A Critical Discourse Analysis of Emotional Language in AI-Generated Climate Communication

Artificial intelligence is increasingly used in public communication campaigns related to environmental awareness, raising important questions about how automated systems construct emotional engagement and ecological responsibility through language. While climate communication frequently relies on affective and moral appeals to encourage collective environmental action, limited research has examined how generative AI reproduces these discursive strategies in climate-related texts. This study investigates the construction of emotional language in AI-generated climate communication using Fairclough’s three-dimensional model of Critical Discourse Analysis. The dataset comprises forty climate-related texts generated by a large language model through prompts centred on environmental responsibility, climate urgency, and sustainability advocacy. The analysis examines the textual features of emotional discourse, the discursive practices embedded within AI-generated narratives, and the broader ideological implications of automated environmental communication. The findings demonstrate that the generated texts consistently employ inclusive pronouns, evaluative adjectives, moral appeals, and future-oriented narratives to construct climate change as a shared human concern requiring immediate collective action. Empathy is strategically produced through emotionally charged vocabulary that positions environmental protection within a framework of moral obligation and global solidarity. However, the analysis also reveals that these AI-generated narratives frequently reproduce simplified and depoliticised representations of climate responsibility by foregrounding individual behavioural change while minimising structural, political, and institutional dimensions of the climate crisis. The study argues that generative AI systems do not merely replicate neutral information but actively reproduce dominant ideological patterns embedded within contemporary environmental discourse. By extending Critical Discourse Analysis into the context of AI-mediated communication, this research contributes to emerging scholarship on automated discourse, emotional persuasion, and the ideological implications of generative artificial intelligence in public communication.

Artificial intelligence, climate communication, critical discourse analysis, emotional language, environmental discourse

1. Brennen, J. S., Howard, P. N., & Nielsen, R. K. (2022). Artificial intelligence and the future of journalism. Digital Journalism, 10(1), 1–19. https://doi.org/10.1080/21670811.2021.1988865 [Google Scholar] [Crossref]

2. Brosch, T. (2021). Affect and emotions as drivers of climate change perception and action: A review. Current Opinion in Behavioral Sciences, 42, 15–21. https://doi.org/10.1016/j.cobeha.2021.02.001 [Google Scholar] [Crossref]

3. Carvalho, A. (2021). Media and climate change communication. Wiley Interdisciplinary Reviews: Climate Change, 12(1), e665. https://doi.org/10.1002/wcc.665 [Google Scholar] [Crossref]

4. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 [Google Scholar] [Crossref]

5. Fairclough, N. (2013). Critical discourse analysis: The critical study of language (2nd ed.). Routledge. [Google Scholar] [Crossref]

6. Marlon, J. R., Ballew, M. T., Roser-Renouf, C., Leiserowitz, A., & Maibach, E. (2021). Climate change worry, risk perceptions, and policy support. Global Environmental Change, 68, 102266. https://doi.org/10.1016/j.gloenvcha.2021.102266 [Google Scholar] [Crossref]

7. Moser, S. C. (2021). Communicating climate change: History, challenges, process and future directions. Wiley Interdisciplinary Reviews: Climate Change, 12(2), e690. https://doi.org/10.1002/wcc.690 [Google Scholar] [Crossref]

8. Nabi, R. L., Gustafson, A., & Jensen, R. (2021). Framing climate change: Exploring the role of emotion in generating advocacy behavior. Communication Research, 48(3), 442–468. https://doi.org/10.1177/0093650218777856 [Google Scholar] [Crossref]

9. O’Neill, S., & Nicholson-Cole, S. (2021). “Fear won't do it”: Promoting positive engagement with climate change through visual and iconic representations. Science Communication, 43(2), 217–243. https://doi.org/10.1177/1075547021999942 [Google Scholar] [Crossref]

10. Schneider, C. R., Zaval, L., Markowitz, E. M., & Weber, E. U. (2021). The influence of anticipated pride and guilt on pro-environmental decision making. PLOS ONE, 16(4), e0248849. https://doi.org/10.1371/journal.pone.0248849 [Google Scholar] [Crossref]

11. Veltri, G. A., & Atanasova, D. (2022). Climate change communication in the digital age. Wiley Interdisciplinary Reviews: Climate Change, 13(1), e740. https://doi.org/10.1002/wcc.740 [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition