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Through this study, the aim is to provide reference for the improvement of CEO related theories, the reform of internal governance mechanisms and compensation systems in enterprises, and ultimately contribute to economic and social development and the achievement of dual carbon goals.

In the context of accelerating the implementation of the “dual carbon” goal and promoting sustainable economic and social development, this paper builds a panel data model based on the panel data of 31 provinces in China from 2011 to 2019 to empirically test the impact of CEO stability on green innovation and total factor productivity of enterprises.

The results show that CEO stability has a positive role in promoting enterprise total factor productivity and green innovation and enterprise green innovation also has a positive role in promoting total factor productivity; Heterogeneity testing found that CEO stability has no significant impact on green innovation in state-owned enterprises. Corporate green innovation has a partial mesomeric effect between CEO stability and total factor productivity; The proportion of independent directors has a negative moderating effect on CEO stability and corporate green innovation, while equity incentive has a positive moderating effect on the relationship between CEO stability, corporate total factor productivity and green innovation.

First, it develops the research on CEO stability and its economic consequences. Second, it expands the impact of CEO stability on heterogeneous enterprises. Third, provides new ideas for the reform of internal governance mechanism and salary system of enterprises.

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 provide new empirical evidence on the impact of credit risk on China banks' total factor productivity.

The paper employs the Malmquist Productivity Index (MPI) which allows for the examination of five different indices: total factor productivity change (TFPCH); technological change (TECHCH); efficiency change (EFFCH); pure technical efficiency change (PEFFCH); and scale efficiency change (SECH) indices.

The empirical findings indicate that the State Owned Commercial Banks (SOCB), Joint Stock Commercial Banks (JSCB), and City Commercial Banks (CCB) have exhibited lower TFPCH levels with the inclusion of risk factor. It was found that the JSCB and CCB have exhibited lower TFPCH due to TECHCH, while the SOCB have exhibited lower TFPCH due to EFFCH. The empirical findings suggest that the inclusion of credit risk factor has resulted in a higher JSCB EFFCH levels. On the other hand, the SOCB and CCB have exhibited a lower EFFCH levels due to SECH and PEFFCH, respectively.

The results clearly highlight the importance of credit risk and lending quality in determining the total factor productivity change of banks operating in the China banking sector. The author demonstrates that the inclusion of credit risk factor has resulted in a lower TFPCH level of all banks operating in the China banking sector. Thus, excluding the credit risk factor from the analysis on the China banking sector may potentially bias the result upwards.

In an environment of heavy government influence over the lending process, a large proportion of loans extended by Chinese banks over the years have gone bad. Policymakers should prevent the flow of new non‐performing loans by separating bad clients from banks that are being restructured and recapitalized in the reform of the banking sector.

By employing the Malmquist Productivity Index (MPI), the present paper contributes to the existing literature by examining, for the first time, the impact of credit risk on China banks' total factor productivity. To the best of the author's knowledge, this type of analysis is completely missing from the literature in regard to the China banking sector.

Production-related industrial zones, super structures and infrastructures are constructed by the construction industry. Nearly all industries and their environmental emissions are influenced by the construction industry including its sub-industries, companies and their supply chains. Furthermore, cities play an important role in economic growth. Cities are hubs for productivity, production, supply and demand, and innovation with the help of their human capital and built environment (e.g. offices, factories, industrial zones, infrastructures, etc.).

Industrial growth fosters urbanisation which is vital for the supply side in the economy to reach to the human resources. Urbanisation which supports industrial growth obstacles industries’ efficiency due to urbanisation problems (e.g. traffic, air and water pollution, health problems).

Construction industry and its sub-industries affect total factor productivity growth in nearly all industries. Construction industry can be a facilitator industry for economic growth and industrial growth considering total factor productivity growth and environment aspects. All industries’ green and sustainable total factor productivity growth can be supported by rethinking construction industry, its sub-industries and their outputs (e.g. construction materials, built environment, cities) as well as construction project management processes.

This chapter aims to introduce carbon capturing smart construction industry model to foster green and sustainable total factor productivity growth of industries. This chapter emphasises current and potential roles of construction industry, its sub-industries and their outputs in fostering other industries’ growth through green and sustainable total factor productivity growth. It focusses on carbon capturing technologies and design at different levels. Furthermore, this chapter emphasises cities’ role in green and sustainable total factor productivity growth. This chapter provides recommendations for construction industry policies and carbon capturing cities/built environment model to solve urbanisation problems and to foster industrial growth and green and sustainable total factor productivity growth. This chapter is expected to be useful to all stakeholders of the construction industry, policy makers, and researchers in the relevant field.

This chapter investigates productivity and cost patterns in the all-cargo US air transport sector. We empirically test the productivity growth influence of changes in unexplained technology, air operations movement characteristics, and factor input prices. Findings show productivity trends depicting negative growth for the 1993–2001 sample, then shifting measurably such that productivity trends depict positive growth for the 2002–2014 sample. The post 2001 growth was fueled by changes in unexplained technological advancements. We interpret this finding as an indication of the importance of technological innovation as a performance enhancer in this transport sector. Findings also reveal a lack of productivity change associated with changes in input prices and movement characteristics. We interpret input price findings as indicating increases in factor input prices such as wages and fuel prices are commensurate with enhanced labor and fuel productivity. The movement characteristic findings are attributable to a lack of sustained increases in load factors, stage length, network size and carrying more volume over the network (density).

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 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.

The objective of this paper is to investigate the effects and relative influence of: grid patterns; variability of foundation sizes; total surface area; and average surface area, on formwork labour productivity of isolated foundations.

To achieve this objective, a sufficiently large volume of productivity data were collected and analyzed at both levels; macro, and micro, using the linear regression method. As a result, the effects and relative influence of the investigated factors on formwork labour productivity are determined and quantified.

The findings show significant impacts of the buildability factors investigated on formwork labour productivity, and substantiate the importance of applying the rationalization and standardization concepts to the design stage of construction projects.

Further research into the effects of buildability factors on formwork, and other related trades of in situ reinforced concrete material, i.e. rebar fixing/installation and concreting, labour productivity, which are common to other structural elements and activities such as, grade/ground beams, columns, walls, beams, and slabs, is recommended, so that the related findings can ultimately be used to develop an automated “Buildability Design Support System” to formalize the buildability knowledge of reinforced concrete construction projects.

The outcomes of this research provide designers with feedback on how well their designs consider the requirements of buildability principles, and the tangible consequences of their decisions on labour productivity. In addition, practical recommendations deduced from the findings are presented, which upon implementation, can improve the buildability level of this activity, hence translate into higher labour efficiency and lower labour costs. On the other hand, the depicted patterns may provide guidance to construction managers for effective activity planning and efficient labour utilization.

The findings fill a gap in buildability knowledge and its influence on formwork labour productivity of an important, labour intensive, activity within the in situ reinforced concrete construction projects.

The hypothesis that the growth in total GNP can also be explained by other factors than the growth in total inputs (capital and labour) and their respective productivities is analysed by the use of 1960‐1985 OECD country data. The OLS estimations of the models of embodied and disembodied technical change in both capital services as measured by the R&D expenditures and labour productivity as measured by investment expenditures in education and health showed very significant results. However, despite the inclusion of these expenditures in the aggregate production function, GNP growth has not been fully exhausted in all OECD countries. Indeed, the unexplained residual which was computed for these countries turned out to be of non‐negligible magnitude and growth. The assumed non‐factor sources of growth containing the unexplained residual which may not be associated by the movement along a production function would include non‐quantifiable political, social and institutional forces which, in some cases, might interact to speed or adversely delay growth unless they remain stable or improved.