Global Warming, Uncertainty and Endogenous Technical Change

What impact does ecological uncertainty have on agents' decisions concerning domestic emissions abatement, physical investments, and R&;D expenditures? How sensitive are the answers to these questions when we move from exogenous to endogenous technical change? To investigate these issues we modify the ETC-RICE model described in Buonanno et al. (2001) by embedding in it a hazard rate function as in Bosello and Moretto (1999). With this model at hand we run numerical optimisations focusing our attention on the control variables of the representative agents, i.e., domestic abatement rate, investments in physical capital, and R&;D spending, as well as on the endogenous patterns of GDP level and CO₂ emissions. Our results show that uncertainty strongly influences agents behaviour; in particular, agents slow down their emissions in order to maintain a more sustainable growth path. In addition, R&;D expenditures trigger the “engine of growth” exclusively when environmental technical change is formalized in an endogenous fashion. However, even if environmental uncertainty may stimulate technical change, long-run growth it turns out to be negatively affected by the former, as also predicted by Clarke and Reed (1994) Tsur and Zemel (1996) and Bosello and Moretto (1999).     

Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty

Concerning the stabilization of greenhouse gases, the UNFCCC prescribes measures to anticipate, prevent, or minimize the causes of climate change and mitigate their adverse effects. Such measures should be cost-effective and scientific uncertainty should not be used as a reason for postponing them. However, in the light of uncertainty about climate sensitivity and other underlying parameters, it is difficult to assess the importance of different technologies in achieving robust long-term climate risk mitigation. One example currently debated in this context is biomass energy, which can be used to produce both carbon-neutral energy carriers, e.g., electricity, and at the same time offer a permanent CO₂ sink by capturing carbon from the biomass at the conversion facility and permanently storing it. We use the GGI Scenario Database IIASA [3] as a point of departure for deriving optimal technology portfolios across different socioeconomic scenarios for a range of stabilization targets, focusing, in particular, on new, low-emission scenarios. More precisely, the dynamics underlying technology adoption and operational decisions are analyzed in a real options model, the output of which then informs the portfolio optimization. In this way, we determine the importance of different energy technologies in meeting specific stabilization targets under different circumstances (i.e., under different socioeconomic scenarios), providing valuable insight to policymakers about the incentive mechanisms needed to achieve robust long-term climate risk mitigation.     

A Satisficing Framework for Environmental Policy Under Model Uncertainty

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

     

Uncertainty in watershed response predictions induced by spatial variability of precipitation

Negligence to consider the spatial variability of rainfall could result in serious errors in model outputs. The objective of this study was to examine the uncertainty of both runoff and pollutant transport predictions due to the input errors of rainfall. This study used synthetic data to represent the “true” rainfall pattern, instead of interpolated precipitation. It was conducted on a synthetic case area having a total area of 20 km² with ten subbasins. Each subbasin has one rainfall gauge with synthetic precipitation records. Six rainfall storms with varied spatial distribution were generated. The average rainfall was obtained from all of the ten gauges by the arithmetic average method. The input errors of rainfall were induced by the difference between the actual rainfall pattern and estimated average rainfall. The results show that spatial variability of rainfall can cause uncertainty in modeling outputs of hydrologic, which would be transport to pollutant export predictions, when uniformity of rainfall is assumed. Since rainfall is essential information for predicting watershed responses, it is important to consider the properties of rainfall, particularly spatial rainfall variability, in the application of hydrologic and water quality models.     

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.     

土壤中有机磷农药分析过程中不确定度的来源及评定

土壤中有机磷农药分析过程中的不确定度主要来源于样品采集、运输和保存、前处理及分析这四个环节。以土壤中马拉硫磷的测定为例,分别评定了各个环节产生不确定度的大小。结果表明,样品前处理产生的不确定度对总不确定度的贡献最大。     

How to Take into Account Uncertainty in Species Extinction Probabilities for Phylogenetic Conservation Prioritization

In this article, we are concerned with the general problem of choosing from a set of taxa T a subset S to protect in order to try to contribute to halting biodiversity loss as efficiently as possible given limited resources. The protection of a taxon decreases its extinction probability, and the impact of protecting the taxa of S is measured by the resulting expected phylogenetic diversity (ePD) of the set T. The primary challenge posed by this approach lies in determining the extinction probability of a taxon (protected or unprotected). To deal with this difficulty, the uncertainty about the extinction probabilities can be described through a set of possible scenarios, each corresponding to different extinction probability values for each taxon. We show how to determine an “optimal robust set” of taxa to protect, defined as the set of taxa that minimizes the maximum “regret,” i.e., the maximum relative gap, over all the scenarios, between (1) the ePD of T obtained by protecting the taxa of this set and (2) the ePD of T which would be produced by protecting the subset of taxa optimal for the considered scenario. In our experimental conditions covering 100 cases, this gap is almost always less than 1%. Consequently, the ePD of T obtained by protecting the taxa of the optimal robust set is not far from the maximum ePD of T that could have been obtained if we had known the true scenario. In other words, a way of escaping (in large part, at least) from the uncertainty related to the extinction probabilities of the taxa consists of choosing to protect those belonging to the optimal robust set. We also compare the optimal robust set to other relevant subsets of T.     

Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty

In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.     

Geostatistical Uncertainty Modelling for the Environmental Hazard Assessment During Single Erosive Rainstorm Events

This paper presents an environmental hazard assessment to account the impacts of single rainstorm variability on river-torrential landscape identified as potentially vulnerable mainly to erosional soil degradation processes. An algorithm for the characterisation of this impact, called Erosive Hazard Index (EHI), is developed with a less expensive methodology. In EHI modelling, we assume that the river-torrential system has adapted to the natural hydrological regime, and a sudden fluctuation in this regime, especially those exceeding thresholds for an acceptable range of flexibility, may have disastrous consequences for the mountain environment. The hazard analysis links key rainstorm energy variables expressed as a single-storm erosion index (EI_sto), with impact thresholds identified using an intensity pattern model. Afterwards, the conditional probabilities of exceeding these thresholds are spatially assessed using non-parametric geostatistical techinques, known as indicator kriging. The approach was applied to a test site in river-torrential landscape of the Southern Italy (Benevento province) for 13 November 1997 rainstorm event.     

便携式GC-MS测定气体样品中6种典型VOCs的不确定度评定

以便携式GC-MS测定气体样品中6种典型挥发性有机物(VOCs)组分(苯、1,1,2-三氯乙烷、四氯乙烯、乙苯、间二甲苯和1,3,5-三甲基苯)为例,应用不确定度理论,从检测过程和计算方法的角度分析了影响测量不确定度的各种因素:标准气体定值、标准气体稀释、工作曲线的非线性及重复性测定.对各测量不确定度分量进行计算和评定...     

IFTCIP: An Integrated Optimization Model for Environmental Management under Uncertainty

In this study, an interval-fuzzy two-stage chance-constrained integer programming (IFTCIP) method is developed for supporting environmental management under uncertainty. The IFTCIP improves upon the existing interval, fuzzy, and two-stage programming approaches by allowing uncertainties expressed as probability distributions, fuzzy sets, and discrete intervals to be directly incorporated within a general mixed integer linear programming framework. It has advantages in uncertainty reflection, policy investigation, risk assessment, and capacity-expansion analysis in comparison to the other optimization methods. Moreover, it can help examine the risk of violating system constraints and the associated consequences. The developed method is applied to the planning for facility expansion and waste-flow allocation within a municipal solid waste management system. Violations of capacity constraints are allowed under a range of significance levels, which reflects tradeoffs between the system cost and the constraint-violation risk. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different risk levels. They are useful for generating desired decision alternatives with minimized system cost and constraint-violation risk under various environmental, economic, and system-reliability conditions. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost.     

A Superiority-Inferiority-Based Inexact Fuzzy Stochastic Programming Approach for Solid Waste Management Under Uncertainty

A superiority–inferiority-based inexact fuzzy stochastic programming (SI-IFSP) model was developed for planning municipal solid waste management systems under uncertainty. The SI-IFSP approach represents a new attempt to tackle multiple uncertainties in objective function coefficients which are beyond the capabilities of existing inexact programming methods. Through introducing the concept of fuzzy random boundary interval, SI-IFSP is capable of reflecting multiple uncertainties (i.e., interval values, fuzzy sets, probability distributions, and their combinations) in both the objective function and constraints, leading to enhanced system robustness. The developed SI-IFSP method was applied to a case study of long-term municipal solid waste management. Useful solutions were generated. A number of decision alternatives could be generated based on projected applicable conditions, reflecting the compromise between system optimality and reliability as well as the tradeoffs between economic and environmental objectives. Moreover, the consequences of system violations could be quantified through introducing a set of economic penalties, reflecting the relationships between system costs and constraint violation risks. The results suggest that the proposed SI-IFSP method can explicitly address complexities in municipal solid waste management systems and is applicable to practical waste management problems.     

Uncertainty Assessment of Voc Emissions from Paint in the Netherlands Using the Nusap System

We demonstrate an innovative approach to uncertainty assessment known as the NUSAP system, to assess qualitative and quantitative uncertainty for the case of emissions of volatile organic compounds (VOC) from paint in The Netherlands. Using expert elicitation, we identified key sources of error, critical assumptions, and bias in the monitoring process. We assessed pedigree and probabilistic uncertainty of all input quantities. We used four pedigree criteria to assess the strength of the knowledge base: proxy representation, empirical basis, methodological rigour and degree of validation. Using Monte Carlo analysis, we assessed sensitivity and propagation of uncertainty. Results for sensitivity and pedigree were combined in a NUSAP Diagnostic Diagram, which effectively highlighted the assumption for VOC percentage of imported paint as the weakest spot in the monitoring of VOC emissions. We conclude that NUSAP facilitates systematic scrutinization of method and underlying assumptions and structures creative thinking on sources of error and bias. It provides a means to prioritise uncertainties and focus research efforts on the potentially most problematic parameters and assumptions, at the same time identifying specific weaknesses in the knowledge base. With NUSAP, nuances of meaning about quantities can be conveyed concisely and clearly, to a degree that is not possible with statistic methods only.     

用波长色散X射线荧光光谱测定土壤中铬的不确定度

运用测量不确定度评定与表示的理论,分析了波长色散X射线荧光光谱仪测定土壤中铬的不确定度,得出测定铬的不确定度为1.0mg/kg.     

Uncertainty in integrated assessment: A social scientific perspective

In the context of integrated assessment, the authors address the issue of uncertainty and subjectivity in modelling. In relating bias to different perspectives, the authors introduce the methodology of multiple model routes, which are reflections of different perceptions of reality and various policy preferences. As heuristic they use three perspectives, which are distinguished in cultural theory. The article describes case-studies on the population and health controversy in order to illustrate the possibilities of their approach. The article concludes with discussing the lessons learned by applying this methodology.     

水中挥发酚的测量不确定度评定

建立了分光光度法测定水中挥发酚的合成标准不确定度的数学模式,它由质量的标准测量不确定度和体积的标准测量不确定度组成。应用一个实例对这两部分标准不确定度的分量作了详尽的分析和计算,得出测量扩展不确定度结果。     

水中放射性核素锶-90测量不确定度的评估

阐述了分析放射性核素产生不确定度因素比一般化学分析多的原因,通过采用鱼骨图法分析放射性核素锶-90测量实验中的不确定度和分析计算公式中的有关参数,了解到分析水中放射性核素锶-90时,主要受到样品测量、仪器探测效率、样品化学回收率和样品取样体积等4个方面的不确定度因素影响。     

硝酸银标准滴定溶液的不确定度评定

在掌握JJF1059-1999的基础上，对硝酸银标准滴定溶液浓度的不确定度进行评定，主要从方法概述、确定测量中不确定度的来源、建立数学模型、计算相对标准不确定度分量、合成相对标准不确定度、扩展不确定度等入手，详细介绍了硝酸银标准滴定溶液的不确定度评定过程。     

运用线性拟合法评定水中总磷的测量不确定度

采用Top—Down不确定度评定理念，利用实验室日常质控数据，结合标准样品的线性校准方法（线性拟合法），评定水中总磷的测量不确定度，并将评定结果与GUM评定法相比较，相对偏差≤20％。指出线性拟合法适用于测量系统校准函数成线性，且实验总残差符合常数剩余标准差假定情况下的不确定度评定。     

Uncertainty from Model Calibration: Applying a New Method to Transport Energy Demand Modelling

Uncertainties in energy demand modelling originate from both limited understanding of the real-world system and a lack of data for model development, calibration and validation. These uncertainties allow for the development of different models, but also leave room for different calibrations of a single model. Here, an automated model calibration procedure was developed and tested for transport sector energy use modelling in the TIMER 2.0 global energy model. This model describes energy use on the basis of activity levels, structural change and autonomous and price-induced energy efficiency improvements. We found that the model could reasonably reproduce historic data under different sets of parameter values, leading to different projections of future energy demand levels. Projected energy use for 2030 shows a range of 44–95% around the best-fit projection. Two different model interpretations of the past can generally be distinguished: (1) high useful energy intensity and major energy efficiency improvements or (2) low useful energy intensity and little efficiency improvement. Generally, the first lead to higher future energy demand levels than the second, but model and insights do not provide decisive arguments to attribute a higher likelihood to one of the alternatives.