High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects.

Accurate spatial distribution of carbon dioxide (CO2) emissions is essential information needed to peaking emissions and achieving carbon neutral in China. The aim of this study was to map CO2 emissions with high spatial resolution at provincial scale and then explore the scale effect on mapping results. As an example, the spatiotemporal pattern and factors influencing CO2 emissions were examined in Guizhou Province in Western China. With the proposed method, a reasonable spatial distribution of CO2 emissions with high spatial resolution was obtained, which had relatively accurate information on spatial details. The optimal resolution of CO2 emissions at the provincial scale under high spatial resolution was approximately 90 m and 1260 m. More detailed grid data can better reflect the spatial variability of CO2 emissions. Emissions of CO2 were spatially heterogeneous in Guizhou, with high emissions in centers of big cities that gradually spread and decreased from city centers. From 2009 to 2019, the spatial distribution of CO2 emissions developed from agglomeration to dispersion. Areas of high carbon emissions decreased, those of medium carbon emissions increased, and many areas changed from no emissions to carbon emissions. Industrial land had the highest emissions, followed by commercial and transportation lands. Over 10 years, changes occurred in the relation between interregional economic level of Guizhou and CO2 emissions, with the relation changing from linear into an inverted U-shaped relation. The effect of industrial structure on CO2 emissions decreased, and the linear increase between CO2 emissions and the urban scale became more evident. The results of this study will contribute to accurate monitoring and management of carbon emissions in Guizhou, as well as provide support to formulate policies related to controls on carbon emissions in different regions.

Adaptive Mesh Strategy for Efficient Use of Interface Elements in a 3D Probabilistic Explicit Cracking Model for Concrete.

In this paper, the development of a 3D adaptive probabilistic explicit cracking model for concrete is reported. The contribution offered herein consists in a new adaptive mesh strategy designed to optimize the use of interface elements in probabilistic explicit cracking models. The proposed adaptive mesh procedure is markedly different from other strategies found in the literature, since it takes into account possible influences on the redistribution of stresses after cracking and can also be applied to purely deterministic cracking models. The process of obtaining the most appropriate adaptive mesh procedure involved the development and evaluation of three different adaptivity strategies. Two of these adaptivity strategies were shown to be inappropriate due to issues related to stress redistribution after cracking. The validation results demonstrate that the developed adaptive probabilistic model is capable of predicting the scale effect at a level similar to that experimentally observed, considering the tensile failure of plain concrete specimens. The results also show that different softening levels can be obtained. The proposed adaptive mesh strategy proved to be advantageous, being able to promote significant reductions in the simulation time in comparison with the classical strategy commonly used in probabilistic explicit cracking models.

Spatiotemporal evolution and interaction of water constraints and their socio-ecological drivers in the Taihu Lake Basin.

To effectively manage water constraints (WCs) within a basin, it is crucial to first scientifically delineate their spatial distribution and thoroughly understand the interactions between WCs. Investigating the complex driving mechanisms at multiple scales is also essential. In this study, a basin WC evaluation framework is constructed using a conflict risk assessment model. The spatiotemporal variations of four types of WCs across three spatial scales in the Taihu Lake Basin (TLB) are thoroughly investigated. Furthermore, the study quantifies the trade-offs, synergy effects, and bundle patterns of these water constraints. The study employs the Optimal Parameters-based Geographic Detector (OPGD) and multivariate linear regression to identify the key socio-ecological drivers of WCs. Our findings indicate that between 2010 and 2020, water resource constraint (WREC), water environment constraint (WENC), water safety constraint (WSAC), water ecology constraint (WECC), and the comprehensive WC (CWC) displayed varying degrees of heterogeneity. Particularly, the mean values of WSAC, WECC, and CWC witnessed an increase over the decade. Additionally, all WCs exhibited a strong positive spatial autocorrelation. Synergistic interactions among WCs were predominantly observed in pairs such as WREC-WSAC, WREC-WECC, and WSAC-WECC, while a weaker trade-off effect was noted in the WENC-WECC pair. At multiple scales, we identified eight types of WC bundles capable of undergoing mutual transformations, especially at the basin scale. The primary drivers of WCs varied across different stages and scales, with most factors collectively exerting a more significant impact than individually. Notably, factors like secondary and tertiary industry GDP (X2), population density (X3), precipitation (X6), and elevation (X7) were identified as core drivers influencing the evolution of WCs in the TLB. Integrating these spatiotemporal characteristics and driving mechanisms of WC interactions into basin planning and management can significantly support the alleviation of multidimensional water constraints in territorial spaces.

Decomposing scale, technique and composition effects of foreign direct investment on environmental quality.

This study aimed to investigate how the decomposing scale effect, technique effect and composition effect of foreign direct investment (FDI) impact on carbon dioxide (CO2) emissions for 115 nations spanning 1999 to 2019 by employing Generalised Method of Moments (GMM) model. The results indicated that FDI, real GDP per capita, capital-labor ratio, institutional quality and urbanization increase CO2 emissions while the square of real GDP per capita and trade openness contributed to reducing CO2 emissions. Also, our findings fail to support Environmental Kuznets Curve (EKC) theory. The outcomes of this research illustrated that scale effect dominates composition effect and followed by technique effect. The interaction effect of FDI and technique effect has the least influence on CO2 levels in reducing the harmful effects of FDI on CO2. Furthermore, it should be highlighted that although FDI increases CO2 emissions, its detrimental impact on CO2 emissions is moderately mitigated by its interactions with three economic mechanisms. Therefore, it is necessary to enhance the technical processes of production as well as the development of modern technologies. We recommended that policymakers balance sustainable economic development with environmental sustainability by considering the indirect effects of factors on CO2 emissions.

Spatial scale effects on the value of ecosystem services in China's terrestrial area.

Identifying the response characteristics of ecosystem service value (ESV) to changes in spatial scales, known as spatial scale effects, is crucial in guiding the development of corresponding management strategies. This paper examines ESV in China's terrestrial area during the year 2020, revealing the spatial aggregation characteristics of ESV and the trade-off and synergistic relationships of ecosystem services at different spatial scales, ranging from 1 km × 1 km-10 km × 10 km, with a gradient of 1 km. The results indicate: 1) The distribution pattern of ESV in China's terrestrial area is "high in the southeast and low in the northwest." 2) The spatial characteristics of ESV in China's terrestrial area undergo a distinct transition at the 3 km × 3 km scale. In detail, the spatial clustering features show a trend of first rising and then falling with the increase in spatial scale, while the synergistic relationships between different ecosystem services strengthen and the trade-off relationships weaken with the increase of the spatial scale. These findings can inform the formulation of differentiated ecological protection compensation policies and enable cross-area trading of ecological values in China.

Can market-oriented and government-led spatial agglomeration of factories reduce carbon emission intensity? Evidence from China.

Both market-oriented and government-led spatial agglomeration of factories profoundly affect China's carbon emission intensity. However, few studies have comprehensively examined the effect of spatial agglomeration of factories driven by different motivations on carbon emission intensity, and often ignore the moderating effect of local government attention to the environment. This study attempts to incorporate market-oriented and government-led spatial agglomeration of factories into the same analytical framework and examine their interaction effects on carbon emission intensity. We first calculate an Agglomeration Index based on the geographic coordinates of micro-factories to measure market-oriented agglomeration of factories, and take development zone policies as a proxy for government-led agglomeration of factories. Based on city-level data from 1998 to 2013, we empirically analyze their impacts on carbon emission intensity and further explore regional heterogeneity and mechanisms. The results show that both the market-oriented and government-led spatial agglomeration of factories can significantly reduce carbon emission intensity through scale effect, innovation effect and structure effect. However, the agglomeration of factories caused by government forces has weakened the original effect of market-oriented agglomeration of factories in reducing carbon emission intensity. Heterogeneity analysis indicates that government-led agglomeration of factories is more effective in reducing carbon emission intensity in less-developed regions. Furthermore, the improvement of the governments' attention to the environment can further exert the positive effect of spatial agglomeration of factories on carbon emission intensity. Our study deepens the understanding of the effects of spatial agglomeration of factories caused by different motivations, and provides a reference for China and other developing countries to formulate relevant policies for reducing carbon emission intensity.

[Spatial scale effects of landscape patterns on non-point source pollution: A case study of Taizi River Basin in Northeast China]. / éžç‚¹æºæ±¡æŸ“å¯¹æ™¯è§‚æ ¼å±€å“åº”çš„ç©ºé—´å°ºåº¦æ•ˆåº”­­ä»¥ä¸œåŒ—太子河流域为例.

River water quality is influenced by natural processes and human activities. Multi-scale landscape patterns can affect river water quality by altering the generation and transport processes of pollutants at different spatial scales. Taking Taizi River Basin in Northeast China as an example, we analyzed the relationship between landscape patterns and non-point source pollution in rivers based on water quality monitoring data and land use data by using correlation analysis and redundancy analysis methods. We aimed to determine the key spatial scales for the responses of landscape patterns to non-point source pollution and identify the key landscape indices influencing river non-point source pollution. The results showed that water quality of Taizi River Basin had seasonal differences, with better water quality during the flood season than non-flood season. Spatially, total nitrogen (TN) and total phosphorus (TP) were higher at the confluence points of tributaries and downstream areas. The impact of landscape patterns on non-point source pollution was stronger during the non-flood season than the flood season, while the influence on TN was stronger than on TP. At the spatial scale of within 500 m buffer zone during the flood season and at the sub-watershed scale during the non-flood season, landscape patterns showed the highest explanatory power for the variations of TN and TP. At the type level, built-up land, cropland, and bare land were positively correlated with TN and TP, while forest was negatively correlated with TN and TP, which were the key types influencing non-point source pollution. At the landscape level, patch density, percentage of like adjacencies, and contagion index were key indicators affecting watershed water quality. Lower patch density was associated with better connectivity and aggregation of "sink" landscapes, leading to better purification effects on TN, but more pronounced retention effects on TP. Conversely, higher landscape diversity and denser pattern of multiple types would cause the deterioration of water quality. Our results suggested that rational allocation of landscape types within the watershed and riparian buffer zones, appropriately enriching landscape diversity, and optimizing landscape aggregation and connectivity would be effective measures for improving water quality and achieving sustainable ecological management.

Crack Resistance of Lightly Reinforced Concrete Structures.

The crack resistance of concrete structures with low reinforcement ratios requires a broader examination. It is particularly important in the case of foundations working in changing subsoil conditions. Unfavorable phenomena occurring in the subsoil (e.g., ground subsidence, landslips, non-uniform settlement) can lead to unexpected cracking. Therefore, it is necessary to check the effectiveness of the low reinforcement provided. As there are limited studies on lightly reinforced concrete structures, we performed our own experimental investigation and numerical calculations. In the beams analyzed, the reinforcement ratio varied from 0.05% to 0.20%. It was found that crack resistance in concrete members depends on the reinforcement ratio and steel bar distribution. A comprehensive method was proposed for estimating the crack resistance of lightly reinforced concrete members in which both the reinforcement ratio and the reinforcement dispersion ratio were taken into account. Furthermore, the method considered the size effect and the fracture properties of concrete. The proposed method provides the basis for extrapolation of the test results obtained for small elements and conclusions for members with large cross-sections, such as foundations, which frequently use lightly reinforced concrete.

[Scale Effects of Landscape Pattern on Impacts of River Water Quality： A Meta-analysis].

The landscape pattern determines water pollution source and sink processes and plays an important role in regulating river water quality. Due to scale effects, studies on the relationship between landscape pattern and river water quality showed variance at different scales. However, there is still a lack of integrated study on the scale effect of landscape pattern and river water quality dynamics. This study collected 4 041 data from results of previous publications to address the characteristics of landscape pattern and river water quality dynamics at different scales and to identify the key temporal and spatial scales as well as landscape pattern indices for regulating river water quality. The results indicated that, compared to precipitation events, base flow periods, and interannual scales, the high-flow period was the key temporal scale for linking landscape pattern on river water quality. Compared to the watershed scale, the landscape pattern of buffer zones had a greater impact on river water quality. The high-flow period-buffer zone scale was the key spatiotemporal coupling scale for linking landscape pattern and river water quality. Compared to croplands, water bodies, grasslands, and the overall landscape of the watershed, the landscape pattern of forests and urban areas had a greater impact on river water quality. Fragmentation degree was the most important landscape pattern factor regulating river water quality. In river water quality management, it is important to focus on the landscape configuration of buffer zones, increase forest area, reduce patch density of forests and water bodies, and decrease the aggregation degree of urban areas.

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region.

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000-2005), sharp decline (2005-2009), slow decline (2009-2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005-2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

Effect of agricultural fiscal expenditures on agricultural carbon intensity in China.

Few studies provide direct evidences that agricultural fiscal affects agricultural carbon intensity. This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders' birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions' carbon intensities are increased due to fiscal rivalry.

How does new energy demonstration city pilot policy affect carbon dioxide emissions? Evidence from a quasi-natural experiment in China.

Energy transition policies are of great significance in adjusting the structure of energy supply and demand and coping with climate change. The new energy demonstration city pilot (NEDCP) policy, as an important pilot project in China's energy transition process, lacks a scientific assessment of the carbon reduction effect of the NEDCP policy and an in-depth explanation of the mechanism of the NEDCP. Based on panel data of 209 Chinese cities at the prefectural and higher levels from 2007 to 2019, this study takes the NEDCP policy as a quasi-natural experiment, using a difference-in-differences model combined with firm-level data to identify the impact of the NEDCP policy on urban carbon dioxide (CO2) emissions. This study analyzes the impact of heterogeneity of urban characteristics on the policy effect from multiple perspectives, and further investigates its mechanism. The conclusions are shown in the following aspects. (1) The implementation of the NEDCP policy decreases urban CO2 emissions significantly. Meanwhile, a series of robustness tests, including the instrumental variables method, propensity score matching difference-in-differences method, placebo test, exclusion of policy interference test, and machine learning method, support this conclusion. (2) The NEDCP policy achieves carbon reduction effects mainly through scale and structure effects. (3) The results of the heterogeneity test show that the NEDCP policy is more effective in cities with higher administrative levels, energy-demanding cities, cities in the southeast of Hu-line, and cities with a higher degree of nationalization. Therefore, the Chinese government should summarize the implementation experience of the NEDCP policy and expand its scope of application. The evaluation of the NEDCP policy in China has important reference value for the energy transition of other developing countries.

Trade-offs and synergies relationships of ecosystem services and their socio-ecological driving factors under different spatial scales in Shaoguan City, Guangdong, China.

Ecosystem services refer to the benefits that human obtain from natural ecosystems. Different ecosystem services are generated by the combination of social-ecological driving factors, and exhibit different spatial patterns across scales. The complex relationships and driving mechanisms among ecosystem services under different spatial scales remain unclear. With Shaoguan City from Guangdong Province as the study area, we analyzed the spatial patterns and relationships of four ecosystem services and their trade-offs/synergies (TOSs), quantified their responses to seven social-ecological drivers at the kilometer grid scale and sub-watershed scale, and proposed regional ecologi-cal management and planning strategies for cross-scale sustainable development. The results showed that the spatial distribution of ecosystem services in Shaoguan City exhibited spatial clustering and cross-scale variations. Habitat quality, water yield, and carbon storage exhibited similar spatial distribution pattern. High supply was mainly distributed in mountainous areas in the east, north, west, and south, while weak supply was distributed in plain areas in the central, northwest, south and northeast. In addition, the spatial clustering of these services intensified with increasing spatial scale. Ecosystem services displayed synergistic relationships at both spatial scales, and the intensity of the synergy changed with scale. At both the kilometer grid and sub-watershed scale, the primary drivers for ecosystem services were the normalized vegetation index and digital elevation model. The main driver for TOSs was the mean annual temperature at the kilometer grid scale, while it was the mean annual evapotranspiration at the sub-watershed scale. Based on the supply levels of ecosystem services, the study area could be divided into five distinct ecosystem service bundles, i.e., mountain ecological balance zone, forest ecological conservation zone, urban forest maintenance zone, ecologically sensitive zone, and ecological risk zone. All bundles exhibited both spatial heterogeneity and cross-scale variations. We integrated the cross-scale variations of four representative ecosystem services and their complex interactions and driving mechanisms in Shaoguan City into spatial planning to facilitate the sustainable ecosystem management across multiple scales, which could offer valuable references for the construction of ecological civilization in other regions.

The effects of surface vegetation coverage on the spatial distribution of PM2.5 in the central area of Nanchang City, China.

The frequent occurrence of haze has caused widespread concern in China, and PM2.5 is thought to be the main cause. Previous research showed that PM2.5 was not only influenced by meteorological conditions but also by land cover especially surface vegetation. It was concluded that PM2.5 concentration is significantly influenced by surface vegetation, but spatially how and in what manner are still unanswered. Taking the central area of Nanchang City, China, as a case, this study firstly applied land use regression (LUR) model to simulate the distribution of PM2.5 in 2020. Then, the dichotomous model was used to determine vegetation coverage. A statistical regression model was used to analyze the influence of vegetation cover on PM2.5 and the scale effects. The results showed that (1) vegetation coverage and PM2.5 concentration were both significantly negatively correlated at the spatial scales selected for this study. (2) The effect of vegetation coverage on PM2.5 varied with season and the 500 m had the best correlation. (3) The non-linear regression model fits better than the linear model, and the effect of vegetation coverage on PM2.5 was complex. (4) The effect of vegetation coverage on PM2.5 concentration was different with PM2.5 concentration level. The higher the PM2.5 concentration, the more pronounced the effect of vegetation coverage on it. This study proposed the idea and method of coupling vegetation coverage with PM2.5 concentration at the regional scale from gradient landscape's point of view and provided some references for mitigating PM2.5 pollution through optimizing urban vegetation patterns.

An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services.

The rapid urbanization has accelerated the destruction of regional ecosystems, triggering ecological risks and threatening sustainable development. Landscape ecological risk (LER) evaluation is an effective tool to mitigate such negative impacts. However, the existing evaluation systems exhibit certain subjectivity. Therefore, an improved LER evaluation method was proposed, which incorporates ecosystem services (ESs) to characterize landscape vulnerability. The method was validated using the Pearl River Delta urban agglomeration (PRDUA) as the study area. The results showed that the optimal grain size and extent for landscape pattern analysis in the PRDUA were determined to be 150 m and 6km × 6 km, respectively. The comparison results with the traditional LER evaluation method demonstrated the improved method's superior rationality and reliability. The hotspot analysis based on the Getis-Ord Gi* method revealed that the hotspots of LER were mainly concentrated in the densely populated areas of the south-central region of the PRDUA. The coupling coordination degree (CCD) between LERs and ESs showed four different levels of development in both temporal and spatial dimensions, generally dominated by moderately balanced development and lagging ESs, reflecting the unbalanced ecological environment and socio-economic development of the PRDUA. It is recommended that the ecosystems in the PRDUA be managed and protected separately according to the delineated Ecological Protection Area (EPA), Urban Built-up Area (UBA), and Urban Ecological Boundary Area (UEBA). This study can provide an important reference for regional ecosystem conservation and management.

Assessing the Scale Effect on Bearing Capacity of Undrained Subsoil: Implications for Seismic Resilience of Shallow Foundations.

This research investigates the influence of the scale effect on the bearing capacity of fine-grained subsoil under undrained conditions. The analyses were conducted based on laboratory tests of silty clay. Uniformly compacted samples were subjected to an unconfined compression test. The research was performed on cylindrical specimens. Three different variants of the diameter D (38 mm, 70 mm, 100 mm) and the corresponding height H = 2D were analyzed. Based on the tests results, the unconfined compression strength qu was determined, and from this, the undrained shear strength cu was calculated. The obtained results showed a clear decrease in cu with increasing sample size. However, in the existing reference documents, there are no specific guidelines for calculations of bearing capacity with consideration of sample size effect on the soil shear strength. Therefore, this study utilized the laboratory soil test data to calculate the bearing capacity of undrained subsoil, taking into account the seismic impacts, with a particular focus on spread foundations.

Analysis of the relationship between landscape fragmentation and ecosystem service value in northern Shaanxi, China.

Landscape fragmentation affected the structure and function of the ecosystem, resulting in an impact on ecosystem service value (ESV). This paper analyzed the correlation between landscape fragmentation and ESV using land-use data from northern Shaanxi covering three periods from 2000 to 2020. The paper employed the granularity deduction method, spatial autocorrelation analysis, and value equivalent method to study the fragmentation characteristics of the regional landscape and the spatial-temporal evolution of ESV. The research findings indicated that the optimal granularity was 150 m, and the amplitude was 5 km × 5 km. The study found that the degree of landscape fragmentation was positively correlated with patch density (PD), division index (DIVISION), and Shannon's diversity index (SHDI), while negatively correlated with the largest patch index (LPI), patch cohesion index (COHESION), and effective mesh size (MESH). Moreover, the total ESV in the study area showed a decreasing trend, with grass, forest, and cultivated land being the three land-use types that contributed the most value. The analysis indicated that there was a negative correlation between the degree of landscape fragmentation and ESV. As the degree of landscape fragmentation increased, ESV decreased. The correlation between landscape fragmentation and ESV discussed in this paper provided valuable insights into the optimal utilization and sustainable development of regional land resources.

Scale Effect of Land Cover Classification from Multi-Resolution Satellite Remote Sensing Data.

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2, and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4-6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increased the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects, and landscape ecology uncertainty studies.

Scale dependence of urban green space cooling efficiency: A case study in Beijing metropolitan area.

Urban Green Space (UGS), providing environmental, social and economic benefits simultaneously, has been regarded as a cost-effective Nature-based Solution (NbS) to combat the effects of urban heat island (UHI). Under the dual pressure of increasing demand for limited land resources and mitigating UHI, how to scientifically and effectively use the limited space to obtain the maximum cooling efficiency (scaling of cooling intensity and UGS size) is an important component of strategic urban green planning. However, the scale dependence of UGS cooling effect has not yet been sufficiently quantified, particularly with respect to involving small and medium size UGS. Here, we explored the size-dependent UGS cooling efficiency in Beijing using 10,003 UGS patches extracted from high-resolution remote sensing images. We found that 5922 UGS (59.20 %) exhibited a "cooling island effect", the cooling service of UGS could reduce land surface temperature by 0.06 ± 0.05 °C to 3.81 ± 1.01 °C, and the cooling intensity enhanced nonlinearly with increasing size and closely related to the complexity of UGS shape and vegetation quality. We further showed that the cooling efficiency of small, medium and large UGS was -0.004 ± 0.03 (n = 2201), 0.79 ± 0.01 (n = 3570), 0.19 ± 0.03 (n = 151), respectively, suggesting that strategic urban greening to combat urban heat should target on increasing medium-sized UGS and managing the layout of green space. These findings emphasize the significance of considering and further exploring the scale dependence of UGS cooling effect in mitigating urban heat.

Scale effect of circularly polarized luminescent signal of matter.

Circularly polarized luminescence (CPL) is an important part in the research of modern luminescent materials and photoelectric devices. Usually, chiral molecules or chiral structures are the key factors to induce CPL spontaneous emission. In this study, a scale-effect model based on scalar theory was proposed to better understand the CPL signal of luminescent materials. Besides chiral structures being able to induce CPL, achiral ordered structures can also have a significant influence on CPL signals. These achiral structures are mainly reflected in the particle scale in micro-order or macro-order, i.e. the CPL signal measured under most conditions depends on the scale of the ordered medium, and does not reflect the inherent chirality of the excited state of the luminescent molecule. This kind of influence is difficult to be eliminated by simple and universal strategies in macro-measurement. At the same time, it is found that the measurement entropy of CPL detection may be the key factor to determine the isotropy and anisotropy of the CPL signal. This discovery would bring new opportunities to the research of chiral luminescent materials. This strategy can also greatly reduce the development difficulty of CPL materials and show high application potential in biomedical, photoelectric information and other fields.