To improve the water resources carrying capacity of the Xiangjiang River Basin and achieve sustainable development, this article evaluates and predicts the Xiangjiang River Basin’s water resources carrying capacity level based on county-level units. This article takes 44 county-level units in the Xiangjiang River Basin as the evaluation target, selects TOPSIS and the entropy weight method to determine weights, calculates the water resources carrying capacity level of the evaluation sample, uses a BP neural network model to calculate the predicted water resources carrying capacity level for the next 5 years, and adds the GIS method for spatiotemporal analysis.(1) The water resources carrying capacity of the Xiangjiang River Basin has remained relatively stable for a long period, with overloaded areas being the majority. (2) There are relatively significant spatial differences in the carrying capacity of water resources: Zixing City, located upstream of the tributary, is far ahead due to its possession of the Dongjiang Reservoir; the water resources carrying capacity in the middle and lower reaches (northern region) is generally higher than that in the upper reaches (southern region). (3) According to the BP neural network model prediction, the water resources carrying capacity of the Xiangjiang River Basin will maintain a stable development trend in 2022, while areas such as Changsha and Zixing City will be in a critical state, and other counties and cities will be in an overloaded state.This study has important references value for the evaluation and early warning work of the Xiangjiang River Basin and related research, providing a scientific and systematic evaluation method and providing strong support for water resource management and planning in Hunan Province and other regions.

This study conducted a systematic review of 222 research articles (2014–2024) from the Web of Science Core Collection database to investigate the ecological and environmental impacts of pumped hydro storage (PHS). Utilizing CiteSpace 6.1R software for visual analysis, the research hotspots and evolutionary trends over the past decade were comprehensively examined. Key findings include the following: (1) Annual publication output exhibited sustained growth, with China contributing 29.7% of total publications, ranking first globally. (2) Research institutions demonstrated broad geographical distribution but weak collaborative networks, as the top 10 institutions accounted for only 21.6% of total publications, highlighting untapped potential for cross-regional cooperation. (3) Current research focuses on three domains: ecological–environmental benefit assessment, renewable energy synergistic integration, and power grid regulation optimization. Emerging trends emphasize multi-objective planning (e.g., economic–ecological trade-offs) and hybrid system design (e.g., solar–wind–PHS coordinated dispatch), providing critical support for green energy transitions. (4) Post-2020 research has witnessed novel thematic directions, including deepened studies on wind–PHS coupling and life-cycle assessment (LCA). Policy-driven renewable energy integration research entered an explosive growth phase, with PHS–photovoltaic–wind complementary technologies emerging as a core innovation pathway. Future research should prioritize strengthening institutional collaboration networks, exploring region-specific ecological impact mechanisms, and advancing policy–technology–environment multi-dimensional frameworks for practical applications.

Evaluation of water resource carrying capacity is one of the methods used to measure the degree of realization of resource and environmental planning. This study takes 26 county-level units in the Dongting Lake area of Hunan Province as the evaluation object and screens 22 indicators for the evaluation of water resource carrying capacity. The comprehensive weight of the water resources carrying capacity of the Dongting Lake area is calculated using double weight methods (DWM) based on the analytical hierarchy process (AHP) and the entropy weighting method. And then, the back propagation (BP) neural network is used to predict the evolution of the alarm situation of the water resources carrying capacity. The results show the following: (1) In terms of time trend, the water resources carrying capacity of Dongting Lake area is generally stable and is between overload and critical state, but the change trends of water resources carrying capacity of each county are very different; (2) in terms of spatial distribution, the water resources carrying capacity in the western and southern regions of the Dongting Lake area is relatively high, while that in the Yueyang urban area in the eastern region is relatively low, showing a pattern of “high in the west and low in the east”; (3) among the criterion layers, the water resources support force has the highest weight and the smallest change, and the economic support force has the largest change in weight. The decline of water resources carrying capacity is closely related to the speed of economic development; (4) by 2025, the water resources carrying capacity of Nanxian County, Hanshou County, and Xiangyin County will be significantly increased from overload to critical state, and Dingcheng District will gradually turn from critical to overloaded status. This study is the first attempt to evaluate and predict the water resources carrying capacity of the Dongting Lake area at the county level so that the evaluation results are more applicable to grass-roots administrative units with decision-making power. The double-weighting method is used to determine the weights of the indicators so that the evaluation results can be more accurate.