde doğru ve net cevaplar ürettiği görülmüştür. YZ tarafından üretilen bilgiler toplumu iklim değişikliği konusunda eğitmek için kullanılabilir. Bu nedenle bu çalışma Birleşmiş Milletler Sürdürülebilir Kalkınma Hedeflerinden 13 numaralı İklim Eylemi’ne katkıda bulunmaktadır.
CR  - Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M., Hua, J., Al-Fatesh, A., Ihara, I., Rooney, D. W., & Yap, P. S. (2023). Artificial intelligence-based solutions for climate change: a review. In Environmental Chemistry Letters (Vol. 21, Issue 5). Springer International Publishing. https://doi.org/10.1007/s10311-023-01617-y
CR  - Cook, B. R., & Overpeck, J. T. (2019). Relationship-building between climate scientists and publics as an alternative to information transfer. Wiley Interdisciplinary Reviews: Climate Change, 10(2), 1–17. https://doi.org/10.1002/wcc.570
CR  - Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023). The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI and Society, 38(1), 283–307. https://doi.org/10.1007/s00146-021-01294-x
CR  - Daugaard, D., Kent, D., Servátka, M., & Zhang, L. (2024). Optimistic framing increases responsible investment of investment professionals. Scientific Reports, 14(1), 1–9. https://doi.org/10.1038/s41598-023-50965-w
CR  - Debnath, R., Creutzig, F., Sovacool, B. K., & Shuckburgh, E. (2023). Harnessing human and machine intelligence for planetary-level climate action. Npj Climate Action, 2(1), 1–11. https://doi.org/10.1038/s44168-023-00056-3
CR  - Duram, L. A. (2021). Teaching a social science course on climate change : Suggestions for active learning. Bulletin of the American Meteorological Society, 102(8), E1494–E1498. https://doi.org/10.1175/BAMS-D-21-0035.1
CR  - Glavinskas, V. (2023). We asked ChatGPT about climate change — it left a lot out. https://www.edf.org/article/we-asked-chatgpt-about-climate-change-it-left-lot-out
CR  - Griffiths, A. (2023). Raising Issues OK computer ? Using artificial intelligence for teaching and learning about climate change. Teaching Geography, 48(2), 2023.
CR  - Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-Mediated Communication: Definition, Research Agenda, and Ethical Considerations. Journal of Computer-Mediated Communication, 25(1), 89–100. https://doi.org/10.1093/jcmc/zmz022
CR  - He, K., Member, R. M., Lin, Q., & Ruan, Y. (2021). A Survey of Large Language Models for Healthcare : from Data , Technology , and Applications to Accountability and Ethics. 14(8), 1–32.
CR  - Hwang, I. C., Tol, R. S. J., & Hofkes, M. W. (2019). Active Learning and Optimal Climate Policy. Environmental and Resource Economics, 73(4), 1237–1264. https://doi.org/10.1007/s10640-018-0297-x
CR  - İşler, B., & Kılıç, M. Y. (2021). Eğitimde Yapay Zekâ Kullanımı ve Gelişimi. Yeni Medya Elektronik Dergi, 5(1), 1–11.
CR  - Keleş, O., & Deniz, N. D. (2024). A comparative study with human data: Do LLMs have superficial language processing? 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings. https://doi.org/10.1109/SIU61531.2024.10600807
CR  - Leal Filho, W., Wall, T., Rui Mucova, S. A., Nagy, G. J., Balogun, A.-L., Luetz, J. M., Ng, A. W., Kovaleva, M., Safiul Azam, F. M., Alves, F., Guevara, Z., Matandirotya, N. R., Skouloudis, A., Tzachor, A., Malakar, K., & Gandhi, O. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662. https://doi.org/10.1016/j.techfore.2022.121662
CR  - Lee, B. Y., Pavilonis, B., John, D. C., Heneghan, J., Bartsch, S. M., & Kavouras, I. (2024). The Need to Focus More on Climate Change Communication and Incorporate More Systems Approaches. Journal of Health Communication, 29, 1–10. https://doi.org/10.1080/10810730.2024.2361566
CR  - Liu, Y., Saleh, S., & Huang, J. (2021). Artificial Intelligence in Promoting Teaching and Learning Transformation in Schools. International Journal of Innovation, Creativity and Change, 15(3), 891–902. https://doi.org/10.53333/ijicc2013/15369
CR  - Luccioni, A., Schmidt, V., Vardanyan, V., & Bengio, Y. (2021). Using Artificial Intelligence to Visualize the Impacts of Climate Change. IEEE Computer Graphics and Applications, 41(1), 8–14. https://doi.org/10.1109/MCG.2020.3025425
CR  - Pirim, H. (2006). Yapay Zeka. Journal of Yasar University, 1(1), 81–93.
CR  - Ramya, Verma, R., & Singh, R. K. (2024). The role of artificial intelligence in climate change visualization. Idealistic Journal of Advanced Research in Progressive Spectrums, 3(4), 48–54.
CR  - Rane, N., Choudhary, S., & Rane, J. (2024). Contribution of ChatGPT and Similar Generative Artificial Intelligence for Enhanced Climate Change Mitigation Strategies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4681720
CR  - Şeko, Y. A. (2024). ClimateGPT iklim değişikliğiyle mücadeleyi desteklemeyi hedefliyor. https://www.aa.com.tr/tr/bilim-teknoloji/climategpt-iklim-degisikligiyle-mucadeleyi-desteklemeyi-hedefliyor/3245317
CR  - Sullivan, Y., & Fosso Wamba, S. (2024). Artificial intelligence and adaptive response to market changes: A strategy to enhance firm performance and innovation. Journal of Business Research, 174(March 2022), 114500. https://doi.org/10.1016/j.jbusres.2024.114500
CR  - Thulke, D., Gao, Y., Pelser, P., Brune, R., Jalota, R., Fok, F., Ramos, M., van Wyk, I., Nasir, A., Goldstein, H., Tragemann, T., Nguyen, K., Fowler, A., Stanco, A., Gabriel, J., Taylor, J., Moro, D., Tsymbalov, E., de Waal, J., … Erasmus, D. (2024). ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change. 1–52. http://arxiv.org/abs/2401.09646
CR  - Urzedo, D., Sworna, Z. T., Hoskins, A. J., & Robinson, C. J. (2024). AI chatbots contribute to global conservation injustices. Humanities and Social Sciences Communications, 11(1), 1–8. https://doi.org/10.1057/s41599-024-02720-3
CR  - Vaghefi, S. A., Stammbach, D., Muccione, V., Bingler, J., Ni, J., Kraus, M., Allen, S., Colesanti-Senni, C., Wekhof, T., Schimanski, T., Gostlow, G., Yu, T., Wang, Q., Webersinke, N., Huggel, C., & Leippold, M. (2023). ChatClimate: Grounding conversational AI in climate science. Communications Earth & Environment, 4(1), 480. https://doi.org/10.1038/s43247-023-01084-x
CR  - Verendel, V. (2023). Tracking artificial intelligence in climate inventions with patent data. Nature Climate Change, 13(1), 40–47. https://doi.org/10.1038/s41558-022-01536-w
CR  - Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
CR  - Zeydan, İ. (2024a). Pazarlamada Yapay Zeka Üzerine Yapılan Çalışmaların İçerik Analizi. Karaelmas Sosyal Bilimler Dergisi, 2/1(June), 1–11.
CR  - Zeydan, İ. (2024b). Yapay zekâ-pazarlama entegrasyonu: örnek uygulamalar. International Journal of Management Economics and Business, 20(ICMEB’24 Special Issue), 423–446. https://doi.org/10.17130/ijmeb.1486507
CR  - Zeydan, Ö., Sunar, B., & Danır, Z. (2017). Greenhouse gas emissions and climate change vulnerabilities of certain European countries. Journal of Young Scientist, 5, 121–128. http://journalofyoungscientist.usamv.ro/index.php/scientific-papers/current?id=516
CR  - Zhang, B., & Soh, H. (2023). Large Language Models as Zero-Shot Human Models for Human-Robot Interaction. IEEE International Conference on Intelligent Robots and Systems, 7961–7968. https://doi.org/10.1109/IROS55552.2023.10341488
UR  - https://doi.org/10.17680/erciyesiletisim.1580090
L1  - https://dergipark.org.tr/tr/download/article-file/4343110
ER  -