Stable and sustainable food production is an important guarantee for national security and social stability. The uneven distribution of cultivated land and water resources will threaten national food security. In this study, we adopt the Gini coefficient and water–land matching coefficient for exploring the water–land nexus in the main grain-producing areas of North China Plain (NCP) from 2000 to 2020. The water–land–food nexus considering grain crop production structure is further explored from spatial and temporal multi-scales. The results show the following: (1) The Gini coefficient presents an increasing trend in the NCP, indicating an increasing imbalance in the water–land matching degree among inter-regions. (2) There are significant differences in the WL nexus and WLF nexus among regions, showing a spatial pattern of “worse in the north and better in the south”. (3) The cities which belonged to the low WL-low WLF and high WL-low WLF should be considered as key targets when formulating policies. (4) Adjusting the wheat–maize biannual system, optimizing the grain cultivation structure, promoting semi-dryland farming, and developing low water-consuming and high-yielding crop varieties are essential measures for these regions. The research results provide significant reference for the optimal management and sustainable agricultural development of agricultural land and water resources in NCP.

Rapid economic growth has a significant impact on land use change, which would threaten the natural ecology. Zhangye city of the Heihe River Basin, China is an ecologically vulnerable region where land use changes significantly due to socioeconomic development and population increases. The study employed a computable general equilibrium of land use change (CGELUC) model to simulate land use change and then used a dynamic land system (DLS) model to spatialize land use change during 2015–2030 under three development scenarios in Zhangye city. The three development scenarios are the baseline scenario (BAU), the resource consumption scenario (RCS) and the green development scenario (GDS). We found that economic growth would lead to land demand increases in high value-added industries and decreases in low value-added industries. The cultivated land would decrease while the built-up area would increase. By 2030, the cultivated land will decrease by 8.16%, 10.89% and 4.16%, respectively, under BAU, RCS and GDS, while the built-up area will increase by 8.61%, 10.39% and 4.75%, respectively. The expansion of built-up area under RCS presents spatial characteristics of centralized distribution, while spatial characteristics of uniform discrete distributions are presented under GDS. The expansion of ecological land under GDS would be considerable, especially in the north of Sunan County and Gaotai County, and around the natural reserve of Ganzhou County. This paper provides a scientific reference for coordinating economic development and ecological protection in the rapidly developing urbanized areas in western China.

Attaining optimal eco-efficiency is of paramount importance in promoting the sustainable and harmonious development of the economy and environment within urban agglomerations. Firstly, this paper utilizes the Super-SBM model with undesirable output to measure the eco-efficiency (EE) of 64 cities in the Beijing–Tianjin–Hebei metropolitan region (BTHMR), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Chengdu–Chongqing Economic Zone (CCEZ) from 2006 to 2019. Secondly, this study puts forth a novel and comprehensive index system aimed at evaluating the urbanization efficiency and sheds light on the spatiotemporal changes in EE and urbanization efficiency. Finally, the STIRPAT model is used to examine the influencing factors of EE and to investigate the correlation between EE and urbanization efficiency. The study found that the overall EE of the four typical urban agglomerations is high, but the trend varies with a decrease of about 12.9% from 2006 to 2019. The mean EE is in the order of CCEZ > PRD > BTHMR > YRD, with mean values of 0.941, 0.909, 0.842, and 0.732, respectively. The level of science and technology and the urbanization efficiency have a significant positive impact on EE, while population, industrial structure, FDI, and greening level have an inhibitory effect on urban eco-efficiency. Based on the results, policy suggestions such as paying attention to regional heterogeneity and giving full play to the government’s macro-regulatory role in shaping the economic and industrial structure are proposed to serve as a guide for the coordinated development of urban agglomerations under the Dual Carbon Target.

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