An Inexact Fuzzy-robust Two-stage Programming Model for Managing Sulfur Dioxide Abatement Under Uncertainty

In this study, an inexact fuzzy-robust two-stage programming (IFRTSP) method is developed for tackling multiple forms of uncertainties that can be expressed as discrete intervals, probabilistic distributions and/or fuzzy membership functions. The model can reflect economic penalties of corrective measures against any infeasibilities arising due to a particular realization of system uncertainties. Moreover, the fuzzy decision space can be delimited into a more robust one with the uncertainties being specified through dimensional enlargement of the original fuzzy constraints. A management problem in terms of regional air pollution control has been studied to illustrate the applicability of the proposed approach. Results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers identify desired pollution-abatement strategy with minimized system cost and maximized environmental efficiency.     

Inexact Fuzzy-Stochastic Programming for Water Resources Management Under Multiple Uncertainties

In this study, an interval-parameter fuzzy-stochastic two-stage programming (IFSTP) approach is developed for irrigation planning within an agriculture system under multiple uncertainties. A concept of the distribution with fuzzy-interval probability (DFIP) is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets, and probability distributions. IFSTP integrates the interval programming, two-stage stochastic programming, and fuzzy-stochastic programming within a general optimization framework. IFSTP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. IFSTP is applied to an irrigation planning in a water resources management system. Solutions from IFSTP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions are generated for objective function values and decision variables; thus, a number of decision alternatives can be generated under different levels of stream flows.     

Interval-Parameter Conditional Value-at-Risk Two-Stage Stochastic Programming Model for Management of End-of-Life Vehicles

The management of end-of-life vehicles conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. Sound management of end-of-life vehicles is vitally important worldwide thus requiring sophisticated decision-making tools for optimizing its efficiency and reducing system risk. This paper proposes an interval-parameter conditional value-at-risk two-stage stochastic programming model for management of end-of-life vehicles. A case study is conducted in order to demonstrate the usefulness of the developed model. The model is able to provide the trade-offs between the expected profit and system risk. It can effectively control risk at extremely disadvantageous availability levels of end-of-life vehicles. The formulated model can produce optimal solutions under predetermined decision-making risk preferences and confidence levels. It can simultaneously determine the optimal long-term allocation targets of end-of-life vehicles and reusable parts as well as capital investment, production planning, and logistics management decisions within a multi-period planning horizon. The proposed model can efficiently handle uncertainties expressed as interval values and probability distributions. It is able to provide valuable insights into the effects of uncertainties. Compared to the available models, the resulting solutions are far more robust.

     

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.     

Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods

This study attempts to determine the scale-dependent hierarchical spatial variation and longitudinal distributions of Sicyopterus japonicus year round. The distribution of S. japonicus in the Datuan Stream in northern Taiwan was surveyed during the fall and winter 2007, as well as the spring and summer of 2008. The spatial structure of S. japonicus density was modeled using geostatistics. The longitudinal distributions of S. japonicus density were then estimated using kriging and hydrology distance with nested variogram models. Variography results indicate that nested variogram models could reflect the hierarchical structure in the spatial variation of seasonal S. japonicus density, with the small, median, and large ranges representing three nested scales. Models for the four seasons were consistent in that they shared the same shape of variogram models with various ranges and sill values. This model shape consistency implies stationary spatial correlations in the longitudinal fish distribution across the four seasons. The Kriging geostatistical method based on the multiple scales nested variogram models also provided robust estimates of S. japonicus densities at unsampled sections. We conclude that S. japonicus densities exhibit hierarchical patterns and variation in the four seasons along the study stream. Geostatistical methods with a nested variograms and hydrological distance are a highly effective means of delineating the hierarchical structure in longitudinal patterns of S. japonicus density in each season, providing estimates of the S. japonicus density for hierarchically structured spatial distributions and expanding knowledge of S. japonicus beyond the limits imposed by spatial and temporal scales.     

Investments in Power Generation Under Uncertainty—a MIP Specification and Large-Scale Application for EU

Investments in power generation constitute a typical budget allocation problem in the context of multiple objectives, while all factors influencing investor’s decisions for power plants are subject to considerable uncertainties. The paper introduces a multi-objective stochastic model designed to optimize budget allocation decisions for power generation in the context of risk aversion taking into account several sources of uncertainty, especially with regard to volatility of fossil fuel and electricity prices, technological costs, and climate policy variability. Probability distributions for uncertain factors influencing investment decisions are directly derived from the stochastic global energy model PROMETHEUS and thus they take into account complex interactions between variables in the systemic context. In order to fully incorporate stochastic characteristics of the problem, the model is specified as an optimization problem in which the probability that an objective exceeds a given threshold is maximized (risk aversion) subject to a set of deterministic and probabilistic constraints. The model is formulated as a mixed integer program providing complete flexibility on the joint distributions of rates of return of technologies competing for investments, as it can handle non-symmetric distributions and take automatically into account complex covariance patterns as emerging from comprehensive PROMETHEUS stochastic results. The analysis shows that risk is a crucial factor for power generation investments with investors not opting for technologies subject to uncertainty related to climate policies and fossil fuel prices. On the other hand, combination of options with negative covariance tends to benefit in the context of risk-hedging behavior.     

Utility of fuzzy cross-impact simulation in environmental assessment

Fuzzy cross-impact simulation is a qualitative technique, where interactions within a system are represented by a cross-impact matrix that includes linguistic elements. It can be used effectively to visualize dynamic evolution of a system. The utility of the fuzzy cross-impact simulation approach is: (1) in dealing with uncertainties in environment-development systems; (2) scoping cumulative effect assessment; and (3) integrating societal response structure in environment impact assessment. Use of the method is illustrated in a case concerning the textile industry in Indore, India. Consequences of policy alternatives for cleanup and pollution abatement are predicted in terms of environmental quality and quality of life, using the simulation model. The consequence analysis is used to arrive at preferred policy options.     

Transition to Lead-Free Products in the US Electronics Industry: A Model of Environmental,Technical, and Economic Preferences

The European Union’s Restriction on the Use of Certain Hazardous Substances (Directive 2002/95/EC) targeted at electronic products took effect in 2006. In contrast, the USA has no comparable national policy on these products. To understand corporate responses to policy differences across jurisdictions, we conducted a structured-questionnaire survey of individuals in 109 companies that are representative of the US electronics industry. The results reveal that 70% of these companies have already adopted lead-free solder for electronics with 49% of the total preferring the SnAgCu formulation, despite uncertainties associated with environmental impacts of this alternative alloy. We use a modified life cycle impact assessment method based on endpoint modeling approach to derive weighting factors that represent the respondents’ value system for tradeoffs among environmental impacts. We use a modified fuzzy Technique for Order Preference by Similarity to Ideal Solution approach to evaluate technical criteria dominance in declared preferences. A statistical model of corporate behavior is also presented. The results provide the first systematic framework that accounts for environmental impact, technological challenge, and business strategy concurrently toward formulating a comprehensive national policy for materials selection in electronic products.     

Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment

In this paper, we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in a harmonized way stochastic programming in a large-scale bottom-up (BU) model and Monte Carlo simulation in a large-scale top-down (TD) model. The BU model we use is the TIMES Integrated Assessment Model, which is run in a stochastic programming version to provide a hedging emission policy to cope with the uncertainty characterizing climate sensitivity. The TD model we use is the computable general equilibrium model GEMINI-E3. Through Monte Carlo simulations of randomly generated uncertain parameter values, one provides a stochastic micro- and macro-economic analysis. Through statistical analysis of the simulation results, we analyse the impact of the uncertainties on the policy assessment.     

DMSP-IEES: A Stochastic Programming Model Based on Dual-Interval and Multi-Stage Scenarios Modeling Approaches for Energy Systems Management and GHG Emissions Control

Energy-related activities contribute a major portion of anthropogenic greenhouse gas (GHG) emissions into the atmosphere. In this study, a dual-interval multi-stage stochastic programming model for the planning of integrated energy-environment systems (DMSP-IEES) model is developed for integrated energy-environment systems management, in which issues of GHG-emission mitigation can be reflected throughout the process of energy systems planning. By integrating methodologies of interval linear programming (when numbers are described as interval values without distribution information), dual-interval programming (when lower and upper bounds of interval values are not available as deterministic values but as discrete intervals), and multi-stage stochastic programming, the DMSP-IEES model is capable of dealing with uncertainties expressed as discrete intervals, dual intervals, and probability distributions within a multi-stage context. Decision alternatives can also be generated through analysis of the single- and dual-interval solutions according to projected applicable conditions. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that the developed model can tackle the dual uncertainties and the dynamic complexities in the energy-environment management systems through a multi-layer scenario tree. In addition, it can reflect the interactions among multiple system components and the associated trade-offs.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

A spatial hierarchical framework for the co-management of ecosystems in Canada and the United States for the upper Great Lakes region

Over the past three decades, considerable effort has been invested in the development of complex and comprehensive ecosystem classifications and inventories in many parts of North America. Paralleling this has been an evolution in those hierarchical frameworks guiding the development and application of classifications. However, resource management agencies continue to grapple with the dilemma of applying multiple classification and inventory templates over large jurisdictions, especially as they attempt to address ecosystem management objectives. Given that Canada and the United States share ecosystems and that commitments have been made by all levels of government to make progress towards ecosystem-based approaches to management, there is a need to provide the proper tools. Comprehensive goals will not be achieved without collaboration and cooperation.This paper outlines the range of ecosystem classification approaches that exist in the Upper Great Lakes region. Canadian and American national hierarchical frameworks are briefly examined. Specific information needs and tasks are outlined which must be followed, independent of national boundaries, for the successful integration of planning and monitoring programs for large regional ecosystems.A general model is proposed for the development and application of an integrated, multi-scale and bi-national ecosystem classification, inventory and information system. This approach would facilitate data sharing and communication across jurisdictional boundaries.     

Using an Allowance Reserve to Manage Uncertain Costs in a Cap-and-Trade Program for Greenhouse Gases

This paper investigates the design and performance of an allowance reserve in the context of a cap-and-trade program for greenhouse gases. We use a Monte Carlo approach in which the parameters of the marginal abatement cost function, and the supply of offsets, are drawn from specified distributions. Our framework focuses on the potential impact of “medium-run shocks” to abatement cost and offset supply, as opposed to either short-run volatility or permanent shifts in the cost curve. Our model suggests that under reasonable (and even fairly conservative) assumptions about abatement cost and offset supply, an allowance reserve broadly similar to recent proposals for US climate legislation can be effective in containing allowance prices. In our core policy scenario, with a trigger price equal to US $32 in 2015, we estimate that the probability of drawing on the allowance reserve is <25% and the probability of requiring more than 7?GT of reserve tons over 20?years is <5%. We also use the model to explore the trade-off among three features of the reserve that are most relevant to policy makers: the total size of the reserve, the trigger price, and the degree of confidence that the reserve will be large enough to limit allowance prices to the target level. Our essential result is that a lower trigger price, or a higher degree of confidence, requires a larger reserve.     

Neutrosophic multi-criteria evaluation of sustainable alternatives for the structure of single-family homes

This paper proposes a methodology for the assessment of the sustainability among three different structural design alternatives for a single-family home. The response associated with each alternative has been measured using 43 indicators considering all stages of the life cycle. A decision-making model is carried out on the basis of a neutrosophic group analytical hierarchy process (NAHP-G) capturing the maximum information in terms of credibility, inconsistency and indetermination. The 9 criteria on which an expert group intervenes are finally evaluated using VIKOR. The results show that non-probabilistic uncertainties influence the weights obtained, with maximum deviations in the criteria between 11.91% and 4.95%, if compared to conventional AHP. From the methodology it is obtained that the technological alternative with non-conventional concrete performs best in sustainable terms. Although the industrialized option has less environmental impact, only the simultaneous consideration of the economic, environmental and social pillars in a project will lead to appropriate sustainable designs.     

Avoiding climate change uncertainties in Strategic Environmental Assessment

This article is concerned with how Strategic Environmental Assessment (SEA) practice handles climate change uncertainties within the Danish planning system. First, a hypothetical model is set up for how uncertainty is handled and not handled in decision-making. The model incorporates the strategies ‘reduction’ and ‘resilience’, ‘denying’, ‘ignoring’ and ‘postponing’. Second, 151 Danish SEAs are analysed with a focus on the extent to which climate change uncertainties are acknowledged and presented, and the empirical findings are discussed in relation to the model. The findings indicate that despite incentives to do so, climate change uncertainties were systematically avoided or downplayed in all but 5 of the 151 SEAs that were reviewed. Finally, two possible explanatory mechanisms are proposed to explain this: conflict avoidance and a need to quantify uncertainty.     

Biological complexity confounds the separation of point- and non-point sources of human impact on the natural world

Identifying process from pattern is one of the most vexing tasks inenvironmental monitoring. Given information on the distribution of speciesin a pre-defined area, together with comprehensive data on how environmentalconditions in that area have altered through time, is it possible toidentify the factors controlling the species‘ layout? Here, the practicalsignificance of this quandary is demonstrated using a series ofenvironmentally-degraded coastal lagoons in New South Wales. The TuggerahLakes (33°17′S,151°30′E) have over the last 50 yearsexperienced significant changes in species‘ distributions. Seagrasses,macroalgae, phytoplankton, molluscs, prawns and the jellyfish Catostylus mosaicus have altered in spatial pattern. Two human activitieshave been blamed for these perturbations: (1) agricultural clearance ofnative vegetation from the catchment, with associated input of top-soil andnutrients; (2) the commissioning of a coal-fired power station in 1967, withmassive uptake and recirculation of lake water for cooling purposes. In thispaper, spatial changes in macrophyte distributions over the last 50 yearsare reviewed in an attempt to identify the true source(s) of perturbation.The model adopted assumes that the power station is a point source of impactwhile nutrient inputs from the catchment are a diffuse source of impact;changes in species distributions can hypothetically be related back to thesesources according to whether they are localised or widespread. However,after a comprehensive analysis of available macrophyte data derived frominterviews, aerial photography and line transect methodologyies theconclusion is reached that changes in biogeographical pattern around theTuggerah Lakes cannot be attributed to specific anthropogenic pressures atanything beyond the coarsest of levels. This is considered to be the normfor most coastal management situations where natural background variation(’noise‘) and the complexity of linkages between physical, chemical andbiological components confounds the identification of causal relationships.The practical implications of this conclusion are discussed in the contextof litigation and remedial management design. Emphasis is placed on theneed to adopt an adaptive approach to estuarine management, incorporatingexplicit recognition of the limitations of available data, and to developnew techniques for identifying cause-effect relationships.     

Evaluation of a real-time monitoring system for river quality — a trade-off between risk attitudes,costs, and uncertainty

Uncertainty is definitely one of the key topics in environmental assessment and management. Typically, attempts to reduce uncertainty are subject to expenses. But how to compare and trade-off expenses and the reduced uncertainty? They only seldom allow the use of a single unit. Instead, the whole analysis and decision procedure is very subjective. This paper presents one approach to handle such problems, namely the combined use of Bayesian influence diagrams, and probabilistic risk attitude analysis. The approach was used in the evaluation of three alternatives for a real time river water quality forecasting system. A trade-off analysis of risk attitudes, costs and uncertainty indicated the levels of socioeconomic utility required for investments in the respective systems, and accordingly illuminated the impact of the uncertainties involved on inference and decision-making with various risk attitudes and discount rates.     

An Integrated Multicriteria Decision Analysis System for Reducing Air Emissions from Mining Process

The selection of a best alternative method to minimize air pollution and energy consumption for mine sites is a critical task because it encompasses evaluation of different techniques. The aim of this paper is to select most suitable technology for mining system which helps in reducing air pollution and carbon footprints by implementing the multicriteria decision analysis (MCDA) method. The existing methods or frameworks in the mining sector, which have been used in the past to select the sustainable solution, are lacking aid of MCDA, and there is a need to contribute more in this field with a promising decision system. The MCDA method is applied as a probabilistic integrated approach for a mine site in Canada. The analysis involves processing inputs to the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method which assists in identifying the alternatives, defining the criteria, and thus outranking of the final choice. Moreover, criteria weighting has been determined using analytical hierarchical process (AHP) method. Three categories: reduction of dust/fugitive emission control strategies, reduction in fuel consumption to minimize carbon footprint, and cyanide destruction methods are selected. The probability distributions of criteria weights and output flows are defined by performing uncertainty analysis using the Monte Carlo simulation (MCS). The sensitivity analysis is conducted using Spearman’s rank correlation method and walking criteria weights. The results indicate that the integrated framework provides a reliable way of selecting air pollution control solutions and help in quantifying the impact of different criteria for the selected alternatives.

     

Climatic Implications of Revised IPCC Emissions Scenarios, the Kyoto Protocol and Quantification of Uncertainties

In December 1997, the United Nations Framework Convention on Climate Change (FCCC) adopted the Kyoto Protocol. This paper describes a framework that models the climatic implications of this international agreement, using Monte Carlo simulations and the preliminary Intergovernmental Panel on Climate Change emissions scenarios (SRES). Emissions scenarios (including intervention scenarios), climate sensitivity, and terrestrial carbon sink are the key sampled model parameters. This framework gives prior probability distributions to these parameters and, using a simple climate model, posterior distributions of global temperature change are determined for the future. Our exercise showed that the Kyoto Protocol's effectiveness will be mostly dependent upon which SRES world evolves. In some worlds the Protocol decreases the warming considerably but in others it is almost irrelevant. We exemplified this approach with a current FCCC issue, namely “hot air”. This modelling framework provides a probabilistic assessment of climate policies, which can be useful for decision-makers involved in global climate change management. This revised version was published online in July 2006 with corrections to the Cover Date.