以Hartwick准则破解“资源的诅咒”

一些资源禀赋较高的国家或地区,一直以来经济社会发展未充分享受自然资源带来的红利,甚至陷入贫困和动荡,这一现象被称作"资源的诅咒";在我国,也有不少资源型城市在发展过程中由于资源的耗竭而陷入困境。经济学家Hartwick对资源枯竭区域发展进行了深入研究,并提出"Hartwick准则"对这一类区域的发展进行了理论指导。本文对Hartwick准则进行简要介绍,以这一准则在非洲国家博茨瓦纳的钻石开发中的应用进行分析,并依据理论和经验,对我国一些资源型区域的发展提出建议。     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

Evaluation of a classification method for biodegradable solid wastes using anaerobic degradation parameters

We studied the biochemical and anaerobic degradation characteristics of 29 types of materials to evaluate the effects of a physical composition classification method for degradable solid waste on the computation of anaerobic degradation parameters, including the methane yield potential (L₀), anaerobic decay rate (k), and carbon sequestration factor (CSF). Biochemical methane potential tests were conducted to determine the anaerobic degradation parameters of each material. The results indicated that the anaerobic degradation parameters of nut waste were quite different from those of other food waste and nut waste was classified separately. Paper was subdivided into two categories according to its lignin content: degradable paper with lignin content of <0.05 g g VS^?1, and refractory paper with lignin content >0.15 g g VS^?1. The L₀, k, and CSF parameters of leaves, a type of garden waste, were similar to those of grass. This classification method for degradable solid waste may provide a theoretical basis that facilitates the more accurate calculation of anaerobic degradation parameters.     

Inner City regeneration: perspectives on the G.E.A.R. Project

Government responses to the problems of the inner city currently focus on comprehensive programmes of positive discrimination. The first and most extensive is the G.E.A.R. project in Glasgow. Regular monitoring of the effectiveness of such large‐scale initiatives is essential. If the physical and social aims of such policies are to be realised, assessments must utilise both objective and subjective indicators.     

Rough Set Rule Induction for Suitability Assessment

The data that characterize an environmental system are a fundamental part of an environmental decision-support system. However, obtaining complete and consistent data sets for regional studies can be difficult. Data sets are often available only for small study areas within the region, whereas the data themselves contain uncertainty because of system complexity, differences in methodology, or data collection errors. This paper presents rough-set rule induction as one way to deal with data uncertainty while creating predictive if–then rules that generalize data values to the entire region. The approach is illustrated by determining the crop suitability of 14 crops for the agricultural soils of the Willamette River Basin, Oregon, USA. To implement this method, environmental and crop yield data were spatially related to individual soil units, forming the examples needed for the rule induction process. Next, four learning algorithms were defined by using different subsets of environmental attributes. ROSETTA, a software system for rough set analysis, was then used to generate rules using each algorithm. Cross-validation analysis showed that all crops had at least one algorithm with an accuracy rate greater than 68%. After selecting a preferred algorithm, the induced classifier was used to predict the crop suitability of each crop for the unclassified soils. The results suggest that rough set rule induction is a useful method for data generalization and suitability analysis.     

Cluster detection using Bayes factors from overparameterized cluster models

In this paper, we consider the use of a partition model to estimate regional disease rates and to detect spatial clusters. Formal inference regarding the number of partitions (or clusters) can be obtained with a reversible jump Markov chain Monte Carlo algorithm. As an alternative, we consider the ability of models with a fixed, but overly large, number of partitions to estimate regional disease rates and to provide informal inferences about the number and locations of clusters using local Bayes factors. We illustrate and compare these two approaches using data on leukemia incidence in upstate New York and data on breast cancer incidence in Wisconsin.     

Trends and sources of particulate matter in the Superstition Wilderness using air trajectory and aerosol cluster analysis

Ambient aerosols adversely affect human health and visibility and impact climate. Identification of sources of particulate matter and its precursors is necessary for developing control strategies. The goal of this research is to utilize long-term speciated particulate matter data and back-trajectory cluster analyses to determine trends and sources of particulate matter in the Superstition Wilderness, a rural area east of Phoenix, Arizona. Twenty-four hour back-trajectories were calculated for every hour of every 24-h particulate matter sample obtained by IMPROVE from 1991 to 2004. Days that included back-trajectories with considerable spatial variance were excluded from further analyses. To minimize uncertainties inherent in single trajectories, all calculated trajectories for each sampling day were averaged to represent the air mass sampled during that day. Cluster analysis of trajectories identified four unique regions, including a region with Phoenix, a region with copper smelters, and one with coal-fired power plants. Yearly averages of sulfate, nitrate, soil, and carbon concentrations were calculated for each region. Statistically significant trends in species concentrations by region and independent of region and differences in concentrations between regions were examined.Sulfate concentrations from the region with smelters were higher than other regions but decreased during the study period. Emissions data from the smelters indicate that much of the sulfate from the region was due to the smelters. The overall 2.2% year⁻¹ decrease in sulfate concentrations at TNM is likely due to decreased emissions from the copper smelters. A 3.6% year⁻¹ increase in nitrate concentrations was driven largely by increasing NO_x concentrations from Phoenix and to a lesser extent the region southwest of the site which includes Tucson and suburban/urban areas between Phoenix and Tucson. Soil concentrations were higher from regions with deserts than the region without desert. This method could not identify trends or source regions of carbonaceous aerosols at this site.     

废黄河水质的多元统计分析

用主成分分析的方法,找出影响废黄河2007年水质的主成分,计算各样本的主成分得分,用聚类分析的方法将样本水质分类,结合分类结果及水质综合评分对各样本水质进行评价。     

泸州市降水监测布点优化研究

在泸州市2016—2020年大气降水监测数据的基础上,借助聚类分析、多元方差分析等统计分析手段,对原有降水监测点位进行优化研究,并验证优化结果。在遵循《酸沉降监测技术规范》(HJ/T 165—2004)点位布设要求的前提下,建立了降水监测点位评价体系,从原有7个降水监测点位中优化筛选出3个。优化后的点位对监测结果的影响显著性均>0.05,表明优化前后全市降水数据无显著性差异,该优化方案不会影响区域整体代表性。     

南昌典型园林树种燃烧性实验研究

选择南昌西郊和北郊12个园林树种不同器官理化性状测定分析和燃烧试验,根据各树种的含水率、燃点、热值、粗灰分、粗脂肪、木质素、粗纤维、燃烧时间、火烧强度和生物生态学特性等10个因子,应用DPS聚类分析法,对12个园林树种的燃烧性进行了排序,结果表明,难燃树种有5个,依次是：乐昌含笑、珊瑚树、大叶黄杨、白玉兰和海桐;较难燃树种有4个,依次是：女贞、山茶、夹竹桃和含笑;易燃树种有3个,依次是：桂花、巴东木连和红花檵木。为南方防火林带的建设提供了目前公认以外的防火树种。