The research aims to discover practices, Artificial Intelligence tools and technologies useful for the sustainability reporting process, in order to improve the accuracy, the quality and the reliability of the sustainability data and disclosure. The research aims to explain the status of Artificial Intelligence (AI) in the sustainability reporting field through the bibliometric analysis and the systematic literature review in order to understand how the AI can be integrated in the sustainability management process to enhance efficiency and accuracy and to automatize some phases of the sustainability reporting such as data collection, data analysis, text generation and disclosure, considering technologies such as dig data analytics, Large Language Model (LLM), machine learning, Natural Language Processing (NLP). The AI also could be also useful in the assurance process and to discover greenwashing. The review analyses were conducted on 73 papers published from 2015 to 2025 extracted from Scopus and Web of Science and the bibliometric analysis was performed through Bibliometrix R-package and VosViewer. The results highlighted the main papers, authors, affiliations and countries in the field and the four theme clusters identified through the keywords co-occurrence: “AI technologies”, “Narrative and disclosure”, “Finance and governance” and “AI techniques”. The study has theoretical and practical implications. From an academic point of view the study fits into the current debate about the impact of the AI in the different phases of the sustainability reporting in order to assure more transparent, accurate and reliable disclosures, considering also the ethical behaviour of the algorithm and advantage and risks of the AI. From the other hand, the theoretical implications have a direct connection with the practical implications since companies need to understand how the AI tools can be integrated in the sustainability management process in order to enhance efficiency and accuracy and to automatize some phases of the sustainability reporting such as the data collection, the analysis and the text generation.
Artificial intelligence for the sustainability reporting: a systematic review and bibliometric analysis
De Matteis C.
;Fasiello R.;Caputo F.;Venturelli A.
2026-01-01
Abstract
The research aims to discover practices, Artificial Intelligence tools and technologies useful for the sustainability reporting process, in order to improve the accuracy, the quality and the reliability of the sustainability data and disclosure. The research aims to explain the status of Artificial Intelligence (AI) in the sustainability reporting field through the bibliometric analysis and the systematic literature review in order to understand how the AI can be integrated in the sustainability management process to enhance efficiency and accuracy and to automatize some phases of the sustainability reporting such as data collection, data analysis, text generation and disclosure, considering technologies such as dig data analytics, Large Language Model (LLM), machine learning, Natural Language Processing (NLP). The AI also could be also useful in the assurance process and to discover greenwashing. The review analyses were conducted on 73 papers published from 2015 to 2025 extracted from Scopus and Web of Science and the bibliometric analysis was performed through Bibliometrix R-package and VosViewer. The results highlighted the main papers, authors, affiliations and countries in the field and the four theme clusters identified through the keywords co-occurrence: “AI technologies”, “Narrative and disclosure”, “Finance and governance” and “AI techniques”. The study has theoretical and practical implications. From an academic point of view the study fits into the current debate about the impact of the AI in the different phases of the sustainability reporting in order to assure more transparent, accurate and reliable disclosures, considering also the ethical behaviour of the algorithm and advantage and risks of the AI. From the other hand, the theoretical implications have a direct connection with the practical implications since companies need to understand how the AI tools can be integrated in the sustainability management process in order to enhance efficiency and accuracy and to automatize some phases of the sustainability reporting such as the data collection, the analysis and the text generation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
