基于能源消费的中国省级区域碳足迹时空演变分析

碳足迹作为衡量生产某一产品在其生命周期所直接或间接排放的CO₂量,其能够反应人类某项活动或某种产品对生态环境的压力程度。本文采用1997-2008年全国省级区域化石能源消费数据和土地利用结构数据,构建碳足迹计算模型,测算不同时间、不同区域的碳足迹、碳生态承载力和碳赤字,并引入物理学中重心的概念,测算1997-2008年全国各省级区域碳足迹的重心,进行碳足迹重心的时空演变趋势分析,掌握区域间能源消费碳排放的差异性;同时构建能源消费碳足迹压力指数模型,计算1997-2008年各省的碳足迹压力指数,对研究区域进行生态压力强度分级,并考察各省级区域碳足迹压力指数在两个相邻时间点之间的变化强度,进行生态压力变化强度的级别划分。     

中国城市网络关联格局的演变及影响因素：基于企业网络视角

基于企业网络视角,利用隶属网络模型,研究中国城市网络关联格局的演变,利用QAP模型识别影响因素,结果发现：（1）网络整体呈现扁平化特征,城市网络在越来越扁平化的同时等级性并没有消失;（2）从网络联系趋势上看,城市之间联系越来越紧密,网络规模越来越大,但总体联系仍比较松散,且东部发育完善,中西部发育不完善,东部城市对其他地区城市的辐射作用越来越强;（3）我国城市网络存在典型的“小世界”效应且“小世界”效应越来越明显;（4）通过凝聚子群分析,发现子群数目越来越多,但是只有少数城市出现在子群中,说明联系大部分发生在核心城市之间;（5）城市规模、城市资源和生产成本对城市网络关联格局的形成有显著影响,城市规模、城市资源差距越小和生产成本差距越大的城市之间越容易发生联系,并且城市网络的发展深受历史因素的影响。     

基于企业并购联系视角的中国城市网络演变研究

以企业并购数据构建企业网络,基于企业之间实际经济联系视角,采用社会网络分析法研究2009年、2012年、2015年和2018年中国城市网络结构及其演化。结果表明：（1）由跨城市并购数据所构建的中国城市网络复杂程度急剧加深,但仍处于低水平-弱连结的分布态势。（2）城市节点遵从典型的长尾分布和幂律分布,“富人俱乐部”现象显著。城市分布格局经历了以京津冀、长三角、珠三角及成渝城市群四区组团的跳跃式分布格局到“大”字型带状分布格局再到“T”字型沿海沿江发展格局的演变,以胡焕庸线为界东西差异显著。此外,并购频次排名前30位的城市大部分为省会城市、直辖市、计划单列市及一些经济强市。（3）列入研究范围的城市其表现类型初期往往为主并型或被并型,后期趋向均衡型发展,且网络中城市的表现类型发生不同形式的变化。（4）以京津冀、长三角、珠三角、成渝城市群地区形成以三角结构为核心的城市网络菱形骨架格局逐渐得到夯实,城市网络由等级联系特征向等级性与空间近邻性并存转变。     

中国入境旅游碳足迹时空特征与演化研究

据统计,旅游碳排放已占全球碳排放总量的4.9%,加强对其研究和控制是关乎人类能否可持续发展的重要命题。本文基于地理学的视角,研究了中国2007年到2017年间30个省、市、自治区入境旅游碳足迹时空分布的特征和演化规律。在利用碳足迹综合计算模型和空间分析方法基础上,深入揭示了中国入境旅游碳足迹的时空分布特征及演化规律。结果表明,2007年到2017年间,中国入境旅游碳足迹呈现急速上升又稍有回落的趋势,总量从562.30万t上升到1088.09万t,增长1.94倍,其中交通和邮电业占比最大;近十年来我国多数省市的入境旅游碳足迹变异程度不高,维持在较平稳的状态;空间维度上,则呈现东南向西北方向递减趋势。     

基于能源消费的江苏省土地利用碳排放与碳足迹

采用2003~2007年江苏省能源消费和土地利用等数据,通过构建能源消费的碳排放模型,对江苏省5年来能源消费碳排放进行了核算,并通过土地利用类型和碳排放项目的对应,对不同土地利用方式的碳排放及碳足迹进行了定量分析。结论如下:(1)江苏省能源消费碳排放总量从2003年的8794.24万t上升到2007年的16329.85万t,涨幅达86%。其中,终端能源消费碳排放占53.6%。(2)江苏全省土地单位面积碳排放从2003年8.24t/hm²上升到2007年15.53 t/hm²,增幅为88.5%。其中,居民点及工矿用地单位面积碳排放最大,为95.62 t/hm²。(3)江苏全省能源消费碳足迹大于生产性土地的实际面积,由此造成的生态赤字达1351.285万hm²。(4)不同土地利用类型的碳足迹大小顺序为:居民点及工矿用地＞交通用地＞未利用地及特殊用地＞农用地和水利用地,其中居民点及工矿用地的碳足迹高达10.89 hm²/ hm²。(5)江苏全省单位面积碳足迹也呈明显的扩大趋势,从2003年的0.938 hm²/ hm²上升到2007年的1.769 hm²/ hm²。     

中国不同产业空间的碳排放强度与碳足迹分析

采用2007 年中国各省区不同产业各种能源消费等数据,通过构建能源消费碳排放和碳足迹模型,对各省区化石能源和农村生物质能源的碳排放量进行了估算;建立了不同产业空间与能源消费碳排放的对应关系,将产业活动空间分为农业空间、生活与工商业空间、交通产业空间、渔业与水利业空间、其他产业空间等五大类;对各省区不同产业空间碳排放强度和碳足迹进行了对比分析。主要结论如下：(1) 中国2007 年能源消费碳排放总量为1.65 GtC,其中化石能源碳排放占89%;(2) 2007 年中国产业空间碳排放强度为1.98 t/hm²,其中,生活及工商业空间、交通产业空间的碳排放强度较高,分别为55.16 t/hm²和49.65 t/hm²;(3) 2007 年中国产业空间碳足迹为522.34×10⁶ hm²,由此造成的生态赤字为28.69×10⁶ hm²,这说明我国的生产性土地面积不足以补偿产业空间的碳排放,补偿率约为94.5%。各地区碳足迹差异明显,不少省份甚至存在生态盈余。总体而言,从产业活动空间的角度来看,中国目前的碳赤字不大;(4) 全国产业空间单位面积碳足迹为0.63 hm²/hm²,其中生活与工商业空间的碳足迹最大,为17.5 hm²/hm²。不同产业空间单位面积碳足迹大都呈现从东到西逐渐下降的趋势。     

中国社会文化地理学研究的发展与反思：基于2015—2020年中英文文献分析

作为一个在中国发轫较晚的人文地理学分支,社会文化地理学逐渐成为人文地理学的重要分支。随着研究话题的日新月异以及研究方法和理论的推陈出新,中国社会文化地理学的理论和应用研究在近年（2015—2020年）得到越来越多学者的关注和参与,同时涌现出一系列新的学科现象和值得反思的问题,如话题的多样性、研究方法的交叉性、研究科学性的反思和质疑等。因此,从延续学科自省的视角出发,有必要坚持从中、西对比的角度,不断度量中国社会文化地理学的发展趋势,定位学科的特点,以期为国内社会文化地理研究提供一定的方向和启示。借助文献分析工具对2015—2020年中、英文领域主要学术期刊的社会文化地理学文献进行采集和分析,辅以德尔菲法对学科专家进行咨询,尝试结合客观和主观数据,综合辨析当前中外社会文化地理学的研究特色,以及未来中国社会文化地理学研究可能的发展方向和潜力所在。从分析结果上看,国外社会文化地理研究话题涵盖：1）从移民研究到流动性研究;2）从文化景观研究到超越人类的地理学研究;3）弱势人群研究与关怀地理学等方面。中国社会文化地理研究话题包括：1）理论引介与反思;2）城镇化、流动性与多元移民;3）旅游与地方;4）乡村转型与乡愁等方面。综合国内外社会文化地理研究成果可知,西方国家（尤其是英国）作为社会文化地理学的重要发源地,在学科知识“生产—消费”循环中保持优势地位;西方社会文化地理学研究重点关注国际大政治和日常政治话题,而中国社会文化地理研究与旅游和乡村话题结合紧密,与西方形成明显区别,反映中国社会发展需要;中国社会文化地理学国际化趋势明显,但中、西方知识交换程度不高,知识边界依然明显。另外,专家咨询意见也凸显社会文化地理研究者的学科归属感有待加强、研究范式系统化不足与共识不充分等问题。未来中国社会文化地理学需要不断推进学科交叉和方法创新,探索其地理学本质,在国际化进程中讲好中国故事,总结中国经验,发展中国理论,服务国家,对话世界。     

中国"互联网+"发展的空间关联网络及其影响因素——基于社会网络分析视角的实证研究

借助社会网络分析方法研究“互联网+”发展的空间关联网络特征,并利用QAP方法探究中国“互联网+”空间关联网络的影响因素。研究发现,“互联网+”发展的空间关联呈现显著网络特征,可划分为“净溢出”“经纪人”“主受益”以及“双向溢出”四大类型板块,并且板块内部具有较明显的“等级”属性。技术创新、基础设施、人力资本、市场发展、对外开放对“互联网+”发展的空间关联网络存在正向影响。地理距离对“互联网+”空间关联网络存在抑制作用,随着地理距离的不断增大,“互联网+”的知识溢出和流动效应逐步衰减。     

Urban carbon footprint and carbon cycle pressure: The case study of Nanjing简

Urban carbon footprint reflects the impact and pressure of human activities on ur- ban environment. Based on city level, this paper estimated carbon emissions and carbon footprint of Nanjing city, analyzed urban carbon footprint intensity and carbon cycle pressure and discussed the influencing factors of carbon footprint through LMDI decomposition model. The main conclusions are as follows： （1） The total carbon emissions of Nanjing increased rapidly since 2000, in which the carbon emission from the use of fossil energy was the largest Meanwhile, carbon sinks of Nanjing presented a declining trend since 2000, which caused the decrease of carbon compensation rate and the increase of urban carbon cycle pressure. （2） The total carbon footprint of Nanjing increased rapidly since 2000, and the carbon deficit was more than ten times of total land areas of Nanjing in 2009, which means Nanjing confronted high carbon cycle pressure. （3） Generally, carbon footprint intensity of Nanjing was on de- crease and the carbon footprint productivity was on increase. This indicated that energy utilization rate and carbon efficiency of Nanjing was improved since 2000, and the policy for energy conservation and emission reduction taken by Nanjing＇s government received better effects. （4） Economic development, population and industrial structure are promoting factors for the increase of carbon footprint of Nanjing, while the industrial carbon footprint intensity was inhibitory factor. （5） Several countermeasures should be taken to decrease urban carbon footprint and alleviate carbon cycle pressure, such as： improvement of the energy efficiency, industrial structure reconstruction, afforestation and environmental protection and land use control. Generally, transition to low-carbon economy is essential for Chinese cities to realize sustainable development in the future.     

中国不同区域能源消费碳足迹的时空变化(英文)

Study on regional carbon emission is one of the hot topics under the background of global climate change and low-carbon economic development, and also help to establish different low-carbon strategies for different regions. On the basis of energy consumption and land use data of different regions in China from 1999 to 2008, this paper established carbon emission and carbon footprint models based on total energy consumption, and calculated the amount of carbon emissions and carbon footprint in different regions of China from 1999 to 2008. The author also analyzed carbon emission density and per unit area carbon footprint for each region. Finally, advices for decreasing carbon footprint were put forward. The main conclusions are as follows: (1) Carbon emissions from total energy consumption increased 129% from 1999 to 2008 in China, but its spatial distribution pattern among different regions just slightly changed, the sorting of carbon emission amount was: Eastern China > Northern China > Central and Southern China > Southwest China > Northwest China. (2) The sorting of carbon emission density was: Eastern China > Northeast China > Central and Southern China > Northern China > Southwest China > Northwest China from 1999 to 2003, but from 2004 Central and Southern China began to have higher carbon emission density than Northeast China, the order of other regions did not change. (3) Carbon footprint increased significantly since the rapid increasing of carbon emissions and less increasing area of pro-ductive land in different regions of China from 1999 to 2008. Northern China had the largest carbon footprint, and Northwest China, Eastern China, Northern China, Central and Southern China followed in turn, while Southwest China presented the lowest area of carbon footprint and the highest percentage of carbon absorption. (4) Mainly influenced by regional land area, Northern China presented the highest per unit area carbon footprint and followed by Eastern China, and Northeast China; Central and Southern China, and Northwest China had a similar medium per unit area carbon footprint; Southwest China always had the lowest per unit area carbon footprint. (5) China faced great ecological pressure brought by carbon emission. Some measures should be taken both from reducing carbon emission and increasing carbon absorption.     

Temporospatial changes of carbon footprint based on energy consumption in China

Study on regional carbon emission is one of the hot topics under the background of global climate change and low-carbon economic development, and also help to establish different low-carbon strategies for different regions. On the basis of energy consumption and land use data of different regions in China from 1999 to 2008, this paper established carbon emission and carbon footprint models based on total energy consumption, and calculated the amount of carbon emissions and carbon footprint in different regions of China from 1999 to 2008. The author also analyzed carbon emission density and per unit area carbon footprint for each region. Finally, advices for decreasing carbon footprint were put forward. The main conclusions are as follows: (1) Carbon emissions from total energy consumption increased 129% from 1999 to 2008 in China, but its spatial distribution pattern among different regions just slightly changed, the sorting of carbon emission amount was: Eastern China > Northern China > Central and Southern China > Southwest China > Northwest China. (2) The sorting of carbon emission density was: Eastern China > Northeast China > Central and Southern China > Northern China > Southwest China > Northwest China from 1999 to 2003, but from 2004 Central and Southern China began to have higher carbon emission density than Northeast China, the order of other regions did not change. (3) Carbon footprint increased significantly since the rapid increasing of carbon emissions and less increasing area of productive land in different regions of China from 1999 to 2008. Northern China had the largest carbon footprint, and Northwest China, Eastern China, Northern China, Central and Southern China followed in turn, while Southwest China presented the lowest area of carbon footprint and the highest percentage of carbon absorption. (4) Mainly influenced by regional land area, Northern China presented the highest per unit area carbon footprint and followed by Eastern China, and Northeast China; Central and Southern China, and Northwest China had a similar medium per unit area carbon footprint; Southwest China always had the lowest per unit area carbon footprint. (5) China faced great ecological pressure brought by carbon emission. Some measures should be taken both from reducing carbon emission and increasing carbon absorption.     

新疆旅游经济网络特征的时空演变研究——基于修正的引力模型及社会网络分析

通过构建"旅游综合质量"评价指标,继而将引力模型进行修正以衡量各区域间旅游经济联系度并运用社会网络分析方法探究了2008—2017年新疆15个地州旅游经济的空间网络特征。结果表明:(1) 2008—2017年间,新疆旅游经济关联网络密度的均值仅为0.356、网络效率均值为0.718、网络等级度均值为0.367。(2) 10 a间,乌鲁木齐市、伊犁州直属、喀什地区、昌吉州、吐鲁番市等地属于度数中心度与中间中心度双高区域;阿勒泰地区、巴州等地属于度数中心度较高、中间中心度较低区域;克拉玛依市属于度数中心度较低,中间中心度较高区域;博州、和田地区、哈密地区、塔城地区、克州、阿克苏地区、石河子市等地属于度数中心度、中间中心度双低区域。(3)乌鲁木齐市、昌吉州、喀什地区、伊犁州直属、阿勒泰地区等地在研究时限内属于"双向溢出板块";石河子市、克拉玛依市、巴州、吐鲁番市属于"经纪人板块";博州、哈密地区、塔城地区属于"净受益板块";阿克苏地区、克州、和田地区属于"主受益板块"。本文旨在丰富旅游经济网络研究视角,同时为新疆各地州旅游经济发展与合作提供量化依据。     

电商快递包装箱的碳足迹空间分解和隐含碳转移研究

伴随电子商务等新兴消费业态的迅猛发展,其碳排放量已不容忽视。本研究从空间分解和隐含碳转移的角度,基于对电商快递包装箱全生命周期的碳足迹研究,识别其在原料、生产、利用各环节与电商行为的地理空间耦合,分析各阶段及总排放的省域尺度格局特征,以及伴随快递流的隐含碳转移网络格局。研究发现：原料阶段的碳排放多在木材原料商区位,生产阶段的碳排放和快递发货区位高度耦合,利用阶段的碳排放和快递收货区位相耦合。全国电商快递包装箱各阶段的碳排放在空间上高度集聚,原料阶段集中在广西,生产和利用阶段集中在广东、浙江、江苏,整体呈现出“东多西少、南多北少”的空间特征。碳排放总量较高的省区多由生产驱动,碳排放总量越少的省区则利用驱动越明显。隐含碳转移网络呈现出“少数省区净流出、多数省区净流入”的“轴辐式”结构特征,浙江、广东两省承担了80%左右的净流出,是绝大多数省区隐含碳流入的最大来源地,北京是净流入最大的省区。基于隐含碳转移进行碳排放责任辨析,是相关决策需要考虑的重要因素;绿色包装的减排贡献较大,亟待寻求技术突破;新兴消费业态的碳排放值得长期关注。     

Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China

Global warming has been one of the major concerns behind the world’s high-speed economic growth. How to implement the coordinated development of the carbon footprint and the economy will be the core issue of the world’s economic and social development, as well as the heated debate of the research at home and abroad in recent years. Based on the energy consumption, integrated with the “Top-Down” life cycle approach and geographically weighted regression (GWR) model, this paper analyzed the spatial differences and multi-mechanism of carbon footprint in provincial China in 2010. Firstly, this study calculated the amount of carbon footprint of each province using “Top-Down” life cycle approach and found that there were significant differences of carbon footprint and per capita carbon footprint in provincial China. The provinces with higher carbon footprint, mainly located in northern China, have large economic scales; the provinces with higher per capita carbon footprint are mainly distributed in central cities such as Beijing, Shanghai and energy-rich regions and heavy chemical bases. Secondly, with the aid of GIS and spatial analysis model (GWR model), this paper had unfolded that the expansion of economic scale is the main driver of the rapid growth of carbon footprint. The growth of population and urbanization also acted as promoting factors for the increase of the carbon footprint. Energy structure had no considerable promoting effect for the increase of the carbon footprint. Improving energy efficiency is the most important factor to inhibit the growing carbon footprint. Thirdly, developing low-carbon economies and low-carbon industries, as well as advocating low-carbon city construction and improving carbon efficiency would be the primary approaches to inhibit the rapid growth of carbon footprint. Moderately controlling the economic scale and population size would also be required to alleviate carbon footprint. Meanwhile, environmental protection and construction of low-carbon cities would evoke extensive attention in the process of urbanization.     

Quality assessment of building footprint data using a deep autoencoder network

Volunteered geographic information (VGI), OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated. However, the quality of VGI remains a valid concern. In this paper, we use the matched results between OSM building footprints and official data as the samples for training an autoencoder network, which encodes and reconstructs the sample populations according to unknown complex multivariate probability distributions. Then, the OSM data are assessed based on the theory that small probability samples contribute little to the autoencoder network and that they can be recognized by the higher reconstructed errors during training. In the method described here, the selected measures, including data completeness, positional accuracy, shape accuracy, semantic accuracy and orientation consistency between OSM and official data, are used as the inputs for a deep autoencoder network. Finally, building footprint data from Toronto, Canada, are evaluated, and experiments show that the proposed method can assess the OSM data comprehensively, objectively and accurately.     

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association(LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China's economic growth and carbon emission load will continue for some time.