基于居民出行行为的北京市交通碳排放影响机理

近年来低碳城市逐渐成为众多学科关注的焦点所在,国内外学者从多个角度对其开展一系列的理论与实证研究,但从微观层面深入探讨城市交通碳排放的影响机理的研究相对较为缺乏。利用北京市居民活动日志调查获取的第一手数据,基于居民日常出行行为计算微观层面的城市交通碳排放,并采用结构方程模型深入挖掘居住空间、个体行为以及交通碳排放三者之间的内在关系。结果表明,出行距离、机动出行概率对交通碳排放产生显著的正效应,而出行频率的影响并不显著;同时,出行结构的影响要远远大于出行总量的影响。另外,城市空间结构对城市交通碳排放产生显著影响,单位社区居民的出行行为整体具有“低碳”性质,应从低碳视角对单位社区进行重新审视。     

居民家庭日常出行碳排放的发生机制与调控策略——以北京市为例

根据2007年北京市居民活动日志调查数据,利用Amos7.0软件建立单效标因素的路径分析模型,试图在"社区-家庭"层面上挖掘"空间利用-出行特征-碳排放"的内在发生机理,藉此寻找城市空间组织低碳化的调控路径。研究发现:影响居民家庭日常出行碳排放的主要因素是出行距离和出行方式。社区空间利用特征对家庭出行的距离总量有显著影响,对小汽车出行比率则没有明显作用效果;私家车的购置对居民家庭出行行为的高碳化具有不可逆的作用特点;在现有设施条件、空间环境和车辆使用政策下,公共交通对私人交通出行没有替代性。研究认为,城市空间组织和调控优化应通过土地混合利用、设施供给等物质空间组织与再组织手段,形成空间行为组织和行为规划策略,引导居民降低交通发生量,优化居民交通发生的时空结构,建构低碳的城市空间结构。     

Multi-scale integrated assessment of urban energy use and CO2 emissions

Accurate and detailed accounting of energy-induced carbon dioxide (CO₂) emissions is crucial to the evaluation of pressures on natural resources and the environment, as well as to the assignment of responsibility for emission reductions. However, previous emission inventories were usually production- or consumption-based accounting, and few studies have comprehensively documented the linkages among socio-economic activities and external transaction in urban areas. Therefore, we address this gap in proposing an analytical framework and accounting system with three dimensions of boundaries to comprehensively assess urban energy use and related CO₂ emissions. The analytical framework depicted the input, transformation, transfer and discharge process of the carbon-based (fossil) energy flows through the complex urban ecosystems, and defined the accounting scopes and boundaries on the strength of ‘carbon footprint’ and ‘urban metabolism’. The accounting system highlighted the assessment for the transfer and discharge of socio-economic subsystems with different spatial boundaries. Three kinds methods applied to Beijing City explicitly exhibited the accounting characteristics. Our research firstly suggests that urban carbon-based energy metabolism can be used to analyze the process and structure of urban energy consumption and CO₂ emissions. Secondly, three kinds of accounting methods use different benchmarks to estimate urban energy use and CO₂ emissions with their distinct strength and weakness. Thirdly, the empirical analysis in Beijing City demonstrate that the three kinds of methods are complementary and give different insights to discuss urban energy-induced CO₂ emissions reduction. We deduce a conclusion that carbon reductions responsibility can be assigned in the light of production, consumption and shared responsibility based principles. Overall, from perspective of the industrial and energy restructuring and the residential lifestyle changes, our results shed new light on the analysis on the evolutionary mechanism and pattern of urban energy-induced CO₂ emissions with the combination of three kinds of methods. And the spatial structure adjustment and technical progress provides further elements for consideration about the scenarios of change in urban energy use and CO₂ emissions.     

多尺度建成环境对居民通勤出行碳排放的影响——来自广州的实证研究

通勤出行碳排放是城市交通碳排放的主要来源。然而,现有研究很少关注不同地理尺度建成环境对居民通勤出行碳排放的影响差异,且尚未得出一致的结论。本文基于居民出行调查数据和多层次混合效应模型对广州多尺度建成环境对居民通勤出行碳排放的影响进行了实证研究。研究发现：在控制居住自选择效应之后,居民通勤出行碳排放具有明显的空间差异,这些差异是由社区间的建成环境差异所导致的。在不同尺度建成环境中,社区尺度的建成环境对居民通勤出行碳排放的影响最显著。而且,居住地建成环境对通勤出行碳排放的影响比工作地建成环境的影响显著。对建成环境进行规划干预应更多着眼于居民所居住的社区以及与居民日常出行活动联系紧密的社区15分钟步行生活圈。虽然实证案例社区的选取可能存在局限性,但研究结论可为构建低碳城市空间结构、引导居民出行行为转变和制定具有针对性的低碳交通与土地利用政策提供一定的科学理论依据。     

信息技术对城市居民出行特征的影响——以南京为例

信息技术在城市生产、生活与管理中的加速应用不仅促使城市空间结构正在发生转型,也影响了城市居民的活动空间特征。论文基于社会调查,以南京为例,考察了信息技术对城市居民交通出行行为特征的影响。总体而言,南京城市居民家庭在接入信息技术上已经有了长足的发展,接入互联网能力迈入快速增长期。但由于各城区间、各郊区间的信息接入能力存在显著差异,这必然会影响居民的出行行为。研究表明:在家办公的出现减少了日常的工作出行。信息网络对居民日常出行的引导作用日益突出,并出现了部分替代。城市居民家庭的交通通讯费用大幅增长,上网时间增长较快,而城区更为明显。     

Urban Form,Air Pollution,and CO2 Emissions in Large U.S. Metropolitan Areas

In this article we explore the relationships between urban form and air pollution among 86 U.S. metropolitan areas. Urban form was quantified using preexisting sprawl indexes and spatial metrics applied to remotely sensed land cover data. Air pollution data included the nonpoint source emission of the ozone (O₃) precursors nitrogen oxides (NOx) and volatile organic compounds (VOCs), the concentration of O₃, the concentration and nonpoint source emission of fine particulate matter (PM2.5), and the emission of carbon dioxide (CO₂) from on-road sources. Metropolitan areas that exhibited higher levels of urban sprawl, or sprawl-like urban morphologies, generally exhibited higher concentrations and emissions of air pollution and CO₂ when controlling for population, land area, and climate.     

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.     

多尺度交通出行碳排放影响因素研究进展

减少交通出行碳排放是全球共同面对的重大议题之一,同时也是城市和交通可持续发展的重要目标。论文首先基于文献计量方法对近20年来的全球交通出行碳排放研究现状与趋势进行梳理与分析,在此基础上,分别从国家、城市和社区3个尺度对国家交通能源消耗及其碳排放的驱动力因素、城市形态对交通碳排放的影响以及社区建成环境对居民出行碳排放的影响研究进行了文献综述与归纳凝练。研究发现：① 国家尺度的研究早期大多基于时间序列数据,采用分解法探究交通能源消耗的主要驱动力;近年来,研究进一步根据能源消耗数据“自上而下”地测算交通碳排放,并通过构建面板数据模型探究社会经济、城市形态和交通发展因素对交通碳排放的影响。② 城市尺度的研究早期围绕紧凑城市是否一种低碳的城市形态而进行讨论,主要使用截面数据和相关分析方法;近年来,进一步拓展使用情景预测、GIS空间分析、空间回归、空间模拟等方法探究城市交通碳排放的空间差异及其与城市形态、城市中心分布形式之间的关系。③ 在社区尺度,研究多以截面、非集计的问卷调查数据为主,采用定量的数学模型探究居民社会经济属性和人口密度,土地利用混合度,与就业地、城市中心的距离,路网与交叉口密度、公共交通供给水平等建成环境要素对居民出行碳排放的影响。最后有针对性地提出了未来中国城市交通出行碳排放影响因素的研究趋势。     

道路交通节能减排途径与潜力分析

交通运输行业是仅次于制造业的第二大油品消耗行业,也是实现低碳生活发展路径的重点行业。基于交通运输部门碳排放量现状分析,本文提出了优先发展公共交通、鼓励发展小排量汽车、降低机动车单耗、控制排放物标准等四种挖潜减排途径,并利用排放量估算和情景分析相结合的方法,对我国道路交通减排潜力进行了预测。按照高中低度三种减排情景发展,测算出2015年及2020年减排量,为实现至2020年我国碳排放较2005年下降40%~45%的目标提供科学基础。研究认为:2010-2020年是中国交通运输行业高速发展阶段,要满足能源安全和温室气体减排要求,我国需要实施更严格的产业和环境政策,若强化低碳情景模式,则2015年、2020年的碳排减少量分别约为2183万t、7148万t,达到我国道路交通部门的最大减排潜力。     

青藏高原生态系统固碳释氧价值动态测评

本文旨在定量评价青藏高原生态系统的固碳释氧价值及其动态变化,为改善区域生态环境提供参考。基于MODIS/NDVI数据,利用光能利用率模型测算净第一性生产(NPP)物质量,并通过光合作用方程式换算成固定CO₂和释放O₂的物质量,以此为基础,采用造林成本法和工业制氧法对青藏高原固碳释氧价值量进行估算。结果表明:2000年、2005年和2010年固定CO₂的价值分别为384.36×10⁹元、393.23×10⁹元和356.41×10⁹元,释放O₂的价值分别为408.31×10⁹元、415.02×10⁹元和378.61×10⁹元。2000-2005年固碳释氧价值增加了15.58×10⁹元,2005-2010年下降了73.23×10⁹元,而2000-2010年下降了57.65×10⁹元。固碳释氧价值在空间上呈现出从东南向西北递减的趋势,这与青藏高原的水热条件分布基本一致。在价值构成中,草原>森林>草甸>其它类型>灌丛>农田。2000-2010年青藏高原生态系统固碳释氧价值呈现减小趋势,表明近年来气候变化和人类活动导致青藏高原的生态环境出现了退化趋势。     

低碳视角下城市空间形态紧凑度研究

全球气候变化背景下,构建功能紧凑的低碳城市空间形态已成为世界各地城市实现低碳化可持续发展的主要途径之一。但目前的城市形态研究主要集中在城市外部空间,缺乏对城市内部功能空间紧凑度的量化。从实现低碳城市的视角,提出了城市内部功能空间形态紧凑度的量化指标,用以探讨实现低碳城市的城市空间形态特征;以商业、医疗、教育和文化娱乐等4种重要服务设施布局为基础,构建了城市功能空间紧凑度指数,对不同规模、不同地理条件类型的8个案例城市进行定量研究,实证了城市内部“功能空间紧凑度”指标较城市外部“形态紧凑度”指标能够更好的反映城市形态的紧凑性内涵,适用于评价各类城市空间形态的低碳发展模式。     

长江经济带主要城市群2h出行交通圈格局特征

城市群作为城镇化高级阶段区域社会经济发展和参与国际竞争的主要空间载体,良好的交通通达性是其高质量发展的必要条件。以《交通强国建设纲要》提出的城市群2h通达目标为研究视角,以长江经济带3个国家级城市群为研究对象,选取加权平均出行时间作为测度指标并引入场强和引力模型,对比分析3个城市群所有城市与中心城市两个层级2h出行交通圈格局特征。研究发现：① 3个城市群出行交通圈均表现为从中心向外围地区呈现出由低到高连续扩大的“圈层式”空间格局特征,且长三角和成渝城市群分别形成了“>”字型和“十”字型2h出行廊道,但长江中游城市群尚未形成2h出行廊道。② 城市群中心城市2h出行交通圈的空间分布受区位条件影响较大,越接近区域几何中心则2h出行交通圈覆盖范围越大,而影响腹地受中心城市综合规模的作用明显,3个城市群各中心城市2h出行交通圈和影响腹地的空间分异显著。③ 不同城市群中心城市2h出行交通圈衔接联系水平差异显著,表现出多元的圈层叠加的经济联系格局。④ 高密度高速公路路网在提高城市群中心城市2h出行交通圈衔接联系水平中发挥重要作用,按衔接联系模式特征可将各城市群总结为多中心网络型、多中心组团型与双中心轴辐型。     

Allocating and mapping carbon footprint at the township scale by correlating industry sectors to land uses

A key part of reducing CO₂ emissions is exploring scientific methods for calculating carbon footprints and allocating their sources. Several limitations in current approaches exist at smaller scales due to shortcomings and uncertainty in data collection. This article implements an improved approach to allocate carbon footprints at the local, neighborhood scale, taking land uses as a criteria, after verifying the correlation between industry sectors and land uses through cointegration test. A case study of the Wuhan Metropolitan Area (WhMA) is provided to examine the method's applicability and effectiveness. Some related spatiotemporal variations in carbon‐footprint values at the township scale are depicted as a spatial tendency from zonal agglomeration to radial diffusion in a core‐periphery structure, which relates to such human‐driven factors as population, transportation, and urban (built‐up) area. The findings provide insight for policymakers to generate appropriate allocative strategies for low‐carbon development.     

Configuring growth coalitions among the projects of urban aggrandizement in Kunming,Southwest China

In this paper, we examine two urban projects in Kunming, Southwest China, one a massive scheme to build a new city center on rural land outside the city, and the other, an act of urban restructuring involving the rebuilding of an old Hui (Muslim) neighborhood in the old city center. We analyze the coalition of forces configured to undertake these large-scale projects; we underline the role of specially formed city-building organizations and the support provided by the state-owned institutions that dominate the city’s political economy; and we draw attention to the caution with which the city government handled resistance to urban restructuring among Hui residents. We argue that urban growth projects in inland cities like Kunming are marked by certain features that distinguish them from similar projects in Eastern Chinese cities but maintain that nonetheless the drive for profit maximization at the expense of residents is unchanged.     

我国低碳经济发展框架初步研究

在哥本哈根世界气候变化大会上,我国向世界承诺2020年单位国内生产总值的二氧化碳排放量比2005年下降40％~45％。本文在对已有研究成果进行系统梳理的基础上,分析了影响我国碳排放的主要因素,核算了主要减排途径的碳减排潜力,提出了至2020年我国发展低碳经济的基本框架。研究发现,碳排放强度与产业结构演化之间存在倒U字形曲线关系,发展模式转变和产业结构调整取得实质性成效是实现2020年减排目标的前提。此外,工业技术节能、建筑节能和道路交通节能也还都有一定的潜力。在不同情景下,工业技术节能对实现2020年减排目标的贡献程度在12%~14%之间,建筑节能和增加非化石能源规模分别可以起到10%左右的贡献,道路交通节能的贡献率在2%~3%之间。     

城市CO2排放结构与低碳水平测度——以京津沪渝为例

在科学设计城市CO₂排放、城市低碳水平数理模型基础上,结合BP神经网络法,综合考虑各种不确定因素的影响,通过1995~2008年京津沪渝4市CO₂排放结构和低碳水平测度以及BP神经网络模型预测,可以发现:4城市CO₂排放量逐年递增,但存在较大差异;城市CO₂排放量和发展态势取决于4城市CO₂排放结构及变化;低碳水平测度结果表明4城市的经济增长仍然依赖于碳基能源消耗,但产业结构的优化升级对提高低碳水平的作用是显著的;基于BP神经网络法的短期预测比传统预测法更为合理和精确。     

Detecting the dynamics of urban structure through spatial network analysis

Urban spatial structure in large cities is becoming ever more complex as populations grow in size, engage in more travel, and have increasing amounts of disposable income that enable them to live more diverse lifestyles. These trends have prominent and visible effects on urban activity, and cities are becoming more polycentric in their structure as new clusters and hotspots emerge and coalesce in a wider sea of urban development. Here, we apply recent methods in network science and their generalization to spatial analysis to identify the spatial structure of city hubs, centers, and borders, which are essential elements in understanding urban interactions. We use a ‘big’ data set for Singapore from the automatic smart card fare collection system, which is available for sample periods in 2010, 2011, and 2012 to show how the changing roles and influences of local areas in the overall spatial structure of urban movement can be efficiently monitored from daily transportation.

In essence, we first construct a weighted directed graph from these travel records. Each node in the graph denotes an urban area, edges denote the possibility of travel between any two areas, and the weight of edges denotes the volume of travel, which is the number of trips made. We then make use of (a) the graph properties to obtain an overall view of travel demand, (b) graph centralities for detecting urban centers and hubs, and (c) graph community structures for uncovering socioeconomic clusters defined as neighborhoods and their borders. Finally, results of this network analysis are projected back onto geographical space to reveal the spatial structure of urban movements. The revealed community structure shows a clear subdivision into different areas that separate the population’s activity space into smaller neighborhoods. The generated borders are different from existing administrative ones. By comparing the results from 3 years of data, we find that Singapore, even from such a short time series, is developing rapidly towards a polycentric urban form, where new subcenters and communities are emerging largely in line with the city’s master plan.

To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.     

CO2 seasonal variation and global change: Test global warming from another point of view

CO₂ and temperature records at Mauna Loa, Hawaii, and other observation stations show that the correlation between CO₂ and temperature is not significant. These stations are located away from big cities, and in various latitudes and hemispheres. But the correlation is significant in global mean data. Over the last five decades, CO₂ has grown at an accelerating rate with no corresponding rise in temperature in the stations. This discrepancy indicates that CO₂ probably is not the driving force of temperature change globally but only locally (mainly in big cities). We suggest that the Earth''s atmospheric concentration of CO₂ is too low to drive global temperature change. Our empirical perception of the global warming record is due to the urban heat island effect:temperature rises in areas with rising population density and rising industrial activity. This effect mainly occurs in the areas with high population and intense human activities, and is not representative of global warming. Regions far from cities, such as the Mauna Loa highland, show no evident warming trend. The global monthly mean temperature calculated by record data, widely used by academic researchers, shows R²=0.765, a high degree of correlation with CO₂. However, the R² shows much less significance (mean R²=0.024) if calculated by each record for 188 selected stations over the world. This test suggests that the inflated high correlation between CO₂ and temperature (mean R²=0.765-0.024=0.741) used in reports from the Intergovernmental Panel on Climate Change (IPCC) was very likely produced during data correction and processing. This untrue global monthly mean temperature has created a picture:human emission drives global warming.     

居民出行活动特征与收入水平的关系——以上海市为例

居民出行活动与居民的收入水平关系是公共交通、城市地理研究的重要问题。传统获取居民出行活动信息主要基于问卷调查的方式,不仅成本高、样本量有限,且研究局限于定性讨论,研究结果易因受访者的主观意识而产生偏颇。随着信息技术的革新,传感器记录的大规模人类活动信息为研究居民出行活动特征与居民收入水平关系提供了可能性。本文利用上海市居民时空轨迹数据,从居民出行活动的角度出发,首先构建居民出行活动指标,并利用主成分分析法提取居民出行活动特征的主要成分;然后对主成分进行K-Means聚类,并针对不同出行活动特征的类别,分析居民出行活动特征与居民收入水平的关系,结果表明：①居民出行地点多样性与居民出行范围大小是反映居民出行活动特征的主要成分;②移动范围越小、移动地点多样性越低的居民类别,其平均工资水平越高;③不同移动性特征的类别平均收入水平差异与各类别居民工作地的产业发展有关。研究结论可为城市规划及相关经济政策制定提供参考。     

Resisting urban renewal in Istanbul

In this article I examine grassroots responses to state-directed urban renewal in two poor neighborhoods in Istanbul. Through detailed analysis of resident perspectives and urban association tactics, I explore various factors that shape the trajectories of resistance to urban renewal, including solidarity networks, levels of participation and trust in local neighborhood associations, strength of neighborhood identity, extra-local support, and the traditions and channels of negotiation with state actors. I argue that these factors are articulated by a distinct “politics of compensation”—a term that I borrow from Ananya Roy—that is particular to the contemporary neoliberal condition. I find that grassroots mobilizations in Istanbul do not necessarily operate in opposition to neoliberalism; in fact, many struggles remain within the conceptual space of neoliberalism and its cost-benefit calculations. These findings contribute to the emerging literature on struggles against urban renewal in the global south.