Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.
Greenhouse gas emissions and global warming are recent issues of upward trend. This study sought to underline the causal relationships between engagement modes with green technology, environmental, social, and governance (ESG) ratio, and circular economy. Our investigation also captured benchmarking of energy companies' circular economy behaviors. A hybrid-stage partial least squares structural equation modeling (PLS-SEM) and multi-criteria decision-making (MCDM) analysis have been adopted. This study collected 713 questionnaires from heads of departments and managers of energy companies. The findings of this study claimed that engagement modes with green technology affect the circular economy and sustainability. The findings revealed that ESG ratings have a mediating role in the nexus among engagement modes with green technology and circular economy. The results of the MCDM application revealed the identification of the best and worst energy companies of circular economy behaviours. This study is exceptional because it is among the first to address the issues of greenhouse gas emissions by providing decisive evidence about the level of circular economy behaviors in energy companies.