基础模型（FMs）是大规模预训练的人工智能（AI）系统，已在自然语言处理与计算机视觉领域引发变革，并正推动地理空间分析与地球观测（EO）的发展。此类模型有望提升跨任务泛化能力、可扩展性，并以极少标注数据实现高效适配。然而，尽管地理空间基础模型迅速涌现，其在现实世界中的实用性及其与全球可持续发展目标（SDGs）的契合度仍缺乏深入探究。本文提出SustainFM——一个植根于17项可持续发展目标的综合性基准评估框架，涵盖从资产财富预测到环境灾害检测等高度多样化的任务。本研究对地理空间基础模型开展了严谨的跨学科评估，为其在实现可持续发展目标中的作用提供了关键洞见。主要发现包括：（1）尽管并非在所有场景下均具普适优势，基础模型在多种任务与数据集上常优于传统方法；（2）对基础模型的评估不应仅限于准确性，还应将迁移能力、泛化能力与能效作为负责任应用的关键指标；（3）基础模型可支撑可扩展、以可持续发展目标为锚点的解决方案，在应对复杂可持续发展挑战方面具有广泛适用性。尤为重要的是，我们倡导从以模型为中心的研发范式转向以实际影响为导向的部署范式，并强调能效、对领域偏移的鲁棒性及伦理考量等指标。

Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global sustainability goals remain underexplored. We introduce SustainFM, a comprehensive benchmarking framework grounded in the 17 Sustainable Development Goals with extremely diverse tasks ranging from asset wealth prediction to environmental hazard detection. This study provides a rigorous, interdisciplinary assessment of geospatial FMs and offers critical insights into their role in attaining sustainability goals. Our findings show: (1) While not universally superior, FMs often outperform traditional approaches across diverse tasks and datasets. (2) Evaluating FMs should go beyond accuracy to include transferability, generalization, and energy efficiency as key criteria for their responsible use. (3) FMs enable scalable, SDG-grounded solutions, offering broad utility for tackling complex sustainability challenges. Critically, we advocate for a paradigm shift from model-centric development to impact-driven deployment, and emphasize metrics such as energy efficiency, robustness to domain shifts, and ethical considerations.