The ecological compensation standard in transboundary river basins should be determined by the basin countries through negotiation on the basis of the base value of the ecological compensation standard. This paper calculated the base value range of the ecological compensation standard, determining the upper limit based on the spillover value of ecosystem services for the ecosystem-service-consuming country and the lower limit according to the cost of ecological protection for the ecosystem-service-supplying country. The final range was determined by integrating this with the willingness to pay and the actual effort in each basin country. Taking, for example, the Lancang–Mekong River basin, the results indicate that the spillover value of ecosystem services in Laos, China and Myanmar was positive and these three countries were ecosystem-service-supplying countries, while in Cambodia, Vietnam and Thailand it was negative and these three countries were ecosystem-service-consuming countries. Among the ecosystem-service-supplying countries, the cost of ecological protection of them was in descending order of Laos, China and Myanmar, which was related to their own level of economic development. Considering the adjustment coefficient for the payment of ecosystem service value and the cost-sharing coefficient of each basin country, the feasible range for the base value of the ecological compensation standard was determined to be [2.47, 229.67] × 10⁸ $, which provided the basis for the negotiation on the determination of the ECS. In addition, implementation suggestions were proposed from three aspects: establishing a basin-information-sharing mechanism and platform, establishing an integrated management organization for transboundary river basins, and strengthening and improving the coordination and supervision model of ecological compensation.

Digital labor platforms have expanded employment in urban service work while shifting much of the operational and occupational risk of that work onto workers. Whether such employment can support decent work—the central labor commitment of SDG 8—remains unresolved. Algorithmic dispatching, real-time monitoring, and continuous performance evaluation have raised delivery efficiency, but they have also intensified the tension between operational speed and the conditions under which that work is performed. The prior literature has largely examined algorithmic control as a relationship between platforms and workers, yet has paid less attention to how intermediary organizations translate platform rules into enforceable—and at times protective—practices. What remains under-theorized is not the existence of intermediaries but the mechanisms through which they absorb, translate, and redistribute algorithmic pressure. Drawing upon a 16-week ethnographic investigation at Subcontracted Station J in Nanjing, China, this study integrates approximately 120 h of participant observation, semi-structured interviews with 24 relevant actors, and 827 digital trace data entries from three workplace WeChat groups to examine how subcontracted stations mediate algorithmic pressure and its impact on decent work. We find that station managers selectively absorb, translate, and redistribute algorithmic pressure through four practices we term penalty buffering, risk buffering, resource buffering, and knowledge buffering. We conceptualize this meso-level governance mechanism as “managerial buffering.” Our findings demonstrate that achieving the decent work championed by SDG 8 cannot rely solely on informal repairs by grassroots organizations; rather, protective practices within these mechanisms must be institutionalized into explicit, universal, and enforceable framework agreements. This study also acknowledges that the applicability of the proposed “managerial buffering” framework may depend on local institutional and socioeconomic contexts. Constrained by a single-case design, future research could conduct comparative studies across cities, platforms, and stations to further test the empirical manifestations of managerial buffering under diverse institutional environments.

【目的】在“节水优先、空间均衡、系统治理、两手发力”十六字治水思路中，“空间均衡”是破解水资源空间分布与经济社会发展需求不适配的根本路径。【方法】本文通过构建水资源空间均衡两阶段诊断模型，对黄河流域2012—2021年的水资源空间均衡进行系统性诊断。第一阶段：黄河流域61个地级市内部的水资源空间均衡诊断，基于空间重心模型对各地级市内部水资源、经济社会和生态环境3个子系统之间的空间均衡性进行诊断；第二阶段：省份间的水资源空间均衡诊断，基于第一阶段获得的各地级市空间均衡度，运用Dagum基尼系数从省份内和相邻省份间诊断黄河流域水资源空间均衡，并分析不均衡原因。【结果】①2012—2021年，黄河流域61个地级市中超过70%达到水资源空间均衡，未达到水资源空间均衡的地级市占比不超过30%，主要分布于内蒙古、甘肃、宁夏和山东；②黄河流域各省份内部的水资源空间均衡状态为：山西和河南两省已实现高水平空间均衡，内蒙古和甘肃处于低水平空间均衡状态，其余省份未达到空间均衡状态；③相邻“省份对”之间基本处于空间不均衡状态，仅有（甘肃，内蒙古）和（山西，河南）两个“省份对”达到了空间均衡状态。【结论】黄河流域地级市内部、省份内部、相邻省份间均未全面达到空间均衡，其中省份间的差异是流域水资源空间不均衡的主导因素，Dagum基尼系数分解显示其贡献率达到59.29%。