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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.

This paper aims to investigate the productivity behavior of Indian banks in the presence of non-performing assets (NPAs) over the period 1999 to 2017. The study examines whether Indian banks withstand the shocks of the global financial crisis (GFC) of 2007–2009 and sustain their total factor productivity (TFP) levels in the post-crisis economic turbulent period or not.

The robust estimates of TFP and its components: efficiency change and technical change are obtained using the state-of-the-art and innovative sequential Malmquist-Luenberger productivity index (SMLPI) approach. The key advantages of this approach are that it explicitly allows the joint production of undesirable output (NPAs in our case) along with desirable inputs and outputs in the production process and precludes the possibility of spurious technical regress.

The empirical results of the study reveal that the Indian banking system has experienced a (−1) percent TFP regress, contributed solely by efficiency loss during the period under investigation. The GFC has slowed down the growth trajectory of TFP growth in the Indian banking industry. Among ownership groups, the effect of the GFC was pronounced on the public sector banks.

The practical implication drawn from the study is that the Indian banks have not been able to successfully transmit the use of installed technology in a way to generate early warning signals and mitigate the risk of defaults so as to maximize their productivity gains in the banking industry.

This study is perhaps the first one to understand the productivity dynamics of the Indian banks in response to both endogenous (i.e. NPA crisis) and exogenous (i.e. global financial and economic stress) crises. Moreover, the authors obtain the robust estimates of TFP growth of Indian banks by explicitly accounting for NPAs as an undesirable output and equity as a quasi-fixed input in the bank production process.

The purpose of this paper is to estimate the relative efficiencies of banks of the Indian domestic banking sector by employing various models of data envelopment analysis (DEA) using the panel data of the recent decade (2008–2017). The paper provides a comparative analysis of these models based on the efficiency outputs. It compares the performance of banks based on their ownership and sizes and studies the decade-long trend of productivity using Malmquist indices.

This paper estimates overall technical, pure technical and scale efficiencies of 21 public sector banks and 17 private banks. It compares the descriptive statistics of efficiency estimates found out through 18 different DEA models and compares them using two non-parametric statistical tests. It studies the difference in efficiencies based on ownership and size by applying the same statistical tests. It employs the Malmquist index method to study the technological and technical progress in the banks’ productivity over the decade of FY 2008–FY 2017.

During FY 2016–2017, only 9 out of 38 banks were overall technically efficient with the whole sample having a mean overall technical inefficiency of 5 percent with scale inefficiency contributing more than pure technical inefficiency. The comparative study ascertains that private sector and public sector banks (PSBs) possess efficiencies that are similar based on super-efficiency slack-based model – variable returns to scale and non-oriented, a model that the authors argue to be the most suitable for the real-life business banking scenarios whereas the private sector banks possess better efficiency than the PSBs. The Malmquist indices prove that private sector banks have a higher increase in productivity based on both technological progress and efficiency improvements whereas PSBs had a loss of efficiency and comparatively less improvement in technology.

This study has a limitation of choosing a single model of inputs and outputs. Improved insights can be drawn by employing more models based on different inputs and outputs. Further, relevance of each input and output can be examined using a regression-based feedback mechanism (Ouenniche and Carrales, 2018). The influence of environmental factors on the efficiencies can be studied using second-stage regression models and the relationship between efficiency scores and financial ratios can be examined.

This study is based on the panel data of the recent decade (2008–2017) and provides insights into the efficiency scenario of the Indian banking industry and how it changed over the past decade, to the leadership of banks, the banking regulators and the policy makers. The comparative analysis of DEA models based on a sample of Indian banks is first of its kind in the Indian context and helps the researchers to select an appropriate model and delve into further research on the same.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

The purpose of this paper is to investigate the effects of technology progress on carbon intensity in China. Abatement of carbon emission has become one of the most important targets for the Chinese government. Numerous studies confirm that technology progress is the main factor responsible for reduction in CI. However, very few studies analyze the impacts of technology progress on CI for various regions. There is also inadequate knowledge on the transmission mechanisms of the impacts. These are the motivations for this research.

Given energy consumption and CO₂ emissions, an improved MLPI, which stands for the generalized technology progress related to energy and environment, is introduced and decomposed into technical and efficiency changes. Using a panel data of 30 provinces from 1997 to 2012, the authors construct different panel data models to investigate the effects of technology progress (and its decomposition elements) on CI.

Results show that technology progress is conducive for reducing CI, with the main factor being technical change. It also finds that the two components of technology progress have completely different effects in the three regions of China. Dynamic panel data models with threshold effects indicate that capital deepening enforces and weakens the negative effects of technical change and efficiency change on CI.

The above conclusions provide new evidences for policy-makers with respect to capital deepening, technical innovation and allocative efficiency enhancement with a view to achieving CI reduction.

The purpose of this paper is to empirically investigate the effect of lean manufacturing on productivity changes and to identify the root sources of productivity changes. Furthermore, the authors explore the moderating effects of research and development (R&D) to examine the relationship between lean manufacturing and productivity changes.

This paper employs the propensity score matching (PSM) model combined with the difference-in-difference (DID) estimation to overcome the selectivity bias. The Malmquist productivity index is used to capture productivity changes. By analyzing 671 Chinese manufacturing listed firms from 2009 to 2014, the moderating effects of R&D on the relationship between lean manufacturing and productivity changes are measured.

The results reveal that lean manufacturing implementation has non-significant effects on productivity changes in principle, while a detailed analysis indicates that lean manufacturing could improve scale efficiency significantly. While engaged in R&D could significantly improve the efficiency of technological changes for lean manufacturing implementation firms, there exist negative effects on pure technical efficiency.

This research only covers manufacturing listed firms in China. Further studies should extend the generalizability of the findings.

This study helps managers to identify the important role of R&D on the relationship between lean manufacturing and productivity changes and provides insights into how to improve the lean manufacturing performance.

This paper appears to be one of the earliest studies on the relationship between lean manufacturing and productivity changes by applying the PSM combined with DID estimation in Chinese manufacturing environment.

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.

This study aims to empirically evaluate the performance of commercial banks operating in India.

The efficiency of the commercial banks is evaluated using the data envelopment analysis (DEA) approach. We measure the technical, pure technical and scale efficiency of the sampled conventional banks using the input-oriented model. We employed an extended DEA window analysis approach based on a panel sample of 47 banks in the Indian scenario. The period of study is from 2009 to 2018.

The results obtained from CRS and VRS measures envisage that Indian banks have failed to manage their inputs efficiently and convert them into outputs. It implies that Indian banks do not operate at an optimum level. Moreover, the results show that public banks exhibit superior efficiency scores followed by private and foreign banks. Apart from the aggregate sector level, we also investigate the performance of Indian banks at the individual level for in-depth analysis. The individual bank-level analysis reports that the public sector banks (PSBs) are the most efficient followed by foreign banks, whereas, the least efficient are the private banks.

The findings of our study have implications for government, financial institutions and policymakers to access the verve and flexibility of the Indian banking system. The government should consider restructuring inefficient banks to enhance overall performance. This can be considered by improvement in managerial efficiency, efficient allocation of scarce resources and appropriate scale of operation. However, the findings of the study should be interpreted in light of the period of study for the banks being operational (as we filter out banks that ceased to exist) in India and empirical methods employed. The results may vary if alternative measures are used.

The present paper investigates the efficiency of the Indian banking sector employing the Data Envelopment Window Analysis (DEWA) technique. To the best of our knowledge, the present study is perhaps the first one to employ the DEWA measure on the Indian banking industry to gauge their performance over time.

This study focuses on examining the impact of energy consumption, economic structure, population, and manufacturing output on the CO₂ emissions of selected emerging countries by utilizing the Structural Time Series Model (STSM). Based on the annual data ranging from 1970 to 2019, the model is built up using total primary energy consumption, GDP per capita, population and manufacturing value-added, and, finally, a stochastic Underlying Emission Trend as explanatory variables. STSM is extended by the introduction of the notion of Underlying Energy Demand Trend (UEDT) as a factor for exogenous effects, including development in technical progress, energy efficiency improvements, changes in human behaviors, economy, and environmental regulations. In this context, STSM and the notion of UEDT are implemented to form a forecasting model for CO₂ emissions of the selected emerging countries. The model discovers the significant influences of all selected variables of CO₂ emissions. The results suggest that the most forceful factor in CO₂ emissions is the total primary energy supply. Furthermore, while the long-term impact of economic growth on CO₂ emissions is negative for some emerging economies, it is positive for several others. The model also measures the long-term manufacturing value-added elasticity of CO₂ emissions in these emerging economies.