High-quality economic development is an important approach for achieving sustainable economic development, and it is an essential condition for coordinated development between economic systems and ecosystems. This paper starts from five key points, namely, “innovation, coordination, opening-up, sharing and greenness”, to construct an evaluation system for the index of high-quality economic development, using the AHP and EVM methods to measure the level of high-quality economic development of 30 regions in China from 2004 to 2019. It uses the kernel density estimation model (hereinafter referred to briefly as KDE) and clustering method to analyze time evolution trends and spatial variation characteristics. Moreover, the LSE model is adopted to explore and analyze the factors influencing high-quality economic development in different regions. Additionally, the driving forces of China’s high-quality economic development are analyzed by means of path analysis combined with the average value of each index. The results show the following: (1) The high-quality economic development of 30 regions in China (excluding Hong Kong, Macao, Taiwan and Tibet) is spatially clustered, with obviously different development levels, characterized by the eastern region being better developed than the central and western regions. (2) With the passage of time, the polarization of China’s 30 regions has been alleviated, but they are still facing challenging development situations; (3) The factors affecting the high-quality economic development of these 30 regions in China can be divided into four types: three-factors, four-factors-I, four-factors-II and five-factors. Contributing regional factors show different distribution characteristics. The above conclusion provides a reference and scientific basis for the government to formulate policies of high-quality economic development and to solve problems facing coordinated sustainable development among regional societies, their economies and the environment.

The progression of agricultural production, ever-increasing industrialization, population boom, and more water-concentrated lifestyles has placed a severe burden on Yellow River Basin’s existing water resources, particularly in the current century. In the context of resource and environmental constraints, improving the green efficiency of agricultural water use (AWGE) is an important measure for alleviating the shortage of water resources as well as meeting the intrinsic requirement to promote the green transformation and upgrading of agriculture. This study used the Super Slack-Based Measure (Super-SBM) to measure the AWGE of 87 regions in the Yellow River Basin from 2000 to 2019. Based on spatial and temporal perspectives, it applied Exploratory Spatial Data Analysis (ESDA) to explore the dynamic evolution and regional differences in AWGE. Then, this study used a spatial econometric model to analyze the main factors that influence AWGE in the Yellow River Basin. The results show that, firstly, the AWGE of the Yellow River Basin shows a steady upward trend from 2000 to 2019, but the differences among regions were obvious. Secondly, the AWGE showed an obvious spatial autocorrelation in the Yellow River Basin and showed significant high–high and low–low agglomeration characteristics. Thirdly, rural per capita disposable income and effective irrigation have a positive influence on AWGE, while rural labor transfer, the input intensity of agricultural machinery and water structure have a negative influence. The spatial econometric model regression results show that the influence factors of AWGE in the Yellow River Basin showed significant spatial spillover effects and spatial heterogeneity in their effect. Finally, when improving AWGE in the Yellow River Basin, plans should be formulated according to local conditions. The results of this study can provide new ideas on the study of AWGE in the Yellow River Basin and provide references for the formulation of regional agricultural water resource utilization policies as well.

The concept of green development proposed by the OECD has effectively promoted the coordinated development of economic growth, resource conservation, and environmental protection. In addition, it has provided effective ways and means of improving the quality of global social and economic development. Therefore, this study constructed the green development evaluation index system of Shaanxi Province based on the OECD green development concept, used the AHP and the entropy weight method for weighting, and measured the green development level of ten urban areas in Shaanxi Province from 2016 to 2021. Based on the score of the green development level, the LSE model, ESDA model, and kernel density model were introduced to decompose the optimization path of green development in Shaanxi Province from the three dimensions of contributing factors, spatial distribution, and temporal evolution. The specific research conclusions are: first, from 2016 to 2021, the green development score of Shaanxi Province was between 0.92 and 1.79, and the level of green development showed an overall upward trend, but the two-level differentiation trend between regions was serious. Second, the green development levels of each region in Shaanxi Province were three-factor type I, type II, four-factor type, and five-factor type. The influencing factors of each region showed different characteristics. Third, the level of green development among various regions in Shaanxi Province did not show spatial aggregation, and the development level was significantly different. The regional characteristics show that Xi’an is obviously superior to other regions and has not played a leading role. Fourth, over time, the situation of two-level differentiation in the ten city-level administrative districts of Shaanxi Province has eased, but the development situation is still very severe. The above research conclusions provide a reference and scientific basis for Shaanxi Province to formulate economic development policies and solve the problem associated with coordinated, sustainable development of the regional society-economy-environment.