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This study used bibliometrics to analyze the characteristics of funded papers in the social sciences in Taiwan from 2015 to 2019, including the number of funded papers, funded paper ratio, document types, fields, type of publishing organizations, journal impact, and paper citation impact. The results indicate that the number of funded papers and the funded paper ratio of SSCI papers from Taiwan have both increased. In addition to articles and reviews, conference papers are a major type of funded paper for local SSCI papers. In terms of fields, the nontraditional social sciences, such as environmental sciences, psychiatry, and public/environmental & occupational health, have a particularly high funded paper ratio. However, the number of funded papers and funded paper ratio is the highest in the field of education & educational research. The fields of economics, management, and business have a large number of funded papers but a low funded paper ratio. Universities are the main funded institutions, but hospitals have the highest funded paper ratio. Funded papers tend to have higher paper citation impacts. Therefore, funding positively affects the productivity and impact of SSCI papers from Taiwan. [ABSTRACT FROM AUTHOR]

[Objective] Previous studies regarding the use of the erosion-productivity impact calculator (EPIC) model from 1991 to 2021 were retrieved in order to conduct bibliometrics and cluster analysis so that the direction for the future application and development of the EPIC model could be determined. [Methods] The R-bibliometrix tool was used to analyze literature related to the EPIC model in the Web of Science Core Collection Database, and 261 papers were ultimately selected. The research hotspot and historical development trend of EPIC model were analyzed. [Results] During 1991—2021, the applications of the EPIC model were mainly focused on four aspects: water resources, crop growth, soil erosion and organic matter loss, and agricultural drought. The EPIC model had high simulation accuracy and was able to well evaluate water erosion, soil nutrient loss, and crop yield in the past, present, and future. However, disadvantages included too many parameters and extensive input data requirements. [Conclusion] The EPIC model has many modules and thus has potential to be applied across broadly different research fields in the future. It also has the potential to be coupled with other models and deep learning methods in the future in order to carry out in-depth simulations regarding "double carbon" , soil erosion, crop production, and climate change responses.