By integrating multiple remote sensing data sources this study accurately assesses the spatiotemporal characteristics of changes in ecosystem service values (ESVs) in the Yellow River Basin from 2000 to 2015 through Theil-Sen median trend analysis and the Mann-Kendall test. The stability and continuity of the ESVs were comprehensively characterized using coefficients of variation and the Hurst exponent. The degree of coherence between ESVs and economic growth (represented by gross domestic product GDP) on the same temporal and spatial scales was analyzed using ecological-economic coordination (EEC) models. The results show that (1) from 2001 to 2015 the total ESV and the ESV per unit area in the Yellow River Basin generally showed a U-shaped pattern (decreasing slightly then increasing rapidly). (2) The areas with increasing ESVs made up approximately 55.6% of the total area of the river basin. The areas with a decreasing pattern were mainly in the west and north of the Yellow River Basin. (3) The stability and continuity of the ESVs showed a clustered, compact distribution. (4) The most common level of EEC was slightly uncoordinated followed by slightly coordinated and highly coordinated. The proportion of coordinated areas was relatively higher in cultivated land and the lowest in built-up land.

The rebound effect exists widely in the fields of energy, irrigation, and other resource utilizations. Previous studies have predicted the evolution of different resource utilizations under the shared socioeconomic pathways (SSPs), but it is still unclear whether total water use has a rebound effect. This study uses the SSPs as the basic prediction framework and evaluates the water resources and economic status of the provinces in China using the hydro-economic (HE) classification method. Then, combined with the SSPs scenario setting parameters, the conditional convergence model and the method recommended by the Food and Agriculture Organization of the United Nations (FAO) are used to simulate the changes in water use efficiency of the different provinces in China under different scenarios. Based on the future GDP forecast data of China’s provinces, combined with the forecast of water use efficiency changes, the total water use changes in China’s 31 provinces under different pathways from 2016 to 2030 are calculated. Among them, the future GDP data is predicted based on the Cobb–Douglas production function and SSPs scenario settings. Using a comprehensive evaluation of the evolution of the efficiency and the total amount, this study reveals whether there is a rebound effect. The results showed that with the continuous growth in the water use efficiency, the total water use had a “U” type trend, which indicated that there was a rebound effect in the total water use of China under the different SSPs. Based on this information, this study proposes some suggestions for irrigation water-saving technologies and policies.

Technological changes in water use efficiency directly influence regional sustainable development. However, few studies have attempted to predict changes in water use efficiency because of the complex influencing factors and regional diversity. The Chinese Government has established a target of 0.6 for the effective utilization coefficient of irrigation water, but it is not clear how the coefficient will change in different provinces in the future. The purpose of this study is to predict irrigation water use efficiency changes using a conditional convergence model and combined with the shared socioeconomic pathways (SSPs) scenario settings and hydro-economic (HE) classification to group 31 Chinese provinces by their different economic and water resources conditions. The results show that the coefficient exponentially converges to 0.6 in half the provinces under SSP1 (sustainability), SSP2 (middle of the road), and SSP5 (conventional development) by 2030, whereas SSP3 (fragmentation) and SSP4 (inequality) are generally inefficient development pathways. HE-3 provinces (strong economic capacity, substantial hydrological challenges) achieve the greatest efficiency improvements (with all coefficients above 0.6), and SSP1 is a suitable pathway for these provinces. HE-2 provinces (strong economic capacities, low hydrological challenges) have relatively low efficiency because they lack incentives to save water, and SSP1 is also suitable for these provinces. For most HE-1 provinces (low economic capacity, low hydrological challenges), the coefficients are less than 0.6, and efforts are required to enhance their economic capacity under SSP1 or SSP5. HE-4 provinces (low economic capacity, substantial hydrological challenges) would improve efficiency in a cost-efficient manner under SSP2.

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC’s) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province’s five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings.