Energy spending and household characteristics of floating population: Evidence from Shanghai

Wei, Taoyuan; Zhu, Qin; Glomsrød, Solveig

doi:10.1016/j.esd.2014.07.001

Cited by 19 publications

(6 citation statements)

References 29 publications

(30 reference statements)

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“…The influenza-associated P&I excess mortality rate for the registered Shanghai population was higher than that for the general population in Shanghai. Possible reasons are: (1) over 80% of temporary migrants in Shanghai were constituted of young and middle-aged labor force while seniors over 60 years old only accounted for 3%, who were the high-risk group for influenza [ 36 ]. However, the proportion of seniors among Shanghai registered population has exceeded 20% by the year 2009 [ 37 ]; (2) many temporary migrants in Shanghai were generally not willing to go to hospitals because they could not afford healthcare, even if they did catch influenza; and (3) a proportion of the temporary migrants in Shanghai might go back to their places of origin when death was approaching because of Chinese tradition, which in turn led to under-reporting of the number of deaths.…”

Section: Discussionmentioning

confidence: 99%

Excess pneumonia and influenza mortality attributable to seasonal influenza in subtropical Shanghai, China

Wang

Chen

et al. 2017

BMC Infect Dis

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BackgroundDisease burden attributable to influenza is substantial in subtropical regions. Our study aims to estimate excess pneumonia and influenza (P&I) mortality associated with influenza by subtypes/lineages in Shanghai, China, 2010–2015.MethodsQuasi-Poisson regression models were fitted to weekly numbers of deaths from causes coded as P&I for Shanghai general and registered population. Three proxies for influenza activity were respectively used as an explanatory variable. Long-term trend, seasonal trend and absolute humidity were adjusted for as confounding factors. The outcome measurements of excess P&I mortality associated with influenza subtypes/lineages were derived by subtracting the baseline mortality from fitted mortality.ResultsExcess P&I mortality associated with influenza were 0.22, 0.30, and 0.23 per 100,000 population for three different proxies in Shanghai general population, lower than those in registered population (0.34, 0.48, and 0.36 per 100,000 population). Influenza B (Victoria) lineage did not contribute to excess P&I mortality (P = 0.206) while influenza B (Yamagata) lineage did (P = 0.044). Influenza-associated P&I mortality was high in the elderly population.ConclusionsSeasonal influenza A virus had a higher P&I mortality than influenza B virus, while B (Yamagata) lineage is the dominant lineage attributable to P&I mortality.Electronic supplementary materialThe online version of this article (10.1186/s12879-017-2863-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Excess pneumonia and influenza mortality attributable to seasonal influenza in subtropical Shanghai, China

Wang

Chen

et al. 2017

BMC Infect Dis

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“…But some scholars come to a different conclusion. Wei et al ( 2014 ) argued that there was not enough evidence to show that the direct carbon emissions of residents moving to Shanghai from rural areas increased. Ala-Mantila et al ( 2014 ) and Rehman et al ( 2021 ) found that urban population has less carbon emissions than rural.…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

Ziyuan

Yibo

Simayi

et al. 2022

Environ Sci Pollut Res

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In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020–2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.

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“…Higher income groups usually have higher total energy consumption compared to lower income households [35]. Transport and non-transport HH energy use is positively associated with high income in different countries [36][37][38][39]. Education as one of the influencing factors complicates households' energy use behaviors.…”

Section: Socioeconomic Characteristics and Demographicsmentioning

confidence: 99%

“…Education as one of the influencing factors complicates households' energy use behaviors. On the one hand, higher Educated households tend to adopt more energy efficient technologies and hence have less total energy expenditures [34,40] as well as less transportation related energy consumption [38]. At the same time they may own energy intensive items or commute longer for work [36,41] that will increase their total energy consumption [36,42].…”

Section: Socioeconomic Characteristics and Demographicsmentioning

confidence: 99%

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

2018

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The U.S. household (HH) energy consumption is responsible for approximately 20% of annual global GHG emissions. Identifying the key factors influencing HH energy consumption is a major goal of policy makers to achieve energy sustainability. Although various explanatory factors have been examined, empirical evidence is inconclusive. Most studies are either aspatial in nature or neglect the spatial non-stationarity in data. Our study examines spatial variation of the key factors associated with HH energy expenditures at census tract level by utilizing geographically weighted regression (GWR) for the 14 metropolitan statistical areas (MSAs) in North Carolina (NC). A range of explanatory variables including socioeconomic and demographic characteristics of households, local urban form, housing characteristics, and temperature are analyzed. While GWR model for HH transportation expenditures has a better performance compared to the utility model, the results indicate that the GWR model for both utility and transportation has a slightly better prediction power compared to the traditional ordinary least square (OLS) model. HH median income, median age of householders, urban compactness, and distance from the primary city center explain spatial variability of HH transportation expenditures in the study area. HH median income, median age of householders, and percent of one-unit detached housing are identified as the main influencing factors on HH utility expenditures in the GWR model. This analysis also provides the spatial variability of the relationship between HH energy expenditures and the associated factors suggesting the need for location-specific evaluation and suitable guidelines to reduce the energy consumption.

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Energy spending and household characteristics of floating population: Evidence from Shanghai

Cited by 19 publications

References 29 publications

Excess pneumonia and influenza mortality attributable to seasonal influenza in subtropical Shanghai, China

Excess pneumonia and influenza mortality attributable to seasonal influenza in subtropical Shanghai, China

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

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