Super-efficiency SBM Research Articles

Research on Green Finance and Green Development Based Eco-Efficiency and Spatial Econometric Analysis

During the “14th Five-Year Plan” period, a question worth exploring in depth would be how China promotes green development through green finance to realize the goal of financially supporting the construction of ecological civilization. This paper builds a super-efficiency SBM window model to evaluate the comparable eco-efficiency of 30 provincial administrative regions in Mainland China (except Tibet) as a measurement of green development, and constructs an evaluation system for the green finance index, as a measurement of regional green finance development levels in China from 2007 to 2019. This paper also constructs spatial econometric models to study the effects of green finance on green development, and the influence of green finance on green development through supporting green technological innovation. Moreover, this paper analyzes the mechanisms of the spatial spillover effects and the heterogeneity in eastern, central, and western regions of China. The results of the study show that green finance only has a positive effect on green development in eastern regions, while in central and western regions, it fails to effectively support green development. The positive effect of green finance on green development by supporting green technological innovation is only in eastern regions, but it is not significant in the central region, while negative effect in the western region. Finally, according to the research conclusions, it is proposed to implement differentiated policies of green finance and the integration policies of green finance and green technological innovation policies in different regions of China.

Spatial-temporal differentiation and influencing mechanism of green development efficiency in three major urban agglomerations in Zhejiang Province, China.

Improving the green development efficiency of the three major urban agglomerations in Zhejiang is an important way to faster build the pilot demonstration area of beautiful China. Based on county panel data from 2000 to 2019 of 41 counties (cities) included in the three major urban agglomerations, we combined the super-efficiency SBM model with window analysis to evaluate the efficiency, and used spatial econometric analysis method to study the spatial distribution and the differences in regional difference. Furthermore, the fixed panel fixed effect model was used to explore the differential influencing mechanism. The results showed that green development efficiency of the three major urban agglomerations increased gradually with fluctuations during the study period. The distribution of efficiency values gradually shifted from a situation of 'high in the north and low in the south' to a 'relatively common and coordinated' development in the various regions. There was a significant spatial agglomeration effect in green development efficiency of the three major urban agglomerations, but it presented fluctuating phenomenon among cities which were at the similar level. The average green development efficiency of central Zhejiang urban agglomeration was the highest. The development within the three major urban agglomerations was relatively stable. The annulus Hangzhou Bay urban agglomeration came next, with stable development. The Wenzhou-Taizhou coastal urban agglomeration was the lowest, but the efficiency enhancement was the largest. The increases in population density, along with the proportion of added value of tertiary industry in GDP, per capita industrial added value, local fiscal expenditures, education expenditures and per capita actual use of foreign capital could improve the efficiency of green development. However, the increases in industrial electricity consumption and fixed asset investment in kennels would inhibit the improvement of green development efficiency. The impacts of per capita industrial added value, local fiscal expenditure, education expenditure and per capita actual use of foreign capital on urban agglomerations were heterogeneous among different cities.

Spatiotemporal Heterogeneity of Agricultural Land Eco-Efficiency: A Case Study of 128 Cities in the Yangtze River Basin

Analysis of spatiotemporal heterogeneity and evolutionary characteristics of agricultural land eco-efficiency is of great significance for achieving a rational use of natural resources and coordinated development of the agricultural economy as well as the ecological environment. In this study, we construct the “ecological space–agricultural production–carbon emission” framework, incorporate carbon emission intensity as an undesired output into the evaluation index system of agricultural land eco-efficiency, calculate the eco-efficiency of agricultural land in 128 cities in the Yangtze River basin from 2009 to 2018 by adopting the super-efficiency SBM model, and discuss the spatial and temporal changes using methodology such as hotspot analysis and kernel density estimation by ArcGIS. The results show the following. The overall trend of agricultural land eco-efficiency in the Yangtze River basin is increasing year by year and still has potential for improvement. However, there are significant discrepancies among cities, with the eco-efficiency of the downstream being much higher than that of the midstream and upstream regions, and demonstrating the pattern of “big dispersion–small agglomeration”. Some cities are still facing pressure to improve the eco-efficiency of agricultural land. Correspondingly, this paper puts forward optimization recommendations: Firstly, the downstream cities should give full play to their geographical advantages, actively introduce advanced production technologies, and reasonably allocate agricultural resources. Secondly, the upstream and midstream regions should formulate reasonable regional strategies in accordance with their natural resource endowments to improve the ecological benefits of agricultural land and narrow the regional disparities. This paper gives targeted policy recommendations at the levels of paying attention to education of farmers, providing incentives for ecological planting, strengthening agricultural infrastructure construction, reasonably controlling the use of agricultural materials, and increasing investment in agricultural pollutant emission management.

Enterprise Environmental Performance Evaluation Based on Super Efficiency SBM

Based on the existing research results, this paper constructs an enterprise environmental performance evaluation index system. Based on the Data Envelopment Analysis, this paper selects the data of 27 representative listed enterprises in Chengdu-Chongqing economic circle from 2017 to 2021 by using the super-efficiency SBM model considering the non-expected output, and conducts static environmental performance. The result shows that the static environmental performance level of enterprises in Chengdu-Chongqing economic circle is low. It is suggested that enterprises should improve their green management ability and take the initiative to assume social responsibilities.

Evaluation of regional university technology transfer performance in China based on super-efficiency SBM

Evaluation of regional university technology transfer performance in China based on super-efficiency SBM

Influence of Technological Innovation on Regional Green Development Efficiency

Under the background of Green Development, the function direction of technological innovation to green development efficiency, which includes economy, resources and environment, needs to be observed by demonstration. In this paper, the green development efficiency of 30 provinces (cities and districts) in China from 2004 to 2017 is measured and its intertemporal changes, regional differences of green development efficiency are analyzed by using the super efficiency SBM model, further through theoretical analysis and empirical study, the influence of technological innovation on regional green development efficiency and its impact mechanism are investigated. The influence mechanisms of the technological innovation on green development efficiency are clarified and empirically tested by spatial econometric models from the perspectives of the growth sources and quantitative analysis. The results show that during the observation period, the green development efficiency in China exhibits a U-shaped variation, but there are huge regional differences with the obvious polarization in Eastern and Midwestern regions, and that technological innovation has some effect in promoting the region green development efficiency, but not significant enough, which are heterogeneous according to the time periods and regions.

Modelling the climate change in regional crop production efficiency in Canada: based on the super-efficiency SBM model and DEA window analysis

This study aims to capture the spatial and time influence of climate change on crop production efficiency (CPE) using the SE-SBM model combined with DEA window analysis. The model is validated with the data from 1976 to 2016 in Saskatchewan, one of the three Canadian prairie provinces. By categorising Saskatchewan into three regions: black soil zone, dark brown soil zone, and brown soil zone, differences are analysed between CPE without considering climate change and CPE under climate change. We find that CPE under climate change is significantly larger than CPE without consideration of climate change, and the positive influence of temperature fluctuations during the growth period on CPE is more important than that of humidity fluctuations, and the beneficial impact of average humidity on CPE is significantly greater than that of average temperature.

Analysis of Agricultural Eco-Efficiency and Influencing Factors in Henan Province

In order to improve the agricultural eco-efficiency and promote the sustainable development of agriculture in Henan Province, China, based on the footprint theory, the super-efficiency SBM model is used to scientifically calculate and analyze the agricultural eco-efficiency in Henan Province. On this basis, the influencing factors of agricultural eco-efficiency in Henan Province are quantitatively analyzed by using the grey incidence analysis model. The results show that unilaterally considering one of grey water footprints and carbon footprints will overestimate or underestimate the agricultural eco-efficiency of Henan Province in different degrees in different time periods, and the agricultural eco-efficiency obtained by comprehensively considering grey water footprint and carbon footprint (GWCAEE) is more in line with the reality of agricultural development in Henan Province. In 2000-2004, GWCAEE in Henan Province was better. During 2005-2014, GWCAEE in Henan Province showed a fluctuating decline and continued to be in an inefficient state. From 2015 to 2019, GWCAEE of Henan Province gradually increased, and it became effective in 2019. In recent years, GWCAEE has developed well. Through the grey incidence analysis between 12 influencing factors including endogenous factors and exogenous factors and GWCAEE, it is found that the six leading factors of GWCAEE in Henan Province are agricultural structure, financial input for agriculture, number of agricultural employees, crop sown area, consumption of chemical pesticide, consumption of agricultural diesel oil. According to the above research conclusions, suggestions for improving agricultural eco-efficiency in Henan Province are put forward.

Environmental performance evaluation of heavy polluting enterprises

Based on this super-efficiency SBM model, this paper evaluates the environmental performance of heavy polluting enterprises and finds that the overall environmental performance value of heavy polluting enterprises is not good and needs to be improved.

Modelling the climate change in regional crop production efficiency in Canada: based on the super-efficiency SBM model and DEA window analysis

Modelling the climate change in regional crop production efficiency in Canada: based on the super-efficiency SBM model and DEA window analysis

Financial Geographical Structure and the Gap of Green Development: Evidence from Chinese Cities

In the context of common prosperity, it is of great significance to explore the influence and mechanism of financial development on the gap of green development. This study takes 250 prefecture‐level cities in China from 2003 to 2019 as the research object and empirically tests the influence of financial development on the gap of green development by using the super‐efficiency SBM model, the two‐way fixed effect model, and the mediating effect model. It is found that local financial effect can significantly narrow the gap of green development, while financial spillover effect of the central city can widen the gap of green development. The mechanism analysis shows that enhancing the advancement of human capital helps to optimize the utilization efficiency of financial resources outside the city and thus narrows the gap of green development. Local financial effect narrows the gap by the optimization of industrial structure, the improvement technological innovation, and the decrease of resource dependence, while financial spillover effect narrows the gap only by the optimization of industrial structure. The heterogeneity analysis demonstrates that local financial effect significantly narrows the gap of green development in cities that are 100–200 KM away from the central city in the central region, while financial spillover effect significantly enlarges the gap of green development in cities that are 100–300 KM away from the central city in the western region. The research in this paper facilitates the understanding of the influence of financial development on the gap of green development and puts forward relevant policy suggestions.

Research on the Nonlinear Impact of Environmental Regulation on the Efficiency of China’s Regional Green Economy: Insights from the PSTR Model

This paper uses the super-efficiency SBM model to measure the green economic efficiency considering undesired output and analyzes the spatial distribution difference of green economic efficiency; secondly, the nonlinear panel threshold model is used to empirically study the nonlinear relationship between environmental regulations and green economic efficiency, and further analyzed the threshold effect of environmental regulations on the efficiency of green economy and concluded as follows. (1) The green economy efficiency index in the eastern region is mostly more significant than 1, and the green economy efficiency in most provinces in the eastern region has improved. These provinces have higher regional production levels and less environmental pollution. The green economy efficiency of the central region is second only to the eastern region. The green economy efficiency of provinces in the western region except Chongqing is less than 1, indicating that these provinces have insufficient regional production, severe environmental pollution, or extensive resource depletion. (2) The impact of environmental regulations on the efficiency of the green economy presents an inverted “U” shape, with a threshold of 0.5128 for environmental regulations. The impact of the industrial structure on the efficiency of the green economy changes from inhibition to promotion after crossing the threshold of the intensity of environmental regulation, and the degree of opening to the outside world has a complementary effect on the efficiency of the green economy. The impact of urbanization on the efficiency of the green economy changes from promotion to suppression after surpassing the threshold of the intensity of environmental regulations.

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Environmental factors have a profound impact on the evaluation of grain production efficiency. Taking environmental factors into account can more accurately measure grain production efficiency and identify the development stage of grain production. Based on the global super-efficiency SBM model, environmental factors are included in the grain production efficiency measurement system, while the temporal and spatial evolution characteristics of grain production environmental efficiency and the sources of inefficiency are analyzed. In addition, the Spatial Durbin Model is used to investigate the influencing factors and spatial spillover effects of China’s grain production environmental efficiency. The results show that: environmental factors have a significant impact on the efficiency of grain production in China; the environmental efficiency of grain production in China fluctuates with a downward trend; the environmental efficiency of grain production in different provinces show obvious spatial differentiation and geographical agglomeration; the main influencing factors of the environmental efficiency loss of grain production in major grain production areas are carbon emissions, non-point source pollution of grain production and labor input; the level of education, the level of technological development, and labor transfer have a significant positive spillover effect on the environmental efficiency of China’s grain production, yet disaster rate has a significantly negative one. Thus, it is important to pay attention to resource conservation and pollution control, strengthen cooperation and exchanges between provinces, and take the path of coordinated development between the environment and grain production.

Pathway of Green Development of Yangtze River Economics Belt from the Perspective of Green Technological Innovation and Environmental Regulation.

The eco-efficiency of the Yangtze River Economic Belt from 2005 to 2019 has been evaluated by the super-efficiency SBM window model, the results of which are taken as the measurement standard for green development. Next, more attempts have been done to figure out the impacts of green technological innovation on the green development in urban clusters of the Yangtze River Economic Belt by a systematic GMM model, further confirming the moderation effect of dual environmental regulations on the relationship between green technological innovation and green development and the heterogeneity in different urban clusters of the Yangtze River Economic Belt. Finally, it is a fact that a cascade has been pointed out in green development of the Yangtze River Economic Zone. With an empirical analysis, it has been found that green technological innovation has a positive moderating effect on green development in the downstream regions, and the relationship between green technological innovation and green development is positively affected by the formal environmental regulations in the overall Yangtze River Economic Zone and the midstream region. Meanwhile, informal environmental regulations play a positive role in moderating the links between green technological innovation and green development in the overall Yangtze River Economic Zone, as well as the upstream, midstream and downstream regions. Based on the conclusions of the research, some policy suggestions of a multi-environmental regulation governance system and regional differentiated environmental regulation are given at last.

Does Urban Agglomeration Promote Urban Land Green Use Efficiency? Take the Yangtze River Economic Zone of China as an Example

This paper theoretically constructs the logical framework of urban agglomeration affecting urban land green use efficiency (ULGUE) and employs the super efficiency SBM model with undesired outputs to measure ULGUE based on the research object from 107 prefecture-level cities and above in the Yangtze River Economic Zone. Then, this paper uses the difference-in-differences method to test the impact of urban agglomeration on the ULGUE. The results show that: (1) urban agglomeration has a positive regulatory effect on the ULGUE, and this research conclusion is robust; (2) the promotion effect of urban agglomeration on the ULGUE has regional differences, showing the basic pattern of ordinary prefecture-level cities > regional central cities > provincial capital cities and medium cities > big cities > super large and megacities; (3) small and medium-sized cities should be encouraged to further agglomerate while moderately controlling the excessive agglomeration of super-large cities and megacities. Moreover, it is suggested to improve the level of regional agglomeration from the aspects of flexible use of market mechanisms and policy tools, innovative technology applications to achieve the effective integration of urban agglomeration, and ULGUE promotion.

Super efficiency SBM-DEA and neural network for performance evaluation

The traditional data envelopment analysis (DEA) method used for performance evaluation has inherent problems such as being easily affected by statistical noise in data. Furthermore, when new evaluation units are added, the performance of all the original units must be re-measured, which restricts the evaluation efficiency. In this study, machine learning algorithms were applied to make up for the shortcomings of the data envelopment analysis method. First, a super-efficiency SBM model was used to construct the relative effective frontier, and then machine learning algorithms were used to construct a regression model and establish the absolute effective frontier. After 15 machine learning algorithms were compared, BPNN demonstrated the best performance, and a SuperSBM-DEA-BPNN model was eventually established. The new model has the following advantages: First, compared with the traditional data envelopment analysis method, the absolute effective frontier displays better evaluation; second, compared with the data envelopment analysis and neural network fusion outlined in the previous literature, the new model can better overcome the problems associated with data envelopment analysis, thereby improving the fusion efficiency. Taking the innovation efficiency evaluation of China's regional rural commercial banks for instance, the new model is proven to be more applicable and offers more effective management tools to improve efficiency. On the whole, the new model not only provides a stable performance evaluation tool but also facilitates comparison, which has good application significance for organizations.

Coupling mechanism and development prospect of innovative ecosystem of clean energy in smart agriculture based on blockchain

With the economic development of various countries in the world, the traditional energy supply has been unable to meet the demand of current economic development, and the environmental problems caused by the use of traditional energy are also a major issue that needs to be urgently solved in all countries in the world, so the development and use of clean energy is an important measure to alleviate these two problems. This paper mainly studies the coupling mechanism and development prospect of the innovation ecosystem of clean energy under the smart agriculture based on blockchain. This paper first analyzes the coupling mechanism between the blockchain smart agriculture and the clean energy innovation ecosystem; secondly, it establishes the evaluation index system of the clean energy innovation ecosystem, and measures the development efficiency of the clean innovation ecosystem by using the SBM model, super efficiency SBM model and the global principal component method; thirdly, it uses the development efficiency of the clean energy innovation ecosystem through coupling and coordination The model calculates the coupling coordination of clean energy innovation ecosystem in different regions, and analyzes the spatial-temporal differences, regional differences and spatial agglomeration of the coupling coordination degree. The calculation results show that the average coupling coordination degree of green innovation development in the East, the middle, the West and the whole country is consistent with the development trend of provinces, autonomous regions and cities, showing an upward trend. The results show that the eastern region increases from 0.62 to 0.70; the average coupling coordination degree of the central region is lower than the national average coupling coordination degree, and the distance is gradually increasing; the western region has been in a serious maladjustment level, the global Moran index I value of the coupling coordination degree of clean energy innovation ecosystem is greater than 0, and the Z statistics are greater than the critical value of normal distribution function at the level of 5 %, P values are all less than 0.05 Local Moran index I and Z statistics have been growing steadily, and the significance of spatial positive correlation is getting higher and higher. The research results of this paper can provide a useful reference for the development of clean energy innovation ecosystem under the smart agriculture based on blockchain.

Assessment and Spatial-Temporal Evolution Analysis of Urban Land Use Efficiency under Green Development Orientation: Case of the Yangtze River Delta Urban Agglomerations

Rapid urbanization has provided a strong impetus for the economic growth of China, but it has also caused many problems such as inefficient urban land use and environmental pollution. With the popularization of the concept of green and sustainable development, the Environmental-Social-Governance (ESG) assessment concept is widely accepted. The government and residents are paying more and more attention to environmental issues in urban development, and environmental protection has formed an important part of urban development. In this context, this study takes 26 cities in the Yangtze River Delta as examples to build an evaluation system for urban land-use efficiency under green development orientation. The evaluation system takes into account the inputs of land, capital, labor, and energy factors in the process of urban development. Based on emphasizing economic output, the social benefits and undesired outputs brought about by urban development are taken into account. This paper measures urban land use efficiency by the super-efficiency SBM model, and on this basis, analyses the spatial-temporal evolution characteristics of urban land-use efficiency. Further, this paper measures urban land use efficiency without considering undesired outputs and compares the two evaluation methods. Again, the comparison illustrates the rationality of urban land use efficiency evaluation system under green development orientation.

Evaluation of the Sustainable Coupling Coordination of the Logistics Industry and the Manufacturing Industry in the Yangtze River Economic Belt

In order to promote the sustainable and coordinated development of the logistics industry and the manufacturing industry in the Yangtze River Economic Belt of China and provide the policy makers with decision-making references, this paper explored the spatio-temporal evolution of the coupling coordination development level of the two industries. A three-stage super-efficiency SBM model, which eliminated the influence of environmental factors and random errors, was constructed to make it possible to conduct an in-depth comparative analysis on the effective decision-making units (DMUs), making the calculation results more accurate. This was the main contribution of this paper. Based on the new model considering undesirable output, this paper analyzed the panel data of 11 provinces and cities in the Yangtze River Economic Belt from 2007 to 2017 and investigated the coordination development level from the dimensions of time and space considering the energy input and carbon emissions of the two industries. Our main research findings were as follows. First, due to the relative lagging of the logistics industry in promoting the development of the manufacturing industry, the overall level of the coordination between the two industries was at a stage of limited coordination. Second, the regional differences were significant with a spatial evolution pattern of “high in the east and low in the west”. Third, environmental factors affected the input efficiency of the logistics industry and the manufacturing industry, especially the latter. Overall, this paper made theoretical and practical contributions to promoting the joint development of the two industries, improving the logistics industry and upgrading the manufacturing industry.

Analysis of carbon emission performance and regional differences in China's eight economic regions: Based on the super-efficiency SBM model and the Theil index.

China's carbon emission performance has significant regional heterogeneity. Identified the sources of carbon emission performance differences and the influence of various driving factors in China's eight economic regions accurately is the premise for realizing China's carbon emission reduction goals. Based on the provincial panel data from 2005 to 2017, the super-efficiency SBM model and Malmquist model are constructed in this paper to measure regional carbon emission performance's static and dynamic changes. After that, the Theil index is used to distinguish the impact of inter-regional and intra-regional differences on different regions' carbon emissions performance. Finally, by introducing the Tobit model, the effect of various driving factors on carbon emission performance differences is analyzed quantitatively. The results show that: (1) There are significant differences in different regions' carbon emission performance, but the overall carbon emission performance presents an upward fluctuation trend. Malmquist index decomposition results show substantial differences in technology progress index and technology efficiency index in different regions, leading to significant carbon emission performance differences. (2) Overall, inter-regional differences contribute the most to the overall carbon emission performance, up to more than 80%. Among them, the inter-regional and intra-regional differences in ERMRYR contributed significantly. (3) Through Tobit regression analysis, it is found that residents' living standards, urbanization level, ecological development degree, and industrial structure positively affect carbon emission performance. On the contrary, energy intensity presents an apparent negative correlation on carbon emission performance. Therefore, to improve the carbon emission performance, we should put forward targeted suggestions according to the characteristics of different regional development stages, regional carbon emission differences, and influencing driving factors.