Hoffmann, F.; Koch, T.; Weber, M.; Weigold, M.; Metternich, J.: Development of Data-based Business Models to Incentivise Sustainability in Industrial Production. In: Herberger, D.; Hübner, M.; Stich, V. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 1. Hannover : publish-Ing., 2023, S. 199-209. DOI:
https://doi.org/10.15488/13439
Zusammenfassung: |
Recent environmental catastrophes highlight the need to curb global climate change. Carbon dioxide (CO2) is responsible for the majority of the anthropogenic greenhouse effect. Politicians and society already exerted pressure for some time on industry and companies as major emitters. Despite continuously decreasing emissions, the savings achieved in the industrial sector fall short of the politically set targets. This is mainly due to the fact, that the combination of economic and ecological interests for companies is not promoted sufficiently. As a result, there is a lack of incentives for production companies to reduce their emissions. By incorporating economic aspects, data-based business models can create such incentives and thus support current and future regulatory measures.
This paper presents an approach of developing data-based business models to incentivise sustainability in industrial manufacturing. For this purpose, existing and potential future incentive mechanisms for the reduction of CO2 are first identified and discussed. Subsequently, the business model approach for "CO2 reduction in product creation" from the Gaia-X lighthouse project EuProGiant is presented. Finally, this approach is discussed in consideration of possible emission savings and the compatibility of economical and ecological company interests.
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Lizenzbestimmungen: |
CC BY 3.0 DE - http://creativecommons.org/licenses/by/3.0/de/
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Publikationstyp: |
BookPart |
Publikationsstatus: |
publishedVersion |
Erstveröffentlichung: |
2023 |
Schlagwörter (deutsch): |
Konferenzschrift
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Schlagwörter (englisch): |
Business Models, Sustainability, Digitalization, Data Analysis, Gaia-X
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Fachliche Zuordnung (DDC): |
620 | Ingenieurwissenschaften und Maschinenbau
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