Models for predicting environmental impacts with artificial intelligence: a bibliometric review - Martinez, Graziela Luiz Franco; Constantino, Michel Angelo; Pistori, Hemerson (Brazilian Journal of Production Engineering, 2026)
This study examines the evolution of international scientific production on environmental impact prediction models based on AI algorithms, with an emphasis on Machine Learning-ML techniques applied to the context of climate change. The objective was to map trends, identify gaps, and assess the consolidation of this field, considering its potential for integration with sustainability metrics. The research used bibliometric methods based on 631 articles indexed in the WoS between 1945-2023, analyzed with the Bibliometrix in R software. The results indicate exponential growth in publications since 2016, accompanied by the predominance of China, the USA, and India in both volume and scientific influence. The application of Bradford’s and Lotka’s Laws revealed a concentration of production in a few journals and high authorial fragmentation. The thematic analysis showed that ML, climate change, and prediction models are the driving themes of the field, although still focused on isolated environmental variables. It was found that there is an absence of integrated models articulating environmental forecasts, socioeconomic impacts, and governance dimensions, as well as a lack of systematic connections between AI techniques and ESG indicators. It is concluded that this integration constitutes a promising frontier for future research and for the formulation of sustainability strategies.