Water, energy and food are the basic resources for human survival and development. The coordination development of water-energy-food (W-E-F) is of great significance to promote regional sustainable development. In this study, Northwest China (Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang) was selected as the research case, and an evaluation index system was constructed to assess the vulnerability and coordination of water-energy-food (W-E-F) system based on PSR model. Then, a coupled model based on cloud-matter element model and coordination degree model was proposed. The cloud-matter element model was adopted to evaluate the vulnerability level of W-E-F system. The coordination degree model was employed to calculate the coordination degrees of W-E-F system. The results showed that, from 2006 to 2015, the vulnerability levels of W-E-F system in Northwest China were mostly at Level 1. The coordination degrees of W-E-F system belonged to the transitional development level (II) in most years. The vulnerability and coordination problems of W-E-F system in Northwest China were severe. The comprehensive vulnerability index values of W-E-F system were generally on the rise, but far from reaching a good level. Moreover, the comprehensive vulnerability index values and coordination degrees of W-E-F system in Northwest China do not match well. Finally, the countermeasures and suggestions to improve the coordinated development of water resource, energy and food in Northwest China were put forward.

As the requirements for environmental protection and high-efficiency economies increase, grain production (GP) across the globe faces more stringent ecological constraints and economic pressure. Understanding the relationships between natural resources and economic and agricultural factors in grain-producing regions is paramount for ensuring global food security. This paper proposes a methodological framework to explore the relationships between water and soil resources (WSRs), economic input factors (EIFs), and GP. We employed the northeast region of China as a case study to advance our understanding of the factors driving the development of grain-producing capacity. We first constructed and calculated the region's water and soil comprehensive index (WSCI) to describe water-soil properties. We then used hotspot analysis to explore the spatial agglomeration characteristics of WSRs, EIFs, and GP. Finally, we used threshold regression analysis to understand the effects of EIFs and GP with the WSCI as the threshold variable. With the improvement of the WSCI, the positive impact of fertilizer and irrigation on GP shows a U-shaped curve in elasticity coefficients. The positive effect of agricultural machinery on GP decreases significantly, and the impact of labor input on GP is insignificant. These results provide new insights into the relationship between WSRs, EIFs, and GP and a reference for improving GP efficiency globally. This work thus contributes to advancing our capabilities to enable food security while considering aspects of sustainable agriculture in important grain-producing regions across the globe.

Seawater intrusion has occurred in the coastal area of the Yellow Sea and the Bohai Sea as early as the 1970s, and the situation is worsening, with rapid socioeconomic development in recent years. Substantial amounts of groundwater have been exploited to support socioeconomic activities, especially agricultural activities, causing a reduction in the groundwater level, and hence the intrusion of seawater. This issue seriously restricts the sustainable socioeconomic development of these coastal areas. To this end, this paper applied the improved Tapio decoupling theory to analyze the degree of decoupling, and the spatial difference between the economic growth and the groundwater consumption of the five provinces and cities in the coastal areas of the Yellow Sea and Bohai Sea in the period of 2003–2016. Based on the improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and panel data, we determined the driving factors of groundwater consumption in the coastal areas of the Yellow Sea and Bohai Sea. The results demonstrated that the effective irrigation area of farmland should be expanded, new water-saving technology should be introduced, the crop planting structure should be readjusted, and the consumption of groundwater should be reduced. By implementing these measures, it would be possible to contain seawater intrusion in the coastal areas of the Yellow Sea and Bohai Sea.

In view of the increased vulnerability of water resources system caused by seawater intrusion in the coastal areas of the Yellow Sea and Bohai Sea, this paper based on the VSD(Vulnerability Scoping Diagram) model to construct the water resource vulnerability evaluation index system of “exposure-sensitivity-adaptive capacity” under seawater intrusion. Then use entropy-TOPSIS method to evaluate water resource vulnerability and divide the levels. In addition, take Longkou City of Shandong Province as an example to conduct empirical research. The results show that the water resource vulnerability of Longkou City is at a strong vulnerability level. The water resources system is under obvious pressure. Population density, per capita GDP, temperature and precipitation change are the main factors of the system pressure. Seawater intrusion has a high degree of impact on water resources vulnerability and water resource system is more sensitive to groundwater level, chloride concentration and other stimuli. The improvement of water use efficiency and scientific and technological investment contribute to the significant enhancement of the adaptability of the water resources system of Longkou City.

Accurate carbon price forecasting is critical for trading decisions, risk management and policy formulation in carbon markets. However, mainstream decomposition-ensemble models suffer from two key drawbacks: point-wise modeling fails to capture long-term temporal dependencies, while independent modeling of decomposed trend and seasonal components leads to serious information loss. To address these limitations, this paper proposes a novel Dual-Path Interactive Attention Network (DPIANet) for carbon price time series forecasting, whose dual-parallel architecture consists of a Dual Interaction Attention (DIA) Block and a Decomposition–Subsequence Interaction Attention (DSIA) Block. First, DPIANet employs a patch-wise partitioning strategy to extract local temporal semantic information inaccessible to traditional point-wise segmentation. The DIA Block jointly captures temporal dependencies between different patches within the same sequence and inter-feature dependencies within the same time step. In parallel, the DSIA Block extracts interactive features between decomposed trend and seasonal subsequences, fusing these features with original subsequences to enhance representation and mitigate decomposition-induced information loss. A dual-layer feature selection method (PMI and XGBoost-SHAP) is adopted to identify key driving factors. Experiments on four representative Chinese regional carbon trading markets covering 2014-2020 show that DPIANet achieves superior prediction performance over state-of-the-art models in terms of MSE and MAE, with competitive robustness across different market characteristics, providing practical decision support for carbon market stakeholders.