DEA-Malmquist Index Research Articles

Financial Support Efficiency of Rural Revitalization: Based on Three-Stage DEA Model and Malmquist Index Model

Financial resources play a crucial role in rural revitalization. Understanding the efficiency of financial support is essential for the scientific and rational allocation of these resources. Therefore, we conducted an assessment over the period 2011–2020 utilizing the three-stage DEA model and the Malmquist index model to measure the efficiency of financial support for rural revitalization across 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan, and Tibet) from both static and dynamic perspectives. The results indicate the following: (1) Despite an overall downward trend, efficiency increased during specific intervals, namely 2012–2013, 2015–2016, and 2018–2019. (2) Regionally, the decline in the efficiency of financial support for rural revitalization is particularly notable in the northeast region. The eastern and central regions also experienced this trend to a lesser extent, whereas the western region experienced a more moderate decrease. However, a detailed analysis revealed that 10 provinces experienced efficiency gains. (3) Stochastic Frontier Analysis (SFA) regression results suggest that environmental variables have a measurable impact on the efficiency of financial support for rural revitalization.

Evaluation of Capital Utilization Efficiency in Agricultural Listed Firms Based on a Three-Stage Data Envelopment Analysis

Rural revitalization is intricately tied to national prosperity, with listed companies in agriculture, forestry, animal husbandry, and fisheries playing pivotal roles in driving this transformative agenda. However, these sectors face critical challenges, notably resource constraints and environmental pressures, which underscore the necessity of evaluating capital utilization efficiency. This study employs a combined three-stage DEA model and Malmquist index to analyze capital utilization efficiency across 85 listed companies within China’s agriculture, forestry, animal husbandry, and fishery industries over the period 2018–2022. The primary objectives are to quantify capital utilization efficiency, identify pathways for industry optimization, advance ecological agricultural sustainability, and offer valuable insights to investors and policymakers. Key findings include: (1) When grouped by sub-sector and adjusted to account for external environmental factors and random disturbances, the initial comprehensive capital utilization efficiency in agricultural companies was found to be significantly overestimated. Subsequent third-stage adjustments revealed decreases in average comprehensive efficiency, technical efficiency, and scale efficiency by 29.79%, 3.03%, and 27.37%, respectively, largely driven by a marked decline in scale efficiency, ultimately diminishing overall efficiency. (2) The capital utilization efficiency in agricultural trading firms remains suboptimal, with scale efficiency posing a critical limitation. High dependency on government subsidies and excess specialized personnel further constrain efficiency improvements. (3) Dynamic analysis using the DEA-Malmquist model indicates low total factor productivity in the agricultural sub-sector, primarily due to inefficiencies in capital management and suboptimal scale allocation. These findings underscore the need for targeted strategies to enhance resource allocation and management, bolster talent development and financial management frameworks, and drive technological research and development, innovation, and efficient capital allocation across the agriculture, forestry, animal husbandry, and fishery sectors.

Analysis of China’s carbon financial Development’s impact on reducing emissions using the BCC and Malmquist models

This study evaluates the impact of China’s carbon finance development on carbon emission reduction and its role in promoting low-carbon economic growth. We construct an evaluation index system and employ Systematic Cluster Analysis (SCA) and Data Envelopment Analysis (DEA) methods to analyze the carbon finance markets in five representative Chinese provinces (autonomous regions). Utilizing the DEA-BCC model and DEA-Malmquist index, we conducted an in-depth analysis of the carbon emission reduction effects between 2022 and 2023. Our findings reveal that while China’s carbon finance development significantly contributes to carbon emission reduction, there are considerable regional disparities in effectiveness. Beijing and Guangdong achieved DEA efficiency, whereas Jilin, Ningxia, and Xinjiang experienced efficiency losses, suggesting a need for structural optimization. The study provides insights into challenges and potential improvements in China’s carbon finance sector and offers policy recommendations for enhancing market mechanisms, technological innovation, and social engagement to support China’s carbon peak and neutrality goals.

Evaluating the efficiency of online course resource allocation in universities of China

Curriculum is the core element of talent cultivation in universities, and its quality directly determines that of the talent cultivation. However, the efficiency of online course resource allocation in universities of China is unacceptable, and the corresponding driving mechanisms are unclear. Based on the data of iCourses platform for a top university (H University) of China, this paper develops the DEA-Malmquist index and Tobit regression model to evaluate the efficiency of university online course resource allocation and explore its main influencing factors. The empirical results indicate that, first of all, humanities and social sciences outperform natural sciences in the resource allocation efficiency of online courses. Compared with non-national premium online courses, the resource investment redundancy of national premium online courses appears relatively lower. Secondly, from the perspective of temporal changes, the total factor productivity of online course resource allocation generally has a downward trend. The innovation effect of technological progress is more significant, while the catch-up effect of technical efficiency is clearly insufficient. Scale efficiency is the main factor hindering its improvement. Finally, in terms of the factors affecting the efficiency of online course resource allocation, teachers’ research guidance capability, teaching research skills, and the number of courses offered have significantly positive impact.

Measuring The Efficiency of Green Development Enabled by Digital Economy in China's Provinces and Regions - Based on DEA Model and Malmquist Index

Measuring The Efficiency of Green Development Enabled by Digital Economy in China's Provinces and Regions - Based on DEA Model and Malmquist Index

Assessment of Climate Adaptability in the Late-Maturing Citrus Industry in Sichuan Province

Sichuan Province is the largest inland area for late-maturing citrus fruit production in China, and its climate conditions are a primary consideration for the cultivation of late-maturing citrus fruits. Based on meteorological data from 2010 to 2020 for the 18 prefecture-level cities and autonomous prefectures in Sichuan Province that cultivate late-maturing citrus fruits, along with the traditional method of dividing the advantages of citrus and the calculation of comparative advantage using factor endowment coefficients, we identified the annual average temperature, annual accumulated temperature ≥ 10 °C, average temperatures in July and January, annual precipitation, and annual sunshine hours as input indicators. We selected the resource endowment coefficient as the output indicator and used the DEA–Malmquist index model to evaluate the climate adaptability of Sichuan’s late-maturing citrus fruit industry. The analysis results indicate that the overall climate conditions in Sichuan are suitable for the growth of late-maturing citrus fruits. However, extensive cultivation of similar varieties has led to a decline in resource endowment across different regions. The use of arable land for cultivating late-maturing citrus fruits has also reduced climate adaptability. Policies that contradict climate adaptability do not support sustainable development within Sichuan’s late-maturing citrus fruit industry.

Eco-Efficiency Evolution of Water-Intensive Polluters in the Yangtze River Economic Belt: A Spatial-Temporal Analysis Using the DEA-Malmquist Index

Occupying a preeminent position as the globe’s most extensive economic corridor, the Yangtze River Economic Belt (YREB) serves as a crucial aquifer for 800 million individuals and harbors almost 50% of the country's paramount water-intensive contaminant producers. The ecological efficiency of these producers within the belt has increasingly come under scholarly scrutiny. Leveraging data from 2004-2012 encompassing 725 such manufacturers and employing the DEA-Malmquist index methodology, this research delineates the eco-efficiency trajectories of these water-centric pollutant producers, shedding light on their spatial-temporal dynamics across the expansive Yangtze Economic Belt. The results are as follows: (1) there’s a discernible enhancement in the eco-efficiency (ML) of these manufacturers situated within the YREB, predominantly propelled by technological change (TC) and shifts in technical efficiency change (TEC); (2) From a spatial perspective, notable disparities emerge in both technological change (TC) and technical efficiency change (TEC) among the belt’s upper, middle, and lower tiers. Intriguingly, the hierarchy for TC and TEC descends as follows: Lower > Upper > Middle; (3) Examining the spatial evolution nuances, 2004 witnessed eco-efficiency distributions ranking from Upper > Middle > Lower. Fast forward to 2012, a marked reconfiguration appears with a distribution pattern of Middle > Lower > Upper.

Evaluation and influencing factors of green transformation in three major urban agglomerations in China

Since the 21st century, the world has increasingly focused on the issue of sustainable development, and the green transformation issues have become a new hot topic worldwide. Green and low-carbon transformation has become an international consensus. Urban agglomerations are important connections between urban development and regional coordination, as well as important spatial carriers for economic activities. They are not only the main source of carbon emissions, but also the main battlefield for energy conservation and emission reduction. As an important field for carbon reduction, the green transformation of cities is crucial for achieving the “dual carbon” goals. This article focuses on 48 cities in the three most mature and influential urban agglomerations in China from 2011 to 2019, namely, the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Pearl River Delta urban agglomeration. The three-stage DEA model and Malmquist index model are used to measure the green transformation efficiency of the three urban agglomerations from both dynamic and static perspectives, and a Tobit regression model is constructed to explore the influencing factors of green transformation efficiency in urban agglomerations. Research has found that: 1) From a static perspective, the overall efficiency of green transformation in the three major urban agglomerations is at a high level, but from a temporal perspective, it shows a downward trend. The Pearl River Delta urban agglomeration is known for its green development, with the highest average efficiency of green transformation, followed by the Yangtze River Delta urban agglomeration. The Beijing-Tianjin-Hebei urban agglomeration has the lowest level of green transformation; 2) From a dynamic perspective, technological progress is the main driving factor for improving the efficiency of green transformation in the three major urban agglomerations. Therefore, the government should pay special attention to the progressiveness of technology when formulating relevant policies to promote urban green transformation; 3) From the perspective of spatiotemporal differences, there are significant differences in the spatiotemporal characteristics of green transformation among the three major urban agglomerations, and there are significant differences in green transformation strategies among different urban agglomerations. Eliminating environmental factors and random interference is necessary for accurately measuring the efficiency of green transformation in urban agglomerations; 4) From the perspective of influencing factors, factors such as industrial structure upgrading, green innovation level, and environmental regulation intensity jointly affect the efficiency of green transformation in urban agglomerations. Based on this, we should pay attention to the differences between urban agglomerations and implement policies tailored to local conditions; Strengthen the network system of urban agglomerations and avoid conflicts between cities; Encourage green technology innovation, accelerate industrial structure upgrading, and so on. This article focuses on the issue of green transformation in urban agglomerations, and conducts research from three perspectives: efficiency measurement, influencing factors, and implementation mechanisms. A relatively systematic theoretical framework for green transformation in urban agglomerations is formed, and an evaluation index system for green transformation efficiency in urban agglomerations is constructed and optimized. The composition mechanism of the five dimensional evaluation system for transformation efficiency is analyzed, and the bottleneck and breakthrough direction of the three major urban agglomerations in China in the development process are grasped. It has a good demonstration effect on the green transformation of other urban agglomerations.

Decomposition and Driving Factors of Total Factor Productivity of Food Crops in the Yellow River Basin, China

The urgency of enhancing agricultural productivity within the Yellow River Basin cannot be overstated, given its critical role in ensuring food security amidst the challenges posed by climate change, natural disasters, and the increasing demand for food crops. Utilizing panel data from nine provinces within the Yellow River Basin for the period 2001 to 2020, this study examines the temporal characteristics and spatial distribution of Total Factor Productivity (TFP) for key grain crops—namely wheat, corn, and soybean—through the application of the Malmquist index which can be decomposed through the DEA-Malmquist index methodology. The empirical results demonstrate that TFP growth rates for these crops have exhibited significant phase variations, with soybean recording the highest TFP growth rate in the basin. Additionally, this study underscores marked regional disparities in soybean productivity. TFP decomposition reveals that the primary drivers of TFP improvement across these crops are attributed to technical progress, with gains in overall technical efficiency largely due to scale efficiency enhancements, whereas pure technical efficiency has shown limited progress. Regional analysis indicates that Inner Mongolia leads in TFP growth for all crops, while Ningxia, Sichuan, and Shaanxi lag behind in wheat, corn, and soybean. Additionally, our analysis delineates natural disasters as a significant barrier to Total Factor Productivity (TFP), notably obstructing technological advancements in wheat cultivation. The investigation further reveals a positive relationship between regional per capita income and the growth of wheat TFP, in contrast to a negative relationship with the TFP growth of corn and soybeans. Moreover, investing in agriculture, forestry, water management, and road infrastructure supports the growth of wheat TFP, while urbanization levels pose constraints. Conclusively, an uptick in annual rural electricity usage, along with improved per capita postal and telecommunication services, exerts a favorable influence on TFP for corn and soybeans.

The Synergistic Relationship between Low-Carbon Development of Road Freight Transport and Its Economic Efficiency—A Case Study of Wuhan, China

Road freight transport, an essential component of the logistics sector, faces challenges: high cost, low efficiency, and environmental impact. The need has become urgent to achieve a synergistic balance between low-carbon and high-efficiency development. This study used a three-stage DEA–Malmquist index model to analyze the road freight efficiency of Wuhan and 16 other cities in China from 2015 to 2020, and we compared Wuhan’s performance with its peers. In addition, grey correlation analysis was used to evaluate the low-carbon development of urban road freight transport in Wuhan. Through the calculation of the degree of synergy between low-carbon development and freight transport efficiency, this study provides insights into the synergistic development of low-carbon and efficient road freight transport in Wuhan. The key findings show that the total factor productivity of road freight transport in Wuhan was generally on a downward trend from 2015 to 2020, and was lower than that of the average of the 17 selected Chinese cities. The carbon emissions of road freight transport in Wuhan inversely related to its scale efficiency. This study also points out that the synergy between low-carbon development and the economic efficiency of road freight transport in Wuhan is not high and needs to be further integrated and optimized.

Efficiency Characteristics and Evolution Patterns of Urban Carbon Metabolism of Production-Living-Ecological Space in Beijing-Tianjin-Hebei Region

This study explored the carbon metabolism efficiency of a production-living-ecological space system, which is of great significance for regional factor integration and spatial optimization. In this study, the material flow analysis method was introduced to establish a framework for evaluating the carbon metabolism efficiency of the production-living-ecological space system, and the super-efficiency DEA model and Malmquist index were used to empirically analyze the spatio-temporal distribution, dynamic change, and evolution patterns of the carbon metabolism efficiency of production-living-ecological space in the Beijing-Tianjin-Hebei Region, China, from 2000 to 2020 on the basis of the urban metabolic perspective. The results showed that:① the carbon metabolism efficiency of the production-living-ecological space showed a fluctuating growth trend, indicating the significant spatial differentiation of carbon metabolism efficiency in each city. There was a low overall carbon metabolism efficiency level, with a distribution pattern of being high in the middle and low in the north and south. ② The Malmquist index showed that the Total Factor Productivity (TFP) of carbon metabolism efficiency was greater than 1, and both the Technical Change (TC) and Pure Efficiency Change (PEC) were less than 1, in which the TFP showed an increasing trend, whereas there was no significant contribution of technological progress or pure technical efficiency to carbon metabolism efficiency. The total factor productivity of more than 50% of the cities showed an improving trend, only 38.46% of which made technological progress in improving carbon metabolism efficiency, and more than half of the urban pure technical efficiency showed a decreasing trend, in which the technical efficiency change and scale efficiency change were greater than 1 in most cities. ③ There were different types of carbon efficiency characteristics in each city, and according to the movement rules of the corresponding points in the quartile map, the evolution patterns of tourism industry efficiency were classified into stable, reciprocating, progressive, and abrupt. Therefore, local governments should adopt differentiated strategies to reasonably allocate spatial resources of production-living-ecological space and improve the technical level and scale efficiency, so as to improve the efficiency of urban carbon metabolism.

Evaluating the impact of the innovation efficiency of high-tech industry on carbon emissions: a case study of the manufacturing industry in China.

Amid China's economic shift to high-quality development, addressing environmental challenges like greenhouse gas emissions and manufacturing pollution, there is a crucial demand for sustainable and eco-friendly development strategies. This study aims to investigate the impact of innovation efficiency in the high-tech industry on carbon emissions. It seeks to explore regional differences, mechanisms, and the influence of energy consumption structures in achieving sustainable development goals. Utilizing data from 30 provinces spanning 2009 to 2020, the study employs the DEA-Malmquist index model, spatial and temporal classification evaluation, and a panel measurement model to assess the efficiency of innovation and development in high-tech industries and their relationship with carbon emissions. The results indicate several key findings: (1) The overall operational efficiency of high-tech industry innovation and development in China is steadily increasing. However, there are distinct characteristics observed among provinces and cities, reflecting diverse input and output types. (2) High-tech industry innovation efficiency significantly contributes to carbon emission reduction, and there is regional heterogeneity in this impact. The central and western regions exhibit greater effects compared to other provinces and cities. (3) The optimization of the energy structure is identified as a mechanism through which high-tech industry innovation efficiency reduces carbon emissions. Moreover, different intervals of high-tech industry innovation efficiency yield varying effects on carbon emissions. This research underscores the importance of fostering high-tech industry innovation efficiency as a means to reduce carbon emissions. It also identifies key areas for future policy development and resource allocation, emphasizing the support needed for low-carbon technology research and development.

Evaluation of the environmental efficiency of the agricultural sector in comparison with other economic sectors of Iran by DEA method and Malmquist index

Evaluation of the environmental efficiency of the agricultural sector in comparison with other economic sectors of Iran by DEA method and Malmquist index

Study on the transformation model of old neighborhoods oriented towards sustainable community economic development

Abstract Urban renewal now dominates the strategic stage of urbanization, shifting from the previous focus on “whether there is” to the pursuit of “good.” In the context of urban renewal, the transformation of old neighborhoods should be carried out reasonably. In this regard, this study focuses on exploring design ideas for the transformation of old neighborhoods in the context of sustainable community economic development, with the aim of proposing a set of comprehensive transformation programs. Then, based on the three perspectives of residents, community, and city, the evaluation indexes for the transformation of old neighborhoods are selected from two perspectives of input-output, and the transformation efficiency evaluation model is constructed by combining the model of DEA-BCC and the model of DEA-Malmquist index. Finally, taking 19 old neighborhoods in QD city as samples, we analyze the citizens’ satisfaction with the transformation effect of the neighborhoods, analyze the efficiency of the transformation investment in the old neighborhoods from the two perspectives of comprehensive efficiency value and scale efficiency value, and analyze the problems and the direction of improvement of the transformation of the old neighborhoods from the redundancy of the slack variables. The results show that the average investment efficiency of the old neighborhood transformation is 0.9185, and the pure technical efficiency and scale efficiency are 0.9239 and 0.9651, respectively.

Measurement and analysis of the total factor productivity of the three major industries in Tianjin

Based on relevant data on the primary, secondary, and tertiary industries in Tianjin from 2009‒2019, combined with the decomposition of the DEA-Malmquist index, the total factor productivity (TFP) of the three major industries in Tianjin in 2009‒2019 and its decomposition were calculated using DEAP2.1 software. Also, the TFP was comprehensively analyzed. The results showed that the TFP of Tianjin’s three major industries showed a downward trend in 2009‒2019, and the most important reason for this phenomenon was the decline in technological progress. When Tianjin develops its three major industries, it should focus on strengthening technological innovation and establishing strict and standardized management systems, rules, and regulations to promote a significant increase in TFP and the steady and high-quality development of its economy.

Can digital economy compensate the effect of aging on total factor productivity?

In China, the number of senior citizens has grown, along with the burden of old age, and aging has hampered economic growth. The advent of the digital age has led to the emergence of the digital economy as a new engine for economic growth. This paper uses DEA-Malmquist index model to measure the total factor productivity growth rate of 31 provinces in China from 2011 to 2021, and uses the moderating effects model to empirically investigate the relationship between the digital economy, aging and total factor productivity, and to verify whether the development of the digital economy can mitigate the negative impact of aging on total factor productivity. The results show that aging inhibits total factor productivity growth, and the digital economy can promote total factor productivity growth. Digital economy can alleviate the negative impact of aging on total factor productivity growth, and has a moderating effect. Digital economy plays a moderating role by improving the level of human capital and facilitating technological progress. The regional heterogeneity analysis shows that the moderating effect of the digital economy exists in the eastern and western regions and the southern region, but not in the central region and the northern region. Furthermore, the digital economy has a moderating effect on both the high and low aging groups. The research in this paper not only helps to evaluate the productivity effects of the digital economy, but also has important implications for finding ways to mitigate the negative effects of aging.

Harmonization of higher education and student management by integrating information technology

Abstract In recent years, higher education management has been improving, but the coordinated development of the overall educational management and student management system of colleges and universities still needs to be further improved and enhanced. This paper uses data envelopment analysis and data fusion technology to establish DEA fusion algorithm and DEA-Malmquist index method to evaluate and analyze students’ learning efficiency in colleges and universities. The results indicated that the average degree of perfection of online teaching management in higher education is low, 86.2%. Students’ overall satisfaction with this is also expected at 56.2%. There is a need to strengthen management and improve classroom interactivity and innovation, to enhance the quality and attractiveness of courses, and to avoid excessive fragmentation. In addition to the existing problems, the results achieved include more accessible access to learning resources, with a percentage of 88.6%. The algorithm of this paper is used to study the efficiency of higher education and student learning, and to provide a more comprehensive and objective approach to the development of the quality of higher education and student management for the later development of higher education.

The impact of technological advancements on carbon emissions in agriculture and rural areas

Rural agriculture is an essential aspect of carbon emission reduction and sequestration and also has the potential to achieve “carbon peak” and “carbon neutrality.” A DEA-Malmquist index model is used to estimate the technical progress of agriculture using panel data from 30 provinces from 2012 to 2021. The evolution trend and spatial correlation of agricultural and rural carbon emissions are measured based on the spatial and temporal dimensions. The dynamic panel spatial lag model was also used to empirically test the mechanism of agricultural technological progress affecting agricultural rural carbon emissions. The results show that: ① There are apparent evolutionary characteristics and spatial agglomeration of agricultural and rural carbon emissions, and carbon pollution between provinces shows a weakening trend ② Agricultural technological progress can effectively drive carbon emission reduction in agriculture and rural areas, and the improvement of the farmers’ income structure can effectively regulate the carbon emission reduction efficacy of the total factor productivity, technological efficiency, and technological upgrading ③ The negative spatial spillover effect of agricultural technological progress on carbon emissions is significant; when the introduction of the farmers’ income structure as an interaction term, the negative spatial spillover effect of agricultural technological progress is significant. The negative spatial spillover effect of agricultural technological progress on carbon emissions is significant; introducing farmers’ income structure as an interaction term weakens the negative spatial spillover effect.

Assessing Operational Efficiency and Technological Progress: An In-depth Study of China’s E-payment Concept Listed Companies Using DEA-Malmquist Index

Electronic payment (e-payment) enterprises are third-party e-commerce platforms that provide services for buyers and sellers through network service platforms. This study evaluates the operational efficiency of 42 e-payment concept listed companies in Shanghai and Shenzhen stock markets from 2014 to 2019, using the data envelopment analysis (DEA)-Malmquist index model. The research results show that the overall operational efficiency of e-payment concept listed companies is in a good state, but some companies are constrained by constant or decreasing returns to scale, and should improve their management ability, streamline their organization, integrate their internal resources, accelerate their structural adjustment, reduce their invalid input, and increase their output level. Moreover, e-payment related enterprises should increase their research and development investment, pay attention to technological innovation, and enhance their core competitiveness. Facing the ever-changing market and more and more competitors, enterprises should use time and opportunity as competitive resources, seize the initiative, and avoid being eliminated by the market. This study provides a comprehensive and objective evaluation of the operational efficiency of e-payment concept listed companies in China, and offers some suggestions for improving their performance and competitiveness in the e-payment industry.

Research on the Operational Efficiency of Chinese Medical Institutions Based on the Three-Stage DEA——Malmquist Index Model

To further investigate the impact of the COVID-19 pandemic on the operational efficiency of hospitals in China, this article uses the DEA-Malmquist model to measure and analyze the operational efficiency of hospitals in 31 provinces across the country from 2015 to 2021. The research results indicate that environmental factors have reduced the operational efficiency of hospitals to some extent. In terms of the regional characteristics of efficiency, the mean total factor productivity in the southern regions is higher than that in the northern regions, but the average annual growth rates of comprehensive technical efficiency, pure technical efficiency, and scale efficiency in the northern regions are higher than those in the southern regions. In addition, the COVID-19 pandemic has had a significant impact on the operational efficiency of hospitals. The total factor productivity of hospitals in 2020 decreased by 19.6% compared to the previous year, and the comprehensive technical efficiency of hospitals in the northern regions decreased by 2.3% during the same period, while the southern regions showed an increase of 0.6%. The northern regions are more vulnerable to the impact of the pandemic.