Ecological effectiveness assessment of the Shan-Shui ecological restoration initiative in China: advances, challenges, and prospects

Against global ecosystem degradation, biodiversity loss and climate change, large-scale integrated ecological restoration is a global priority under the UN Sustainable Development Goals. China's Shan-Shui Initiative—one of the world's largest systemic ecological restoration programs—has drawn wide attention for its ecological outcomes. Yet existing assessments lack a systematic synthesis of prevailing gaps and future priorities, urgently needed by researchers and policy makers for adaptive restoration governance. To address this, we systematically synthesize advances in Shan-Shui initiative effectiveness assessment, identify prevailing methodological and indicator gaps, and outline a forward-looking research agenda to support adaptive restoration governance. We found that current assessments remain nascent with limited spatiotemporal coverage. A multi-dimensional, integrated indicator system has been established for assessing the ecological effectiveness of the Shan-Shui initiative, operating on a progressive framework focused on mitigating anthropogenic stressors, optimizing ecosystem structure and composition, improving ecosystem functions, and ultimately enhancing ecosystem services. The methodological system, rooted in before-and-after comparison and increasingly integrating spatial controls and causal inference models, is supported by remote sensing, field surveys, and modeling data. Evaluations indicate overall positive effectiveness, but with obvious scale dependency and dimensional heterogeneity, and frequent trade-offs among ecosystem services. This review identifies key challenges, including uneven spatiotemporal coverage, the lack of integrated indicators tailored to specific restoration types, insufficient causal inference to distinguish project effects from climate variability, and limited consideration of post-restoration management costs. Future work should establish multi-scale, long-term monitoring networks, establish a dual-level indicator system combining macro-scale integrative metrics with project-type-specific indicators, promote cross-validation of methods, and translate findings into decision-support information for adaptive management to build a more reliable assessment paradigm.