Mechanism Study on the Impact of China Population Structure Change on the Water Use of the Three Main Industries

As the principal part of economic and social development, the demographic factor is the fundamental factor driving the change of water resources, and achieving the harmony of human and water has been one of the most important tasks to promote high-quality development. Based on Maslow’s hierarchy of needs theory, this article applied panel data for 19 years and employed impulse response functions and threshold models to do a mechanism analysis of the impact of population structure changes on the water consumption changes of the three main industries. The study found the following: Firstly, the urban population promotes an increase of the total water consumption, industrial water consumption, and domestic water consumption, which suppresses agricultural water consumption and shows an inverted “N” trend. Secondly, the aging population has expanded the total water consumption, and agricultural and domestic water demand, and reduced industrial water consumption. Thirdly, food consumption helps to reduce the total water consumption and agricultural water consumption, but increases the industrial water consumption and the growth rate rises. Fourthly, the increase in the proportion of agricultural employment reduces the total water consumption, and agricultural and domestic water consumption, and increases industrial water consumption. Fifthly, the total water consumption and domestic water consumption both increase with the improvement of the population education level, while the agricultural water consumption declines first and then rises. The empirical results can provide a reference for analyzing the driving mechanisms of regional water consumption changes.

Research on the Measurement and Influencing Factors of Implicit Water Resources in Import and Export Trade from the Perspective of Global Value Chains

In this study, China’s virtual water trade was measured on the basis of multi-region input/output tables, and its influencing factors of change were decomposed. The results revealed that virtual water export and import increased from 161.5 billion tons and 114.07 billion tons in 2007 to 193.31 billion tons and 157.1 billion tons in 2014, respectively. Eight economies accounted for more than 50% of China’s total virtual water export and import, whereby the total of the United States, Japan, and Europe reached 44% (export) and 31.3% (import). The export scale, export of intermediate products, export industry structure, domestic water consumption coefficient, and domestic intermediate input structure were the main factors of the change in virtual water export. The growth of export scale was the primary reason for the growth of virtual water export. A decline in the domestic water consumption coefficient was the primary reason for the restrained growth of virtual water export. The import scale, import of intermediate products, import industry structure, water consumption coefficient of foreign countries, and the correlation among domestic industries were the main factors affecting the change in virtual water import. The growth of import scale was the primary reason for the growth of virtual water import in most sectors. A decline in the water consumption coefficient abroad was the primary reason for the restrained growth of virtual water import.

Prediction and analysis of domestic water consumption based on optimized grey and Markov model

With the rapid development of urbanization and the continuous improvement of living standards, China's domestic water consumption shows a growing trend. However, in some arid and water deficient areas, the shortage of water resources is a crucial factor affecting regional economic development and population growth. Therefore, it is essential important to reliably predict the future water consumption data of a region. Aiming at the problems of poor prediction accuracy and overfitting of non-growth series in traditional grey prediction, this paper uses residual grey model combined with Markov chain correction to predict domestic water consumption. Based on the traditional grey theory prediction, the residual grey prediction model is established. Combined with the Markov state transition matrix, the grey prediction value is modified, and the model is applied to the prediction of domestic water consumption in Shaanxi Province from 2003 to 2019. The fitting results show that the accuracy grade of the improved residual grey prediction model is “good”. This shows that the dynamic unbiased grey Markov model can eliminate the inherent error of the traditional grey GM (1,1) model, improve the prediction accuracy, have better reliability, and can provide a new method for water consumption prediction.