Enhancing pooling levels strengthens the risk resilience of healthcare insurance: a case study of basic medical insurance fund operations data in Gansu, China.

BACKGROUND: In China, enhancing the pooling levels of basic health insurance has consistently been regarded as a pivotal measure to promote the refinement of the healthcare insurance system. From 2020 to 2022, the widespread outbreak of COVID-19 posed new challenges to China's basic health insurance. METHODS: The research utilizes Data Envelopment Analysis (DEA), Malmquist index assessment, and fixed-effects panel Tobit models to analyze panel data from 2020 to 2022, assessing the efficiency of basic health insurance in Gansu Province. RESULTS: From 2020 to 2022, the average overall efficiency of the municipal pooling of Basic Medical Insurance for Urban and Rural Residents was 0.941, demonstrating a stable trend with a modest increase. The efficiency frontier regions have expanded from 5 (35.71%) to 7 (50%). Operational efficiency exhibited a negative correlation with per capita hospitalization expenses and per capita fund balance but a positive correlation with per capita accumulated fund balance and reimbursement rates for hospitalized patients. In 2021, compared to 2020, the county-pooling Basic Medical Insurance for Urban Employees saw a decline of 0.126 in overall efficiency, reducing the efficiency frontier regions from 8 to 3. However, from 2021 to 2022, the municipal-coordinated Basic Medical Insurance for Urban Employees experienced a 0.069 increase in overall efficiency, with the efficiency frontier regions expanding from 3 to 5. Throughout 2020 to 2022, the operational efficiency of the Urban Employee Basic Medical Insurance showed a consistent negative correlation with per capita fund balance. CONCLUSION: From 2020 to 2022, the overall operational performance of basic health insurance in Gansu Province was satisfactory, and enhancing the pooling level is beneficial in addressing the impact of unforeseen events on the health insurance system.

Dynamic changes in water resources and comprehensive assessment of water resource utilization efficiency in the Aral Sea basin, Central Asia.

The Aral Sea Basin in Central Asia faces significant challenges in improving water utilization and treatment because of frequent transboundary river water disputes and shortages of water resources. However, the traditional water resource utilization efficiency (WRUE) assessment models generally have the defect of over-validating evaluation results. To solve this problem, this study used the Coefficient of Variation method to constrain the self-contained weights in the traditional Data Envelopment Analysis (DEA) to construct an improved CV-DEA model, and assessed the WRUE of the Aral Sea Basin countries during 2000-2018 and compared the WRUE with that of the countries in the Mekong River Basin and Northeast Asia, then explored the factors influencing water utilization. The conclusions were drawn: since 1960, the runoff from the upper Amu Darya and Syr Darya rivers increased significantly, while the runoff from the lower Amu Darya River into the Aral Sea declined. Meanwhile, the water area of the Aral Sea shrank from 2.56 × 104 km2 to 0.70 × 104 km2 in 2000-2018, with the Northern Aral Sea remaining stable while the southern part shrinking sharply. The WRUE of the Aral Sea Basin (0.599, on average) was higher than that of the Mekong River Basin (0.547) and lower than that of Northeast Asia (0.885). Kazakhstan and Uzbekistan had the highest WRUE of 0.819 and 0.685 respectively, and the WRUE in both two countries improved from 2000 to 2018. Tajikistan (0.495) and Turkmenistan (0.402) experienced decreases in WRUEs. The high input redundancy of agricultural water consumption was the main driving force affecting WRUE in the basin.

Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach.

BACKGROUND: Primary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such institutions. This study is designed to gauge the efficiency of PHC institutions by using 2012-2020 panel data covering 31 provinces in China. METHODS: This study applied an improved three-stage data envelopment analysis (DEA) model to evaluate the efficiency of PHC institutions in China. Unlike the traditional three-stage DEA model, the input-oriented global super-efficiency slack-based measurement (SBM) DEA model is used to calculate the efficiency in the first and third stages of the improved three-stage DEA model, which not only allows the effects of environmental factors and random noise to be taken into account but also deal with the problem of slack, super-efficiency and the comparability of interperiod efficiency values throughout the efficiency measurement. RESULTS: The results show that the efficiency of PHC institutions has been overestimated due to the impact of external environmental factors and random noise. From 2012 to 2020, the efficiency of PHC institutions displayed a downward trend. Moreover, there are significant differences in the efficiency of PHC institutions between regions, with the lowest efficiency being found in the northeast region. The efficiency of PHC institutions is significantly affected by residents' annual average income, per capita GDP, population density, the percentage of the population aged 0-14, the percentage of the population aged 65 and older, the number of people with a college education and above per 100,000 residents, and the proportion of the urban population. CONCLUSIONS: Substantial investment in PHC institutions has not led to the expected efficiency gains. Therefore, more effective measures should be taken to improve the efficiency of PHC institutions in China based on local conditions. This study provides a new analytical approach to calculating the efficiency of PHC institutions, and this approach can be applied to efficiency evaluation either in other fields or in other countries.

Changes in efficiency of tertiary public general hospitals during the reform of public hospitals in Beijing, China.

BACKGROUND: In March 2009, the Chinese government formally launched a new round of healthcare reform. As the city with the highest concentration of high-quality medical resources in China, in the past 10 years, Beijing has also been exploring medical reforms. This article studied the performance and development of the 10 tertiary general public hospitals managed by Beijing Municipal Hospital Management Centre to provide policy basis for further deepening Beijing public hospital reform and improving hospital efficiency. METHODS: The 2011, 2015 and 2018 'Beijing Health Work Statistics' were used to evaluate the performance of Beijing's tertiary public general hospitals, based on the Pabon Lasso model and the data envelopment analysis (DEA) model. RESULTS: Based on the Pabon Lasso model, 60%, 70% and 70% of the hospitals were entirely efficient (zone 3) in 2011, 2015 and 2018. It shows that among the 10 general public hospitals in Beijing, efficient hospitals accounted for the majority and further increased during the reform period. The DEA model further illustrates this point and shows more effective hospitals (80%) than the Pabon Lasso model, showing the efficiency of these hospitals to be improved during the reform period. CONCLUSIONS: The efficiency of the 10 hospitals has gradually improved during the reform period, and the difficulty of seeing a doctor in Beijing at a national medical centre has been relieved to a certain extent. Combining the Pabon Lasso model and the DEA model can analyse hospital efficiency more comprehensively, and can prompt initial information for improving hospital efficiency, but the results also reflect some problems.

Sustainability efficiency of climate change and global disasters based on greenhouse gas emissions from the parallel production sectors - A modified dynamic parallel three-stage network DEA model.

This study employed dynamic three-stage network data envelopment analysis (DEA), considering parallel production in the agricultural and industrial sectors, to assess the impact of greenhouse gas emissions on the climate change and natural disaster stages. The results revealed the following: (1) The dynamic overall efficiencies of more countries are decreasing than are increasing. The seven countries with the poorest overall efficiency ranking (Myanmar, Vietnam, Thailand, Bangladesh, the Philippines, Pakistan, and India) are mostly located in Southeast Asia. (2) The number of countries that maintained low efficiency over the long term is greater than those that retained high efficiency over the long term. Myanmar, Mexico, India, the Philippines, Thailand, and Vietnam maintained efficiency scores below 0.5, whereas South Korea, Japan, China, and New Zealand maintained efficiency scores above 0.8. (3) More than one-third of countries exhibited declines in efficiency over time, and half were European countries. Less than one-third of countries maintained their efficiency, and less than one-third of countries gradually improved. (4) Approximately half of the countries' efficiency scores were lower than the global average. The efficiency scores of the industrial sector exhibited a greater room for improvement on the input factors than did those of the agricultural sector. (5) Total factor energy efficiency analysis revealed that methane emissions and CO2 emissions have a similar level but large room for improvement across countries. Improving input factors in the production stage can ultimately mitigate inefficiencies in the climate change and natural disaster stages. There are still other important factors related to climate change, such as sea surface temperature, forest areas, or air pollution indicators, that could be considered in future research. The occurrence of global disasters could also be discussed in groups according to the region where the countries are located in the future.

Analysis of the spatial characteristics and influencing factors of agricultural eco-efficiency: evidence from Anhui Province, China, during the period 2011-2018.

Sustainable agriculture is important for preserving environmental health and simultaneously gaining economic profits while maintaining social and economic equity. One way to evaluate sustainable agriculture is by studying agricultural eco-efficiency (AEE). Hence, this study constructed a data-driven method to evaluate and optimize AEE with the aim of providing a basis for improving the sustainable development of regional agriculture. Sixteen cities in Anhui Province, China, were considered in the study, and the variables used were agricultural resource inputs, environmental pollution, and agricultural economic development. Agricultural non-point source pollution (NPSP) emissions were considered the undesired output to build an AEE evaluation index system. Furthermore, a data envelopment analysis (DEA) model was established to analyse AEE from the static and dynamic perspectives. The spatial development and the temporal and spatial characteristics of AEE were also analysed. In addition, we applied a random effect (RE) panel Tobit model to quantitatively analyse the influencing factors of AEE from the input perspective and then proposed reasonable suggestions for improving the sustainable development of regional agriculture. Our findings show that the overall agricultural development in the 16 cities in Anhui Province has been continuously improving, even though there is an agglomeration of spatial development in some regions. In conclusion, this study provides suggestions and references for policy makers and agricultural practitioners regarding how to improve regional AEE and promote the sustainable development of the regional agricultural economy.

Is the allocation of medical and health resources effective? Characteristic facts from regional heterogeneity in China.

BACKGROUND: Over the last decade, the expenditure on public medical and health has increased greatly in China, however, problems as low efficiency and unfairness still exist. How to accurately describe the effectiveness of existing medical and health resources in combination with regional heterogeneity is of great significance to China's medical and health reform. METHODS: Based on provincial panel data for the period of 2005 to 2017, combining expected output and unexpected output, this paper constructs a super-efficiency three-stage SBM-DEA model, to measure and analyze the spatial-temporal heterogeneity characteristics and influencing factors of public medical and health efficiency (PMHE). RESULTS: (1) After the impacts of random error and external environmental factors are removed, the mean value of overall PMHE is 0.9274, failing to reach DEA efficiency, and PMHE shows a fluctuated downward trend. (2) The adjusted PMHE level shows a prominent spatial imbalance at the stage 3. The average efficiency level is ranked by the East > the West > the Central > the Northeast. (3) The increases of GDP per capita and population density are beneficial to the improvement of PMHE, while income level and education level are disadvantageous to PMHE, and last, the urbanization level, an uncertain effect. (4) There is no σ convergence of the PMHE in the East, the Central and the West, that is, the internal differences may gradually expand in the future, while the Northeast shows a significant σ convergence trending of PMHE. (5) The state's allocation of medical and health resources has undergone major changes during "The Twelfth Five-Year Plan". CONCLUSION: This study innovatively incorporates undesired outputs of health care into the efficiency evaluation framework by constructing the main efficiency evaluation indicators. The results of the robust evaluation conclude that China's existing investment in medical and health resources is generally not effective. Therefore, although China's health care reform has made certain achievement, it is still necessary to expand the investment in health care resources.

Efficiency evaluation of urban wastewater treatment: Evidence from 113 cities in the Yangtze River Economic Belt of China.

To resolve the increasing water pollution crisis, scientifically evaluating the urban wastewater treatment efficiency (UWTE) is an essential prerequisite to ensure the success of any policies aiming to decrease water pollution. Using 113 city-level panel data in the Yangtze River Economic Belt (YREB)-the biggest economic belt in China, during 2008-2017, this study aims to evaluate the UWTE based on a Bootstrap-DEA and to measure the efficiency changes through a Malmquist index model. The results show that the overall UWTE is at a low level, evidenced by the fact the average efficiency score is 0.51 during 2008-2017, and no cities have an efficiency score equal to 1. The UWTE is in the trend of decreasing, which is mainly caused by the decline of technical progress change. 69.02% of cities are in the state of decreasing returns to scale. The UWTE shows considerable disparities both between regions and city sizes, with the highest efficiency score observed in the midstream area and large-sized cities, the lowest efficiency score observed in the downstream area and small-sized cities. The findings of this study are expected to have great practical significance for governing wastewater pollution.

Provincial-scale assessment of vulnerability and resilience to drought in China.

Frequent droughts have caused severe disaster losses in China. Such events can be minimized by enhancing the country's resilience and reducing its vulnerability, where this can ensure socioeconomic stability and sustainable development. Evaluating the vulnerability and resilience to drought is thus crucial for effectively managing the risk of disasters and promoting sustainable socioeconomic development. In this study, we constructed a comprehensive framework to assess the spatiotemporal characteristics of China's vulnerability and resilience to drought at the provincial scale from an input-output perspective by using the Super-efficiency Data Envelopment Analysis (DEA) model and the Super-efficiency Slacks-Based Measure DEA (SBM-DEA) model. This study focused on drought drivers, the disaster-forming environment, drought bearers, disaster intensity, and recovery. The results showed that the vulnerability to drought of 42 % of China's provinces decreased from 2010 to 2022, that of only 29 % of the provinces increased, while the status of a majority of provinces improved in general. The center of gravity of the vulnerability to drought moved toward the southwest over time and a spatial clustering of vulnerability was observed, with High-High clusters moving from the north to the south. Moreover, the resilience to drought declined in 36 % of provinces and increased in only 20 %, reflecting poor resilience overall. The center of gravity of China's overall resilience to drought moved northward, with a relatively stable spatial pattern and prominent clusters of Low-Low resilience indicating a pressing need for improvement. Areas with high vulnerability and low resilience were concentrated in inland western and eastern regions, and this highlights the importance of drought prevention and mitigation in provinces like Xinjiang, Inner Mongolia, Jiangxi, and Fujian. The findings here provide valuable insights for mitigating the risk of drought and promoting sustainable socioeconomic development.

What explains the operational efficiency of listed commercial banks in China? Evidence from a three-stage DEA-tobit modeling analysis.

Based on the actual situation of listed commercial banks in China, this study utilizes a three-stage DEA model to eliminate external macro-environmental factors and random error factors. As a result, the study objectively measures the true efficiency values of 42 listed commercial banks in China from 2015 to 2021. On this basis, a Tobit model is constructed to analyze the micro-level factors influencing the operational efficiency of listed commercial banks in China. Empirical research shows that the operational efficiency values in the first stage are significantly lower than those in the third stage. Analyzing the proportion of banks with an efficiency value of 1, it is found that state-owned banks and joint-stock banks account for a large proportion. The proportion of city commercial banks decreases significantly, while agricultural banks and commercial banks have the lowest proportion. In addition, the Tobit regression results show that bank size, innovation level, profitability, and development capacity have a significant positive impact on the operational efficiency of listed commercial banks in China at the current stage. Based on the above empirical results and analysis, specific measures are proposed to improve the operational efficiency of listed commercial banks in China. Based on the above empirical results and analysis, the study finally suggests the following specific measures to enhance the operational efficiency of listed commercial banks in China: 1. Strengthening technological investment to achieve diversified operations and cultivate new niche markets; 2. Emphasizing intermediate business income to enhance market competitiveness; 3. Undertaking digital transformation and upgrading to improve sustainable profitability.

Spatial differentiation and influencing factors of red tourism resources transformation efficiency in China based on RMP-IO analysis.

This study employs modified data envelopment analysis (DEA) models and spatial autocorrelation methods to analyze the characteristics of red tourism transformation efficiency and categorize them into efficiency zones. By utilizing geographic detector models, the interactive driving mechanisms behind spatial differentiation are revealed, providing valuable insights for the high-quality transformation and development of China's red tourism economy. The application of modified DEA models facilitates the evaluation of red tourism resource transformation efficiency by decomposing comprehensive efficiency into single-factor efficiency for individual input and output variables. The results indicate that: (1) Expansion of tourism factors is crucial for achieving red tourism resource transformation in China, with low efficiency in resource endowment investment acting as the primary constraint. (2) Local spatial correlation between production efficiency and resource transformation efficiency demonstrates a decreasing trend from east to west, leading to the classification of China's red tourism resources into five types of efficiency zones. (3) Endogenous ability factors predominantly affect red tourism resource transformation efficiency, with interaction between internal and external factors driving spatial differentiation.

Green transformation and performance synergy efficiency of china's thermal power enterprises on the basis of the environmental tax burden.

Global climate change and the collection of environmental protection taxes are accelerating the green transformation of thermal power enterprises. This study selected Chinese thermal power listed companies as samples and used a dynamic three-stage (operational, green transformation, and market performance) network DEA model to evaluate their transformation efficiency and corporate performance. This paper incorporates targeted indicators such as ESG (environment, society, governance) and stock prices into the model and conducts a comparative study on the basis of macro policies and the geographical location of the enterprise. A comparative analysis was conducted on the efficiency of enterprises before and after the adjustment of the environmental tax burden, using the environmental tax burden as an exogenous variable. Thus, the following conclusions can be drawn: there is a certain positive correlation between the collaborative efficiency of the two links of thermal power enterprises and the economic development of their respective regions. Moreover, the green transformation efficiency of most thermal power enterprises is superior to the market performance efficiency. The environmental tax burden mainly improves the overall efficiency of thermal power enterprises by improving their operational efficiency and efficiency in the green transformation stage without affecting market performance. To further improve efficiency, thermal power enterprises should actively communicate with stakeholders to strive for more financial relief.

Measuring Technical Efficiency and Productivity of Secondary Maternal and Child Health Hospitals in Hubei, China: Some Evidences from Hubei Province of China 2019 to 2021.

Environmental factors like COVID-19 can have significant impact on technical efficiency (TE) and total factor productivity (TFP) of health services provided. In this study, focusing on Maternal and Child Health (MCH) hospitals in Hubei Province of China in 2019 to 2021, we aimed to measure their TE and TFP, identify some influential environmental factors, and propose some policy recommendations. Altogether 62 secondary MCH hospitals were selected as the study sample. Four input indicators, 3 output indicators, and 4 environmental indicators were selected to analyze the panel data from 2019 to 2021. Three-stage Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI) model were employed to estimate the TE and TFP of these hospitals. During 2019 to 2021, the inputs of the sample hospitals had increased, while the outputs had decreased. The inputs redundancy was negatively associated with birth rate, number of residents, and GDP per capita (P < .05). It was positively associated with number of COVID-19 infections (P < .05). The adjusted TE scores in 2019 to 2021 were 0.822, 0.784, and 0.803, respectively. The TFP declined in 2020 and 2021 compared to 2019, with scores being 0.845 and 0.762. The technical efficiency change (TEC) scores from 2019 to 2021 were 0.926 and 1.063. The technological change (TC) scores from 2019 to 2021 were 0.912 and 0.716. During 2019 to 2021, the operation of sample hospitals had been significantly influenced by environmental factors like COVID-19 pandemic, low birth rate, number of residents, and GDP per capita. The inputs had increased but outputs had decreased, leading to an increase in inputs redundancy and a decline in TE. The TFP showed a downward trend, with TC and SEC being the priority directions for improvement. Some recommendations are made for both hospitals and government to continuously improve the TE and TFP.

Regional coordinated development of low-carbon sports industry in China.

This study develops a systematic modeling framework, comprising a prediction model, a super-SBM model, and a spatial autocorrelation analysis model, to explore the spatial-temporal evolution tendencies of development efficiency within China's 30 regions in the low-carbon sports industry from 2006 to 2025. This framework aims to provide theoretical insights for the formulation of more targeted policies. Based on the empirical findings, the main conclusions of this study are as follows: (1) The optimal buffer operator grey prediction model demonstrates the highest accuracy among the prediction models examined. (2) The development efficiency curves of the 30 regions exhibit a significant increasing trend from 2006 to 2021, with values generally peaking between 0.4 and 0.6. (3) Notably, the disparity in development efficiency between developed and less developed regions is expected to progressively widen. (4) The development efficiency of the low-carbon sports industry across the 30 regions typically displays high-high clustering and low-low clustering during China's four five-year plan periods. This underscores the importance and urgency of promoting regional coordinated development within the low-carbon sports industry.

Spatio-temporal evolution and its policy influencing factors of agricultural land-use efficiency under carbon emission constraint in mainland China.

In the context of the vision to reach peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060, Mainland China's agricultural development will face strict carbon constraints. This paper analyzes the agricultural land-use efficiency of Mainland China's agriculture under carbon emission constraint from 1996 to 2020, based on the unexpected super SBM (Slack-based measure)-Undesirable DEA, Malmquist index model, and quartile division-GIS method. The results show that: from 1996 to 2020, the agricultural output value per land and grain output per land show an upward trend, and the agricultural carbon emissions per land of most provinces show an increasing trend and larger emissions. The agricultural land-use efficiency in Mainland China rises first and then decreases, and technological progress is the decisive path to improving the agricultural land-use efficiency in Mainland China. The average MI in the prominent grain-selling area during 1996-2020 was as high as 1.071, which was significantly higher than that in the prominent grain-producing area (1.039) and the balance area (1.030). The improvement of agricultural land-use efficiency is mostly due to technological progress, but the instability of technical input and management in land use. To improve agricultural land-use efficiency in Mainland China, we should pay attention to the precise policy formulation of low-carbon and high-quality development and strengthen government investment in the difference between space resource endowment and development status.

[Efficiency Characteristics and Evolution Patterns of Urban Carbon Metabolism of Production-Living-Ecological Space in Beijing-Tianjin-Hebei Region].

This study explored the carbon metabolism efficiency of a production-living-ecological space system, which is of great significance for regional factor integration and spatial optimization. In this study, the material flow analysis method was introduced to establish a framework for evaluating the carbon metabolism efficiency of the production-living-ecological space system, and the super-efficiency DEA model and Malmquist index were used to empirically analyze the spatio-temporal distribution, dynamic change, and evolution patterns of the carbon metabolism efficiency of production-living-ecological space in the Beijing-Tianjin-Hebei Region, China, from 2000 to 2020 on the basis of the urban metabolic perspective. The results showed that:① the carbon metabolism efficiency of the production-living-ecological space showed a fluctuating growth trend, indicating the significant spatial differentiation of carbon metabolism efficiency in each city. There was a low overall carbon metabolism efficiency level, with a distribution pattern of being high in the middle and low in the north and south. ② The Malmquist index showed that the Total Factor Productivity (TFP) of carbon metabolism efficiency was greater than 1, and both the Technical Change (TC) and Pure Efficiency Change (PEC) were less than 1, in which the TFP showed an increasing trend, whereas there was no significant contribution of technological progress or pure technical efficiency to carbon metabolism efficiency. The total factor productivity of more than 50% of the cities showed an improving trend, only 38.46% of which made technological progress in improving carbon metabolism efficiency, and more than half of the urban pure technical efficiency showed a decreasing trend, in which the technical efficiency change and scale efficiency change were greater than 1 in most cities. ③ There were different types of carbon efficiency characteristics in each city, and according to the movement rules of the corresponding points in the quartile map, the evolution patterns of tourism industry efficiency were classified into stable, reciprocating, progressive, and abrupt. Therefore, local governments should adopt differentiated strategies to reasonably allocate spatial resources of production-living-ecological space and improve the technical level and scale efficiency, so as to improve the efficiency of urban carbon metabolism.

Efficiency and optimization of government service resource allocation in a cloud computing environment.

According to the connotation and structure of government service resources, data of government service resources in L city from 2019 to 2021 are used to calculate the efficiency of government service resource allocation in each county and region in different periods, particularly by adding the government cloud platform and cloud computing resources to the government service resource data and applying the data envelopment analysis (DEA) method, which has practical significance for the development and innovation of government services. On this basis, patterns and evolutionary trends of government service resource allocation efficiency in each region during the study period are analyzed and discussed. Results are as follows. i) Overall efficiency level in the allocation of government service resources in L city is not high, showing an increasing annual trend among the high and low staggering. ii) Relative difference of allocation efficiency of government service resources is a common phenomenon of regional development, the existence and evolution of which are the direct or indirect influence and reflection of various aspects, such as economic strength and reform effort. iii) Data analysis for the specific points indicates that increased input does not necessarily lead to increased efficiency, some indicators have insufficient input or redundant output. Therefore, optimization of the physical, human, and financial resource allocation methods; and the intelligent online processing of government services achieved by the adoption of government cloud platform and cloud computing resources are the current objective choices to realize maximum efficiency in the allocation of government service resources.

Environmental quality assessment and spatial spillover effects of three urban agglomerations in China: A Meta-EBM approach.

The new development form of urban agglomeration has greatly promoted economic and social progress in recent years, but it is also facing severe environmental pollution problems. Understanding the status quo of environmental efficiency in urban agglomerations and its leading driving forces is an important prerequisite for formulating energy conservation and emission reduction policies. This research uses the Meta Epsilon Based Measure (Meta-EBM) model to measure the environmental emission efficiency of the Beijing-Tianjin-Hebei（BTH）, Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations in China from 2014 to 2018 so as to improve on the inability of traditional Data Envelopment Analysis (DEA) to combine linear and non-linear characteristics, and employs Moran's I index and spatial econometric methods to analyze their spatial dependence and main driving factors. The results demonstrate that the overall environmental efficiency of the three major urban agglomerations in the five years from 2014 to 2018 presents a wave-like development and then tends to be flat. The itemized efficiency of economic outputs has maintained a relatively high level with the environmental output index exhibiting the best efficiency for industrial wastewater, followed by industrial sulfur dioxide (SO2). The scores of the two indicators for inhalable fine particle emissions (PM2.5) and industrial smoke and dust in each urban agglomeration are not ideal, and there are obvious differences between regions. Among them, YRD and PRD are relatively inferior. From the perspective of spatial spillover effects, various indicators show diverse characteristics at different development stages of the regions. Population and Normalized Difference Vegetation Index (NDVI) have a positive effect on environmental efficiency, while both Gross Domestic Product (GDP) per capita and transportation tend to show greater negative effects on regional environmental optimization. This study proposes countermeasures as follows. Each urban agglomeration should set up measures suitable to local conditions and give full play to their location advantages. They can also use space radiation to promote sector economic development and optimize urban environmental benefits.

Measurement and influencing factors of industrial carbon unlocking efficiency under the background of "double carbon".

Based on the panel data of 11 provinces and cities in the Yangtze River Economic Belt of China from 2011 to 2018, the DEA model and Malmquist index were used to calculate the industrial carbon unlocking efficiency of the Yangtze River Economic Belt in a static and dynamic way, and the Tobit model was used to empirically test its influencing factors. The results show that (1) the overall efficiency of industrial carbon unlocking in the Yangtze River Economic Belt is on the rise, but there are regional differences. The efficiency of industrial carbon unlocking in the lower reaches of the Yangtze River is higher than that in the upper and middle reaches. (2) The total factor productivity of industrial carbon unlocking in the Yangtze River Economic Belt has increased steadily on the whole, and technological progress is the main source of growth. (3) Industrial carbon unlocking efficiency is affected by many factors. Environmental regulation and degree of openness have a promoting effect on industrial carbon unlocking efficiency, while government investment and innovation level have a restraining effect on industrial carbon unlocking efficiency.

Measuring Productivity and Relative Efficiency of Public Tertiary Traditional Chinese Medicine Hospitals in Hubei, China.

Improving the productivity and relative efficiency of traditional Chinese medicine (TCM) hospitals is pivotal for hospital managers and policymakers to optimize the utilization of TCM resources in China. This study aimed to measure the productivity and relative efficiency of public tertiary TCM hospitals in Hubei Province. The input and output indicators data were extracted from the Health Commission of Hubei Province (HCHP) from 2019 to 2021. The Bootstrap-Malmquist-DEA model was employed to measure the productivity and relative efficiency of the hospitals. The statistical significance was set at P < .05. The numbers of total diagnostic patients and discharged patients declined by 23.44% and 28.34% from 2019 to 2020, and then increased by 25.76% and 20.44% respectively from 2020 to 2021. The average bias-corrected technical efficiency (TE) scores of the TCM hospitals from 2019 to 2021 were 0.8391, 0.8048, and 0.8559, indicating good efficiency. The average total factor productivity (TFP) in 2020 and 2021 decreased compared to that in 2019, with scores of 0.7479 and 0.8996, respectively. Between 2019 and 2020, the TFP changes among 19 out of 21 (90.48%) TCM hospitals and the technological changes (TC) among 20 out of 21 (95.24%) were less than 1.0000 (P < .05). The TFP changes of 17 out of 21 (80.95%) TCM hospitals and the TC of 20 out of 21 (95.24%) were less than 1.0000 (P < .05) between 2019 and 2021. COVID-19 might have constrained the provision of healthcare services by the public tertiary TCM hospitals in Hubei Province. Priority should be given to the utilization of healthcare resources, performance evaluation, information system strengthening, and internal hospital management to boost technical efficiency. TCM hospitals need to focus further on technology innovation to improve their technological progress.