BackgroundAIDS is a worrying public health issue in China and lacks timely and effective surveillance. With the diffusion and adoption of the Internet, the ‘big data’ aggregated from Internet search engines, which contain users’ information on the concern or reality of their health status, provide a new opportunity for AIDS surveillance. This paper uses search engine data to monitor and forecast AIDS in China.MethodsA machine learning method, artificial neural networks (ANNs), is used to forecast AIDS incidences and deaths. Search trend data related to AIDS from the largest Chinese search engine, Baidu.com, are collected and selected as the input variables of ANNs, and officially reported actual AIDS incidences and deaths are used as the output variable. Three criteria, the mean absolute percentage error, the root mean squared percentage error, and the index of agreement, are used to test the forecasting performance of the ANN method.ResultsBased on the monthly time series data from January 2011 to June 2017, this article finds that, under the three criteria, the ANN method can lead to satisfactory forecasting of AIDS incidences and deaths, regardless of the change in the number of search queries.ConclusionsDespite the inability to self-detect HIV/AIDS through online searching, Internet-based data should be adopted as a timely, cost-effective complement to a traditional AIDS surveillance system.

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Purpose The purpose of this paper is to analyse the impact of high-speed railway (HSR) on industrial pollution emissions using the data for 285 prefecture-level cities in China from 2004 to 2016. Design/methodology/approach The research method used in this paper is the multi-period difference-in-differences (DID) model, which is an effective policy effect assessment method. To further address the issue of endogeneity, the DID integrated with the propensity score matching (PSM-DID) approach is employed to eliminate the potential self-selection bias. Findings The results show that the HSR has significantly reduced industrial pollution emissions, which is validated by several robustness tests. Compared with peripheral cities, HSR exerts a greater impact on industrial pollution emissions in central cities. In addition, the mechanism test reveals that the optimised allocation of inter-city industries is an important channel for HSR to mitigate industrial pollution emissions, and this is closely related to the location of HSR stations. Originality/value Previous studies have paid more attention to evaluating the economic effects of HSR, however, most of these studies overlook its environmental effects. Consequently, the impact of HSR on industrial pollution emissions is led by using multi-period DID models in this paper, in which the environmental effects are measured. The results of this paper can provide a reference for the pollution reduction policies and also the coordinated development of economic growth and environmental quality.

Purpose Rural credit cooperatives (RCCs) have long dominated China’s rural credit market and met most of agricultural credit demands while the existing literature seldom examines their contribution to agricultural sector. The purpose of this paper is to provide empirical evidence on the contribution of RCCs to agricultural growth, using China’s provincial panel data from 1997 to 2014. Design/methodology/approach Both static fixed effects models and two-step generalized method of moment dynamic panel data models, which control the endogeneity, are employed to identify the causality from RCC credit to agricultural growth in China. Findings The results show that the credit from RCCs increases the agricultural output significantly. A 1 percent increase in RCC credit leads to agricultural growth of about 0.08 percent, which is robust to various empirical specifications. Further study shows that the contribution of RCC credit to agricultural growth decreases from the most developed eastern region to the least developed western region and increases over time. Research limitations/implications The results imply that RCC credit is critical in financing agricultural activities by relaxing rural credit constraints and intense competition strengthens the contribution of RCCs to agricultural growth by improving managerial efficiency and developing diversified financial products to meet better rural credit demands. Originality/value To the authors’ knowledge, this is first empirical study on the effect of RCC credit on agricultural growth despite of many on the role of financial development in agricultural growth.

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