Nexus between air pollution and NCOV-2019 in China: Application of negative binomial regression analysis
TL;DR: In this paper, a negative binomial regression (NBR) model was applied to examine the difference between the number of people infected with the novel coronavirus (NCOV-2019) and deaths in China.
About: This article is published in Process Safety and Environmental Protection.The article was published on 2021-06-01. It has received 163 citations till now.
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TL;DR: Wang et al. as discussed by the authors measured the relationship between green innovation and the performance of financial development by using an econometric estimation during the year of 2000 to 2018 in 28 Chinese provinces.
Abstract: This research measures the relationship between green innovation and the performance of financial development by using an econometric estimation during the year of 2000 to 2018 in 28 Chinese provinces. It is intended to explore the relative role of green technological innovation in driving green financial development in the west and central China, as well as how it influences economic growth in these regions. Ordinary least square (OLS) framework was utilized in mainland China to perform empirical studies by using an econometric estimation. This study claims that China has adopted research-based education system, while those for economic growth and expenditure in the regions while the innovation parts results shows that the tertiary education were 12.42% and 13.53% versus the 10.50% and 10.6% in the eastern area. The research-based education increases the patents in green innovation and boosts the environmental policy. The financial development led to green technological development and innovation. Green innovation and financial development decrease the emissions, and it is apparent that as environmental regulations stimulate technical development, the superiority of human resources increases. The findings indicate that green financing reduces short-term lending, thus limiting clean energy overinvestment, while the long-term loans have little impact on renewable energy overinvestment, and the intermediary effect is unmaintainable. Meanwhile, the green financial growth will reduce renewable energy overinvestment and increase renewable energy investment productivity to certain amount.
127 citations
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TL;DR: In this paper , the authors examined the financial resource curse hypothesis by scrutinizing the linkage between financial development and natural resources rent from 1984 to 2018, and the cross-sectional augmented ARDL model was used for analysis purposes.
119 citations
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TL;DR: In this article, the authors present a measurement and analysis of G7 countries' energy, economic, social, and environmental performance associated with energy poverty indexes using data envelopment analysis (DEA).
Abstract: The aim of the study is to estimate the nexus between energy insecurity and energy poverty with the role of climate change and other environmental concerns. We used DEA like WP methods and properties of MCDA, a most common form of data envelopment analysis (DEA) to estimate the nexus between constructs. This paper presents a measurement and analysis of G7 countries’ energy, economic, social, and environmental performance associated with energy poverty indexes. The study used the multiple, comprehensive, and relevant set of indicators, including energy economics and environmental consideration of energy poverty. The net energy consumption of al G7 economies is equal to 34 percent of the entire world along with the net estimate GDP score of around 50 percent. Using DEA modelling and estimation technique, our research presented valuable insights for readers, theorists and policy makers on energy, environment, energy poverty and climate change mitigation. For this reasons, all these indicators combined in a mathematical composite indicator to measure energy, economic, social, and environmental performance index (EPI). Results show that Canada has the highest EPII score, which shows that Canada’s capacity to deal with energy self-sufficiency, economic development, and environmental performance is greater than the other G7 countries. France and Italy rank second and third. Japan comes next with 0.50 EPI scores, while the USA has the lowest average EPI score environment vulnerable even though have higher economic development among the G7 group countries. We suggest a policy framework to strengthen the subject matter of the study.
94 citations
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TL;DR: In this article, the authors analyzed the relationship between external debt and economic growth in the South Asian region and showed that gross capital formation and trade openness have a positive effect on economic growth.
88 citations
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22 Nov 2021
TL;DR: In this paper, the authors aim to realize their potential for sustainable production in achieving zero-carbon agenda in emerging economies, due to natural resource constraints, businesses must focus on green production.
Abstract: Emerging economies are striving to realize their potential for sustainable production in achieving zero-carbon agenda. Due to natural resource constraints, businesses must focus on green production...
68 citations
References
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01 Jan 2007TL;DR: In this article, the authors introduce the concept of risk in count response models and assess the performance of count models, including Poisson regression, negative binomial regression, and truncated count models.
Abstract: Preface 1. Introduction 2. The concept of risk 3. Overview of count response models 4. Methods of estimation and assessment 5. Assessment of count models 6. Poisson regression 7. Overdispersion 8. Negative binomial regression 9. Negative binomial regression: modeling 10. Alternative variance parameterizations 11. Problems with zero counts 12. Censored and truncated count models 13. Handling endogeneity and latent class models 14. Count panel models 15. Bayesian negative binomial models Appendix A. Constructing and interpreting interactions Appendix B. Data sets and Stata files References Index.
2,967 citations
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TL;DR: The latest literatures on genetic, epidemiological, and clinical features of COVID‐19 in comparison to SARS and MERS are summarized and special measures on diagnosis and potential interventions are emphasized to improve understanding of the unique features ofCOVID‐ 19 and enhance control measures in the future.
Abstract: By 27 February 2020, the outbreak of coronavirus disease 2019 (COVID-19) caused 82 623 confirmed cases and 2858 deaths globally, more than severe acute respiratory syndrome (SARS) (8273 cases, 775 deaths) and Middle East respiratory syndrome (MERS) (1139 cases, 431 deaths) caused in 2003 and 2013, respectively. COVID-19 has spread to 46 countries internationally. Total fatality rate of COVID-19 is estimated at 3.46% by far based on published data from the Chinese Center for Disease Control and Prevention (China CDC). Average incubation period of COVID-19 is around 6.4 days, ranges from 0 to 24 days. The basic reproductive number (R0 ) of COVID-19 ranges from 2 to 3.5 at the early phase regardless of different prediction models, which is higher than SARS and MERS. A study from China CDC showed majority of patients (80.9%) were considered asymptomatic or mild pneumonia but released large amounts of viruses at the early phase of infection, which posed enormous challenges for containing the spread of COVID-19. Nosocomial transmission was another severe problem. A total of 3019 health workers were infected by 12 February 2020, which accounted for 3.83% of total number of infections, and extremely burdened the health system, especially in Wuhan. Limited epidemiological and clinical data suggest that the disease spectrum of COVID-19 may differ from SARS or MERS. We summarize latest literatures on genetic, epidemiological, and clinical features of COVID-19 in comparison to SARS and MERS and emphasize special measures on diagnosis and potential interventions. This review will improve our understanding of the unique features of COVID-19 and enhance our control measures in the future.
1,196 citations
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TL;DR: There is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease.
858 citations
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TL;DR: A small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate, and the results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the CO VID-19 crisis.
Abstract: Objectives United States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States. Design A nationwide, cross-sectional study using county-level data. Data sources COVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. Main outcome measures We fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses. Results We found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses. Conclusions A small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely. Summary Box What is already known on this topic Long-term exposure to PM2.5 is linked to many of the comorbidities that have been associated with poor prognosis and death in COVID-19 patients, including cardiovascular and lung disease. PM2.5 exposure is associated with increased risk of severe outcomes in patients with certain infectious respiratory diseases, including influenza, pneumonia, and SARS. Air pollution exposure is known to cause inflammation and cellular damage, and evidence suggests that it may suppress early immune response to infection. What this study adds This is the first nationwide study of the relationship between historical exposure to air pollution exposure and COVID-19 death rate, relying on data from more than 3,000 counties in the United States. The results suggest that long-term exposure to PM2.5 is associated with higher COVID-19 mortality rates, after adjustment for a wide range of socioeconomic, demographic, weather, behavioral, epidemic stage, and healthcare-related confounders. This study relies entirely on publicly available data and fully reproducible, public code to facilitate continued investigation of these relationships by the broader scientific community as the COVID-19 outbreak evolves and more data become available. A small increase in long-term PM2.5 exposure was associated with a substantial increase in the county’s COVID-19 mortality rate up to April 22, 2020.
833 citations