CLUE-S模型对村镇土地利用变化的模拟与精度评价

利用航片、IKONOS和Quickbird影像得到研究区3期（1991、2001和2009年）土地利用历史数据，运用CLUE S模型，基于1991、2001年的土地利用数据对2009年土地利用格局进行模拟预测，并将模拟结果与2009年真实土地利用数据进行比较。类型水平上，选择ROC曲线统计和偏离度指数分别对各地类的Logistic回归拟合精度和CLUE S模型模拟精度进行评价；景观水平上，采用景观指数和Kappa指数系列方法，从综合预测能力、景观格局、数量和空间位置等方面对CLUE S模型的模拟精度进行全面评估。结果表明:①CLUE S模型对各土地利用类型的模拟精度均较高，各地类的Logistic回归的拟合精度随着模拟分辨率的提高而逐渐增加；②随着模拟时间的缩短，CLUE S模型对整体景观格局的模拟精度提高；③该模型对土地利用数量的模拟精度明显优于其对空间位置的模拟精度。总体而言，CLUE S模型在村镇尺度的模拟效果良好，预测精度较高，但在空间位置和景观格局精度方面还有待提高     

人力资本、社会资本与市民化抑制

新生代农民工问题的终结是市民化,但是目前许多新生代农民工存在市民化阻碍,这不利于我国城市化进程的推进和经济的持续健康发展。对沈阳和余姚两地区进行了案例调查,得到了两地区新生代农民工的调查数据。运用调查数据,首先采用需求可识别的biprobit模型对新生代的农民工市民化抑制程度进行了测度,并预测出新生代农民工个体的市民化抑制程度。在对新生代农民工个体的市民化抑制程度指标进行分位数回归分解的基础上,分析了人力资本和社会资本对新生代农民工市民化抑制程度的影响,以期促进新生代农民工的城市融入。研究发现:新生代农民工的社会资本水平是影响其市民化的关键因素,人力资本水平的提高只有在市民化抑制程度高于80%时才会使市民化抑制程度下降。因此,相应的政策建议为,政府应加强新生代农民工城市公共服务的享用水平;发展和健全农民工工会组织;在市民化抑制程度较高的地区应特别加强新生代农民工职业技能的培训。     

人口与消费对碳排放影响的分析模型与实证

从消费压力人口视角探讨碳排放问题,有利于正确判断和把握气候变化压力的人文因素,以及人口与消费可持续发展的动力学机制.本文通过对STIRPAT模型的扩展,应用岭回归方法计量分析人口、消费及技术因素对碳排放的影响.对我国1980至2007年碳排放情况的统计实证结果表明,扩展的STIRPAT模型对中国国情有较高的解释力.居民消费水平、人口城市化率、人口规模三个因素对我国碳排放总量的变化影响明显;现阶段我国居民消费水平与人口结构变化对碳排放的影响力已高于人口规模变化的影响力,居民消费水平与消费模式等人文因素的变化有可能成为我国碳排放的新的增长点;技术进步因素在此模型中对我国该阶段碳排放的解释力有限,表明我国未来通过技术进步减缓碳排放的潜力巨大.     

中国FDI环境效应的再检验

改革开放以来,外商直接投资(FDI)的涌入为中国经济发展注入了新活力,并成为中国经济高速增长的重要引擎之一。然而,随着康菲石油渤海漏油事故、江苏启东王子造纸排污和阿玛尼污染投诉等涉外污染事件报道频出,学术界对于FDI涌入可能带来的负面环境效应表现出一定担忧。现有文献多聚焦于整体层面的FDI环境效应检验,而对其区域差异及原因产生机制的系统探究并不多见。为此,本文从理论判断和命题推演出发,利用2003—2014年中国省际面板数据,以改进熵值法拟合环境污染综合指数,并构建普通面板模型,实证检验了FDI流入对中国环境污染的影响及其区域差异。结果表明,总体而言,"污染光环"假说在中国基本成立,即FDI流入在一定程度上改善了中国环境质量,但不同地区FDI的环境效应有所不同,其主要表现为沿海和内陆地区FDI具有显著的"污染光环"效应,且前者强于后者,而沿边地区FDI具有不明显的"污染天堂"效应。在此基础上,构造面板门槛模型,进一步系统考察了FDI对中国环境污染影响的4种主要吸收能力因素的门槛效应。门槛回归结果表明,FDI对中国环境污染的影响显著存在基于经济发展、环境规制、人力资本和金融发展水平的双门槛效应。经济发展、人力资本和金融发展水平越高,FDI的"污染光环"效应越明显,而适度的环境规制才更有利于FDI环境技术溢出的发挥。基于此,本文建议应进一步全方位地提高对外开放水平,一如既往地积极引进并监督FDI;同时地方政府也需认清地区发展的不平衡性,进而采取差异化的引资政策。     

基于数字图像比色法测定水中氟化物

在酸性溶液中，氟能破坏二甲酚橙〖CD*2〗锆显色体系，使溶液颜色从紫红色变为橙色或黄色，通过采集图像颜色信息构建3个特征颜色分量的多元线性回归模型，用以定量测定水中氟。该方法在0 mg/L~1.5 mg/L范围内线性良好，方法检出限为0.03 mg/L。两个质量浓度水平的氟化物标准溶液6次测定结果的RSD分别为1.4%和1.3%，相对误差分别为0.3%和1.7%。     

安全投入各要素分配方向的研究

针对灰色关联分析法存在的分辨系数的选择主观性较大,用关联系数的平均值得到的关联度降低了评价准确性等缺点,提出用经济学中的偏相关分析和不变替代弹性(CES)生产函数分析方法,建立安全投入与事故函数分析模型。应用SPSS和EViews软件,找出安全投入和事故之间偏相关系数绝对值最大的要素和标准化回归系数绝对值最大的要素,作为优先投入方向,并按照系数绝对值大小进行排序,为以后企业进行安全投入提供理论参考,避免企业资源的浪费。这两种方法可操作性强,主观随意性小,得到的结果可信性高。     

Model evaluation of the phytoextraction potential of heavy metal hyperaccumulators and non-hyperaccumulators

Evaluation of the remediation ability of zinc/cadmium in hyper- and non-hyperaccumulator plant species through greenhouse studies is limited. To bridge the gap between greenhouse studies and field applications for phytoextraction, we used published data to examine the partitioning of heavy metals between plants and soil (defined as the bioconcentration factor). We compared the remediation ability of the Zn/Cd hyperaccumulators Thlaspi caerulescens and Arabidopsis halleri and the non-hyperaccumulators Nicotiana tabacum and Brassica juncea using a hierarchical linear model (HLM). A recursive algorithm was then used to evaluate how many harvest cycles were required to clean a contaminated site to meet Taiwan Environmental Protection Agency regulations. Despite the high bioconcentration factor of both hyperaccumulators, metal removal was still limited because of the plants' small biomass. Simulation with N. tabacum and the Cadmium model suggests further study and development of plants with high biomass and improved phytoextraction potential for use in environmental cleanup.     

基于回归分析的火灾发生气象因子的研究

利用二元回归分析法,对火灾发生次数、空气湿度、风速进行回归分析并检验,得出了火灾发生次数、空气湿度、风速三者之间的显著线性关系。     

Assessment Tools for Urban Catchments: Defining Observable Biological Potential1

Abstract: Urbanization represents a strong and increasingly more prevalent impact on stream quality worldwide. One of the characteristic effects of increased urbanization is a consistent decline in biological stream condition. The characterization of this biological degradation with increasing urbanization presents a number of advantages for the study and management of urban streams and catchments. In this paper, the limitation of biological condition with urbanization, called observed biological potential, is characterized. Using an urban intensity index and a biological index developed specifically for urban systems in the Baltimore, Maryland; Cleveland, Ohio; and San Jose, California regions, two principal techniques were compared (quantile regression and bin regression) to define observed biological potential along urban gradients. Quantile regression was selected as the preferable tool for describing observed biological potential given the consistency with which it can be applied and its statistical efficiency, however, bin quantile regression performed similarly. Having identified a numeric approximation of observed biological potential, two methods for identifying factors related to distance from potential as a way of identifying critical environmental factors affecting biological condition in urban areas were explored. The results of this work can be used for identifying benchmarks for urban stream biological condition, identifying limiting catchment characteristics, and prioritizing urban stream management efforts.     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.