基于模糊评判法的陕西农村公共物品供给模式选择分析

陕西一直实行"以农补工、优先发展城市、优先发展工业"的倾斜性战略政策,使得城乡在资源分配和社会事业发展等方面存在较大的差距,农村发展处于劣势地位,尤其是农村公共物品未能得到有效供给,其供给主要是采取以政府为主导的模式。研究利用陕西农村1990-2008年的时间序列统计数据,通过构建陕西农村经济发展的9个评判指标并对其分别赋予不同的隶属度,运用模糊评判法,对陕西农村公共物品供给的经济发展阶段进行了定量评判。实证研究表明,自20世纪90年代以来,陕西农村地区整体上处于经济发展的第二个阶段,即经济发展的成长阶段。因此,陕西应主要采取以政府为主导型的农村公共物品供给模式,而私人(市场)供给只能作为农村公共物品供给的少许补充。农村公共物品供给模式应根据经济发展阶段作出选择,以期实现各个时期农村公共物品供给的最优化:考虑不同地区农民的需求意愿,改革"一刀切"的供给模式;扭转农民负担部分公共物品供给成本的局面。     

采用支持向量机算法优化电化学处理油田污水的工艺参数

采用支持向量机(SVM)算法,将Box-Behnken设计法与支持向量回归算法(SVR)实验参数优化软件相结合,优化电化学去除油田污水COD的工艺参数。通过量子粒子群算法对SVM算法参数进行优化,从建立的回归模型中找到工艺参数的全局最佳点:电解时间60 min,电解电流3 A,三维电极填充料中石英砂质量695 g。模型得到的COD理论最优去除率为92.48%,验证实验得到的COD去除率为91.43%。     

Equity in neighbourhood walkability? A comparative analysis of three large U.S. cities

This study examines the spatial and statistical relationships between social vulnerability (SV) and neighbourhood walkability across three large U.S. cities with different urban typologies and development patterns: Charlotte, NC (a low-density, fast-growing “Sunbelt” city); Pittsburgh, PA (a moderate density, shrinking “Rust Belt” city); and Portland, OR (a progressive West Coast city known for its sprawl-containment policies). Binary logistic regression, independent-samples t-tests, and mapping techniques are employed to determine whether neighbourhoods with high SV (i.e. older populations, higher poverty rates, more service occupations, lower educational attainment, and a higher proportion of minorities) are as likely as those with low SV to exhibit a high degree of walkability. The publically available Walk Score® metric, based on proximity to amenities, street network connectivity, and density, was used as a proxy for neighbourhood walkability. The results indicate significant variability among cities, with Charlotte exhibiting the greatest potential for inequitable access to walkable urban environments and the most prominent concentration of “walk-vulnerable” block groups (BGs) with high SV and low walkability. Both Portland and Pittsburgh exhibited more equitable access when comparing BGs with high and low SV; however, they each presented unique spatial patterns, visualised using a series of maps.     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box–Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.     

饮食业油烟道火灾的模糊事件树分析

对饮食业油烟道火灾事故进行事件树分析时,历史数据的缺乏常常导致无法进行事件序列的定量计算.针对这一问题,利用专家对事故的模糊评判,引入模糊事件树进行定量计算研究.在模糊集理论的基础上,结合德尔菲法和判断矩阵法, 将模糊性语言转换为三角模糊数或梯形模糊数,应用模糊数截集以及模糊数清晰化方法,对后果事件发生概率进行计算.研究表明,饮食业油烟道火灾事故概率的模糊事件树分析是切实可行的,有助对饮食业油烟道火灾的认识、预防和扑救工作.     

煤与瓦斯突出影响因素评价分析的模糊层次分析方法

在综合分析煤与瓦斯突出的影响因素的基础上,采用模糊层次分析法（FAHP）建立了煤与瓦斯突出模糊层次分析模型并进行了实例分析,确定了煤与瓦斯突出各影响因素的权重系数。评价结果表明：地应力、地质构造、瓦斯压力等是影响煤与瓦斯突出的主要因素。为制定相应的煤与瓦斯突出防治措施,提供了科学的理论依据和切合实际情况的评价方法。     

基于卫星夜间灯光数据的中国分省碳排放时空模拟

中国能源统计数据"横向不可比,纵向不可加"现象依然突出,尤其是分省能源消费统计千差万别,给分省碳排放评估带来了较大困难,如何利用卫星遥感数据科学合理地估算中国分省碳排放是当前亟须研究的问题。本文运用DMSP/OLS全球稳定夜间灯光数据,在通过相互校正、年内融合和年际间校正等系列处理得到中国分省稳定夜间灯光数据的基础上,首先分别构建中国分省稳定夜间灯光亮度DN值与人均碳排放和单位面积碳排放之间的时空地理加权回归模型,两个模型整体效果均较好,拟合优度分别高达96.74%和99.24%;其次运用稳定夜间灯光亮度DN值对分省人均碳排放和单位面积碳排放进行时空模拟;最后运用人口规模和土地面积对分省碳排放进行估算。估算结果显示:(1)整体来看,2000—2013年年均碳排放模拟值与实际值6.3349×109t较为接近,两个模型的相对误差均在0.5%以内。(2)分年度来看,所有年份的相对误差均在5%以内,2006年分省加总碳排放模拟值与实际碳排放6.2036×109t最为接近,绝对误差和相对误差均较小,两个模型模拟值的相对误差均为0.04%。(3)分省域来看,2000—2013年年均碳排放模拟值与实际碳排放均非常接近,除海南和宁夏外,其余28个省区市的相对误差均在1%以内。(4)分年度分省域来看,以2013年为例,40%省份的相对误差在2%以内,70%省份的相对误差在5%以内。从整体、分年度、分省域、分年度分省域的估算结果来看,基于稳定夜间灯光数据的中国分省碳排放时空模拟效果良好。因此,运用卫星夜间灯光数据可以较为准确地对中国分省碳排放进行估算和预测,为卫星遥感影像数据服务分省碳排放监测和评估提供一种补充性参考。     

高铁网络可达性测度及经济潜力分析——以安徽省为例

高速铁路的开通给沿线城市的建设带来了重大影响。基于可达性测度以及修正的经济潜力模型,利用GIS对安徽省高铁可达性以及经济潜力进行深入分析。研究表明:(1)高铁开通后安徽省可达性显著提高,但总体格局保持不变,呈现出以合肥为中心,淮南、芜湖等地为副中心的"多核心"模式。池州、铜陵两城市可达性上升幅度最大,黄山紧随其后,芜湖、滁州变化最小。高铁开通后,滁州可达性排名下降5名,下降幅度最大,合肥、淮南、黄山最为稳定,其他城市均有小幅变化。(2)可达性的提升对城市经济潜力造成了一定影响,总体呈现出以合肥为中心的外围扩展模式。池州、铜陵经济潜力增长最为显著,黄山位列第3,可达性的提升直接影响了池州、铜陵、黄山三市的经济发展。高铁开通后,合肥、芜湖经济潜力排名始终位于前二,传统经济中心地位不变。马鞍山排名上升4名,幅度最大,池州、黄山两市处于末端,经济潜力仍然较为薄弱。(3)高铁开通后,时空收敛效应明显,可达性的提升令城市经济潜力增长显著,但受多方面因素影响,可达性与经济潜力呈现不完全契合关系。     

Is energy efficiency capitalized into home prices? Evidence from three U.S. cities

We test for evidence that energy efficiency features are capitalized into home prices in three U.S. metropolitan areas. Using hedonic regressions and multiple matching procedures, we find that Energy Star certification is associated with higher sales prices in two of the markets: the Research Triangle region of North Carolina and Portland, Oregon. We find that local “green” certifications in Portland and in Austin, Texas, are also associated with higher prices and that the estimated price impacts are larger than those from Energy Star. Matching on observables proves to be important in some cases, reducing the estimated impacts compared with models without matching. We calculate the implied energy savings from the estimated premiums and find that, in the Research Triangle market, the Energy Star premiums approximately equal the savings that program is designed to achieve, but in Portland, the premiums are slightly greater than the program's savings due to low energy costs in the region.