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ChatGPT (GPT) has become one of the most talked-about innovations in recentyears, with over 100 million users worldwide. However, there is still limitedknowledge about the sources of information GPT utilizes. As a result, wecarried out a study focusing on the sources of information within the field ofenvironmental science. In our study, we asked GPT to identify the ten mostsignificant subdisciplines within the field of environmental science. We thenasked it to compose a scientific review article on each subdiscipline,including 25 references. We proceeded to analyze these references, focusing onfactors such as the number of citations, publication date, and the journal inwhich the work was published. Our findings indicate that GPT tends to citehighly-cited publications in environmental science, with a median citationcount of 1184.5. It also exhibits a preference for older publications, with amedian publication year of 2010, and predominantly refers to well-respectedjournals in the field, with Nature being the most cited journal by GPT.Interestingly, our findings suggest that GPT seems to exclusively rely oncitation count data from Google Scholar for the works it cites, rather thanutilizing citation information from other scientific databases such as Web ofScience or Scopus. In conclusion, our study suggests that Google Scholarcitations play a significant role as a predictor for mentioning a study inGPT-generated content. This finding reinforces the dominance of Google Scholaramong scientific databases and perpetuates the Matthew Effect in science, wherethe rich get richer in terms of citations. With many scholars already utilizingGPT for literature review purposes, we can anticipate further disparities andan expanding gap between lesser-cited and highly-cited publications.

ChatGPT cites the most-cited articles and journals, relying solely on  Google Scholar's citation counts. As a result, AI may amplify the Matthew  Effect in environmental science