IFRP: a hybrid interval-parameter fuzzy robust programming approach for waste management planning under uncertainty

In this study, an interval-parameter fuzzy-robust programming (IFRP) model is developed and applied to the planning of solid waste management systems under uncertainty. As an extension of the existing fuzzy-robust programming and interval-parameter linear programming methods, the IFRP can explicitly address system uncertainties with complex presentations. Parameters in the IFRP model can be represented as interval numbers and/or fuzzy membership functions, such that the uncertainties can be directly communicated into the optimization process and resulting solution. Furthermore, highly uncertain information for the lower and upper bounds of interval parameters that exist due to the complexity of the real world can be effectively handled through introducing the concept of fuzzy boundary interval. Consequently, robustness of the optimization process and solution can be enhanced. Results of the case study indicate that useful solutions for planning municipal solid waste management practices can be generated. They reflect a compromise between optimality and stability of the study system. Willingness to pay higher costs will guarantee the system stability; however, a desire to reduce the costs will run the risk of potential instability of the system. The results also suggest that the proposed hybrid methodology is applicable to practical problems that are associated with highly complex and uncertain information.     

An interval-based regret-analysis method for identifying long-term municipal solid waste management policy under uncertainty

In this study, an interval-based regret-analysis (IBRA) model is developed for supporting long-term planning of municipal solid waste (MSW) management activities in the City of Changchun, the capital of Jilin Province, China. The developed IBRA model incorporates approaches of interval–parameter programming (IPP) and minimax–regret (MMR) analysis within an integer programming framework, such that uncertainties expressed as both interval values and random variables can be reflected. The IBRA can account for economic consequences under all possible scenarios associated with different system costs and risk levels without making assumptions on probabilistic distributions for random variables. A regret matrix with interval elements is generated based on a matrix of interval system costs, such that desired decision alternatives can be identified according to the interval minimax regret (IMMR) criterion. The results indicate that reasonable solutions have been generated. They can help decision makers identify the desired alternatives regarding long-term MSW management with a compromise between minimized system cost and minimized system-failure risk.     

A reappraisal of WRI's estimates of greenhouse gas emissions

Excessive emissions of certain trace gases such as carbon dioxide, methane, nitrous oxide, carbon monoxide and chlorofluorocarbons are likely to result in global warming due to increased concentration of these greenhouse gases (GHGs). Therefore, measures to control GHG emissions are essential and the current international debate is on how to arrive at optimal GHG limitation strategies. To set emission targets or to distribute the burden of costs of various control measures, it is necessary to identify the major emitters. The World Resources Institute has assessed countrywide contributions to global GHG emissions for 1987. This paper disagrees with the basic approach adopted by WRI because it fails to apportion sinks on a logical basis by keeping in mind equity considerations; it does not account for the residence time of different GHGs; and it uses unreliable and outdated data for estimating the emissions. Using recent and reliable data for India and Brazil as well as the IPCC global warming potential for various GHGs, the shares in global emissions have been recalculated. The paper concludes that if only current emissions are considered there is considerable bias against those countries which are latecomers to the process of industrialization.     

A LINEAR ANALYSIS OF AN URBAN WATER SUPPLY SYSTEM1

The use of linear programming as a planning tool for determining the optimal long-range development of an urban water supply system was explored. A stochastic trace of water demand was synthesized and used as an input to the model. This permitted evaluating the feasibility of imposing demand restrictions as an effective cost reduction mechanism. The City of Lincoln, Nebraska, was used as the urban model. The fundamental problem was to allocate limited water supplies from several sources to an urban load center to minimize costs and comply with system constraints. The study period covered twenty years, and findings indicate the planning direction for stage development during this period. Sensitivity analyses were performed on cost coefficients and demands. Thirteen sources were included in the initial computations. Conclusions were that linear programming and generated demand traces are useful tools for both short- and long-term urban water supply planning. Lowering peak demands results in long-range development of fewer sources of supply and more economic and efficient use of the supplies developed.     

A Recourse‐Based Interval Fuzzy Programming Model for Point‐Nonpoint Source Effluent Trading under Uncertainty

In this study, a recourse‐based interval fuzzy programming (RIFP) model is developed for tackling uncertainties expressed as fuzzy, interval, and/or probabilistic forms in an effluent trading program. It can incorporate preregulated water‐pollution control policies directly into its optimization process, such that an effective linkage between environmental regulations and economic implications (i.e., penalties) caused by improper policies due to uncertainty existence can be provided. The RIFP model is applied to point‐nonpoint source effluent trading of the Xiangxi River in China. The efficiency of trading efforts between water quality improvement and net system benefit under different degrees of satisfying discharge limits is analyzed. The results are able to help support (1) formulation of water‐pollution control strategies under various economic objectives and system‐reliability constraints, (2) selection of the desired effluent trading pattern for point and nonpoint sources, and (3) generation of tradeoffs among system benefit, satisfaction degree, and pollutant mitigation under multiple uncertainties. Compared with the traditional regulatory approaches, the results demonstrate that the water‐pollution control program can be performed more cost‐effectively through trading than nontrading.     

Transaction costs and the clean development mechanism

The emissions trading provisions of the Kyoto Protocol and its clean development mechanism (CDM) are designed to permit greenhouse gas (GHG) emission reductions at the lowest cost globally. However, to ensure climate integrity, these reductions must pass through vigilant approval, monitoring and evaluation procedures that create additional transaction costs unrelated to the physical process of eliminating GHGs. Moreover, the CDM's additionality criterion creates constraints that magnify the influence of these transaction costs on project viability. If these costs are extreme, they could undermine the success of the CDM, and possibly of the Kyoto Protocol itself. This article describes the trading provisions of the treaty, creates a working definition of transaction costs, and discusses their effects. It then analyzes the process of creating a CDM project to identify the sources of transaction costs, illustrated by an example of a fuel substitution project in Ghana. The conditions for project profitability are analyzed and compared with recent GHG emission credit prices in Europe. The specific Ghanaian results are not generalizable to all CDM projects, but the model does suggest a template that can be used to analyze the effects of project and transaction costs in other contexts.     

Inexact multistage stochastic integer programming for water resources management under uncertainty

In this study, an inexact multistage stochastic integer programming (IMSIP) method is developed for water resources management under uncertainty. This method incorporates techniques of inexact optimization and multistage stochastic programming within an integer programming framework. It can deal with uncertainties expressed as both probabilities and discrete intervals, and reflect the dynamics in terms of decisions for water allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the IMSIP can facilitate analyses of the multiple policy scenarios that are associated with economic penalties when the promised targets are violated as well as the economies-of-scale in the costs for surplus water diversion. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that reasonable solutions have been generated for both binary and continuous variables. For all scenarios under consideration, corrective actions can be undertaken dynamically under various pre-regulated policies and can thus help minimize the penalties and costs. The IMSIP can help water resources managers to identify desired system designs against water shortage and for flood control with maximized economic benefit and minimized system-failure risk.     

Optimization of regional economic and environmental systems under fuzzy and random uncertainties

Environmental problems associated with socio-economic development have been growing concerns faced by many regional and/or national authorities. However, effective planning may encounter difficulties since uncertainties existing in a number of impact factors and pollution-related processes are often not well acknowledged and reflected. This study advances an interval-fuzzy chance-constrained programming (IFCP) method for planning regional economic and environmental systems, where uncertainties presented as intervals, fuzzy sets and probability distributions can be tackled. The developed method is applied to a real-world case for economic and environmental planning in the New Binhai District in the Municipality of Tianjin, China. Two scenarios based on multiple environmental constraints are examined. The results can help identify desired alternatives for planning regional development strategies, where compromised schemes are provided under an integrated consideration of economic efficiency and environmental protection under multiple uncertainties.     

A dual-interval fixed-mix stochastic programming method for water resources management under uncertainty

In this study, a dual-interval fixed-mix stochastic programming (DFSP) method is developed for planning water resources management systems under uncertainty. DFSP incorporates interval-parameter programming (IPP) and fuzzy vertex analysis (FVA) within a fixed-mix stochastic programming (FSP) framework to address uncertain parameters described as probability distributions and dual intervals. It can also be used for analyzing various policy scenarios that are associated with different levels of economic consequences since penalties are exercised with recourse actions against any infeasibility. A real case for water resources management planning of Zhangweinan River Basin in China is then conducted for demonstrating the applicability of the developed DFSP method. Solutions in association with α-cut levels are generated by solving a set of deterministic submodels, which are useful for generating a range of decision alternatives under compound uncertainties. The results can help to identify desired water-allocation schemes for local sustainable development that the prerequisite water demand can be guaranteed when the available water resource is scarce.     

An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection

Cut-off grade strategy (COGS) is a concept that directly influences the financial, technical, economical, and environmental issues in relation to the exploitation of a mineral resource. Despite the simple definition of cut-off grade, the COGS problem is one of the complex and complicated problems in the mine planning process. From the optimization point of view, the COGS with an objective of maximizing the present value of future cash flows is a non-linear and a non-convex problem that even in its deterministic form can be solved using approximate optimization methods. This optimization problem will also be more complex and complicated under uncertainty conditions. This paper proposes an uncertainty based multi-criteria ranking system to investigate the problem of COGS selection considering metal price and geological uncertainties. The proposed system aims at selection of the best COGS among technically feasible alternative COGSs under uncertainty circumstances. Our developed system is based on integrating metal price and geological uncertainties as well as operating flexibility to close the mine early. We incorporate this operating flexibility into the proposed system using a Monte Carlo based real options (RO) valuation model. For this purpose, in addition to the expected value, other risk criteria are considered to rank the alternatives. These risk criteria include abilities of strategies in producing extra profits, minimizing losses, and achieving the predefined goals of the production. In this study, the technically possible COGSs are generated using the Lane comprehensive algorithm. To demonstrate the effectiveness of the proposed system, we utilize data of an Iranian gold mine. Results show that the proposed system outperforms conventional methods in the sense that it shows significantly lower average mis-ranking than the other methods and also selects a strategy with a higher value. The sensitivity analysis of the proposed system relative to the gold price shows that the system is highly dependent on the parameters of the stochastic process used to model the evolution of the metal price. Therefore, special consideration should be given in estimating stochastic process parameters.     

Estimating local greenhouse gas emissions—A case study on a Portuguese municipality

This paper describes the study that led to the development of a carbon dioxide emissions matrix for the Oeiras municipality, one of the largest Portuguese municipalities, located in the metropolitan area of Lisbon. This matrix takes into account the greenhouse gases (GHG) emissions, due to an increase of electricity demand in buildings as well as solid and liquid wastes treatment, from the domestic and services sectors.Using emission factors that were calculated from the relationship between the electricity produced and amount of treated wastes, the GHG emissions in the Oeiras municipality, were estimated for a time series of 6 years (1998–2003).The obtained results showed that the electricity sector accounts for about 75% of the municipal emissions in 2003. This study was developed in order to obtain tools to base options and actions to be undertaken by local authorities such as energy planning and also public information.     

Harnessing Ecosystem Models and Multi-Criteria Decision Analysis for the Support of Forest Management

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.     

A Level‐of‐Service Concept for Planning Future Water Supply Projects under Probabilistic Demand and Supply Framework

One of the most challenging tasks of water supply utilities is planning the timing and quantity of new water supply sources as demand for water consumption grows. Many water supply utilities target on meeting 100% of their customers' needs based on scenario‐based deterministic demand projections numbers even though there are uncertainties in both supply and demand values. This may result in under or overly conservative approach in assessing future needs. In this article, a level‐of‐service concept is introduced to capture a utility's willingness to accept a given level of risk, plan for it, and invoke a management strategy during extreme events than build a facility to accommodate those in planning for new water supply sources. Accounting for uncertainties in both supply and demand help quantify reliability by achieving a prescribed level of service. The major benefit of such an approach for planning future water supply is that it allows policy makers to evaluate the use of adaptive water management strategies and develop supply in an incremental fashion as demand warrants it. For example, if a given level of service cannot be reliably met with the existing system at a future time t, an incremental water supply project would come online to bring the required reliability level up but no more.     

Inexact fuzzy-stochastic mixed-integer programming approach for long-term planning of waste management – Part A: Methodology

In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada.     

The pricing of water services in developing countries

This paper focuses on improving efficiency in the water and sewerage sectors through long-run marginal cost pricing, adjusted for financial needs, externalities, second best considerations, lifeline tariffs and cross subsidization within an integrated water resource planning (IWRP) framework. Supply efficiency suggests that for a given price structure, an optimal long-run investment plan and a corresponding level of supply quality should be determined which maximizes net social benefits. Supply efficiency also requires efficient operation of the water system, including optimization of losses.     

Economics and GHG emission reduction of a PLA bio-refinery system—Combining bottom-up analysis with price elasticity effects

This paper analyses energy savings, GHG emission reductions and costs of bio-refinery systems for polylactic acid (PLA) production. The systems comprise ‘multi-functional’ uses of biomass resources, i.e. use of agricultural residues for energy consumption, use of by-products, and recycling and waste-to-energy recovery of materials. We evaluate the performance of these systems per kg of bio-based polymer produced and per ha of biomass production. The evaluation is done using data of Poland assuming that biomass and PLA production is embedded in a European energy and material market. First, the performance of different bio-refinery systems is investigated by means of a bottom-up chain analysis. Second, an analysis is applied that derives market prices of products and land depending on the own-price elasticity of demand. Thus, the costs of bio-refinery systems depending on the demand of land and material are determined. It is found that all PLA bio-refinery systems considered lead to net savings of non-renewable energy consumption of 70–220 GJ/(ha yr) and net GHG emission reductions of 3–17 Mg CO_2eq/(ha yr). Most of these PLA bio-refinery systems lead to net costs for the overall system of up to 4600 €/(ha yr). PLA production from short rotation wood leads to net benefits of about 1100 €/(ha yr) if a high amount of a high value product, i.e. fibres, is produced. Multi-functionality is necessary to ensure the viability of PLA bio-refinery systems from biomass with regard to energy savings and GHG emission reduction. However, the multi-functional use of biomass does not contribute much to overall incomes. Multifunctional biomass use – especially the use of biomass residues for energy consumption – contributes significantly to savings of non-renewable energy sources. Own-price elasticity of the demand for materials influences the overall costs of the bio-refinery system strongly. The own-price elasticity of land demand markets could become important if bio-refineries are introduced on a large scale.     

Land Resources Allocation Strategies in an Urban Area Involving Uncertainty: A Case Study of Suzhou,in the Yangtze River Delta of China

A large number of mathematical models have been developed to support land resource allocation decisions and land management needs; however, few of them can address various uncertainties that exist in relation to many factors presented in such decisions (e.g., land resource availabilities, land demands, land-use patterns, and social demands, as well as ecological requirements). In this study, a multi-objective interval-stochastic land resource allocation model (MOISLAM) was developed for tackling uncertainty that presents as discrete intervals and/or probability distributions. The developed model improves upon the existing multi-objective programming and inexact optimization approaches. The MOISLAM not only considers economic factors, but also involves food security and eco-environmental constraints; it can, therefore, effectively reflect various interrelations among different aspects in a land resource management system. Moreover, the model can also help examine the reliability of satisfying (or the risk of violating) system constraints under uncertainty. In this study, the MOISLAM was applied to a real case of long-term urban land resource allocation planning in Suzhou, in the Yangtze River Delta of China. Interval solutions associated with different risk levels of constraint violation were obtained. The results are considered useful for generating a range of decision alternatives under various system conditions, and thus helping decision makers to identify a desirable land resource allocation strategy under uncertainty.     

Bulgaria Country Study to address climate change mitigation

Future developments of the Bulgarian economy, energy demand, energy supply, and greenhouse gas (GHG) emissions are projected and evaluated for baseline and mitigation scenarios. Different methods and approaches are used at different stages of the study with a tendency to incorporate them in a single integrated resource planning tool such as the MARKAL-MACRO model. The results obtained indicate that the aim of Framework Convention of Climate Change to have year 2000 GHG emissions below the base year 1988 emissions will be achieved without further mitigation steps. Reducing the expected increase of GHG emissions in the decade 2000 to 2010 requires a package of mitigation measures to be implemented in the next few years.

     

An interval type-2 fuzzy stochastic approach for regional-scale electric power system under parameter uncertainty

In this study, an interval type-2 fuzzy stochastic linear programming method (IT2FSLP) is developed to support regional-scale electric power system (REM) planning. The IT2FSLP-REM model is based on an integration of interval type-2 fuzzy sets boundary programming and stochastic linear programming techniques enable it to have robust abilities to the deal with uncertainties expressed as type-2 fuzzy intervals and probabilistic distributions within a general optimization framework. Moreover, it can reflect dynamic decisions for energy supply and energy conversion processes, as well as provide capacity expansion options with multiple periods. The developed model is applied to a case of planning regional-scale energy and environmental systems to demonstrate its applicability. Based on a two-step solution algorithm, reasonable solutions have been obtained, which reflect tradeoffs among economic cost, environmental requirements, and energy-supply security. Thus, the lower and upper solutions of IT2FSLP-REM would then help energy authorities adjust or justify allocation patterns of regional energy resources and services.