Three-stage DEA Research Articles

Spatial-Temporal Evolution and Driving Mechanism of Urban Land Use Efficiency Based on T-DEA Model: A Case Study of Anhui Province, China

As China’s urbanization has shifted from high-speed to high-quality development, Urban Land Use Efficiency (ULUE) has become an important scale for evaluating urban connotative development. However, existing research has paid less attention to errors caused by different urban environmental factors and random disturbances in ULUE. Therefore, the purpose of this study is to eliminate the impact of environmental factors and random disturbances on ULUE measurement results by placing different cities under the same environmental conditions. First, a three-stage DEA envelopment analysis (T-DEA) model is introduced to calculate the ULUE of 16 prefecture-level cities in Anhui Province from 2001 to 2020. On this basis, the kernel density estimation model, gravity center model, and geographic detector models are used to study the spatial and temporal evolution and driving factors. The results show that (1) the ULUE increases nonlinearly with time, with an increase of 12.74%; (2) the overall peak of ULUE is on the rise, and changes from a single peak to a multi-peak, indicating that ULUE is constantly improving and that there is multi-level differentiation between different cities; (3) during the study period, the center of gravity of the ULUE value moved 22.66 km to the northwest; the overall moving distance was small, and the moving rate was slow; and (4) the influence of the interaction of double driving factors on ULUE is significantly greater than that of a single driving factor, and the factors of urban built-up area and degree of openness, as the key driving factors affecting ULUE, also have a degree of duality. In addition, to achieve efficient urban land use and to coordinate the environmental differences faced by different cities, the government must formulate systematic policies and development strategies considering the spatial characteristics of urban land use efficiency and the complexity of the driving factors.

The Impact of Industrial Agglomeration on Supply Chain Efficiency in Manufacturing

The 20th National Congress of the Communist Party of China pointed out that China's high-quality economic development needed to improve the stability and resilience of the supply chain. And the improvement of manufacturing supply chain efficiency affected markedly on this. Firstly, this paper quantified the supply chain efficiency by using the three-stage DEA model, and established an evaluation system of digital capability from the perspective of green economy. Secondly, the bidirectional fixed effect model was used to further analyze the influence of specialized agglomeration, diversified agglomeration on supply chain efficiency and the moderating effect of digital capability. Based on the above, the conclusion was drawn specialized agglomeration and diversified agglomeration affected positively on manufacturing supply chain efficiency, and digital capability moderated them positively. Then, the result of the robustness analysis showed the model is robust. Finally, this paper put forward suggestions from the aspects of government and enterprise, advocating the development of specialized agglomeration, diversified agglomseration and digital capability.

Research on the Efficiency of Cross-Region Scientific and Technological Innovation Resources Allocation in Chengdu-Chongqing Economic Circle

Analyzing the efficiency of cross-region scientific and technological innovation resources allocation in Chengdu-Chongqing economic circle is helpful to understand the achievements in the construction of economic circle and development trend, and it is great significance to important growth pole for promoting the high-quality development of Chengdu-Chongqing economic circle. Applying the method of three-stage DEA, combined with the input-output data of scientific and technological innovation resources in 44 regions of Chengdu-Chongqing economic circle from 2014 to 2019, this paper studies the allocation efficiency and influencing factors of scientific and technological innovation resources in this region during this period. Results indicate that: Firstly, after excluding the influence of external environmental factors, the average allocation efficiency of scientific and technological innovation resources in Chengdu-Chongqing economic circle changes little from year to year and is at a low level. The allocation efficiency composition of each region is generally characterized by high pure technical efficiency and low scale efficiency. Secondly, the improvement of the degree of opening to the outside world and the level of education investment is conducive to the improvement of the allocation efficiency of scientific and technological innovation resources, while the improvement of the level of economic development and the adjustment of industrial structure have not effectively driven the improvement of the allocation efficiency of scientific and technological innovation resources to a certain extent.

Research on Logistics Efficiency Evaluation of Dalian Port Based on Three-stage DEA

Research on Logistics Efficiency Evaluation of Dalian Port Based on Three-stage DEA

A comparative study of green growth efficiency in Yangtze River Economic Belt and Yellow River Basin between 2010 and 2020

The Yangtze River Economic Belt and the Yellow River Basin are important economic regions and ecological barriers in China. Promoting the development of the Yangtze River Economic Belt and promoting ecological protection and high-quality development in the Yellow River Basin are major regional strategies implemented by the Chinese government, and the green growth of the two regions is important for the high-quality sustainable development of the whole China. To investigate the regional differences in green growth efficiency of the same type of geographical units, this paper measures the green growth efficiency and decomposition indicators of the Yangtze River Economic Belt and the Yellow River Basin from 2010 to 2020 using a three-stage DEA model and the Malmquist index method and establishes a panel Tobit model to identify the influencing factors of green growth efficiency. The results show that: ①After using the three-stage DEA model to remove the influence of external environment and stochastic factors, the mean values of green growth efficiency of Yangtze River Economic Zone and Yellow River Basin from 2010 to 2020 are 0.996 and 1.089, respectively. The change of green growth efficiency of Yangtze River Economic Zone is slightly higher than that of Yellow River Basin. ②The Malmquist indexes of the Yangtze River Economic Belt and the Yellow River Basin have generally increased, with the Technological Progress Index, which characterizes technological innovation, being the main endogenous driver of green growth efficiency in the Yellow River Basin, while the technical efficiency index, which characterizes factor mix and management level, is more significant in the Yangtze River Economic Belt. ③The Tobit model regression results show that the factors influencing green growth efficiency are also different in the two regions. Among them, the urbanization rate has a significantly positive effect on the two regions, while the effects of environmental regulation and research intensity are not significant. External openness has a suppressive effect on green growth efficiency in the Yangtze River Economic Belt, while the level of financial development and human capital negatively affect green growth efficiency in the Yellow River Basin. Therefore, green development in the new era should pay attention to the differences between different regions, and make appropriate development policies according to the local conditions of the development status of different regions.

The Technological Innovation Efficiency of China’s Renewable Energy Enterprises: An Estimation Based on a Three-Stage DEA Model

The advantages of clean, ecologically friendly, and renewable energy have drawn considerable attention from all nations in the world. The growth of the renewable energy industry has frequently been elevated to the status of national policy. By evaluating the technical innovation effectiveness of China’s renewable energy sector, the energy crisis may be alleviated, and the innovation potential of renewable energy can be boosted. At present, the research content of domestic renewable energy enterprises mainly adopts DEA and Cobb–Douglas production functions. Moreover, there is limited literature on the factors impacting efficiency, and most research results center on efficiency assessment. This study employs a three-step DEA method to determine the technological innovation efficiency for China’s A-share renewable energy firms from 2016 to 2020. To investigate the factors influencing technological innovation’s effectiveness, the panel Tobit model is then developed. In light of the empirical data, the main conclusions of this paper are as follows: First, despite a slow but steady improvement, Chinese renewable energy companies still need to increase their technological innovation efficiency. Pure technical efficiency is the main factor contributing to low innovation efficiency. Second, environmental laws such as reliance on global commerce, industrial structure, and local science and technology affect the innovation effectiveness of listed renewable energy enterprises. After excluding environmental factors, the comprehensive technical efficiency of listed renewable energy companies has decreased. Finally, the innovation and technological efficiency of renewable energy firms are positively impacted by government subsidies, top operational revenue, and enterprise scale.

Evaluation of Green Industry Innovation Efficiency Based on Three-Stage DEA Model: A Case Study of Chinese Information Technology Industry

The information technology industry as a new engine driving China’s economy has made more and more contributions to Chinese sustainable development. At present, it has overtaken real estate as the new cradle of Chinese billionaires. The information technology industry not only has its own characteristics of high economic, social benefits, and small impact on the ecological environment, but also can enable the green development of the economy and society. So it is the core industry to support the realization of the “double carbon” goal. This paper evaluated the innovation efficiency of 80 enterprises in the software and information technology service from 2017 to 2018 by constructing a three-stage DEA model. It puts forward countermeasures, which points out the direction for the development of environmental protection and green low-carbon industry. Empirical results show that environmental variables have different effects on innovation efficiency. After excluding the influence of environmental and random factors, the increase in innovation efficiency, while generally significant, is not high. Low innovation efficiency is caused by both pure technical efficiency and scale efficiency, especially pure technical efficiency. Enterprises’ adjusted scale returns are mostly increasing; the innovation investment scale is not optimal. Regional differences of enterprise innovation exist; the East and Midwest have obvious polarization both in quantity and quality. These results quantify the effect of the factors affecting enterprise innovation efficiency and put forward policies and suggestions for promoting the development of China’s information technology industry accordingly.

Assessing the Static and Dynamic Efficiency of Digital Economy in China: Three Stage DEA–Malmquist Index Based Approach

The digital economy, a new economic form, has become an essential economic development engine in various countries. Recently, less research has been conducted on the efficiency of the digital economy, with the majority of studies instead concentrating on the industrial size of the digital economy. Therefore, to quantify and analyze the efficiency of China’s digital economy from 2013 to 2020 from both a static and dynamic perspective, this research utilized a three-stage DEA model and the Malmquist index. The findings demonstrated that after excluding external environmental factors, the scale efficiency value, integrated technical efficiency value, and pure technical efficiency value all significantly increased. This confirmed that external environmental factors uniquely influence the efficiency of the digital economy. The efficiency of the digital economy varies by location, with the eastern region tending to perform the best, and the central region tending to perform the worst. The efficiency decomposition results demonstrated that the positive growth trend of the efficiency of the digital economy is primarily due to technological advancement. Overall, there is a lot of room for growth in China’s digital economy. Each province and city should combine their own capabilities to accelerate digital construction.

The evaluation of innovation efficiency of China’s high-tech manufacturing industry based on the analysis of the three-stage network DEA-Malmquist model

The paper applies the three-stage network DEA and DEA-Malmquist method to measure the innovation efficiency of the high-tech Manufacturing industry for the period 2009–2017 in China. The analysis is divided into two parts to study. In the first analysis, we analyse the innovation efficiency of the high-tech manufacturing industry by using a three-stage network DEA, to open the black box of high technology innovation process in Chia. In the second stage, we use the DEA-Malmquist index method to evaluate the efficiency of technological innovation dynamically. We first found that the average innovation efficiency in the three stages from 2009 to 2017 shows a ‘Λ’ trend, there is room for improvement, especially in the basic and applied innovation stages. Moreover, the innovation is mainly driven by the east. Through the years 2009–2017, the development of high technology is unbalanced as well as insufficient. Our analyses show that the efficiency of technological innovation has been affected by the SEC and national policies. Therefore, we should adopt strategic measures to improve the innovation efficiency.

Efficiency evaluation of urban employee's basic endowment insurance expenditure in China based on a three-stage DEA model.

With the deepening of population aging, the expenditure of basic endowment insurance in China is increasing. The urban employees' basic endowment insurance(UEBEI) system for is an important part of China's basic social endowment insurance system, which is the most important institutional guarantee for the basic needs of employees after retirement. It not only relates to the living standards of retired employees but also relates to the stability of the whole society. Especially considering the acceleration of urbanization process, the financial sustainability of the basic endowment insurance for employees is of great significance for safeguarding the pension rights of retired employees and realizing the normal operation of the whole system, and the operation efficiency of urban employees' basic endowment insurance(UEBEI) fund inevitably becomes the focus of increasing attention. Based on the panel data of 31 provinces in China from 2016 to 2020, this paper established a three-stage DEA-SFA model, and compared the differences of comprehensive technical efficiency, pure technical efficiency and scale efficiency with radar chart, aiming to explore the operating efficiency of the UEBEI in China and how environmental factors affect it. The empirical results show that at present, the overall level of the expenditure efficiency of the UEBEI fund for urban workers is not high, and all provinces have not reached the efficiency frontier level, and there is still a certain space for efficiency improvement. Fiscal autonomy and elderly dependency ratio are negatively correlated with fund expenditure efficiency, while urbanization level and marketization level are positively correlated with fund expenditure efficiency. The regional difference of fund operation efficiency is significant, from high to low, it is East China, Central China and West China. Reasonable control of environmental variables and narrowing of regional economic development and fund expenditure efficiency differences can provide some enlightenment for better realization of common prosperity.

Evaluation of Tourism Efficiency in Jiangsu Province Based on Three-stage DEA Model and Malmquist Index Model

This paper innovatively uses a three-stage DEA model in combination with the Malmquist index to assess the tourism development efficiency and situation of municipalities in Jiangsu Province from 2016-2019 in both a cross-sectional and vertical comparison perspective. The results showed that: all cities in Jiangsu Province are in the state of increasing scale payoff or efficiency frontier surface, total factor productivity is almost all greater than 1, the tourism industry as a whole is developing well, and management inefficiency is the main factor that restricts the further development of tourism. The tourism industry in southern Jiangsu [1] is in a highly de- veloped and mature stage, with high tourism efficiency, and tourism development is more closely related to technological progress; the tourism industry in central Jiangsu[2]and northern Jiangsu[3] is in a rapidly developing and rising stage, with low tourism efficiency but rapid development, and tourism development is more closely related to technological and management efficiency.

Research on the Improvement Path of Regional Green Technology Innovation Efficiency in China Based on fsQCA Method

Improvements in green technology innovation efficiency is the core factor to promote to shape new advantages in resource-saving and environmental friendliness under the new pattern of double-cycle development. It is also the main driving force needed to establish a high-quality development model of an efficient and sustainable economy. Taking 30 provinces of China as case samples, this paper establishes the appraisal system of green technology innovation efficiency. The first step is using the three-stage DEA model to measure green technology innovation efficiency. Then, according to the configuration perspective, the paper uses fuzzy set qualitative comparative analysis (fsQCA) to explore multiple paths for promoting green technology innovation efficiency. The findings are as follows: (1) A single factor of environmental support or technology supply cannot effectively stimulate the improvement of green technology innovation efficiency. Therefore, the impacting factors must be matched to jointly improve green technology innovation efficiency. (2) There are three configuration paths for high green technology innovation efficiency. Namely, they are the driven by economic environment and environmental regulation type; the driven by industrial structure and supply of finance type; and the driven by industrial structure, supply of finance, and supply of manpower type. (3) The paths to generate non-high green technology innovation efficiency can be summarized as one. The shortage of human resources and a poor economic environment are the main reasons for the inhibition of improvements in green technology innovation efficiency; additionally, the configuration of high and non-high green technology innovation efficiency is asymmetrical. On the one hand, our results are helpful for the study of the efficiency of regional green technology innovation at the provincial level. On the other hand, the results also provide practical solutions and a theoretical basis for provinces to promote regional green technology innovation efficiency under the new economic normal.

Assessing the implementation effect of Shanghai's compulsory municipal solid waste classification policy.

Waste treatment is a problem faced by cities all over the world. In recent years, China, as a developing country, regards the municipal solid waste (MSW) classification as one of the important strategies to deal with the MSW problem. The previous MSW classification policies in China were all only advocacy in nature. It was not until January 2019 that the "Regulations on the Management of MSW in Shanghai" was officially promulgated as China's first compulsory MSW classification policy, marking the beginning of an era of compulsory MSW classification in China. How effective is the implementation of Shanghai's compulsory MSW classification policy 18months after its implementation and can developing countries continuously and effectively implement compulsory MSW classification policies? These are important issues of concern to the government, academia, and the public. This paper establishes a three-stage DEA model to evaluate the implementation effect of the compulsory MSW classification policies in Shanghai during the period of February 2019 and July 2020. The study found that the average efficiency of the compulsory MSW classification policy in Shanghai reached 0.906 during the study period, indicating that the policy was executed reasonably well. However, there are only 5months in 18months that the policy was fully effective (reaching efficiency level 1), suggesting that there is still room for improvement. The main reason for not being able to achieve full effectiveness in some months is attributed to scale efficiency. At the same time, the general public budget revenue and expenditure of environmental variables have positive and negative impacts on the policy implementation effect in Shanghai. The research results can provide experience for China to comprehensively implement the compulsory MSW classification policy in the future and can also provide valuable case study information for cities in other developing countries to implement the compulsory MSW classification policy.

Operating efficiency of the innovation ecosystem of college students’ start-ups based on three-stage DEA model

Taking 20 innovation and entrepreneurship bases (college students’ start-ups) in Liaoning Province as the research object, an evaluation indicator system of the operating efficiency of the innovation ecosystem of college students’ start-ups was constructed. The innovation incubation ability and the operating efficiency of the innovation ecosystem of these bases were evaluated using the three-stage DEA model, and the factors affecting the operating efficiency were analyzed. Then, the effects of five environment variables (including the per capita GDP, the business volume of telecommunications service, the number of graduates, the local financial expenditure on science and technology, and the Internet penetration rate, as the explanatory variables) on the operating efficiency of the innovation ecosystem of college students’ start-ups were studied by using efficiency values excluding environment variables and random factors as the explained variables, and five environment variables as the explanatory variables.

How the environmental protection measurements influence transport freight efficiency from the perspective of three-stage DEA model

How the environmental protection measurements influence transport freight efficiency from the perspective of three-stage DEA model

The Efficiency of Energy Infrastructure Investment and Its Regional Economic Impact.

This study constructed an input-output and environmental indicator combination framework to evaluate the efficiency of energy infrastructure investment. Furthermore, the study used a three-stage DEA model to evaluate the efficiency of energy infrastructure investment projects in Jiangsu Province. Subsequently, the study constructed a system of indicators to measure regional economic development and assigns weights to them using the entropy value method, to obtain a comprehensive regional economic development score. Finally, this study analyzed the impact of energy investment efficiency on regional economic growth, economic stability, and industrial structure optimization. The study results show that the efficiency of energy infrastructure investment varies widely across Jiangsu and is highly correlated with the regional economic development model, the level of economic development, and the importance of the industry. The study also reveals that the improvement of energy infrastructure investment efficiency in Jiangsu fails to reflect the level of regional economic development; however, it has a crucial role in increasing social wealth and transforming the regional industrial structure and economy. Based on these results, this study further proposes countermeasures, such as planning a reasonable scale of investment, implementing differentiated regional investment, and upgrading management and technology.

Research on the efficiency evaluation of the integration of technology and finance in China's strategic emerging industries.

The combination of technology and finance has a great potential impact on regional social and economic development, and strategic emerging industries are the convergence of technological innovation and financial support, the development of which have great significance for industrial structure adjustment and industrial quality improvement. The geographical distribution of emerging strategic industries in China is uneven. We selected 18 provinces with relatively concentrated emerging industries. 206 STI board enterprises in 2020 were collected. A three-stage DEA model was used to measure the input-output efficiency of the integrated development of science and technology finance. Also, we used a regression model to examine the path of integrated development of science and technology innovation and financial support. The research finds that the development of strategic emerging industries has a large demand for financial support and obvious regional differences, and the government plays a strong guiding role in their development as well. At the end of the research, countermeasures and suggestions for the development of strategic emerging industries are given.

An Empirical Analysis of Small and Medium-sized China’s Insurance Industry's Core Competitiveness by Three-stage DEA Method

An Empirical Analysis of Small and Medium-sized China’s Insurance Industry's Core Competitiveness by Three-stage DEA Method

Impact of Digital Economy on Energy Supply Chain Efficiency: Evidence from Chinese Energy Enterprises

The global industrial chain and energy supply chain are being reconfigured at an accelerated pace, and the uncertainty of China’s energy supply security is growing significantly. Empowering energy supply chains through the digital economy (diec) has a positive effect on accelerating the transformation of China’s energy supply structure. This paper discusses the effect and mechanisms of the digital economy on energy supply chain efficiency (esce). Specifically, based on the panel data of 112 energy enterprises in China from 2011 to 2019, energy supply chain efficiency and digital economy at the enterprise level were evaluated through three-stage DEA and content analysis, respectively. A two-way fixed effects model and mediation effect mode were adopted to investigate the nexus of diec and esce. The results show that the digital economy improves energy supply chain efficiency, and the conclusion holds water even after a series of robustness tests and endogenous treatment. Meanwhile, its promotion effect is more significant among large enterprises, non-state enterprises and enterprises in high market-oriented regions. The main impact mechanisms are regional industrial agglomeration and technological innovation of enterprises. Based on the above conclusions, it is suggested to take advantage of the industrial aggregation effect and technological innovation effect of the digital economy to further improve the efficiency of the energy supply chain for the purpose of maintaining energy supply security.

Credit Rating Model of Family Farms and Ranches Based on Dynamic Dichotomous DEA

The key to the sustainable development of family farms and ranches is to solve the problem of financing difficulty, and the construction of the credit rating system of family farms and ranches is the basis of solving the financing difficulty. Therefore, the construction of the credit rating system of family farms and ranches is not only a theoretical problem but also has strong practical significance. On the basis of dichotomous method and improved DEA technology, this paper sets up a credit rating model based on dynamic cycle dichotomous DEA method with the goal of maximizing the efficiency value of credit scoring of family farm and ranch and the constraint of increasing default loss rate. The first feature of this paper is that it constructs a direct mapping from the credit index system to the credit score through the three‐stage DEA model, which avoids the disadvantages of the classical linear weighting formula that relies heavily on the measurement results of different weights, and obtains more accurate and stable credit score results of family farms and pastures. Second, the three‐stage DEA efficiency value is used as the credit score value of the family farm and pasture, and whether it is located in the optimal preset interval is taken as the basis for efficiency dichotomy. The DEA credit score of the family farm and pasture is calculated by dynamic cycle, and the samples belonging to each level of the family farm and pasture are calculated successively according to the order of the credit grade from high to low. Finally, the result of reasonable credit rating of family farm and ranch with high default loss rate and low credit score was obtained. The pyramid test results of matching credit rating and default loss rate show that this research method is effective and has a good pyramid shape.