The hidden carbon footprint of short videos created by generative artificial intelligence: examining the sustainability paradox in social media marketing
DOI:
https://doi.org/10.15170/MM.2026.60.03.01Keywords:
Green AI, algorithmic greenwashing, short-form video marketing, digital carbon footprint, generative AI, ESG reporting, sustainable marketingAbstract
THE AIM OF THE PAPER
The rapid uptake of generative artificial intelligence (AI) for producing short-form marketing videos is reshaping digital content creation, while creating growing tension between corporate Net Zero objectives and the European Union’s sustainability reporting demands. This paper addresses that sustainability paradox, focusing on the largely hidden carbon footprint of AI video-creation tools such as Runway, Pika and Sora in marketing use.
METHODOLOGY
The study adopts a two-pillar approach. First, it conducts a structured review of the 2019-2026 literature across AI ethics, environmental science and marketing. Second, it uses scenario-based carbon modelling to estimate the environmental burden of generative video production under different model-architecture and grid carbon intensity scenarios.
MOST IMPORTANT RESULTS
The results show that generating a video with AI consumes, on average, thirty times more energy than image generation and orders of magnitude more than text creation. The annual carbon footprint of a two-person team producing 2,500 AI-generated videos varies by orders of magnitude depending on model choice and grid mix: about 22.6 kg CO₂ for a medium model on an average grid, rising to as much as 900 kg CO₂ in the worst case (large model, fossil-based grid, including the estimation band). These emissions remain largely invisible in corporate ESG and Scope 3 reporting due to shadow AI practices and the absence of AI-specific sustainability indicators, thereby contributing to algorithmic greenwashing.
RECOMMENDATIONS
To address these reporting gaps, the paper proposes a new Carbon Per Mille (C-CPM) index that adds a carbon-intensity dimension to existing marketing-performance metrics. It also shows that lightweight and quantized AI models and carbon-aware render scheduling can substantially reduce CO₂ emissions without materially affecting output quality. Together, these findings offer a conceptual and practical framework for measuring and managing the sustainable use of generative AI in marketing.
References
A * jel a strukturált mintába tartozó 37 forrást jelöli; a többi célzott hivatkozáskereséssel bevont kontextuális mű.
Adobe Express (2025). How often should you really post on social? https://www.adobe.com/express/learn/blog/how-often-should-you-post-content
* Alzoubi, Y., & Mishra, A. (2024). Green artificial intelligence initiatives: Potentials and challenges. Journal of Cleaner Production, 468, 143090. https://doi.org/10.1016/j.jclepro.2024.143090
Animoto (2026). State of video 2026: Balancing AI innovation and human authenticity. https://animoto.com/state-of-video
* Anthony, L. F. W., Kanding, B., & Selvan, R. (2020). Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv. https://doi.org/10.48550/ar-Xiv.2007.03051
Benbya, H., Pachidi, S., & Jarvenpaa, S. (2021). Artificial intelligence in organizations: Implications for information systems research. Journal of the Association for Information Systems, 22(2). https://doi.org/10.17705/1jais.00662
* Bouza, L., Bugeau, A. and Lannelongue, L. (2023). How to estimate carbon footprint when training deep learning models? A guide and review. Environmental Research Communications, 5(11), 115014. https://doi.org/10.1088/2515-7620/acf81b
Bozsik, N., Fűrész, Á., & Szeberényi, A. (2025). Smart Energy, Conscious Future: The Role of Artificial Intelligence in Energy Efficiency. In: Belak, J., Nedelko, Z. (eds.), 9th FEB International Scientific Conference: Sustainable Management in the Age of ESG and AI: Navigating Challenges and Opportunities, Maribor (pp. 511–520). https://doi.org/10.18690/um.epf.5.2025.47
* Budennyy, S., Lazarev, V., Zakharenko, N., Korovin, A., Plosskaya, O., Dimitrov, D., Arkhipkin, V., Oseledets, I., Barsola, I., Egorov, I., Kosterina, A., & Zhukov, L. (2022). eco2AI: Carbon emissions tracking of machine learning models as the first step towards sustainable AI. Doklady Mathematics, 106, 118–128. https://doi.org/10.1134/s1064562422060230
* Chien, A. A., Lin, L., Nguyen, H. B., Rao, V., Sharma, T., & Wijayawardana, R. (2023). Reducing the carbon impact of generative AI inference (today and in 2035). In: HotCarbon ’23: Proceedings of the 2nd Workshop on Sustainable Computer Systems (Article 11, pp. 1–7.) https://doi.org/10.1145/3604930.3605705
* Chung, J.-W., Wu, R., Ma, J. J., & Chowdhury, M. (2026). Where do the Joules go? Diagnosing inference energy consumption. arXiv. https://doi.org/10.48550/arXiv.2601.22076
* Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). The AI gambit: Leveraging artificial intelligence to combat climate change – opportunities, challenges, and recommendations. AI & Society, 38(1), 283–307. https://doi.org/10.1007/s00146-021-01294-x
Delavande, J. (2025a). How much power does a SOTA open video model use? Hugging Face. https://huggingface.co/blog/jdelavande/text-tovideo-energy-cost
* Delavande, J., Pierrard, R., & Luccioni, S. (2025b). Video Killed the Energy Budget: Characterizing the latency and power regimes of open Text-to-Video models. arXiv. https://doi.org/10.48550/arXiv.2509.19222
* Desislavov, R., Martínez-Plumed, F., & Hernández-Orallo, J. (2023). Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems, 38, 100857.https://doi.org/10.1016/j.suscom.2023.100857
* Dodge, J., Prewitt, T., Combes, R. T. D., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W. (2022). Measuring the carbon intensity of AI in cloud instances. arXiv. https://doi.org/10.48550/arXiv.2206.05229
Dudás, K. (2013). Fenntarthatatlan növekedés, fenntarthatatlan fogyasztás. Fenntarthatatlan marketing? Marketing & Menedzsment, 47(4), 24–35.
Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835–2857. https://doi.org/10.1287/mnsc.2014.1984
European Commission (2023). Commission Delegated Regulation (EU) 2023/2772 of 31 July 2023 supplementing Directive 2013/34/EU of the European Parliament and of the Council as regards sustainability reporting standards. (Official Journal of the European Union.) https://eur-lex.europa.eu/eli/reg_del/2023/2772/oj (utolsó letöltés: 2026.03.12.)
* Famularo, J. (2023). Corporate social responsibility communication in the ICT sector: Digital issues, greenwashing, and materiality. International Journal of Corporate Social Responsibility, 8, 1–25. https://doi.org/10.1186/s40991-023-00082-8
* Fischer, R. (2025). Ground-truthing AI energy consumption: Validating CodeCarbon against external measurements. arXiv. https://doi.org/10.48550/arXiv.2509.22092
Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial intelligence in advertising: Advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. SAGE Open, 13(4). https://doi.org/10.1177/21582440231210759
* Garg, S., Haralayya, B., Qudah, A. A. M., Maguluri, P. L., Szeberényi, A., & Sameen, Z. A. (2024). The impact of artificial intelligence on management productivity and efficiency. Business Management and Economics Engineering, 22(1), 424–434. https://doi.org/10.2139/ssrn.5000221
Horváth, K. G. (2020). Az Európai Zöld Megállapodás és a 2021–2027-es többéves pénzügyi keret kapcsolata. Európai Tükör, 23(1), 91–
110. https://doi.org/10.32559/et.2020.1.5
Horváth, K. G. (2023). Vállalati fenntarthatóság és fenntartható vállalkozások. Acta Humana: Emberi Jogi Közlemények, 11(3), 165–184. https://doi.org/10.32566/ah.2023.3.8
HubSpot (2025). Not another state of marketing report: 2025 marketing trends. https://www.hubspot.com/state-of-marketing (utolsó letöltés: 2026.02.13.)
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
* Karaarslan, E., & Aydın, Ö. (2024). Generate impressive videos with text instructions: A review of OpenAI Sora, Stable Diffusion, Lumiere and comparable models. TechRxiv. https://doi.org/10.36227/techrxiv.170862194.43871446/v1
* Khan, S., Naz, N. S., Mazhar, T., Tariq, M. U., Shahzad, T., Guizani, S., & Hamam, H. (2025). Green AI techniques for reducing energy consumption in AI systems. Array, 29, 100652. https://doi.org/10.1016/j.array.2025.100652
* Kirkpatrick, K. (2023). The carbon footprint of artificial intelligence. Communications of the ACM, 66, 17–19. https://doi.org/10.1145/3603746
Kovács, Sz., Végh, M., & Szabó, I. (2025). Nyílt innováció és mesterséges intelligencia: az új innovációmenedzsment dimenziói. Marketing & Menedzsment, 59(különszám), 52–61. https://doi.org/10.15170/MM.2025.59.KSZ.01.05
* Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the carbon emissions of machine learning. arXiv. https://doi.org/10.48550/arXiv.1910.09700
* Li, B., Jiang, Y., & Tiwari, D. (2025). Carbon in motion: Characterizing Open-Sora on the sustainability of generative AI for video generation. ACM SIGEnergy Energy Informatics Review. 4. 160–165. https://doi.org/10.1145/3727200.3727224
Lin, B. (2025). Research on the impact, risks and ethical boundaries of AI-generated content in advertising creativity and delivery. Frontiers in Humanities and Social Sciences, 5(8), 285–289. https://doi.org/10.54691/c5z3a890
* Liu, Y., Zhang, K., Li, Y., Yan, Z., Gao, C., Chen, R., Yuan, Z., Huang, Y., Sun, H., Gao, J., He, L., & Sun, L. (2024). Sora: A review on background, technology, limitations, and opportunities of large vision models. arXiv. https://doi.org/10.48550/arXiv.2402.17177
* Luccioni, A. S., Jernite, Y., & Strubell, E. (2024). Power hungry processing: Watts driving the cost of AI deployment? In: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ‚24) (pp. 85–99.). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658542
Luo, C., Hasan, N. A. M., Zamri bin Ahmad, A. M., & Lei, G. (2025). Influence of short video content on consumers’ purchase intentions on social media platforms with trust as a mediator. Scientific Reports, 15(1), 16605. https://doi.org/10.1038/s41598-025-94994-z
* Montgomery, A. W., Lyon, T. P., & Barg, J. (2023). No end in sight? A greenwash review and research agenda. Organization &
Environment, 37(2), 221–256. https://doi.org/10.1177/10860266231168905
Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389–404. https://doi.org/10.1016/j.jbusres. 2020.11.001
Najafloo, A., Soltani, A., & Szeberényi, A. (2025). Enhanced Marketing Strategies in the Metaverse: Opportunities and Challenges. Acta Carolus Robertus, 15(1), 246–263. https://doi.org/10.33032/acr.6235
* Patterson, D., Gonzalez, J., Hölzle, U., Le, Q. V., Liang, C., Munguía, L.-M., Rothchild, D., So, D. R., Texier, M., & Dean, J. (2022). The carbon footprint of machine learning training will plateau, then shrink. arXiv. https://doi.org/10.48550/arXiv.2204.05149
* Pimenow, S., Pimenowa, O., & Prus, P. (2024). Challenges of artificial intelligence development in the context of energy consumption and impact on climate change. Energies, 17(23), 5965. https://doi.org/10.3390/en17235965
* Reco (2025). 2025 state of shadow AI report. https://www.reco.ai/state-of-shadow-ai-report
* Sætra, H. (2021). A framework for evaluating and disclosing the ESG related impacts of AI with the SDGs. Sustainability, 13(15), 8503. https://doi.org/10.3390/su13158503
Schellewald, A. (2021). Communicative forms on TikTok: Perspectives from digital ethnography. International Journal of Communication, 15, 1437–1457.
* Schmidt, V., Luccioni, A., Lacoste, A., & Dandres, T. (n.d.). Machine learning CO2 impact calculator. https://mlco2.github.io/impact/ (utolsó letöltés: 2026.03.01.)
* Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2019). Green AI. arXiv. https://doi.org/10.48550/arXiv.1907.10597
* Sharma, V., Kour, M., Vass, V., & Szeberényi, A. (2026). How Does Sustainability Governance Shape the Green Finance and Climate Nexus?. Sustainability, 18(2), 1022. https://doi.org/10.3390/su18021022
* Silic, M., Silic, D., & Kind-Trüller, K. (2025). From shadow IT to shadow AI – threats, risks and opportunities for organizations. Strategic Change, 35(1). https://doi.org/10.1002/jsc.2682
* Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., Parikh, D., Gupta, S., & Taigman, Y. (2022). Make-A-Video: Text-to-video generation without text-video data. arXiv. https://doi.org/10.48550/arXiv.2209.14792
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104(1), 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Somov, A. G., Ergunova, O. T., & Szeberényi, A. (2025). The influence of artificial intelligence on decision-making in the field of personnel management of large corporations. In: Krastev, V., Rani, C., Ergunova, O., Kansra, P. and Kajla, T. (eds.), Empowering human resources through human-computer interaction (pp. 181–206.). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-1676-5.ch007
* Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the
57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. https://doi.org/10.18653/v1/p19-1355
Szeberényi, A., Akbar, S., Araz, N., & Balku, R. (2025). Interactive and Immersive Advertising in the Metaverse: User Perceptions and Engagement Compared to Traditional Online Environments. Acta Carolus Robertus, 15(Special Issue), 3–19. https://doi.org/10.33032/acr.7019
Szeberényi, A., Fűrész, Á., & Rokicki, T. (2024). Renewable energy in Hungary: Awareness, challenges, and opportunities. In: Belak, J., Nedelko, Z. (eds.), 8th FEB International Scientific Conference: Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development (pp. 509–518.). Maribor. https://doi.org/10.18690/um.epf.5.2024
Synthetic Labs. (2025). Wan2.1 vs Sora, Runway, Kling, Pika. Synthetic Labs. https://syntheticlabs.xyz/2025/03/10/wan2-1-ai-videocomparison/
* van Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics, 1(1), 213–218. https://doi.org/10.1007/s43681-021-00043-6
* Verdecchia, R., Sallou, J., & Cruz, L. (2023). A systematic review of Green AI. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13, 1–26. https://doi.org/10.1002/widm.1507
* Vergallo, R., Aprile, M., Cruz, L., Vadacca, R., & Mainetti, L. (2025). Large-scale evaluation of quantization for reducing the energy footprint of deep learning models. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5719661
World Resources Institute (WRI), & World Business Council for Sustainable Development (WBCSD) (2011). Corporate value chain (Scope 3) accounting and reporting standard. GHG Protocol. https://ghgprotocol.org/scope-3-standard
* Xiao, L., Li, X., & Zhang, Y. (2023). Exploring the factors influencing consumer engagement behavior regarding short-form video advertising: A big data perspective. Journal of Retailing and Consumer Services, 70, 103170. https://doi.org/10.1016/j.jretconser.2022.103170
* Xing, Z., Dai, Q., Hu, H., Wu, Z., & Jiang, Y.- G. (2023). SimDA: Simple diffusion adapter for efficient video generation. arXiv. https://doi.org/10.48550/arXiv.2308.09710
* Yu, Y., Wang, J., Liu, Y., Yu, P., Wang, D., Zheng, P., & Zhang, M. (2024). Revisit the environmental impact of artificial intelligence: The overlooked carbon emission source? Frontiers of Environmental Science & Engineering, 18, 158. https://doi.org/10.1007/s11783-024-1918-y
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 The Hungarian Journal of Marketing and Management
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
