Three-stage Data Envelopment Analysis Model Research Articles

Does water rights trading promote the efficiency of water use? Empirical evidence from pilot water rights trading in China

ABSTRACT Water rights trading policy is an important economic tool in China's water resource management. Accurately estimating the level of water resource utilization efficiency in experimental areas and impartially assessing the impact of the water rights trading market's operation on water resource utilization efficiency have policy implications for improving water resource governance tools and capabilities. Based on the panel data of 19 provinces (regions) in China from 2010 to 2019, this paper describes the development of a three-stage data envelopment analysis model to measure water use efficiency and the application of the difference-in-differences method based on propensity score matching (PSM-DID) to examine the effect of water rights trading policies on the model. The results indicate that (1) after the introduction of the water rights trading policy, the water use efficiency in the experimental areas improved substantially; (2) the PSM-DID method also confirms this finding, and water rights trading has a positive influence on water use efficiency. The research outcomes of this study can be used to objectively appraise the policy performance of the water rights trading policy and provide a theoretical foundation for its nationwide implementation.

Analysis of the Operational Efficiency of Public Hospitals in Tianjin City based on a Three-Stage Data Envelopment Analysis Model

The purpose is to analyze the operational efficiency of 132 public hospitals in Tianjin in 2021, providing reference for them to fully play the role of "leading institutions" and optimize resource allocation. We constructed an evaluation index system with multiple inputs, outputs, and environmental variables and utilized a three-stage DEA model to assess efficiency by removing the influence of random errors and environmental factors. The average comprehensive technical efficiency, pure technical efficiency, and scale efficiency of public hospitals were 0.865, 0.989, and 0.875, respectively. Among them, seven districts, including Heping, Hedong, and Hexi, had a comprehensive technical efficiency of 1. Among the remaining nine districts, except for Baodi, the rest showed an increasing trend in scale efficiency. Public hospitals in Tianjin City exhibit high operational efficiency, but significant variations exist among different districts, leading to uneven development. The main reason for this disparity is the presence of scale inefficiency. It is recommended to prioritize resource optimization and address the shortcomings that hinder efficiency improvement in a targeted manner. Emphasizing the "triple integration" of service capacity, efficiency, and quality in public hospitals is essential to maximize both social and economic benefits."

Research on the Operational Efficiency of Basic Medical Insurance for Urban Employees in China-Based on Three-Stage DEA Model.

To conduct a three-stage data envelopment analysis (DEA) of the operational efficiency of urban employee basic medical insurance (UEBMI) across 31 provinces in China from 2012 to 2021. Utilized a three-stage DEA model, this study measured and evaluated the operational efficiency of urban employee basic medical insurance within China's 31 provinces. The operational efficiency of urban employee basic medical insurance in China was notably low, displaying significant disparities across different regions and periods. Substantial room for improvement exists. Environmental factors, including urbanization level and unemployment rate, wielded a pronounced influence on the operational efficiency of China's employee medical insurance. Conversely, the fiscal revenue and expenditure ratio hindered the enhancement of employee medical insurance efficiency. This study held valuable insights for enhancing the operational efficiency of China's urban employee basic medical insurance. To effectively improve insurance efficiency, the following recommendations are put forth: firstly, relevant departments should escalate resource investments and optimize resource utilization based on rational allocation; secondly, enhance legislation and regulations, fortify fund oversight, and ensure equitable and judicious utilization of the medical insurance fund; finally, leveraging modern high-tech advancements can comprehensively elevate the operational efficiency of the employee medical insurance fund. Ultimately, with the advent of cutting-edge technology, the operational efficiency of employee medical insurance fund can be comprehensively elevated.

Evaluation and spatial convergence of carbon emission reduction efficiency in China's power industry: Based on a three-stage DEA model with game cross-efficiency

Reducing carbon emissions is essential for achieving sustainable development in China. In this study, we developed a framework for measuring carbon emission reduction considering both thermal power and clean energy generation perspectives. Subsequently, we constructed a three-stage data envelopment analysis (DEA) model with game cross-efficiency to eliminate the influence of external environmental factors, random factors, and regional competition. Thereafter, we calculated the carbon emission reduction efficiencies (CEREs) of the power industries of 30 provinces in China from 2010 to 2020. Based on this, we conducted temporal and spatial analyses of the carbon emission reduction amount (CERA), evaluated CERE, compared different DEA models, and assessed spatial convergence effects. The evaluation results of CERE across 30 provinces, cities, and autonomous regions in China showed that: (1) although China's CERA increased, substantial regional differences exist in the carbon emission reduction structure. (2) The overall average CERE ranged from 0.485 to 0.737. Since 2016, the highest CERE values have been observed in southwest China, followed by the eastern coastal and central regions, while the northwestern region experienced notable fluctuations. (3) The three-stage DEA model with game cross-efficiency, which eliminates the influence of competition and external environmental factors, can be used to accurately measure CERE. (4) CERE has a spatial convergence effect on China's power industry and is promoted by energy structure, upgrading of industrial structure and government interventions. These findings provide important insights for optimizing the carbon emissions reduction structure and improving CERE.

Financing Efficiency and Influencing Factors of High-Tech Small and Medium-Sized Enterprises of Information Technology Industry in the China Yangtze River Delta

High-tech small and medium-sized enterprises (high-tech SMEs) play a driving role in the economic transformation of the China Yangtze River Delta. However, comparatively little research and attention has been paid to evaluating the financing efficiency and investigating both external and internal influencing factors on financing efficiency based on the information of these enterprises. This study evaluates the financing efficiencies in a homogeneous environment of high-tech SMEs of information technology industry in the China Yangtze River Delta for the period 2014 to 2020 employing the panel three-stage Data Envelopment Analysis model. We have concluded that the financing technical efficiency presents a U-shaped trend from 2014 to 2020, at a moderate level due to the low pure technical efficiency; financing efficiency shows different spatial distribution patterns; most decision-making units are in the quasi-relatively inefficient range, while only a minority are at the efficiency frontier. Furthermore, the external and internal environmental factors are investigated based on Panel Stochastic Frontier Analysis and Tobit model. According to the empirical results, the optimization of the financial and technical environment contributes to financing efficiency; by contrast, a good economic and policy environment appears to restrict it; internal environmental factors such as growth ability, debt solvency, and the average age of managers have significant impacts on financing efficiency.

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

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

How can China's medical manufacturing listed firms improve their technological innovation efficiency? An analysis based on a three-stage DEA model and corporate governance configurations

China's medical manufacturing industry is in a critical stage of transition to independent innovation. The government should create a business environment that is conducive to the development of the industry, and firms need to accurately identify obstacles to their innovation governance. However, the relationship between corporate governance (CG) and innovation efficiency remains unclear. Based on the CG configuration research framework, this study analyzes, using a three-stage data envelopment analysis (DEA) model and panel data fuzzy-set qualitative comparative analysis (fsQCA), the relationship between the internal governance and technological innovation efficiency (TIE) of China's medical manufacturing listed firms (CMMLFs) with the consideration of the impact of environmental parameters on innovation performance. The results show that (1) government support and medical spending among environmental factors create innovation input redundancy, and education investment and industrial competition promote innovation efficiency; and (2) the high TIE of firms is the outcome of multiple internal mechanisms, and there are five possible paths conducive to improving TIE and two mainstream, stable configurations for high technical efficiency. Our study systematically analyzes the external and internal organizational factors as two situational factors for CG and provides a reference for decision-making to further promote the efficient innovation of medical manufacturing firms.

A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency

Traditional three-stage data envelopment analysis (DEA) models do not consider the problem of functional form and multicollinearity. This study develops a new stochastic semi-parametric frontier-based three-stage DEA model. The frontier incorporates the effects of both external environmental factors and statistical noise on efficiency. We adopt the StoNED (stochastic non-smooth envelopment of data) approach and use the quasi-likelihood estimation method to estimate the parameters of inefficiency term and stochastic noise. We conduct Monte Carlo experiments to examine the performance of the new frontier under different circumstances. Our results show that the new frontier provides a more realistic and accuracy estimator for efficiency measures. An empirical analysis is used to evaluate green economic efficiency (GEE) in China. We empirically compare different models and the results show that external environmental factors cause significant differences. We provide each provincial average GEE evaluated by the improved QLE-StoNED model, which are outperforms compared with other recently developed estimators. And a gradient difference emerges in the GEE among the eastern, central and western areas of China. The results also offer practical implications for the harmonious development of industrial production and a green economy in China.

Assessing the marine ecological welfare performance of coastal regions in China and analysing its determining factors

This study used three-stage data envelopment analysis (DEA) and Tobit regression models to comprehensively evaluate the performance levels, trends, and differences of marine ecological welfare (MEW) in China, and explore its influencing factors to derive relevant policy suggestions. The evaluation results indicate that the three-stage DEA model performed better on MEW than the traditional DEA model by effectively eliminating the influence of random errors and external environmental factors. However, it underestimated the coastal MEW performance of the country. The southern marine economic circle showed an overall improvement, whereas the overall northern marine economic circle did not improve much; the eastern marine economic circle was stable. Moreover, the external environmental factors significantly impacted the performance of MEW, and the value of average MEW performance of each province changed to some extent under the influence of the slack variables of resource consumption and environmental consumption input. Other factors that significantly influenced China’s coastal MEW performance were the level of economic developments on land and government environmental regulations, which correlated positively. Meanwhile, inadequate development momentum in marine research and technology or poor achievement transformation rate negatively influenced it. Finally, countermeasures and suggestions were proposed based on the results of empirical analysis.

Innovative Production Efficiency in Chinese High-Tech Industries During the 13th Five-Year Plan Considering Environmental Factors: Evidence from a Three-Stage DEA Model

Innovative production in high-tech industries is seen as a promoter of corporate profitability and a driver of China's economic growth. However, some scholars point out that high-tech industry is in its infancy and has insufficient innovative production efficiency, which severely restricts regional economic development. To explore this further, we studied the innovation production efficiency of China's high-tech industry during the 13th Five-Year Plan period (2016-2020). The three-stage Data Envelopment Analysis model was utilized to calculate the efficiency of the innovation production in this industry, and we initially employed the DEA-BCC model to calculate the efficiency for 31 provinces and applied similar-stochastic frontier analysis regression to eliminate the potential influence of external environmental factors. The empirical results findings reveal significant inter-regional differences in the efficiency of innovation production, with the Eastern region is the most efficient in innovation production, the Western region has greater growth potential, and the Central region requires to improve its overall efficiency by increasing technological inputs. In addition, we attempt to provide recommendations to policy makers based on our conclusions.

Evaluating the Financial Efficiency of the Healthcare System: A Three-Stage DEA Model Analysis

Relevance. Public health effectiveness is crucial for population health, especially in the face of global challenges like the COVID-19 pandemic. The study applies the Data Envelopment Analysis (DEA) model to measure the efficiency of operating costs in the health care system across various regions of Kazakhstan from 2017 to 2021. Existing methods for assessing healthcare effectiveness often overlook the system's complexity, which turns DEA into a valuable tool to identify inequalities in healthcare availability and quality. Research objective. This study aims to employ the DEA model to measure the efficiency of operating costs in the health care system across regions of Kazakhstan in 2017-2021. Data and methods. The DEA model was chosen for its ability to analyze the efficiency of operating costs. Data were collected from the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. Results. Our findings indicate the need for increased healthcare financing in specific regions, emphasizing the importance of transparent spending. The study concludes that the DEA model can regularly assess health financing, ensuring resources are directed where most needed. The novelty lies in establishing a link between financing and health outcomes. Conclusions. The study's results and methodology can be used by public health authorities in assessing operating costs' effectiveness, allocating resources judiciously, and making informed decisions to enhance the healthcare system.

Research on the Spatial-Temporal Distribution Characteristics and Influencing Factors of Carbon Emission Efficiency in China’s Metal Smelting Industry—Based on the Three-Stage DEA Method

The threat of global climate change has encouraged the international community to pay close attention to the levels of greenhouse gases, such as carbon dioxide, in the atmosphere. China has the world’s largest metal smelting industry, which is a major energy-consuming and carbon-emitting industry. Thus, this industry’s low-carbon transition is of great significance. Carbon emission efficiency (CEE) is a key indicator for the metal smelting industry to prioritize sustainable development. This paper applies a three-stage data envelopment analysis model with undesirable outputs to estimate CEE for 30 provinces from 2005 to 2020 in China, and analyzes the influencing factors using a spatial Durbin model. The results show that the CEE level generally improved in all Chinese provinces during the sample period, but the average CEE in the eastern region was 1.05 compared to 1.07 in the western and central regions, with the latter two regions progressing faster in terms of low carbon production capacity. The national average Malmquist–Luenberger (ML) index demonstrates a significant increase in technical efficiency across regions in 2010 and 2017, peaking in 2017. The study also suggests that current green credit and environmental regulations are not effective in promoting CEE improvements in the metal smelting industry, and that existing policies should be modified. Moreover, the spatial regression results indicate that the cross-regional transfer of low-carbon production technologies in China is largely complete. This study provides a more objective evaluation of the CEE levels of metal smelting across China, providing the government with a new perspective to guide the green transformation of energy-intensive industries.

Water, energy, and food nexus efficiency in China: A provincial assessment using a three-stage data envelopment analysis model

Understanding the region-specific efficiency of the water, energy, and food (WEF) nexus is critical for the effective design of governance strategies. To exclude the heterogeneous influence of the external environment and unveil the real efficiency of the WEF nexus in 30 provinces in China, a three-stage data envelopment analysis was used for efficiency measurement from 2005 to 2016. The results showed that after eliminating external environmental factors and statistical noise, the number of frontier-efficiency provinces decreases, and most provinces experience rank change, except for Tianjin. The average value of the WEF real efficiency in China rose from 0.202 in 2005 to 0.599 in 2016. The provinces were then categorized into four groups based on water and energy endowment for efficiency comparison. Between 2005 and 2016, the efficiency of each group increased, with that of the “water scarcity-energy importing” group being the highest. Furthermore, external environmental factors impede the enhancement of provincial WEF nexus efficiency, mainly because the extensive growth of external environmental factors focuses on scale expansion, rather than on quality improvement, leading to a large-scale slack in WEF inputs. The results of this study provide implications for governance strategies designed from external environmental factors and WEF resource endowment aspects.

Applying an improved three-stage DEA model to evaluate the innovation resource allocation efficiency in industrial enterprises

PurposeThe existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.Design/methodology/approachA three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.FindingsThe efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.Originality/valueA three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

Application of Three-Stage DEA Model Combined with BP Neural Network in Microfinancial Efficiency Evaluation.

The research performed here intends to explore the future development model of new rural financial institutions and determine the financial efficiency goals, thereby providing a huge stage for the development of new rural financial institutions. It applies data envelopment analysis (DEA) to assess financial efficiency to make up for the research gap. First, the relevant theories of rural finance are discussed. Then, some indicators are selected to build an evaluation system. In addition, the DEA method is used to evaluate the rural financial efficiency in Hebei Province by listing the input indexes and output indexes. After training, the backpropagation neural network (BPNN) model is designed and simulated to obtain the evaluation results. The research results show that before 2015, the comprehensive efficiency of Xingtai, Hengshui, Shijiazhuang, and Langfang showed a downward trend. After 2015, the comprehensive efficiency of all cities in Hebei Province tended to be stable, generally stable at about 0.95. It suggests that rural finance in Hebei Province has developed stably in recent years, and the overall efficiency of rural finance has been improved to a certain extent. The simulation results of BPNN demonstrate that the operation efficiency evaluation result of the poverty alleviation development model in the financial support industry is 0.6995, in the interval (0.6, 0.8). In addition, the model operation efficiency is better, indicating that the poverty alleviation development model of the financial support industry has achieved good results and promoted the development of poor rural areas. The research content can provide reference and inspiration for the continuous promotion of rural finance and the formulation and implementation of financial institution reform policies.

An investigation of the innovation efficacy of Chinese photovoltaic enterprises employing three-stage data envelopment analysis (DEA)

The authors found that only a few investigations have been performed on the success of Chinese PV companies in terms of inventiveness and the classic or the two-stage DEA model are the approaches utilized to assess the innovation efficacy of solar enterprises. Neither model takes into account the influence of external factors . The three-stage DEA model is adopted in this essay to find the actual impact of environmental variables on the innovation efficacy of Chinese photovoltaic entities. The real innovation efficacy value of Chinese photovoltaic enterprises is then calculated once the influence of environmental parameters on the efficacy of innovation has been accounted for. In the course of empirical research, it was discovered that the average innovation efficacy of Chinese solar-energy firms is 0.567. In addition, the average innovation pure technical efficacy is 0.829, and the median innovation scale efficacy value is 0.683; the median innovation scale efficacy value is 0.683. The poor innovation scale efficacy of Chinese solar firms is a major contributor to the low innovation efficacy of the industry. The poor innovation scale efficacy of Chinese solar firms is primarily responsible for their low innovation efficacy. Further study reveals that the size of a company’s total assets and its age have a beneficial influence on its innovation efficacy, with state-owned property rights, equity concentration, government subsidies, and Internet penetration all limiting the development of photovoltaic firms. This research aims to help photovoltaic enterprises to enhance their technological innovation inefficacy, and to provide the government ideas to implement differentiated industrial support for photovoltaic enterprises with different innovation efficiencies, so as to achieve long-term development of the photovoltaic industry.

Evaluation of Green Logistics Efficiency in Northwest China

This paper develops an index system for assessing green logistics efficiency in Northwest China with the goal of “double carbon” (peak carbon dioxide emissions and carbon neutrality). The study employs a three-stage data envelopment analysis model of super efficiency to assess the spatial and temporal efficiency of the green logistics industry in Northwest China from 2010 to 2019. Our findings reveal that the overall logistics efficiency in Northwest China fluctuates. Each province has a high pure technical efficiency, but significant regional differences in comprehensive technical efficiency and scale efficiency are apparent. In Northwest China, government support and science and technology levels have a greater impact on green logistics efficiency. Based on the implementation of the “double carbon” strategy, this study offers pertinent insights and suggestions for the high-quality development of the green logistics industry in Northwest China.

Regional Differences and Influencing Factors of Carbon Emission Efficiency in the Yangtze River Economic Belt

This paper attempts to measure the carbon emission efficiency of the Yangtze River Economic Belt, which has nine provinces and two cities, by analyzing the panel data of the economic belt from 2010 to 2019. Firstly, we used a three-stage DEA (data envelopment analysis) model to eliminate the influence of environmental factors and random noise and measured the carbon emission of the Yangtze River Economic Belt. Secondly, we used a spatially lagged time-fixed effects model to conduct an in-depth analysis of the factors affecting carbon emissions efficiency in the Yangtze River Economic Zone. The study shows that: (1) There is a significant positive spatial correlation related to the carbon emission efficiency of the Yangtze River Economic Zone. (2) There is a significant regional variability in carbon emission efficiency, and scale efficiency is the primary constraint on its improvement. (3) The higher the share of coal energy, the higher the level of urbanization, and the higher the population density, the less conducive to carbon efficiency. The more developed a region’s economy is, the more open it is to the outside world. The stronger its technological capabilities and ecological development, the higher the carbon emission value. (4) Carbon emission efficiency is influenced by a variety of factors, and each factor has a different influence on the carbon emission efficiency of the region and neighboring regions.

What kinds of building energy-saving retrofit projects should be preferred? Efficiency evaluation with three-stage data envelopment analysis (DEA)

Building energy-saving retrofit projects (BERPs) have been widely implemented due to pressure to reduce emissions to meet carbon neutrality targets. However, it is still not clear which kind of BERP is most efficient during implementation. This paper proposed a three-stage DEA model for evaluating the efficiency of BERPs. Our research found that ineffective scale was the main reason for the low efficiency of BERPs. Buildings could obtain better energy-saving effects by appropriately expanding the scale of the BERPs. Geographic areas with developed construction industries had larger BERPs with better energy-saving effects. From the perspective of building types, BERPs on shopping malls had the best energy saving efficiency, followed by those on multifunctional buildings and hotel buildings. From the perspective of the retrofit objects, BERPs on heating, ventilation and air conditioning (HVAC) systems had the greatest efficiency, followed by BERPs on building envelopes. This study could suggest preferred strategies for the future development of BERPs.

Data Envelopment Analysis in Three-Stage Network Structure with Slack Based Measure (SBM)

Data envelopment analysis (DEA) is a non-parametric technique for measuring and analyzing the relative efficiency of the Decision-Making Unit (DMU) having multiple homogeneous types of inputs and outputs. Traditional DEA treats DMU as black boxes and calculates their efficiencies by considering their initial inputs and final outputs. But the overall efficiency of DMU is directly depended upon the performances of intermediate processes of operations. In many situations, DMUs have a three-stage network structure. Against the aforesaid backdrop, this paper introduces a non-oriented and non-radial the slacks-based measure (SBM) of efficiency framework for the three-stage network production process. In the proposed model we have considered both series and parallel relationship between the inputs and outputs of different independent and dependent stages. Additionally, this research is aimed at measuring the Indian banking efficiency by using the three-stage network DEA model.