Energy Gen-AI technology framework: A perspective of energy efficiency and business ethics in operation management
Considering the mounting request for viable energy solutions emphasising sustainability, this study addresses the context by positioning Generative Artificial Intelligence (Gen-AI) as a pivotal remedy for improved process efficiency. Concerns about its uncontrolled use and ethical implications prompted a thorough examination of problems and gaps in this domain. This study innovates by positioning Gen-AI as a critical solution for improving energy efficiency amidst rising demand for sustainable energy at the operation and supply chain management levels. Based on technological determinism, it introduces an Energy Gen-AI Technology Framework (EnGen-AI) that integrates Gen-AI, energy efficiency, Business Ethics (BE), and Corporate Social Responsibility (CSR) principles for implementing Gen-AI to enhance sustainable energy management. The methodology includes a scoping review, a multi-criteria analysis, and a technology framework. The study harmonises BE-CSR with Gen-AI practices, offering essential guidance for management implementing Gen-AI in sustainable energy solutions whilst observing its impacts on ethical and social aspects. • Promotes innovation by integrating Gen-AI with ethical and social responsibility. • EnGen-AI framework integrates Gen-AI, energy efficiency, and BE-CSR principles. • Explores the impact of Gen-AI technologies on BE-CSR. • Gen-AI solutions to reduce BE-CSR impact, adapting to countries' development levels. • Offers a comprehensive perspective on BE-CSR issues for enhanced energy control.
Authors
- Daniel Luiz de Mattos Nascimento (ORCID: https://orcid.org/0000-0002-4977-8652)
- Rodrigo Goyannes Gusm�ão Caiado (ORCID: https://orcid.org/0000-0002-3290-8385)
- Leonardo Fontoura (ORCID: https://orcid.org/0000-0002-8201-1541)
- Júlio Vieira Neto (ORCID: https://orcid.org/0000-0001-5974-7086)
Publication Details
- Journal
- Technology in Society
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1016/j.techsoc.2025.102847
- Citations
- 16
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 19.88
Funders
- Ministerio de Ciencia, Innovación y Universidades