Accurate and quantitative assessments of rural development can offer important information for the formulation of rural development strategies. The purpose of this study was to provide a new perspective on the evaluation of rural sustainable development, overcoming the limitations of past studies that were based on simple analyses using a single method. In doing so, we aimed to provide a theoretical reference for formulating differentiated policies for regional rural development. In the present study, the rural development levels of 31 provincial administrative regions in China from 2000 to 2020 were analyzed based on the rural revitalization index framework, using five dimensions proposed in a previous study, i.e., industrial prosperity, ecological livability, rural civilization, effective governance, and a rich life. China’s rural sustainable development level was calculated using kernel density estimation and least squares estimation of temporal and spatial analyses. The results revealed that the development levels of rural areas in China are improving, but the improvement is not spatially consistent across rural areas. On the basis of the driving factors and causal mechanism, seven types of rural development levels were identified. We further analyzed the main reasons for the spatial differences in rural development levels and offer suggestions for improvement.

The scientific evaluation of the development of rural industrial integration is of great significance to understanding the changes occurring in the industrial structure of China’s agricultural sector. Combined with the practical problems related to the development of China’s rural industrial integration on the basis of four dimensions, i.e., agricultural industrial chain extension, agricultural multifunctionality, agricultural service industry integration development and the economic effect of rural industrial integration, the level of China’s rural industrial integration development is calculated for the period from 2008 to 2020 by means of the entropy method, and its spatial–temporal evolution characteristics and the regional heterogeneity of its driving factors are discussed on the basis of kernel density estimation, hotspot analysis and a fixed effect panel data model. The results showed the following: (1) From 2008 to 2020, the level of rural industrial integration development in 31 provinces in China improved to varying degrees. The growth rate of agricultural service industry integration was the highest, while the economic effect of rural industrial integration was second; the growth rate of the agricultural industrial chain extension was the lowest, and agricultural multifunctionality exhibited a fluctuating upward trend, peaking in 2017. In terms of spatial distribution, the representative provinces and cities with high and low levels of each dimension were different. (2) Rural industrial integration development in China was characterized by a regional imbalance, with polarization in the central and western regions. In terms of spatial distribution, an “east–middle–west” ladder pattern was obvious. The hotspots were mainly concentrated in the southeast region, and they showed a gradually expanding trend, while the coldspots were mainly concentrated in the northwest region, and they showed a trend of gradually decreasing size. (3) In terms of driving factors, the urbanization level, rural human capital, rural transportation facilities, rural ecological environment, intensity of financial support for agriculture and rural digitalization had significantly positive effects, while the degree of industrial upgrade had a significantly negative effect. Each driving factor had different effects on the rural industrial integration development in the eastern, central and western regions.

The scientific evaluation of water pollution in the Yellow River Basin was directly related to the sustainable utilization of water resources and the green development of the agricultural economy in this region. In this study, we focused on the planting industry, and measured the agricultural grey water footprint of 73 prefecture-level cities in the Yellow River Basin from 2000 to 2019. We used spatial autocorrelation analysis to reveal temporal and spatial differentiation characteristics, and we used the path analysis method to study the factors influencing the temporal evolution and spatial distribution. Taking 2015 as the study period, the agricultural grey water footprint showed a trend of first rising and then falling. The values and growth rates of the agricultural grey water footprint in different regions were quite different. According to the natural breakpoint method, the agricultural grey water footprints were divided into low, middle, high, and very high groups. There were obvious spatial differences in the agricultural grey water footprints, and these differences gradually decreased. Generally, the H–L and the L–L types were dominant. From 2000 to 2019, most prefecture-level cities maintained the same transition changes as those in the neighboring regions. Crop yield, economic scale, population scale, urban and rural structure, and technological innovation were found to be the key elements of spatiotemporal variation in the agricultural grey water footprint.

The coordinated development of the economy, water resources, and environment is key to the concept of sustainable development. In this study, in respect to the water footprint, it calculated the water resource input and sewage dilution for the Yangling Agricultural Demonstration Area during the agricultural economic growth period from 1999 to 2019. This study also established the Tapio decoupling analysis model on this basis to study the decoupling relationship between economic growth and the water resource environment, as well as its evolution law. A residual free-complete decomposition model was introduced to analyze the influence of the water resource input and sewage dilution on the agricultural economic growth in the Yangling Demonstration Area and its transmission mechanism. The results showed the following: (1) the economic growth and the blue and green water footprints of the Yangling Demonstration Area were decoupled from one another from 1999 to 2019, and the degree of decoupling between economic growth and the grey-water footprint was poor, indicating that economic growth had a more obvious promotion effect on the reduction of water resource consumption, and the pressure on the water environment was increased year by year; (2) the main factor affecting the reduction of water resource consumption in the Yangling Demonstration Area was the effect of technology, and this was greater than the effect of water resource consumption increments resulting from the expansion of the economic scale; (3) the progress of environmental governance technology was the main reason for the decrease in the grey-water footprint in the Yangling Demonstration Area. To improve the quality of our economic development, the pattern of economic development should be transformed to regulate economic growth and expand the scale, reducing water consumption, improving pollutant emission control technology, and making full use of water resources to provide evidence for a reasonable water resource management policy.