LAND PRICE INFLATION AND AFFORDABLE HOUSING

Prices for standard lots and acreage were collected for 1975 and 1980 in 30 metropolitan areas and then analyzed, using multiple regression, to identify factors which would explain variations among metropolitan areas. Extreme price variations were observed. For example, from 1975 to 1980, the price of a standard residential lot increased as little as 31% in one area, while the price rose 1 76% in another. Over 80% of the variation in lot price increases was explainable by a model combining land supply and demand factors. In their order of importance, the factors were: (1) an index of regulatory restriction, (2) population increases, (3) per-capita income increases, and (4) job increases. The analysis suggests that public regulatory, infrastructure and tax policies can significantly affect land supply and demand and, in turn, prices. Communities that choose to manage growth must monitor land supply and demand and adjust their policies to ensure competitive noninflationary land markets. Otherwise major increases in land prices for housing and businesses may result.     

An assessment of oil supply and its implications for future prices

This paper examines three issues related to both the U.S. and world oil supply: (1) the nature of the long-term, postpeak production profile for the U.S. and, by inference, other regions (the Hubbert curve is used as a “strawman” model); (2) implications on U.S. energy security of using a modified Hubbert-type conceptual model of prepeak production, testing the adequacy of Latin America to be the primary source of U.S. oil imports; and (3) the cyclic behavior of oil prices. it shows that U.S. production will exhibit a more attenuated decline than that simulated by the Hubbert curve and not decline to zero. it asserts that U.S. production is better predicted by past reserves than past production, but that this argument does not apply to nations that keep a much larger proportion of reserves in the ground. Such nations could considerably expand production without any growth in reserves. The paper concedes that the potential total production for these nations could be examined with a Hubbert curve model linked to reserves, but with great uncertainty. Such an uncertain optimistic forecast predicts that the cumulative production of Latin America could far exceed that of the United States. Nevertheless, a statistical model of oil prices since 1870 implies that real wellhead oil prices in the United States are on a long-term upward path, underlying a much more “noisy” cyclical pattern estimated to include 22- and 27-year cycles. The statistical model predicts a severe oil shock within a few years (of 1998) but also predicts that through 2030, real oil prices will not reach 1981 levels again. The paper examines U.S. and world trends in seismic exploration, drilling locations and depths, drilling costs, oil/gas reserves, oil/gas use rates, and oil demand. After taking these factors into consideration, it concludes that the statistical model of oil prices cannot be disputed, despite its lack of basis in economic theory.     

Effect of land prices on the spatial differentiation of housing prices: Evidence from cross-county analyses in China

This study analyzes the spatial patterns and driving forces of housing prices in China using a 2,872-county dataset of housing prices in 2014. Multiple theoretical perspectives on housing demand, supply, and market, are combined to establish a housing price model to explore the impact of land prices on housing prices. The relative impacts of land prices on housing prices at different administrative levels are then analyzed using the geographical detector technique. Finally, the influencing mechanism of land prices on housing prices is discussed. The main conclusions are as follows. (1) Housing prices have a pyramid- ranked distribution in China, where higher housing prices are linked to smaller urban populations. (2) Land prices are the primary driver of housing prices, and their impacts on housing prices vary over different administrative levels. To be specific, the effect of land prices is the strongest in the urban districts of provincial capital cities. (3) The internal influence mechanisms for land prices driving housing prices are: topographic factors, urban construction level, the agglomeration degree of high-quality public service resources, and the tertiary industrial development level. The urban land supply plan (supply policies) is the intrinsic driver that determines land prices in cities; through supply and demand, cost, and market mechanisms, land prices then impact housing prices.     

Grand Challenges in Metal Life Cycles

Metal life cycles define and quantify the rates of supply and demand for metals currently used in modern society. Each metal life cycle stage is potentially influenced by material transformation losses, import/export flows, and stockpiling. Flows and stocks in metal life cycles, the rates of by-product extraction, determinations of metal stocks in use, and scrap generation rates remain difficult to quantify, rendering it difficult to make reasonable estimates of the future supply of secondary metals and, therefore, of the future demands for primary metals. This paper presents four grand challenges to geologists, materials scientists, product designers, and industrial ecologists. Addressing these challenges is a prerequisite to achieving better resource efficiencies, as well as to addressing our moral obligation to enable a resource-sufficient life for future generations.     

土地价格对住房价格空间分异的影响——基于中国县域单元的实证分析（英文）

This study analyzes the spatial patterns and driving forces of housing prices in China using a 2,872-county dataset of housing prices in 2014.Multiple theoretical perspectives on housing demand,supply,and market,are combined to establish a housing price model to explore the impact of land prices on housing prices.The relative impacts of land prices on housing prices at different administrative levels are then analyzed using the geographical detector technique.Finally,the influencing mechanism of land prices on housing prices is discussed.The main conclusions are as follows.(1) Housing prices have a pyramid-ranked distribution in China,where higher housing prices are linked to smaller urban populations.(2) Land prices are the primary driver of housing prices,and their impacts on housing prices vary over different administrative levels.To be specific,the effect of land prices is the strongest in the urban districts of provincial capital cities.(3) The internal influence mechanisms for land prices driving housing prices are:topographic factors,urban construction level,the agglomeration degree of high-quality public service resources,and the tertiary industrial development level.The urban land supply plan(supply policies) is the intrinsic driver that determines land prices in cities;through supply and demand,cost,and market mechanisms,land prices then impact housing prices.     

农产品现货价格与期货价格关联研究

农产品现货价格无论从短期还是长期影响期货价格的统计特征都是显著的,通过建立假设,利用VEC模型,选取农产品现货月度数据,根据期货市场日数据加权计算其月度数据,进一步探析了现货影响期货的程度及其机理。逐步控制模型中的其他变量,结果验证其经济意义是历史现货价格数据影响期货价格也是显著的,但是单个农产品现货和期货价格的影响程度是不一致的,有的影响方向是相反的。从单一品种农产品价格来看,现货市场与期货市场相关度极高,对于大豆、棉花、豆粕和强麦来说,现货价格上涨1%,期货价格上涨幅度则分别为0.948%、0.836%、0.873%和0.845%,对于小麦和大豆来说,现货价格上涨1%,期货价格上涨幅度接近1%;期货价格的变动一方面直接受到现货价格的影响,另一方面还受到影响现货价格变动的因素的间接影响。拆借利率、M2、进出口总值的估计系数均显著为正,证实了农产品供求因素(生产量、进口量、出口量、世界总产量、世界总出口量)、货币政策、货币供应量、利率和汇率等的变动,都直接影响到期货价格。农产品现货价格和期货价格是具有联动影响的,因此,适度且分类引导现货价格,建立单品种农产品价格预警机制,平抑农产品期货市场上大的波动,有利于我国农产品期货市场的健康发展;同时,因为期货市场具有价格发现功能,期货价格的稳定对当前的农产品现货也具有引导作用,会促进农产品现货市场的良性发展。     

中国城市住宅价格的空间分异格局及影响因素

分别研究2009年中国286个地级以上城市住宅均价和房价收入比的空间分异格局、总体趋势、空间异质性和相关性;根据供需理论和城市特征价格理论建立了影响中国城市住宅价格空间分异的初选因素,并根据半对数模型分析主要影响因素。结果表明：① 中国城市住宅价格空间分异显著,呈现出空间集聚性分异（东南沿海三大城市群与内陆城市之间）和行政等级性分异（省会与地级市之间）的双重格局;② 房价收入比较高的城市数量更多,分布范围更广,购房难度较大的城市已超过一半;③ 住宅均价的总体分异趋势和空间异质性都强于房价收入比;④ 城市居民收入与财富水平和城市区位与行政等级特征是住宅价格空间分异的两大核心影响因素。     

Role of initial depth at basin margins in sequence architecture: field examples and computer models

A delay in the onset of sedimentation during fault‐related subsidence at a basin margin can occur in both extensional settings, where footwall tilting may cause a diversion of drainage patterns, and in strike‐slip basins, where a source area may be translated along the basin margin. The ‘initial depth’ created by this delay acts as pre‐depositional accommodation and is a partly independent variable. It controls the geometry of the first stratal units deposited at the basin margin and thus modifies the response of the depositional system to subsequent, syndepositional changes in accommodation. In systems with a sharp break in the depositional profile, such as the topset edge in coarse‐grained deltas, the initial depth controls the foreset height and therefore the progradational distance of the topset edge. The topset length, in turn, influences topset accommodation during cyclical base level variations and therefore is reflected in the resulting stacking patterns at both long‐ and short‐term time scales. In the simplified cases modelled in this study, it is the relationship between the initial depth and the net increase in depth over the interval of a relative sea‐level cycle (ΔH) that governs long‐ and short‐term stacking patterns. In situations where the initial depth is significantly larger than ΔH, the topset accommodation of the first delta is insufficient to contain the volume of sediment of younger sequences formed during subsequent relative sea‐level cycles. Therefore, the depositional system tends to prograde over a number of relative sea‐level cycles before the topset area increases so that the long‐term stacking pattern changes to aggradation. Stacking patterns of high‐frequency sequences are influenced by a combination of topset accommodation available and position of the short‐term relative sea‐level cycles on the rising or falling limb of a long‐term sea‐level curve. This determines whether deposits of short‐term cycles are accommodated in delta topsets or foresets, or in both. Variations in stacking pattern caused by different initial depths may be misinterpreted as due to relative sea level or sediment supply changes and it is necessary to consider initial bathymetry in modelling and interpretation of stacking patterns, especially in fault‐bounded basins.     

Local Spatiotemporal Modeling of House Prices: A Mixed Model Approach

The real estate market has long provided an active application area for spatial–temporal modeling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they share similar characteristics, but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property values from previous years and in the spatial–temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research on house prices has adopted either a spatial perspective or a temporal one; relatively little effort has been devoted to situations where both spatial and temporal effects coexist. Using ten years of house price data in Fife, Scotland (2003–2012), this research applies a mixed model approach, semiparametric geographically weighted regression (GWR), to explore, model, and analyze the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates that the mixed modeling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales.     

扬州市住宅价格的空间分异与模式演变

以2001-2012 年扬州中心城区各居住小区的住宅平均单价为基本数据,通过建立住宅价格总体分异测度指数(GDI) 计算其总体分异趋势及各住宅类型内部的分异趋势;采用核密度函数等方法探索住宅价格的分布形态和分异格局的演变规律;利用趋势面分析不同住宅类型价格的空间分异趋势;基于上述结果总结空间分异的演变模式,并分别探索空间分异与格局演变的驱动力。结果表明：① 2001 年以来扬州市住宅价格差距显著增大,分异趋势在波动中增强,与城市住宅均价的年增长率耦合;住宅价格呈现西高东低的空间分异格局,同档次价格小区由空间集聚转为相对分散,高、低价格住宅区分别沿固定扇面由中心向外围扩散。②不同住宅类型内的价格分异走势差别显著,各类型住宅间的价格趋势面差距明显,但其空间形态类似。③ 空间分异模式由2001 年西高东低的扇形同档次价格集聚式分异转变为2012 年扇形与圈层相结合的多档次价格混合式分异。④ 2001 年以来住宅价格总体分异的核心驱动力是城市居住空间的迅速扩展、居民收入差距的增大、房地产市场的繁荣和住宅类型的多元化,其住宅价格空间格局演变的驱动力为城市发展方向的确立与变化、特定住宅类型建设的区位指向和古城保护、旧城改造与新区建设。     

基于高铁网络的中国省会城市经济可达性

基于列车时刻表中的省会城市间交通时间以及相应票价数据和各省市城镇居民家庭人均收入数据,以铁路服务价格、城镇居民消费铁路服务能力两方面为突破口,分析高铁网络对中国省会城市经济可达性的影响。结果表明：① 高铁网络不同程度地提升了省会城市铁路服务价格,降低了铁路服务价格与时间可达性的相关性,同时改变了铁路服务价格的分布格局。② 城镇居民消费铁路服务能力等级差异明显,其中收入水平发挥着决定性作用,而铁路服务价格的影响也不容忽视。③ 高铁网络缩小了“所有城市”铁路服务价格、铁路服务消费能力分布的不公平性,但扩大了“高铁城市”铁路服务价格、铁路服务消费能力分布的不公平性;铁路服务消费能力分布的不公平程度高于铁路服务价格分布的不公平程度。     

Availability of Bauxite Reserves

Based upon a large database, this paper analyzes the record of bauxite mine production, exploration success, and resource depletion and evaluates the availability of bauxite reserves in the near future. The record clearly shows that for the past 50 years world bauxite production rose by an annual increase of over 5% while per capita consumption rose during the same period by about 4%. Time trends of the world bauxite reserve life index (RLI); that is, known world reserves of a given year divided by world production of the same year, are episodic and seem to follow bauxite price cycles. The present-day RLI indicates adequate bauxite supply for about 180 years and is the same as it was in 1950. However, if an annual growth rate of 5% is considered, the currently known reserves will be exhausted within the next 20 years and the reserve base will be adequate for not more than 25 years. This scenario is based, of course, on the unrealistic assumption that future exploration efforts fail to discover additional reserves. Evaluation of the quality, in terms of bauxite signatures, and quantity of presently known bauxite prospects that may be mined in future suggests that there is sufficient potential for adequate bauxite supply for the next 20 to 25 years at least. Bauxite signatures cover a wide range of values that allows selection of the most favorable bauxite prospects for future mining, both in economic as well as environmental terms. Although, there is the general believe that the world abundance of bauxite resources will ensure sufficient supply to meet future demands significant additional reserves have to be discovered if exponential growth rates continue. As the question of future bauxite supply is subject to economic and geologic principles one has to take into consideration that increasing exploration maturity in many mineral provinces will make it difficult to locate additional bauxite reserves and that decreasing real commodity prices will influence the level of investment in bauxite exploration.     

上海市三甲医院对周边地区住房价格的空间影响效应分析

选取上海市区7家三级甲等综合性医院,运用特征价格模型和多元回归分析方法,实证分析它们对周边住宅价格的空间影响效应。研究结果显示：① 总体上,相比其他环境区位因素,三甲医院对周边住宅价格的影响程度较小,而建筑面积、物业管理费、学区房、轨道交通、建筑楼高、至 CBD 的距离等因素的影响程度较大。② 三甲综合医院对周边住宅价格产生负面影响。在相同情况下,住宅离医院距离越近,则价格越低。住宅与医院的距离每缩短50 m,住宅价格平均下浮0.602%。③ 由于三甲综合医院区位各不相同,距离CBD远近不一,对住宅价格的影响也存在空间差异。一般来说,距离CBD越近,住宅价格受医院的负面影响也越小。     

全球石油供应下新地缘政治经济格局分析

本文选取1994年1月到2011年12月的时间序列数据,从地缘政治视角研究国际石油供给的格局及石油价格波动主导权。首先通过ArcGIS分类来展现石油供给的地缘政治格局总体面貌;其次对国际石油价格波动主导权进行实证分析,结果显示石油存储量与石油价格之间并非简单的线性关系,且在金融危机时期呈现负相关关系,在地缘政治事件频发时期呈现正相关关系;进一步对主导权量化估算,结果表明,国际石油价格波动主导权更多的掌控在OECD需求方,即金融业主导国家。随着时间的推移,NonOECD需求方与石油供给方对国际石油价格主导权逐渐增加,同时,金融因素对国际油价波动的主导权不容忽视。     

上海市住房价格梯度及其影响因素分析

选择从上海市中心区至宝山区的一条南北向区段,通过采集沿线内环以内、内环和中环之间、中环和外环之间以及外环以外四个区间内二手房、新房的价格样本,分析其价格梯度差,发现二手房价格一般要高于新房价格,但其价格递减速度比新房更快。根据实际情况,提取繁华程度、市场供求比例、地理区位、交通条件、人口状况、基础设施、环境质量七个影响住房价格的主要因子,运用多元回归分析方法对样本区域的房地产价格进行分析,得出了多元线性回归方程,并进行了回归分析效果检验;最后分别运用偏相关系数分析法和单项因子权重度量法来估算各因子的影响程度。结果表明,二手房市场和新房市场具有明显差异,市场供求是影响二手房价格的最主要因子,而环境质量则是影响新开楼盘价格的首要因子;繁华程度和交通条件的重要影响作用在本次回归模型中没有得到验证。     

Geostatistical space–time mapping of house prices using Bayesian maximum entropy

Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space–time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.     

城市更新对房地产市场的时空影响效应:基于杭州市的实证研究

以杭州市为例,采用双重差分法研究城市更新对房地产市场的时空影响效应。研究结果发现： 城市更新在不同阶段对周边住宅价格产生的影响效应不同,项目建设和运营期分别对周边房价产生28.6%和32.0%的推升作用; 城市更新对周边住房价格的影响在空间上存在显著差异,对城市边缘区的提升作用比核心区高23.0%; 城市更新对周边房价影响随着距离增加显著降低,距离城市更新项目中心2.0 km以内、2.0~3.5 km范围对房价的推升作用分别为41.9%、24.9%和9.1%。最后建议政府完善房地产市场微观调控,建立完善长效机制。     

南京城区住宅售租价格时空分异与影响因素

住宅价格空间分异是中国城市地理学和经济地理学近年来关注的热点前沿课题。以南京4560个居住小区为研究总样本,采集2009-2017年间30个季度各小区平均住宅售价和租金,选取6个特征时段和小区分布相对集中的重点研究区,采用克里格插值法分析研究区内住宅售租价格的空间分异与演变特征,发现售价空间分异明显加剧,高值区渐显于河西新城、江心洲和鼓楼名校学区;租金空间则从城市中心向外围递减格局转变为整体更加均衡的新老城区多中心结构。在此基础上,重点围绕住宅“区位”属性,构建售租价格分异影响因素指标体系,通过逐步多元回归分析发现,中心位势变量对售租价格的解释度最高,而配套服务类区位因素对售租价格的解释力在降低。南京城市房价快速增长背景下,常规“区位”因素对房价分异的重要性持续减弱,学区、政策偏向等特殊“区位”因子对房价的决定性作用则逐步突显,而“售租比”全面快速增长则预示着城市房价风险程度的整体提高。     

基于绿色投入产出表的环境污染治理成本及影响模拟

在编制绿色投入产出表的基础上,对中国废水、大气污染物和固体废物的直接产生系数和完全产生系数进行分析,并对这三大类污染治理的单位成本和污染物治理的经济影响进行模拟。结果显示：① 电力热力的生产和供应业、化学原料及化学制品制造业、黑色金属冶炼及压延加工业、石油加工及炼焦业、石油和天然气开采业等行业废水、废气和固废的完全产污系数都较高,属污染减排的重点行业。② 模拟的废水完全治理成本为6.4元/t,废气为2136元/t,固体废物为101元/t。③ 以模拟的单位治理成本进行污染物治理,将对电力热力的生产和供应业、有色金属矿采选业、黑色金属矿采选业、造纸及纸制品业、有色金属冶炼及压延加工业、黑色金属冶炼及压延加工业、非金属矿物制造业等行业的产品价格影响大。     

Recent Environmental Change and Human Impact on Svalbard: The Lake-Sediment Geochemical Record

As part of a broader investigation into recent environmental change on Svalbard, the inorganic geochemical record of six lake-sediment cores was analysed. The major temporal trends in sediment elemental composition are driven by variations in two contrasting sediment components, both derived from catchment soils: (1) mineral matter, and (2) soil organic matter (SOM), enriched in Fe and Mn oxides and heavy metals. Two environmental impacts are recorded in most or all of the lake sediment sequences. An up-core increase in organic matter can be partly attributed to diagenetic effects, but also requires an enhanced supply of SOM relative to mineral matter. In addition, the central and southern sites all show a ca. 1970 event characterised by an enhanced mineral matter accumulation rate. This requires either an enhanced allochthonous supply or an enhanced mobilisation of littoral sediments. In either case a regional-scale driving force, such as a shift in climate, is required. At five of the lakes the sediment heavy metal concentration profiles can be explained entirely by natural factors. However, at Tenndammen (U), situated close to the Svalbard’s largest settlement at Longyearbyen, possible anthropogenic Pb enrichment is found. Comparison of observed and expected heavy metal profiles (based on Greenland ice-core data) shows that the lakes are generally too insensitive to have recorded a long-transported heavy metal pollution signal.