Spatial Effects Research Articles

Digital economy and regional innovation: unveiling the spatial dynamics and human capital influence in China

Purpose The paper aims to examine the digital economy's influence on China's regional innovation and development. It focuses on direct effects and spatial spillover across regions, and the mediating role of human capital. This analysis is vital for policy and strategic planning in the digital era. Design/methodology/approach This study uses panel data from 30 Chinese provinces (2004–2019) and uses the entropy method to quantify the digital economy's development. It investigates its impact on regional innovation using a dynamic spatial Durbin model (SDM) and mediation effect model, assessing direct effects, spatial spillover and human capital's mediating role. Various control variables are included for comprehensive analysis. Findings Findings show the digital economy significantly boosts regional innovation, acting as a growth driver. However, impacts vary regionally, with the central region gaining more than the eastern and western areas. Spatial spillover effects are mixed, showing negative short-term and positive long-term impacts under different weight matrices. Human capital is crucial for fostering innovation through the digital economy. Originality/value The paper offers unique insights into the spatial dynamics of the digital economy's impact on regional innovation in China. It advances understanding of the digital economy's role in regional development using innovative methods like the entropy method and dynamic SDM. Highlighting human capital as a key mediating factor enriches discussions on digital economy strategies for regional innovation.

Spatial response of urban land use intensity to ecological networks: a case study of Xi'an Metropolitan Region, China.

In the face of the persistent degradation of ecological environments and fragmentation of ecological networks brought about by rapid urbanization, this study focuses on examining the interaction between urban land use intensity and ecological networks in the Xi'an Metropolitan Region (XAMR), China, and their impact on ecological equilibrium and sustainable development. By comprehensively evaluating the changes in land use intensity in XAMR from 2010 to 2020, the aim is to underscore the pivotal role of ecological networks in maintaining urban ecological balance and promoting sustainable development. The findings indicate a transition in land use intensity in the XAMR from low to high concentration, reflecting an intensification in land resource utilization during urbanization. However, the establishment and management of ecological networks can significantly enhance urban ecological security and biodiversity. Notably, this research identified crucial ecological corridors and source areas, augmenting the connectivity of urban green infrastructure and providing vital support for urban biodiversity. Additionally, a significant finding of this study is the spatial spillover effects generated by socioeconomic factors such as the proportion of tertiary and secondary industries and per capita GDP through the ecological network, which have profound impacts on land use intensity in the surrounding areas. These insights offer a novel understanding of the complex interactions within urban ecosystems, emphasizing the importance of incorporating ecological network construction in urban planning. Overall, through a comprehensive analysis of the relationship between the ecological network and land use intensity in the XAMR, this study proposes new directions for urban ecosystem management and land use planning, highlighting the significance of scientific ecological network planning and management in achieving long-term sustainable development in urbanization processes.

Study on urban green development efficiency of Jiangsu, Zhejiang and Fujian in China: a mixed network SBM approach

PurposeBy studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.Design/methodology/approachFirst, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.FindingsSubsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.Originality/valueThis study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

Microbial communities change along the 300 km length of the Grand River for extreme high- and low-flow regimes.

The Grand River watershed is the largest catchment in southern Ontario. The river's northern and southern sections are influenced by agriculture, whereas central regions receive wastewater effluent and urban runoff. To characterize in-river microbial communities, as they relate to spatial and environmental factors, we conducted two same-day sampling events along the entire 300 km length of the river, representing contrasting flow seasons (high flow spring melt and low flow end of summer). Through high-throughput sequencing of 16S rRNA genes, we assessed the relationship between river microbiota and spatial and physicochemical variables. Flow season had a greater impact on communities than spatial or diel effects and profiles diverged with distance between sites under both flow conditions, but low-flow profiles exhibited higher beta diversity. High-flow profiles showed greater species richness and increased presence of soil and sediment taxa, which may relate to increased input from terrestrial sources. Total suspended solids, dissolved inorganic carbon, and distance from headwaters significantly explained microbial community variation during the low-flow event, whereas conductivity, sulfate, and nitrite were significant explanatory factors for spring melt. This study establishes a baseline for the Grand River's microbial community, serving as a foundation for modeling the microbiology of anthropogenically impacted freshwater systems affected by lotic processes.

Spatial spillover effects of fiscal expenditure and economic opportunity on internal migration in Taiwan

The purpose of this study was to understand the determining factors of in-migrant population registration in various counties or cities in Taiwan. Panel data from counties and cities in Taiwan from 2000 to 2021 were adopted, and spatial econometric analysis was conducted using the spatial Durbin model. In terms of spatial spillover effects, the in-migrant population of each county or city is influenced by the fiscal expenditure and economic opportunity factors of neighboring counties or cities, such as (1) expenditure on education, science and culture, (2) number of employed population, (3) average annual regular income per household, and (4) average disposable income per household.

Impacts of Low-Carbon City Pilot Policy on Urban Land Green Use Efficiency: Evidence from 283 Cities in China

On the global scale, the low-carbon city pilot policy (LCCPP) has important significance for and influence on the study of urban land green use efficiency (ULGUE). Based on the panel data of 283 cities in China from 2007 to 2019, this study uses the super-SBM model, multi-period DID model, spatial econometric model, intermediary effect model, and heterogeneity analysis methods to deeply explore the specific impact mechanism of LCCPP on ULGUE. The results show the following: (1) During the study period, the average ULGUE of the selected samples increased by 11.71 percentage points overall and showed a certain spatial agglomeration effect. (2) LCCPP has a significant promoting effect on the improvement of ULGUE, and there is a positive spatial spillover effect. (3) The impact of LCCPP on ULGUE is mainly achieved through two paths: reducing energy utilization intensity and improving urban innovation level. (4) In cities with different levels of land green use efficiency, geographical location, and resource endowment, there are significant differences in policy effects. This paper puts forward countermeasures and suggestions to comprehensively promote the sustainable development of global cities and the improvement of land green use efficiency.

Peregrine soliton emits dispersive waves within graded-index multimode fibers without higher-order dispersion

We investigate the propagation dynamics of the Peregrine soliton, a significant prototype of rogue waves, within the graded-index multimode fibers, in the absence of higher-order dispersion. The Peregrine soliton keeps the approximate evolution trend when propagating within the graded-index multimode fibers to replace the single-mode fibers when preserving the equivalent nonlinear effect. In addition, a series of dispersive waves (also called resonant radiation) can be emitted by the Peregrine soliton, perturbated by the periodic beam oscillation caused by the spatial self-imaging effect within the graded-index multimode fibers. To be more exact, the location of the multiple resonant frequencies can be predicted using the modified quasi-phase-matching conditions, which are verified by the numerically calculated results. We can also manipulate the locations of spectral sidebands and the peak power of dispersive waves by changing the self-imaging parameter of the graded-index multimode fibers. Our findings can provide a deeper comprehension of the propagation characteristic of the Peregrine soliton within the graded-index multimode fibers and provide valuable instruction for further rich nonlinear experiments.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or “niches” followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon’s rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

The impact of regional sports industry aggregation on residents’ health level in China

Health is the basis for human survival and development and is an important symbol to evaluate a country's economic growth and social progress. This article measures the degree of sports industry agglomeration in different regions of China and uses the Moran index to verify the existence of global autocorrelation in sports agglomeration. Next, the spatial Durbin model was used to verify the spatial spillover effect of sports industry agglomeration on the health level of residents, and the following conclusions were obtained.Firstly, there is spatial autocorrelation and heterogeneity in the clustering level of China's sports industry. The spatial distribution is extremely uneven, and different regions have formed relatively stable spatial patterns. Secondly, the degree of aggregation of the sports industry can reduce the number of per capita visits and have a positive spatial spillover effect on the health of residents. Not only can it promote the health level of residents in the province, but it also has spatial spillover effects on surrounding areas.Finally, based on the research results, the following conclusions are proposed in this article. Policy recommendations include increasing investment in sports talent cultivation, accelerating the construction of sports center cities, and increasing residents' attention to sports to improve residents' health.

Boron Cluster Renders Organic Radicals Water-Stable for Photothermal Anti-Infections.

Water-stable organic radicals are promising photothermal conversion candidates for photothermal therapy (PTT). However, organic radicals are usually unstable in biological environments, which greatly hinders their wide application. Here, we have developed a chaotropic effect-based and photoinduced water-stable supramolecular radical (MB12-2) for efficient antibacterial PTT. The supramolecular radical precursor MB12-1 was constructed by the chaotropic effect between closo-dodecaborate cluster (B12H122-) and N,N'-dimethylated dipyridinium thiazolo [5,4-d] thiazole (MPT2+). Subsequently, with triethanolamine (TEOA) serving as an electron donor, MB12-1 could transform to its radical form MB12-2 through photoinduced electron transfer (PET) under 435-nm laser irradiation. The N2 adsorption-desorption analysis confirmed that MB12-2 was tightly packed through the introduction of B12H122-, which effectively enhanced its stability via a spatial site-blocked effect. Moreover, the half-life of MB12-2 in water was calculated through ultraviolet-visible light (UV-vis) absorption spectra results for periods as long as 20 days. In addition, in the skin infection model, MB12-2, as a wound dressing, showed remarkable photothermal antibacterial activity (>97%) under 660-nm laser irradiation and promoted wound healing. This study presents a simple method for designing long-term water-stable supramolecular radicals, offering a novel avenue for noncontact treatments for bacterial infections.

Research on the Accounting and Spatial Effects of Emergy Ecological Footprint and Industrial Green GDP--The Case of Yangtze River Economic Belt

The booming development of industrial enterprises at the cost of ecological resource consumption has inevitably harmed the ecological environment, especially since the phenomenon of water resource pollution is more serious. Exploring the relationship between the ecological environment and the industrial economy is crucial to the green and sustainable development of the region. This study uses the Spatial Durbin Model (SDM) to investigate the impact of emergy ecological footprint on the industrial green of gross domestic product (GDP) under the perspective of water resources, based on the panel data of 11 provinces in Chinas Yangtze River Economic Belt (YREB) from 2012 to 2021. The results show that first, the overall industrial green GDP of the YREB has increased year by year, and the effect of water resource protection is outstanding. Second, the emergy ecological footprint has a significant positive effect on industrial green GDP, and the spillover effect is also significant. Third, heterogeneity analysis shows that the spatial relationship between emergy ecological footprint and industrial green GDP is different in different regions, and the local effect and neighborhood effect of the high industrial green economic development areas are consistent with those of the YREB, while the relationship between the low industrial green economic development areas is not obvious. This paper is of great significance for the green development of industrial enterprises in the YREB and guides the formulation of sustainable development policies.

A Gaussian Function Model of Mesoscale Eddy Temperature Anomalies and Research of Spatial Distribution Characteristics

Mesoscale eddies are ubiquitous oceanic phenomena and play an important role in ocean circulation, ocean dynamics, and the transport of material energy. Temperature anomalies are a crucial parameter that reflects the state of mesoscale eddies. This study proposes a Gaussian function model to fit the vertical temperature anomaly (TA) profile to facilitate the analysis of variations, and the principle of the model is based on the fact that each TA profile tends to fluctuate around one or more peaks. The model is extracted and validated using Argo profiles within cyclonic and anticyclonic eddies in the Northwest Pacific Ocean spanning over the period from 2002 to 2021. The validation demonstrates that the model can accurately recover the vertical TA profiles with a limited number of parameters. This makes it suitable for analysing the spatial distribution patterns that require a large sample count. The analysis indicates that eddies with different TA profiles have a spatial aggregation effect in geographic distribution. Eddies with lower extreme temperature anomalies, at depths of 200–300 m, are mainly distributed along two bands on the north side of the Kuroshio Extension (KE) and the North Equatorial Current. Eddies with extreme TAs at the deepest depth (500–600 m) are distributed along the KE.

Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China's experience.

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China's eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran's index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China.

Seasonal changes in ecosystem health and their spatial relationship with landscape structure in china's Loess plateau

Environmental managers have sought to optimize landscape structure to maintain regional ecosystem health (EH). However, we still lack a full understanding of the effects of changing landscape structure on EH, especially in terms of shorter time scales (e.g., monthly or seasonal scales) and spatial heterogeneity. To address this gap, this study used the vigor-organization-resilience-services (VORS) model to assess the annual and seasonal variation of EH in the Loess Plateau (LP) during 2000–2015, and then applied the geographically weighted regression (GWR) model to investigate the spatial effects of different aspects (i.e., landscape composition and configuration) of the landscape structure change on EH over time. Our results showed that the EH level in the LP from 2000 to 2015 displayed temporal non-stationarity at annual and seasonal scales, and a spatial pattern of decreasing from southeast to northwest. The effects of landscape structure on EH had obvious spatial and temporal heterogeneity, depending on the local landscape composition and configuration. In general, landscape configuration exerted a greater impact on EH than landscape composition apart from Shannon’s diversity index and percentage of forestland area, but the intensity and direction of the impact displayed higher temporally instability and therefore should receive more attention. Furthermore, regulation of the dominant patch size, landscape connectivity and forest spatial spacing is the optimal choice for regional ecosystem management. The results suggest that EH responses to changes in landscape structure in the LP vary by time and place, and that the integrating temporal variation and spatial characteristics can help decision-makers make precise spatial arrangements and temporal adjustments to keep the negative ecological impacts within reasonable limits.

Differential Analysis of Carbon Emissions between Growing and Shrinking Cities: A Case of Three Northeastern Provinces in China

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China’s three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies.

Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China.

As an important way for China to achieve its dual-carbon goal, green finance has become the foundation for promoting high-quality economic development in China. In order to clarify the mechanism of green finance on carbon emissions, this paper puts green finance into the economic model and deduces the relationship between green finance and carbon emission reduction. This paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2008 to 2019, using the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology based on the measurement of the green finance development index of each province by the entropy method. The findings show that the development of green finance can reduce carbon emission significantly, which can be sustained until at least the third phase and generates spatial spillover effects; regional heterogeneity analysis finds that the development of green finance shows geographical discrepancies: compared with the eastern and western regions, the development of green finance in central region can reduce carbon emissions more significantly; not only can the development of green finance directly reduce carbon emission, but also through the upgrading of industrial structure and technological innovation. The research not only provides a new perspective and supplementary empirical evidence for understanding the carbon emission reduction effect of green finance, but also offers some useful references for green finance to contribute to carbon emission reduction.

Analysis of Spatial Spillover Effects of Digital Inclusive Finance on Urban-Rural Income Gap

This article mainly analyzes the changes in urban-rural disparities under digital inclusive finance, which can be divided into three levels of research objectives, gradually and layer by layer. Firstly, based on the theoretical foundation of accelerating the development of rural financial markets and reducing economic disparities between rural and urban areas through digital inclusive finance, a panel threshold model is constructed to quantitatively analyze the threshold effect of digital inclusive finance on the urban-rural income gap, and to study how to reduce the threshold effect and narrow the digital divide between rural and urban areas; Secondly, from the perspective of spatial spillover, this study examines the positive or negative spatial spillover effects of digital inclusive finance on regional urban-rural income disparities, in order to analyze the direction of the impact of digital inclusive finance on urban-rural income disparities. Using the Spatial Panel Lag Model (SAR) and Spatial Durbin Model (SDM) to empirically test their inherent correlation and impact; Thirdly, there are differences in policies and resource allocation among provinces and cities, as well as differences in urban-rural income disparities in different regions. Based on this, a quantitative analysis model is constructed to analyze the path and methods of digital inclusive finance promoting urban-rural income disparities.

Thermal shock triggers microexplosion combustion in graded fuel and oxidizer encapsulation microspheres with improved combustion efficiency

The combustion agglomeration of aluminum (Al) and the long heat and mass transfer distance between Al and oxidizer cause serious two-phase flow loss and slag accumulation in the engine of solid propellants. Here, we present an effective strategy by in-situ encapsulating graded nano-aluminum (nAl) and micron-aluminum (μAl) in ammonium perchlorate (AP) simultaneously by using hydroxymethyl cellulose (HMC) as adhesive and constructing a compact coupling fuel oxidizer complex high-energy microsphere (μAl/nAl@AP/HMC) to enhance the energy release efficiency and combustion efficiency. Compared with physical mixture (PM), the ignition delay time and combustion time of μAl/nAl@AP/HMC microspheres are reduced by 69.58 % and 69.87 %, owing to the decomposition process control of AP through the adsorption and spatial constraint effect on the interface between metal fuels and oxidizer. Furthermore, the nAl embedded in the AP surface forms a hot spot around the μAl to promote the combustion reaction, thereby shortening the cavitation threshold and tensile limit time of μAl and accelerating the occurrence of microexplosion. Microexplosion effect in μAl/nAl@AP/HMC microsphere significantly weakens combustion agglomeration and improves combustion efficiency and energy release efficiency by sharp shrinkage of combustion condensation products, which addresses the challenges on energy output and combustion efficiency in high-density solid propellants.

Correction: Limited access to improved drinking water, unimproved drinking water, and toilet facilities among households in Ethiopia: Spatial and mixed effect analysis

Correction: Limited access to improved drinking water, unimproved drinking water, and toilet facilities among households in Ethiopia: Spatial and mixed effect analysis

A multiscale examination of heat health risk inequality and its drivers in mega-urban agglomeration: A case study in the Yangtze River Delta, China

Increased frequency of extreme urban heat and its exposure to urban populations is one of the challenges presented by climate change, especially in urban clusters. Due to the rapid but unequal development, heat exposure disproportionately increased in the underdeveloped regions compared to the developed regions in urban agglomeration. To address this issue, it is crucial to clarify the spatial pattern of heat health risk (HHR) inequality for urban heat resilience. However, analyses for the disparity of HHR inequality often used a single scale, neglecting important spatial context effects at other scales. Moreover, the rationale of HHR inequality remains unclear. Here, we took the well-developed and highly urbanized Yangtze River Delta (YRD) region as a case study and employed multiscale approaches to examine how and why the HHR inequality varied at and within the regional scale. We first assessed HHR using a comprehensive assessment framework at a 1 km grid level. Then, we quantified the inequality between regions using local Moran's I and KS distance. Therefore, we utilized the Gini coefficient and Bayes quantile regression to quantify inequality and identify its drivers within the regional scale. Finally, we proposed a conceptual framework to inform policymaking in regions with different patterns of multiscale equality. Our results found that the HHR in YRD exhibited significant spatial inequality at the regional scale (Moran's I = 0.562, P < 0.001) and within the regional scale (Gini coefficient: 0.27–0.54). Higher population concentrations and building densities often led to higher HHR. In high HHR areas, intra-regional inequality was often lower due to high and coordinated socioeconomic levels (Gini coefficient: 0.27–0.34). Additionally, in areas with low and medium levels of risk, healthcare resource availability and local temperatures had a greater impact on intra-regional inequities, which varied at different levels of inequality. This study contributes to a better understanding of multiscale HHR inequality, which helps optimize heat risk management strategies and regional sustainable development.