Bike-sharing is an environmentally friendly shared mobility mode, but its self-loop phenomenon, where bikes are returned to the same station after several time usage, significantly impacts equity in accessing its services. Therefore, this study conducts a multiscale analysis with a spatial autoregressive model and double machine learning framework to assess socioeconomic features and geospatial location's impact on the self-loop phenomenon at metro stations and street scales. The results reveal that bike-sharing self-loop intensity exhibits significant spatial lag effect at street scale and is positively associated with residential land use. Marginal treatment effects of residential land use is higher on streets with middle-aged residents, high fixed employment, and low car ownership. The multimodal public transit condition reveals significant positive marginal treatment effects at both scales. To enhance bike-sharing cooperation, we advocate augmenting bicycle availability in areas with high metro usage and low bus coverage, alongside implementing adaptable redistribution strategies.

翻译：共享单车是一种环境友好的共享出行模式，但其自循环现象——即单车在使用若干时段后被归还至同一站点——显著影响了服务获取的公平性。为此，本研究采用空间自回归模型与双重机器学习框架进行多尺度分析，以评估社会经济特征与地理空间位置在地铁站点和街道尺度上对自循环现象的影响。结果表明，共享单车自循环强度在街道尺度上呈现显著的空间滞后效应，并与居住用地呈正相关。居住用地的边际处理效应在中年居民比例高、固定就业率高、汽车保有量低的街道上更为突出。多模式公共交通条件在两个尺度上均显示出显著的正向边际处理效应。为提升共享单车的协同效能，我们建议在地铁使用率高而公交覆盖低的区域增加单车供给，并实施灵活的自适应再调度策略。