A breakthrough in macro-scale circularity and eco-efficiency assessment: A case study of OECD countries.

Countries' circularity performance and CO emissions should be addressed as a part of the UN net-zero Sustainable Development Goals (SDGs) 2030. Macro-scale circularity assessment is regarded as a helpful tool for tracking and adjusting nations' progress toward the sustainable Circular Economy (CE) and SDGs. However, practical frameworks are required to address the shortage of real-world circularity assessments at the macro level.

How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis.

Covid-19 virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) threatens the health of human beings worldwide, imposing a concern for the world and prompting governments to control the contagion. Although vaccination is a proper tool to control the transmission, the efficient allocation of limited health-care resources to massive patients can improve the effectiveness of medical services. Relying on the Artificial Neural Network (ANN), the aim of this research is to enhance the future efficiency of Covid-19 treatment centers by forecasting their efficiency and providing benchmarks.