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The spatial–temporal pattern, influencing factors and driving variables of carbon emissions are essential considerations for achieving China’s carbon peak and neutrality targets, which support high-quality development. This study was designed to explore and evaluate the spatial–temporal evolutionary characteristics, trends and main influencing factors of carbon emissions in the Yangtze River Economic Belt (YREB), focusing on the decoupling of carbon emissions and socioeconomic development in the YREB. In total, 11 provinces and key cities were focused on as the research objects of the YREB district Tapio decoupling model, which examined the decoupling relationship between carbon emissions and socioeconomic development. Combined with a geographic detector, the Tapio, Logarithmic Mean Divisia Index (LMDI) and gray prediction models were employed in a comprehensive evaluating pipeline, which was constructed to decouple the main influencing factors and corresponding impacts of carbon emissions. Particularly, the gray prediction model was employed to predict the carbon emission differences in the YREB sub-regions in 2030. The results indicated the following: (1) The total carbon emissions showed a periodic fluctuation and upward trend with obvious spatial differences, and energy consumption was mainly dominated by coal. (2) The center of carbon emissions was located in Hubei Province in the middle reaches of the Yangtze River, with a standard deviation ellipse showing a “Southwest–Northeast” trend, and most provinces were concentrated in the L-H (low-high) cluster. (3) The entire YREB had achieved carbon emissions decoupling, but it was mainly in a weak decoupling state. (4) Carbon emissions were significantly affected by the indicator E for economic growth, with the indicators EI for energy consumption and I for the added ratio of GDP also bringing greater impacts on carbon reduction contributions. The carbon emission prediction results indicated that the upper and middle reaches of the YREB were more likely to achieve carbon neutrality. Full article

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions. Full article

As a typical coastal tourist city, Sanya has experienced large-scale urbanization driven by tourism development, leading to landscape fragmentation, disorderly urban sprawl, and irrational resource utilization. These factors have intensified regional ecological risks and caused the degradation of ecosystem service functions, thereby constraining sustainable urban development. Therefore, establishing urban ecological zoning can identify the dynamic relationship between ecological conditions and urban growth, ease human-land conflicts, and promote high-quality urban development. This study employed the value equivalency method and the landscape ecological risk index method to calculate the ecosystem service value (ESV) and the ecological risk index (ERI) of Sanya City from 2000 to 2020 and to delineate ecological zones. The PLUS model was used to predict the changes in ecological zoning of Sanya City under a natural development scenario in 2030. The results demonstrate the following: (1) The ecological risk in the study area shows a distribution pattern of “high in the south and low in the north,” with low-risk areas being the dominant type, accounting for about 80% of the total area. Over time, the area of high-risk zones has shown an increasing trend, while that of low-risk zones has decreased year by year. (2) The ecosystem service value in the study area shows a distribution pattern of “high in the north and low in the south,” with a decreasing trend over time, with a cumulative reduction of 2.11 × 10⁸ ten thousand yuan from 2000 to 2020. (3) Among the four ecological zones, the ecological protection zone is the dominant type, accounting for about 50%. The increase in the ecological early warning zone is the most significant. In contrast, the ecological optimization and improvement zones show a marked decrease. The prediction results show that by 2030, the ecological early warning and ecological protection zones will increase, while the other zones will decrease. This study adopts a temporal-dynamic approach by constructing a framework that integrates historical evolution with future simulation, providing scientific evidence for building Sanya’s ecological security pattern and spatial governance. It offers practical significance for coordinating regional ecological conservation with urban development. Full article

In recent years, the frequent occurrence of extreme weather events has prompted increased global attention to greenhouse gas emissions. This study analyzes the spatio-temporal evolution of carbon emission intensity (CEI) across land use types in China’s 30 provinces from 2009 to 2022. Based on the data from China Rural Statistical Yearbook, China City Statistical Yearbook, China Energy Statistical Yearbook, China Natural Resources Statistical Yearbook, and China Statistical Yearbook, this study aims to reveal the spatio-temporal differentiation patterns of CEI, analyze the decoupling status between development mode and carbon emissions, and establish a three-dimensional collaborative emission reduction framework. Firstly, employing the carbon emission factor method, provincial carbon emissions, sinks, and net emissions are calculated, with intensity levels derived from gross domestic product (GDP). Secondly, spatio-temporal trends and inter-provincial disparities are analyzed using the decoupling index. The spatial effects among the provinces are investigated based on Moran’s I index. The results show that while the overall CEI has declined since 2009, significant regional disparities persist, with the southern provinces showing lower carbon emission intensities compared to the northern and western regions. The spatial analysis reveals a strong aggregation effect, with provinces clustering into high-high (HH) and low-low (LL) regions regarding CEI. This study concludes with policy recommendations for emission reduction and climate change mitigation, emphasizing industrial structure adjustment, enhanced regional coordination, and optimized land use planning. Full article

Against the backdrop of urban–rural integrated development, special ecological function zones, as spatial carriers with significant regional ecological value and rural development functions, are confronted with a striking conflict between ecological conservation and regional advancement. This contradiction is comprehensively reflected in the interactions among land use functions (LUFs) that differ in nature and intensity. Therefore, exploring the trade-off and synergy (TOS) among regional LUFs is not only of great significance for optimizing territorial spatial patterns and advancing rural revitalization but also provides scientific evidence for the differentiated administration of regional land use. Taking 185 townships in the Funiu Mountain area of China as research units, this study constructs a land use assessment system based on the ‘Production–Living–Ecological’ (PLE) framework, utilizing multi-source datasets from 2000 to 2020. Spearman correlation analysis, geographically weighted regression (GWR), and bivariate local spatial autocorrelation methods are employed to examine the spatio-temporal dynamics of LUFs and the spatial non-stationarity of their TOSs. The findings indicate that, throughout the research period, the production function (PF) displayed a fluctuating declining trend, whereas the living function (LF) and ecological function (EF) demonstrated a fluctuating increasing trend. Notably, EF held an absolute dominant position in the overall structure of LUFs. This is highly consistent with the region’s positioning as a special ecological function zone and also a direct reflection of the effectiveness of continuous ecological construction over the past two decades. Spatially, PF is stronger in southern, eastern, and northern low-altitude townships, correlating with higher levels of economic development; LF is concentrated around townships near county centers; and high EF values are clustered in the central and western areas, showing an opposite spatial pattern to PF and LF. A synergistic relationship is observed between PF and LF, while both PF and LF exhibit trade-offs with EF. The TOSs between different function changes demonstrate significant spatial non-stationarity: linear synergy was the primary type for PF-LF, PF-EF, and LF-EF combinations, but each combination exhibited unique spatial characteristics in terms of non-stationarity. Notably, towns identified as having different types of trade-off relationships in the study of spatial non-stationarity are key areas for township spatial governance and optimization. Through the allocation of regional resources and targeted policy tools, the functional relationships can be adjusted and optimized to attain sustainable land use. Full article

To address biodiversity degradation in Nationally Important Agricultural Heritage Systems, this study integrates multi-temporal remote sensing data (2000–2023) with the Biodiversity Maintenance Function (BMF) and InVEST Habitat Quality (HQ) models. We assess ecological changes in the Beijing–Tianjin–Hebei (BTH) region and 14 nationally recognized heritage systems. A dual-factor HQ–BMF coupling matrix was developed to trace ecological trajectories shaped by both natural and anthropogenic influences. Results show that (1) regional BMF followed a non-linear trend of increase, decline, and rebound between 2003 and 2023. The mean value rose from 0.1036 in 2003 to 0.1397 in 2023, despite intermediate fluctuations. In contrast, HQ declined steadily from 0.8734 in 2003 to 0.7729 in 2023, reflecting a continuous loss of high-quality habitats. (2) Nearly all heritage systems experienced phased BMF fluctuations—an initial rise, subsequent decline, and eventual recovery. At the same time, HQ showed a continuous decline in 8 of the 14 systems, indicating that more than half of the systems experienced sustained habitat degradation. (3) The HQ–BMF matrix revealed strong spatial heterogeneity. By 2023, only one site remained in a “dual-high” zone, while another had fallen into a “dual-low” condition, suggesting localized ecological degradation. These findings provide quantitative support for conservation strategies, ecological compensation, and land-use regulation in agricultural heritage systems. Full article

Climate change alters climatic factors, which in turn affect the suitability of crops to grow. Winter wheat is a major crop in the Beijing-Tianjin-Heibei region of China. To assess the climate factors on winter wheat production, the meteorological data (temperature, precipitation, sunshine, etc.) from 25 stations in the target region the Beijing-Tianjin-Hebei region of China from 1961 to 2010, the winter wheat yield data from 1978 to 2010, and the growth stages were used. A model of the suitability of light, temperature, and water was subsequently developed to quantitatively analyze the spatial and temporal variability of the suitability of the winter wheat to the climate of the region. Temperature suitability was high during the sowing and grouting periods (temperature suitability peaks at 0.941 during grouting) and lowest in the rejuvenation period. In terms of spatial distribution, it is strong in the south and low in the north, and it exhibits a gradual increase in interannual variation. Precipitation suitability fluctuates steadily, with a peak in the tillering stage and a trough in the jointing stage. In terms of spatial distribution, it is highest in the northeast and decreases in the west; in inter-annual changes, it fluctuates strongly with weak overall growth. Sunshine suitability is stable at 0.9 or above. In spatial distribution, it is high in the northwest and low in the southeast, and it decreases slowly in the interannual variations. The trend of climatic suitability is consistent with temperature and precipitation, showing a pattern of falling first and then rising. In terms of spatial distribution, the overall climate suitability is high in the south and low in the north. In inter-annual changes, climate suitability generally increases slowly. Temperature and precipitation are key factors. Moisture stress became the most important factor for winter wheat cultivation in the region. Sunshine conditions are typically sufficient. This study provides a theoretical basis for a rational layout of winter wheat growing areas in the Beijing-Tianjin-Hebei region and the full utilization of climatic resources. Full article

As industrial and urban growth advances, the challenge of rural population shrinkage has grown more pronounced, impacting rural functions. Northeast China is an example in this study, and a rural function evaluation index system is constructed based on four dimensions: agricultural production, economic development, social security, and ecological conservation. The spatio-temporal heterogeneity of the impact of rural population shrinkage on rural functions is quantified in this study using bivariate spatial autocorrelation and geographically and temporally weighted regression (GTWR). The results show that from 2000 to 2020, the rural population in most counties in Northeast China declined, while agricultural production, economic development, social security, and ecological conservation functions generally trended upwards. According to the GTWR model, the positive effect of rural population density on agricultural production weakened over time, slightly promoting social security and continuing to inhibit ecological conservation. In contrast, the supporting effect of average rural population size on economic development strengthened, its inhibitory effect on ecology decreased, and it slightly inhibited social security. While rural population shrinkage generally promoted agricultural development, economic growth, social security, and ecological improvements, its positive impact on agricultural development declined over time, and the promotion effects on social security and ecological conservation partially turned into inhibition after 2020. Policy recommendations are presented in this paper, providing a solid scientific foundation for the sustainable development of rural areas in Northeast China. Full article

Enhancing tourism eco-efficiency (TEE) is crucial for achieving China’s “dual carbon” objectives. This study examines nine provinces in the Yellow River Basin from 2010 to 2022, employing a super-efficiency SBM model, kernel density estimation, gravity center migration, standard deviation ellipse, Tobit regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate spatial-temporal variations and influencing factors. The results show that TEE increased steadily before 2019, declined during the COVID-19 pandemic, and recovered after 2021. Spatially, widening disparities and a polarization trend were observed, with the efficiency center remaining relatively stable in Shaanxi Province. Factors such as advancements in tourism economic development, regional economic growth, technological innovation, and infrastructure improvements significantly promote TEE, whereas stringent environmental regulations and greater openness exert constraints, and the impact of human capital remains uncertain. Four types of condition combinations were identified—economic-driven, market-innovation-driven, scale-innovation-driven, and balanced development. Managerial implications highlight the need for region-specific pathways and regional cooperation, with a dual focus on technological and institutional drivers as well as ecological value orientation, to sustainably enhance TEE in the Yellow River Basin. Full article

Background and Objective: Stunting remains a critical public health concern affecting child growth and development, particularly among children under two years of age in low- and middle-income countries, including Rwanda. This study investigates spatiotemporal trends in stunting prevalence from 2020 to 2024 at the sector level using national surveillance data. Methods: To capture regional disparities and temporal trends, we used hierarchical Bayesian spatiotemporal models, which accounted for spatial structure, temporal correlations, and interactions, to estimate stunting prevalence across districts and sectors over time. Results: Between 2020 and 2024, the national prevalence of stunting among children under two years decreased from 33.1% to 21.7%, representing a 34.4% change. Three districts, Kamonyi, Nyarugenge, and Ngoma, achieved reductions of >70%, whereas Rubavu, Nyabihu, and Nyaruguru saw minimal change (14–15%). By 2024, several sectors in Kicukiro, Nyanza, Nyarugenge, and Kirehe had reduced levels of stunting below the national target of 19%. Conclusions: Despite considerable gains, significant geographical differences persist in the stunting prevalence of children under two, underscoring the need for targeted, decentralized interventions to mitigate and eliminate this in lagging areas. Full article

Land resources are the basis of human production and life, and they face many challenges in the process of urbanization, such as the prominent contradiction between land supply and demand and the inefficient use of land, which in turn restricts the socio-economic development and the promotion of urbanization. This paper takes China’s ten largest urban agglomerations as its research object and constructs a land resource drag effect model based on the C-D production function. The geographical weighted regression method is used to calculate the coefficient of the land drag effect. Combining kernel density analysis and spatial autocorrelation analysis, the paper reveals the temporal and spatial evolution patterns of the drag effect and discusses the impact of land resources on new urbanization and its temporal and spatial differentiation characteristics. The study shows that during the period of 2006–2022, China’s new-type urbanization as a whole rises, but the development of each urban agglomeration has significant differences, showing a spatial pattern of “east high, west low”; the drag effect of land resources shows a decreasing trend, but regional differences are obvious, showing a distribution of “east strong, west weak”; the kernel density curve of drag effect of land shows a “right-skewed-left-skewed” change, with the overall level weakening and the degree of concentration increasing; the drag effect of land resources shows significant positive global autocorrelation, and there are spatial proximity effect and spillover effect in space. The findings provide a theoretical basis for land resource utilization and spatial development in China’s new-type urbanization process. Therefore, it is necessary to implement differentiated land resource allocation and urban planning policies according to different types of urban spatial agglomeration and to give full play to the cooperative linkage effect of urban agglomerations in reducing the drag effect of land resources. Full article

Jilin Province, an important commodity grain base in China, relies on groundwater resources for its agricultural development. The implementation of a series of policies, including agricultural subsidies and food security policies, has led to a rapid expansion of the sowing area in recent decades, resulting in an increase in agricultural water demand. This has had a significant impact on the groundwater system. It is therefore imperative to understand the dynamics of the groundwater to ensure the security of water resources, ecological security, and food security. An evaluation of the sustainability of groundwater resources in Jilin Province was conducted through a quantitative analysis of the reliability, resilience, and vulnerability of groundwater. This analysis was informed by the inversion of changes in groundwater reserves over a period of 249 months, commencing from 2002-04 to 2022-12. The inversion process utilized data from the Gravity Recovery and Climate Experiment (GRACE) gravity satellite and Global Land Data Assimilation System (GLDAS), offering a comprehensive view of the temporal dynamics of groundwater reserves in the region. The results indicated the following: (1) Groundwater storage (total amount of water below the surface) in Jilin Province exhibited an overall decreasing trend, with the highest groundwater level recorded in June and the lowest in September on a monthly basis. (2) Prior to September 2010, groundwater reserves were in surplus most of the time. From October 2010 to August 2018, however, they began to fluctuate between surplus and deficit states. Since September 2018, the reserves have been in a long-term deficit, showing an overall downward trend. (3) Prior to 2005, the groundwater system was at a high/extremely high level of sustainability. However, following 2011, it fell to a very low level of sustainability and has continued to deteriorate. (4) The maximum information coefficient and correlation analysis indicate that the sown area is the most significant factor contributing to the decline in the sustainability of the groundwater system. This study reveals the spatial and temporal distribution pattern and evolution trend of groundwater resources sustainability in Jilin Province, and provides theoretical and data support for regional groundwater resources protection and management. Full article

The scientific evaluation of the coordinated development level of the Yangtze River Delta Urban Agglomeration is crucial for promoting the localization of the Sustainable Development Goals (SDGs). This study, based on the SDGs framework, utilizes data from 41 prefecture-level cities in the Yangtze River Delta from 2013 to 2023 to establish a five-dimensional evaluation index system, covering urban–rural integration (SDG 10), scientific and technological innovation (SDG 9), infrastructure (SDG 9.1), ecological environment (SDG 13/14/15), and public services (SDG 3/4/11). By applying the coupling coordination degree model, kernel density estimation, and the standard deviation ellipse method, the study systematically assesses the regional coordinated development level and its spatio-temporal evolution patterns. The findings reveal that from 2013 to 2023, the development indices of the five subsystems showed a fluctuating upward trend, with significant disparities in growth rate and stability. The overall regional coordination degree continuously improved, and differences diminished, with the coupling degree and coupling coordination degree exhibiting a “polarization followed by an overall leap” pattern. The coupling coordination degree evolved in three stages: “imbalance in mutual feedback among elements, strengthening of coordination mechanisms, and deepening of policy innovation”, with spatial differentiation and clustered development coexisting. Spatially, the distribution center shifted through three phases: “policy-driven”, “market-regulated”, and “technology-led”, forming an axial reconstruction from northwest to southeast, ultimately establishing a multi-center coordinated development system. Full article

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region. Full article

Tourism ecological efficiency (TEE) is a significant indicator of the development level of green and intensive tourism. However, conventional directional and radial TEE measurement approaches overlook critical factors such as intermediate process influences and input–output slack variables, potentially leading to biased estimates. Urban areas are key to coordinating tourism across provinces, so accurately assessing the TEE is vital for sustainable regional tourism. This study uses an improved TEE measurement model to measure the TEE of the Guangdong–Fujian–Zhejiang (GFZ) coastal city clusters from 2010 to 2021. The improved TEE measurement model is a three-stage super-efficiency SBM approach. It then uses standard deviation ellipses and geographic detectors to analyze the TEE’s spatiotemporal characteristics and influencing factors. The findings indicate the following: (1) The three-stage super-efficiency SBM approach improves the accuracy and validity of measurement results by removing external environmental variables. (2) During the study period, the TEE values of the GFZ coastal city clusters were above average (except for Meizhou, where the efficiency improved). Temporally, the TEE values of 75% of the cities showed an increasing trend; spatially, the high-value areas increased significantly, the middle- and low-value areas decreased, and the center of gravity shifted to the north and south. (3) The years 2016–2021 saw an increase in external development factors and the use of external resources. The study’s findings can serve as scientific benchmarks for TEE measurement, as well as the low-carbon and environmentally friendly growth of tourism in urban agglomerations. Full article