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This study aims to analyze the total factor productivity (TFP) performance of Chinese counties and cities over the period from 2007 to 2010. Chinese regional and urban–rural TFP performance are investigated by using county-level data, and the impact of the urbanization policy on TFP is discussed.

The data envelopment analysis (DEA)-Malmquist technique and Kumbhakar–Sun’s semi-parametric model are used for TFP change measurement and comparison. The county-level TFP performances are summarized and studied by statistical methods. Their spatial distribution is exhibited in a geographical thematic map.

The county-level analysis proves that China underwent a large-scale TFP decline over the period from 2007 to 2010. Statistically speaking, cities’ TFP growth is more positive than counties’; however, different provinces also have their regional characteristics. In addition, the Chinese Hukou (household registration) institution divides Chinese urbanization into halves, which have the opposite correlation on TFP growth.

Because the collection of county-level data is enormous and costly, this study only focuses on a very short period (2007-2010) with estimated data. This TFP change analysis is limited to the short-term phenomenon around the 2008 international financial crisis.

This study provides a visual spatial distribution for county-level TFP change in China over the period 2007-2010. Results of the analysis demonstrate that the Chinese Hukou system is among the policy factors that can influence productivity in the course of urbanization.

The achievement of the first nationwide county-level TFP change study for economic growth in China is innovative. This study provides a unique perspective for understanding productivity performance at the regional level over the period investigated, which provides invaluable data for investigating the impact of urbanization and the rural–urban gap on TFP growth.

The high-quality development of the real estate industry is crucial to the transformation of China's economy. However, few studies apply the productivity to explore the development path of the real estate industry in China. To fill this gap, this study mainly investigates the total factor productivity (TFP) of the real estate industry of 30 sample provinces in mainland China from 2007 to 2016.

The Malmquist index is applied to estimate the productivity (i.e. TFP) of the real estate industry, based on the data envelopment analysis (DEA). Then, the truncated tobit regression analysis explores the external influencing factors on the TFP of the real estate industry.

Through empirical analysis, it is found that the high-quality development of the real estate industry depends on the technological innovation by the real estate enterprises and the targeted policies by the provincial government. Moreover, the development of the real estate industry has a positive correlation with the growth of China's economy but a negative correlation with the development of other industries.

TFP mainly reveals the development status of the provincial real estate industry and identifies the driving force for exploring the high-quality development mode of the real estate sector. Furthermore, the fluctuation rule of TFP can be applied to predict the development trend of the real estate industry in the future.

As an application, this study measures the TFP of the Chinese real estate industry in different provinces and periods. The results have meaningful policy implications for policymakers regulating the real estate industry.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

This paper analyses the efficiency and productivity of Brazilian football clubs in the post-world cup 2014 period (2014–2022) using a network dynamic DEA-Malmquist model.

Financial and sporting efficiency and productivity in Brazilian football clubs.

The financial division’s average efficiency is higher than that of the sporting division and overall efficiency from 2014 to 2022. Fourteen clubs exhibited increased productivity during this period. Regression models revealed a statistically significant positive relationship between the debt ratio and DEA dependent variable models at a 1% significance level and a significant negative relationship with the three Malmquist dependent variable models. Additionally, the models identified a statistically significant relationship with the “Covid” (2020 years) variable across all models.

Our findings suggest that increased expenditures can lead to higher liabilities, reducing the ability to afford high-quality players and thus diminishing overall club value. Additionally, the inefficiencies observed among some of the largest football clubs reveal room for improvement in both financial and sportive aspects.

This is the first study to investigate efficiency and productivity in two dimensions for Brazilian football clubs, incorporating an analysis of productivity over an extended period and examining the impact of debt and other determinants on club performance.

This study provides a configurational examination of how policy designs influence the innovation performance of the emergency industry in China.

This study employs the Data Envelopment Analysis Malmquist index (DEA-Malmquist) to quantify the innovation performance of the emergency industry and then codes the innovation policies to calculate the syntactic components based on institutional grammar tools (IGTs). The configurations of syntactic components were determined by applying the fuzzy-set qualitative comparative analysis (fsQCA).

The results indicate that rules- and norms-oriented policy designs would improve the innovation performance of China's emergency industry. In the developed provinces, the “Deontic” and “aIm” combinations in the policy are useful for improving performance. In the developing provinces, the ambiguity of the “aIm” and “Context” conditions in the policy is leading to low performance. Additionally, a lack of strategy-oriented policy design would also result in poor performance.

Most previous studies used substitute variables to understand policy impacts. This study contributes to identifying the impacts of the syntactic components of policy designs on the innovation performance of the emergency industry. The findings can assist policymakers in developing more effective policies to stimulate innovation development in the emergency industry.

This paper aims to examine whether the African commercial banks selected as the best African banks by Global Finance Magazine really are the best.

Panel data envelopment analysis (DEA) was used, as well as the Malmquist total factor productivity index, to distinguish productive banks from unproductive banks. Nineteen commercial banks were selected from the 30 best African banks as identified by the Global Finance Magazine.

Of the 19 banks, five were found to be unproductive. Bank productivity was attributed mainly to technological change, and different methods marked different results, for example, the regional winner bank (Standard Bank of South Africa) selected by Global Finance Magazine ranked ninth in this study, whereas the Bank Windhoek Limited, Namibia, ranked first.

The study confirms the applicability of DEA for the banking industry. The model shows variability among the banks’ efficiency and productivity and provides different results to the Global Finance Magazine’s best bank selection. For example, the Standard Bank of South Africa, which is selected as the regional winner, is now ranked ninth under the DEA Malmquist’s total factor productivity.

The study shows that the DEA model can be applied not only for analysing the firm’s efficiency but also for objective rating, ranking and selecting best banks.

This study aims to measure the intensity of innovation in the Brazilian food sector and compares it to other manufacturing sectors in the country.

The authors used economic and financial data provided by the annual survey of industry [Pesquisa Industrial Anual (PIAs), in Portuguese] and other supporting data provided by the survey of innovation [Pesquisa de Inovação (PINTEC), in Portuguese] and the classification of technology intensity (TI) proposed by the Organization for Economic Co-operation and Development. The authors subsequently applied the Malmquist index in addition to the data envelopment analysis to measure innovation.

The results reveal that the Brazilian food sector is classified as a sector with low TI and investment in research and development (R&D), which represents one of the lowest rates when compared to other sectors. Thus, the Brazilian food sector is far from achieving its full potential. Nevertheless, the authors noticed that the sugar refinery industry showed an evolution in its technology frontier and presented a frequency of innovation similar to the average of high-tech industries.

This study contributes to the debate on innovation in the food sector, emphasizing the need to accomplish higher investments in R&D to increase the productivity of the sector.

Mergers and acquisitions (M&A) are increasingly being adopted as a strategic approach to consolidating financial institutions and banks, with a focus on enhancing capital strength, broadening business operations and maintaining financial stability. Operational efficiency within the banking sector is crucial for effective functioning and delivering quality services to customers. This study analyzes the efficiency of large-scale mergers involving several Indian public sector banks announced between 2016 and 2019.

The study used Data Envelopment Analysis (DEA), Logistic Regression, Malmquist Productivity Index (MPI) and Stochastic Frontier Analysis for the analysis purpose.

The results from DEA indicate that the average efficiency of merged public sector banks improved post-merger, with four out of six banks achieving technical efficiency in the post-merger period. However, efficiency varied, with OTE scores ranging from 65.8 to 100%. The SFA analysis shows that loanable funds are key drivers of both interest and non-interest income, while significant inefficiencies, particularly in labor, require attention. Physical capital plays a secondary role in income generation. The Malmquist productivity index analysis reveals a 1.6% average productivity growth in the post-merger year Y+1, driven by technological change, with positive TFP in Y+1 and Y+2 and a decline in Y+3. Only four of the six merged banks, namely Bank of Baroda, Union Bank of India, Canara Bank and Punjab National Bank, achieved positive TFP growth, primarily due to improvement in technical efficiency. Additionally, the logistic regression analysis indicates that asset quality and size have statistically significant regression coefficients in predicting operational technical efficiency (OTE).

This paper will contribute to the existing literature of banking, mergers and acquisitions and financial economies.

The forest products processing industry is a key component of the forestry economy, and the level of companies’ operating efficiency directly affects its profitability and market competitiveness.

In order to deeply study the operation status of forest product processing industry, this paper takes the panel data of 70 listed forest product processing companies from 2015 to 2022 as the basis, and adopts BBC, CCR and DEA-Malmquist models to measure the operating efficiency of these companies. Meanwhile, the Tobit model is applied to deeply explore the impact of innovation input on operating efficiency.

The results of the paper show that: (1) the overall operating efficiency of listed forest product processing companies performs well, and the improvement of technology level promotes the growth of total factor productivity; (2) innovation input plays a significant positive role in listed forest product processing companies, which positively affects the operating efficiency.

A scientific and reasonable evaluation of the operating efficiency of listed forest product companies is of great practical significance to the development of the forestry industry The study of forest product processing industry is of key significance to the social economy.

This paper explores the improvement of production and operation efficiency of forest products processing enterprises for the purpose of in-depth analysis of the current situation of China's forest products processing enterprises, which is conducive to improving the innovation and operation efficiency of China's forest products processing enterprises, and realizing the high-quality development of China's forest products processing industry.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.