Sustainable pattern analysis of a publicly owned Material Recovery Facility in a fast-growing urban setting under uncertainty

Sustainable development goals are achievable through the installation of Material Recovery Facilities (MRFs) in certain solid waste management systems, especially those in rapidly expanding multi-district urban areas. MRFs are a cost-effective alternative when curbside recycling does not demonstrate long-term success. Previous capacity planning uses mixed integer programming optimization for the urban center of the city of San Antonio, Texas to establish that a publicly owned material recovery facility is preferable to a privatized facility. As a companion study, this analysis demonstrates that a MRF alleviates economic, political, and social pressures facing solid waste management under uncertainty. It explores the impact of uncertainty in decision alternatives in an urban environmental system. From this unique angle, waste generation, incidence of recyclables in the waste stream, routing distances, recycling participation, and other planning components are taken as intervals to expand upon previous deterministic integer-programming models. The information incorporated into the optimization objectives includes economic impacts for recycling income and cost components in waste management. The constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening restrictions. Due to the fragmented data set, a grey integer programming modeling approach quantifies the consequences of inexact information as it propagates through the final solutions in the optimization process. The grey algorithm screens optimal shipping patterns and an ideal MRF location and capacity. Two case settings compare MRF selection policies where optimal solutions exemplify the value of grey programming in the context of integrated solid waste management.     

Negotiating environmental agreements under ratification constraints

We analyze the impact of ratification constraints on the optimal terms of international environmental agreements (IEAs). For this, we combine the literature on IEAs with the public choice literature on two-level games. By also incorporating uncertain preferences of the pivotal voter in the ratification stage, we make several important contributions: (i) we contribute to a more realistic modeling of the temporal structure of international agreements, (ii) we show a surprising, yet intuitive non-monotonic relationship between the optimal commitment level and the variance of ratification decisions, (iii) we identify reasons to expect a larger number of countries to join international negotiations than predicted by most of the coalition formation literature based on a representative agent model. Ratification constraints thereby can improve the welfare gains from stable international agreements.     

原子吸收法测定海水中铁含量的不确定度评定

为了保证海水中铁元素含量的检测质量,更好地深入了解铁在整个海水体系的生物地球化学中扮演的角色,需科学地评定检测结果的分散性。文章依据《测量不确定度的评定与表示》(JJF1059-1999)的理论,以浙江近海海水为例,评定原子吸收法测定海水中铁含量的不确定度。测得浙江近海样品中铁的浓度为5.2μg/L,扩展不确定度U=0.8μg/L(k=2)。通过对各不确定度分量进行评定发现,利用该方法测定海水中铁含量时,对其合成标准不确定度的主要贡献来自于样品制备过程,尤其是萃取过程。     

Decision making under uncertainty in case of soil remediation

Decisions on soil remediation are one of the most difficult management issues of municipal and state agencies. The assessment of contamination is uncertain, the costs of remediation are high, and the impacts on the environment are multiple. This paper presents a general, transparent, and consistent method for decision making among the remediation alternatives. Soil washing, phytoremediation, and no remediation are exemplarily considered. Multi-criteria utility functions including (a) the cost of remediation (b) the impact on human health and agricultural productivity, and (c) the economic gain after remediation are constructed using probability density functions representing contamination for all site coordinates. Herewith, the probability of different types of (i) correct decisions such as a hit or a true rejection and (ii) erroneous decisions such as a false alarm or miss are examined. The decision theoretic model is applied to a case study on heavy metal contaminated soil. This case study reveals the non-linear structure of multi-criteria-decision making. The case study shows that the geostatistical uncertainties of the log-normal distributed soil contamination must be taken into account: When uncertainties are not considered and the utilities are assessed according to the estimated value for a spatial unit, only few (N=26) spatial units result where the utility score of the alternative soil washing are higher than the utility score to the no remediation alternative. However, when taking into account geostatistical uncertainties of the log-normal soil distribution this number is about ten times greater (N=237). Furthermore, the use of 'maximizing expected utility' as decision rule is critical in that it may lead to a high probability of misses.     

Discovering meaningful information from large amounts of environment and health data to reduce uncertainties in formulating environmental policies

This study uses knowledge discovery concepts to analyze large amounts of data step by step for the purpose of assisting in the formulation of environmental policy. We performed data cleansing and extracting from existing nation-wide databases, and used regression and classification techniques to analyze the data. The current water hardness in Kaohsiung, Taiwan contributes to the prevention of cardiovascular disease (CVD) but exacerbates the development of renal stones (RS). However, to focus on water hardness alone to control RS would not be cost effective at all, because the existing database parameters do not adequately allow for a clear understanding of RS. Analysis of huge amounts of data can most often turn up the most reliable and convincing results and the use of existing databases can be cost-effective.     

Multi-criteria decision-making considering risk and uncertainty in physical asset management

In this work we present a method for risk-informed decision-making in the physical asset management context whereby risk evaluation and cost-benefit analysis are considered in a common framework. The methodology uses quantitative risk measures to prioritize projects based on a combination of risk tolerance criteria, cost-benefit analysis and uncertainty reduction metrics. There is a need in the risk and asset management literature for a unified framework through which quantitative risk can be evaluated against tolerability criteria and trade-off decisions can be made between risk treatment options. The methodology uses quantitative risk measures for loss of life, loss of production and loss of property. A risk matrix is used to classify risk as intolerable, As Low As Reasonably Practicable (ALARP) or broadly tolerable. Risks in the intolerable and ALARP region require risk treatment, and risk treatment options are generated. Risk reduction benefit of the treatment options is quantified, and cost-benefit analysis is performed using discounted cashflow analysis. The Analytic Hierarchy Process is used to derive weights for prioritization criteria based on decision-maker preferences. The weights, along with prioritization criteria for risk reduction, tolerance criteria and project cost, are used to prioritize projects using the Technique for Order Preference by Similarity to Ideal Solution. The usefulness of the methodology for improved decision-making is illustrated using a numerical example.     

Estimates of synthetic fertilizer N-induced direct nitrous oxide emission from Chinese croplands during 1980-2000

There is increasing concern that agricultural intensification in China has greatly increased N₂O emissions due to rapidly increased fertilizer use. By linking a spatial database of precipitation, synthetic fertilizer N input, cropping rotation and area via GIS, a precipitation-rectified emission factor of N₂O for upland croplands and water regime-specific emission factors for irrigated rice paddies were adopted to estimate annual synthetic fertilizer N-induced direct N₂O emissions (FIE-N₂O) from Chinese croplands during 1980-2000. Annual FIE-N₂O was estimated to be 115.7 Gg N₂O-N year⁻¹ in the 1980s and 210.5 Gg N₂O-N year⁻¹ in the 1990s, with an annual increasing rate of 9.14 Gg N₂O-N year⁻¹ over the period 1980-2000. Upland croplands contributed most to the national total of FIE-N₂O, accounting for 79% in 1980 and 92% in 2000. Approximately 65% of the FIE-N₂O emitted in eastern and southern central China.     

Policy and Science Implications of the Framing and Qualities of Uncertainty in Risks: Toxic and Beneficial Fish from the Baltic Sea

Policy and research issues in the framing and qualities of uncertainties in risks are analyzed, based on the assessments of dioxin-like compounds (DLCs) and other ingredients in Baltic Sea fish, a high-profile case of governance. Risks are framed broadly, to then focus on dioxins and beneficial fatty acids, fish consumption, human health, and science-management links. Hierarchies of uncertainty (data, model, decision rule, and epistemic) and ambiguity (of values) are used to identify issues of scientific and policy contestation and opportunities for resolving them. The associated complexity of risks is illustrated by risk–benefit analyses of fish consumption and by evaluations of guideline values, highlighting value contents and policy factors in presumably scientific decision criteria, and arguments used in multi-dimensional risk and benefit comparisons. These comparisons pose challenges to narrow assessments centered, for e.g., on toxicants or on food benefits, and to more many-sided and balanced risk communication and management. It is shown that structured and contextualized treatment of uncertainties and ambiguities in a reflexive approach can inform balances between wide and narrow focus, detail and generality, and evidence and precaution.     

Projecting Climate Change Effects on Forest Net Primary Productivity in Subtropical Louisiana,USA

This study projected responses of forest net primary productivity (NPP) to three climate change scenarios at a resolution of 5 km × 5 km across the state of Louisiana, USA. In addition, we assessed uncertainties associated with the NPP projection at the grid and state levels. Climate data of the scenarios were derived from Community Climate System Model outputs. Changes in annual NPP between 2000 and 2050 were projected with the forest ecosystem model PnET-II. Results showed that forest productivity would increase under climate change scenarios A1B and A2, but with scenario B1, it would peak during 2011–2020 and then decline. The projected average NPP under B1 over the years from 2000 to 2050 was significantly different from those under A1B and A2. Forest NPP appeared to be primarily a function of temperature, not precipitation. Uncertainties of the NPP projection were due to large spatial resolution of the climate variables. Overall, this study suggested that in order to project effects of climate change on forest ecosystem at regional level, modeling uncertainties could be reduced by increasing the spatial resolution of the climate projections.     

A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment

A highly resolved temporal and spatial Pearl River Delta (PRD) regional emission inventory for the year 2006 was developed with the use of best available domestic emission factors and activity data. The inventory covers major emission sources in the region and a bottom–up approach was adopted to compile the inventory for those sources where possible. The results show that the estimates for SO₂, NO_x, CO, PM₁₀, PM_2.5 and VOC emissions in the PRD region for the year 2006 are 711.4 kt, 891.9 kt, 3840.6 kt, 418.4 kt, 204.6 kt, and 1180.1 kt, respectively. About 91.4% of SO₂ emissions were from power plant and industrial sources, and 87.2% of NO_x emissions were from power plant and mobile sources. The industrial, mobile and power plant sources are major contributors to PM₁₀ and PM_2.5 emissions, accounting for 97.7% of the total PM₁₀ and 97.2% of PM_2.5 emissions, respectively. Mobile, biogenic and VOC product-related sources are responsible for 90.5% of the total VOC emissions. The emissions are spatially allocated onto grid cells with a resolution of 3 km × 3 km, showing that anthropogenic air pollutant emissions are mainly distributed over PRD central-southern city cluster areas. The preliminary temporal profiles were established for the power plant, industrial and on-road mobile sources. There is relatively low uncertainty in SO₂ emission estimates with a range of −16% to +21% from power plant sources, medium to high uncertainty for the NO_x emissions, and high uncertainties in the VOC, PM_2.5, PM₁₀ and CO emissions.