Academic influence index evaluation report of geographic simulation models (2023)

As the availability and number of geographic simulation models across various domains have surged, evaluating their relative value has become increasingly challenging. Traditional model evaluation typically involves comparing simulation results with measured data or outputs from other models. In contrast to these traditional approaches, this report continues the application of the “Model Academic Influence Index (MAI)” method from the previous year, which assesses the relative value of the model from the perspective of academic influence, emphasizing its academic contributions. We evaluate the MAI of 207 models and 22 methods collected from credible digital repositories in 2023 and establish a model leaderboard. Based on this ranking, we briefly explore the proportional representation of open-source versus closed-source models and further investigate the distribution of models across different open-source licenses. These findings provide support for model selection and optimization within the academic community and beyond, while offering new insights into the development of the open-source ecosystem.