Financial inclusion and low-carbon architectural design strategies: solutions for architectural climate conditions and architectural temperature on new buildings.

The objective of this research is to explore the potential of financial inclusion and low-carbon architectural design strategies as solutions to improve the thermal comfort and energy efficiency of new buildings in different architectural climate conditions. The manufacture sector, which accounts for about 40% of all yearly greenhouse gas releases, has been stimulating with trying to reduce the amount of energy it consumes and the detrimental effects it has on the climate, in accordance with the standards outlined in the 2016 Paris Agreement. In this study, panel data analysis is used to examine the connection between green property financing and carbon dioxide emissions from the building sector in one hundred and five developed and developing countries. Although this analysis finds a negative correlation among the development of environmentally friendly real estate financing and firms' worldwide carbon dioxide emissions, it finds that this correlation is most robust in developing nations. A number of these countries are experiencing an unregulated and rapid population explosion, which has boosted their demand for oil, making this discovery essential for them. The difficulty in securing green funding during this crisis is slowing and even reversing gains made in past years, making it all the more important to keep this momentum going during the COVID-19 outbreak. It's critical to keep the momentum going by doing something.

Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis.

In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface temperature day (LSTD) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%. When coupled with relevant socioeconomic-governmental factors, the model's average precision score improves to 77%. At the local scale analysis for selected countries, our proposed model can provide insights into the relationship between the fused data features and the respective local G parameter by achieving an average accuracy score of 79%.

COVID-19 in the environment.

In recent months, the presence of an emerging disease of infectious etiology has paralyzed everyone, already being a public health problem due to its high rate of infection, a life-threatening disease. The WHO has named it COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV2). New studies provide information of the role of the environment in COVID-19 transmission process, mortality related to this infectious disease and the impact on human health. The following review aims to analyze information on the implications of COVID-19 infection on human health and the impact of its presence on the environment, from its transmission capacity and the role of air pollutants and climatological factors to reducing the air pollution during confinement. Likewise, it provides a vision of the impact on the environment and human health of exposure to disinfectants and the presence of COVID-19 in wastewater, among other actions.