Academic influence index evaluation report of geographic simulation models (2022)

Recent years have witnessed a significant increase in the availability and number of geographic simulation models across various domains, leading to challenges in evaluating their relative value. Traditional model evaluations typically compare simulation results with measured data or other models. This report presents the application of the newly “Model Academic Influence Index (MAI)" method which focuses on evaluating a model's academic contributions. It offers both annual and lifetime index, and reflects the model's major application areas covered. The report evaluates the MAI of 205 models and 22 methods in 2022 from trusted digital repositories and emphasizes the importance of open-source models, providing URLs and licenses. Recognizing the complexity and importance of this task, we invite ongoing discussion and feedback from the modeling community. This report aims to support more informed decision-making in academia and the public and promote the development of a more open and scientific modeling profession and community. •This report evaluates annual and lifetime MAI of 227 models and methods in 2022.•MAI offers developers a channel to understand model academic popularity and recognition.•The MAI will provide decision support information for academia and the public.•We advocate for the enhancement of open-source models via platforms adopted FAIR.