  ChatGPT (GPT) has become one of the most talked-about innovations in recent
years, with over 100 million users worldwide. However, there is still limited
knowledge about the sources of information GPT utilizes. As a result, we
carried out a study focusing on the sources of information within the field of
environmental science. In our study, we asked GPT to identify the ten most
significant subdisciplines within the field of environmental science. We then
asked it to compose a scientific review article on each subdiscipline,
including 25 references. We proceeded to analyze these references, focusing on
factors such as the number of citations, publication date, and the journal in
which the work was published. Our findings indicate that GPT tends to cite
highly-cited publications in environmental science, with a median citation
count of 1184.5. It also exhibits a preference for older publications, with a
median publication year of 2010, and predominantly refers to well-respected
journals in the field, with Nature being the most cited journal by GPT.
Interestingly, our findings suggest that GPT seems to exclusively rely on
citation count data from Google Scholar for the works it cites, rather than
utilizing citation information from other scientific databases such as Web of
Science or Scopus. In conclusion, our study suggests that Google Scholar
citations play a significant role as a predictor for mentioning a study in
GPT-generated content. This finding reinforces the dominance of Google Scholar
among scientific databases and perpetuates the Matthew Effect in science, where
the rich get richer in terms of citations. With many scholars already utilizing
GPT for literature review purposes, we can anticipate further disparities and
an expanding gap between lesser-cited and highly-cited publications.
