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This paper aims to investigate the situation and evolution of sustainability performance in China.

This paper adopts the global Malmquist-Luenberger (GML) productivity index based on data envelopment analysis and Tobit regression for analysis.

The results indicate the following: China’s sustainability performance has been improving since 2005 and is closely related to the national development strategy and supportive policy; regional gaps in sustainability are a prominent problem represented by the fact that South Central China is becoming a sustainability collapse zone; interprovincial heterogeneity is evident with the varying development speed and conditions; and the level of sustainability performance has a significantly positive correlation with the urbanization rate, investment completed in the treatment of industrial pollution, government appropriation for education and per capita area of paved roads, but it has a negative correlation with the possession of private vehicles.

As an application, this study assessing the GML productivity index of 30 provinces in China from 2005 to 2015 and analyse the sustainability performance on three regional levels (i.e. country level, regional level and provincial level). Tobit regression is also applied to recognize the factors related to the GML index with the results taken as references for policy suggestions. The results have implications for a comprehensive understanding of China's sustainability performance and policymaking in this field.

This paper aims to measure Chinese regional thermal industries’ evolution.

This paper uses data envelopment analysis (DEA) and global Malmquist–Luenberger productivity (GMLP) index.

The results reveal that the development of Chinese thermal power industry varies significantly in different regions, and it is highly correlated with the level of local economic development. Although the change of technical efficiency and scale efficiency had different impacts on different regions from year to year, the overall GMLP index change shows a close relationship with the contemporaneous frontier shift.

The results indicate that the Chinese Government should make efforts to promote its policy implementations and regulations in thermal industries so that the contemporaneous frontier will shift toward the global technology frontier with more desirable outputs and less undesirable outputs.

As an application, this study uses DEA and GMLP index to measure the productivity of Chinese thermal industries in 30 Chinese provinces from 2006 to 2013. The results have the meaningful policy implications for decision makers in charge of Chinese thermal industries.

The purpose of this paper is to present an approach for productivity measurement that considers both construction growth and carbon reduction.

The approach applied is a sequential Malmquist-Luenberger productivity analysis based on a directional distance function and sequential benchmark technology using the data envelopment analysis (DEA) technique. The sequential Malmquist-Luenberger productivity change index is decomposed into pure technical efficiency, scale efficiency, and technological change indices, in order to investigate the driving forces for productivity change.

The construction industries of the Australian states and territories were selected implement the new approach. The results indicate that construction growth and carbon reduction can be achieved simultaneously through the learning of techniques from benchmarks.

Current research on total factor productivity (TFP) in construction generally neglects carbon emissions. This does not accurately depict the nature of construction and therefore yields biased estimation results. TFP measurement should consider carbon reduction, which is beneficial for policymakers to promote sustainable productivity development in the construction industry.

The approach developed here is generic and enhances productivity and DEA research levels in construction. This research can be used to formulate policies for evaluating performance in worldwide construction projects, organizations and industries by considering undesirable outputs and desirable outputs simultaneously, and for promoting sustainable development in construction by identifying competitiveness factors.

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.

Increasing carbon productivity is an effective way to reduce carbon emissions, while boosting economic prosperity. For appropriate formulating and enforcement of energy saving and carbon emissions reduction policies in various sectors, it is of great significance to investigate the evolution characteristics and convergence modes of carbon productivity across the manufacturing sectors.

Using slack-based measure directional distance function (SBM-DDF) and global Malmquist–Luenberger (GML) productivity index, this paper measures the carbon productivities of 29 manufacturing subsectors in Shanghai, China, from 2001 to 2016 under the total factor framework. Furthermore, based on the convergence theories, it empirically examines the convergence of carbon productivity across these manufacturing sectors.

The measurement results suggest that the carbon productivities of the manufacturing sectors in Shanghai show an increasing tendency on the whole, and technical efficiency instead of technological change makes a main contribution to the increase. It is found that there is no obvious σ convergence across the manufacturing sectors in Shanghai, but there exist both absolute ß convergence and conditional ß convergence. Moreover, there is heterogeneity in convergence characteristics between the clean sectors and polluting sectors. The findings also show that firm size and industry structure have significant positive impacts on the growth of carbon productivities of the manufacturing sectors, whereas the impacts of capital deepening and energy consumption structure are significantly negative.

This paper measures the carbon productivities of the manufacturing subsectors by applying SBM-DDF and GML index, so as to improve the accuracy. It provides an insight into the convergence of carbon productivity across the manufacturing sectors.

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 monitor the environmental efficiency of suppliers in the presence of undesirable output and dual-role factors with static and dynamic aspects. Meanwhile, it also aims to explain the main reason for the low efficiency of suppliers.

The authors propose a modified data model considering undesirable output and dual-role factors. The study integrates the modified data envelopment analysis model into the distance function of the Malmquist–Luenberger index. Moreover, this study uses the global benchmark technology to formulate a two-stage model. To verify the validity of this model, a model application is conducted on an automotive spare components company in China.

The results identify the unique status of dual-role factors based on the global optimality of the model and then categorize inefficient suppliers in an individual evaluation cycle. In addition, each supplier is projected on a frontier curve after obtaining the improved data. Furthermore, through the status plot of M-L and its components, this paper concludes that efficiency scale change is the main reason for the gap in ecological performance between different suppliers.

The proposed model considers both undesirable output and dual-role factors; however, variables with different features, such as imprecise, fuzzy and qualitative characteristics, can be embedded into the presented two-stage model.

Evaluating green suppliers through multiple consecutive evaluation cycles will aid a company in effectively managing its key suppliers. Furthermore, the evaluation provides policy guidance for further improvement of suppliers.

The research objective of this paper is to investigate the direct and indirect impacts of green finance on agricultural carbon total factor productivity (ACTFP) within the framework of the carbon peaking and carbon neutrality (dual carbon) goals, while also identifying the driving factors through an exponential decomposition of ACTFP, aiming to provide policy recommendations to enhance financial support for low-carbon agricultural development.

In this paper, the Global Malmquist Luenberger (GML) Index method was employed to analyze and decompose the ACTFP, while the direct and spillover effects of China’s green finance pilot policy (GFPP) on ACTFP were assessed using the difference-in-differences (DID) method and the spatial differences-in-differences (SDID) method, respectively.

After the implementation of the GFPP, the ACTFP in the pilot area has experienced significant improvement, with the enhancement of technical efficiency serving as the main driving force. In addition, the GFPP exhibits a positive low-carbon spatial spillover effect, indicating it benefits ACTFP in both the pilot and adjacent areas.

Within the framework of the dual carbon goals, the paper highlights agriculture as a significant carbon emitter. ACTFP is assessed by considering the agricultural carbon emission factor as the sole non-desired output, and the impact of the GFPP on ACTFP is investigated through the DID method, thereby providing substantial validation of the hypotheses inferred from the mathematical model. Subsequently, the spillover effects of GFPP on ACTFP are analyzed in conjunction with the spatial econometric model.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

By discussing the spatial spillover effect and regional heterogeneity of digital economy and green agricultural development level, this paper aims to provide countermeasures and suggestions for the better development of green agriculture in the contemporary era when digital economy is universally developed and at the same time provide development suggestions suitable for green agriculture's development characteristics and initial conditions for different regions.

This paper discusses the theoretical foundation of the digital economy and green agriculture development and utilizes panel data from 30 provinces in China from 2011 to 2018. By employing the Super-Efficiency Slack-based Measure and Malmquist-Luenberger (SBM-ML) model based on unexpected output to measure the total factor productivity of green agriculture and employing the spatial panel Durbin model to empirically test the spatiotemporal effects of the digital economy on green agriculture development from both temporal and spatial dimensions. Finally, the model is tested for robustness as well as heterogeneity.

The research findings are as follows: First, from the perspective of time effect, digital economy has a continuous driving effect on the development of green agriculture and with the passage of time, this effect becomes more and more prominent; second, from the perspective of spatial effect, digital economy has a significant positive impact on the development of local green agriculture, while digital economy has a significant negative impact on the development of surrounding green agriculture. Finally, the impact of digital economy on the development of green agriculture shows significant differences in different dimensions and regions.

As an important driver of economic growth, the digital economy has injected new impetus into agricultural and rural development. Along with the intensifying environmental pollution problems, how to influence the green development of agriculture through the digital economy is a proposition worthy of attention nowadays. This paper analyzes the relationship between the digital economy and agricultural green development in multiple dimensions by exploring the temporal and spatial spillover effects of the digital economy on agricultural green development, as well as the heterogeneity in different dimensions and in different regions and derives policy insights accordingly in order to improve relevant policies.