Understanding the nexus CO2 emissions and economic growth helps economies in formulating energy policies and developing energy resources in sustainable ways. Although during recent years, numerous of the previous studies have been very thoroughly investigated the nexus between economic growth and CO2 emissions, there is a lack of research regarding the qualitative systematic review and meta-analysis in these areas. The main purpose of this review paper is to present the comprehensive overview of the relationship between CO2 emissions and economic growth. In this regard, the Web of Science database has been chosen and a qualitative systematic and meta-analysis method which called "Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)" has been proposed. Therefore, a review of 175 published articles appearing in 55 scholarly international journals between 1995 and 2017 has been achieved to reach a broad review of the nexus between economic growth and CO2 emissions with other indicators. Consequently, the selected articles have been categorized by the author name, the year of publication, data duration, types of techniques, data analysis method, the name of indicators, country, scope (individual country and multi-countries), journals, results, and outcome in which they appeared. The results of this paper demonstrated that the nexus between CO2 emissions and economic growth gives reasons for policy options that have to reduce emissions by imposing limiting factors on economic growth as well. Given the fact that bidirectional causality exists, as far as economic growth increases or decreases, further CO2 emissions are stimulated in higher or lower levels and consequently, a potential reduction of the emissions should have an adverse influence on economic growth.
Performance measurement plays an important role in the successful design and reform of regional healthcare management systems. In this study, we propose a hybrid data envelopment analysis (DEA) and game theory model for measuring the performance and productivity in the healthcare centers. The input and output variables associated with the efficiency of the healthcare centers are identified by reviewing the relevant literature, and then used in conjunction with the internal organizational data. The selected indicators and collected data are then weighted and prioritized with the help of experts in the field. A case study is presented to demonstrate the applicability and efficacy of the proposed model. The results reveal useful information and insights on the efficiency levels of the regional healthcare centers in the case study.
As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.
Conventional building materials (CBMs) made from non-renewable resources are the main source of indoor air contaminants, whose impact can extend from indoors to outdoors. Given their sustainable development (SD) prospect, green building materials (GBMs) with non-toxic, natural, and organic compounds have the potential to reduce their overall impacts on environmental and human health. In this regard, biocomposites as GBMs are environmentally friendly, safe, and recyclable materials and their replacement of CBMs reduces environmental impacts and human health concerns. This study aims to develop a model of fully hybrid bio-based biocomposite as non-structural GBMs and compare it with fully petroleum-based composite in terms of volatile organic compound (VOC) emissions and human health impacts. Using a small chamber test (American Society for Testing and Materials (ASTM)-D5116) for VOC investigation and SimaPro software modeling with the ReCiPe method for evaluating human health impacts. Life cycle assessment (LCA) methodology is used, and the results indicate that switching the fully hybrid bio-based biocomposite with the fully petroleum-based composite could reduce more than 50% impacts on human health in terms of indoor and outdoor. Our results indicate that the usage of biocomposite as GBMs can be an environmentally friendly solution for reducing the total indoor and outdoor impacts on human health.