Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Comparison of Geographically Weighted Regression of Benthic Substrate Modeling Accuracy on Large and Small Wadeable Streams

Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (<10 m wetted width) habitat assessments difficult. Geographically weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales. This study examined the ability of GWR to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution. We performed GWR on two groups of 60 randomly selected substrate patches from large and small streams and used depth measurements to model substrate. Our large and small stream substrate models responded equally well to GWR. Results showed no statistically significant difference between GWR R2 values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time.

Spatiotemporal Characteristics of Typical Ecosystem Services and Their Spatial Responses to Driving Factors in Ecologically Fragile Areas in Upper Yellow River,China

The identification of dominant driving factors for different ecosystem services(ESs)is crucial for ecological conservation and sustainable development.However,the spatial heterogeneity of the dominant driving factors affecting various ESs has not been adequately elucidated,particularly in ecologically fragile regions.This study employed the integrated valuation of ESs and trade-offs(InVEST)model to evaluate four ESs,namely,water yield(WY),soil conservation(SC),habitat quality(HQ),and carbon storage(CS),and then to identify the dominant driving factors of spatiotemporal differentiation of ES and further to characterize the spatial heterogeneity characteristics of the dominant driving factors in the eco-fragile areas of the upper Yellow River,China from 2000 to 2020.The results demonstrated that WY exhibited northeast-high and northwest-low patterns in the upper Yellow River region,while high values of SC and CS were distributed in central forested areas and a high value of HQ was distributed in vast grassland areas.The CS,WY,and SC exhibited decreasing trends over time.The most critical factors affecting WY,SC,HQ,and CS were the actual evapotranspiration,precipitation,slope,and normalized difference vegetation index,respectively.In addition,the effects of different factors on various ESs exhibited spatial heterogeneity.These results could provide spatial decision support for eco-protection and rehabilitation in ecologically fragile areas.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.

Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

In the Anthropocene era,human activities have become increasingly complex and diversified.The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities,especially in arid and semi-arid areas.In the study,we chose the economic belt on the northern slope of the Tianshan Mountains(EBNSTM)in Xinjiang Uygur Autonomous Region of China as a case study.By collecting geographic data and statistical data from 2010 and 2020,we constructed an ecological resilience assessment model based on the ecosystem habitat quality(EHQ),ecosystem landscape stability(ELS),and ecosystem service value(ESV).Further,we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis,and explored its responses to climate change and human activities using the geographically weighted regression(GWR)model.The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020.The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of"high in the western region and low in the eastern region",and the spatial clustering trend was enhanced during the study period.Desert,Gobi and rapidly urbanized areas showed low level of ecological resilience,and oasis and mountain areas exhibited high level of ecological resilience.Climate factors had an important impact on ecological resilience.Specifically,average annual temperature and annual precipitation were the key climate factors that improved ecological resilience,while average annual evapotranspiration was the main factor that blocked ecological resilience.Among the human activity factors,the distance from the main road showed a negative correlation with ecological resilience.Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions,whereas in the areas with poorer ecological conditions,the correlations were positive.The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990-2018

To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and geographically and temporally weighted regression(GTWR)model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.The results are as follows.First,the urban-rural coupling coordination degree in Northeast China was very low and improved slowly,but its stages of evolution is a good interpretation of the strategic arrangements of China's urbanization.Second,the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization,converging on urban agglomeration,which was high in the south and low in the north.Moreover,the gap between the north and south weakened.Third,the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities,pushing from rural transformation,and government regulations.The influence intensity of the three mechanisms was weak,but the pulling from the central cities was stronger than that of the other two mechanisms.Furthermore,the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China.Fourth,to promote the development of urban-rural coordination in Northeast China,it is essential to advance urban-rural economic correlation,enhance the government^role in regulating and guiding,and adopt different policies for each region in Northeast China.

Spatial non-stationary characteristics between temperate grasslands based on a mixed geographically weighted regression model

Spatial models are effective in obtaining local details on grassland biomass,and their accuracy has important practical significance for the stable management of grasses and livestock.To this end,the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China(August 2020),combined with hydrometeorology,elevation,net primary productivity(NPP),and other auxiliary data over the same period.Accordingly,non-stationary characteristics of the spatial scale,and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression(MGWR)model.The results showed that the model was suitable for correlation analysis.The spatial scale of ratio resident-area index(PRI)was the largest,followed by the digital elevation model,NPP,distance from gully,distance from river,average July rainfall,and daily temperature range;whereas the spatial scales of night light,distance from roads,and relative humidity(RH)were the most limited.All influencing factors maintained positive and negative effects on grass yield,save for the strictly negative effect of RH.The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield.Regression parameters revealed that the results of Ordinary least squares(OLS)(Adjusted R^(2)=0.642)and geographically weighted regression(GWR)(Adjusted R^(2)=0.797)models were worse than those of MGWR(Adjusted R^(2)=0.889)models.Based on the results of the RMSE and radius index,the simulation effect also was MGWR>GWR>OLS models.Ultimately,the MGWR model held the strongest prediction performance(R^(2)=0.8306).Spatially,the grass yield was high in the south and west,and low in the north and east of the study area.The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.

藏东-川西生态维护水源涵养区产水量驱动机制

藏东-川西生态维护水源涵养区位于西南高山峡谷区北部,是中国典型的生态脆弱区,生态系统抗干扰能力差,对气候变化的反应敏感。产水量是生态系统服务中的一项重要功能,研究产水量空间分布特征及其影响因子对该地区水资源保护、涵养,开发和利用有重要意义。基于2000—2020年地表覆盖产品、气候、基岩深度、土壤和地形等数据,运用InVEST模型Annual Water Yield模块模拟了藏东-川西地区产水量;结合地理探测器(GDM)分析了造成产水量空间分异的各因子的解释能力;对q>0.1的因子引入多尺度地理加权回归(MGWR)分析它们对研究区不同地理位置产水量的影响程度,并运用Theil-Sen趋势分析、Mann-Kendall显著性检验得到了产水量及其主导因素时空变化特征;同时利用Hurst指数预测了未来短期内产水量上升或下降趋势和评估了研究时段内产水量在不同空间位置的波动水平。结果表明:1)降水量和产水量空间分布在研究区内呈现“东西高,中部低”的分布格局,岷江流域降水量为最大,金沙江流域蒸散水平最高,怒江流域产水量领先其余三个流域;2)除降水量和蒸散发外,藏东-川西地区产水量主要影响因子有气候因子(年均湿度和年均风速)、地形因子(高程)、土壤类型、植被因子(归一化植被指数和净初级生产力)与社会因子(地表覆盖类型和人类活动强度指数);其中降水量、蒸散发、高程、归一化植被指数、净初级生产力和地表覆盖类型是产水量的主导影响因素。3)降水、高程与人类活动强度指数对产水服务的影响有较强的正向作用,而蒸散发、归一化植被指数和净初级生产力展现出较强的负向作用。4)研究区南部区域产水量波动水平高。在未来短期内,研究区95.30%的区域有下降趋势。5)藏东-川西地区不应将盲目提升人工植被覆盖度作为提高产水量首要方案,应注意对天然林的保护和预防石漠化。

黄河流域区域经济差异影响因素的空间异质性--基于MGWR模型的实证研究

利用变异系数、基尼系数、GIS空间分析等方法,剖析黄河流域区域经济差异的时空演化格局,并运用多尺度地理加权回归模型(MGWR)对其影响因素的空间异质性进行分析。结果表明:(1)2002—2020年黄河流域不同尺度区域经济绝对与相对差异均较为明显,区域经济差异从大到小依次为地级市、省级、城市群、上中下游。(2)2002—2020年黄河流域区域经济发展水平呈波动上升趋势,呈由东至西递减格局,且尺度越小区域经济异质性越强。(3)各影响因素呈现显著的空间分异格局,对黄河流域区域经济差异的影响具有不同的空间模式和影响范围。其中城镇化、分权化、产业结构对黄河流域地级市经济发展的影响从东至西递减,区域投资水平对黄河流域上游城市经济发展的影响较大,海拔对流域内城市经济发展的负向影响由东至西递增。

2000—2018年内蒙古沙区“生态-经济-社会”复合系统脆弱性演变及关键影响因素

在气候变化和人类活动共同影响下,沙区“生态-经济-社会”复合系统快速变化,为沙漠化治理及区域可持续发展带来了严峻的挑战。因此,揭示沙区“生态-经济-社会”复合系统脆弱性演变规律,辨识影响脆弱性演化的关键因素及区域差异对沙区可持续管理具有重要意义。以内蒙古自治区为研究区,利用“暴露-敏感-适应”评估模型构建沙区“生态-经济-社会”复合系统脆弱性评价指标体系,将空间主成分分析与时空地理加权回归模型相结合,探讨2000—2018年内蒙古“生态-经济-社会”复合系统脆弱性时空演变及关键因素作用机制。结果表明:(1)脆弱性及暴露度、敏感性和适应性三个维度在空间上均有较强的异质性特征,脆弱性具有整体分散、局部聚集的分布格局;暴露度的空间分布为中部高,两边低;敏感性的空间分布为西部高,东部低;适应性的高低值分布与脆弱性相反。(2)脆弱性的变化在总体上呈现大部分区域降低,局部区域升高的趋势,显著升高区域主要聚集在内蒙古南部地区。(3)影响脆弱性分布的因素在每年均有所不同,但总体上植被覆盖度、沙漠化程度、气象指数等生态指标是影响脆弱性显著变化区域的主要因素。

Spatial Heterogeneity Association of HIV Incidence with Socio-economic Factors in Zimbabwe

This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males,unemployed females,percentage of poor households i.e.,poverty prevalence,night lights index,literacy rate,household food security,and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios(LLR),global Moran’s I,local indicator of spatial association-LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression(GWPR)and semi-parametric GWPR(s-GWPR)were used to determine the spatial association between HIV incidence and socio-economic factors.HIV incidence(number of cases per 1000)ranged from 0.6(Buhera district)to 13.30(Mangwe district).Spatial clustering of HIV incidence was observed(Global Moran’s I=-0.150;Z score 3.038;p-value 0.002).Significant clusters of HIV were observed at district level.HIV incidence and its association with socio-economic factors varied across the districts except percentage of females unemployed.Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.

可达性对城市群多模式交通碳排放的空间异质性影响

当前,在交通行业碳减排存在巨大挑战以及未来中国城市群交通发展长远规划双重背景下,如何通过改善交通可达性提高居民出行效率和减少碳排放是亟待解决的关键问题之一。本文基于城际出行手机信令数据,从居民出行的角度提出城际多模式交通客运碳排放量方法,并采用梯度提升决策树(GBDT)模型及多尺度地理加权回归(MGWR)模型探讨可达性对区域碳排放量的空间异质性影响。以关中平原城市群为例进行验证,结果表明:城际公路客运碳排放量远大于铁路,呈现沿交通基础设施线路分布的特征;在整体区域范围内,可达性指标对碳排放水平具有一定的正向边际效应;MGWR能够刻画碳排放与可达性指标关系的空间异质性及尺度差异;经济潜能可达性、介数中心性及接近中心性对城际碳排放具有显著的正向空间异质性影响,但影响尺度不同;公路客运碳排放对介数中心性及接近中心性要素较为敏感,经济潜能对碳排放的影响较为平稳;铁路出行可达性的提升对中心城市的影响效应低于周边区县城市。

京津冀地区PM_(2.5)与CO_(2)的协同控制效应及调控因素研究

京津冀地区面临着二氧化碳(CO_(2))减排和细颗粒物(PM_(2.5))浓度降低的双重挑战,研究该地区CO_(2)与PM_(2.5)的协同控制效应及调控因素演变特征,对其经济高质量发展与环境改善具有重要意义。文章基于2005-2020年人口栅格数据、夜间灯光数据以及统计年鉴数据,利用协同控制效应坐标系法和时空地理加权回归模型评估了京津冀地区CO_(2)排放量和PM_(2.5)浓度的协同控制效应及调控因素作用机制的时空异质性。结果表明:(1)京津冀地区实现CO_(2)和PM_(2.5)协同控制效应的区域呈现上升-下降-上升的变化趋势,在2019年达到最高,最高占比68.15%。(2)第三产业发达、清洁能源丰富或产业布局合理的城市更易实现CO_(2)排放量与PM_(2.5)浓度的共控。(3)产业结构、研发支出、对外开放程度和降雨量为京津冀地区协同控制CO_(2)排放量和PM_(2.5)浓度的主要因素,但这些调控因素的作用存在时空异质性。

湖南省城镇土地利用多样性对人口密度的影响研究

城镇人口密度降低已经成为制约部分城镇发展的重要因素之一。选取湖南省为案例区,在定量刻画湖南省城镇人口密度与土地利用多样性格局基础上,运用地理加权回归模型探索两者的关系,并构建压力-状态-响应模型探索湖南省城镇土地利用多样性对人口密度的影响路径。结果表明:①湖南省城镇人口密度、城镇建设用地土地利用多样性均存在明显的空间正相关性,但聚集趋势相反;②单一化的城镇建设用地利用方式既不利于湖南省城镇土地利用多样性的提升,也不利于湖南省城镇人口密度的提升,工业布局可以引导湖南省城镇人口密度的提升,注重经济发展、提高城镇公共服务能力是提高城镇土地利用多样性进而提高城镇人口密度的有效方法;③湖南省二、三产业增加值与城镇人口密度呈现弱负相关且呈现“南高北低”的格局;④湖南省城镇居民可支配收入与城镇人口密度的相关性呈现出“西北高、东南低”的格局,并在长沙市中心城区一带出现正相关与负相关的分界。最后,从优化城镇职能体系对应对历史遗留问题、促进人口密度有序提升方面提出了政策建议。

中国省域旅游环境系统服务价值的时空分异及影响因素研究

探究旅游环境系统服务价值的时空分异及影响因素对于科学评估旅游环境系统服务价值,实现旅游业的可持续发展具有重要意义。文章界定旅游环境系统服务价值的内涵,以我国31个省域为研究对象,从经济、生态和社会三个层面构建省域旅游环境系统服务价值指标体系,采用熵权TOPSIS法和空间自相关法对31个省域的旅游环境系统服务价值进行测度和时空分异特征探究,通过地理探测器和地理加权回归模型分析其影响因素。结果表明:(1)2012—2019年,31个省域旅游环境系统服务价值均值在时间层面总体呈上升趋势,2020年因新冠疫情出现轻微下降,2021年有所回升;空间层面上,东、中、西三大区域空间格局差异显著,年均值东部>中部>西部。(2)31个省域旅游环境系统服务价值的全局莫兰指数呈现下降—上升—下降—上升—平稳的小幅波动趋势,存在空间正向集聚效应,且局部自相关集聚模式变化较大。(3)对外经济贸易水平、政府扶持力度和科技投入对旅游环境系统服务价值均为正向影响,高等教育规模呈负向影响,对外经济贸易水平的影响强度由北向南递增,政府扶持力度和高等教育规模的影响强度由西北向东南递增,科技投入的影响强度由西向东递减。

赣州市生境质量和景观格局间时空非平稳性关联

在城市化的高速推进造成景观破碎、威胁生境质量的大背景下,赣州市较好地实现了经济发展与生态保护并行。为探明赣州市景观格局对生境质量的驱动机理,回答“怎样的景观格局有利于生态保护”这一问题,该研究以赣州市为研究区,对其2000—2020年的生境质量进行空间测度并分析其时空跃迁动态特征;分析其景观格局演变特征,并基于时空地理加权回归模型(geographically and temporally weighted regression,GTWR)探明二者关联机制。结果表明:赣州市生境质量呈现“四周高,中部低”的空间分布特征,2000—2020年整体均处于较高水平,呈现积极协同进步的空间动态特征;赣州市景观格局维持在较稳定状态,人造地表扩张优先占用非林非水地类,且与水域增量存在空间伴随;生境质量与景观格局间关联呈现显著的时空非平稳性特征;散布与并列指数(interspersion juxtaposition index,IJI)显示人造地表、林地和水域交错分布可能降低人造地表对生境质量的负向影响。该研究以提升生境质量为目标,探明了10 km×10 km格网尺度下赣州市各景观格局指数的最优驱动阈值区间,人造地表斑块密度应控制在0.68个/km^(2)之内,最大斑块占林地总面积比不宜低于9.8%,且林地斑块密度应控制在7.8个/km^(2)之内,斑块IJI指数应大于20%,草地和水域斑块IJI指数应控制在60%内以及超出13%。研究结果可为国土空间规划和其他区域的生态管理决策提供参考。

四川省县域数字乡村发展水平的地域特征与影响因素

以北京大学新农村发展研究院发布的2020年县域数字乡村指数作为测度指标,采用空间自相关分析、半变异函数、地理加权回归、地理探测器等方法,研究四川省172个县域数字乡村发展水平的空间分异特征及影响因素。结果表明:(1)四川省县域数字乡村发展水平整体偏低,空间分异格局明显,呈现出以成都平原为核心高值区、自东向西递减的趋势。(2)数字乡村发展水平的空间关联性较强,显著高-高区主要分布在成都平原及其周围,如德阳、眉山的县域;显著低-低区主要在川西北及攀西地区分布,如阿坝、甘孜、凉山的县域。(3)数字基础设施指数和治理数字化指数的地域分异受空间相关性影响的比重大,经济数字化指数和生活数字化指数的地域分异主要受随机成分影响。(4)从回归系数平均数的绝对值看,影响四川省县域数字乡村发展水平的因素由大到小为农村金融、收入水平、互联网发展水平、人口受教育水平、政府支持和交通条件。并且这些因素不是独立、直接作用于数字乡村,而是各因素两两交互作用。

耕地“非粮化”影响因素空间效应研究——以珠三角为例

为科学管控耕地非粮化,本研究以珠三角县级行政区为研究单元,在揭示耕地非粮化空间分异特征基础上,采用空间杜宾模型和地理加权回归模型探究耕地非粮化及其空间效应。结果表明:珠三角2019年各县平均非粮化率为47.8%,高于全国平均水平。从非粮化率来看,耕地非粮化集聚于珠三角周边县域及部分中部县域,以低-低和高-高集聚为主;从非粮化面积来看,耕地非粮化集聚于研究区东北部,以高-高集聚为主。珠三角非粮化存在空间依赖性。从直接效应看,第一产业GDP占比、到市中心的距离与非粮化呈负相关,劳均耕地面积、有效耕地灌溉面积与非粮化呈正相关;从溢出效应看,人均GDP与非粮化呈正相关。第一产业GDP占比和有效耕地灌溉面积对非粮化的影响均呈现中部高、周边低的空间异质性特征。研究表明,经济发展水平较高区域更易产生“非粮化”,非粮化治理应当因地制宜、分级整治,坚决落实“非粮化”管理政策,提高种粮收益和粮食综合生产力,促进粮农降本增效。

基于Geodetector和MGWR的贵州工业碳排放效率时空演化及影响因素分析

探究工业碳排放效率的时空演化及影响因素对区域产业绿色发展具有重要意义。基于2010—2020年贵州省9个市州的面板数据,利用super-SBM模型与Malmquist指数对工业碳排放效率进行静态和动态分析,并采用探索性时空数据分析方法揭示时空交互特征;基于此结合地理探测器和多尺度地理加权回归模型研究其影响因素。结果表明:①贵州工业碳排放效率整体呈上升趋势,年均增长率为8.45%。②技术进步是贵州工业碳排放效率提升的主要内动力。③工业碳排放效率空间自相关的时间路径长度呈现由东部市州向中、西部增大的趋势;贵州各市州的工业碳排放效率随时间演变呈现出较强的空间依赖关系。④对外开放程度、城市化水平、能源消耗强度、产业结构、重工业水平、生产力水平6个因素是影响工业碳排放效率的主导因子,且影响显著性出现不同程度的提高;对外开放程度、能源消耗强度与工业碳排放效率存在负相关,其余主导因子与工业碳排放效率呈正相关。

福建省县域森林损失时空演变特征及其影响因素分析

森林是陆地生物圈重要的生态资源,厘清区域长期森林损失状况及其影响因素,对森林资源保护和社会经济协调发展具有重要意义。基于全球森林变化(Global Forest Change)数据集,利用ArcGIS 10.8空间分析技术,对2001—2019年福建省58个县域森林损失的时空演变特征进行分析,并结合时空地理加权回归模型,探究县域森林损失的主要影响因素及其时空分异特征。结果表明:(1)2001—2019年福建省县域森林损失率呈波动上升的基本态势,森林损失面积累计达112.89万hm^(2),森林损失情况可划分为显著上升阶段、先降后升阶段和显著下降阶段。(2)整体上,福建省县域森林损失具有较强的空间正相关性和空间集聚性特征,相邻区域森林损失率的空间相关性显著,而其空间集聚效应呈现西北低、东南高的基本态势。(3)城镇化率、人均GDP、公路通车里程、人口密度和木材产量对福建省县域森林损失均具有一定的影响,但存在明显的时空异质性特征。该研究有效揭示了福建省县域森林损失时空动态特征及其影响因素,可为森林资源可持续经营提供理论依据。