泛长江三角洲城市旅游经济发展的空间效应

基于泛长江三角洲2002~2012年市域面板数据,并借助面板SDM及空间效应分解方法,对区域城市旅游经济发展及其动力因子的空间效应进行实证分析。结果表明:1区域城市入境和国内旅游发展都存在明显的空间依赖性和空间溢出效应,当邻近城市入境和国内旅游发展水平每提高1%,将促进本城市入境和国内旅游发展水平增加约0.257%和0.312%;2区域城市入境和国内旅游发展各动力因子的空间交互作用也较为明显。旅游资源禀赋、旅游服务设施、旅游交通设施和城市经济发展水平对入境旅游发展具有显著的空间溢出效应,旅游服务设施和城市经济发展水平对国内旅游发展的空间溢出效应也较显著。3保障性因子多为正向溢出,吸引性因子多为负向溢出。前者体现了动力因子的空间模仿效应,后者反映了空间竞争效应。保障性因子和吸引力因子的空间效应强度及作用路径差异推动着区域旅游发展格局不断演进。     

资源产业空间集聚与区域经济增长:“资源诅咒”效应实证

针对"资源诅咒"研究中自然资源度量指标选取存在的科学性问题,利用资源产业空间集聚代替自然资源丰裕度和资源产业依赖度作为自然资源丰裕程度的解释变量。首先,在Krugman空间基尼系数的基础上,构建适用于测度资源产业空间集聚的计算公式,基于我国30个省(区、市)层面的数据,量化1999—2013年间资源产业的空间集聚程度,分析其排名情况和分布格局。研究显示:我国有15个省(区、市)存在资源产业空间集聚现象,这些地区大多位于长江以北,且均为自然资源富集地区。其次,构建经济增长计量模型,选取存在资源产业空间集聚现象的省(区、市)作为样本空间,并采用多种计量分析和检验方法考察资源产业空间集聚度与区域经济增长之间的直接和间接关系,分析"资源诅咒"的传导机制。研究显示:1在模型整体估计中,资源产业空间集聚的系数符号均为负,且在5%的水平上显著,说明资源产业空间集聚与区域经济增长间的关系呈现负相关,符合"资源诅咒"的基本假设。2在模型分步估计中,人力资本投资、技术创新投入和制造业发展与区域经济增长正相关,符合经典经济增长理论的逻辑关系;物质资本投资与区域经济增长负相关,可能是由于当地经济主体的消费跨期替代弹性较小,导致物质资本投资对经济增长的直接增益效应小于其对社会人力资本积累的负向效应,进而阻碍区域经济增长;政府干涉程度越高越不利于经济增长。根据研究结果,建议应明确市场调节为主、政府调控为辅的导向机制,进一步提升资源产业空间集聚程度,强化行业人员素质与技术水平匹配,逐步提高自然资源生产和利用效率,从而实现加快地区经济转型,有效促进经济可持续发展的全面提升。     

长江经济带新型城镇化协调性的空间差异与时空演化

从城乡、产城、区域和城镇化与资源环境协调发展4个方面建立指标体系,衡量长江经济带105个地级及以上市新型城镇化的协调性,并运用泰尔系数和探索性空间分析考察协调性的空间差异和时空演化,结果发现:长江经济带新型城镇化协调性存在明显的反地势分布和"城市圈俱乐部"特征;东、中、西部城镇化协调性差异随时间越来越明显,东部城市主要体现在非省会城市之间的差异,中部主要体现在省会城市与非省会城市的组间差异,西部主要体现在省会城市之间以及省会城市和非省会城市之间的组间差异;长江经济带新型城镇化协调性表现出正的全局空间自相关,协调性高的区域开始集聚在一起,这种趋势随时间不断增强并且空间溢出效应明显。     

长湖流域水质时空分布特征及影响因子

利用2009—2014年长湖5个水质监测点数据,采用时间序列法分析了长湖水质的时间变化规律,采用相关性分析法,分析了流域水污染的影响因子。结果表明:在时间上,长湖水污染物质量浓度季节变化明显,COD、TN、NH3-N均为7、9月较低,1、3月较高,丰水期水质好于枯水期。入湖地区TP质量浓度7月达最高值,且7月份入湖地区的桥河口、关沮口的NH3-N、TN含量稍高于5月。空间上,西北部入湖地区水质劣于湖心及东南部出湖地区。工业、生活等点源污水,以耕地为主的农业非点源以及天然降水量和径流量是影响水质的主要因素,入湖排污量、降水量和径流量与长湖水质呈显著相关关系(P0.05)。     

2000~2014年武汉市城市扩展时空特征分析

基于不同时相的高分辨率遥感影像，采用面向对象的解译方法提取了武汉市2000、2005、2010和2014年的土地覆被信息，从城市扩展强度指数，城市中心坐标迁移和分形维数等方面分析了武汉市2000～2014年城市扩展时空特征。结果表明：2000～2014年间，武汉市城市扩展强度指数为1.41，各主城区城市扩展强度不一，洪山区建设用地的增加对主城区城市扩展的贡献最大；在扩展过程中，武汉市城市分形维数增加，城市空间形态变复杂；其扩展呈现核心-放射扩展模式，并逐渐转向圈层式；自然条件、经济、人口、交通、政策和城市规划是武汉市城市扩展的主要驱动力，但随着交通的发展，自然条件和经济对武汉市城市扩展的驱动作用正逐渐减弱。     

中国碳排放强度的时空演进及跃迁机制

面对气候变化所带来的生存危机以及环境治理的复杂状况,对中国碳排放强度时空演进的动态监测与预警治理的研究是实现碳排放强度下降目标的关键。文章测算1997—2015年中国大陆30个省区碳排放强度的空间面板数据,采用探索性时空数据分析(ESTDA)方法对中国碳排放强度的空间相关性、集聚特征及其时空跃迁进行空间统计分析,借助分位数回归与时空跃迁嵌套模型,揭示在时间和空间推移的双重作用下中国各省区碳排放强度的时空跃迁机制。研究结果表明:(1)中国30个省区的碳排放强度在时空分布上并不是完全随机状态,各个省区碳排放强度之间具有显著的空间相关性特征,碳排放强度的变动趋势会受到其相临近省区碳排放强度的影响,省域间的碳排放强度在空间分布上呈现"集聚"与"分异"并存的时空演进特征。(2)中国碳排放强度空间集聚趋势增强,具有高度的凝固性和较低的流动性,10个高碳排放强度省区碳排放强度的稳定性将成为制约中国碳排放强度整体跃迁的重点省区,相关省区的跃迁性将成为驱动中国碳排放强度整体跃迁的关键省区。(3)各省区的碳排放强度空间集聚过程中存在时空跃迁的驱动模式和制约模式,分位数回归模型能够很好地解释各驱动因素对碳排放强度时空跃迁的驱动机制,不同响应阶段的驱动因素的分位数与碳排放强度时空跃迁类型之间具有很强的嵌套性。(4)根据各省区碳排放强度时空演进及其跃迁机制的分析结果,进一步提出加强对关键省区碳排放强度的有效监测与治理,加大碳排放的约束力度等差异化的碳减排调控措施。     

Community gardening: cultivating subjectivities,space, and justice

Community gardens have been lauded for being inherently resistant to neoliberalism and criticised for underwriting it. To move beyond this either/or debate, we need to employ more focused lenses and specify both the processes of neoliberalisation at play and the outcomes they can produce. This paper explores the ways in which neoliberal processes of privatisation, state entrepreneurialism, and devolution intersect with community gardens, and the subjectivities that may be cultivated, the spaces that may be created and the types of justice that may be advanced as a result. It argues that certain characteristics and orientations of gardens are more conducive to resisting neoliberalism. These include the cultivation of producer, citizen, and activist subjectivities (over those of consumer, entrepreneur, and volunteer); the elevation of the use value of shared lived space (over a site’s potential exchange value) and the advancement of spatial justice through community access to non-privatised space; and food justice, through non-commodified means of obtaining food. Holding these ends in mind can help ensure that proponents of community gardening sow the seeds of the fruits they most wish to reap.     

Toward accurate and precise estimates of lion density

Reliable estimates of animal density are fundamental to understanding ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation because wildlife authorities rely on estimates to make decisions. However, it is notoriously difficult to accurately estimate density for wide‐ranging carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores, such as lions (Panthera leo). Although abundance indices for lions may produce poor inferences, they continue to be used to estimate density and inform management and policy. We used sighting data from a 3‐month survey and adapted a Bayesian spatially explicit capture‐recapture (SECR) model to estimate spatial lion density in the Maasai Mara National Reserve and surrounding conservancies in Kenya. Our unstructured spatial capture‐recapture sampling design incorporated search effort to explicitly estimate detection probability and density on a fine spatial scale, making our approach robust in the context of varying detection probabilities. Overall posterior mean lion density was estimated to be 17.08 (posterior SD 1.310) lions >1 year old/100 km², and the sex ratio was estimated at 2.2 females to 1 male. Our modeling framework and narrow posterior SD demonstrate that SECR methods can produce statistically rigorous and precise estimates of population parameters, and we argue that they should be favored over less reliable abundance indices. Furthermore, our approach is flexible enough to incorporate different data types, which enables robust population estimates over relatively short survey periods in a variety of systems. Trend analyses are essential to guide conservation decisions but are frequently based on surveys of differing reliability. We therefore call for a unified framework to assess lion numbers in key populations to improve management and policy decisions.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado