The integration of Artificial Intelligence (AI) into sustainability practices is transforming industries by enabling innovative solutions to complex challenges. However, while the nexus between AI and economic or environmental sustainability has been the subject of substantial academic inquiry, its potential role in advancing social sustainability remains comparatively underexplored. Social sustainability, encompassing critical aspects such as health and well-being, equitable access to education, and community resilience, is fundamental to the development of sustainable societies and is explicitly linked to Sustainable Development Goals (SDGs) such as SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities). In the energy sector, where innovation is a primary driver, AI is increasingly employed to address issues including energy equity, workforce safety, and community development. The success of such initiatives, however, critically hinges on stakeholder perception, which influences the legitimacy and acceptance of AI-driven practices. This study aims to bridge the gap between AI applications and their social implications by examining how AI-driven innovative practices in the energy sector can enhance social sustainability and Environmental, Social, and Governance (ESG) performance. Specifically, it focuses on the role of stakeholder perception as a key determinant of the success of socially sustainable initiatives. By integrating concepts from stakeholder theory and knowledge management frameworks, this research seeks to advance theoretical understanding while offering practical strategies for achieving sustainability goals in knowledge-intensive and innovation-driven industries. The paper presents a review of the extant literature and outlines a research proposal predicated on a mixed-methods approach, focusing on case studies of energy companies implementing AI for social sustainability.
Artificial Intelligence and Social Sustainability in the Energy Sector: Enhancing Stakeholder Perception and ESG Performance throught Innovative Practices
Antonio Capasso
;Spatola Giovanni;D'Avino Mario;
2025-01-01
Abstract
The integration of Artificial Intelligence (AI) into sustainability practices is transforming industries by enabling innovative solutions to complex challenges. However, while the nexus between AI and economic or environmental sustainability has been the subject of substantial academic inquiry, its potential role in advancing social sustainability remains comparatively underexplored. Social sustainability, encompassing critical aspects such as health and well-being, equitable access to education, and community resilience, is fundamental to the development of sustainable societies and is explicitly linked to Sustainable Development Goals (SDGs) such as SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities). In the energy sector, where innovation is a primary driver, AI is increasingly employed to address issues including energy equity, workforce safety, and community development. The success of such initiatives, however, critically hinges on stakeholder perception, which influences the legitimacy and acceptance of AI-driven practices. This study aims to bridge the gap between AI applications and their social implications by examining how AI-driven innovative practices in the energy sector can enhance social sustainability and Environmental, Social, and Governance (ESG) performance. Specifically, it focuses on the role of stakeholder perception as a key determinant of the success of socially sustainable initiatives. By integrating concepts from stakeholder theory and knowledge management frameworks, this research seeks to advance theoretical understanding while offering practical strategies for achieving sustainability goals in knowledge-intensive and innovation-driven industries. The paper presents a review of the extant literature and outlines a research proposal predicated on a mixed-methods approach, focusing on case studies of energy companies implementing AI for social sustainability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
