Search Results (2,656)

With the significant increase in solar power generation’s proportion in power systems, the uncertainty of its power output poses increasingly severe challenges to grid operation. In recent years, solar forecasting models have achieved remarkable progress, with various developed models each exhibiting distinct advantages and characteristics. To address complex and variable geographical and meteorological conditions, it is necessary to adopt a multi-model fusion approach to leverage the strengths and adaptability of individual models. This paper proposes a photovoltaic power prediction framework based on multi-stage ensemble learning, which enhances prediction robustness by integrating the complementary advantages of heterogeneous models. The framework employs a three-level optimization architecture: first, a recursive feature elimination (RFE) algorithm based on LightGBM–XGBoost–MLP weighted scoring is used to screen high-discriminative features; second, mutual information and hierarchical clustering are utilized to construct a heterogeneous model pool, enabling competitive intra-group and complementary inter-group model selection; finally, the traditional static weighting strategy is improved by concatenating multi-model prediction results with real-time meteorological data to establish a time-period-based dynamic weight optimization module. The performance of the proposed framework was validated across multiple dimensions—including feature selection, model screening, dynamic integration, and comprehensive performance—using measured data from a 75 MW photovoltaic power plant in Inner Mongolia and the open-source dataset PVOD. Full article

Understanding the tradeoff and synergy among ecosystem services (ESs) and their influencing factors is a prerequisite for simultaneously managing multiple ESs and holds significant importance for achieving harmonious regional development between humans and nature. Existing research predominantly focuses on the overall characteristics of tradeoff and synergy, while studies on spatially differentiated tradeoff and synergy characteristics remain limited. In addition, their driving mechanisms are not yet fully understood, especially in large-scale river basins. This study, taking the Yellow River Basin (YRB) from 2000 to 2023 as the study area, employed multi-source data and multiple models to quantify three ESs, including soil erosion negative service (indirectly reflecting the soil conservation service function), carbon sequestration, and water yield. Combining Pearson correlation analysis, a geographically weighted regression model, and optimal parameter geographical detection, we identified the spatiotemporal interaction relationships and their dominant drivers. The results indicated that soil erosion negative services decreased by 24.89%, while carbon sequestration and water yield increased by 53.30% and 38.47%, respectively. The most significant improvements in the three ESs were observed in the midstream of the YRB. Spatially, soil erosion negative service decreased from west to east. Carbon sequestration exhibited a spatial pattern of higher values in the south and east and lower values in the north and west. Water yield decreased from south to north. Tradeoff relationships existed between soil erosion negative service and carbon sequestration and between soil erosion negative service and water yield. A synergistic relationship existed between carbon sequestration and water yield. Over time, the proportion of areas showing synergy among these three ESs decreased. However, synergistic areas remained more common than tradeoff areas. This was especially evident in the relationship between carbon sequestration and water yield, where synergy consistently accounted for over 78% of the YRB. Rainfall, soil properties, and fractional vegetation cover were identified as important drivers of the tradeoff/synergy between soil erosion negative service and carbon sequestration. Rainfall, temperature, fractional vegetation cover, and elevation were significant drivers of the interactions between carbon sequestration and water yield. Population density, fractional vegetation cover, GDP density, and rainfall were the main influencing factors for the tradeoff/synergy between soil erosion negative service and water yield. Our general methodology and results provide valuable decision-making references for policymakers, highlighting the necessity of considering the spatiotemporal heterogeneity in ESs tradeoff characteristics and their underlying driving factors. 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

The digital economy injects developmental momentum into urban–rural integration through technological penetration, while urban–rural integration expands application scenarios for the digital economy via spatial restructuring. By clarifying the coupling coordination mechanism between these two subsystems, this study employs the coupling coordination degree model, spatial autocorrelation analysis, Markov chain, and spatiotemporal geographically weighted regression model to systematically investigate the development levels of the digital economy and urban–rural integration, the dynamic evolution characteristics of their coupling coordination degree, and the spatiotemporal heterogeneity of influencing factors across 31 provinces of China from 2012 to 2022. The main findings are as follows: (1) The digital economy level exhibited a pronounced upward trajectory with substantial inter-provincial disparities, while urban–rural integration level displayed a modest upward trend accompanied by evident polarization. (2) The coupling coordination degree increased steadily, with the number of provinces experiencing moderate and mild imbalance declining markedly and the contiguous zone of near imbalance expanding. Spatially, the pattern was characterized as “high in the east, low in the west.” (3) The coupling coordination degree exhibited significant positive spatial correlation. High-High agglomeration was concentrated in the eastern coastal regions, while Low-Low agglomeration dominated the western inland areas. The dynamic transfer of the coupling coordination degree revealed a distinct “club convergence” phenomenon. (4) Government support and technological innovation exerted increasingly positive effects on the coupling coordination degree in northeast and north China. Economic development initially exerted a significant positive effect in northwest and southern China, but its impact subsequently shifted to regions north of the Yellow River basin. In several southwest provinces, the influence of industrial structure transitioned from positive to negative. Full article

Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models (DEMs) with hydrological parameters, applying weighted sum analysis to classify 18 sub-watersheds into different flood priority levels. Morphometric parameters, including basin relief, drainage density, and slope, were analyzed to establish correlations between watershed morphology and flood susceptibility. The results indicate that approximately 74.38% of the watershed exhibits high to very high flood risk, with the most vulnerable sub-watersheds characterized by steep slopes, high drainage densities, and compact morphometric configurations. The correlation matrix confirms that watershed topography significantly influences surface runoff behavior, underscoring the necessity of incorporating geospatial analysis into flood risk assessment frameworks. The classification of sub-watersheds into priority levels provides a scientific basis for optimizing resource allocation in flood mitigation strategies. This study highlights the importance of integrating advanced geospatial technologies, such as GISs and remote sensing, into hydrological risk assessments. The findings emphasize the need for proactive watershed management, including the use of real-time monitoring and digital tools for climate adaptation. Future research should explore the influence of land-use changes and climate variability on flood dynamics to enhance predictive modeling. These insights contribute to evidence-based decision-making for disaster risk reduction, reinforcing resilience in climate-sensitive regions. Full article

Revealing the trade-offs, synergies, and driving mechanisms among land use functions is essential for mitigating conflicts between functions, optimizing territorial spatial patterns, and providing policy support for regional sustainable development. Taking the Central Yunnan Urban Agglomeration as a case study, this study adopts a grid-based evaluation unit and employs a multi-model fusion approach to systematically analyze the interaction mechanisms among land use functions. By integrating the Pearson correlation method and root mean square deviation (RMSD) model, the trade-off and synergy relationships and their spatiotemporal evolution were quantitatively assessed. The XGBoost–SHAP model and optimized parameter-based geographical detector (OPGD) were introduced to identify the nonlinear characteristics and interaction effects of influencing factors on land use function trade-offs and synergies. In addition, a geographically weighted regression (GWR) model was used to explore spatial heterogeneity in these effects. The results indicate that (1) from 2010 to 2020, the overall synergy between production and ecological functions (PF&EF) in the urban agglomeration was enhanced, while trade-offs between production and living functions (PF&LF) intensified, and the trade-off intensity between living and ecological functions (LF&EF) decreased. Significant spatial heterogeneity exists among land use function interactions: PF&EF and PF&LF trade-offs are concentrated in the central and eastern parts of the urban agglomeration, while LF&EF trade-offs are more scattered, mainly occurring in highly urbanized and ecologically sensitive areas; (2) the dominant factors influencing land use function trade-offs and synergies include precipitation, slope, land use intensity, elevation, NDVI, Shannon diversity index (SHDI), distance to county centers, and distance to expressways; (3) these dominant factors exhibit strong nonlinear effects and significant threshold responses in shaping trade-offs and synergies among land use functions; and that (4) compared with the OLS model, the GWR model demonstrated higher fitting accuracy. This reveals that the impacts of natural, socio-economic, and landscape pattern factors on land use function interactions are characterized by pronounced spatial heterogeneity. 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

Oil consumption brings both energy security risks and environmental responsibilities. While traditional studies assign environmental responsibility primarily to oil producers, our research uncovers a geographical displacement of accountability: substantial oil volumes are embedded in traded goods and ultimately consumed in distant regions. Although China’s “dual control” policy regulates fossil energy use, it fails to account for the complexities of embodied oil flows. This oversight leads to imbalanced interregional responsibility allocation and resource exploitation issues. Adopting the “consumer pays” principle, this study makes methodological advances by innovatively combining multi-regional input–output (MRIO) modeling with geographically and temporally weighted regression (GTWR) analysis. The integrated approach provides spatial–temporal resolution in tracking embodied oil flows and their drivers across China’s provinces. Key findings include (1) strong concentration of oil inflows in developed eastern and central provinces, alongside rapid growth in southwestern regions; (2) evolving temporal patterns where economic growth and distance remain persistent drivers, while green technology and urbanization emerge as growing mitigating factors; (3) spatially, northwestern regions rely heavily on external supplies due to economic growth and urbanization, southeastern areas face rising transport costs, while green technologies in coastal regions have yet to significantly curb inflows due to rebound effects. These insights provide a new analytical framework for energy policy, supporting region-specific solutions to balance development and sustainability from a consumption perspective. Full article

To systematically reveal the influence mechanisms and spatial effects of industrial intellectual property (IP) reform strategies and manufacturing craftsmanship spirit on regional energy intensity, this study aims to provide theoretical support and practical pathways for emerging market economies pursuing dual goals of energy efficiency governance and manufacturing transformation. Based on a “technology–culture synergistic innovation ecology” theoretical framework, the study deepens the understanding of energy intensity governance and introduces two spatial weight matrices—the economic distance matrix and the nested economic–geographic matrix—to uncover the spatial heterogeneity of policy and cultural effects. Using panel data from 30 Chinese provinces from 2010 to 2022 (excluding Tibet, Hong Kong, Macao, and Taiwan), we construct an index of manufacturing craftsmanship spirit (CSM) and its four dimensions—excellence in detail, persistent dedication, breakthrough orientation, and innovation inheritance—via the entropy method. Empirical analysis is conducted through Spatial Difference-in-Differences (SDID) and Double Machine Learning (DML) models. The results show that: (1) Industrial IP reform strategies significantly reduce local energy intensity through improved property rights definition and technology transaction mechanisms, but may increase energy intensity in economically proximate regions due to intensified technological competition. (2) All four dimensions of craftsmanship spirit indirectly mitigate regional energy intensity via distinct pathways, with particularly strong mediating effects from persistent dedication and innovation inheritance. In contrast, breakthrough orientation shows no significant impact, possibly due to limitations from the current stage of the technology lifecycle. (3) Spatial spillover effects are heterogeneous: under the nested economic–geographic matrix, IP reform strategies reduce neighboring regions’ energy intensity through synergistic effects, while under the economic distance matrix, competitive spillovers lead to an increase in adjacent energy intensity. Based on these findings, we propose the following: deepening IP reform strategies to build a technology–culture synergistic ecosystem; enhancing regional policy coordination to avoid technology lock-in; systematically cultivating the core of craftsmanship spirit; and establishing a dynamic incentive mechanism for breakthrough orientation. These measures can jointly drive systemic improvements in regional energy efficiency. Full article

Food insecurity remains a persistent global challenge, despite significant advancements in agricultural production and technology. The main objective of this study is to identify and quantitatively assess some of the structural determinants influencing country-level food insecurity and provide an empirical background for policy-making aimed at achieving the Sustainable Development Goal of Zero Hunger (SDG 2). This study employs a beta regression model in order to study moderate or severe food insecurity across 153 countries, using a cross-sectional dataset that integrates economic, agricultural, political, and demographic independent variables. The analysis identifies low household per capita final consumption expenditure (β = −9 × 10⁻⁵, p < 0.001), high income inequality expressed as a high GINI coefficient (β = 0.047, p < 0.001), high long-term inflation (β = 0.0176, p = 0.003), and low economic globalization (β = −0.021, p = 0.001) as the most significant predictors of food insecurity. Agricultural variables such as land area (β = −1 × 10⁻⁵, p = 0.02) and productivity per hectare (β = −9 × 10⁻⁵, p = 0.09) showed limited but statistically significant inverse effects (lowering food insecurity), while factors like unemployment, political stability, and conflict were not significant in the model. The findings suggest that increased economic capacity, inequality reduction, inflation control, and global trade integration are critical pathways for reducing food insecurity. Future research could employ beta regression in time-series and panel analyses or spatial models like geographically weighted regression to capture geographic differences in food insecurity determinants. Full article

Assessing habitat quality and quantifying human disturbance are fundamental prerequisites for ecological conservation. However, existing studies predominantly focus on single dimensions. There is an urgent need to integrate habitat quality and human disturbance, and quantify their spatially coupled coordination relationships to address the disconnect between them in current research. As a critical ecological reserve in southeastern China, Fujian Province faces threats of ecological degradation. This study employed the InVEST model to evaluate habitat quality in Fujian from 1980 to 2020, utilized a human disturbance index to quantify spatiotemporal patterns of anthropogenic activities, analyzed their changes using landscape indices, and applied coupling coordination analysis to examine their interrelationships. Machine learning and geographically weighted regression were further integrated to identify driving factors of coupling coordination patterns. The results showed that: (1) Habitat quality remained relatively high while human disturbance levels stayed low throughout 1980–2020, though both showed gradual deterioration over time, particularly during 2010–2020, with riverine and coastal eastern regions exhibiting the lowest habitat quality and highest disturbance levels. (2) Coupling coordination relationships predominantly exhibited synergy, with moderate imbalance zones concentrated in the eastern coastal areas. Temporally, regions with lower imbalance expanded significantly during 2010–2020. (3) Landscape metric analysis revealed declining dominance of high-quality habitat/low-disturbance/synergistic zones, contrasted by increased fragmentation of low-quality habitat/high-disturbance/imbalanced zones. (4) Socioeconomic factors exerted stronger influence on coupling coordination patterns than natural environmental variables, proximity to urban areas, road density, and nighttime light indices. Each driver displayed spatially variable positive/negative effects. The results enhance our understanding of human–nature sustainable development dynamics, urban expansion–ecological conservation trade-offs, and provide methodological insights for regional ecological management and achieving sustainable development goals. Full article

Background/Objectives: Forced degradation studies are critical for identifying potential degradation pathways of monoclonal antibodies (mAbs), particularly under thermal stress. Due to their structural complexity and sensitivity to elevated temperatures, mAbs are prone to fragmentation, aggregation, and post-translational modifications. This study aimed to evaluate and compare the degradation profiles of biosimilar anti-VEGF mAb and its originator counterparts sourced from both the United States (U.S.) and the European Union (EU) under thermal stress conditions. To our knowledge, this represents one of the few studies conducting a direct head-to-head comparability assessment across biosimilar and two geographically sourced originators, integrating orthogonal analytical approaches. Methods: Biosimilar candidate and originator products (U.S. and EU) were incubated at 37 °C and 50 °C for 3, 7, and 14 days. Fragmentation profiles were assessed using validated non-reduced and reduced capillary electrophoresis–sodium dodecyl sulfate (CE-SDS) methods. Additionally, size-exclusion ultra-performance liquid chromatography (SE-UPLC) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) assays were performed on samples stressed for 14 days to provide deeper insights into degradation pathways. Results: Non-reduced CE-SDS analysis indicated a time- and temperature-dependent increase in low-molecular-weight fragments and a corresponding decrease in the intact form, with more pronounced effects observed at 50 °C. Reduced CE-SDS revealed a more rapid increase in total impurity levels at 50 °C, accompanied by a decrease in total light and heavy chain content. SE-UPLC showed enhanced aggregation under thermal stress, more pronounced at 50 °C. LC-MS/MS analysis identified increased asparagine deamidation in the PENNY peptide and pyroglutamic acid formation (pE) at the N-terminus of the heavy chain. Conclusions: The degradation profiles of the biosimilar and originator mAbs were highly comparable under thermal stress, with no significant qualitative differences detected. By applying a multi-tiered analytical characterization technique, this study provides a comprehensive comparability assessment that underscores the robustness of biosimilarity even under forced degradation conditions. Full article

Urban greenspace exposure associates with improved health outcomes, particularly chronic disease mitigation. Based on the need to characterize spatial heterogeneity in the health benefits of urban greenspaces, this study quantified the association between greenspace accessibility and chronic disease mortality in London, while examining the modulating effects of urbanization and age. Utilizing nighttime light (NTL) data to define urbanization gradients and road-network analysis to measure greenspace accessibility, we applied geographically weighted regression (GWR) across 983 neighborhoods. Key findings reveal that over 60% of central London residents live within 300 m of greenspace, yet 20% fall short of WHO standards. Greenspace accessibility showed significant negative associations with standardized mortality ratios for both cancer (β = −0.0759) and respiratory diseases (β = −0.0358), and this relationship was more pronounced in highly urbanized areas and neighborhoods with higher working-age populations. Crucially, central urban zones show amplified effects: a 100 m accessibility improvement was associated with a potential reduction in cancer deaths of 1.9% and in respiratory disease deaths of 2.4% in high-sensitivity areas. Urbanization levels and working-age population proportions exert significantly stronger moderating effects on greenspace–respiratory disease relationships than on cancer outcomes. While observational, our findings provide spatially explicit evidence that the greenspace–mortality relationship is context-dependent. This underscores the need for precision in urban health planning, suggesting interventions should prioritize equitable greenspace coverage in highly urbanized cores and tailor functions to local demographics to optimize potential co-benefits. This study reframes understanding of greenspace health benefits, enhances spatial management precision, and offers models for healthy planning in global high-density cities. Full article

China’s major function-oriented zoning (MFOZ) serves as a crucial policy instrument for functional regulation of land use, playing a significant role in the latest territorial spatial planning. Studies on the implementation of MFOZ have been conducted since its release in 2012, but there is a lack of comprehensive methods to assess the effectiveness of its implementation. In China, the newly initiated City Examination provides novel technical support for verifying MFOZ planning, addressing the gap in comprehensive evaluation methodologies and channels. This study proposes a comprehensive urban assessment framework and a major function classification approach based on City Examination data, enabling the identification of implementation deviations in MFOZ planning based on the current urban conditions reflected by City Examination. The methodology incorporates dimensionality reduction, multi-indicator clustering, entropy-weighted overlays, and natural break classification techniques and examines the degree of strategic deviation in China’s MFOZ through a comprehensive and systematic assessment. Due to the timeliness and long-term nature City Examination data, the method allows for the long-time dynamic tracking and evaluation of the real-time progress in MFOZ. Empirical analysis of Hubei Province revealed that 77.9% of its urban development aligns with the 2011 MFOZ scheme while demonstrating discernible deviation types and hierarchical discrepancies, with geographically clustered patterns observed among cities exhibiting such deviations. Full article

Climate change has emerged as a critical global issue. Land-use/cover change (LUCC) plays a pivotal role in influencing terrestrial ecosystem carbon cycles and further regulates carbon emission intensity by reshaping the spatial characteristics of landscape patterns. Taking 300 Chinese cities as the study area, an analytical framework encompassing carbon emission accounting, regional land-use landscape pattern analysis, spatiotemporal correlation between landscape patterns and carbon emissions, and economic “core-periphery” disparities was presented. The land-use carbon emissions and landscape pattern indices of each city from 2005 to 2020 were calculated, and the geographically weighted regression (GWR) model was employed to examine the impact of land-use landscape pattern changes on carbon emissions from an urban perspective. Furthermore, the cities were categorized into developed and underdeveloped groups based on the median per capita GDP to compare how economic development levels moderate this impact mechanism. The results indicate that the relationship between landscape patterns and carbon emissions exhibits significant spatial heterogeneity, highlighting the complexity of the influence of land-use morphology on carbon emissions. Sustainable land-use strategies must account for regional disparities in economic levels, planning capacity, and administrative characteristics rather than pursuing a uniform urban form. Economic development significantly moderates the carbon mitigation effects of landscape patterns through its influence on spatial governance capacity, leading to pronounced differences between cities at varying development levels. Moving forward, regionally tailored approaches that integrate landscape optimization with industrial transformation and ecological conservation should be prioritized to provide spatial solutions for achieving the carbon peaking and carbon neutrality goals. Full article