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.

近年来，各个领域的地理模拟模型的可用性和数量显着增加，导致评估其相对价值面临挑战。传统的模型评估通常将仿真结果与测量数据或其他模型进行比较。本报告介绍了新的“模型学术影响力指数（MAI）”方法的应用，该方法侧重于评估模型的学术贡献。它提供年度指数和终生指数，反映模型所涵盖的主要应用领域。报告评估了模型的MAI到 2022 年，来自受信任的数字存储库的 205 个模型和 22 个方法将被纳入其中，并强调开源模型、提供 URL 和许可证的重要性。认识到这项任务的复杂性和重要性，我们邀请建模社区进行持续的讨论和反馈。本报告旨在支持学术界和公众做出更明智的决策，促进更加开放和科学的模特职业和社区的发展。