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.

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.

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.

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.

[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.

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.

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.

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.

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.

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.

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.

Spatiotemporal evolution and driving factors of carbon emission efficiency of resource-based cities in the Yellow River Basin of China.

As an important part of regional coordinated development, the high-quality development of the Yellow River Basin has become a national strategy. It is imminent for resource-based cities to perform a high-quality transformation. The analysis of carbon emission efficiency in the Yellow River Basin includes the examination of spatiotemporal evolution characteristics and the main driving factors. This is done by utilizing the super-efficiency SBM-DEA and panel Tobit regression models, with the assistance of night light data. Our findings are as follows: (1) Carbon emissions continue to grow. The "Jiziwan" basin is an area where plenty of high-emitting cities agglomerate. The carbon emission of resource-based cities presents a W-shaped pattern in time. (2) In time, the carbon emission efficiency follows a U-shaped curve. Spatially, the carbon emission efficiency in the middle reaches is comparatively low, whereas it is relatively high in both the upper and lower reaches. And that in high carbon-emitting resource-based cities are in the low to medium range. (3) Carbon emission efficiency has a significant negative relationship with energy intensity, urbanization rate, and population density and a significant positive relationship with industrial proportion. Energy intensity is the most direct driving force. That is to say, we can increase carbon emission efficiency effectively by reducing energy intensity.

Industrial eco-efficiency of resource-based cities in China: spatial-temporal dynamics and associated factors.

Promoting the greening of industry is the key to achieving high-quality and sustainable development of the urban economy. It is particularly important for resource-based cities (RBCs) that exploit natural resources as the leading industries. In this paper, the Windows-Bootstrap-DEA model was used to calculate the industrial eco-efficiency (IEE) of 114 RBCs in China from 2003 to 2016, and the regional differences and dynamic evolution characteristics of the IEE were analyzed. The panel Tobit model was used to explore the factors associated with IEE in RBCs. The results showed that the IEE of RBCs in China was at a low level during the study period, and the resource utilization process had not reached an optimal state. There were large regional differences in IEE, and there was a significant degree of spatial agglomeration. The results of conditional probability density estimation showed that the distribution of IEE had strong internal stability on the whole, and the distributions of IEE of RBCs in different regions, different resource types, and different development stages showed significant differences. The results of the panel Tobit model showed that per capita GDP, ownership structure, science and technology input, and industrial agglomeration had significant positive effects on IEE, while industrial structure and employment structure showed significant negative effects. The conclusions of this paper can provide a scientific decision-making basis for industrial transformation planning of RBCs.

A ZSG-DEA model with factor constraint cone-based decoupling analysis for household CO2 emissions: a case study on Sichuan province.

This study employed an input-output approach to measure household CO2 emissions (HCEs) in Sichuan province, China from 2010 to 2017. A ZSG-DEA model with a factor constraint cone was built, to allocate such emission allowances to provincial subordinate cities and investigate their spatiotemporal heterogeneity. Using Tapio decoupling analysis, this study further examined the nexus between regional economic performance and HCEs. The results indicated that HCEs in Sichuan province initially increased and then decreased, peaking at 69.60 million tons in 2015. The structure of the HCEs changed from coal- to petroleum-dominated from 2010 to 2017. Indirect emissions from consumption-related sectors accounted for 60% of the total HCEs, including food, transportation, communication, and accommodation. The capital city of Chengdu, with the largest economic scale, overwhelmingly dominated the total household CO2 emissions, whereas Panzhihua, a heavy-industry-dominated city, had the highest HCEs  per capita. The Tapio decoupling results suggested that seven cities, led by Chengdu, had strong decoupling statuses, indicating that these cities performed well in the trade-off between economic growth and HCEs reduction. These results indicated that HCEs had a head effect centered on cities with advanced urbanization, but there was a virtuous cycle between CO2 emissions reduction and economic development. Given the spatial and temporal heterogeneity of HCEs, differentiated policymaking on emissions reduction is the key to facilitating green transformations.

The impact of manufacturing servitization on consumption-based carbon rebound effect: A global value chain research perspective.

This study investigates the causal effect of manufacturing servitization on the consumption-based carbon rebound effect, employing the empirical approach based on the value-added trade and SBM-DEA model. The results show that improving the servitization level will lead to a significant decrease in the consumption-based carbon rebound effect of the global manufacturing industry. Moreover, the main paths through which the manufacturing servitization inhibits the consumption-based carbon rebound effect are the human capital and government management. We also find that the impact of manufacturing servitization is more substantial in advanced manufacturing and developed economies, which is smaller in manufacturing sectors with higher global value chain positions and lower export penetration levels. These findings suggest that improving the level of manufacturing servitization helps alleviate the consumption-based carbon rebound effect and is conducive to achieving the target of global carbon emission reduction.

Fiscal decentralization, local government environmental protection preference, and regional green innovation efficiency: evidence from China.

Green technological innovation has gained in importance in regional policy making towards gaining competitive advantage and sustainable development. This paper used the data envelopment analysis method to calculate regional green innovation efficiency in China, and empirically tested the effect of fiscal decentralization through Tobit model. The regression results show that the local governments with higher fiscal autonomy would prefer to strengthen environmental protection; thus, the regional green innovation efficiency was improved. After the guidance of relevant national development strategies, these effects became more apparent. Our research provided theoretical support and practical guidance for promoting regional led green innovation, improving environmental quality, achieving carbon neutrality, and promoting the high-quality and sustainable development.

Analysis of financial support efficiency and influencing factors of listed seed companies from the perspective of energy consumption and carbon emissions.

The seed industry has a prominent role in strengthening ecological stability and national food security as it provides the basic ground for agriculture sector. In the current research, the effectiveness of financial support provided to listed seed enterprises and its influencing factors from the perspectives of energy consumption and carbon emissions are examined using a three-stage DEA-Tobit model. The dataset for the underlined study variables mainly comes from the financial data published by 32 listed seed enterprises and the China Energy Statistical Yearbook from 2016 to 2021. In order to make the results more accurate, the influence of external environmental factors such as economic development level, total energy consumption, and total carbon emission on listed seed enterprises are excluded. The results revealed that the mean value of financial support efficiency of listed seed enterprises increased significantly after excluding the influence of external environmental and random factors. External environmental factors such as regional energy consumption and carbon dioxide emission played an important role in the process of the financial system supporting the development of listed seed enterprises. The development of some listed seed enterprises with high financial support efficiency came at the cost of high local carbon dioxide emission and high energy consumption. Internal factors such as operating profit, equity concentration, financial structure, and enterprise size are the key intra-firm factors that affect the efficiency of financial support for listed seed enterprises. Thus, it is suggested that enterprises must pay attention to the environmental performance to reach a win-win situation in reducing and improving the energy consumption and financial performance, respectively. Similarly, the improvement of energy use efficiency through endogenous and external innovation should be prioritized to achieve sustainable economic development.

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.

Maximize Eco-Economic Benefits with Minimum Land Resources Input: Evaluation and Evolution of Land Use Eco-Efficiency of Agglomerations in Middle Reaches of Yangtze River, China.

Increasing land-use eco-efficiency can alleviate human-land conflict in urban areas as well as improve regional urbanization quality to achieve sustainable development. As the central urban agglomeration in China, the Middle Reaches of Yangtze River (MRYR) has experienced rapid urbanization and huge land-use change during 2000 to 2020, which poses great threats to its ecological environment. This study adopted the Super-Slack-Based Data Envelopment Analysis (Super SBM-DEA) model to evaluate the eco-efficiency of land use in MRYR. The result shows that the average eco-efficiency value of land use is above 0.77 for each year, indicating that the general efficiency is at a middle level. The trend of the evolution of the eco-efficiency can be summarized as a "U-shape" style curve. The variance between the four urban agglomerations of the MRYR changed over time. Not all capital cities or cities with higher GDP per capita obtain higher eco-efficiency in this study. Policy intervention, population and land use, technique, and environmental pollution are influencing factors of land-use eco-efficiency. Based on slacks analysis, this study proposed the optimization of the land-use structure to improve eco-efficiency from four aspects of land-use structure, investment and labor, ecosystem services value (ESV) and environment pollution, and industry structure.

How financial development scale and R&D influence regional innovation efficiency: empirical evidence from the financial industry.

Financial elements and R&D elements are significant drivers for enhancing regional innovation efficiency. This paper measures financial elements by financial development scale and the marketization level of the financial industry and R&D elements by the inputs and flow of R&D personnel and R&D capital and specifically considers R&D element flow to quantify the consequential spatial spillover effects. Based on provincial panel data from 2008 to 2018, the paper firstly estimates the regional innovation efficiency of China's 30 provincial-level administrative regions using super-efficiency DEA and then conducts an empirical analysis of the influence of financial elements and R&D elements on regional innovation efficiency by the use of the Tobit model and three spatial econometric models. It is found that, by and large, the financial development scale, the marketization level of the financial industry, the inputs of R&D personnel and R&D capital, and R&D capital flow all have significant effects on regional innovation efficiency. Nonetheless, by region, R&D personnel flow in central China can significantly boost regional innovation efficiency while fails in eastern and western China. From the spatial perspective, both financial elements and R&D elements have significant positive spatial spillover effects. Therefore, in order to bolster regional innovation efficiency, it is crucial to improve the allocation of financial elements and R&D elements and build a tight regional collaborative innovation network.