Analysis of Demand and Structural Changes in China’s Water Resources over the Next 30 Years
The medium and long-term demand prediction for water resources is critical to the country’s macro-allocation of water resources and the formulation of a long-term regional economic development strategy. However, the official prediction report of China’s water resources was created ten years ago, and its results are far from accurate in practice making it difficult to direct China’s water resource planning in the coming ten years.
By introducing the demand forecasting method for the first time, this study built a mathematical prediction model that integrates “data constraint + mathematical prediction + actuarial prediction”.
The conclusions are as follows:
(1) The new method developed in this research is more accurate than previous prediction methods and has a higher degree of matching with actual data. Despite the problem of trend extrapolation, this model can accurately understand the long-term trend. However, the amount of data must be calculated.
(2) China’s consumption of total water, agricultural water, industrial water, domestic water, and ecological water will predict to be 552.9 billion m3, 319.1 billion m3, 95.7 billion m3, 95.5 billion m3 and 42.6 billion m3 in 2030, respectively, as well as 504.7 billion m3, 281.1 billion m3, 61.4 billion m3, 101.4 billion m3, and 60.8 billion m3 in 2050, respectively, indicating obvious structural changes.
(3) China’s industrial structure evolves frequently; therefore, water resource forecasting must account for changes in industrial structure as well as geographical variances. In North China, agricultural water demand will fall, but ecological water investment will rise. The planning and distribution of the nation’s water resources will significantly be influenced theoretically and practically by the development of this methodology study. This method is universal and can be used for long-term prediction of future water demand in other countries.
Keywords: water resources; demand forecasting; model building; coupled analysis