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The purpose of this paper is to upgrade the collaborative emergency ability of government in the tier of towns, realizing emergency resource share, emergency cost reduction and emergency efficiency improving. This paper mainly aims to solve the problem of forecasting the natural disaster happening year of every township collaborative region in Fangshan District.

First, classify the townships into five collaborative regions through grey clustering. Second, set up a grey disaster forecast model for the whole Fangshan District according the annals of disaster from 1985 to 2012, and forecast the disaster grade. Third, build a grey disaster forecast model for the collaborative regions after constructing the buffer operators of catastrophic sequence according the annals of disaster from 1949 to 2012.

The authors forecasted the happening year and flood grade of future disaster for the whole Fangshan District. The accurate degrees of both flood and drought year model are greater than 90 per cent. The accurate degree of insects calamity year is a little low, but the relative errors are all lower than 3 per cent in recent continuous three times, so in the whole, it can be used. For the collaborative regions, the authors forecasted the future disaster years of them. The accuracy rate of every model is greater than 90 per cent. The result shows that the forecast errors are acceptable.

In the models, for the purpose of good accuracy, the authors used different initial data. For example, in the forecast model for whole Fangshan District disaster year, the authors used the data from 1985 to 2012, while in the forecast model for the disaster grade of it, the authors used the data from 1949 to 2012. In the disaster year forecast model for collaborative region, the authors also used the data from 1949 to 2012. If the authors can find a model that has high accuracy rate by using all the date information, it will be better.

Township is the most basic level of government organization in China, researching on collaborative emergency in township will do help to take targeted precautions measures against calamity according to the characteristic there. At the same time realizing emergency cost reduction and emergency efficiency improving based on the advantages of emergency resource share, short rescue distance, little effects of communication destruction.

Because of the stochastic occurring of disasters, it is very important to forecast the happening time of disasters accurately. This paper forecasted the natural disaster happening time of Fangshan District through grey catastrophic model, aimed at giving decision support to related department and strengthen the disaster prevention power targetedly.

It is well known that the greater the system, the steadier it is, and the easier to forecast it. Fangshan District, Beijing, is a medium-sized and small system in regional research, while townships are small systems. It is rarely a big challenge for the authors to forecast the disaster years in Fangshan and its collaborative townships. In this paper, the authors used grey system model and Markov transfer matrix in forecasting the disaster years and the disaster grade of flood in Fangshan District. All of them are new trying to using grey system theory in disaster forecast for Fangshan District, Beijing.

This study aims to build a model about students’ behavioural intention (BI) to use the university WeChat Library (WL).

Firstly, after the unified theory of acceptance and use of technology model was modified, a modified theory of acceptance and use of technology (MTAUT) model is built; a questionnaire survey, in which 1,003 valid observations are randomly collected from six universities in a city in eastern China, is performed; and then the results of the survey are used to build the model about students’ BI to use the WL based on regression analysis.

This study finds five factors, which are information quality (IQ), effort expectancy (EE), facilitating conditions (FC), performance expectancy (PE) and social influence (SI), that constitute independent variables of the model about students’ BI to use the university WL. And IQ has the greatest effect on students’ BI to use the WL, followed by EE, FC, PE and SI.

This study builds and tests a MTAUT model about students’ BI to use the university WL.