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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.

The aim of this article is to research on forestry green total factor productivity and explore the impact of financial support on forestry green total factor productivity.

The methods used in this study are super efficiency SBM model of undesired output and empirical model. SBM model is a kind of Data Envelopment Analysis (DEA). The SBM model with non-expected outputs (slacks-based measure) can be used to deal with the problem of efficiency measurement with multiple input and output variables and can be used to analyze the efficiency of green development of forestry economy.

First, the overall green total factor productivity of the authors’ country's forestry has shown a trend of first decline and then an increase from 2008 to 2018, and there are significant spatiotemporal differences; second, financial support has a significant positive impact on forestry green total factor productivity; third, environmental regulation has a significant threshold effect in the process of financial support on forestry green total factor productivity, and the role of financial support shows a trend of first increasing and then decreasing.

Secondly, taking the data of 30 provinces and cities in the authors’ country from 2008 to 2018 as the research object, using the super-efficiency SBM-Malmquist index to measure the country's forestry green total factor productivity and analyze its temporal and spatial changes; finally, a dynamic panel model was established to explore the impact of financial support on forestry green total factors quantitative impact on productivity, and adding environmental regulation as a threshold variable to establish a dynamic threshold regression, and found that financial support has a nonlinear impact on forestry green total factor productivity.

The purpose of this paper is to propose and analyze a model to obtain a total factor productivity of an industry through quantitative empirical analysis in order to determine the joint contribution of the production and technology function, and the change and technical progress. The case of the Peruvian large shipbuilding industry between the years 1969 and 1990 was considered for the analysis of the proposed model. The large shipbuilding in Peru finished in 1992 and has restarted in 2014. The importance of the study lies in the fact that the analysis is focused on an industry which is resurfacing, and in this regards, the study of the first production period will yield more and accurate information to make decisions regarding its future development.

One way of considering the several effects of technical progress, in line with Sato (1970) such as growth and bias, is to specify a production function maintaining the linear homogeneity property, such as: Y(t)=F [A(t)K(t), B(t)L(t)], where Y(t) is the aggregate product over a period of time (t); K(t) is the capital; L(t) is the labor; and A(t) and B(t) are the efficiencies or augmentations of K(t) and L(t), respectively. Based on the regression analysis data, the value of σ can be estimated to a residual growth rate (Kennedy and Thirlwall, 1972) that allows assessing the technical knowledge that is not attributable to the factors’ efficiency grains: TCTR = T ˙ / T − ( α ( A ˙ / A ) + β ( B ˙ / B ) ) . This last expression measures the residual technological growth rate (TCTR, by its Spanish acronym).

The results of the analysis of the large shipbuilding at SIMA-Callao during the given period (22 years of operation, between 1969 and 1990) show that the necessary installed capacity and the technological knowledge was available in order to develop a complex industrial process in the South Pacific region, thus, contributing to the sector’s growth in the country. The evolution of the shipbuilding activities coincides with the GDP expansion and decline periods in Peru. According to the results, the total factor productivity increased during 1969-1976, 1979-1982, and 1986-1987 periods and it has been confirmed that the contribution of the efficiencies of the production factors were inversely related to the economies of scale and output growth.

The analysis is based on the activities carried out throughout 22 years of operations in SIMA-Callao shipyards (1969-1990). The data regarding the product, labor, imported materials costs, local material costs, direct expenses, wages, and man-day costs was obtained from several sources within the shipyard. Direct expenses correspond to classification, inspections, administrative expenses (dock, quality control, equipment rental, etc.), drawings, technical data, insurance, and materials freight. Additionally, the sources of information are project construction contracts, annual expenses reports, and man-day cost quarterly reports of the shipbuilding area. The man-day cost includes salary, social benefits, and the company’s functional cost.

There are different ways to obtain productivity index. In this case, the authors used the stated model. In addition, based on this experience, this can be applied to other industries.

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 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.

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.

The purpose of this paper is to attempt to measure the performance of the Indian banking sector in terms of efficiency and productivity levels and their determinants during the post-reform period.

The present study is a novel attempt as it has used pooled data for a duration of 15 years (i.e. 1997/1998-2010/2011) from 59 selected banks for estimating the Hicks-Moorsteen (HM) total factor productivity (TFP) index.

Poor technical efficiency has experienced with scale efficiency change exerting dominant factors; whereas relatively better productivity growth has been experienced by the banks with major contributions from technical change components. The study found relatively underestimated efficiency and productivity levels by traditional data envelopment analysis-based Malmquist index. Additionally, the study brings into account the results for external and environmental determining factors contributing to the TFP growth.

Using HMTFP indices has helped to eliminate certain drawbacks of data envelopment and provided the more elaborative decomposition of productivity growth along with their components so as to have lucid and multidimensional insights about the performance of the Indian banking industry after the initiation of financial reforms.

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.

The purpose of this paper is to develop a methodology to study profit vs non‐profit seeking firms usefully to compare corporate performance. It aims to apply the methodology to measure if state vs non‐state firms with different objectives are comparable in performance. If relevant, the paper also aims to comment on the applicability of this method to analysis of other firms, e.g. Islamic banks in Indonesia.

The paper applies Malmquist data envelopment analysis method to different classes of firms: state vs non‐state firms; aggregated at the industry and at national levels; and develop appropriate time trend analysis as well. Findings – The common belief that all state firms are inefficient is not upheld by test results: in some sectors (agriculture and chemicals) state firms are more efficient than private firms. Efficiency is very low, but did improve over time across all sectors and types of firms particularly before the 1997‐1998 and in recent years. Efficiency is mostly achieved through technology adoption (technological change) accounts for most efficiency gains.

This study overturns findings of many accounting performance based studies and revisits policy implications.

No one policy fits all in Indonesia for privatization programme.

The paper provides more valid methodology to compare state firms with non‐state firms for the first time.