Disequilibrium and Indivisibility of Pollution Control Equipment Under a Permit-Trading System: A Case Study of Kao-Hsiung and Ping-Tung Counties in Taiwan

A mixed-integer programming model that minimizes the social abatement cost is used to investigate whether a market equilibrium condition could be reached in a newly proposed permit-trading market for nitrogen oxide control in Taiwan. Unlike in previous studies, unit pollution abatement cost is determined endogenously by incorporating technology adoption as a binary decision variable. The results show that when technologies are lumpy and irreversible, disequilibrium might occur due to firms’ inability to manage their emission levels after installing equipment with fixed size and control capacity.

Optimal Selection of Priority Development Areas Considering Tradeoffs Between Hydrology and Development Configuration

Mixed-integer linear programs are proposed for siting development and conservation areas in watersheds, addressing economic objectives (development perimeter and proximity) and ecological objectives. Links between watershed hydrology and ecology need not be well defined. Parameters for the linear programs are obtained from linearization of the SWAT hydrologic model.     

关于植物中氟化物测定结果的计算

针对植物中氟化物测定时由于绘制校准曲线的标准溶液体积与分析样品的定容体积不一样而引出的结果计算时的某些问题,提出了正确的计算方法和建议。     

线性修正趋势分析技术在环境质量预警预测中的应用

线性修正趋势分析法根据历年数据建立线性模型并进行修正,预测年度、季环境要素污染物变化情况,可应用于空气质量、水环境质量、噪声环境质量、污染源污染物排放等预警预测数据统计分析评价,为环境决策提供技术支持。本文通过实例论述了该方法在实际工作中的应用。     

DPX快速萃取程序升温大体积进样GC/MS法测定水中19种多溴联苯化合物

建立DPX快速吸附萃取、程序升温(PTV)大体积进样与气相色谱/质谱(GC/MS)联用,SIM模式同时测定水中19种多溴联苯(PBBs)单体的方法。该方法在PBBs质量浓度1~50μg/L范围内线性良好,19种PBBs单体检出限为0.147~0.230μg/L,相对标准偏差为6.61%~10.5%,加标回收率为61.5%~82.6%。     

Optimal Expansion Strategy for a Sewer System under Uncertainty

Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands—the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of uncertainties to propagate throughout the optimization context, simultaneously leading to determine a wealth of optimal decisions within a reasonable range. Both models ran for three 5-year periods beginning in 2005 and ending in 2020. The dynamic outputs of this analysis reflect the systematic concerns about integrative uncertainties within this decision analysis, which enable decision makers and stakeholders to make all-inclusive decisions for sanitary sewer system expansion in an economically growing region.     

Multi-stand Forest Management under a Climatic Risk: Do Time and Risk Preferences Matter?

We propose a stochastic dynamic programming framework to model the management of a multi-stand forest under climate risk (strong wind occurrence). The preferences of the forest-owner are specified by a non-expected utility in order to separately analyze intertemporal substitution and risk aversion effects. A numerical method is developed to characterize the optimal forest management policies and the optimal consumption-saving strategy. The stochastic dynamic programming framework is applied to a non-industrial private forest-owner located in North-East of France. We show that the optimal decisions both depend upon risk and time preferences. The authors would like to thank participants at the international conference on Economics of Sustainable Forest Management in Toronto, at the PARIS 1 seminar on Environmental and Natural Resource Economics, at the 2004 Applied Microeconomics Conference in Lille and at the 13th annual conference of the European Association of Environmental and Resource Economists at Budapest.     

A Model for Landscape Planning Under Complex Spatial Conditions

We present a new mathematical programming framework that is adaptable to a variety of spatially explicit landscape problems in environmental investment, conservation, and land-use planning, transport planning, and agriculture. As part of capturing spatial interdependencies, the framework considers decision variables at two levels, finely spaced grid cells and landholdings. We applied the framework to an environmental investment problem using objective functions representing biodiversity and carbon sequestration. We also tested the model to optimize the path of a road through part of the landscape. Using the Nambucca case study in eastern Australia, we applied a hybrid greedy randomised adaptive search procedure (GRASP) to find solutions to the model.     

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.     

Theoretical and Empirical Analysis on Influence Factors of Modern Agricultural Equipment Level in the Processof Agricultural Sustainable Development

To promote modern agricultural equipment level is one characteristic of constructing and developing modern agriculture in China.This paper makes up stepwise linear regression analysis model of influence factors of modern agricultural equipment level,and chooses rural labor,per capita income of rural residents,rural investment,proportion of people at secondary education level and at higher level in per hundred rural labor force and arable land area as independent variables,and total power of machine as induced variable.The major results show that the relativity of modern agricultural equipment level,rural investment and education level of peasants is remarkable,and they are the major influence factors of modern agricultural equipment level.Raising investment level of rural infrastructure construction as well as and research and development and promotion of advanced and applicable modern agricultural equipment,improving quality and education level of peasants can accelerate the development of China’s modern agricultural equipment effectively in the process of agricultural sustainable development.