Performance evaluation and enrollment quota allocation for higher education institutions in China

Estimation for running time and energy losses due to unproductive stops at bus stations in urban-rural traffic corridors

To provide data support for developing fixed-route DRT based on FRT to reduce operating costs inside base routes in urban-rural traffic corridors, this paper estimated running time and energy losses due to unproductive stops at bus stations in urban-rural traffic corridors. Firstly, 14 urban-rural bus routes without ticket sellers in Xi'an are selected to demonstrate the universality of unproductive stops at bus stations. Secondly, a model for estimating running time and energy losses based on the VT-CPFM model is developed. Finally, running time and energy losses due to unproductive stops in two representative urban-rural traffic corridors are estimated. Estimated results show that the average running time loss ratios of different rounds in Routes 332, 333, 335, 338 and G1 range from 8.30% to 17.52% and that average fuel loss ratios range from 9.16% to 13.30%. In addition, the monetary loss in energy consumption of Route G1 in 2019 is estimated to be up to 193213 yuan. This study proves that unproductive stops at bus stations generally exist in urban-rural bus routes and can result in significant running time and energy losses and that developing fixed-route DRT based on FRT leveraging V2I with mobile APP in representative urban-rural traffic corridors is very necessary, which is expected to reduce energy consumption and running time.

A proposed multistage evaluation approach for Higher Education Institutions based on network Data envelopment analysis: A Brazilian experience

The evaluation of Higher Education Institutions (HEIs) is a challenging task, due to the complexity inherent in the context in which they are inserted, the different institutional profiles, the variety of resources used and the results offered in the performance of their mission. In this way, the present study proposes an evaluative approach that aims to measure efficiency taking into account the diverse activities characteristic of university institutions. To this end, a data envelopment analysis (DEA) approach, known as Network DEA, is used, which encompasses the different processes and subprocesses that take place within these institutions. In all, the evaluation considers eleven variables organized in three distinct stages, which reflect the performance of the HEIs in different perspectives: financial, undergraduate level and graduate level (student training and scientific production and innovation). Finally, the model was applied at 45 Brazilian federal universities. This case study allows a comprehensive efficiency analysis to be carried out for the set of institutions considered, from each perspective taken into account in the approach.

The Impact of Health Care Reform Since 2009 on the Efficiency of Primary Health Services: A Provincial Panel Data Study in China

Background: Primary health care (PHC) is an important part of health systems in the world and in China. To improve the efficiency of PHC institutions (PHCIs), many countries have implemented reforms, including China's health care reform since 2009. This study aims to evaluate the impact of this reform on the efficiency of PHCIs from the perspective of the whole health system. Methods: Data were collected from China Health Statistical Yearbooks and China Statistical Yearbooks published from 2005 to 2019. By taking the number of beds, health technicians and PHCIs as inputs and the proportion of diagnosis, treatment and admission in PHCIs as outputs, Malmquist DEA was used to evaluate the efficiency change of PHCIs, and panel data regression was performed to analyze the impact of the reform and other factors on such efficiency. The interaction between reform and economic level was also estimated. Results: The MPI in Beijing, Tianjin, Shanghai, Hunan, and Guangdong improved after the reform. The efficiency improvement in Beijing, Tianjin and Shanghai is mainly reflected in the growth of TC, whereas the efficiency improvement in Guangdong and Hunan is mainly reflected in the growth of EC. Meanwhile, the EC and TC in Hebei, Heilongjiang, Shandong, and other provinces deteriorated. The deterioration of MPI in Shanxi, Inner Mongolia and Jilin was mainly attributed to EC. while the deterioration of MPI in Liaoning, Anhui, and Fujian provinces is mainly attributed to TC. Since 2009, the reform exerted a negative impact on MPI (β = -0.06; P < 0.01), TC (β = -0.048; P < 0.01) and EC (β = -0.03; P < 0.01). And such negative impact was weaker in economically developed areas (β = 0.076; P < 0.01). Conclusions: Attention should be paid to future reforms: China should continue investing in PHCIs, establish a structurally integrated and functionally complementary delivery system and promote the coordination of reform policies to avoid the adverse impacts of other reform policies on PHCIs.