China has witnessed accelerated urbanization since the reforms and open policies which began in 1978. This eventually resulted in increased residential water requirements and worsening water shortages, particularly in the current century. In the context of resource and environmental constraints, improving agricultural water use efficiency (AWUE) is a crucial issue to ensure food security, improve the ecological environment, and meet the needs of sustainable agricultural development. Based on the panel data of 30 provinces in China from 1999 to 2018, the article uses the Super-SBM model to measure the AWUE. Moreover, the study uses the entropy method to establish the urbanization evaluation index system from the dimensions of population, land, economy, measures the comprehensive level of urbanization development, and further constructs a dynamic spatial econometric model. We use the unconditional maximum likelihood estimation method to evaluate the impact of urbanization development on AWUE and its heterogeneity. The findings reveal that the AWUE considering undesired outcomes has generally shown a steady improvement, but there is ample space for resource conservation and environmental protection, and there are noticeable differences among regions. The decomposition of spatial effects shows that urbanization development in each region has a short-term positive effect on AWUE in the region and neighboring regions, and a long-term effect exists only in the western region. The impact of urbanization in different dimensions has been found that both land urbanization and economic urbanization contribute to the improvement of AWUE, while population urbanization helps to improve AWUE by improving the awareness level of the farmers.

Improving agricultural water use efficiency (AWUE) is an important way to solve the shortage of water resources in arid and semi-arid regions. This study used the Super-DEA (data envelopment analysis) to measure the AWUE of 52 cities in Northwest China from 2000 to 2018. Based on spatial and temporal perspectives, it applied Exploratory Spatial Data Analysis (ESDA) to explore the dynamic evolution and regional differences of AWUE. A spatial econometric model was then used to analyze the main factors that influence the AWUE in Northwest China. The results showed firstly that the overall AWUE in Northwest China from 2000 to 2018 presented a steady upward trend. However, only a few cities achieved effective agricultural water usage by 2018, and the differences among cities were obvious. Secondly, AWUE showed an obvious spatial autocorrelation in Northwest China and showed significant high–high and low–low agglomeration characteristics. Thirdly, economic growth, urbanization development, and effective irrigation have significant, positive effects on AWUE, while per capita water resource has a significant, negative influence. Finally, when improving the AWUE in arid and semi-arid regions, plans should be formulated according to local conditions. The results of this study can provide new ideas on the study of AWUE in arid and semi-arid regions and provide references for the formulation of regional agricultural water resource utilization policies as well.

The coordinated development of the economy, resources, and environment is a key aspect of sustainable development. China’s rapid agricultural modernization has been accompanied by the continuous growth of rural economic aggregate and carbon emissions from the planting industry. However, the quantitative relationship between these two factors and its internal mechanism are not yet fully understood. In this paper, the Intergovernmental Panel on Climate Change (IPCC) method is used to calculate the carbon emissions of the planting industry in China from 1998–2019. Based on this, the Tapio decoupling analysis model was constructed to study the decoupling relationship between economic development and carbon emissions of the planting industry in China from 1998–2019 and the associated spatial and temporal evolution patterns. The effect of the complete decomposition model (without residuals), in terms of carbon emissions from the planting industry, on the process of economic development and its transmission mechanism are introduced. The results show that: (1) The carbon emissions of the planting industry in China increased with the economic development occurring from 1998–2005, where agricultural economic development was highly dependent on resource factors and the environment. The growth trend of carbon emissions of the planting industry slowed from 2006 to 2019, while economic development has gradually realized the decoupling of carbon emissions from the planting industry. (2) From 1998–2019, in Heilongjiang, Sichuan, and Hunan, the economic development was given priority, showing strong and negative decoupling with carbon emissions from farming. The economic development in most regions were given priority, showing strong decoupling with carbon emissions from farming. Up to 2019, decoupling was observed with a significant trend of spatial agglomeration. (3) Economic scale effects had a positive influence on the carbon emissions of the planting industry, while the technology effect and population effect had an inhibiting influence on the carbon emissions of the planting industry. The key policy implication of this paper is that improvement of the quality of economic development serves as the premise for the transformation of the economic development mode. It is necessary to reasonably regulate the economic growth rate and expansion scale, reduce resource consumption and pollutant emission technology, and to make full use of resources, in order to provide a basis for the formulation of reasonable emission reduction policies. An effective way to realize the sustainable development of the agricultural economy would be to improve the technical efficiency, control the population scale appropriately, and optimize the agricultural industrial structure.

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.

For developing countries, an adequate domestic water supply is conventionally assessed based on the proportion of communities that are covered by improved water sources. However, it is difficult to evaluate water poverty accurately, as it is multidimensional. For this reason, this paper used the Water Poverty Index (WPI) to measure water poverty in rural communities of arid areas in China. This study also uses the Least Square Error (LSE) model to analyze the influencing factors of water poverty. Based on the WPI and LSE, the results showed that the rural communities of Sheshu, Fanyao, Dongcao, Qiaodi, and Gouershang (listed in order of priority of need for intervention) are in a water poverty situation. In rural communities with high water poverty, the suggested priority order for the study was environment, capacity, use, resources, and access, with the environmental factor needing to be improved. The results are useful for prioritizing areas and identifying the extent of the need for policy intervention on different scales. The research findings are intended to complement the evaluation of water poverty and to provide a strategy for regional water resources management to relieve water poverty.