In the context of the digital economy and based on the characteristics of digital financial development in China, this paper investigates the effect of digital finance on economic growth and explores its influencing mechanism. A panel econometric model, mediating effect model, and instrumental variable method were employed to evaluate yearly data from 30 provinces of China from 2011 to 2018. The results show that the development of digital finance has significantly driven economic growth, which is quantitatively robust after the selection of historical data as instrumental variables and other robustness tests. A heterogeneity analysis proved that provinces in the central and western regions, which have a lower urbanization rate and lower physical capital, more clearly embody the facilitating impacts of digital finance on economic growth compared to their counterparts in other regions. Further analysis found that the development of digital finance has spurred the liberation of regional entrepreneurship, which in turn promoted economic growth—that is, there is an entrepreneurial channel by which digital finance could boost economic growth.

BackgroundPerfluoroalkyl and polyfluoroalkyl substances (PFASs) have been reported to suppress immune function. However, previous studies on prenatal exposure to PFASs and allergic disorders in offspring provided inconsistent results. We aimed to examine the association between prenatal exposure to PFASs and childhood atopic dermatitis (AD) in offspring up to 24 months of age.MethodsA prospective birth cohort study involving 1056 pregnant women was conducted in two hospitals in Shanghai from 2012 to 2015. Prenatal information was collected by an interview with the women and from medical records. Fetal umbilical cord blood was collected at birth. Cord blood plasma PFASs were measured. Children were followed at 6, 12 and 24 months and information on the development of AD was recorded. AD was diagnosed by 2 dermatologists independently based on the questionnaires. Multiple logistic regression was used to compute odds ratio (OR) and corresponding 95% confidence interval (CI) for the association between AD and each PFASs, adjusting for potential confounders.ResultsA total of 687 children completed a 2-year follow-up visit and had PFASs measurement. AD was diagnosed in 173 (25.2%) children during the first 24 months. In female children, a log-unit increase in perfluorooctanoic acid (PFOA) was associated with a 2.1-fold increase in AD risk (AOR 2.07, 95% CI 1.13–3.80) after adjusting for potential confounders. The corresponding risk was 2.22 (1.07–4.58) for perfluorononanoic acid (PFNA). The highest PFOA quartile was significantly associated with AD (2.52, 1.12–5.68) compared with the lowest quartile. The highest quartile of PFNA, perfluorodecanoic acid (PFDA) and perfluorohexane sulfonic acid (PFHxS) were associated with AD with AOR (95% CI) being 2.14 (0.97–4.74), 2.14 (1.00–4.57), and 2.30 (1.03–5.15), respectively. Additionally, the second quartile of perfluorododecanoic acid (PFDoA) was associated with a 3.2-fold increase in AD risk (3.24, 1.44–7.27). However, no significant associations were found in male children.ConclusionsPrenatal exposure to PFOA, PFDA, PFDoA and PFHxS significantly increased the risk of childhood AD in female children during the first 24 months of life. In addition, the associations between AD with prenatal exposure to PFNA were close to statistical significance.Electronic supplementary materialThe online version of this article (10.1186/s12940-018-0352-7) contains supplementary material, which is available to authorized users.

BackgroundThe COVID-19 pandemic and physical distancing guidelines have compelled stroke practices worldwide to reshape their delivery of care significantly. We aimed to illustrate how the stroke services were interrupted during the pandemic in China.MethodsA 61-item questionnaire designed on Wenjuanxing Form was completed by doctors or nurses who were involved in treating patients with stroke from 1 February to 31 March 2020.ResultsA total of 415 respondents completed the online survey after informed consent was obtained. Of the respondents, 37.8%, 35.2% and 27.0% were from mild, moderate and severe epidemic areas, respectively. Overall, the proportion of severe impact (reduction >50%) on the admission of transient ischaemic stroke, acute ischaemic stroke (AIS) and intracerebral haemorrhage (ICH) was 45.0%, 32.0% and 27.5%, respectively. Those numbers were 36.9%, 27.9% and 22.3%; 36.5%, 22.1% and 22.6%; and 66.4%, 47.5% and 41.1% in mild, moderate and severe epidemic areas, respectively (all p<0.0001). For AIS, thrombolysis was moderate (20%–50% reduction) or severely impacted (>50%), as reported by 54.4% of the respondents, while thrombectomy was 39.3%. These were 44.4%, 26.3%; 44.2%, 39.4%; and 78.2%, 56.5%, in mild, moderate and severe epidemic areas, respectively (all p<0.0001). For patients with acute ICH, 39.8% reported the impact was severe or moderate for those eligible for surgery who had surgery. Those numbers were 27.4%, 39.0% and 58.1% in mild, moderate and severe epidemic areas, respectively. For staff resources, about 20% (overall) to 55% (severe epidemic) of the respondents reported moderate or severe impact on the on-duty doctors and nurses.ConclusionWe found a significant reduction of admission for all types of patients with stroke during the pandemic. Patients were less likely to receive appropriate care, for example, thrombolysis/thrombectomy, after being admitted to the hospital. Stroke service in severe COVID-19 epidemic areas, for example, Wuhan, was much more severely impacted compared with other regions in China.

Urban residential buildings make large contributions to energy consumption. Energy consumption per square meter is most widely used to measure energy efficiency in urban residential buildings. This study aims to explore whether it is an appropriate indicator. An extended STIRPAT model was used based on the survey data from 867 households. Here we present that building area per household has a dilution effect on energy consumption per square meter. Neglecting this dilution effect leads to a significant overestimation of the effectiveness of building energy savings standards. Further analysis suggests that the peak of energy consumption per square meter in China’s urban residential buildings occurred in 2012 when accounting for the dilution effect, which is 11 years later than it would have occurred without considering the dilution effect. Overall, overlooking the dilution effect may lead to misleading judgments of crucial energy-saving policy tools, as well as the ongoing trend of residential energy consumption in China.

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Current resident lifestyles pose a significant threat to urban sustainable development. Therefore, low-carbon behavior is receiving increasing attention from scholars and policy makers. Ascertaining residential self-selection is essential in order to study the relationship between the built environment and travel behavior. While several studies have explored the relationship between the urban form, socioeconomic factors, and travel behavior, only a few of them have studied the impact of self-selection on household energy consumption and other forms of consumption, which are also contribute to household carbon emissions. Using large-scale field surveys of 1,485 households and high-resolution images, sourced from Google Maps in 2018, of Zhengzhou city, the present study estimated the low-carbon level of three kinds of behavior: daily energy use at home, daily travel, and daily consumption. The study investigated the influence factors on low-carbon behavior using the hierarchical linear model. We found that residential self-selection impacts both energy use and daily travel. Residents in some built environments consumed less energy at home and contributed less CO2 emissions through daily travel than others. In particular, individual-level variables significantly affected the low-carbon energy use behavior. The female, elderly, highly educated, married, and working-class residents with children had higher levels of low-carbon energy use. Community-level variables significantly affected the level of low-carbon travel and low-carbon consumption. If residents lived in areas with high density, mixed land use, and high accessibility, their travel mode and consumption behavior would entail low carbon emissions. There is a relationship between individual variables and community variables. Different individual attributes living in the same built environment have different impacts on low-carbon behaviors.

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