Multiple Concurrent Causal Relationships and Multiple Governance Pathways for Non-Pharmaceutical Intervention Policies in Pandemics: A Fuzzy Set Qualitative Comparative Analysis Based on 102 Countries and Regions

The global outbreak of COVID-19 has been wreaking havoc on all aspects of human societies. In addition to pharmaceutical interventions, non-pharmaceutical intervention policies have been proven to be crucial in slowing down the spread of the virus and reducing the impact of the outbreak on economic development, daily life, and social stability. However, no studies have focused on which non-pharmaceutical intervention policies are more effective; this is the focus of our study. We used data samples from 102 countries and regions around the world and selected seven categories of related policies, including work and school suspensions, assembly restrictions, movement restrictions, home isolation, international population movement restrictions, income subsidies, and testing and screening as the condition variables. A susceptible-exposed-infected-quarantined-recovered (SEIQR) model considering non-pharmaceutical intervention policies and latency with infectiousness was constructed to calculate the epidemic transmission rate as the outcome variable, and a fuzzy set qualitative comparative analysis (fsQCA) method was applied to explore the multiple concurrent causal relationships and multiple governance paths of non-pharmaceutical intervention policies for epidemics from the configuration perspective. We found a total of four non-pharmaceutical intervention policy pathways. Among them, L1 was highly suppressive, L2 was moderately suppressive, and L3 was externally suppressive. The results also showed that individual non-pharmaceutical intervention policy could not effectively suppress the spread of the pandemic. Moreover, three specific non-pharmaceutical intervention policies, including work stoppage and school closure, testing and screening, and economic subsidies, had a universal effect in the policies grouping for effective control of the pandemic transmission.

The impact trilemma of energy prices, taxation, and population on industrial and residential greenhouse gas emissions in Europe

As a major source of pollution and the cause of climate change, greenhouse gas emissions are attracting the attention of scholars, policymakers, and governors in Europe and the world. This article assesses the impact of population, energy taxes, and energy prices on greenhouse gas emissions from the residential and industrial energy consumption in Europe. The paper establishes a theoretical framework that predicts that rising energy prices and increased energy taxes will reduce residential and industrial GHG emissions. According to this framework, it is expected that the labor force will have an impact on industrial greenhouse gas emissions depending on wages elasticity. Between 2007 and 2017, panel data from 21 European countries were used to test the proposed hypothesis. First, a complete sample test was conducted. The results confirmed the proposed hypothesis. In addition, it was found that the size of the population increased residential greenhouse gas emissions, while the urbanization process reduced these emissions. Next, the sample was divided according to the economic development level. The split sample analysis shows the regional heterogeneity of population factors and energy costs impacts on GHG emissions. Finally, the time-varying coefficient test indicates that during the study period, the negative impact of urbanization has decreased over time, while the positive impact of industrial production on greenhouse gas emissions has increased. We believe this article will contribute to the formulation of environmental policies and provide additional insights for environmentally sustainable development.

MLP Modeling and Prediction of IP Subnet Packets Forwarding Performance

In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.