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The Yellow River Basin (YRB) is China's substantial energy consumption base. The issue of carbon emission efficiency directly affects the ecological protection and high-quality development of the YRB. It is the key to achieving carbon peak in 2030 and carbon neutralization in 2060 ("30.60") double carbon emission reduction targets. Therefore, taking YRB as the research object, this paper first calculates the carbon emission and the decoupling state in the YRB. Secondly, the super-efficiency slacks-based measurement (SE-SBM) model is combined with the Malmquist index to analyze the temporal and spatial evolution characteristics of YRB's carbon emission efficiency from static and dynamic perspectives. Thirdly, the dynamic evolution characteristics of carbon emission efficiency are analyzed with the help of the Kernel density function. Finally, the Tobit model analyzes the influencing factors of YRB's and China's carbon emission efficiency. The results show that: (1) Among the nine provinces of YRB, the decoupling state between carbon emissions and economic growth in most provinces changes from weak decoupling to strong decoupling, and the decoupling elasticity index shows a fluctuating downward trend. (2) There are significant differences in carbon emission efficiency among provinces, but on the whole, it shows a stable growth trend. The high-value area of carbon emission efficiency is increasing, and the phenomenon of two-level differentiation is improving. The decline of the technological progress index causes the Malmquist index in Qinghai and Ningxia. On the contrary, the rise of the Malmquist index in the other seven provinces is caused by improving the technical efficiency index. (3) Industrial structure, economic development, and industrialization are the main positive factors affecting YRB's carbon emission efficiency. Urbanization level, green development level, and energy consumption level are the leading negative indicators hindering YRB's improvement of carbon emission efficiency. Therefore, targeted emission reduction suggestions should be formulated according to YRB's resource endowment and development stage characteristics. [ABSTRACT FROM AUTHOR]

Sanjiangyuan region is located in the hinterland of Qinghai-Tibet plateau, covering an area of 30.25 square kilometers, with the total population of 556000 people, including Tibetans, constituting more than 90%. Since it is the source of the Yangtze River, Yellow River, Lancang River catchment area, biodiversity concentration, and a beautiful environment, known as the "Chinese water tower," occupy an irreplaceable strategic position in national development. Although Sanjiangyuan region is blessed with unique natural landscape and rich tourism resources, its ecological environment has been seriously deteriorated due to long-term man-made destruction, backward economic development, and increasing poverty, which have seriously affected and restricted the survival and development of local ethnic groups. Since the 18th National Congress of the Communist Party of China, General Secretary Xi Jinping has repeatedly emphasized that "Lucid waters and lush mountains are invaluable assets." China advocates harmonious coexistence between humans and nature and sticks to the path of green and sustainable development. At the 19th National Congress of the Communist Party of China, the rural revitalization strategy was clearly proposed, which includes not only industrial revitalization but also ecological revitalization. In this paper, the area belonging to Sanjiangyuan National Park is taken as the main research object. By constructing economic, tourism, and ecological environment (TEE) evaluation indexes, the coupling degree and coupling coordination degree models are established, and the data from 2000 to 2019 are measured. The relationship of the three indicators is analyzed, and corresponding proposals for its development are put forward, such as implementing ecological compensation mechanism to improve the level of ecosystem management; blending into ethnic cultural elements to create a unique tourist resort; paying attention to economic development quality, building energy conservation and emissions reduction green engine; Doing a good job in the top-level design of ecological economy, and promoting the coordinated development of various indicators, etc. We will focus on quality control of economic development and build green engines for energy conservation and emission reduction. We will do a good job in top-level ecological and economic design and promote coordinated development of various indicators. [ABSTRACT FROM AUTHOR]

Water yield is an important ecosystem service, which is directly related to human welfare and affects the sustainable development. Using the integrated valuation of environmental services and tradeoffs model (InVEST model), we simulated the dynamic change of water yield in Qinghai lake watershed, Qinghai, China, and verified the simulation results. This paper emphatically explored how precipitation change and land use/land cover change (LUCC) affected the change of water yield on the spatial and temporal scales. Before 2004, the areas of cultivated land and unused land showed a dramatic increasing tendency, while forestland and water area presented a decreasing trend. After 2004 cultivated land changed slowly, unused land decreased. Grassland revealed a general trend of decline during 1977–2018, while built-up land basically presented a linear increase. The results show that water yield fluctuated and increased during 1977–2018. From 1977 to 2000, the mean water yield in each sub-watershed showed an increasing trend and afterward a decreasing one. After 2000, the sub-watersheds basically showed an increasing tendency. There was a strong correlation, with a correlation coefficient of 0.954 ** (** correlation is significant at the 0.01 level), between precipitation and water yield. Land use/land cover change can change the hydrological state of infiltration, evapotranspiration, and water retention. Meanwhile, the correlation between built-up land and water yield was the highest, with a correlation coefficient of 0.932, followed by forestland, with a correlation coefficient of 0.897. Through the analysis of different scenarios, we found that compared with land use/land cover change, precipitation played a more dominant role in affecting water yield. [ABSTRACT FROM AUTHOR]