With the ever-increasing focus on sustainable development, recycling waste and renewable use of waste products has earned immense consideration from academics and policy makers. The serious pollution, complex types, and strong infectivity of medical waste have brought serious challenges to management. Although several researchers have addressed the issue by optimizing medical waste management networks and systems, there is still a significant gap in systematically evaluating the efficiency of medical waste recycling systems. Therefore, this paper proposes a two-stage data envelopment analysis (DEA) approach that combines the virtual frontier and the global bounded adjustment measure (BAM-VF-G), considering both undesirable inputs and outputs. In the first stage, the BAM-G model is used to evaluate the efficiency of medical waste recycling systems, and the BAM-VF-G model is used to further rank super-efficient medical waste recycling systems. In the second stage, two types of efficiency decomposition models are proposed. The first type of models decompose unified efficiency into production efficiency (PE) and environment efficiency (EE). Depending upon the system structure, the second type of models decompose unified efficiency into the efficiency of the medical waste collection and transport subsystem (MWCS) and the efficiency of the medical waste treatment subsystem (MWTS). The novel approach is used to measure the efficiency of the medical waste recycling systems in China’s new first-tier cities, and we find that (1) Foshan ranks the highest in efficiency, followed by Tianjin and Qingdao, with efficiency values of 0.386, 0.180, and 0.130, respectively; (2) the EE lacks resilience and fluctuated the most from 2017 to 2022; and (3) the efficiency of MWCSs has always been lower than that of MWTSs and is a critical factor inhibiting the overall efficiency of medical waste recycling systems.

The scientific site selection for pallet pooling systems is pivotal to enhancing logistics efficiency and environmental performance. However, previous studies mainly adopt single-objective optimization approaches, which fail to simultaneously account for economic, environmental, and operational performance factors. The contribution of this paper lies in proposing an integrated decision-making method based on BWM-GIS-DEA to address the site selection problem for pallet pooling service centers. First, the Best-Worst Method (BWM) determines the weights of 13 criteria across 5 dimensions: economic, transportation, geographical location, technological, and service coverage. These criteria include factors such as the distribution density of pallet manufacturers and potential customers. Then, suitability maps are generated using Geographic Information System (GIS) spatial overlay technology to identify 6 alternative cities. Finally, a two-layer Data Envelopment Analysis (DEA) model is applied to measure the efficiency of the alternative sites. This method is applied in Inner Mongolia, China, and Ejin Horo Banner is identified as the optimal site with an efficiency score of 1.156, demonstrating superior resource allocation characterized by lower land costs and higher pallet turnover rates. The proposed framework not only fills a methodological gap in sustainable facility location research but also provides a replicable and policy-ready tool to guide practical decision-making.