Identifying the Determinants of Nongrain Farming in China and Its Implications for Agricultural Development

Promoted by rapid industrialization and urbanization, the structure and spatial pattern of farming in China has changed greatly, and nongrain farming (NGF) has become more common. However, excessive NGF in some areas is not conducive to sustainable agricultural development and threatens China’s food security. In this study, we briefly analyze the stage characteristics of NGF in China and investigate the spatial agglomeration of NGF and its influencing factors from the perspective of spatial econometrics. The results showed that the average annual growth rate of NGF in China from 1985 to 2019 was 0.64%, and there was a growing positive spatial correlation between NGF in each province. Spatial Durbin model (SDM) estimation showed that both the per capita disposable income of local rural residents and the local urbanization rate promoted the development of NGF, while local per capita farmland, road density, and the functional orientation of the main grain-producing areas had a negative impact on NGF. The per capita disposable income of rural households and urbanization rate in neighboring areas had a promoting effect on the development of NGF, while road density in neighboring areas was negatively correlated with NGF. Ultimately, some targeted measures are proposed to promote China’s agricultural development in the new era.

The impact of COVID-19 on Farmers' Economic Income in Hubei Province of China

This paper discusses the statistical measurement of the impact of COVID-19 major emergencies on farmers' economic income in Hubei Province. Hubei Province was selected as the object of analysis, and five data of total output value of agriculture, forestry, animal husbandry, fishery and per capita disposable income of farmers in Hubei Province from the first quarter of 2013 to the second quarter of 2020 were collected by using the Internet. Since all the collected data were macroeconomic data, these data were taken the logarithm to meet the economic significance. The per capita disposable income of farmers was taken as the response variable, and the main factors affecting farmers' income were obtained by factor analysis. Livestock husbandry and fishery industries were the main industries in Hubei Province. Then the score of factor analysis were taken as explained variable to establish regression model composed of influencing factors. This paper use the multiple linear regression, support vector regression to fitting and forecasting data, ARIMA model of time series analysis, introduced at the same time, through the AIC model choice, with the first quarter of 2013 to 2019 in the second quarter fitting training, backward prediction two quarters, and three or four quarter of 2019 compared with the real data, through to the predicted results of the sequence diagram and evaluation index model to compare the mean square error (RMSE). Three models predict per capita disposable income of farmers in the first and second quarter of 2020. It has been found that performance better ARIMA model in the model compare is worse than before, and three kinds of predicted values are higher than the real value of the model, showed the outbreak to the influence of the agricultural economy in hubei province is serious. On this basis, taking into account the characteristics of geomorphic climate in Hubei province, the constructive suggestions are put forward.

On farmers’ Economic Income in Hubei Province of China During the COVID-19 Epidemic

This paper discusses the statistical measurement of the impact of COVID-19 major emergencies on farmers’ economic income in Hubei Province. Hubei Province was selected as the object of analysis, and five data of total output value of agriculture, forestry, animal husbandry, fishery and per capita disposable income of farmers in Hubei Province from the first quarter of 2013 to the second quarter of 2020 were collected by using the Internet. Since all the collected data were macroeconomic data, these data were taken the logarithm to meet the economic significance. The per capita disposable income of farmers was taken as the response variable, and the main factors affecting farmers’ income were obtained by factor analysis. Livestock husbandry and fishery industries were the main industries in Hubei Province. Then the score of factor analysis were taken as explained variable to establish a regression model composed of influencing factors. This paper uses the multiple linear regression, support vector regression to fitting and forecasting data, ARIMA model of time series analysis, introduced at the same time, through the AIC model choice, with the first quarter of 2013 to 2019 in the second quarter fitting training, backward prediction two quarters, and three or four quarter of 2019 compared with the real data, through to the predicted results of the sequence diagram and evaluation index model to compare the mean square error (RMSE). Three models predict per capita disposable income of farmers in the first and second quarter of 2020. It has been found that performance better ARIMA model in the model compare is worse than before, and three kinds of predicted values are higher than the real value of the model, showed the outbreak to the influence of the agricultural economy in Hubei province is serious. On this basis, taking into account the characteristics of geomorphic climate in Hubei province, constructive suggestions are put forward.

Analysis of China’s Pure Electric Vehicle Sales Based on Spatial Econometric Models

This paper empirically investigates the spatial dependence and serial correlation structures among different China’s brands of pure electric vehicle (EV) sales using spatial econometric models. Based on the newly proposed economic distance spatial weight matrix, the empirical results show that EV endurance mileage, power battery capacity, charging time, government subsidy, retail price, and each brand market share have important impacts on EV sales. The per capita disposable income of urban households, gasoline price, loan rate and the number of charging pile are statistically significant determinants of EV sales. In particular, the improvements of the number of charging pile and the rise of gasoline price can increase EV sales, while the rise of loan rate or tight monetary policy may increase the consumers’ cost of purchasing EVs and then decrease EV sales. Another interesting finding is that though the per capita disposable income of urban households increases the EV sales decreases. A plausible explanation would seem to be that the impact of the per capita disposable income of urban households on the EV sales is offset by the decline in government subsidies or the incomplete infrastructures such as the inconvenient of charging stations. Besides, the significantly positive spatial dependence and serial correlation exist among EV manufactures indicates that when developing EV sales strategies, EV manufacturers must consider not only the properties of the EVs they produce, but also the properties of similar types of EVs produced by other brands in the EV market.

A study on the impact of China’s urbanization on the quantity of municipal solid waste produced

Based on the IPAT model, this study selects the two-order lag period that is then applied to the dynamic model created to explore the impact of China’s urbanization on the quantity of municipal solid waste (MSW) produced. The study uses panel data collected from 27 provinces, autonomous regions, and municipalities in China that report directly to the central government. Results show that nationwide urbanization and urban per capita disposable income are positively correlated with the quantity of MSW produced. However, specifically, urbanization in the eastern and midwestern areas of China is insignificantly correlated with the quantity of MSW produced. It is, therefore, recommended that citizens should make sensible and environmental consumption decisions based on per capita disposable income. It is also suggested that quality development and Stead’s urbanization plan should become national policy, and that MSW categorization and a recycling policy should be implemented to treat MSW effectively.

Influential Factors of Residential Commodity Price Changes in Sanya

Residential commodity price is not only an important index of government macro-control, but also an important livelihood topic in society. The purpose of this paper is to take Sanya as an example to make an empirical analysis on the relationship between the factors affecting commodity housing prices, and their effects on housing prices. By using pearson correlation analysis, factor analysis and principal component regression method, it is found that the main factors influencing the housing price in Sanya are the housing sales area, the gross domestic product, the per capita disposable income, and the land price level of residential land. Among them, housing sales area and Sanya city house price changes in the opposite direction, the regional gross domestic product, per capita disposable income, residential land price level plays a positive role in housing prices.