Willingness to pay for clean air in China

We use a residential sorting model incorporating migration disutility to recover the implicit value of clean air in China. The model is estimated using China Population Census Data along with PM2.5 satellite data. Our study provides new evidence on the willingness to pay for air quality improvements in developing countries and is the first application of an equilibrium sorting model to the valuation of non-market amenities in China. We employ two instrumental variables based on coal-fired electricity generation and wind direction to address the endogeneity of local air pollution. Results suggest important differences between the residential sorting model and a conventional hedonic model, highlighting the role of moving costs and the discreteness of the choice set. Our sorting results indicate that the economic value of air quality improvement associated with a one-unit decline in PM2.5 concentration is up to $8.83 billion for all Chinese households in 2005.     

Extinction rate of discovered and undiscovered plants in Singapore

Extinction is a key issue in the assessment of global biodiversity. However, many extinction rate measures do not account for species that went extinct before they could be discovered. The highly developed island city–state of Singapore has one of the best-documented tropical floras in the world. This allowed us to estimate the total rate of floristic extinctions in Singapore since 1822 after accounting for sampling effort and crypto extinctions by collating herbaria records. Our database comprised 34,224 specimens from 2076 native species, of which 464 species (22%) were considered nationally extinct. We assumed that undiscovered species had the same annual per-species extinction rates as discovered species and that no undiscovered species remained extant. With classical and Bayesian algorithms, we estimated that 304 (95% confidence interval, 213–414) and 412 (95% credible interval, 313–534) additional species went extinct before they could be discovered, respectively; corresponding total extinction rate estimates were 32% and 35% (range 30–38%). We detected violations of our 2 assumptions that could cause our extinction estimates, particularly the absolute numbers, to be biased downward. Thus, our estimates should be treated as lower bounds. Our results illustrate the possible magnitudes of plant extirpations that can be expected in the tropics as development continues.     

A regional environmental quality management model: An assessment

The principal purpose of this paper is to discuss the research and policy lessons learned from a large environmental quality management model constructed for the Lower Delaware River Valley Region, The policy lessons involve estimates of the costs of meeting varying standards on air and water quality and the impact on those costs of certain region-wide management alternatives. The research lessons concern: (a) the desirability of considering air and water quality, and solid waste disposal, simultaneously in a single model; (b) the costs of and returns to including nonlinear models of natural systems in a regional optimization framework; and (c) the feasibility of working with constraints on the geographic distribution of the costs of environmental quality improvement within the regional, nonlinear model.     

Reconstructibility of Density Dependence and the Conservative Assessment of Extinction Risks

Abstract: The probability of extinction is sensitive to the presence and character of density dependence controlling the dynamics of a population. This means that our capacity to estimate a population's risks of extinction under varying environmental conditions or competing management regimes is linked to our ability to reconstruct from data the density-dependence relationships governing the natural dynamics, especially when data do not reveal a trend of population growth or decline. In an example using Gadus morhua , we show that even 10- or 20-year data sets are too short to make precise estimates of these risks. We also observe, however, that under moderate or weak density dependence, the computed risks are lower than when density dependence is not included in the model. We propose, therefore, that when available data sets are insufficient for reconstructing reliable measurements of density dependence, conservative estimates of extinction probabilities can be made from models that simply omit density dependence.     

Hedonic housing prices and the demand for clean air

This paper investigates the methodological problems associated with the use of housing market data to measure the willingness to pay for clean air. With the use of a hedonic housing price model and data for the Boston metropolitan area, quantitative estimates of the willingness to pay for air quality improvements are generated. Marginal air pollution damages (as revealed in the housing market) are found to increase with the level of air pollution and with household income. The results are relatively sensitive to the specification of the hedonic housing price equation, but insensitive to the specification of the air quality demand equation.     

Specification of the logit model: The case of valuation of nonmarket goods

We address the proper specification of the logit model for estimation of nonmarket commodity demand. Economic theory is used to show the conditions which must be met by the logit functional form if it is to yield valid estimates of welfare change measures and demand relationships. An empirical example is used to illustrate the argument. Recreational boating data were collected using the contingent valuation method (CVM) in Texas. In one instance the demand curve is positively sloped due to the specific functional form chosen. The estimated demand curve for the theoretically preferred functional form has the expected negative slope.     

Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling

Studying evolutionary mechanisms in natural populations often requires testing multifactorial scenarios of causality involving direct and indirect relationships among individual and environmental variables. It is also essential to account for the imperfect detection of individuals to provide unbiased demographic parameter estimates. To cope with these issues, we developed a new approach combining structural equation models with capture-recapture models (CR-SEM) that allows the investigation of competing hypotheses about individual and environmental variability observed in demographic parameters. We employ Markov chain Monte Carlo sampling in a Bayesian framework to (1) estimate model parameters, (2) implement a model selection procedure to evaluate competing hypotheses about causal mechanisms, and (3) assess the fit of models to data using posterior predictive checks. We illustrate the value of our approach using two case studies on wild bird populations. We first show that CR-SEM can be useful to quantify the action of selection on a set of phenotypic traits with an analysis of selection gradients on morphological traits in Common Blackbirds (Turdus merula). In a second case study on Blue Tits (Cyanistes caeruleus), we illustrate the use of CR-SEM to study evolutionary trade-offs in the wild, while accounting for varying environmental conditions.     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Multinomial mixture model with heterogeneous classification probabilities

Royle and Link (Ecology 86(9):2505?C2512, 2005) proposed an analytical method that allowed estimation of multinomial distribution parameters and classification probabilities from categorical data measured with error. While useful, we demonstrate algebraically and by simulations that this method yields biased multinomial parameter estimates when the probabilities of correct category classifications vary among sampling units. We address this shortcoming by treating these probabilities as logit-normal random variables within a Bayesian framework. We use Markov chain Monte Carlo to compute Bayes estimates from a simulated sample from the posterior distribution. Based on simulations, this elaborated Royle-Link model yields nearly unbiased estimates of multinomial and correct classification probability estimates when classification probabilities are allowed to vary according to the normal distribution on the logit scale or according to the Beta distribution. The method is illustrated using categorical submersed aquatic vegetation data.     

Valuing nature in a general equilibrium

We explore the consequences of modeling the demand for environmental quality improvements as a fully integrated part of a general equilibrium demand system in an applied general equilibrium (or CGE) analysis. Demand for non-market goods depends on a full range of relative prices as well as environmental outcomes. We simulate the effects of reducing two air pollutants to improve human health and three ecosystem services provided to households. The ecosystem services make non-separable contributions to household utility. We find that willingness to pay measures of use-based ecosystem services are impacted by changes in demand for complementary market goods. Demand for these goods shifts due to pollution reductions that enhance ecosystem services. Partial equilibrium estimates of these use values can be measured with substantial error if they fail to account for the general equilibrium adjustments caused by pollution. Over 300 calibrations of the model identify the model features important to these errors. We find that effects on ecosystem services associated with non-use values have important implications for the feedback effects on use related measures of economic tradeoffs. This is due to how our model integrates market and non-market effects, reflecting the non-market services importance to general equilibrium market outcomes.     

The demand for site-specific recreational activities: A characteristics approach

A model of constrained utility maximizing behavior is developed to explain how a representative individual allocates his ski days among alternative sites. The physical characteristics of the ski areas and the individual's skiing ability are explicit arguments in the utility function; the budget allocation is given along with the parametric costs to ski (including travel costs, entrance fees, equipment costs, and the opportunity cost of his time). Shares (a site's share being the proportion of ski days that the individual spends at that site) are derived and assumed multinomially distributed, a stochastic specification which maintains the inherent properties of the shares. Maximum likelihood estimation confirms the basic hypothesis that costs, ability, and characteristics all are important determinants of the sites' shares. The model explains a large proportion of the skier's allocation of ski days. A multinomial logit model of skier behavior is also developed and maximum likelihood estimates of its parameters are obtained. Examination of the summary statistics from my model and the logit model indicates that my model predicts the skier's choice of sites better than the logit model.     

A framework for evaluating regional-scale numerical photochemical modeling systems

This paper discusses the need for critically evaluating regional-scale (~200–2,000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model’s credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.     

Congestion and participation in outdoor recreation: A household production function approach

This paper examines the effects of congestion on recreational behavior within a household production model of consumer behavior. We assume that congestion affects the household's ability to produce constant quality recreational service flows and derive a reduced-form model for participation decisions in remote and developed camping. Empirical estimates of the effects of a congestion measure on the conditional probability of participation as well as on the level of participation are estimated for each activity by the type of trip using information from the 1972 National Recreation Survey. The findings suggest that congestion was most likely to affect the decision to participate and not the level once that decision had been made. While differences in these effects were observed across the activities studied, it is not clear how they should be interpreted since our congestion measure was a proxy variable likely to perform better for remote camping than developed camping.     

Bayesian predictive inference for the proportion of eroded land in a small area in Iowa

There is an increasing interest in the quality of soil, especially for small geographical areas. We present a method to estimate the percent of the area in a county or hydrological basin that is eroded. There are sample data (for several counties in eastern Iowa) from the National Resources Inventory and population data on land use, land capability class, rainfall and slope length and steepness. Using the Gibbs sampler we perform Bayesian predictive inference to obtain estimates for the non-sampled units. These estimates, together with the sample data, provide an estimate of the proportion of the total area that is eroded. We assess the quality of fit of our model using two cross-validation exercises and graphical methods.     

基于PLS1的哈尔滨市PM2.5与空气污染物相关性分析

以往对PM2.5的研究多集中在气象因子或单一空气污染物对PM2.5质量浓度变化的影响,未考虑多种空气污染物对PM2.5质量浓度的协同作用。通过哈尔滨市环保局发布的2014年1月份（共31 d）市区内主要空气污染物SO2、NO2、PM10、CO、O3、PM2.5的质量浓度数据,运用相关性分析、PLS1和通径分析方法,研究哈尔滨市区内主要空气污染物对PM2.5质量浓度变化的直接影响、通过其他空气污染物的间接影响及污染物之间的协同作用。结果表明,SO2、NO2、PM10、CO 与 PM2.5质量浓度显著性相关,O3与 PM2.5质量浓度相关性不显著,SO2、NO2、PM10、CO 之间存在严重的复相关性。依据相关性分析结果,建立了SO2、NO2、PM10、CO对PM2.5质量浓度的PSL1模型,模型的拟合优度r2为0.852,模型拟合良好。对所建立的模型进行通径分析,结果显示,SO2、NO2、PM10、CO对PM2.5质量浓度变化的直接作用分别为0.005、-0.142、-0.140、1.191,CO对PM2.5质量浓度变化的直接影响作用最大。SO2、NO2、PM10通过CO对PM2.5质量浓度变化的间接作用分别为0.706、1.011、1.118均大于它们对PM2.5质量浓度变化的直接作用。SO2、NO2、PM10、CO对PM2.5质量浓度变化的总决定系数为85.9%。CO是主要空气污染物中影响PM2.5质量浓度变化的主要因素,降低冬季煤炭供暖期CO的排放量,有利于提高空气环境质量,降低对人体的健康危害。     

Measuring the benefits of air quality improvements: A hedonic salary approach

This paper estimates the compensating variation associated with nonmarginal changes in air quality using a hedonic salary model and 1970 data collected for a national sample of university professors. Recent advances in the theory of hedonic prices are utilized in constructing the theoretical model and formulating a procedure for generating empirical estimates. Lower bound estimates of the compensating variation associated with a one standard deviation increase in total suspended particulates (27.6 mgs/cu. meter/24 hours) were $419 for full professors, $234 for associate professors, and $209 for assistant professors in 1970. These results were not sensitive to the specification of the hedonic salary equation.     

兰州地区苯并(a)芘的环境多介质迁移和归趋模拟

利用Level Ⅲ逸度模型模拟计算了稳态假设下苯并(a)芘在兰州地区大气、水体、土壤和沉积物中的相间迁移通量、浓度分布.结果表明:大气的平流输入和化石燃料燃烧是该区域苯并(a)芘的主要来源,土壤是其最大的储库,占总残留量的99.6%;在大气、水体、土壤和沉积物中的浓度分别为1.61×10-10 mol·m-3、9.39×10-7 mol·m-3、7.13×10-4 mol·m-3和9.17×10-5mol·m-3,模型计算浓度与同期实测浓度吻合较好,验证了模型的可靠性,并通过灵敏度分析,确定了模型的关键参数.     

天津市环境空气中SO2污染特征分析

在全面监测天津城区环境空气中SO2现状污染水平的基础上,分析天津市SO2大气污染特征,影响因素及治理效果,并且分析天津市环境空气质量的变化趋势。     

A robust weighted EM algorithm for use-availability data

Wildlife sampling for habitat selection often combines a random background sample with a random sample of used sites, because the background sample could contain too few used sites to be informative for rare species. This approach is referred to as use-availability sampling. Two variants are considered where there is: (1) a random background sample including used and unused sites augmented with a sample of used sites, and (2) a sample of used sites augmented with a contaminated background sample, i.e. use is not recorded. A weighted estimator first proposed by Manski and Lerman (Econometrica 45(8):1977?C1988, 1977) forms the basis for our suggested approach. The weighted estimator has been shown to perform better than the usual unweighted approach with uncontaminated data and mis-specified logit models (Xie and Manski in Sociol Methods Res 17(3):283?C302, 1989). A weighted EM algorithm is developed for use with contaminated background data. We show that the weighted estimator continues to perform well with contaminated data and maintains its robustness to model mis-specification. The weighted estimator has not been previously used for use-availability sampling due to reliance on the assumption that only the intercept is biased, which is valid for a correct logit model. We show that adjusting the intercept may not eliminate the bias with an incorrect logit model. In this case, the weighted estimator is a relatively simple and effective alternative.     

武汉市城区大气PM2．5的碳组分与源解析

大气细颗粒物（PM2．5）和碳组分（OC,EC）是影响大气能见度、气候变化以及人体健康的重要污染物,研究大气颗粒物及其中碳组分的污染特征及各类典型污染源对大气细颗粒物及碳组分的贡献,对于认识区域和城市大气污染状况,控制细颗粒物的污染,具有重要意义。2011年7月至2012年2月,利用大流量PM2．5采样器采集武汉市大气细颗粒物样品并对其碳组分进行测定。武汉市城区大气中PM215、OC和EC的质量浓度平均值分别为（127±48．7）、（19．4±10．5）和（2．9±1．48）μg·m-3。其PM2．5的浓度处于我国主要城市的中等偏高水平,而OC、EC的浓度则属中等偏下水平,但均高于国外城市。武汉市大气PM2,质量浓度的季节性变化呈现出秋季〉冬季〉夏季的趋势,是气象因素和污染源排放综合影响的结果。OC浓度和EC浓度具有较好的相关性（r2=0．69）,表明二者存在来源联系。OC／EC的比值为6．7,指示武汉市大气中OC和EC的来源受汽车尾气排放和生物质燃烧的共同影响。SOA的平均质量浓度值为12．5μg·m-3约占PM2．5平均质量浓度的9．8％,表明SOA对武汉市城区大气PM2．5具有重要贡献。结合PM2．5所含的水溶性离子、微量元素组成,利用正矩阵因子分析（PMF）模型对武汉市城区大气PM2．5来源进行解析,结果表明,其主要来源及贡献率分别为机动车源（27．1％）、二次硫酸盐和硝酸盐（26．8％）、工厂排放（26．4％）和生物质燃烧（19．6％）。