Artificial intelligence and corporate environmental sustainability: insights from a bibliometric analysis

Purpose This study aims to review the prior literature on the nexus between artificial intelligence (AI) and corporate environmental sustainability using a well-known quantitative approach of bibliometric analysis. Design/methodology/approach To produce robust findings from the two bibliometric analysis approaches; “performance analysis and mapping analysis”, the authors rigorously followed the preferred reporting items for systematic reviews and meta-analysves (PRISMA) protocol. Furthermore, the data constituting our sample was extracted from the Scopus database, which is recognized as a leading database. For the bibliometric analysis, the study used VOSviewer to divulge crucial trends, emerging themes, map co-authorship networks and identify bibliographic relationships. The initial sample of 1,618 articles retrieved from the Scopus database was precisely filtered and screened following the PRISM framework, using rigorous inclusion and exclusion criteria to generate the final sample. Findings The findings unveil a remarkable surge in research on AI and environmental sustainability in 2025, highlighting the novelty and growing significance of this evolving research frontier. Furthermore, the findings divulge this emerging topic is being investigated across multiple disciplines, namely, “Business, Management and Accounting; environmental Science, and computer science”, stressing the necessity of cross-disciplinary collaboration to build a robust body of knowledge. Furthermore, most prior articles have concentrated on the environmentally sensitive sectors, which is logical and justified; nevertheless, it would be meaningful to explore the nexus between AI models and environmental sustainability within other sectors as well. More importantly, the findings reveal that there is a pressing need for enhanced international academic collaboration, especially with developing countries. Originality/value This research provides original insights by systematically mapping the intellectual landscape of publications linking AI with corporate environmental sustainability. More obviously, it contributes by identifying emerging themes, leading journals, prominent countries, foremost universities, influential articles and co-authorship network. Thereby, this research serves as a foundational reference for future researchers, top management and policy-makers aiming to use AI models in improving corporate environmental sustainability on a global scale.