SBM Model Research Articles

The coupling coordination characteristics of China's health production efficiency and new urbanization and its influencing factors.

Urbanization leads to dramatic changes in habitat quality, which significantly affects population health. Research on the coupling coordination relationship between new urbanization and health production efficiency is conducive to improving residents' well-being and urban sustainable development. In this article, we adopted the super-efficient SBM model and entropy value method separately to evaluate the spatiotemporal variation characteristics of health production efficiency and new urbanization in China. Then, we used the coupling coordination degree model to investigate the interactive coercing relationship between new urbanization and health production efficiency. Finally, the panel Tobit model is used to analyze the factors influencing the coupled coordination of the two systems. The results showed that the new urbanization levels of 31 provinces in China have all steadily increased from 2003 to 2018. Health production efficiency exhibited a fluctuating but increasing trend, and its regional differences are gradually narrowing. Health production efficiency and new urbanization have developed in a more coordinated direction, with a spatial pattern of "high in the southeast and low in the northwest." Meanwhile, the relative development characteristics between the two systems have constantly changed, from the new urbanization lagged type to the two systems synchronized type and the health production efficiency lagged type. Population density, economic development level, government financial investment, and government health investment positively impact the coupling coordination degree of the two systems. In comparison, individual health investment harms the harmonization of the two systems.

The impact of the free trade zones construction on green technological innovation efficiency ——evidence from 288 cities in Chinese

We measure the green technology innovation efficiency of 288 cities in China from static and dynamic dimensions using the super−SBM model and Malmquist−Luenberger index, and employ "Difference in Difference" (DID) model to evaluate the impact of FTZs construction on green technology innovation efficiency using panel data from 288 prefecture-level cities from 2008 to 2020. The findings show: (1) The FTZs significantly improve green technology innovation efficiency. The decomposition indexes promote the green technology innovation efficiency more from the dynamic productivity dimension (GTFP) functioning on technological advancement. (2) The FTZs can boost the efficiency of green technology innovation through industrial agglomeration, digital economy, and government financial support; (3) The effect of FTZs on the efficiency of green technology innovation differs based on the size and location of the city. Green technology innovation will reach maximum potential when promoting FTZ policy in less developed central, western, and interior regions. This study addresses whether FTZ policies can genuinely support regional green innovation and policy insights to expand opening up and enhance high-quality economic growth.

Spatial-temporal variation of economic resilience and its influencing factors under COVID-19: Evidence from Henan province, China

In order to explore the impact of COVID-19 on regional economy, the resilience characteristics of regional economy and influencing factors, this paper takes Henan province as an example and innovatively employ PSR and SBM model to measure the economic resilience of 18 areas in Henan province under the epidemic. The results show that (1) after the outbreak of COVID-19, the economic resilience of Henan province reduced with a tiny interannual variation, the distribution of resilience shifted from concentration to dispersion.(2) The economic resilience was higher in the central, west and south, and lower in the north and east. (3)Generally speaking, the economic resilience in Henan province did not significantly show certain spatial correlation, but formed an obvious "circle structure". The surrounding areas of Zhengzhou belonged to the "first circle", where the economic resilience was in the low-high cluster area and had a negative relationship. Areas outside the "first circle" gathered in the outer edge and formed the "second circle", where the economic resilience was in the low-low agglomeration area, with significant positive spatial spillover effect. (4) The level of health services had the strongest influence and GDP per capita played a fundamental role in improving economic resilience. This study is helpful for the policymakers to formulate relevant policies to improve economic resilience under public health emergencies in the future.

An Empirical Analysis of the Impact of Green Investment on China's Green Development Level: Based on Mediating and Spatial Spillover Effects

This study, based on panel data from 30 provinces in China from 2007 to 2020, employs the Super Efficiency SBM model to measure the green total factor productivity of each province. By constructing mediating effect models and spatial econometric models, it conducts an empirical investigation into the mechanisms and spatial characteristics of how green investments promote China's green development. The results reveal that, at an overall level, green investments play a positive role in influencing China's green development. Regarding the influence pathways, innovative green technology advancements, both in terms of inventions and improvements, are important mediating channels through which green investments promote green development, with inventions having a stronger mediating effect. In terms of regional heterogeneity, green investments significantly promote green development in the eastern and central regions, while their promotion effect in the western regions is less pronounced. Concerning spatial spillover characteristics, green investments not only drive local green development levels but also stimulate the green development in surrounding areas through spatial spillover effects.

The association of farmers’ cognition, intention and behaviour towards sustainable intensification of cultivated land use in Shandong Province, China

ABSTRACT Cultivated land system confronts with a series of problems hindering its socioeconomic and ecological sustainability. Sustainable intensification of cultivated land use (SICLU) is considered as an efficient method to achieve three aspects of sustainable goals in society, economy and ecology simultaneously. Farmers, the main executor of all agricultural strategies, are the core of SICLU research. However, there is a small quantity of researches on SICLU from the perspective of farmers’ social psychology. Therefore, this article measured the level of SICLU at the farmer scale using super-efficiency SBM model, and relying on structural equation modeling and an innovative research framework, explored the association mechanism among farmers’ cognition, intention and behavior of SICLU. Finally, the results showed that (1) most of samples are at a medium or higher level of SICLU. (2) all samples’ cognition are significantly positively related to their behavior and their behavior significantly negatively affect their intention. (3) correlation characteristics among them are slightly different under each level. The proposed research outline not only expand the scope of SI research to the perspective of social psychology in breadth, but in depth take targeted actions to protect agricultural environment, make cultivated land system sustainable and alleviate food crisis.

Fiscal expenditure efficiency and green economic efficiency: evidence from Chinese cities

This study develops indicators for evaluating Green Economy Efficiency (GEE) and Fiscal Expenditure Efficiency (FEE) at the prefecture level and above within Chinese cities. Utilizing the super-efficiency SBM model and a three-stage DEA model, we empirically examine the influence of FEE on GEE. To address the issue of endogeneity arising from reverse causality between FEE and GEE, this research employs fiscal autonomy and officials’ tenure as instrumental variables for FEE. The key findings are summarized as follows: (1) GEE exhibits a fluctuating upward trend from 2006 to 2020, while FEE remains relatively stable overall. Notably, the GEE and FEE in the eastern region surpass those in the central and western regions. (2) A one percentage point increase in FEE corresponds to a substantial 7.597% rise in GEE, indicating a significant promotional effect, a result consistently upheld in robustness tests. Furthermore, factors such as urbanization level, economic development, education level, and industrial structure also contribute to urban green economic growth. (3) FEE primarily contributes to GEE through local economic growth, urban infrastructure development, and regional innovation levels. (4) The impact of FEE on GEE is more pronounced in developed cities, larger cities, those with high fiscal freedom, and highly urbanized cities. Based on these conclusions, policy recommendations are proposed: (1) contingent upon local circumstances, each municipal government should optimize its fiscal expenditure structure and enhance FEE based on its unique endowments; (2) make full use of spatial correlation and knowledge spillover effect to promote the coordinated growth of FEE and GEE; (3) improve urban infrastructure construction and encourage regional technological innovation.

Spatial–Temporal Characteristics and Influencing Factors of Eco-Efficiency of Cultivated Land Use in the Yangtze River Delta Region

The sustainable utilization of regional cultivated land systems in the Yangtze River Delta (YRD) region over the past 40 years has been severely impacted by rapid urbanization processes. Improving the eco-efficiency of cultivated land use (ECLU) plays a significant role in achieving the sustainable utilization of farmland and high-quality development of agriculture and rural areas. In this study, the spatial–temporal features and influencing factors of the ECLU in the YRD are investigated by various methods, such as a super-efficient SBM model, hot spot analysis, Dagum Gini coefficient, and panel tobit model. The findings indicate the following: the ECLU showed an overall high level from 2000 to 2020; the ECLU varied significantly over time and space in the YRD. The ECLU presented obvious spatial agglomeration in the YRD: southern regions exhibited a concentration of cold spots, while hot spots were primarily found in the east and north of the YRD. The trend of regional differences in ECLU during the research period fluctuated upwards in the YRD, and the density difference super-variable was the main source of regional differences. Increases in urbanization level and GDP per capita contributed to ECLU enhancement in the YRD, and agricultural intensity levels and agricultural industrial structures played a negative role in ECLU improvement. Finally, we suggest that different regions should adapt to local conditions, scientifically and reasonably allocate cultivated land production resources, and promote the coordinated improvement of ECLU. This study could provide a reference for policymakers to formulate better decisions on cultivated land utilization and management.

Coupling coordination and interactive effects of new urbanization efficiency and eco-efficiency-A case study of Fujian Province.

This study explores the coupling and coordinated development of new urbanization and eco-efficiency and their interaction mechanisms from the perspective of efficiency, and it has significant implications for promoting high-quality development and surpassing in China's regional development. The study aims to investigate the spatiotemporal distribution pattern of new urbanization efficiency and eco-efficiency and its coupling and coordinated development relationship. Using panel data from 2010 to 2020 for nine cities in Fujian Province, this study employs the undesired super-efficiency SBM model to measure the efficiency of new urbanization and eco-efficiency. Additionally, a spatial panel Durbin model is constructed to analyze the interaction effect between the two efficiencies. During the study period, both new urbanization efficiency and eco-efficiency in Fujian Province showed a fluctuating upward trend, with higher efficiency in the southeast than the northwest, exhibiting significant spatial agglomeration effects. Despite high double efficiency coupling, coordinated development was low, evolving from near-disorder to barely coordinated. The high coupling and coordination areas were mainly concentrated in the southeast, with gaps between different regions gradually narrowing. There was positive interaction between dual efficiency in the same region, with new urbanization efficiency showing a positive spatial spillover effect on eco-efficiency. Conversely, the spatial spillover effect of eco-efficiency on new urbanization efficiency was not significant.

The Efficiency of China’s Hub Economy and Its Influencing Factors: A Two-Stage Analysis Based on the Super SBM-Malmquist-Tobit Model

Hub economy is a kind of emerging economic form. Developing a hub economy is essential to strengthen domestic and foreign connectivity and build a powerful country in transportation. This paper designs a two-stage analysis framework for the evaluation and impact study of the input-output efficiency of the hub economy based on the super SBM model, Malmquist model, and Tobit model. In the first stage, the Super SBM and Malmquist models are used to measure the static and dynamic efficiency of the hub economy. In the second stage, the Tobit model is used to analyze the factors influencing the efficiency of the hub economy. Among them, the explained variable in the second stage is the measurement result of technical efficiency in the first stage. The empirical results of 30 provinces and cities in China from 2012 to 2021 show that (1) the technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) of China’s hub economy are 0.585, 0.740, and 0.820, respectively, which do not reach the effective state; (2) the technical efficiency change index (Effch), technical progress change index (Techch), and total factor productivity change index (Tfpch) of China’s hub economy are 0.994, 0.945, and 0.939, respectively, indicating that the corresponding efficiencies show a downward trend; and (3) industrial structure, innovation, and technology are significantly and positively correlated with the efficiency of the hub economy; policy and enterprises are significantly negatively correlated with the efficiency of the hub economy; and education does not correlate with the efficiency of the hub economy.

Evaluation of Cropland Utilization Eco-Efficiency and Influencing Factors in Primary Grain-Producing Regions of China

Under the backdrop of the “double-carbon” target, the primary grain-producing regions in China are confronted with the tasks of mitigating pollution and carbon emissions and ensuring food security. This paper explores the eco-efficiency of cropland utilization and the factors influencing the primary grain-producing regions in China, utilizing panel data from 13 provinces spanning the period from 2000 to 2019. The analysis employs three models: the super-efficiency SBM model, the Malmquist index model, and the random-effect panel Tobit model. The findings suggest the following: (1) Although the eco-efficiency of cropland utilization in China’s primary grain-producing regions did not reach the production frontier during the period of 2000–2019, it exhibited a high level with an overall upward trend. The limiting factor inhibiting the growth of total factor productivity is lower technical efficiency. (2) There is evident spatial variation in the eco-efficiency of cropland utilization across China, displaying a dynamic evolution from northeast > western > central > eastern to northeast > western > eastern > central. Total factor productivity in each province demonstrates an upward trend, with the east > northeast > west > central ranking. (3) Regarding the influencing factors, the utilization of agricultural production chemicals exerts a negative influence, while the proportion of government financial input, labor input, and irrigation index have a positive impact.

Structural characteristics and influencing factors of a spatial correlation network for tourism environmental efficiency in China

The tourism industry in China presents uneven tourism efficiency but deepening spatial associations; thus, tourism resources must be more rationally allocated. In this study, the highly efficient SBM model was used to measure the tourism environmental efficiency of 31 provinces in China. A spatial correlation network is then constructed based on the gravity model, and the structural characteristics and influencing factors of the network are analyzed. The results show that (1) the overall tourism environmental efficiency in China presents a fluctuating growth trend, with significantly higher values observed in the eastern region than in the central and western regions; moreover, the growth in efficiency in the eastern region has been relatively stable in recent years, that in the central region has increased, while that in the western region has significantly declined. (2) A spatially linked network with a stable tourism environmental efficiency structure has been formed in China. The number of network relations and density of the network fluctuate and increase, while the network efficiency continues to decrease; however, a strong small-world nature is observed. (3) An obvious network core–edge structure is observed, with Shanghai, Beijing, Zhejiang, and Jiangsu at the center showing a significant intermediary role and remote provinces such as Tibet, Xinjiang, Ningxia, and Inner Mongolia at the edge showing fewer connections. (4) The four major plates of China based on the CONCOR algorithm are sparsely connected internally and show strong inter-plate connections and spillover effects. (5) The industry support capacity difference matrix, technological development level difference matrix, transportation accessibility difference matrix, and environmental regulation level difference matrix significantly and positively affect spatial associations, while the geographical distance matrix significantly and negatively affect spatial association relationship establishment. These findings have important theoretical and practical significance for the sustainable development of tourism in China's provinces and cities.

Operational low-flow forecasting using LSTMs

This study focuses on exploring the potential of using Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily scale with lead times up to 46 days ahead. A novel LSTM-based model architecture is designed to leverage both historical observation and forecasted meteorological data to carry out multi-step discharge time series forecasting. The feature and target selection for this deep learning (DL) model involves evaluating the use of different spatial resolutions for meteorological forcing (basin-averaged or subbasin-averaged), the impact of incorporating past discharge observations, and the use of different target variables (discharge Q or time-differenced discharge dQ). Then, the model is trained using the ERA5 dataset as meteorological forcing, and employed for operational forecast with ECMWF seasonal forecast (SEAS5) data. The forecast results are compared to a benchmark process-based model, wflow_sbm. This study also explores the flexibility of the DL model by fine-tuning the pretrained model with limited SEAS5 dataset. Key findings from feature and target selection include: (1) opting for subbasin-averaged meteorological variables significantly improves model performance compared to a basin-averaged approach. (2) Utilizing dQ as the target variable greatly boosts short-term forecast accuracy compared to using Q, with a mean absolute error (MAE) of 25 m3 s−1 and mean absolute percentage error (MAPE) of 0.02 for the first lead time, ensuring reliability and accuracy at the onset of the forecast horizon. (3) While incorporating historical discharge improves the forecasting of Q, its impact on predicting dQ is less pronounced for short lead times. In the operational forecast with SEAS5, compared to the wflow_sbm model, the DL model exhibits skill in forecasting low flows as evidenced by Continuous Ranked Probability Skill Score (CRPSS) median values of all lead times above zero, and better accuracy in forecasting drought events within short lead times. The wflow_sbm model shows higher accuracy for longer lead times. In the exploration of fine-tuning approach, the fine-tuned model generates marginal short-term enhancements in forecasting low-flow events over a non-fine-tuned model. Overall, this study contributes to advancing the field of low-flow forecasting using deep learning approach.

Does regional carbon market construction promote urban carbon emission efficiency?—taking Fujian carbon emissions trading system as an example

ABSTRACT Carbon market construction is a crucial tool in meeting sustainable development goals (SDGs). Based on data from prefecture-level cities in Fujian Province, China, between 2011 and 2020, the SBM model is employed to determine the carbon emission efficiency for each city. Additionally, the event analysis method is deployed to evaluate the influence of the carbon market construction on carbon emission efficiency in Fujian Province, and project carbon emission efficiencies for different scenarios. The findings reveal that: (1) The construction of the carbon market has led to an improvement in carbon emission efficiency in Fujian Province, the duration of this effect needs to be strengthened. (2) No significant difference has been observed in carbon emission efficiency between coastal and inland cities due to the carbon market construction. It is feasible to investigate the formation of a carbon market with distinct local features, ultimately achieving SDGs.

文化产业公司经营效率评价——基于SBM模型的分析

文化产业公司经营效率评价——基于SBM模型的分析

Evaluation model of carbon emission efficiency of land intensive use based on SBM model

Evaluation model of carbon emission efficiency of land intensive use based on SBM model

Could Digital Literacy Contribute to the Improvement of Green Production Efficiency in Agriculture?

The digital development in rural areas has become an important driving force for promoting green production in agriculture. But the lack of digital skills among farmers remains a problem. The main reason why farmers cannot master digital skills well is their lack of digital literacy, which leads to market information asymmetry and the inability to truly distinguish the difference between green production and traditional production. Based on this, this article uses field research methods to collect data from 1,116 farmers in China, calculates agricultural green production efficiency using the SBM model, and empirically tests the relationship between farmers’ digital literacy and green production efficiency in agriculture. The results showed that digital literacy has a significant promoting effect on agricultural green production efficiency. Secondly, it was found that digital literacy can effectively enhance farmers’ cognition of green agricultural products and green production technology adoption. Among them, the effect of cognition of green agricultural products is reflected in farmers’ self-cognition and market cognition. The green production technology adoption is reflected in the breadth and depth of technology adoption. Third, we discuss the effect of different dimensions of digital literacy on agricultural green production efficiency. The effect of the digital literacy on the production efficiency of the five typical green technologies used by farmers is further discussed. Although there are certain differences, overall, they all have a significant promoting effect. It is necessary to comprehensively improve farmers’ digital literacy, bridge the digital divide and promote the green transformation of agricultural production.

Research on the enhancement path of green technology innovation efficiency under the group perspective.

China is at a critical moment of transforming high-speed development to high-quality development, and it is significant to improve the efficiency of green technological innovation. In this paper, under the perspective of two-stage innovation value chain, we construct the evaluation index system of green technology innovation efficiency, adopt the super efficiency SBM model to measure the green technology innovation efficiency of China's high-tech industries, and based on the results obtained, we assume the fuzzy set qualitative comparative analysis method (fs-QCA) based on the group theory to explore the complex causal mechanism and grouping paths of the interaction between enterprises, government and market that affects the green technology innovation efficiency Mechanism and group path. The study results show that (1) enterprise, government, and market are not necessary conditions to influence the efficiency of green technological innovation, and even if a particular party plays a central role, it needs the assistance of other parties. (2) The improvement of green technological innovation efficiency requires the interaction of enterprises, government, and market, and even if any party does not have the core conditions, it can still produce high green technological innovation efficiency. (3) The path of the "innovative compensation" effect is identified, which indicates that enterprises will generate a high level of green innovation efficiency under sufficient investment brought about by the enterprise scale effect and matched with a good level of economic development. (4) The market economy-led pathway suggests that when the market economy is highly developed, firms do not need environmental regulation and government support to generate efficient levels of green technological innovation.

Carbon unlocking efficiency study based on super-efficiency SBM-Malmquist

Carbon Neutrality goals and Sustainable Development Goals (SDGs), as new requirements for global development at this stage, have raised higher requirements for achieving the coordination of economic efficiency and ecological development of transportation infrastructure, especially highways. To promote the achievement of Carbon neutrality goals and SDGs, this research intends to study the effect of highways on the efficiency of carbon unlocking in each province. In this paper, we take China as an example, use the data of 18 listed highway companies and their provinces from 2010–2021 to conduct the study, measure the static carbon unlocking efficiency by using the super-efficiency SBM model with undesirable outputs, combine with the Malmquist index model for the decomposition of the efficiency and the dynamic analysis, and use the Tobit regression model to analyze the factors affecting the carbon unlocking efficiency. The results show that (1) From a static perspective, the carbon unlocking efficiency shows a steady upward trend, and the carbon unlocking efficiency has been in a state of low efficiency in general, with obvious regional differences. (2) From a dynamic perspective, carbon unlocking efficiency changes are relatively flat, and the carbon unlocking efficiency change index is in the rising stage, relying on technical efficiency can effectively improve the level of carbon unlocking efficiency. (3) The regional economic level, industrial structure, and urbanization level have a significant positive correlation with carbon unlocking efficiency, while the level of scientific and technological development and the level of opening up have a negative effect.

Spatial effects of township health centers' health resource allocation efficiency in China.

China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Spatiotemporal trends and coordination of agricultural carbon efficiency in the Yangtze River Economic Belt and Yellow River Basin, China: An analysis of influencing factors and green finance integration.

As China's second-largest source of greenhouse gas emissions, agriculture is essential to achieving the goal of "carbon peak" and "carbon neutrality." Based on the measurement of agricultural carbon emissions (ACE) and agricultural carbon intensity (ACI) in 19 regions along the Yangtze River Economic Belt (YEB) and Yellow River Basin (YRB) in China from 2001 to 2020, this paper first uses the super-efficiency SBM model to measure ACE efficiency from static and dynamic perspectives. Then, the coupling coordination degree (CCD) between ACE efficiency and green finance in each region of the two basins is explored. Finally, Grey Relation Analysis (GRA) is used to obtain the influencing factors of CCD. The following conclusions are drawn: (1) The ACE in the YEB is almost twice that of the YRB. The ACE of the two basins generally experienced a trend of first growth and then declined, but the peak time was different. The ACI of the two basins showed a trend of continuous decline, and the decline rate of the YRB was faster. (2) The ACE efficiency of the two basins showed an overall upward trend, and the growth degree of different regions was vastly different. From the factor decomposition, the technological progress (TP) of the two basins significantly impacts the total factor productivity (TFP). (3) The CCD of ACE efficiency and green finance in the two basins increased from near imbalance to barely coordination level, and the CCD of the YEB increased slightly faster. The CCD of the two basins has a spatial difference of "downstream > midstream > upstream." (4) Among the influencing factors of the CCD of the two systems, the influencing degree of the factors is as follows from large to small: quality of human capital, level of economic development, government regulation, scientific and technological innovation ability.