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We highlight the state of the art in the eco-efficiency measurement using data envelopment analysis, including Malmquist–Luenberger productivity index. We also consider productivity change over time, provide directions for future studies in the field and gather the most recent policy suggestions for governments, organizations and sectors for reducing CO2 emissions. A structured literature search of the Web of Science academic database reveals 311 papers published between 1989 and 2022. We carry out network analysis of citations to show the evolution of the literature in this research topic. In doing so, we (a) examine the key-route main path of knowledge flows, (b) provide basic bibliometric information about the most active journals and authors, (c) conduct a qualitative in-depth analysis of the identified most important studies and (d) identify the research fronts and relate them to the emerging issues on the topic researched, focusing on the most recent period between 2000 and 2022. Based on the insights of the literature review, the second part of this paper critically analyses the papers on the key-route (main path) of this subject. This review can be used as guidance and a starting point for researchers and practitioners who want to further investigate optimal policies to reach NetZero.

PurposeThe 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.Design/methodology/approachThe 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.FindingsThrough 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.Practical implicationsTFP 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.Originality/valueAs 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.

Congestion is an economic phenomenon of overinvestment that occurs when excessive inputs decrease the maximally possible outputs. Although decision-makers are unlikely to decrease outputs by increasing inputs, congestion is widespread in reality. Identifying and measuring congestion can help decision-makers detect the problem of overinvestment. This paper reviews the development of the concept of congestion in the framework of data envelopment analysis (DEA), which is a widely accepted method for identifying and measuring congestion. In this paper, six main congestion identification and measurement methods are analysed through several numerical examples. We investigate the ideas of these methods, the contributions compared with the previous methods, and the existing shortcomings. Based on our analysis, we conclude that existing congestion identification and measurement methods are still inadequate. Three problems are anticipated for further study: maintaining the consistency between congestion and overinvestment, considering joint weak disposability assumption between desirable outputs and undesirable outputs, and quantifying the degree of congestion.

The ex-post assessment of institutional performance has been applied to allocate scientific and technological (S&T) resource to universities and public research institutes. However, over-emphasis on particular types of performance could lead to unintended results and harm the science system. This research assesses the performance of these universities and public research institutes using ‘technical efficiency’ and their potential using ‘capacity utilization’, which are obtained by data envelopment analysis methods. Moreover, a comprehensive S&T resource allocation framework is proposed, where the organizations can be classified into four groups according to their performance and potential assessment results. An empirical study is conducted using the data of 58 Chinese research institutes from 2011 to 2018. Results indicate different patterns in the distribution and evolution of the performance and potential of these research institutes. The approaches proposed by this research are expected to complement existing performance-based S&T resource allocations.

Purpose This paper studies the relationship between the impact factor (IF) and the number of journal papers in Chinese publishing system. Design/methodology/approach The method proposed by Huang (2016) is used whereas to analysis the data of Chinese journals in this study. Findings Based on the analysis, we find the following. (1) The average impact factor (AIF) of journals in all disciplines maintained a growth trend from 2007 to 2017. Whether before or after removing outlier journals that may garner publication fees, the IF and its growth rate for most social sciences disciplines are larger than those of most natural sciences disciplines, and the number of journal papers on social sciences disciplines decreased while that of natural sciences disciplines increased from 2007 to 2017. (2) The removal of outlier journals has a greater impact on the relationship between the IF and the number of journal papers in some disciplines such as Geosciences because there may be journals that publish many papers to garner publication fees. (3) The success-breeds-success (SBS) principle is applicable in Chinese journals on natural sciences disciplines but not in Chinese journals on social sciences disciplines, and the relationship is the reverse of the SBS principle in Economics and Education & Educational Research. (4) Based on interviews and surveys, the difference in the relationship between the IF and the number of journal papers for Chinese natural sciences disciplines and Chinese social sciences disciplines may be due to the influence of the international publishing system. Chinese natural sciences journals are losing their academic power while Chinese social sciences journals that are less influenced by the international publishing system are in fierce competition. Research limitation More implications could be found if long-term tracking and comparing the international publishing system with Chinese publishing system are taken. Practical implications It is suggested that researchers from different countries study natural science and social sciences journals in their languages and observe the influence of the international publishing system. Originality/value This paper presents an overview of the relationship between IF and the number of journal papers in Chinese publishing system from 2007 to 2017, provides insights into the relationship in different disciplines in Chinese publishing system, and points out the similarities and differences between Chinese publishing system and international publishing system.

Myeloid differentiation factor 88 (MyD88) is a crucial adaptor protein for Toll-like receptor (TLR)-mediated signaling pathways and plays an important role in immune response. In this study, the full-length cDNA of MyD88 from Macrobrachium rosenbergii (MRMyD88) was cloned. The MRMyD88 cDNA is 1758 bp long and contains a 1398-bp open reading frame. Multiple sequence alignment and phylogenetic analysis revealed that the amino acid sequence of MRMyD88 shared high identity with the known MyD88 proteins. The MRMyD88 mRNA was widely expressed in all examined tissues, with highest level in intestine, followed by gonad and pleopod. Furthermore, the MRMyD88 promoter region, spanning 1622 bp, contains several transcription factor-binding sites, including nine GATA-1 box motifs. Electrophoretic mobility shift assay showed that Gfi-1, SRF, and Oct-1 bind to the upstream region of MRMyD88. Additionally, the results showed that the expression levels of TLR1, TLR2 and TLR3 were different in response to Vibrio anguillarum, Lactobacillus plantarum and Aeromonas hydrophila infections. However, these bacteria significantly increased the expression levels of MyD88 and prophenoloxidase. These data suggest that the TLR-mediated signaling pathway is MyD88-dependent in response to pathogenic and probiotic bacteria in M. rosenbergii.

As the inputs of investment and employees in real estate sectors have continued to increase in recent years, it is crucial to study the evaluation of real estate performance. However, these rarely ...

This research focuses on a sample of European and Chinese elite universities for the period 2011–2015. We adopt a meta-frontier methodology to decompose their overall productivity in three main determinants: (1) technical efficiency compared with contemporaneous technology, (2) change in technical efficiency and (3) technology relative superiority of the two groups of universities. The results reveal different patterns of evolution: Chinese institutions’ productivity grows faster than that of their European counterparts (+ 7.15%/year vs 4.51%/year), however the latter maintain a higher level of technology in efficient production as a group.

Considering that the real estate industry is a critical industry to promote the economy in China, it is necessary to measure the real estate performance. However, few studies about the performance evaluation of China’s real estate industry have focused on the production process. To fill this gap, this paper proposes a two-stage framework to investigate the real estate productivity of 30 sample provinces on mainland China from 2008 to 2015, based on a common-weight global Malmquist productivity index (MPI). The major findings are shown as follows: (a) the real estate efficiency is low, and it is mainly caused by the inefficiency in the sales stage, not the development stage; (b) the development trend of the real estate sector in China is sensitive to the government policies, and the fluctuations of MPI are consistent with the direction of policy adjustment during the observation period; (c) as for the regional analysis of MPI, we introduce the concept of the dependence degree of the economy on the real estate industry and predict that MPI in economically underdeveloped regions may decline in the future. Finally, policy recommendations are provided for the high-quality development of China’s real estate industry. First published online 25 January 2021

This paper aims to investigate the capacity utilization (CU) of selected large Chinese iron and steel enterprises by using the data envelopment analysis (DEA) approach. In this paper, we first propose a DEA model with an unrestricted capacity directional output distance function by incorporating two joint cost disposability relations and differentiating between non-emission-causing inputs and emission-causing inputs. Second, we define the status of decision-making units (DMUs) with excess capacity and design a method to identify insufficient variable inputs. We use a group of 48 largest Chinese iron and steel enterprises as our sample to investigate their performance in terms of CU indicator. The main findings are summarised and can be used to support the corresponding policymaking for the managers of both government and enterprises.

Highlights • This study provides a risk score that predicts critical care admission and death in COVID-19. • Chest radiography severity scores are highly predictive of outcome. • The findings may inform admit/discharge decisions as well as patient selection for clinical trials., Background The COVID-19 pandemic continues to escalate. There is urgent need to stratify patients. Understanding risk of deterioration will assist in admission and discharge decisions, and help selection for clinical studies to indicate where risk of therapy-related complications is justified. Methods An observational cohort of patients acutely admitted to two London hospitals with COVID-19 and positive SARS-CoV-2 swab results was assessed. Demographic details, clinical data, comorbidities, blood parameters and chest radiograph severity scores were collected from electronic health records. Endpoints assessed were critical care admission and death. A risk score was developed to predict outcomes. Findings Analyses included 1,157 patients. Older age, male sex, comorbidities, respiratory rate, oxygenation, radiographic severity, higher neutrophils, higher CRP and lower albumin at presentation predicted critical care admission and mortality. Non-white ethnicity predicted critical care admission but not death. Social deprivation was not predictive of outcome. A risk score was developed incorporating twelve characteristics: age>40, male, non-white ethnicity, oxygen saturations3, neutrophil count>8.0 x109/L, CRP>40 mg/L, albumin100 µmol/L, diabetes mellitus, hypertension and chronic lung disease. Risk scores of 4 or higher corresponded to a 28-day cumulative incidence of critical care admission or death of 40.7% (95% CI: 37.1 to 44.4), versus 12.4% (95% CI: 8.2 to 16.7) for scores less than 4. Interpretation Our study identified predictors of critical care admission and death in people admitted to hospital with COVID-19. These predictors were incorporated into a risk score that will inform clinical care and stratify patients for clinical trials.

Meat yield is an important indicator of the commercial value of crustaceans. We investigated the correlations between morphometric characteristics and meat yield in Macrobrachium rosenbergii (De Man, 1879), in order to determine which traits were correlated with high meat yield. The association between morphometric characteristics and meat yield were estimated using statistical methods, including correlation coefficients, determination coefficients, path coefficients, and regression equations. In the same “family” [= cultured lineage or strain], the meat yield of males was often slightly lower than that of females. The meat yield of different male morphotypes ranged from 19.90% to 38.12%, while that of different female stages ranged from 30.14% to 35.59%. Among male morphotypes, the “small male” had the largest meat yield, and “old blue claw” had the lowest meat yield (). Among the various female developmental stages, stage I had the largest meat yield, and stage III female had the lowest meat yield (). The multivariate linear regression equation for meat yield of males contained four independent variables, and the determination coefficient was 0.744. The regression equation for females contained five independent variables, and the determination coefficient was 0.456. The characteristics that had the largest direct positive effect for meat yield were abdominal weight/body weight in males (0.862) and body length in females (0.441). These results can provide reference data for selecting M. rosenbergii with high meat yield, in order to improve the economic benefit of the prawn processing industry.

Purpose This paper aims to investigate the situation and evolution of sustainability performance in China. Design/methodology/approach This paper adopts the global Malmquist-Luenberger (GML) productivity index based on data envelopment analysis and Tobit regression for analysis. Findings 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. Originality/value 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.

Angiopoietin-2 (Ang-2) is a proangiogenic factor that mediates inflammation and atherosclerosis. We evaluated the predictive value of circulating Ang-2 levels for periprocedural myocardial injury (PMI) in 145 patients undergoing elective percutaneous coronary intervention (PCI), and investigated whether post-PCI Ang-2 levels are influenced by PMI. PMI was defined as a post-procedural troponin elevation above the 5×99th percentile upper reference limit. Blood samples for Ang-2 analysis were collected at admission and on postoperative days 1 and 3. PMI occurred in 40 patients (28%). At baseline, there was no difference in Ang-2 levels between PMI and non-PMI patients (P=0.554). However, a significant interaction effect between PMI occurrence and time on Ang-2 levels was observed (interaction P=0.036). Although serum Ang-2 levels in non-PMI patients gradually decreased, Ang-2 levels in PMI patients did not change between different time-points. Multiple logistic regression analysis revealed that age, total stent length, and serum levels of N-terminal pro-brain natriuretic peptide were independent PMI predictors. These findings indicate that pre-procedural Ang-2 levels do not impact PMI occurrence after elective PCI. However, changes in Ang-2 levels after the procedure are closely related to PMI.

This study investigates the capacity utilization (CU) of Chinese manufacturing industries, using a CU indicator based on data envelopment analysis and directional distance functions (DDFs). The inputs are separated into variable inputs and a quasi-fixed input to measure the gap of DDFs, which indicated either under-utilization of inputs or overcapacity. Moreover, we define an indicator for CU change over time and introduced the corresponding decomposition. We note that, during the study time period (2007–2010), the CU of Chinese manufacturing industries improved, which implies that Chinese manufacturing industries expanded their production and got closer to their capacity during the examined period. The driving force of this improvement is technical changes. The higher average CU values of light manufacturing industries than that of the heavy industries and the extremely high CU values of two light industries reveal a severe overcapacity problem in the light industries. We also provided the methods and conduct analysis on determining optimal variable inputs and the type of the overcapacity on specific DMUs. The bootstrap regression procedures are employed to test the influence of environmental variables on CU values. Finally, we provide policy implications and suggestions for policymakers who oversee the development of Chinese manufacturing industries.

Congestion is one of the most important phenomena in the production processes that reducing (increasing) one or more inputs can increase (decrease) one or more outputs. Recently, Karimi, Khorram, and Moeini (2016) presented a data envelopment analysis (DEA) procedure to identify congestion of decision-making units (DMUs) in integer-valued DEA. This study first provides two counterexamples and shows that their procedure may not be capable of identifying congestion, correctly; and then proposes an integer-valued slack-based DEA approach for recognizing the right- and left-hand congestion status of the DMUs who are all characterized by the technology dealing with both negative and/or non-negative continuous and integer data. Finally, a numerical example is provided to make a comparison between our proposed approach and the Karimi et al.’s (2016) approach, and then an illustrative empirical application regarding Japanese banking is presented to demonstrate the applicability of our proposed approach.

The Chinese government announced to cut its carbon emissions intensity by 60%–65% from its 2005 level. To realize the national abatement commitment, a rational allocation into its subunits (i.e. in...

Network data envelopment analysis (NDEA) deals with evaluating the performance of a set of homogeneous production processes (or decision making units (DMUs)) taking into account the internal structure of DMUs. A large number of studies in NDEA are based on two-stage structures with intermediate products. For a two-stage DMU, if the intermediate products increase by increasing the inputs of the first stage, then increasing of the intermediate products may lead to some variations on the outputs of the second stage. In this research, this issue is called “variations effect” of the intermediate products on the outputs of the second stage. These variations are very significant for a decision maker from the economic viewpoint because, in this case, he/she can identify variations of the outputs of the second stage and also recognize congestion by increasing the intermediate products; and then decides whether increasing the intermediate products is good or not for improving the performance of the two-stage DMU. This fact is the motivation for creating this study which hitherto, none of the studies in NDEA pay attention to this issue. The current research with the aim of tackling this issue proposes a DEA approach to determine the range of variations of the intermediate products and specify the effect of these variations on the outputs of the second stage in the presence of both negative and non-negative data. Finally, two numerical and empirical examples are provided to illustrate the potential application of the proposed approach.

This paper aims to investigate the regional sustainable performance of real estate industry of 30 sample provinces on China's mainland from 2007 to 2013, based on a slack-based data envelopment analysis (DEA) approach. We assume that the real estate industry operates with a dynamic three-stage network process. The proposed model specification is dynamic because carryover variables produced in a previous period that constrain the current production are considered. The empirical results show the major findings: (a) the growing speeds of Assets, Completed investment on land and Completed investment in houses are far faster than that of gross domestic product (GDP) in the same period, which indicates the possibility of too much existing investment; (b) the inefficiency status of the Chinese real estate industry increased after 2012; (c) there is severe imbalance among different provinces; (d) there is a need to improve the operational efficiencies of real estate industries in some provinces, especially Qinghai, Gansu, Ningxia, Xinjiang, Hainan, Guizhou, and Shanxi; and (e) GDP and Price of commercial real estate both have negative effects on slack-based inefficiency, on average, in the examined period. (f) The Herfindahl-Hirschman index and other variables can be used to explain the level and difference in inefficiency bias over time and across regions. Based on those findings, we propose several policy suggestions for sustainable development of Chinese real estate industry.

Purpose This paper aims to investigate the scientific productivity of China’s science system. Design/methodology/approach This paper employs the Malmquist productivity index (MPI) based on Data Envelopment Analysis (DEA). Findings The results reveal that the overall efficiency of Chinese universities increased significantly from 2009 to 2016, which is mainly driven by technological progress. From the perspective of the functions of higher education, research and transfer activities perform better than the teaching activities. Research limitations As an implication, the indicator selection mechanism, investigation period and the MPI model can be further extended in the future research. Practical implications The results indicate that Chinese education administrative departments should take actions to guide and promote the teaching activities and formulate reasonable resource allocation regulations to reach the balanced development in Chinese universities. Originality/value This paper selects 58 Chinese universities and conducts a quantified measurement during the period 2009–2016. Three main functional activities of universities (i.e. teaching, researching, and application) are innovatively categorized into different schemes, and we calculate their performance, respectively.

The blind expansion and increasing carbon emissions of the coal-fired power generation industry in China have attracted wide attention at home and abroad. To detect the technical ineffectiveness in the coal-fired power generation industry and the effects of carbon emission reductions, this study developed energy congestion models using data envelopment analysis (DEA). Energy congestion is classified into undesirable energy congestion (UEC) and desirable energy congestion (DEC) under natural disposability and managerial disposability. The UEC and DEC models were used to identify energy congestion, measure the amounts of UEC and DEC, and analyze the sources of inefficiency for regional coal-fired generation industry in China from 2004 to 2013. Our empirical analysis revealed: i) The UEC of coal-fired generation industry has occurred in many regions, most of which are less-developed areas. This indicates that energy is wasted in coal-fired generation industry due to congestion inefficiency. ii) DEC has occurred in a few regions and did not occur in 2013. These provinces where DEC occurred may have a high potential for eco-technology innovation. The method of energy congestion measurement proposed in this study and the research conclusion have reference effect on regional energy conservation and eco-technology innovation.

Cross-efficiency evaluation in data envelopment analysis (DEA) is a useful tool in evaluating the performance of decision-making units (DMUs). It is generally assumed that decision makers (DMs) are completely rational in common cross-efficiency evaluation models, which fail to consider the DM's risk attitude that plays an important role in the evaluation process. To fill this gap, we investigate the cross-efficiency evaluation in DEA based on prospect theory. First, we introduce a prospect value of the DMU to capture the non-rational psychological aspects of a DM under risk. Second, based on the prospect value, we propose a new cross-efficiency model termed the prospect cross-efficiency (PCE) model. Particularly, some existing cross-efficiency evaluation models can be deemed as the special cases of the PCE model with suitable adjustments of the parameters. Furthermore, this paper provides an empirical example to evaluate cross-efficiency with several selected universities directly managed by the Ministry of Education of China to illustrate the effectiveness of the PCE model in ranking DMUs.

Acute influenza infection has been reported to be associated with neurological symptoms such as influenza-associated encephalopathy (IAE). Although the pathophysiology of this condition remain unclear, neuroinflammation and associated alterations in the central nervous system (CNS) are usually induced. Microglia (MGs), CNS-resident macrophages, are generally the first cells to be activated in response to brain infection or damage. We performed reverse transcriptase droplet digital PCR (RT-ddPCR) and luminex assays to investigate virus proliferation and immune reactions in BV2 MGs infected with influenza A(H1N1)pdm09 virus. Furthermore, isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics methods were used to investigate the dynamic change in the protein expression profile in BV2 MGs to gain insight into the CNS response to influenza A (H1N1) pdm09 infection. Our results showed that the influenza A(H1N1)pdm09 virus was replicative and productive in BV2 MG cells, which produced cytokines such as interleukin (IL)-1β, IL-6, tumour necrosis factor (TNF)-α and monocyte chemoattractant protein (MCP)-1. The expression of osteopontin (OPN) in the influenza A (H1N1) pdm09-infected BV2 MGs was upregulated at 16 and 32 h post-infection (hpi) compared to that in the control group, resulting in aggravated brain damage and inflammation. Our study indicates that OPN signalling might provide new insights into the treatment of CNS injury and neurodegenerative diseases in IAE.

This paper aims to address the problem of allocating the CO2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO2 emission reduction is c...

Reduction of CO2 emissions is a strategic priority for the construction industry, however current schemes do not provide the level of performance that is required. There is also a lack of understanding of how to allocate CO2 emissions targets within regions. Therefore, this research study develops a three-stage empirical system to identify the CO2 emissions allocation scheme for the Chinese construction industry at the provincial level. The results indicate that (a) the construction industry's CO2 emissions need to be reduced by ca. 10% from the base level in 2017; (b) 86.7% of the provinces have a relatively large capacity for CO2 emissions reduction; (c) China's East region accounts for 44.0% of the total amount for CO2 emissions reduction; and (d) about one-third of the provinces face enormous pressure to reduce CO2 emissions by more than 10% on the base of 2017. This research study provides unique insights and guidance to support assessment of the regional allocation of CO2 emissions for the construction industry, which is a valuable reference for other countries and industries.

Environmental performance allows measuring the existing gap between countries regarding their environmental policy objectives. Improving environmental performance also allows countries to achieve their sustainable development goals. However, a systematic identification of factors influencing environmental performance constitutes a premise to improve it and such a review of factors has not been conducted in previous research. This paper develops a quantitative literature review of the factors influencing environmental performance in which a total of 84 journal papers were identified by keyword retrieval between 2004 and 2019. Literature metrological and literature content analyses are performed and two major research outcomes are obtained: first, a list of environmental performance influencing factors and a classification of the five main research streams related to environmental performance: enterprise, government, economy, technology and society. Second, building on the previous classification, a research agenda is proposed which points out current shortcomings and potential research directions for environmental performance research. The results of this piece of research provides a theoretical reference for improving environmental performance. It can also help countries target better environmental management practices when seeking global sustainable development. Highlights 84 journal papers are analysed with metrological and content analysis. Grounded theory produces a list of factors affecting environmental performance. Environmental performance factors are classified in five relevant categories. These categories are: enterprise, government, economy, society and technology. A research agenda is proposed for meeting the 2030 Sustainable development goals.