基于改进场模型的中国中部地区城市影响范围研究

Due to unique advantages in clearly understanding the interrelationship between city and its hinterland, as well as city and city, the study of urban spheres of influence is becoming highlight in regional research. This paper improves traditional field model from two aspects: the composite indicator and regional accessibility, in order to delineate urban spheres of influence more reasonably. Taking three years of central China as a case study, this paper investigates dynamic evolution of urban spheres of influence. Focusing on the evolution of spatial pattern, we abstract five types and its corresponding three stages theoretically. Finally, recommendation of development has been made for each stage. This study undertakes certain exploration in the study of urban spheres of influence from the perspective of theory and practice, hoping to provide some references for the study in this field and other regional research.     

中国能源消费碳排放的空间计量分析(英文)

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 con-sumption,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 in-creased 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.     

基于能源消费的中国不同产业空间的碳足迹分析

Using energy consumption and land use data of each region of China in 2007,this paper established carbon emission and carbon footprint model based on energy consumption,and estimated the carbon emission amount of fossil energy and rural biomass energy of dif-ferent regions of China in 2007.Through matching the energy consumption items with indus-trial spaces,this paper divided industrial spaces into five types:agricultural space,living & industrial-commercial space,transportation industrial space,fishery and water conservancy space,and other industrial space.Then the author analyzed the carbon emission intensity and carbon footprint of each industrial space.Finally,advices of decreasing industrial carbon footprint and optimizing industrial space pattern were put forward.The main conclusions are as following:(1) Total amount of carbon emission from energy consumption of China in 2007 was about 1.65 GtC,in which the proportion of carbon emission from fossil energy was 89%.(2) Carbon emission intensity of industrial space of China in 2007 was 1.98 t/hm2,in which,carbon emission intensity of living & industrial-commercial space and of transportation in-dustrial space was 55.16 t/hm2 and 49.65 t/hm2 respectively,they were high-carbon-emission industrial spaces among others.(3) Carbon footprint caused by industrial activities of China in 2007 was 522.34 106 hm2,which brought about ecological deficit of 28.69 106 hm2,which means that the productive lands were not sufficient to compensate for carbon footprint of industrial activities,and the compensating rate was 94.5%.As to the regional carbon footprint,several regions have ecological profit while others have not.In general,the present ecologi-cal deficit caused by industrial activities was small in 2007.(4) Per unit area carbon footprint of industrial space in China was about 0.63 hm2/hm2 in 2007,in which that of living & indus-trial-commercial space was the highest (17.5 hm2/hm2).The per unit area carbon footprint of different industrial spaces all presented a declining trend from east to west of China.     

近30年中国东北地区玉米种植体系的时空动态分析(英文)

Understanding crop patterns and their changes on regional scale is a critical requirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model(SPAM) has been developed for presenting spatio-temporal dynamics of maize cropping system in Northeast China during 1980–2010. The simulated results indicated that(1) maize sown area expanded northwards to 48°N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation(less than 100 m) as well as higher elevation(mainly distributed between 200 m and 350 m);(2) maize yield has been greatly promoted for most planted area of Northeast China, especially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region;(3) maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.     

中国城市群分布及范围的定量评价(英文)

Urban clusters are the expected products of high levels of industry and urbanization in a country, as well as being the basic units of participation in global competition. With respect to China, urban clusters are regarded as the dominant formation for boosting the Chinese urbanization process. However, to date, there is no coincident, efficient, and credible methodological system and set of techniques to identify Chinese urban clusters. This research investigates the potential of a computerized identification method supported by geographic information techniques to provide a better understanding of the distribution of Chinese urban clusters. The identification method is executed based on a geographic information database, a digital elevation model, and socio-economic data with the aid of ArcInfo Macro Language programming. In the method, preliminary boundaries are identified accord-ing to transportation accessibility, and final identifications are achieved from limiting city numbers, population, and GDP in a region with the aid of the rasterized socio-economic dataset. The results show that the method identifies nine Chinese urban clusters, i.e., Pearl River Delta, Lower Yangtze River Valley, Beijing-Tianjin-Hebei Region, Northeast China Plain, Middle Yangtze River Valley, Central China Plains, Western Taiwan Strait, Guanzhong and Chengdu-Chongqing urban clusters. This research represents the first study involving the computerized identification of Chinese urban clusters. Moreover, compared to other related studies, the study’s approach, which combines transportation accessibility and socio-economic characteristics, is shown to be a distinct, effective and reliable way of identifying urban clusters.     

中国城市碳排放强度的空间溢出效应及驱动因素探究（英文）

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.     

基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测

由CO₂排放所引起的气候变化是当今社会所关注的热点话题,提高碳排放绩效是碳减排的重要途径。目前关于碳排放绩效的研究多从国家尺度和行业尺度进行探讨,由于能源消耗统计数据有限,缺乏城市尺度的研究。基于遥感模拟反演的1992—2013年中国各城市碳排放数据,采用超效率SBM模型对城市碳排放绩效进行测定,构建马尔可夫和空间马尔可夫概率转移矩阵,首次从城市尺度探讨了中国碳排放绩效的时空动态演变特征,并预测其长期演变的趋势。研究表明,中国城市碳排放绩效均值呈现波动中稳定上升的趋势,但整体仍处于较低的水平,未来城市碳排放绩效仍具有较大的提升空间,节能减排潜力大;全国城市碳排放绩效空间格局呈现“南高北低”特征,城市间碳排放绩效水平的差异性显著;空间马尔科夫概率转移矩阵结果显示,中国城市碳排放绩效类型转移具有稳定性,且存在“俱乐部收敛”现象,地理背景在中国城市碳排放绩效类型转移过程中发挥重要作用;从长期演变的趋势预测来看,中国碳排放绩效未来演变较为乐观,碳排放绩效随时间的推移而逐步提升,碳排放绩效分布呈现向高值集中的趋势。因此未来中国应继续加大节能减排力度以提高城市碳排放绩效,实现国家节能减排目标;同时不同地理背景的邻域城市之间应建立完善的经济合作联动机制,以此提升城市碳排放绩效水平并追求经济增长与节能减排之间协调发展,从而实现低碳城市建设和可持续发展。     

1978-2016年中国农业生态效率时空演变及趋势预测

基于1978-2016年中国各省市面板数据,采用超效率SBM模型测算省际农业生态效率,在时间序列分析和空间相关性分析的基础上,构建传统和空间马尔可夫概率转移矩阵,探讨中国农业生态效率的时空动态演变特征,并预测其长期演变的趋势。研究发现：① 中国农业生态效率呈现出在波动中稳定上升的“双峰”分布特征,且波峰高度的差距在缩小,但整体仍处于较低水平,农业生态效率仍存在较大提升空间,东部地区农业生态效率提升较中西部地区更加显著;② 中国农业生态效率整体上向高水平方向转移的趋势显著,但农业生态效率的演变具有维持原有状态的稳定性,且较难实现跨越式转移。地理空间格局在农业生态效率时空演变过程中发挥着重要作用,空间集聚特性显著,农业生态效率较高的省市具有正向的溢出效应,而农业生态效率较低的省市具有负的溢出效应,从而在空间格局上逐渐形成“高高集聚、低低集聚、高辐射低、低抑制高”的“俱乐部收敛”现象;③ 从长期演变的趋势预测来看,多数省市农业生态效率逐渐向上转移为较高水平,并逐渐演变为由低到高渐次递增的格局,在农业生态效率较低的地理背景下,其长期演变的稳定状态表现为偏“单峰”分布,而在农业生态效率较高的地理背景下,其长期演变为较高水平集聚的偏“双峰”分布。最后,分析当前研究需要改进的方向,并提出控制农业污染排放量、地区间农业生态政策联动、加强地区间农业生态合作交流与借鉴等能够有效提升中国农业生态效率及缩小省市间差距。     

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.     

西北地区居民生活碳排放现状分析及预测

我国西北地区经济发展、居民生活水平和碳排放均低于全国平均水平,随着国家政策倾斜,居民生活条件逐步改善,居民生活碳排放量提升空间较大,排放格局将受影响,这对西北地区本就脆弱的生态环境更加不利。目前有关居民碳排放的研究多集中在人类活动频繁、碳排放量大的我国东、南部地区,较少关注西北地区,但碳排放增加、环境成本加重对于欠发达地区的影响更加深远。其次,研究者关注居民碳排放预测时,通常着眼于数量预测,忽视了空间格局预测,不利于区域间协同发展。基于1997—2016年西北五省居民能源消耗和消费支出数据,首先利用直接系数法和投入产出法测算了1997—2016年西北地区居民生活碳排放,对其现状进行分析;第二,基于标准差椭圆和ARIMA模型从数量和空间格局上对2017—2021年西北居民生活碳排放进行预测。结果表明:1997—2016年,西北居民生活碳排放呈先缓慢后快速的上升趋势。直接碳排放稳定在0.3~0.4×10^8 t;间接碳排放达到2.38×10^8 t;空间分布总体稳定,呈西北—东南分布,移动趋势为西北—东南—西北,标准差椭圆中心在(99.07°E,38.19°N)附近移动。2017—2021年,直接碳排放达到0.543×10^8t;间接碳排放为3.631×10^8 t;主体区域沿X轴发散,Y轴收敛,旋转轴变化小,随着西部大开发和脱贫的推进,新疆排放量次于陕西,增速较快,推动碳排放主体区域向西北移动。旨在为实现西北地区人口、消费、环境协调发展、引导居民树立低碳消费的价值理念建言献策。     

Spatiotemporal dynamics of carbon intensity from energy consumption in China

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 spatiotemporal dynamics and dominating factors of China’s carbon intensity from energy consumption 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 agglomeration 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 econometric 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.     

中国省域二氧化碳排放的时空格局及影响因素

通过测算全国30个省域2000—2015年的二氧化碳排放量,采用自然段点法,分别对2000年、2005年、2010年和2015年各省碳排放量进行分类,分析其空间分异化特征。采用空间自相关分析法揭示了相邻各省份碳排放量的空间关联性。在此基础上,运用对数均值迪氏分解法,从能源结构、能源强度、经济发展和人口规模等角度,对碳排放影响因素进行无残差分解。结果表明：1）时间上,我国碳排放总量整体呈上升趋势,在2014—2015年仅下降2%。除北京市外,其余各省份的碳排放量呈增长趋势;空间上,高值碳排放由环渤海及东部沿海省份逐步蔓延至中西部个别省份;2）各省域碳排放主要呈现高高集聚和低高集聚的特征,高高集聚稳定集中在辽宁、河北、山东、山西和江苏省,北京市和天津市与高碳排放的省份形成一个低高集聚区域;3）东、中部比西部省份更易受能源结构、能源强度、经济发展和人口规模等因素的影响。经济发展对碳排放是驱动作用,能源强度对碳排放是抑制作用,能源结构对各省份碳排放的影响有正向驱动和负向抑制作用,除贵州省外,其余省份人口规模对碳排放均是正向驱动作用。     

公平和效率视角的中国碳排放区域差异及其固化研究

分别从公平和效率的角度考察中国省际碳排放的区域差异,可以比较区域碳减排配额分摊中公平原则和效率原则重要性大小。论文选取1997—2015年中国29个省级地区数据,首先通过包含非期望产出的Super-0SBM模型测算出各地区的碳排放效率,并用人均碳排放量来衡量各地区的碳排放公平情况。随后基于Dagum基尼分解方法以及Markov链方法,分别从整体差异程度和内部差异固化两个方面比较了中国碳排放公平和效率的区域差异情况。研究发现：（1） 中国碳排放公平和碳排放效率呈现出不一致的空间非均衡格局。（2） 不管是整体、还是三大地区之间,中国碳排放效率的差异程度都大于碳排放公平,二者差异没有缩小的趋势。（3） 各地区间碳排放效率的差异固化程度也要高于碳排放公平,中国碳排放的区域“长期低效率”固化问题比区域“长期不公平”固化问题更严重。因此,在计算中国区域碳减排潜力以及进行碳减排配额分摊时,效率原则比公平原则更加重要。中央政府也应更加重视区域碳排放效率的差异问题,更多的从碳排放效率上入手,挖掘其存在的更大碳减排潜力。     

中国能源消费碳排放强度及其影响因素的空间计量

碳排放所引起的全球气候变化对人类经济社会发展带来了严峻的挑战。中国政府承诺到2020 年GDP碳排放强度较2005 年降低40%~45%,这一目标的实现有赖于全国层面社会经济和产业结构的实质性转型,更有赖于省区层面节能减排的具体行动。基于联合国政府间气候变化专门委员会(IPCC) 提供的方法,本文估算了全国30 个省区1997-2010 年碳排放强度,采用空间自相关分析方法和空间面板计量模型,探讨了中国省级尺度碳排放强度的时空格局特征及其主要影响因素,旨在为政府制定差异化节能减排的政策和发展低碳经济提供科学依据。研究结果表明:① 1997-2010 年,中国能能源消费CO₂排放总量从4.16 Gt 增加到11.29Gt,年均增长率为7.15%,而同期GDP年均增长率达11.72%,碳排放强度总体上呈逐年下降的态势;② 1997-2010 年,碳排放强度的Moran's I 指数呈波动型增长,说明中国能源消费碳排放强度在省区尺度上具有明显的空间集聚特征,且集聚程度有不断增强的态势,同时,碳排放强度高值集聚区和低值集聚区表现出一定程度的路径依赖或空间锁定;③ 空间面板计量模型分析结果表明,能源强度、能源结构、产业结构和城市化率对中国能源消费碳排放强度时空格局演变具有重要影响;④ 提高能源利用效率,优化能源结构和产业结构,走低碳城市化道路,以及实行节能减排省区联动策略是推动中国实现节能减排目标的重要途径。     

Spatiotemporal evolution of national development zones and their impact on urban land growth in China

Journal of Geographical Sciences - Development zones are important growth poles for promoting regional economic development. However, the spatiotemporal relationship between development zone...     

中国城市群高质量发展的时空特征及分异机理

以中国“十三五”规划纲要提及的19个城市群为研究对象,从创新、协调、绿色、开放和共享5个维度构建中国城市群高质量发展综合评价指标体系,运用熵值法、地理探测器等研究方法,剖析2006—2018年中国城市群高质量发展的时空格局特征及其分异机理。结果表明：(1)总体来看,中国城市群高质量发展水平呈增长态势,各维度发展水平与发展速度存在差异。(2)从空间格局看,总体保持“东中西”梯度递减格局,但随着中、西部部分城市群高质量发展水平稳步上升,空间梯度格局呈减弱趋势。(3)从空间集聚特征看,城市群高质量发展集聚态势逐渐增强,集聚类型中HH型主要集中在珠三角、长三角城市群;HL型出现在各城市群的核心省会城市;LH型主要位于长三角城市群高值集聚地区的周边城市;LL型主要位于成长早期与初级阶段城市群的交界处。(4)从探测因子作用力强度看,“十三五”规划中期各因子作用强度排序为收入水平、人口密度、教育水平、政府调控和投资强度。其中,投资强度作用力于“十二五”时期下滑趋势明显,说明城市群高质量发展依赖投资驱动的作用减弱。此外,不同发育阶段的城市群高质量发展的主导因素在不同时期也发生变化。(5)针对不同发育阶...