中国城市群地区PM2.5时空演变格局及其影响因素

城市群作为中国新型城镇化主体形态,是支撑全国经济增长、促进区域协调发展、参与国际分工合作的重要平台,也是空气污染的核心区域。本文选取2000-2015年NASA大气遥感影像反演PM_2.5数据,运用GIS空间分析和空间面板杜宾模型,揭示了中国城市群PM_2.5的时空演变特征与主控因素。结果显示：① 2000-2015年中国城市群PM_2.5浓度呈现波动增长趋势,2007年出现拐点,低浓度城市减少,高浓度城市增多。② 城市群PM_2.5浓度以胡焕庸线为界呈现东高西低的格局,城市群间空间差异性显著且不断扩大,东部、东北地区浓度提升更快。③ 城市群PM_2.5年均浓度空间集聚性显著,以胡焕庸线为界,热点区域集中东部,范围持续增加,冷点集中在西部,范围持续缩小。④ 城市群内各城市间PM_2.5浓度存在空间溢出效应。不同城市群影响要素差异显著,工业化和能源消耗对PM_2.5污染有正向影响;外商投资在东南沿海和边境城市群对PM_2.5污染具有负向影响;人口密度对本地区PM_2.5污染主要具有正向影响,对邻近地区则相反;城市化水平在国家级城市群对PM_2.5污染有负向影响,在区域性和地方性城市群则相反;产业结构高级度对本地区PM_2.5污染有负向影响,对邻近地区则相反;技术扶持度对PM_2.5污染的影响显著,但存在滞后性和回弹效应。     

京津冀城市群水资源开发利用的时空特征与政策启示

京津冀城市群是中国在国际经济体系中具有最强竞争力的支撑平台之一,也是中国乃至全世界水安全保障难度最大的地区之一。本文主要以2000-2014年数据为基础,采用泰尔系数、变异系数、曲线分析和空间分级分类分析等方法,揭示了京津冀城市群水资源与用水变化的时空特征。结果显示：①绝大多数城市多年平均水资源总量显著减少,干旱化的长期趋势明显,而且北部、西部城市减少幅度更大;②京津冀城市群水资源极度短缺,空间不均衡性呈现先减小后增大的趋势,而且中部和南部城市水资源开发利用潜力更低,缺水更甚;③绝大多数城市用水总量零增长或缓慢负增长,用水结构以工农业用水比重下降为主要特征,各类用水的空间不均衡性保持相对稳定;④用水效率普遍快速提升,空间分布差异呈现先增大后减小的总体趋势。基于此,建议京津冀城市群实施“以水量城”的城镇化政策和“以水定产”的产业政策,完善水生态补偿政策,落实水资源管理红线政策,实现水资源约束下各城市间经济社会与生态环境的协同发展。     

长三角PM2.5时空格局演变与特征——基于2013-2016年实时监测数据

长三角是中国空气污染较为严重的区域。基于地统计、探索性空间数据分析等方法,使用实时监测数据,探讨该区域41个地级以上城市PM_2.5在2013-2016年的时空格局演变与污染特征。结果显示：① PM_2.5表现出不同的年度、月度以及逐日特征。年均值逐年下降,月均值则体现出鲜明季节性,呈现冬高、夏低的“U型”特征,日均值有“脉冲型”波动特点。② 2013-2016年,长三角PM_2.5污染情况已经得到显著改善,但超过三分之二的地区仍存在不同程度超标现象,呈现“西北高、东南低”的污染格局。③ PM_2.5高值区从以合肥、扬州为中心向江苏、安徽北部边界地区转移,低值区逐步扩展形成东至舟山、西至黄山、北至上海、南至温州的低值集聚带。④ 趋势分析来看,PM_2.5年均值东西差异和南北差异均逐步减小,但东西向差异略大于南北向。同时,两个方向中部隆起现象得到改善,城市投影点趋于线性拟合。     

黄河流域PM2.5时空演变特征及其影响因素分析

选取2000—2017年大气PM_2.5遥感反演数据集,综合运用标准差椭圆及地理探测器等方法,揭示了黄河流域PM_2.5的时空演变特征及其影响因素。结果表明：2000—2017年黄河流域PM_2.5年均浓度整体呈现先快速增加后又波动变化的演变趋势,空气污染状况不容乐观;黄河流域PM_2.5空间集聚性明显,低值区稳定分布在内蒙古中部和西南部高原地区,高值区一方面分布在自然条件较差的西北内陆,另一方面集中在人类活动强度较高的地带;黄河流域PM_2.5污染总体呈现出“西北-东南”方向的分布格局,其浓度在地理空间上呈现分散化的趋势,即污染的主要范围有所扩大;人口密度、工业化水平、外商投资以及科技支出等经济社会因素对PM_2.5浓度存在显著影响,但其作用强度及方向存在差异。     

京津冀城市群创业风险投资的时空分布特征及影响机制

创业风险投资是京津冀城市群一体化协同创新进程中的重要驱动力。本文利用社会网络分析方法分析了京津冀城市群创业风险投资的时空分布特征,在此基础上利用引力模型和计量分析模型揭示京津冀城市群创业风险投资时空分布的影响机制。主要结论为：京津冀城市群的创业风险投资网络在总额方面具有一定的周期波动性,其时空分布的不均衡性缓慢降低,创业风险投资的三中心(北京、天津和唐山)对周边城市具有一定的带动作用;创业风险投资在城市间的流动呈现出一定的网络性,但是发育缓慢,具有很强的向心性;创业风险投资对创业项目不同阶段的投资从偏重创业后期逐步趋向各阶段均衡发展;信息基础设施发展水平和经济发展水平与创业风险投资之间具有较强的正相关作用;金融环境、服务业发展水平相对滞后,对创新创业成果的转化和资金吸引能力较弱。     

2003-2016年中国PM2.5质量浓度时空格局演变及影响因素解析

以中国286个主要地级市为研究区域,基于2003—2016年中国地级市大气PM_2.5质量浓度栅格数据及各地级市社会经济数据,运用空间自相关和空间面板杜宾模型,揭示了中国地级市PM_2.5质量浓度的时空格局与影响因素。研究显示：(1)中国PM_2.5污染在时序演化上呈“M”型波动态势,在空间分布上以胡焕庸线为界呈现出“东高西低”的集聚型格局;(2)中国地级市PM_2.5污染在空间效应上呈现出显著的正相关性,表明区域间大气污染存在交互影响;(3)人口密度与非农产业从业人员占比对PM_2.5质量浓度升高的贡献最大,而液化石油气供气总量与第三产业占GDP的比重对PM_2.5有较为显著的负向消减作用。     

2003—2016年中国PM2.5质量浓度时空格局演变及影响因素解析

以中国286个主要地级市为研究区域,基于2003—2016年中国地级市大气PM_2.5质量浓度栅格数据及各地级市社会经济数据,运用空间自相关和空间面板杜宾模型,揭示了中国地级市PM_2.5质量浓度的时空格局与影响因素。研究显示：(1)中国PM_2.5污染在时序演化上呈“M”型波动态势,在空间分布上以胡焕庸线为界呈现出“东高西低”的集聚型格局;(2)中国地级市PM_2.5污染在空间效应上呈现出显著的正相关性,表明区域间大气污染存在交互影响;(3)人口密度与非农产业从业人员占比对PM_2.5质量浓度升高的贡献最大,而液化石油气供气总量与第三产业占GDP的比重对PM_2.5有较为显著的负向消减作用。     

京津冀城市群旅游城镇化与影响因素研究北大核心CSCD

旅游城镇化已成为区域经济由高速发展向高质发展转型升级的重要途径,在实现区域绿色发展、协调发展等方面具有重要意义。该文基于2007-2019年面板数据定量评估京津冀城市群旅游城镇化发展进程,通过绘制折线图、雷达图和箱线图表征其时序演进特征,借助标准差椭圆、立体趋势面和核密度估计方法辨析其空间分布格局,并结合灰色关联分析探寻其关键影响因素。结论如下:2007-2019年京津冀城市群旅游城镇化指数整体呈上升趋势,虽偶有起伏但较多城市向中高水平集聚,空间上大体呈不均衡分布态势、差异较显著,最终形成西北强势、南部次之、中部弱势的分布格局;京津冀城市群各市旅游城镇化的关键影响因素存在较大差异。最后基于发展战略协同性、差异性和前瞻性原则,为京津冀城市群旅游城镇化出谋划策。     

中国城市群产业协同集聚的时空特征及影响因素

以十四五规划纲要中提及的19个中国城市群共201个城市数据为样本，运用ArcGIS空间分析模型、空间误差模型等方法对城市群2006—2018年产业协同集聚水平的时空特征进行分析，并剖析其影响因素。结果表明：(1)中国城市群第二、三产业协同集聚水平总体呈现波动下降的态势，其中第二产业区位熵呈先降后升的趋势，第三产业区位熵则持续缓慢下降。(2)中国城市群第二、三产业协同集聚空间格局保持相对稳定，城市群产业协同集聚水平较高的城市群依旧主要分布于西部地区和沿海地区，而中部地区的产业协同集聚水平相对较为落后。(3)中国城市群第二、三产业协同集聚水平存在显著的空间正相关性，总体呈现波动下降趋势，集聚态势逐渐减弱、内部差异逐渐增大。(4)运用空间误差模型进行影响因素分析，发现城镇化、政府调控和外商投资强度对中国城市群第二、三产业协同集聚水平的空间布局影响较为显著，而科技支出强度的影响不显著。(5)产业协同集聚发展需因地制宜，东部地区城市群应发挥好政府的调控作用推动城市群内部的协调分工；中部和东北地区城市群应加快提升第三产业，确保其配套服务业能适应第二产业发展；西部城市群则应加强自身城镇化建设水平，保...     

中国城市PM2.5浓度时空特征及其影响因素(英文)

在科学识别城市建成区的基础上,运用探索性空间分析和空间计量模型,分析了2000–2015年中国城市PM_2.5浓度的时空特征及其影响因素。结果表明：2000–2015年中国城市PM_2.5浓度呈倒“L”型增长,而PM_2.5浓度高的城市具有大规模集聚的特征,城市群即是PM_2.5浓度高的城市聚集区,受自然因素、社会经济因素和城市形态因素共同作用。在2000–2005年,中国城市PM_2.5年平均浓度从31.19μg m^–3增加到46.00μg m^–3,河北、山东、河南交汇地区出现小规模高浓度集聚。在2005–2010年、2010–2015年两个阶段,城市PM_2.5浓度年平均增长率放缓,2010年为47.67μg m^–3,2015年为48.72μg m^–3。高浓度集聚区域不断扩大,在2010年扩张至京津冀、长江中部、长江三角洲、成都平原,研究期末已经扩大至整个华北平原、哈长地区。     

东京都市圈大气污染防治政策对京津冀的启示

京津冀城市群与东京大都市圈不仅在疏解非首都功能、优化城市空间结构等方面存在共通之处,其区域大气污染防治过程所显现的污染源及污染物种类等特征也相似。本文以京津冀城市群和东京大都市圈作为比较研究对象,在全面介绍东京大都市圈大气污染防治的过程与成效,并系统分析大气污染防治政策路径的基础上,根据京津冀城市群大气污染特征、大气污染防治政策体系以及需要解决的问题,从政策形成、政策框架、政策实施3个方面,提出制定大气污染防治政策的建议。     

Spatio-temporal evolution and dynamic simulation of the urban resilience of Beijing-Tianjin-Hebei urban agglomeration

Journal of Geographical Sciences - The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for...     

An analysis of the differentiation rules and influencing factors of venture capital in Beijing-Tianjin-Hebei urban agglomeration

Under China’s innovation-driven development strategy, venture capital has become an important driving force in urban agglomeration integration and collaborative innovation. This paper uses social network analysis to analyze spatiotemporal differences of venture capital in the Beijing-Tianjin-Hebei urban agglomeration for the period 2005–2015. A gravity model and panel data regression model are used to reveal the influencing factors on spatiotemporal differences in venture capital in the region. This study finds that there is a certain cyclical fluctuation and uneven differentiation in the venture capital network in the Beijing- Tianjin-Hebei urban agglomeration in terms of total investment, and that the three centers of venture capital (Beijing, Shijiazhuang and Tangshan) have a stimulatory effect on surrounding cities; flows of venture capital between cities display certain networking rules, but they are slow to develop and strongly centripetal; there is a strong positive correlation between levels of information infrastructure development and economic development and venture capital investment; and places with relatively underdeveloped financial environments and service industries are less able to apply the fruits of innovation and entrepreneurship and to attract funds. This study can act as a reference for the Beijing-Tianjin-Hebei urban agglomeration in building a world-class super urban agglomeration with the best innovation capabilities in China.     

京津冀城市物流联系时空演变及驱动因素研究

基于2005年、2010年和2017年三期数据,采用修正引力模型、社会网络分析法解析京津冀城市物流联系时空演变,并剖析城市物流联系强度的驱动因素。得出如下结论:(1)2005—2017年物流联系强度增长经历了快速到缓慢的过程,物流功能疏解降低了北京的增速,河北省各城市增长显著,京津两城市物流联系强度占比虽持续降低,但物流联系量集中于京津两地的格局没有发生根本改变;(2)京津冀城市物流网络结构日趋复杂,京津两城市网络资源控制力下降,河北各城市网络参与度与资源控制力大幅提升,逐渐形成以北京、天津、石家庄为中心的多核心发展态势;(3)城市基础设施建设、对外开放水平和物流专业化程度是京津冀物流联系的持续驱动,研究期内京津冀城市物流联系的驱动力由基础设施及物流专业化建设驱动,向以外向型为主的工业化驱动转变,进而向以出口加内需消费的双驱动转换。     

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

High concentrations of PM_2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_2.5 concentrations for regional air quality control and management. In this study, PM_2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM_2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m³. (2) PM_2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_2.5. (3) The center of gravity of PM_2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_2.5 concentrations have moved eastward, while low-value PM_2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM_2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM_2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_2.5 concentration in China.

     

Driving factors and spatiotemporal effects of environmental stress in urban agglomeration: Evidence from the Beijing–Tianjin–Hebei region of China

Environmental stress is used as an indicator of the overall pressure on regional environmental systems caused by the output of various pollutants as a result of human activities. Based on the pollutant emissions and socioeconomic databases of the counties in Beijing–Tianjin–Hebei region, this paper comprehensively calculates the environmental stress index(ESI) for the urban agglomeration using the entropy weight method(EWM) at the county scale and analyzes the spatiotemporal patterns and the differences among the four types of major functional zones(MFZ) for the period 2012–2016. In addition, the socioeconomic driving forces of environmental stress are quantitatively estimated using the geographically weighted regression(GWR) method based on the STIRPAT model framework. The results show that:(1) The level of environmental stress in the Beijing–Tianjin–Hebei region was significantly alleviated during that time period, with a decrease in ESI of 54.68% by 2016. This decrease was most significant in Beijing, Tangshan, Tianjin, Shijiazhuang, and other central urban areas, as well as the Binhai New Area. The level of environmental stress in counties decreased gradually from the central urban areas to the suburban areas, and the high-level stress counties were eliminated by 2016.(2) The spatial spillover effect of environmental stress increased further at the county scale from 2012 to 2016, and spatial locking and path dependence emerged in the cities of Tangshan and Tianjin.(3) Urbanized zones(development-optimized and development-prioritized zones) were the major areas bearing environmental pollution in the Beijing–Tianjin–Hebei region in that time period. The ESI accounted for 65.98% of the whole region, where there was a need to focus on the prevention and control of environmental pollution.(4) The driving factors of environmental stress at the county scale included population size and the level of economic development. In addition, the technical capacity of environmental waste disposal, the intensity of agricultural production input, the intensity of territorial development, and the level of urbanization also had a certain degree of influence.(5) There was spatial heterogeneity in the effects of the various driving factors on the level of environmental stress. Thus, it was necessary to adopt differentiated environmental governance and reduction countermeasures in respect of emission sources, according to the intensity and spatiotemporal differences in the driving forces in order to improve the accuracy and adaptability of environmental collaborative control in the Beijing–Tianjin–Hebei region.     

时空因素对中国城市火灾态势变化的影响

基于空间动态面板数据模型,对2000～2009年中国市域城市火灾数据进行分析,探讨经济发展与气候变化共同作用下时空因素对中国城市火灾态势变化的宏观影响。结果显示:气候变干促使火灾恶化,而经济发展扭转了这种趋势并促使火灾态势整体改善。时空因素对中国城市火灾态势变化具有显著影响,并可被引申为火灾同化效应、火灾惯性效应、火灾警示效应。火灾安全管理部门应该充分利用这些效应,采取积极措施改善火灾态势。本文补充了城市地理学在城市火灾领域的研究不足;将抽象的时空因素引申为具有实际物理含义的虚拟变量,对相关研究具有借鉴意义。     

Spatiotemporal evolution of PM2.5 concentrations in urban agglomerations of China

As the main form of new urbanization in China,urban agglomerations are an im-portant platform to support national economic growth,promote coordinated regional devel-opment,and participate in international competition and cooperation.However,they have become core areas for air pollution.This study used PM2.5 data from NASA atmospheric re-mote sensing image inversion from 2000 to 2015 and spatial analysis including a spatial Durbin model to reveal the spatio-temporal evolution characteristics and main factors con-trolling PM2.5 in China's urban agglomerations.The main conclusions are as follows:(1)From 2000 to 2015,the PM2.5 concentrations of China's urban agglomerations showed a growing trend with some volatility.In 2007,there was an inflection point.The number of low-concentration cities decreased,while the number of high-concentration cities increased.(2)The concentrations of PM2.5 in urban agglomerations were high in the west and low in the east,with the"Hu Line"as the boundary.The spatial differences were significant and in-creasing.The concentration of PM2.5 grew faster in urban agglomerations in the eastern and northeastern regions.(3)The urban agglomeration of PM2.5 had significant spatial concentra-tions.The hot spots were concentrated to the east of the Hu Line,and the number of hot-spot cities continued to rise.The cold spots were concentrated to the west of the Hu Line,and the number of cold-spot cities continued to decline.(4)There was a significant spatial spillover effect of PM2.5 pollution among cities within urban agglomerations.The main factors control-ling PM2.5 pollution in different urban agglomerations had significant differences.Industriali-zation and energy consumption had a significant positive impact on PM2.5 pollution.Foreign direct investment had a significant negative impact on PM2.5 pollution in the southeast coastal and border urban agglomerations.Population density had a significant positive impact on PM2.5 pollution in a particular region,but this had the opposite effect in neighboring areas.Urbanization rate had a negative impact on PM2.5 pollution in national-level urban agglomer-ations,but this had the opposite effect in regional and local urban agglomerations.A high degree of industrial structure had a significant negative impact on PM2.5 pollution in a region,but this had an opposite effect in neighboring regions.Technical support level had a signifi-cant impact on PM2.5 pollution,but there were lag effects and rebound effects.