In this paper, the autocorrelations of maximal period Feedback with Carry Shift Register sequences （l-sequences） are discussed. For an l-sequence a with connection integer q = p^e（e ≥ 2） and period T = p^t-1（p- ...In this paper, the autocorrelations of maximal period Feedback with Carry Shift Register sequences （l-sequences） are discussed. For an l-sequence a with connection integer q = p^e（e ≥ 2） and period T = p^t-1（p- 1）, and for any integer i, 1 ≤ i ≤ e/2, by calculating the number of certain sets, it is shown that the autocorrelation of a with shift τ= kT/2p^i is Ca（τ） =（-1）^k-1 T/p^2i-1, where 1 ≤ k ≤ 2p^i - 1, and gcd（k,2p^i） = 1. This result shows there do exist some shifts such that the autocorrelations of l-sequences are high although most autocorrelations are low. Such result also holds for the decimations of l-sequences.展开更多
Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Si...Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.展开更多
Quantifying and mapping how ecosystem services impact agricultural competitiveness is crucial for attaining the Sustainable Development Goals of United Nations.However,few study quantified agricultural competitiveness...Quantifying and mapping how ecosystem services impact agricultural competitiveness is crucial for attaining the Sustainable Development Goals of United Nations.However,few study quantified agricultural competitiveness and mapped the effects of ecosystem services on agricultural competitiveness using multiple models.In this study,multi-source data from 2000 to 2020 were utilized to establish the indicator system of agricultural competitiveness;five ecosystem services were quantified using computation models;Geographic Information System(GIS)spatial analysis was used to explore the spatial patterns of agricultural competitiveness and ecosystem services;geographic detector models were applied to investigate the effects and driving mechanisms of ecosystem services on agricultural competitiveness.Shandong Province of China was selected as the case study area.The results demonstrated that:1)there was a significant increase in agricultural competitiveness during the study period,with high levels observed mainly in the east region of the study area.2)The spatial distribution patterns of ecosystem services and agricultural competitiveness primarily exhibited High-High and Low-Low Cluster types.3)Habitat quality emerged as the main driving factor of agricultural competitiveness in 2000 and 2020,while water yield played a substantial role in 2010.4)The coupling of two ecosystem services exerted a greater effect on agricultural competitiveness compared to individual ecosystem service.The innovations of this study are constructing an indicator system to quantify agricultural competitiveness,and exploring the effects of ecosystem services on agricultural competitiveness.This study proposed an indicator system to quantify agricultural competitiveness,which can be applied in other regions,and explored the effects of ecosystem services on agricultural competitiveness.The findings of this study can serve as valuable insights for policymakers to formulate tailored agricultural development policies that take into account the synergistic effects of ecosystem services on agricultural competitiveness.展开更多
Species distribution patterns is one of the important topics in ecology and biological conservation.Although species distribution models have been intensively used in the research,the effects of spatial associations a...Species distribution patterns is one of the important topics in ecology and biological conservation.Although species distribution models have been intensively used in the research,the effects of spatial associations and spatial dependence have been rarely taken into account in the modeling processes.Recently,Joint Species Distribution Models(JSDMs)offer the opportunity to consider both environmental factors and interspecific relationships as well as the role of spatial structures.This study uses the HMSC(Hierarchical Modelling of Species Communities)framework to model the multispecies distribution of a marine fish assemblage,in which spatial associations and spatial dependence is deliberately accounted for.Three HMSC models were implemented with different structures of random effects to address the existence of spatial associations and spatial dependence,and the predictive performances at different levels of sample sizes were analyzed in the assessment.The results showed that the models with random effects could account for a larger proportion of explainable variance(32.8%),and particularly the spatial random effect model provided the best predictive performances(R_(mean)^(2)=0.31),indicating that spatial random effects could substantially influence the results of the joint species distribution.Increasing sample size had a strong effect(R_(mean)^(2)=0.24-0.31)on the predictive accuracy of the spatially-structured model than on the other models,suggesting that optimal model selection should be dependent on sample size.This study highlights the importance of incorporating spatial random effects for JSDM predictions and suggests that the choice of model structures should consider the data quality across species.展开更多
The canonical description of structures comprises two aspects:(1)basic structural elements and(2)arrangement pattern between those elements.This tidy description has been very successful and facilitates the developmen...The canonical description of structures comprises two aspects:(1)basic structural elements and(2)arrangement pattern between those elements.This tidy description has been very successful and facilitates the development of structural physics tremendously,enabling the classification,comparison and analysis of an extremely wide range of structures,including crystals,quasi-crystals,liquid crystals,semi-crystalline materials and so on.However,it has been gradually realized that many novel materials and devices exhibit random structures in which either basic elements or arrangement patterns may not exist.With the rapid development of modern advanced materials,this type of apparently random structure pops up frequently,leaving researchers struggling with how to describe,classify and quantitatively compare them.This paper proposes the utilization of statistical characteristics as the major indicators for the description of apparently random structures.Specifically,we have explored many statistical properties,including power spectral density,histograms,structural complexity,entropic complexity,autocorrelation,etc.,and found that autocorrelation may serve as a promising statistical proxy to distinguish similar-looking random structures.We discuss eight atomic force microscope images of random structures,demonstrating that autocorrelation can be used to distinguish them.In addition,14 more diverse datasets are used to support this conclusion,including atomic force microscopy images of polymers and non-polymers,transmission electron microscopy images of nanocomposite layers and scanning electron microscopy images of non-polymers.展开更多
Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of a...Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of agricultural planting,unregulated aquaculture,and poultry farming.However,relevant esti-mations of the pollutant content in its sediment have not been carried out.This study analyzed the spatial patterns of heavy metal pollution and eutrophication at 36 water sampling sites in the Xiaonanhai Lake area,focusing on eight heavy metals:Cd,Cr,Cu,Ni,As,Pb,Hg,and Zn.The nutrient status of the lake area was evaluated using the nitrogen-phosphorus comprehensive pollution index,and heavy metal pollution status of the lake area was evaluated using geo-accumulation and the potential ecological risk index.Spatial autocorrelation analysis revealed the spatial correlation and aggregation of eutrophication levels in Xiaonanhai Lake.The results showed that the overall trophic state of the Xiaonanhai Lake area was moderate eutrophication,with a gradually decreasing eutrophication level from north to south.The Chengnan Wastewater Treatment Plant in the northern part of the lake area and surface source pollution from aquaculture were the main nitrogen and phosphorus sources.The overall eco-logical risk index of heavy metal pollution was medium and gradually weakened from north to south,consistent with the thickness of the bottom mud.The heavy metal pollution load was mainly precipitated from the bottom mud in the lake area.The eutrophication and heavy metal pollution levels in the lake area showed significant positive spatial autocorrelation,the influence range of the regional eutrophication level was small,and the spatial heterogeneity of the eutrophication and heavy metal pollution levels in Xiaonanhai Lake was relatively high.The northern part of the lake was a hotspot(high/high aggregation)of eutrophication(p<0.01)while the southern part was a cold spot(low/low concentration;p<0.05).The middle and northern part of the lake area was the hot spot(high/high concentration)of heavy metal pollution level(p<0.1)while the southern part was the cold spot(low/low concentration;p<0.1).Therefore,when carrying out water environment management in Xiaonanhai Lake,the northern area and the middle area should be prioritized for eutrophication prevention and control and dredging.展开更多
In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the couplin...In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the coupling coordination and spatial-temporal correlation between urbanization and ecosystem service,and the hotspot analysis is used to judge the spatial-temporal trend of urbanization and ecosystem service.The results show that:(1)The urbanization level from 2000 to 2020 continued to rise,the areas with relatively high urbanization were concentrated in the central part of the study area,and the relatively high terrain areas on both sides of the study area,the urbanization was relatively slow,and the hotspot areas with highly significant and significant urbanization level from 2000 to 2020 were distributed as bands in the central part of the study area and the area was rising,and there was no Cold spot area distribution;between 2000 and 2020,the ecosystem service value in the study area increased by 2.6800×10^(8) yuan.Over these two decades,it exhibited a development trend that first rose and then declined.The woodland and grassland agglomeration areas were located on the two sides of the study area,forming highly significant and significant hotspots.Conversely,the central and northeastern parts of the study area were characterized by concentrated man-made land surfaces and croplands,resulting in the formation of highly significant and significant cold spots.(2)In the central part of the study area where man-made land surface and cultivated land are concentrated,the coupling coordination between urbanization and ecosystem service is in the intermediate dislocation and mild dislocation interval;the woodland and grassland concentration areas on both sides of the study area are ecologically fragile,and the coupling coordination between the two is in the level of less than intermediate dislocation.(3)From 2000 to 2020,urbanization and the value of ecosystem services were both negatively correlated,although the correlation coefficient was low.In the central and northeastern parts,urbanization and ecosystem service exhibited patterns of high-low,high-high,and low-low clustering.Conversely,on both sides of the study area,most of the clusters showed a low-high pattern.展开更多
Sustainable development,underpinned by robust systemic driving forces,is central to the growth of high-quality tourism.Therefore,identifying these forces at the regional level is crucial for advancing China’s goal of...Sustainable development,underpinned by robust systemic driving forces,is central to the growth of high-quality tourism.Therefore,identifying these forces at the regional level is crucial for advancing China’s goal of becoming a leading nation for tourism.This study accordingly constructs a new evaluation system that covers tourism market demand,industry supply,and structural transformation,and analyzes data from 31 Chinese provincial regions(2010–2019).The entropy method and spatial autocorrelation analysis were applied to examine the driving forces for sustainable regional tourism development.The results revealed that:First,at the national level,the driving forces for sustainable regional tourism development exhibited a clear upward trend from 2010 to 2019,with an acceleration in growth after 2015.However,there was significant regional heterogeneity:The eastern region displayed the highest levels of driving forces,followed by the central and western regions.Second,high-value clusters of these driving forces expanded from the eastern to the western regions,while the central provinces remained relatively balanced.Specifically,provincial regions such as Guangdong,Beijing,and Zhejiang were able to successively enter the high-value clusters,whereas the Xinjiang Uygur autonomous region,Gansu,and Qinghai consistently remained in the low-value clusters.Third,the driving forces exhibited a significant spatial agglomeration effect.The degree of clustering followed an inverted“U”trend over the study period,while the spatial patterns of the provincial regions remained relatively stable.展开更多
Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)an...Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)and its impact factors.Hence,we constructed an assessment system for the URIDL from spatial,economic,social,life,and ecological integration.The spatial autocorrelation and Spearman rank correlation coefficients were used to assess the spatiotemporal variation of the URIDL and the trade-off synergistic relationship among the subsystems at the provincial scale in China using socio-economic statistical data from 2000 to 2020.A spatial panel quantile regression model was used to analyze the driving mechanism.The results showed that the URIDL of China increased by 0.19%from 2000 to 2020,and a high-high(H-H)spatial agglomeration pattern occurred in the Yangtze River Delta and the Beijing-Tianjin-Hebei regions.Spatial integration significantly contributed to the other subsystems,whereas economic integration had a significant negative impact on the other subsystems in the eastern coastal and southwestern regions.Per capita Gross Domestic Product(GDP)improved the URIDL,whereas other factors,such as fiscal revenue decentralization,had inhibiting effects.Notably,the impact of factors on URIDL varies across different quantiles.Finally,we proposed policy recommendations for differentiated improvement of URIDL based on its evolution and regional development level during the research period.展开更多
This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving ...This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.展开更多
The upper reaches of the Yellow River in Sichuan Province are critical area for water conservation and ecological protection in China. However, they are experiencing a range of ecological and environmental challenges,...The upper reaches of the Yellow River in Sichuan Province are critical area for water conservation and ecological protection in China. However, they are experiencing a range of ecological and environmental challenges, including grassland desertification, wetland degradation, and soil erosion, all of which pose significant threats to the environmental sustainability and overall development of the Yellow River Basin. Urbanization can lead to irreversible damage to ecosystems. Therefore, understanding the relationship between urbanization and ecosystems is crucial for fostering sustainable development in the region. With land use and meteorological data in the upper reaches of the Yellow River in Sichuan Province in 2000-2020, and using InVEST model and standardized processing methods, we analyzed the spatiotemporal evolution characteristics of urbanization and four ecosystem services: water conservation, carbon storage, habitat quality, and soil retention. Additionally, we employed the GeoDa bivariate spatial autocorrelation analysis model to reveal the spatial correlations and interactions between urbanization and ecosystems. The results reveal a significant spatial mismatch between urbanization and ecosystem services in the upper Yellow River region of Sichuan Province. While the composite urbanization index decreased from 0.0075 to 0.0042 and remained concentrated in county centers, all ecosystem services showed improvement: water conservation increased from 17.38×10^(9) mm to 23.37×10^(9) mm, carbon storage rose from 936.60 Tg to 938.42 Tg, habitat quality improved from 0.875 to 0.879, and soil retention enhanced from 13.56×10^(8) t to 17.59×10^(8) t. However, these ecological gains were mainly driven by restoration in non-urban southern areas, creating a clear spatial disconnection from urban centers and leading to persistently weak and declining coordination between systems. This spatial decoupling underscores the inadequacy of the current urbanization model in promoting regional ecological synergy. We therefore recommend implementing differentiated zoning strategies: promoting compact development coupled with ecological restoration in county centers, strictly protecting core water conservation and carbon sequestration areas in the southern key ecological zones, and enhancing soilwater conservation and ecological restoration in the vulnerable northern belt, so as to establish an ecological security framework compatible with sustainable urbanization.展开更多
Near-surface geological defects pose a serious threat to human life and infrastructure.Hence,the exploration of geological hazards is essential.Currently,there are various geological hazard exploration methods;however...Near-surface geological defects pose a serious threat to human life and infrastructure.Hence,the exploration of geological hazards is essential.Currently,there are various geological hazard exploration methods;however,those require improvements in terms of economic feasibility,convenience,and lateral resolution.To address this,this study examined an extraction method to determine spatial autocorrelation velocity dispersion curves for application in near-surface exploration.展开更多
In the Rocky Mountain and Pacific Northwest regions of the United States,forests include extensive portions of standing dead trees.These regions showcase an intriguing phenomenon where the combined biomass of standing...In the Rocky Mountain and Pacific Northwest regions of the United States,forests include extensive portions of standing dead trees.These regions showcase an intriguing phenomenon where the combined biomass of standing dead trees surpasses that of fallen and decomposing woody debris.This stems from a suite of factors including pest disturbances,management decisions,and a changing climate.With increasingly dry and hot conditions,dead timber on a landscape increases the probability that a fire will occur.Identifying and characterizing the presence of standing dead trees on a landscape helps with forest management efforts including reductions in the wildfire hazard presented by the trees,and vulnerability of nearby park assets should the trees burn.Using forest-based classification,exploratory data analysis,and cluster vulnerability analysis,this study characterized the occurrence and implications of standing dead trees within Yellowstone National Park.The findings show standing dead trees across the entire study area with varying densities.These clusters were cross-referenced with vulnerability parameters of distance to roads,distance to trails,distance to water,distance to buildings,and slope.These parameters inform fire ignition,propagation,and impact.The weighted sum of these parameters was used to determine the vulnerability incurred on the park assets by the clusters and showed the highest values nearest to park entrances and points of interest.High vulnerability clusters warrant priority management to reduce wildfire impact.The framework of this study can be applied to other sites and incorporate additional vulnerability variables to assess forest fuel and impact.This can provide a reference for management to prioritize areas for resource conservation and improve fire prevention and suppression efficiency.展开更多
In order to study the scale characteristics of heterogeneities in complex media, a random medium is constructed using a statistical method and by changing model parameters （autocorrelation lengths a and b）, the scal...In order to study the scale characteristics of heterogeneities in complex media, a random medium is constructed using a statistical method and by changing model parameters （autocorrelation lengths a and b）, the scales of heterogeneous geologic bodies in the horizontal and the vertical Cartesian directions may be varied in the medium. The autocorrelation lengths a and b represent the mean scale of heterogeneous geologic bodies in the horizontal and vertical Cartesian directions in the randQm medium, respectively. Based on this model, the relationship between model autocorrelation lengths and heterogeneous geologic body scales is studied by horizontal velocity variation and standard deviation. The horizontal velocity variation research shows that velocities are in random perturbation. The heterogeneous geologic body scale increases with increasing autocorrelation length. The recursion equation for the relationship between autocorrelation lengths and heterogeneous geologic body scales is determined from the velocity standard deviation research and the actual heterogeneous geologic body scale magnitude can be estimated by the equation.展开更多
To overcome the shortcomings of the single-shot autocorrelation SSA where only one pulse width is obtained when the SSA is applied to measure the pulse width of ultrashort laser pulses a modified SSA for measuring the...To overcome the shortcomings of the single-shot autocorrelation SSA where only one pulse width is obtained when the SSA is applied to measure the pulse width of ultrashort laser pulses a modified SSA for measuring the spatiotemporal characteristics of ultrashort laser pulses at different spatial positions is proposed. The spatiotemporal characteristics of femtosecond laser pulses output from the Ti sapphire regenerative amplifier system are experimentally measured by the proposed method. It was found that the complex spatial characteristics are measured accurately.The pulse widths at different spatial positions are various which obey the Gaussian distribution.The pulse width at the same spatial position becomes narrow with the increase in input average power when femtosecond laser pulses pass through a carbon disulfide CS2 nonlinear medium.The experimental results verify that the proposed method is valid for measuring the spatiotemporal characteristics of ultrashort laser pulses at different spatial positions.展开更多
The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon inte...The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spati- otemporal dynamics and dominating factors of China＇s carbon intensity from energy con- sumption in 1997-2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China＇s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP （11.72%）. Secondly, the trend of Moran＇s I indicated that China＇s carbon intensity has a growing spatial agglom- eration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel economet- ric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China＇s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.展开更多
Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regiona...Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy consumption, spatial autocorrelation analysis of carbon emissions, spatial regression analysis between carbon emissions and their influencing factors. The analyzed results are shown as follows. （1） Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly. （2） The global spatial autocorrelation of carbon emissions from energy consumption in- creased from 1997 to 2009, the spatial autocorrelation analysis showed that there exists a ＂polarization＂ phenomenon, the centre of ＂High-High＂ agglomeration did not change greatly but expanded currently, the centre of ＂Low-Low＂ agglomeration also did not change greatly but narrowed currently. （3） The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population, R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population. The contribution of population to carbon emissions increased but the contribution of GDP decreased from 1997 to 2009. The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population, so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.展开更多
In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission inten...In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.展开更多
The question of how to generate maximum socio-economic benefits while at the same time minimizing input from urban land resources lies at the core of regional ecological civilization construction. We apply stochastic ...The question of how to generate maximum socio-economic benefits while at the same time minimizing input from urban land resources lies at the core of regional ecological civilization construction. We apply stochastic frontier analysis （SFA） in this study to municipal input-output data for the period between 2005 and 2014 to evaluate the urbanization efficiency of 110 cities within the Yangtze River Economic Belt （YREB） and then further assess the spatial association characteristics of these values. The results of this study initially reveal that the urbanization efficiency of the YREB increased from 0.34 to 0.53 between 2005 and 2014, a significant growth at a cumulative rate of 54.07%. Data show that the efficiency growth rate of cities within the upper reaches of the Yangtze River has been faster than that of their counterparts in the middle and lower reaches, and that there is also a great deal of ad- ditional potential for growth in urbanization efficiency across the whole area. Secondly, results show that urbanization efficiency conforms to a ＂bar-like＂ distribution across the whole area, gradually decreasing from the east to the west. This trend highlights great intra-provincial differences, but also striking inter-provincial variation within the upper, middle, and lower reaches of the Yangtze River. The total urbanization efficiency of cities within the lower reaches of the river has been the highest, followed successively by those within the middle and upper reaches. Finally, values for Moran＇s / within this area remained higher than zero over the study period and have increased annually; this result indicates a positive spatial correlation between the urbanization efficiency of cities and annual increments in agglomeration level. Our use of the local indicators of spatial association （LISA） statistic has enabled us to quantify characteristics of ＂small agglomeration and large dispersion＂. Thus, ＂high- high＂ （H-H） agglomeration areas can be seen to have spread outwards from around Zhejiang Province and the city of Shanghai, while areas characterized by ＂low-low＂ （L-L） patterns are mainly concentrated in the north of Anhui Province and in Sichuan Province. The framework and results of this research are of considerable significance to our understanding of both land use sustainability and balanced development.展开更多
基金the 863 Project of China (No.2006AA01Z417) the National Natural Science Foundation of China (No.60673081).
文摘In this paper, the autocorrelations of maximal period Feedback with Carry Shift Register sequences （l-sequences） are discussed. For an l-sequence a with connection integer q = p^e（e ≥ 2） and period T = p^t-1（p- 1）, and for any integer i, 1 ≤ i ≤ e/2, by calculating the number of certain sets, it is shown that the autocorrelation of a with shift τ= kT/2p^i is Ca（τ） =（-1）^k-1 T/p^2i-1, where 1 ≤ k ≤ 2p^i - 1, and gcd（k,2p^i） = 1. This result shows there do exist some shifts such that the autocorrelations of l-sequences are high although most autocorrelations are low. Such result also holds for the decimations of l-sequences.
基金supported by the Humanities and Social Sciences Project of the Ministry of Education of the Peoples Republic(No.21YJCZH099)the National Natural Science Foundation of China(Nos.41401089 and 41741014)the Science and Technology Project of Sichuan Province(No.2023NSFSC1979).
文摘Majority of carbon emissions originate from fossil energy consumption,thus necessitating calculation and monitoring of carbon emissions from energy consumption.In this study,we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions.We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration.Furthermore,we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation,and we used the Geographically and Temporally Weighted Regression(GTWR)model to analyze the influencing factors of carbon emissions at this scale.The results of this study are as follows:(1)from 2000 to 2019,the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease,with an average annual growth rate of 4.24%.However,in recent years,it has stabilized,and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration;(2)carbon emissions exhibited significant spatial clustering,with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration;(3)factors such as GDP,population(Pop),urbanization rate(Ur),and industrialization structure(Ic)all showed a significant influence on carbon emissions;(4)the spatial heterogeneity of each influencing factor was evident.
基金Under the auspices of the National Key Research and Development Program of China(No.2022YFC3204404)。
文摘Quantifying and mapping how ecosystem services impact agricultural competitiveness is crucial for attaining the Sustainable Development Goals of United Nations.However,few study quantified agricultural competitiveness and mapped the effects of ecosystem services on agricultural competitiveness using multiple models.In this study,multi-source data from 2000 to 2020 were utilized to establish the indicator system of agricultural competitiveness;five ecosystem services were quantified using computation models;Geographic Information System(GIS)spatial analysis was used to explore the spatial patterns of agricultural competitiveness and ecosystem services;geographic detector models were applied to investigate the effects and driving mechanisms of ecosystem services on agricultural competitiveness.Shandong Province of China was selected as the case study area.The results demonstrated that:1)there was a significant increase in agricultural competitiveness during the study period,with high levels observed mainly in the east region of the study area.2)The spatial distribution patterns of ecosystem services and agricultural competitiveness primarily exhibited High-High and Low-Low Cluster types.3)Habitat quality emerged as the main driving factor of agricultural competitiveness in 2000 and 2020,while water yield played a substantial role in 2010.4)The coupling of two ecosystem services exerted a greater effect on agricultural competitiveness compared to individual ecosystem service.The innovations of this study are constructing an indicator system to quantify agricultural competitiveness,and exploring the effects of ecosystem services on agricultural competitiveness.This study proposed an indicator system to quantify agricultural competitiveness,which can be applied in other regions,and explored the effects of ecosystem services on agricultural competitiveness.The findings of this study can serve as valuable insights for policymakers to formulate tailored agricultural development policies that take into account the synergistic effects of ecosystem services on agricultural competitiveness.
基金supported by the National Key R&D Program of China(No.2022YFD2401301)。
文摘Species distribution patterns is one of the important topics in ecology and biological conservation.Although species distribution models have been intensively used in the research,the effects of spatial associations and spatial dependence have been rarely taken into account in the modeling processes.Recently,Joint Species Distribution Models(JSDMs)offer the opportunity to consider both environmental factors and interspecific relationships as well as the role of spatial structures.This study uses the HMSC(Hierarchical Modelling of Species Communities)framework to model the multispecies distribution of a marine fish assemblage,in which spatial associations and spatial dependence is deliberately accounted for.Three HMSC models were implemented with different structures of random effects to address the existence of spatial associations and spatial dependence,and the predictive performances at different levels of sample sizes were analyzed in the assessment.The results showed that the models with random effects could account for a larger proportion of explainable variance(32.8%),and particularly the spatial random effect model provided the best predictive performances(R_(mean)^(2)=0.31),indicating that spatial random effects could substantially influence the results of the joint species distribution.Increasing sample size had a strong effect(R_(mean)^(2)=0.24-0.31)on the predictive accuracy of the spatially-structured model than on the other models,suggesting that optimal model selection should be dependent on sample size.This study highlights the importance of incorporating spatial random effects for JSDM predictions and suggests that the choice of model structures should consider the data quality across species.
基金supported by the School Important Direction Project Cultivation Fund and Key Fund Project for Youth Innovation(Grant Nos.WK2310000101,YD2310002006,and BJ2310000055).
文摘The canonical description of structures comprises two aspects:(1)basic structural elements and(2)arrangement pattern between those elements.This tidy description has been very successful and facilitates the development of structural physics tremendously,enabling the classification,comparison and analysis of an extremely wide range of structures,including crystals,quasi-crystals,liquid crystals,semi-crystalline materials and so on.However,it has been gradually realized that many novel materials and devices exhibit random structures in which either basic elements or arrangement patterns may not exist.With the rapid development of modern advanced materials,this type of apparently random structure pops up frequently,leaving researchers struggling with how to describe,classify and quantitatively compare them.This paper proposes the utilization of statistical characteristics as the major indicators for the description of apparently random structures.Specifically,we have explored many statistical properties,including power spectral density,histograms,structural complexity,entropic complexity,autocorrelation,etc.,and found that autocorrelation may serve as a promising statistical proxy to distinguish similar-looking random structures.We discuss eight atomic force microscope images of random structures,demonstrating that autocorrelation can be used to distinguish them.In addition,14 more diverse datasets are used to support this conclusion,including atomic force microscopy images of polymers and non-polymers,transmission electron microscopy images of nanocomposite layers and scanning electron microscopy images of non-polymers.
基金China Institute of Water Resources and HydropowerResearch(IWHR)Innovative Team for Theoreticaland Technological Research on EcologicalLandscape Construction of Urban and Rural WaterSystems,Grant/Award Number:WE0145B042021。
文摘Located in Nanhai Town,Songzi City,Hubei Province,Xiaonanhai Lake is the largest natural lake in Songzi.It was once severely polluted due to the discharge of urban and rural domestic sewage,disorderly development of agricultural planting,unregulated aquaculture,and poultry farming.However,relevant esti-mations of the pollutant content in its sediment have not been carried out.This study analyzed the spatial patterns of heavy metal pollution and eutrophication at 36 water sampling sites in the Xiaonanhai Lake area,focusing on eight heavy metals:Cd,Cr,Cu,Ni,As,Pb,Hg,and Zn.The nutrient status of the lake area was evaluated using the nitrogen-phosphorus comprehensive pollution index,and heavy metal pollution status of the lake area was evaluated using geo-accumulation and the potential ecological risk index.Spatial autocorrelation analysis revealed the spatial correlation and aggregation of eutrophication levels in Xiaonanhai Lake.The results showed that the overall trophic state of the Xiaonanhai Lake area was moderate eutrophication,with a gradually decreasing eutrophication level from north to south.The Chengnan Wastewater Treatment Plant in the northern part of the lake area and surface source pollution from aquaculture were the main nitrogen and phosphorus sources.The overall eco-logical risk index of heavy metal pollution was medium and gradually weakened from north to south,consistent with the thickness of the bottom mud.The heavy metal pollution load was mainly precipitated from the bottom mud in the lake area.The eutrophication and heavy metal pollution levels in the lake area showed significant positive spatial autocorrelation,the influence range of the regional eutrophication level was small,and the spatial heterogeneity of the eutrophication and heavy metal pollution levels in Xiaonanhai Lake was relatively high.The northern part of the lake was a hotspot(high/high aggregation)of eutrophication(p<0.01)while the southern part was a cold spot(low/low concentration;p<0.05).The middle and northern part of the lake area was the hot spot(high/high concentration)of heavy metal pollution level(p<0.1)while the southern part was the cold spot(low/low concentration;p<0.1).Therefore,when carrying out water environment management in Xiaonanhai Lake,the northern area and the middle area should be prioritized for eutrophication prevention and control and dredging.
基金supported by the Natural Science Foundation of Shanxi Province(Grant No.20210302124437)the Graduate Student Research and Innovation Project of Shanxi Province(Grant No.2023KY551).
文摘In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the coupling coordination and spatial-temporal correlation between urbanization and ecosystem service,and the hotspot analysis is used to judge the spatial-temporal trend of urbanization and ecosystem service.The results show that:(1)The urbanization level from 2000 to 2020 continued to rise,the areas with relatively high urbanization were concentrated in the central part of the study area,and the relatively high terrain areas on both sides of the study area,the urbanization was relatively slow,and the hotspot areas with highly significant and significant urbanization level from 2000 to 2020 were distributed as bands in the central part of the study area and the area was rising,and there was no Cold spot area distribution;between 2000 and 2020,the ecosystem service value in the study area increased by 2.6800×10^(8) yuan.Over these two decades,it exhibited a development trend that first rose and then declined.The woodland and grassland agglomeration areas were located on the two sides of the study area,forming highly significant and significant hotspots.Conversely,the central and northeastern parts of the study area were characterized by concentrated man-made land surfaces and croplands,resulting in the formation of highly significant and significant cold spots.(2)In the central part of the study area where man-made land surface and cultivated land are concentrated,the coupling coordination between urbanization and ecosystem service is in the intermediate dislocation and mild dislocation interval;the woodland and grassland concentration areas on both sides of the study area are ecologically fragile,and the coupling coordination between the two is in the level of less than intermediate dislocation.(3)From 2000 to 2020,urbanization and the value of ecosystem services were both negatively correlated,although the correlation coefficient was low.In the central and northeastern parts,urbanization and ecosystem service exhibited patterns of high-low,high-high,and low-low clustering.Conversely,on both sides of the study area,most of the clusters showed a low-high pattern.
基金funded by the Ministry of Education’s Humanities and Social Sciences Research Planning Project(23YJA790070).
文摘Sustainable development,underpinned by robust systemic driving forces,is central to the growth of high-quality tourism.Therefore,identifying these forces at the regional level is crucial for advancing China’s goal of becoming a leading nation for tourism.This study accordingly constructs a new evaluation system that covers tourism market demand,industry supply,and structural transformation,and analyzes data from 31 Chinese provincial regions(2010–2019).The entropy method and spatial autocorrelation analysis were applied to examine the driving forces for sustainable regional tourism development.The results revealed that:First,at the national level,the driving forces for sustainable regional tourism development exhibited a clear upward trend from 2010 to 2019,with an acceleration in growth after 2015.However,there was significant regional heterogeneity:The eastern region displayed the highest levels of driving forces,followed by the central and western regions.Second,high-value clusters of these driving forces expanded from the eastern to the western regions,while the central provinces remained relatively balanced.Specifically,provincial regions such as Guangdong,Beijing,and Zhejiang were able to successively enter the high-value clusters,whereas the Xinjiang Uygur autonomous region,Gansu,and Qinghai consistently remained in the low-value clusters.Third,the driving forces exhibited a significant spatial agglomeration effect.The degree of clustering followed an inverted“U”trend over the study period,while the spatial patterns of the provincial regions remained relatively stable.
基金Under the auspices of National Key Research and Development Program(No.2023YFC3804001)National Natural Science Foundation of China(No.42201440)。
文摘Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)and its impact factors.Hence,we constructed an assessment system for the URIDL from spatial,economic,social,life,and ecological integration.The spatial autocorrelation and Spearman rank correlation coefficients were used to assess the spatiotemporal variation of the URIDL and the trade-off synergistic relationship among the subsystems at the provincial scale in China using socio-economic statistical data from 2000 to 2020.A spatial panel quantile regression model was used to analyze the driving mechanism.The results showed that the URIDL of China increased by 0.19%from 2000 to 2020,and a high-high(H-H)spatial agglomeration pattern occurred in the Yangtze River Delta and the Beijing-Tianjin-Hebei regions.Spatial integration significantly contributed to the other subsystems,whereas economic integration had a significant negative impact on the other subsystems in the eastern coastal and southwestern regions.Per capita Gross Domestic Product(GDP)improved the URIDL,whereas other factors,such as fiscal revenue decentralization,had inhibiting effects.Notably,the impact of factors on URIDL varies across different quantiles.Finally,we proposed policy recommendations for differentiated improvement of URIDL based on its evolution and regional development level during the research period.
基金financially by the National Research Council of Thailand(NRCT)under Contract No.N42A670894.
文摘This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.
基金supported by the funding provided by the State Key Laboratory of Hydraulics and Mountain River Engineering (SKHL2210)National Natural Science Foundation of China (42171304)the Sichuan Science and Technology Program (2023YFS0380,2023YFS0377,2023NSFSC1989)。
文摘The upper reaches of the Yellow River in Sichuan Province are critical area for water conservation and ecological protection in China. However, they are experiencing a range of ecological and environmental challenges, including grassland desertification, wetland degradation, and soil erosion, all of which pose significant threats to the environmental sustainability and overall development of the Yellow River Basin. Urbanization can lead to irreversible damage to ecosystems. Therefore, understanding the relationship between urbanization and ecosystems is crucial for fostering sustainable development in the region. With land use and meteorological data in the upper reaches of the Yellow River in Sichuan Province in 2000-2020, and using InVEST model and standardized processing methods, we analyzed the spatiotemporal evolution characteristics of urbanization and four ecosystem services: water conservation, carbon storage, habitat quality, and soil retention. Additionally, we employed the GeoDa bivariate spatial autocorrelation analysis model to reveal the spatial correlations and interactions between urbanization and ecosystems. The results reveal a significant spatial mismatch between urbanization and ecosystem services in the upper Yellow River region of Sichuan Province. While the composite urbanization index decreased from 0.0075 to 0.0042 and remained concentrated in county centers, all ecosystem services showed improvement: water conservation increased from 17.38×10^(9) mm to 23.37×10^(9) mm, carbon storage rose from 936.60 Tg to 938.42 Tg, habitat quality improved from 0.875 to 0.879, and soil retention enhanced from 13.56×10^(8) t to 17.59×10^(8) t. However, these ecological gains were mainly driven by restoration in non-urban southern areas, creating a clear spatial disconnection from urban centers and leading to persistently weak and declining coordination between systems. This spatial decoupling underscores the inadequacy of the current urbanization model in promoting regional ecological synergy. We therefore recommend implementing differentiated zoning strategies: promoting compact development coupled with ecological restoration in county centers, strictly protecting core water conservation and carbon sequestration areas in the southern key ecological zones, and enhancing soilwater conservation and ecological restoration in the vulnerable northern belt, so as to establish an ecological security framework compatible with sustainable urbanization.
基金supported by the Henan Province science and technology research project(Grant No.242102321031)National Natural Science Foundation of China(grant numbers 42207200).
文摘Near-surface geological defects pose a serious threat to human life and infrastructure.Hence,the exploration of geological hazards is essential.Currently,there are various geological hazard exploration methods;however,those require improvements in terms of economic feasibility,convenience,and lateral resolution.To address this,this study examined an extraction method to determine spatial autocorrelation velocity dispersion curves for application in near-surface exploration.
基金Wyoming NASA EPSCoR Faculty Research Grant(Grant#80NSSC19M0061)Yellowstone National Park Services for their generous support and funding that made this research possible.
文摘In the Rocky Mountain and Pacific Northwest regions of the United States,forests include extensive portions of standing dead trees.These regions showcase an intriguing phenomenon where the combined biomass of standing dead trees surpasses that of fallen and decomposing woody debris.This stems from a suite of factors including pest disturbances,management decisions,and a changing climate.With increasingly dry and hot conditions,dead timber on a landscape increases the probability that a fire will occur.Identifying and characterizing the presence of standing dead trees on a landscape helps with forest management efforts including reductions in the wildfire hazard presented by the trees,and vulnerability of nearby park assets should the trees burn.Using forest-based classification,exploratory data analysis,and cluster vulnerability analysis,this study characterized the occurrence and implications of standing dead trees within Yellowstone National Park.The findings show standing dead trees across the entire study area with varying densities.These clusters were cross-referenced with vulnerability parameters of distance to roads,distance to trails,distance to water,distance to buildings,and slope.These parameters inform fire ignition,propagation,and impact.The weighted sum of these parameters was used to determine the vulnerability incurred on the park assets by the clusters and showed the highest values nearest to park entrances and points of interest.High vulnerability clusters warrant priority management to reduce wildfire impact.The framework of this study can be applied to other sites and incorporate additional vulnerability variables to assess forest fuel and impact.This can provide a reference for management to prioritize areas for resource conservation and improve fire prevention and suppression efficiency.
基金sponsored by the 973 Program (No. 2009CB219505)the Talents Introduction Special Project of Guangdong Ocean University (No. 0812182)
文摘In order to study the scale characteristics of heterogeneities in complex media, a random medium is constructed using a statistical method and by changing model parameters （autocorrelation lengths a and b）, the scales of heterogeneous geologic bodies in the horizontal and the vertical Cartesian directions may be varied in the medium. The autocorrelation lengths a and b represent the mean scale of heterogeneous geologic bodies in the horizontal and vertical Cartesian directions in the randQm medium, respectively. Based on this model, the relationship between model autocorrelation lengths and heterogeneous geologic body scales is studied by horizontal velocity variation and standard deviation. The horizontal velocity variation research shows that velocities are in random perturbation. The heterogeneous geologic body scale increases with increasing autocorrelation length. The recursion equation for the relationship between autocorrelation lengths and heterogeneous geologic body scales is determined from the velocity standard deviation research and the actual heterogeneous geologic body scale magnitude can be estimated by the equation.
基金The National Natural Science Foundation of China(No.61171081,No.61471164)the Natural Science Foundation of Hunan Province(No.14JJ6043)
文摘To overcome the shortcomings of the single-shot autocorrelation SSA where only one pulse width is obtained when the SSA is applied to measure the pulse width of ultrashort laser pulses a modified SSA for measuring the spatiotemporal characteristics of ultrashort laser pulses at different spatial positions is proposed. The spatiotemporal characteristics of femtosecond laser pulses output from the Ti sapphire regenerative amplifier system are experimentally measured by the proposed method. It was found that the complex spatial characteristics are measured accurately.The pulse widths at different spatial positions are various which obey the Gaussian distribution.The pulse width at the same spatial position becomes narrow with the increase in input average power when femtosecond laser pulses pass through a carbon disulfide CS2 nonlinear medium.The experimental results verify that the proposed method is valid for measuring the spatiotemporal characteristics of ultrashort laser pulses at different spatial positions.
基金Key Research Program of the Chinese Academy of Sciences, No.KZZD-EW-06-03 No.KSZD-EW-Z-021-03+2 种基金 Key Project of Chinese Ministry of Education, No. 13JJD790008 National Natural Science Foundation of China, No.41329001 No.41071108
文摘The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spati- otemporal dynamics and dominating factors of China＇s carbon intensity from energy con- sumption in 1997-2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China＇s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP （11.72%）. Secondly, the trend of Moran＇s I indicated that China＇s carbon intensity has a growing spatial agglom- eration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel economet- ric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China＇s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.
基金Foundation： National Social Science Foundation of China, No.10ZD＆M030 Non-profit Industry Financial Program of Ministry of Land and Resources of China, No.200811033+2 种基金 A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions National Natural Science Foundation of China, No.40801063 No.40971104
文摘Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy consumption, spatial autocorrelation analysis of carbon emissions, spatial regression analysis between carbon emissions and their influencing factors. The analyzed results are shown as follows. （1） Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly. （2） The global spatial autocorrelation of carbon emissions from energy consumption in- creased from 1997 to 2009, the spatial autocorrelation analysis showed that there exists a ＂polarization＂ phenomenon, the centre of ＂High-High＂ agglomeration did not change greatly but expanded currently, the centre of ＂Low-Low＂ agglomeration also did not change greatly but narrowed currently. （3） The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population, R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population. The contribution of population to carbon emissions increased but the contribution of GDP decreased from 1997 to 2009. The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population, so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.
基金National Natural Science Foundation of China,No.41601151Natural Science Foundation of Guangdong Province,No.2016A030310149Pearl River S&T Nova Program of Guangzhou(201806010187)
文摘In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.
基金National Natural Science Foundation of China,No.41501593,No.41601592National Program on Key Research Project,No.2016YFA0602500
文摘The question of how to generate maximum socio-economic benefits while at the same time minimizing input from urban land resources lies at the core of regional ecological civilization construction. We apply stochastic frontier analysis （SFA） in this study to municipal input-output data for the period between 2005 and 2014 to evaluate the urbanization efficiency of 110 cities within the Yangtze River Economic Belt （YREB） and then further assess the spatial association characteristics of these values. The results of this study initially reveal that the urbanization efficiency of the YREB increased from 0.34 to 0.53 between 2005 and 2014, a significant growth at a cumulative rate of 54.07%. Data show that the efficiency growth rate of cities within the upper reaches of the Yangtze River has been faster than that of their counterparts in the middle and lower reaches, and that there is also a great deal of ad- ditional potential for growth in urbanization efficiency across the whole area. Secondly, results show that urbanization efficiency conforms to a ＂bar-like＂ distribution across the whole area, gradually decreasing from the east to the west. This trend highlights great intra-provincial differences, but also striking inter-provincial variation within the upper, middle, and lower reaches of the Yangtze River. The total urbanization efficiency of cities within the lower reaches of the river has been the highest, followed successively by those within the middle and upper reaches. Finally, values for Moran＇s / within this area remained higher than zero over the study period and have increased annually; this result indicates a positive spatial correlation between the urbanization efficiency of cities and annual increments in agglomeration level. Our use of the local indicators of spatial association （LISA） statistic has enabled us to quantify characteristics of ＂small agglomeration and large dispersion＂. Thus, ＂high- high＂ （H-H） agglomeration areas can be seen to have spread outwards from around Zhejiang Province and the city of Shanghai, while areas characterized by ＂low-low＂ （L-L） patterns are mainly concentrated in the north of Anhui Province and in Sichuan Province. The framework and results of this research are of considerable significance to our understanding of both land use sustainability and balanced development.
