不同地区经济增长与环境污染关系的VAR模型分析——基于广州、佛山、肇庆经济圈的实证研究

利用向量自回归(VAR)计量技术对广州.佛山一肇庆(简称"广佛肇")经济圈各城市表征环境污染的指标(工业废水排放量、工业废气排放量和工业固废产生量)和表征经济增长的指标(人均GDP)的时序数据进行脉冲响应函数及预测方差分解分析,研究处于工业化后期的广州、佛山市及工业化前期的肇庆市经济发展与环境污染在时序维度上的关系及其...     

南方稻区农户适应气候变化行为实证分析 ——基于江西省36县(市)346份农户调查数据

利用江西省36县346个农户调查数据,运用Heckman Probit选择模型实证分析了影响农户气候变化感知及其适应行为决策的因素,结果表明,户主年龄、文化程度、与村民交流频率、来往亲戚数、赶集频率、看电视频率、距离市场远近及气象信息服务等因素能显著影响农户对气候变化的感知;而户主性别、年龄、文化程度、可借款人数、来往亲戚数、赶集频率、看电视频率及气象信息服务等因素对农户气候变化适应行为决策有显著影响。最后,根据实证分析结果,提出了促进农户采用气候变化适应行为的政策建议。     

长江流域南京段河流非使用价值评估

采用逻辑回归与相关性分析法,探讨影响公众支付意愿的因素及其影响程度。结果表明,政府信任度、个人经济收入、年龄和环保意识4个变量因子对支付与否具有显著影响;受教育程度和环保意识这两个变量因子对支付意愿的高低具有显著影响。     

改进的未确知测度评价模型在水环境质量评价中的应用

构筑了一种新的单指标未确知测度模式,在此基础上建立了改进的未确知测度模型,并应用该模型对某经济开发区的水环境质量状况进行了评价.结果表明,改进的未确知测度模型的样本排序与样本识别所得的判断结果相吻合,避免了传统未确知测度模型在固有的情况下,样本排序与样本识别判断不一致的问题,从而能够更加客观、合理地反映水环境质量状况.     

化工罐区大气环境影响评价中的技术探讨

就化工罐区大气环境影响评价中储罐正常排放时预测模式的选取、风险事故后果评价预测模式和参数的选取、环境监测计划的制定等技术问题进行了探讨.储罐大小呼吸损耗应采用面源模式进行预测更符合罐区无组织低矮源排放的实际情况.在风险事故后果评价中,泄漏蒸发和火灾事故中C0、SO2等伴生污染物的扩散模拟所选取的扩散参数的不同将导致预测...     

西安生活垃圾排放量影响因素分析及灰色预测研究

城市生活垃圾的产生量是城市环境卫生事业规划和建设的重要依据,本文运用灰色关联度方法,定量分析了影响西安市生活垃圾产生量的因素,并通过建立灰色预测模型,对西安市未来几年的生活垃圾产生量进行了预测,为西安市的城市规划提供了定量的参考依据。     

工业点源环境影响预测系统研发

采用Visual C#为开发工具,以Microsoft Office Access2003建立污染源数据库,开发了基于高斯模式的工业多点源污染扩散环境影响预测系统,实现了对工业点源下风向敏感点污染物浓度的快速、准确计算。通过加载Surfer9 Type Library COM组件,绘制污染物浓度分布云图,结合电子地图上,直观地显示了污染源对敏感区域的环境影响程度,为城市空气环境问题的管理和决策提供了科学的依据。     

基于SCGE的国土空间规划模拟框架CTSPM及其在国土空间安全模拟仿真中的应用

土地利用指标的区域配置与部门配置是我国国土空间规划体系的核心问题。如何在贯彻落实国家意志和中央战略的前提下,根据我国不同类型自然地理区划和经济社会区划的发展实际,特别是根据城镇化格局定型区、发展型城镇化地区和收缩型城镇化地区等三类不同地区的土地利用的实际,充分顾及各级地方政府的发展意愿,优化配置各类用地指标,是本轮国土空间规划不可或缺的首要的基本任务。基于此,建立一个基于空间可计算一般均衡模型（SCGE）的多尺度、多区域、多情景国土空间规划模拟分析框架（CTSPM）,提出该框架在国土空间安全模拟仿真中的实际应用场景,以支持多种国家发展情景和土地指标分配方案下国土空间格局模拟分析,及其经济社会影响和生态环境影响分析,为各地区、各部门之间的土地指标“博弈”提供“辩论”平台,为科学编制国土空间规划以及深度理解国土空间安全的跨区域影响提供分析性框架。     

An evaluation of environmental factors affecting species distributions

Many different models can be built to explain the distributions of species. Often there is no single model that is clearly better than the alternatives, and this leads to uncertainty over which environmental factors are limiting species’ distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an area under the curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected model compares to alternative models, and they provide weak inference to accept any selected model.     

Estimation methods for nonlinear state-space models in ecology

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.