A critical review of the current research mainstreams and the influencing factors of green total factor productivity

The current world economy needs to undergo a green transformation. Green total factor productivity provides the basis for judging whether a country or region can attain long-term sustainable development. However, there is little research into the factors that influence green total factor productivity and this has become an obstacle in the transition to a greener economy. On filtering relevant articles and interviews data collected from 2009 to 2019, open decoding, spindle decoding, and selective decoding are carried out to classify research conducted into green total factor productivity. From this analysis, cutting-edge research and knowledge gaps in green total factor productivity are identified. Also, an influencing factor model of green total factor productivity is built. Findings suggest that technical, economic, and government are the three main research streams involved in this transformation process. In particular, technology plays a decisive role, economy plays a guaranteeing role, and government plays a regulatory role. Moreover, the impact of these factors cannot be isolated, as each influence and mediate the other two. Results from this study will help further popularize green total factor productivity and provide a new starting point for reducing energy consumption and environmental pollution.</p

Health-related quality of life and its correlates among chinese migrants in small-and medium-sized enterprises in two cities of Guangdong

Objectives: To explore the relationship between health-related quality of life (HRQOL) status and associated factors among rural-to-urban migrants in China. Methods: A cross-sectional survey was conducted with 856 rural-to-urban migrants working at small-and medium-size enterprises (SMEs) in Shenzhen and Zhongshan City in 2012. Andersen's behavioral model was used as a theoretical framework to exam the relationships among factors affecting HRQOL. Analysis was performed using structural equation modeling (SEM). Results: Workers with statutory working hours, higher wages and less migrant experience had higher HRQOL scores. Need (contracting a disease in the past two weeks and perception of needing health service) had the greatest total effect on HRQOL (_ =20.78), followed by enabling (labor contract, insurance purchase, income, physical examination during work and training) (_ = 0.40), predisposing (age, family separation, education) (_ = 0.22) and health practices and use of health service (physical exercise weekly, health check-up and use of protective equipments) (_ =20.20). Conclusions: Priority should be given to satisfy the needs of migrant workers, and improve the enabling resources.sch_iih9pub3406pub

Clearance Rate and BP-ANN Model in Paraquat Poisoned Patients Treated with Hemoperfusion

In order to investigate the effect of hemoperfusion (HP) on the clearance rate of paraquat (PQ) and develop a clearance model, 41 PQ-poisoned patients who acquired acute PQ intoxication received HP treatment. PQ concentrations were determined by high performance liquid chromatography (HPLC). According to initial PQ concentration, study subjects were divided into two groups: Low-PQ group (0.05–1.0 μg/mL) and High-PQ group (1.0–10 μg/mL). After initial HP treatment, PQ concentrations decreased in both groups. However, in the High-PQ group, PQ levels remained in excess of 0.05 μg/mL and increased when the second HP treatment was initiated. Based on the PQ concentrations before and after HP treatment, the mean clearance rate of PQ calculated was 73 ± 15%. We also established a backpropagation artificial neural network (BP-ANN) model, which set PQ concentrations before HP treatment as input data and after HP treatment as output data. When it is used to predict PQ concentration after HP treatment, high prediction accuracy (R=0.9977) can be obtained in this model. In conclusion, HP is an effective way to clear PQ from the blood, and the PQ concentration after HP treatment can be predicted by BP-ANN model

Factor structure of the SF-12 derived from principal component analysis.

<p>Factor structure of the SF-12 derived from principal component analysis.</p

The latent and measured variables used in the analysis (N = 856).

<p>SD: standard deviation; ¥: the currency symbol of RMB; PCS: physical component summary; MCS: mental component summary.</p

Measurement model of latent constructs (ellipses) and manifest indicator variables (rectangles).

<p>Values represent standardized factor loadings and all are statistically significant (<i>P</i><0.01). GFI = 0.973; CFI = 0.925; TLI = 0.886; RMSEA = 0.045; SRMR = 0.045. Chi-Square/DF = 2.736.</p

General information of study cities (2011).

<p>Available from: <a href="http://www.gdstats.gov.cn/tjnj/2012/ml1.htm" target="_blank">http://www.gdstats.gov.cn/tjnj/2012/ml1.htm</a></p><p>GDP: gross domestic product; RMB: renminbi (China's currency in circulation, the unit of the RMB is the yuan).</p

How Do Different Types of Environmental Regulations Affect Green Innovation Efficiency?

Environmental regulation policies are being continuously enriched today. To effectively improve green innovation efficiency through environmental regulations, it is urgent to better understand the impact of different environmental regulations on green innovation efficiency (GIE). However, due to the defects of previous methods for measuring GIE, existing studies may have deviations when analysing the effect of environmental regulations on GIE. To fill this gap, using Shaanxi, China, as a case study, the present study proposes a network data envelopment analysis (DEA) model based on neutral cross-efficiency evaluation to accurately measure the GIE of Shaanxi during the period of 2001–2017. On this basis, this study further analysed the impact of different types of environmental regulations on GIE from three aspects: causality, evolutionary relationships, and effect paths. The results indicate that (1) the GIE of Shaanxi Province showed a “fluctuation-slow growth-steady growth” trend during 2001–2017, and after 2014, the problem of an uncoordinated relationship between technology research and design (R&D) and technology transformation began to appear; (2) there was a linear evolutionary relationship between command-and-control environmental regulation and GIE and a “U”-shaped evolutionary relationship between market-based/voluntary environmental regulation and GIE; and (3) command-and-control environmental regulation and voluntary environmental regulation affected GIE mainly at the technology R&D stage, while market-based environmental regulation ran through the entire process of green innovation activities. This study improves the evaluation methods and theoretical systems of GIE and provides the scientific basis for government decision-makers to formulate environmental regulation policies

Andersen's behavioral model.

<p>Andersen's behavioral model.</p