Rural energy transitions are essential for climate governance and sustainable development, particularly in the Global South. However, the evolution of rural photovoltaic (PV) deployment remains insufficiently understood, partly because existing research rarely distinguishes rural deployment from urban expansion. To address this gap, this study introduces an urban-rural differentiated framework to identify rural PV deployment in China. The analysis employs Moran's I, dynamic time warping (DTW), K-means clustering, LightGBM, and SHAP to examine the spatial distribution, temporal evolution, and driving mechanisms using prefecture-level data from 2013 to 2022. The results demonstrate that rural PV deployment is highly uneven and spatially clustered, with its evolution divided into four distinct phases: high growth, steady growth, volatile growth, and low growth. Path dependence is the most consistent predictor across all types, although the enabling conditions for further expansion differ. Industrial agglomeration exerts a greater influence in high-growth regions, while digital finance and service-related infrastructure are more significant in steady-growth regions. Volatile-growth regions remain insufficiently stabilized, and low-growth regions are more constrained by land and solar resources. These findings indicate that the rural PV transition in China does not follow a single national pathway; instead, it is a geographically uneven, mechanism-specific process, which highlights the need for more differentiated rural energy governance.
Against the backdrop of global climatic shift and pressure exerted by anthropogenic disturbance, the health of water balance has come to be seen as a pivotal regard in the pursuit of sustainable progress in the Yellow River Basin (YRB). This research employed a groundbreaking paradigm to assess the health of water balance, focusing specifically on three key water-related issues: Water shortage (WSI), unequal water distribution (WII), and system incoordination (SID). The results suggest that total water storage anomalies (TWSA) and groundwater storage anomalies (GWSA) displayed significant decrease during 2003–2022 at the rates of −0.47 mm/yr and −0.65 mm/yr, respectively. Geographically, TWSA and GWSA showed regional differences in the source region, midstream area and downstream area. From the perspective of water balance health, the sub-basins with WSI<1.0 were predominantly situated in the source area, middle reaches and interior drainage area of YRB, accounting for 52.39
The arid Central Asia, one of the world's largest non-zonal drylands, is highly vulnerable to global climate change due to its fragile water-ecosystem. Recent warming in this region far exceeds the global average, driving profound hydrological and ecological shifts. This study analyzes water cycle changes and ecological risks to reveal how warming is reshaping regional dynamics. Results show declining solid precipitation and an increasing frequency of compound drought-hot events. Extreme droughts have intensified in recent decades. At the same time, clear signs of cryospheric degradation are evident, including widespread glacier retreat, declining glacier coverage, and a shortening of snow cover duration. These changes disrupt seasonal runoff, expand glacial lakes, and elevate flood hazards. Intensified evaporation accelerates soil desiccation, while aridification is spreading into adjacent zones. Overall, climate-driven water cycle reorganization is amplifying ecological risks, and posing a growing threat to regional sustainability.
Northern China’s deserts are critical zones for combating desertification. Recent advances in photovoltaic technology and declining costs have facilitated the construction of utility-scale photovoltaic (PV) power stations in these regions, offering a new approach to sand control that integrates ecological restoration with economic development. Yet, their spatial dynamics and underlying nonlinear deployment drivers remain poorly understood. This study examines the spatiotemporal distribution of PV stations across eight major deserts and four sandy lands from 2010 to 2022 and identifies the potential driving mechanisms using an interpretable machine learning framework. The results indicate a rapid increase in both the number and the total area of PV stations, alongside a shift toward smaller scale projects. Deployment expanded from eastern clusters to western areas, with core concentrations in the Mu Us and Horqin Sandy Lands. Crucially, the SHAP attribution reveals an infrastructure-locked paradigm: proximity to power grids and roads outweighs natural resource endowments. The model uncovers a nonmonotonic climatic dual-effect, wherein moderate winds provide cooling benefits whereas extreme winds act as severe deterrents due to abrasion risks. To balance ecological risks and economic benefits, stricter management, fair benefit-sharing, and diversified evaluation frameworks are recommended. These findings provide a scientific basis for the sustainable and high-quality development of PV stations in desert regions.
In the context of global change, a central challenge in ecology is to establish the multi-scale evolution and coupling mechanisms of ecosystems. In particular, it is necessary to clarify the structural mismatch between hierarchical levels that arises when ecological networks (ENs) are constructed across varying extents or grains. Using city- and central urban-level examples, we introduced a cross-level ENs spatial mismatch measurement index (SMI) and a scale-effect analysis framework. Five representative cities from the Northeast, Northwest, Central, Southwest, and Southeast China were selected as research areas. A unified approach combining minimum cumulative resistance and XGBoost models enable the preliminary construction of two-level ENs. SMI is then applied to evaluate cross-level mismatches, followed by analysis of extent and grain effects. Mechanisms underlying EN disconnection and potential solution pathways are further examined. The results show that: (1) spatial mismatch indicators (SMI_source, SMI_corridor, SMI_nodes) defined on ecological source areas (ESA), corridor, and strategic nodes, reflect mismatch degrees across EN levels; (2) scale effects reveal decreasing SMI with expanding observation extent in central urban area, fluctuating values with synchronous change in data grain, and increasing values with higher statistical grid density; (3) the largest patch index and landscape division index exert a strong influence on SMI_source, while patch number and Shannon’s diversity index play key roles in SMI_corridor; and (4) variation in landscape composition and configuration heterogeneity across scales provides explanatory power of mismatch phenomenon and scale effects. The study contributes to ecological pattern analysis by offering a quantitative method for multi-scale EN research, with implications for ecological protection and regional landscape planning.
