199 research outputs found

    Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic?

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    Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an example, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations; an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the sample and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics

    Digitalization and Climate Change Adaptation in China

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    The effects of climate change are increasingly transforming natural environments, threatening human living and socioeconomic developments. While mitigation action remains a priority, government efforts must also focus on helping people adapt to today’s climate impacts. Emerging digital technologies, which provide more efficient, rapid, and reliable risk monitoring and forecasting and enable better decision-making, can play a critical role to this end. This study develops policy recommendations for the utilization of digital tools to enhance climate change adaptation in China. This article first identifies China's primary climate change adaptation challenges, followed by an examination of successful digital solutions from countries outside of China. Successful application cases include using advanced machine learning models to develop more accurate rainfall predictions and applying digital twin systems to manage urban sewers in real time. These solutions are then evaluated in the Chinese context, leading to the formation of policy recommendations to advance similar initiatives
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