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This study utilized census data from Henan Province for the years 2000, 2010, and 2020 to investigate the spatiotemporal evolution of population aging, defined by the proportion of the population aged 65 and above. Employing spatial analysis techniques such as spatial autocorrelation and the standard deviation ellipse, the research mapped out the progression and distribution of aging demographics. Furthermore, the study delved into the influencing factors of aging using an optimal parameters-based geographical detector. Results indicate a deepening degree of population aging in Henan Province, transitioning from an adult type to an old type structure. There is a marked positive spatial correlation among counties, with high-value aging areas initially decreasing, then increasing, and notably spreading from the central to the central and southern regions of the province. The center of gravity for population aging, specifically around Changge City and Xuchang City, exhibits a trajectory moving southeast before shifting northwest. Factor detection reveals that in 2000, 2010, and 2020, the elderly dependency ratio predominantly influences the aging trend, with explanatory powers of 88.4%, 87.9%, and 90.9%, respectively. Interaction analysis indicates that the interaction between the old-child ratio and the elderly dependency ratio has a strong explanatory power for the aging patterns in Henan Province, reaching 97.3%, 97.0%, and 97.4%, respectively.

Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.

This paper focuses on the scenario where relativistic electron nonlinear Thomson forward scattering is driven by circularly polarized laser pulses, using both theoretical analysis and numerical simulations. For the first time, we investigate how the magnetic flux density and beam waist radius jointly influence the spatiotemporal properties of radiation. For the superimposed field under different parameters, the forces exerted by the laser field and the applied magnetic field on electrons will show cooperative or competitive relationships at different moments of action. And the transformation of this relationship promotes the evolution of the electron radiation pattern in the superimposed field, which greatly changes the spatiotemporal properties of Thomson scattering. Based on this, completely new spatial distributions of radiation-ripple and lotus phenomena were discovered. In addition, the combination of parameters with the best radiation properties was selected through Big Data, and the quasi-periodicity of the azimuth angle ${{\phi }_{\bm{p}}}$ was discovered for the first time. These results will contribute to the understanding of the radiation mechanism of Thomson scattering in superimposed field and will be instructive for laboratory modulation of ultra-high-performance ${\bm{X}}/{\bm{\gamma }}$-rays.

As areas with high population and economic concentration, metropolitan areas are important geographical units for innovation transformation and radiation. However, studies on innovation space have mainly focused on the entire country, urban agglomerations, provinces, and cities, with less attention paid to metropolitan areas as important scales of scientific and technological innovation. In addition, existing studies often emphasize the overall characteristics of the innovation output space, neglecting diverse innovation subjects such as enterprises, scientific research institutions, and individuals. Based on the data of authorized invention patents in the Guangzhou metropolitan area, this study applied spatial research methods, such as spatial autocorrelation, kernel density analysis, and buffer analysis, to explore the overall evolution pattern of innovation output space in the metropolitan area as well as the evolution characteristics and influencing factors of the three types of innovation outputs, namely enterprises, scientific research institutions, and individuals, to provide a scientific basis for the development of innovation space and the optimization of innovation resources. The results show that: (1) the evolution of innovation output space generally follows the pattern of "gathering in the core area of the main district of the central city in a metropolitan region, generating innovation output growth points in the secondary cities and forming a number of innovation output cross-border integration areas." (2) The innovation output space of enterprises continues to cluster toward industrial parks and technology parks with clear boundaries, and the extensive construction of parks has driven the decentralized development of innovation output space of enterprises; The innovation output space of scientific research institutions, with strong stability, has always been highly concentrated in the core area of the main city of Guangzhou and has expanded with the construction of university towns within the metropolitan area; The individual innovation output has shifted from "explosive spatial dispersion" to "organized spatial convergence", and the adsorption effect of urban rail transit on individual innovation output is becoming increasingly significant. (3) The three types of innovation outputs always have spatial proximity characteristics, and enterprise innovation is the main driving force for innovation diffusion in the periphery of metropolitan areas. (4) Regional innovation output is influenced by both the innovation environment and innovation inputs, among which government support is the most critical driving force affecting the three types of innovation outputs. Economic base and location conditions are important pillars for the three types of innovation outputs, while the degree of openness has a greater contribution to enterprise innovation output by facilitating technological spillovers and knowledge flows. In general, the overall spatial evolution of innovation output in the metropolitan area follows the development pattern of "single core-expansion-multicore," with the spatial evolution of the three types of innovation outputs being closely related to the construction of regional industry-university-research centers and major transportation facilities.

[Objective] The increasingly complex international situations and frequent trade frictions have significantly intensified the supply risk of global industrial chains. The trade pattern evolution identifying and risk propagation modeling of global wind power industrial chains would provide essential implications for increasing the resilience of industrial chains and securing energy supplies. [Methods] This study depicted the spatial-temporal characteristics of major wind power industrial chains during 2000-2021 based on complex network analysis, revealed the resilience of the industrial networks by network resilience assessment, and tracked the impacts of supply risks from different countries (regions) based on cascading failure modeling. [Results] (1) The overall scale of global wind power industrial chains continued to expand with trade connections between countries (regions) intensifying, and the complexity and efficiency of industrial networks increased significantly. (2) China as a major exporter played an increasingly important role in the global wind power industrial chains, establishing intense trade relations with numerous countries (regions). (3) Supply risks originating from China may generate profound impacts on the tower and generator trade networks, while supply risks originating from the United States and Germany may generate profound impacts on the blade and gearbox trade networks, respectively. [Conclusion] Optimizing industrial layout, collaborating with emerging markets, and strengthening technological innovation are thus of great significance for systematically preventing potential risks within wind power industry chains worldwide.

The development of mining cities has led to changes in land use and the evolution of landscape patterns. Constructing an ecological security evaluation system can reflect the ecological security status of mining city areas and provide planning references for these cities. This study, based on Heihe City’s land use data from 1980 to 2020, systematically analyzes the spatial and temporal evolution characteristics of land use, landscape patterns, and landscape ecological security levels by constructing a land use transfer matrix and calculating landscape pattern indices and landscape ecological security indices. The results show that: 1) Forest land is the main type of land use in Heihe City, accounting for over 50% of the total area. Land use changes primarily occurred between 2000–2010 and 2010–2020, with the spatial pattern characterized by overall stability and localized dramatic shifts, mainly involving the conversion of forest land to farmland and unused land. 2) From 1980 to 2020, the landscape ecological security pattern in Heihe City improved. Landscape diversity and landscape contagion increased, while landscape fragmentation, the largest patch area, and the average patch area decreased. Land use was optimized overall, but the trend of forest fragmentation became noticeable. 3) Between 1980 and 2020, the landscape ecological security level in Heihe City improved significantly. Driven by ecological restoration policies, the ecological security level in the southern region greatly improved, while the spatial pattern continued to show a trend of lower security in the south and higher security in the north. Specifically, the area proportions of low-security and relatively low-security areas increased by 6.23% and 9.55%, respectively. The spatial clustering of landscape ecological security levels is evident, with high-high value clustering mainly in the north and low-low value clustering mainly in the south. It is necessary to further promote ecological protection in the north to ensure the ecological barrier function, while strengthening ecological restoration in the south to improve ecological security levels. Additionally, continuous macro-policy regulation is needed to maintain long-term ecological security in Heihe City. The ecological security level of mining city landscapes is influenced by economic dependence, policy constraints, and environmental issues. This study can provide guidance for planning improvements in the study area and other mining cities.

[Objective] The temporal and spatial variation of ecological environmental quality and its heterogeneity were analyzed in order to provide a scientific basis for the government and relevant departments to formulate ecological environmental protection policies. [Methods] The google earth engine (GEE) platform was used to select Landsat images of central Yunnan Province from 2000 to 2020, and the remote sensing ecological index (RSEI) was extracted year by year. Spatial analysis methods such as hotspot analysis, standard deviation ellipse, and ecological environment gravity center shift were used to explore the temporal and spatial evolution of ecological environmental quality. [Results] ① The overall ecological environmental quality of the central Yunnan urban agglomeration showed a declining trend, and the average RSEI dropped from 0.51 in 2000 to 0.46 in 2020. ② The ecological environmental quality in Central and Eastern Yunnan Province was obviously better than in the western region, except for a few years. Most of the areas with better ecological environmental quality had greater forest coverage. ③ In the past 20 years, the spatial distribution of the ecological environment of the Central Yunnan urban agglomeration showed a north-south direction, and the overall ecological environmental quality of the urban agglomeration showed a certain degree of aggregation. [Conclusion] The deterioration of the ecological environment of the Central Yunnan urban agglomeration was closely related to the rapid expansion of the built-up areas of the urban agglomeration, climatic anomalies, vegetation destruction, and aggravation of soil erosion.

The exponential expansion of urban areas has precipitated a concomitant deterioration in the natural environment. Constructing ecological networks is vital in improving landscape connectivity, protecting biodiversity, and maintaining regional sustainable development. Ningbo, China was set as the research area. The geographic information system (GIS) and the morphological spatial pattern analysis (MSPA) were employed to determine the ecological source area. Subsequently, the corridor design model Linkage Mapper was employed to ascertain and assess the linkages between the designated ecological source areas. The results showed that: (1) From 2000 to 2020, there was a large-scale change in land use type in Ningbo, with increasing complexity of patches and landscape fragmentation. The ecological sources of the three periods in Ningbo were primarily situated in the western, southern, and Hangzhou Bay coastal regions, exhibiting an uneven distribution in the eastern and western areas. (2) The number of primary ecological corridors in Ningbo underwent a significant reduction, from 26 to 17, between the years 2000 and 2020. In terms of the distribution of ecological corridors, the primary corridors were concentrated in the central, southern, and western regions of the study area in 2000. By 2020, however, the primary ecological corridors within the study region were distributed mainly in a southerly direction. The interaction between North and South ecological sources was weakened, which adversely affected the species spread and ecosystem stability. (3) After optimization, 12 ecological corridors, and 4 ecological nodes were incorporated into the Ningbo, 67 ecological breakpoints were identified, and 4 stepping stone patches were added. The study employed spatio-temporal change trends, including land use type and landscape pattern, to examine the ecological network of Ningbo. In conclusion, the proposed optimisation strategy is aligned with the current urban development context, offering a particularly pertinent reference point for Ningbo's ecological protection initiatives., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Society of Environmental Toxicology and Chemistry. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Rapid socioeconomic growth has altered land use patterns, resulting in a surge in worldwide CO 2 emissions, triggering global climate challenges and adversely affecting human health, safety, and sustainable socioeconomic development. As a result, immediate action is required to undertake climate mitigation and adaptation strategies. This study, based on the causal logic of climate change, blue-green space patterns, and carbon emissions, uses the system dynamics (SD) model, patch-generating land use simulation (PLUS) model, and integrated valuation of ecosystem service and trade-offs (InVEST) models to simulate the evolution of blue-green space patterns and predict the spatial distribution of carbon storage in Wuhan to 2060 from 2030 under three SSP-RCP scenarios from CMIP6 and investigates their mechanisms. The findings show that across various SSP-RCP scenarios, the blue-green space patterns in Wuhan would decline over the next 30 years, with green spaces decreasing to some amount and blue spaces growing marginally. The carbon storage is also expected to decline due to the shrinking blue-green space patterns. The SSP126 scenario has the least shrinkage of blue-green spaces, resulting in a reduction of 7.18Tg in carbon storage. Under the SSP245 scenario, the expansion of non-blue-green spaces encroaches on blue-green spaces, resulting in an 8.13 Tg decrease in carbon storage. Across the SSP585 scenario, non-blue-green spaces expand the fastest, resulting in the highest loss of blue-green spaces and a considerable drop in carbon storage of 11.67 Tg. This research is extremely important for optimizing regional land use patterns, coordinating green and high-quality development in Wuhan, and assisting with the implementation of urban climate change adaptation plans., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

With the promotion of marine power strategies and marine ecological civilization construction, marine green development has become an important issue. This study utilizes the mixed distance function Epsilon-based measure and the global Malmquist–Luenberger index model, which accommodates both radial and non-radial models, to evaluate the marine green total factor productivity (GTFP) across 11 coastal regions of China. The findings of this study are as follows. First, the marine GTFP showed a gradual increase in China’s coastal regions during the study period, with notable differences observed among the three primary marine economic circles. Second, technological advancement was the primary driver of the transformation of marine GTFP. Third, the distribution across the space pattern of marine GTFP tended to be concentrated continuously, which shows that the overall spatial distribution pattern of marine GTFP remained relatively stable. Fourth, there is no obvious σ convergence in a northern, eastern marine economic circle. Both absolute and conditional β convergence exists, indicating that the marine GTFP converged to their respective steady-state levels during the study period. This study can provide decision-making reference for accelerating the green growth and sustainable development of China’s marine economy.

By constructing the competitiveness evaluation index system of cultural and creative industries and adopting the methods of factor analysis, cluster analysis and spatial autocorrelation analysis, this paper empirically analyses the cultural and creative industry competitiveness and its spatial pattern evolution characteristics of 41 cities in the Yangtze River Delta from 2005 to 2020. The findings are as follows: (1) significant differences exist in the competitiveness of cultural and creative industries in the Yangtze River Delta. Shanghai's cultural and creative industry has the highest level of competitiveness. In Zhejiang and Jiangsu, 82% and 54% of the cities cultural and creative industry competitiveness are above the average level, respectively. Only Hefei in Anhui Province is above the average level. (2) The competitiveness of cultural and creative industries in 41 cities can be divided into five clusters from strong to weak. Shanghai ranks in the strongest category, but its comparative advantage weakens. The competitiveness of Nanjing, Hangzhou and Suzhou is the second-strongest category and maintains a rapid improvement. Wuxi and the other eight cities are for the stronger class. Xuzhou and other cities belong to the weaker and weakest categories. (3) The positive spatial correlation of the competitiveness of cultural and creative industries in the Yangtze River Delta. Suzhou, Wuxi, Changzhou, Nantong, and other cities are significant high-high spatial correlation and become the hot spots of developing cultural and creative industry. Xuzhou, Suqian, Fuyang, Bozhou and other cities are significant low-low spatial correlation and they become the cold spots of developing cultural and creative industry. [ABSTRACT FROM AUTHOR]