Based on the concept and connotation of the main function optimization of the river basin, this paper systematically analyzes various main function goals of the basin, and establishes the main function water resource allocation mechanism based on the evaluation index system of the main function of the basin, which is solved by genetic algorithm. The multi-objective optimization model obtained 4 types of water resource allocation plans for 9 provincial-level regions in the Yellow River Basin in 2017. The research results show that: (1) The main functions of the river basin to realize the water resource allocation mechanism makes the Yellow River Basin the largest possible water saving of 2.301 billion m³. (2) The water resources allocation mechanism for the main functions of the basin has brought about 434.448 billion yuan in production function increments, and 99.135 billion yuan in ecological function value increments, which can feed 81.948 million more people. (3) Reducing agricultural water use in Ningxia and Shandong and industrial water distribution in Inner Mongolia is crucial to the optimal distribution of water resources in the study basin.

Changes in the water and sediments of the Yellow River are associated with ecosystem security and quality development throughout the basin. It is of great significance to quantify the contribution of climate change and human activities to the reduction of runoff in order to analyze the causes of water and sediment changes in the Yellow River. Due to different research scales and research methods, the factors and influence degree of runoff variation vary greatly. Therefore, based on the same time scale, this paper uses MK trend test and Double Mass Curve method to systematically analyze the changing trend of hydrological elements in four typical basins (Huangfuchuan, Kuye River, Wuding River and Yanhe River) in the middle reaches of the Yellow River from 1960 to 2015. The Budyko water heat balance equation is used to clarify the role of climate change and human activities in the water and sediment change of the basin. The results showed that the runoffs in Huangfuchuan, Kuye River, Wuding River and Yanhe River basins all decreased significantly from 1960 to 2015 (P<0.01), and the runoffs in 1979 and 1999 experienced sudden changes without significant variations in precipitation. Compared with the base period (1960-1979), the contribution rate of climate change to runoff reduction in the P2 period (1980-1999) reached 64%-76%; with the large-scale implementation of the project of returning farmland to forestland and grassland, human activities in the P3 period (2000-2015) have become the main influencing factor leading to runoff reduction, with a contribution rate of 71%-88%.

Improving urban resources and environmental efficiency is of great significance to the high-quality development of the river basin. This article uses the SBM super-efficiency model based on undesired output and the Malmquist-Luenberger index, the resources and environmental efficiency of cities above prefecture level in the Yellow River Basin in 2000, 2005, 2010, 2015 and 2017 were measured and calculated, and their spatial and temporal characteristics were analyzed in combination with the kernel density function. Finally, the Tobit model is used to measure the main influencing factors of urban resources and environmental efficiency, which is expected to provide a scientific reference for improving the quality and efficiency of the resources and environment of the study area. The results show that the urban resources and environmental efficiency of the Yellow River Basin has generally shown a U-shaped change trend, and the overall level is not high. The number of non-DEA effective cities is much larger than that of DEA effective cities. Urban total factor productivity has shown a downward trend, but efficiency changes and pure technical efficiency changes show an improvement trend; industrial structure and government intervention are positively correlated with resources and environmental efficiency. The level of economic development, urbanization, energy consumption and environmental governance have a negative effect on the resources and environmental efficiency.

Eco-efficiency is the comprehensive embodiment of regional development quality, and the synthetic reflection of coordination degree between socio-economic system and environmental system. Taking the Yellow River Basin as the research area, this study explored the spatio-temporal evolution and driving forces of eco-efficiency of resource-based cities. At first, eco-efficiencies of 37 resource-based cities from 2003 to 2017 were evaluated, using the TOPSIS method. Then, the spatio-temporal changing trends were revealed through the Theil index, the Global Spatial Autocorrelation analysis and the Hotspot analysis. At last, the key driving factors of eco-efficiency change were explored by the Panel Tobit Regression model. The results showed that: (1) On the whole, the eco-efficiency of resource-based cities in the Yellow River Basin stabilized first and then increased during 2003 and 2017, with 2007 as the turning point. However, the quantity and rate of eco-efficiency growth varied considerably among resource-based cities. The two indexes of downstream cities were significantly higher than those of middle and upstream cities, and these two indexes of regenerative cities were significantly higher than those of growing cities, grow-up cities and recessionary cities. (2) The eco-efficiency gap between resource-based cities decreased slightly at first and then increased continuously during 2003 and 2017. Meanwhile, the eco-efficiency spatial distribution pattern of resource-based cities evolved from random state to aggregate state. Specifically, the low-value agglomeration areas were distributed at the junction of Shanxi and Henan provinces at first, and then moved upstream to the central and northern parts of Shanxi province. The high-value agglomeration areas, however, remained consistently in Shandong province, which is located in the lower reaches of the Yellow River. (3) In general, industrial transformation, scientific and technological innovation, infrastructure improvement and location conditions had significant positive effects on the improvement of eco-efficiency in the study area. However, export-oriented economy, resource dependence and environmental regulation had significant inhibitory effects, and urbanization, industrialization and foreign capital utilization had no significant impact. It is worth noting that the driving factors of eco-efficiency were heterogeneous across different types of resource-based cities, which means that different resource-based cites should take different measures to improve their eco-efficiencies.