The Imapact of the Use Artificial Intelligence in the Prodicton Environment on the Ecological Footprint

Authors

Štefan Žun
University of Maribor, Faculty of Organizational Sciences
https://orcid.org/0009-0000-9568-1627

Synopsis

Artificial intelligence (AI) is increasingly recognized as an important tool for improving energy efficiency and reducing the ecological footprint (EF). This study analyzes the impact of AI implementation within the observed system on changes in EF. Results indicate that the development and application of AI can reduce EF primarily through more efficient energy use. Once system energy efficiency exceeds a certain threshold, further AI use can additionally decrease environmental pressures. Automation and robotization of industrial processes improve resource efficiency, reduce emissions, and lower the ecological footprint, while also enhancing industrial and workplace environments. Although economic growth often increases environmental pressures, wider AI adoption can help mitigate these impacts. In Slovenia, AI use is growing, but challenges remain, including limited funding for fundamental research, the need for appropriate regulation and greater investment in human capital and education. The study also compares Slovenia with EU countries and highlights the importance of policies.

Author Biography

Štefan Žun, University of Maribor, Faculty of Organizational Sciences

Kranj, Slovenia. E-mail: stefan.zun@guest.um.si

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Published

March 17, 2026

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Žun, Štefan. (2026). The Imapact of the Use Artificial Intelligence in the Prodicton Environment on the Ecological Footprint. In P. Šprajc, D. Maletič, N. Petrović, I. Iztok, A. Škraba, D. Tomić, & A. Žnidaršič Mohorič (Eds.), & (Ed.), 45th International Conference on Organizational Science Development: Organization and the Longevity Society, Conference Proceedings (Vols. 45., pp. 927-940). University of Maribor Press. https://doi.org/10.18690/um.fov.3.2026.68