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Abstract
Famine poses a significant threat to human food security and sustainable development. This study investigates the prevalence and magnitude of famine and its connection to climatic change/disasters at different spatiotemporal scales. The famine index was reconstructed using 13 828 famine-related literature records in China during the Qing Dynasty (CE 1644–1911). The study found that extreme drought/flood events instantaneously triggered famine at the seasonal to interannual scale, leading to intermittent occurrences of great famines. Drought-induced famine was the most prominent. Famine was positively correlated with drought in both short-term variations and long-term trends across different regions. The effect of floods on famines was double-edged and varied between the north and south of China. The severe famines that occurred between 1811 and 1878 were related to both climatic cooling and an increase in drought/flood events under a situation of growing population pressure on resources. The greatest famine of 1876–78 was probably the result of long-term interactions among intensifying human–land contradictions since the early nineteenth century, periodic droughts in north China, and a weakening of regional buffering mechanisms due to flood-induced declines in south China.

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation. Qingquan Tony Zhang is an Adjunct Professor at the University of Illinois at Champaign, R.C. Evan Fellow, Gies Business School, focusing on finance, quantitative investment and entrepreneurship. He is President of the Chicago chapter of the Chinese American Association for Trading and Investment, who has long worked in FinTech, including artificial intelligence and big data. Beibei Li is an Associate Professor of IT & Management and Anna Loomis McCandless Chair at Carnegie Mellon University. Dr. Li has extensive experience at leveraging large-scale observational data analytics and experimental analysis with a strong focus on modeling individual user behavior across online, offline, and mobile channels for decision support. Danxia Xie is an Associate Professor in Economics at Tsinghua University, China. Dr. Xies teaching and research focuses on digital economy, finance, law and economics, and macroeconomics. Dr. Xie has also worked at Peterson Institute for International Economics, a top think tank at Washington, DC.