Search results

The purpose of this paper is to explore the effects of cross-border acquisitions on the efficiency and productivity of acquired companies in the cement industry in the context of a transitional economy.

The Data Envelopment Analysis (DEA) and Malmquist Productivity Index were used to assess the efficiency and productivity of the acquired companies over the period 2000–2018. DEA and Malmquist index are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. The study assesses partial efficiency and productivity of three inputs: material, capital and labour, as well as the total factor efficiency and productivity of the acquired companies in the short and long term after the acquisitions.

The research results suggest that efficiency of material, efficiency of labour and the total factor efficiency of the acquired companies are higher after the acquisitions than before, while efficiency of capital is lower. In addition, the results show that the acquisitions had a positive impact on total factor productivity of the acquired companies.

The results of this study have practical implications for managers, especially for policy-makers and industry analysts in deciding whether to encourage or discourage cross-border acquisitions in transitional economies.

The study contributes to a better understanding of the impact of cross-border acquisitions on efficiency and productivity of acquired companies in the manufacturing industry. Research in transitional economies related to subject matter is limited, and this study is the first empirical investigation of the effect of cross-border acquisitions on the efficiency and productivity in the cement industry in Serbia by applying the Data Envelopment Analysis.

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.

The purpose of this paper is to examine the industrial production efficiency, pollution treatment efficiency, total factor energy efficiency and water efficiency in China with the consideration of technological innovation. This study also explores the distribution proportion of technological innovation between industrial production substage and pollution treatment substage.

A nonparametric method, data envelopment analysis (DEA), is used as the model foundation of this study. Specifically, a novel two-stage range-adjusted measure (RAM-DEA) with shared inputs is constructed to analyze the China’s industrial system. In this study, the panel data of 30 provinces from 2008 to 2015 are used.

This study found that although the current environmental regulation reduced the efficiency of industrial production, it could significantly improve the pollution treatment level. However, the lack of pollution treatment capacity was still an obstacle for development of China's industrial system. Compared with the total factor energy efficiency, the total factor water efficiency had more room for improvement. The optimal distribution of technological innovation in the two substages performed little change and the distribution roughly followed the “three-seven principle”.

More attention should be paid to improve the pollution treatment level and total factor water efficiency. And more R&D expenditure should be used in the industrial production substage in the eastern coastal areas, while in the inland areas, more R&D expenditure should be used in the pollution treatment substage.

This study proposed a model to environmental efficiency score with considering interval data under two-stage evaluation structure, which could strengthen the theory and expand the application scope of DEA approach.

Agricultural carbon productivity considers the dual goals of “agricultural economic growth” and “carbon emission reduction”. Improving agricultural carbon productivity is a requirement for promoting green and low-carbon development of agriculture. Agricultural production agglomeration is widespread worldwide, but the relationship between agricultural production agglomeration and agricultural carbon productivity is inconclusive. This paper aims to study the impact of agricultural production agglomeration on agricultural carbon productivity, which is conducive to a better understanding of the relationships among agglomeration, agricultural economic development and carbon emission, better planning of agricultural layout to build a modern agricultural industrial system and achieve the goal of carbon peaking and carbon neutrality.

Based on China's provincial data from 1991 to 2019, this paper uses non-radial directional distance function (NDDF) and Metafrontier Malmquist–Luenberger (MML) productivity index to measure total factor agricultural carbon productivity. Subsequently, using a panel two-way fixed effect model to study the effect and mechanism of agricultural production agglomeration on agricultural carbon productivity, and the two-stage least squares method (IV-2SLS) is used to solve endogeneity. Finally, this paper formulates a moderating effect model from the perspective of the efficiency of agricultural material capital inputs.

The empirical results identify that Chinese provincial agricultural carbon productivity has an overall growth trend and agricultural technological progress is the major source of growth. There is an inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity. The input efficiency of agricultural film, machine and water resources have moderating effects on the inverted U-shaped relationship. Agricultural production agglomeration also promotes agricultural carbon productivity by inhibiting agricultural carbon emissions in addition to affecting agricultural input factors and its internal mechanisms are agricultural green technology progress and rural human capital improvement.

This paper innovatively adopts the NDDF–MML method to measure the total factor agricultural carbon productivity more scientifically and accurately and solves the problems of ignoring group heterogeneity and the shortcomings of traditional productivity measurement in previous studies. This paper also explains the inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity theoretically and empirically. Furthermore, from the perspective of agricultural material capital input efficiency, this paper discusses the moderating effect of input efficiency of fertilizers, pesticides, agricultural film, agricultural machines and water resources on agricultural production agglomeration affecting agricultural carbon productivity and answers the mechanism of carbon emission reduction of agricultural production agglomeration.

The discussion on energy efficiency has been increasing due to the increasing population, emissions of degradable and harmful pollutants, and clean energy substitutes are being developed in order to manage and control the energy requirements all over the world. Against this backdrop, the factors of technological innovation and environmental regulations have been determined as key indicators for the evaluation of sustainable developments and practices in the energy efficiency evaluation studies.

A two-stage analysis process has been configured for evaluation of the energy efficiency. The first stage includes the estimation of the Total factor energy efficiency scores using the data envelopment Multiplier input-oriented methodology, while the second stage includes the exploration of the impact of technological innovation and government environmental regulations on the Total factor energy efficiency scores obtained in the first step through the application of a spatial regression model.

This paper highlights the link between the need for and impact of energy efficiency innovations and shows that the energy efficiency goal can be fulfilled by incorporating laws on sustainability and incorporating strict regulations that allow for the use of clean energy, low carbon energy technologies.

The present study, furthermore, provides evidence from 15 countries, five from three different continents, i.e. Asia, Europe and Africa so that a cross-country performance of these factors can be evaluated. The main contribution of the present study is the evaluation of the technological innovation on energy efficiency. There have been studies evaluating various factors on the development of energy-efficient practices; however, the focus on the role of technological innovation and governmental regulations has been scarce.

The nature and extent of productivity change of ten commercial banks in Malaysia is analysed over the period 2000‐2001. Utilising non‐parametric methodology, Data Envelopment Analysis (DEA) and Malmquist total factor productivity index (MPI), individual bank efficiency and productivity changes which took place within this period is estimated. The MPI calculated within the framework of DEA further decompose productivity growth into technical efficiency change and technological change. The results from this analysis indicate that total factor productivity increased in all eight banks except for EON, which remain the same while PBB, recorded a decrease in productivity. AFB recorded the highest growth in total factor productivity. The growth in productivity is attributed to technological change rather than technical efficiency change.

Because of growing energy consumption and increasing absolute CO₂ emissions, the recent calibrations about the environmental sustainability across the globe have mandated to achieve the minimal energy consumption through employing energy-efficient technology. This study aims to estimate linkage between simple measure of energy efficiency indicator that is reciprocal of energy intensity and total factor productivity (TFP) in case of Indian paper industry for 21 major states. In addition, the study incorporates the other control variables like labour productivity, capital utilization and structure of paper industry to scrutinize their likely impact on energy efficiency performance of the industry.

To derive the plausible estimates of TFP, the study applies the much celebrated Levinsohn and Petrin (2003) methodology. Using the regional level data for the period 2001-2013, the study employs instrumental variable-generalized method of moments (GMM-IV) technique to examine the nature of relationship among the variables involved in the analysis.

An elementary examination of energy intensity shows that not all states are equally energy intensive. States like Goa, Rajasthan, Jharkhand and Tamil Nadu are less energy intensive, whereas Uttar Pradesh, Kerala, Chhattisgarh, Assam and Punjab are most energy-intensive states on the basis of their state averages over the whole study period. The results estimated through GMM-IV show that increasing level of TFP is associated with lower level of energy per unit of output. Along this better skills and capacity utilization are also found to have positive impact on energy efficiency performance of industry. However, the potential heterogeneity within the structure of industry itself is found responsible for its higher energy intensity.

States should ensure and undertake substantial investment projects in the research and development of energy-efficient technology and that targeted allocations could be reinforced for more fruitful results. Factors aiming at improving the labour productivity should be given extra emphasis together with capital deepening and widening, needed for energy conservation and environmental sustainability. Given the dependence of structure of paper industry on the multitude of factors like regional inequality, economic growth, industrial structure and the resource endowment together with the issues of fragmented sizes, poor infrastructure and availability and affordability of raw materials etc., states should actively promote the coordination and cooperation among themselves to reap the benefits of technological advancements through technological spill overs. In addition, owing to their respective state autonomies, state governments should set their own energy saving targets by taking into account the respective potentials and opportunities for the different industries. Despite the requirement of energy-efficient innovations, however, the cons of technological advancements and the legal frameworks on the employment structure and distributional status should be taken care of before their adoption and execution.

To the best of our knowledge, this is the first study that empirically examines the linkage between energy efficiency and TFP in case of Indian paper industry. The application of improved methods like Levinsohn and Petrin (2003) to derive the TFP measure and the use of GMM-IV to account for potential econometric problems like that of endogeneity will again add to the novelty of study.

The Association of Southeast Asian Nations (ASEAN) members want to efficiently promote the flow of commodities and personnel within its service areas under given limited resources. Based on panel data from 2007 to 2014, this study applies the output-oriented data envelopment analysis method and focuses on the disaggregated output efficiencies of 42 ASEAN airports. Results show that the international airports of ASEAN members have significantly better output efficiency for passenger and movement output than regional airports. This work provides a relatively fair perspective in evaluating ASEAN’s airport operating efficiency. It helps policymakers measure the frontier forward or backward shift of an airport over the research period, in order to reveal the characteristics of airport efficiency and to present a new interpretation along with managerial implications.

This paper aims to estimate the values of environmental efficiency of each province in China. Subsequently, it analyzes the changes of total factor productivity (TFP) before and after taking the environmental factors into account. Finally, the paper measures the effect of the components of environmental TFP on the convergence of economic growth.

The environmental control variables are taken as output variables and combined with a traditional data envelopment analysis model to build an environmental TFP Index. The growth of environmental TFP is compared among regions and the differences are analyzed. Furthermore, the decomposed components (advances in environmental technology and environmental technical efficiency) are used for regression analysis with labor productivity.

The environmental efficiency of most provinces in China has improved although some regions' efficiency remains low in Northeastern China. The value of environmental TFP is higher and more fluctuant during the period of 1997-2009 than that of traditional TFP. Technical efficiency has a convergence effect on economic growth of regions/provinces, but, after adding environmental factors, it turns into a divergence effect.

Because of data limitations, this paper does not consider the impacts of human capital and other factors on in the convergence analysis on economic. Neither does it consider the use of panel data to analyze the convergence of economic growth and validate the conclusions. These are potential further research directions.

The study confirms that improvements in environmental technologies play a dominant role in enhancing China's environmental TFP. Furthermore, it demonstrates that China's economic growth largely depends on technological progress. This finding, that China's economic growth depends on advances in environmental technology, implies that China should strive to improve its capacity for technological innovation.

The paper measures the value of environmental efficiency in the China's provinces and analyzes the relationship between pollution and economic development in each province. Panel data are used to compare the difference among environmental TFP, environmental factors and traditional TFP. The convergence of economic growth is analyzed in respect of environmental control variables.

Concern for health, safety and environment (HSE) is increasing in many developing countries, especially in energy industries. Improving power plants efficiencies in terms of HSE issues requires considering these issues in performance assessment of power generation units. This study aims to discuss the use of data envelopment analysis methodology for the performance assessment of electrical power plants in Iran by considering HSE and conventional indicators.

Installed capacity, fuel consumption, labor cost, internal power, forced outage hours, operating hours and total power generation along with HSE indices are taken into consideration for determining the efficiency of 20 electric power plants or decision-making units (DMUs). Moreover, DMUs are ranked based on their relative efficiency scores.

Results show that HSE factors are significant in performance assessment of the power plants studied in this research, and among HSE factors, health has the most powerful impact on the efficiency of the power plants.

The approach of this study could be used for continuous improvement of combined HSE and conventional factors. It would also help managers to have better comprehension of key shaping factors in terms of HSE.