ABSTRACT
Informal settlements house millions of poor urban dwellers in developing countries. Although many studies have been conducted on urban poverty in other developing countries, there has been little research conducted in Myanmar. The country was isolated from the rest of the world for many decades and only reopened its doors in 2011, so there is insufficient data for social policy makers. There have been some studies on the informal settlements in Yangon, but these are qualitative and focus on issues in specific settlements;therefore, there is academic space to explore the Yangon slums in their totality to assess their socio-economic conditions quantitatively. This study aimed to provide quantitative analyses of the socio-economic conditions of Yangon informal settlements, particularly regarding their variations in terms of formation according to the timeline of Myanmar's political regimes. The results indicate that the main socio-economic conditions of Yangon's slums differ depending on the time of settlement. © 2023 Elsevier Ltd
ABSTRACT
Waste in the archipelagic border area must be appropriately managed to maintain diplomatic relations. Indonesia's Riau Islands Province is an archipelagic region in Indonesia with limited solid waste infrastructure development. The capacity of the waste infrastructure depends on the rate of waste generation and is influenced by the socioeconomic conditions of the community. This study aims to study the model for estimating the rate of waste generation in the Riau Islands. This study uses data before and during the Covid-19 pandemic in 2019 and 2020. The estima-tion model uses a multiple linear regression model with independent variables such as gross regional domestic product, access sanitation, total population, and human development index. The fixed variable is the incidence of waste generation rate. During the pandemic Covid-19, the generation and composition of waste in the Riau Islands Archipelago did not experience significant changes, so the waste generation and composition characteristics are the same. However, the variable human development index (0.053) and the population (0.012) significantly increase the waste generation rate. The gross regional domestic product (0.017) negatively correlates, reducing the waste generation rate. The Riau Islands, which has an ocean area of 96%, is a source of life and significant to manage because the waste can be released into the ocean. Therefore, management from sources through policies considering the gross regional domestic product, total population, and human development index needs to be considered to reduce waste generation in the archipelago. © 2023, Polskie Towarzystwo Inzynierii Ekologicznej (PTIE). All rights reserved.
ABSTRACT
BACKGROUND: COVID-19 caused a worldwide outbreak leading the majority of human activities to a rough breakdown. Many stakeholders proposed multiple interventions to slow down the disease and number of papers were devoted to the understanding the pandemic, but to a less extend some were oriented socio-economic analysis. In this paper, a socio-economic analysis is proposed to investigate the early-age effect of socio-economic factors on COVID-19 spread. METHODS: Fifty-two countries were selected for this study. A cascade algorithm was developed to extract the R0 number and the day J*; these latter should decrease as the pandemic flattens. Subsequently, R0 and J* were modeled according to socio-economic factors using multilinear stepwise-regression. RESULTS: The findings demonstrated that low values of days before lockdown should flatten the pandemic by reducing J*. Hopefully, DBLD is only parameter to be tuned in the short-term; the other socio-economic parameters cannot easily be handled as they are annually updated. Furthermore, it was highlighted that the elderly is also a major influencing factor especially because it is involved in the interactions terms in R0 model. Simulations proved that the health care system could improve the pandemic damping for low elderly. In contrast, above a given elderly, the reproduction number R0 cannot be reduced even for developed countries (showing high HCI values), meaning that the disease's severity cannot be smoothed regardless the performance of the corresponding health care system; non-pharmaceutical interventions are then expected to be more efficient than corrective measures. DISCUSSION: The relationship between the socio-economic factors and the pandemic parameters R0 and J* exhibits complex relations compared to the models that are proposed in the literature. The quadratic regression model proposed here has discriminated the most influencing parameters within the following approximated order, DLBL, HCI, Elderly, Tav, CO2, and WC as first order, interaction, and second order terms. CONCLUSIONS: This modeling allowed the emergence of interaction terms that don't appear in similar studies; this led to emphasize more complex relationship between the infection spread and the socio-economic factors. Future works will focus on enriching the datasets and the optimization of the controlled parameters to short-term slowdown of similar pandemics.
Subject(s)
COVID-19 , Aged , COVID-19/epidemiology , Communicable Disease Control , Data Science , Humans , SARS-CoV-2 , Socioeconomic FactorsABSTRACT
Despite all the scientific and technological developments in the past one hundred years, biologic issues such as pandemics are a constant threat to society. While one of the aspects of a pandemic is the loss of human life, the outbreak has multi-dimensional impacts across regional and global societies. In this paper, a comparative regressive and neural network model is developed to analyze the impacts of COVID-19 (coronavirus) on the electricity and petroleum demand in China. The environmental analysis shows that the epidemic severeness significantly affects the electricity and the petroleum demand, both directly and indirectly. The outputs of the model stated that the elasticity of petroleum and electricity demand toward the population of the infected people is -0.1% and -0.65%, respectively. The mentioned results show that pandemic status has a significant impact on energy demand, and also its impacts can be tracked into every corner of human society.