Integrated Waste Management Volume I Part 13 potx

Cost-Benefit Analysis of the Clean-Up of Hazardous Waste Sites 411 2.3 Time adjustment for environmental benefits and costs The cost and the benefit of a hazardous waste site cleanup, especially in the case of permanent cleanup, materialise over lengthy periods. Thus, discounting plays a crucial role in the estimation of the value of future costs and benefits. Where different types of interventions are compared, discounting future costs and benefits to present values renders them more easily comparable. Discounting implies that the further in the future the benefits and the costs occur, the lower the weight that should be attached to them. Thus, the general formula of discounting is the following (Pearce et al. 2006): t t 1 W (1 s)   Where w t is the discount factor for time t and s is the discount rate. Thus, the conversion of future benefits to a present value can be estimated with the following formula:  tt PresentValue = FutureValue × w Where economists use discounting to adjust the value of costs and benefits occurring in the future, the standard approach is to assume a constant discount rate common to both costs and benefits. For example, since 1992 the US discount rate suggested as base case for cost- benefit analyses was a fixed at 7% for both cost and benefit estimates. A 3% discount rate was also suggested for sensitivity analysis. The European Commission (2001) recommends for environmental cost benefit analyses the use of a discount rate of 4% and to perform sensitivity analyses using a discount rate of 2 and 4%. However, there has been extensive discussion of whether the discount rate for health benefits should be lower than that applied to monetary costs. Also, where the effects under consideration are long-lived the case for discount rates declining over time has been made. Mainly due to the lack of empirical studies, there is uncertainty regarding the discount rate to be adopted in the economic evaluation of toxic waste cleanup interventions. A recent study conducted by Alberini et al. (2007) in four Italian cities with significant toxic waste problems applied a contingent choice methodology and evaluated that individuals discount future risk with a 7% rate. Recent studies also suggest that the discount rate might not be fixed and that s should be varying with t. According to Viscusi and Hubert (2006) the discount rate shown for improvements in environmental quality does not follow the standard discounted utility model but its pattern is consistent with the hyperbolic model. Time lag between the cleanup policy and its related benefits is also an important issue. The annual number of health outcomes (for example number of asthma cases) observable in a given area increases after the creation of a waste site which is producing toxic emissions. After a latency period, which denotes the lag between emissions and onset of the negative health effects, the number of health effects will increase at either a proportional or non- proportional rate. Eventually, if both the emission dose and the population exposed remain constant over the years, the incremental number of health outcomes attributable to pollution exposure is likely to remain the same. When a cleanup policy is implemented, there are no immediate reductions in the number of health outcomes. This is referred to as the “cessation lag”. Following the cessation lag, there will be a gradual (proportional/non proportional) decline in the effects of the reduced emission on health up to the point where the number of health outcomes is the same as observed before the creation of the waste site. Integrated Waste Management – Volume I 412 The formula used to account for both discounting and latency of benefits is the following:     lt a *X*1/1 d *1 1/1 d /dPresent value of Benefits   Where: X a is the number of health endpoints averted by the cleanup, t is the number of years over which the benefits accrue, and d is the discount rate. λ is the WTP for the health outcome a and latency period l, which is the time occurring between the reduction of the exposure and the improvement in the health of the population. 2.4 Cost-benefit evaluation The main condition for the adoption of a clean-up intervention is that the present value of the benefit exceeds the present value of the cost or that the: Net present value >0. The Net present value (NPV) rule is usually adopted to decide whether to accept or reject an option, to rank different projects and to choose between mutually exclusive projects. An equivalent feasibility test is the benefit cost ratio (BCR) test (Pearce et al. 2006): PVB / PVC 1. However, there are differences between the two tests. The first evaluates the excess in benefits and is a more direct way of measuring the social benefits of a cleanup intervention. The second evaluates the benefits per dollar of cost incurred. For example, a cost ratio of 2.2 means that for each dollar invested $2.20 of social benefit is realized (Pearce et al. 2006). There is general agreement that BCR can be misleading when used outside the rationing context (when only one project should be evaluated: implemented versus rejected). 2.5 Risk and uncertainty As mentioned in the previous paragraphs, cost and benefits are difficult to ascertain. In this context, it is important to define risk and uncertainty given that these are often used as interchangeable elements in the literature. Risk denotes the possibility of attaching a probability to costs or benefits that are not known with certainty. Uncertainty denotes a case in which the probability distribution is not available, but crude end points like the min and max are known. If the decision maker is risk neutral, the expected values of benefits and cost are evaluated. In this case, the net present value equation is as follows (Pearce et al. 2006):  Ii i I jj NPV=( p ×B)-( p ×C) Where Pi is the probability that the benefit Bi occurs and pj is the probability that the cost j occurs. A recent study evaluating the potential benefit of reducing the pollution exposure in the two industrial areas of Gela and Priolo (Sicily) adopted, for the first time, cost benefit acceptability curves to assess uncertainty in benefit/cost estimates. To build cost benefit acceptability curves Guerriero et al. (2011) assign to each parameter a probability distribution (e.g. gamma for cost, normal for excess cases). Then, from each distribution they generate 10,000 Monte Carlo simulation samples. Cost benefit acceptability curves are built plotting the proportion of simulations producing a positive net benefit given a range of remediation cost. Cost-Benefit Analysis of the Clean-Up of Hazardous Waste Sites 413 4. Conclusion Hazardous waste sites are a major environmental problem. There is a large body of literature showing an association between hazardous waste (mis)management and negative health outcomes. Substances resulting from industrial production (e.g. arsenic, cadmium and mercury) once released into landfills without proper treatment can be fatal for the populations exposed. In the US, the public has ranked toxic wastes sites as the number one national environmental priority. A recent study of a contaminated site in the Italian region of Campania, found that 87% of survey respondents believed that they are going to suffer from cancer because of waste exposure (Cori & Pellegrino 2011). Responding to public concerns, national reclamation projects have been created in several countries, e.g. Superfund program in the US, and programma nazionale di bonifica in Italy. The objective of these programs is collecting public and private resources to prioritize the clean-up of hazardous waste sites. Cost benefit analysis is a transparent decision informing procedure to prioritize the cleanup of those sites that for a given remediation budget would allow to produce the highest benefit in terms of negative health outcomes averted. Despite the potential benefits resulting from the application of cost benefit analysis in waste management there are few empirical studies using this tool. The study conducted by Hamilton and Viscusi (1999) evaluating the cost effectiveness of EPA Superfund decisions showed that the majority of clean-up decisions are ineffective and highlights the importance of conducting site level analysis. Further studies conducted in US found that other factors such as media coverage were prevailing in determining the stringency of clean-up standards and the selection of clean-up sites/size. As long as the true benefits and costs of cleanup interventions are ignored resources will be allocated inefficiently. Despite measurement problems and the equity issues, cost-benefit analysis should be conducted routinely to address National Superfund’s decisions. (Zimmerman and Rae, 1993). 5. References Alberini A, Tonin S & Turvani M 2009. The Value of Reducing Cancer Risks at Contaminated Sites: Are More Heavily Exposed People Willing to Pay More? Fondazione Eni Enrico Mattei. Alberini A, Tonin S, Turvani M & Chiabai A 2007. Paying for Permanence: Public Preferences for Contaminated Site Clean-up. Fondazione Eni Enrico Mattei. Cori L & Pellegrino V. 2011. Corpi in trappola. Vite e storie tra i rifiuti. Editori Riuniti EC. 2001. European Commission 2001. Recommended interim values for the value of preventing a fatality in DG Environment Cost Benefit analysis [Online]. Available: ec.europa.eu/environment/enveco/others/pdf/recommended_interim_values.pdf [Accessed]. Enhealth 2003. Enhealth-guidelines for economic evaluation of environmental health planning and assessment Volume 1 EPA. 2010a. Regulatory Impact analysis [Online]. Available: http://www.epa.gov/ttnecas1/ria.html [Accessed]. EPA. 2010b. Regulatory Impact Analysis for the proposed Federal Transport Rule. Final National Ambient Air Quality Standard for SO2 [Online]. Available: http://www.epa.gov/ttnecas1/ria.html [Accessed]. EPA. 2010c. Toxologic Profile of Mercury [Online]. Available: http://www.epa.gov/mercury/effects.htm [Accessed]. Integrated Waste Management – Volume I 414 Gilbreath, J. 2007. IOM: The economics of better environmental health. Environ Health Perspect, 115, A80-1. Goldberg, M. S., Siemiatyck, J., Dewar, R., Desy, M. & Riberdy, H. 1999. Risks of developing cancer relative to living near a municipal solid waste landfill site in Montreal, Quebec, Canada. Arch Environ Health, 54, 291-6. Grosse, S. D., Matte, T. D., Schwartz, J. & Jackson, R. J. 2002. Economic gains resulting from the reduction in children's exposure to lead in the United States. Environmental Health Perspectives, 110, 563-569. Guerriero, C. & Cairns, J. 2009. The potential monetary benefits of reclaiming hazardous waste sites in the Campania region: an economic evaluation. Environ Health, 8, 28. Goldman LR, Paigen B, Magnant M, Highland J.1985. Low Birth Weight, Prematurity and Birth Defects in Children Living Near the Hazardous Waste Site, Love Canal. Hazardous Waste and Hazardous Materials. Summer 1985, 2(2): 209-223 Gupta S, Van Houtven G & Cropper M (eds.) 1995. "Do Benefits and Costs Matter in Environmental Regulation? Ana analysis of EPA decisions under Superfunds." In Richard L Revesz and Richard B. Steward Gupta S, Van Houtven G & Cropper M 1998. Paying for permanence: an economic analysis of EPA's cleanup decisions at Superfund sites. RAND Journal of Economics, 27, 563-582. Hamilton Jt & Viscusi WK 1999. How Costly is "Clean"? An Analysis of the Benefits and Costs of Superfund Remediations. Journal of Policy Analysis and Managment, 18, 2-27. Kochi, I., Hubbell, B. & Kramer, R. 2006. An empirical Bayes approach to combining and comparing estimates of the value of a statistical life for environmental policy analysis. Environmental & Resource Economics, 34, 385-406. Mrozek Jr & Taylor M. 2001. What detemines the valu of life? a meta-analysis [Online]. Available: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0483-08.pdf/$file/EE-0483- 08.pdf [Accessed]. Pearce, D. Year. Valuing Risks of life and health. Towards Consistent Transfer Estimates in the European Union and Accession States. In: European Commission Workshop on Valuing Mortality and Valuing Morbidity, Nov 13 2000 Brussel Pearce D, Atkinson G, Mourato S. 2006. Cost Benefit Analysis and the Environment. OECD publishing Pukkala, E. & Ponka, A. 2001. Increased incidence of cancer and asthma in houses built on a former dump area. Environmental Health Perspectives, 109, 1121-1125. Revesz R.L. May 1999. Environmental Regulation, Cost-Benefit Analysis and the discounting of Human lives. Columbia Law Review. Shepard, D. & Zeckhauser, R. J. 1984. Survival versus Consumption. MANAGEMENT SCIENCE, 30. Viscusi, W. & Huber J 2006. "Hyperbolic Discounting of Public Goods". Center for Law,Economics and Business Discussion Paper Series. Paper 543, Discussion Paper Series. Paper 543. Viscusi, W. K. & Aldy, J. E. 2003. The value of a statistical life: A critical review of market estimates throughout the world. Journal of Risk and Uncertainty, 27, 5-76. Who, Iss, Cnr & Regionecampania. 2004. Trattamento dei rifiuti in Campania. Impatto sulla salute umana. Studio Pilota. [Online]. Available: www.protezionecivile.it/cms/attach/editor/rischi- nucleare/Sintesi_dei_risultati_e_indicazioni_preliminari.pdf [Accessed]. Winpenny , J. T. 1991. Values for the Environment. A Guide for Economic Appraisal, London HMSO. Zimmerman & Rae 1993. Social Equity and Environmental Place. Risk Analysis 13, 649-65. Part 4 Software Applications 22 Benefits from GIS Based Modelling for Municipal Solid Waste Management Christos Chalkias and Katia Lasaridi Harokopio University, Department of Geography Greece 1. Introduction Waste management issues are coming to the forefront of the global environmental agenda at an increasing frequency, as population and consumption growth result in increasing quantities of waste. Moreover, technological development often results in consumer products of complex composition, including hazardous compounds, which pose extra challenges to the waste management systems and environmental protection at the end of their useful life, which may often be fairly short (e.g. cell-phones and electronic gadgets). These end-of-pipe challenges are coupled with the deepening understanding that the Earth’s natural resources are finite by nature and their current exploitation rate unsustainable, even within a midterm perspective. The self-cleaning capacity of the Earth systems is often also viewed as a «natural resource» under stress, with climate change being the most pronounced expression of this risk. In the context of the above mentioned challenge a New Paradigm for waste management has emerged, shifting attention to resources efficiency and minimisation of environmental impacts throughout the life cycle of waste management, from waste prevention to safe disposal. This is best expressed, but not confined, in the relevant EU policy and legislation (e.g. the Thematic Strategy on the prevention and recycling of waste, the Thematic Strategy on the Sustainable Use of Natural Resources and the revised Waste Framework Directive, WFD-2008/98/EC). Especially the latter is of particular interest as it has a legally binding nature for all EU member states and sets a benchmark which is often also taken into consideration by the waste management systems of non-EU countries. The WFD reaffirms the need to move waste management higher in the so called “waste hierarchy”, preferring, in this order, prevention, reuse, recycling and energy recovery over disposal. Separate collection for dry recyclables in municipal solid waste (MSW) should be implemented while separate collection of biowaste should be promoted (although no specific legislative requirements are set) (Nash, 2009). Overall, EU and national waste management policies and legislation in many parts of the world are becoming increasingly demanding for the providers of these services, namely municipalities and their associations, demanding high recovery and recycling rates for a wide range of materials and goods, high diversion targets for the biodegradable fraction of the waste, advanced treatment processes, long after-care periods for existing and future landfills etc (COM, 2005; Lasaridi, 2009). Moreover, this increased level of service will need to be provided at the minimum possible cost, as the public will not be able to bear large Integrated Waste Management – Volume I 418 increases in its waste charges and municipalities are increasingly being required to benchmark their performance, to ensure they offer their waste management services at the most efficient manner (Eunomia, 2002; Karadimas et al., 2007). The current economic crisis inevitably intensifies this need. The need for improved performance at low costs is not restricted to developed countries seeking to apply increasingly complex separate waste collection, treatment and recovery systems. Under a different context, it also exerts its pressure to the municipal services of the developing countries, which strive to ensure waste collection and public health protection for the large populations of highly urbanised areas with severe infrastructure and economic limitations (Gautam & Kumar, 2005; Ghose et al., 2006; Kanchanabhan et al., 2010; Vijay et al., 2005). Local authorities (LAs) constitute worldwide the main providers of municipal solid waste (MSW) management services, either directly or indirectly through subcontracting part or all of these services. Especially waste collection and transport (WC&T) are typically provided at the local municipality level and constitute the main interface between the waste generator and the waste management system. Assessing the different components of the solid waste management costs is a complex, poly-parametric issue, governed by a multitude of geographic, economic, organisational and technology selection factors (Eunomia, 2002; Lasaridi et al., 2006). However, in all cases WC&T costs constitute a significant component of the overall waste management costs, which may approach 100% in cases where waste is simply dumped. For modern waste management systems WC&T costs vary in the range of 50-75% of the total, which overall is significantly higher, as advanced treatment and safe disposal take their own, large share of the total costs (Sonesson, 2000). Therefore, the sector of WC&T attracts particular interest regarding its potential for service optimisation as (a) waste management systems with more recyclables’ streams usually require more transport (Sonesson, 2000) and (b) this sector, even for commingled waste services only, already absorbs a large fraction of the municipal budget available to waste management (Lasaridi et al., 2006). Optimisation of WC&T making use of the novel tools offered by spatial modelling techniques and geographic information systems (GIS) may offer large savings, as it is analysed further in this chapter. In spite of their proved utility and a significant development of the relevant research in the last decades in many parts of the world, including most Greek local authorities, WC&T is typically organised empirically and in some cases irrationally, under public pressures. The aim of this chapter is to present a methodology for the optimisation of the waste collection and transport system based on GIS technology. The methodology is applied to the Municipality of Nikea (MoN), Athens, Greece based on real field data. The strategy consists of replacing and reallocating the waste collection bins as well as rescheduling the waste collection via GIS routing optimisation. The benefits of the proposed strategy are assessed in terms of minimising collection time, distance travelled and man-effort, and consequently financial and environmental costs of the proposed collection system. 2. The role of GIS for sustainable waste management Geographic Information Systems (GIS) are one of the most sophisticated modern technologies to capture, store, manipulate, analyse and display spatial data. These data are usually organised into thematic layers in the form of digital maps. The combined use of GIS with advanced related technologies (e.g., Global Positioning System – GPS and Remote Benefits from GIS Based Modelling for Municipal Solid Waste Management 419 Sensing - RS) assists in the recording of spatial data and the direct use of these data for analysis and cartographic representation. GIS have been successfully used in a wide variety of applications, such as urban utilities planning, transportation, natural resources protection and management, health sciences, forestry, geology, natural disasters prevention and relief, and various aspects of environmental modelling and engineering (among others: Brimicombe, 2003). Among these applications, the study of complex waste management systems, in particular siting waste management and disposal facilities and optimising WC&T, have been a preferential field of GIS applications, from the early onset of the technology (Esmaili, 1972; Ghose et al., 2006; Golden et al., 1983; Karadimas et al., 2007; Sonesson, 2000). Nowadays, integrated GIS technology has been recognised as one of the most promising approaches to automate the process of waste planning and management (Karadimas & Loumos, 2008). As mentioned above, the most widespread application of GIS supported modelling on waste management lies in the areas of landfill siting and optimisation of waste collection and transport, which are discussed in detail in the following section. Additionally, GIS technology has been successfully used for siting of recycling drop-off centres (Chang & Wei, 2000), optimising waste management in coastal areas (Sarptas et al., 2005), estimating of solid waste generation using local demographic and socioeconomic data (Vijay et al., 2005), and waste generation forecasting at the local level (Dyson & Chang 2005; Katsamaki et al., 1998). 2.1 GIS-based modelling for landfill selection The primary idea of superimposition of various thematic maps in order to define the most suitable location according to the properties of the complex spatial units derived after the map overlay, was first introduced in the late 60’s (McHarg, 1969). This idea was applied next within the context of early GIS in many optimal siting applications (Dobson, 1979; Kieferand & Robins, 1973). The allocation of a landfill is a difficult task as it requires the integration of various environmental and socioeconomic data and evolves complicated technical and legal parameters. During this process the challenge is to make an environmentally friendly and financially sound selection. For this purpose, in the last few decades, many studies for landfill site evaluation have been carried out using GIS and multicriteria decision analysis (Geneletti, 2010; Higgs, 2006; Nas et al., 2010; Sener et al., 2006), GIS in combination with analytic hierarchy process (Saaty, 1980) – AHP (Vuppala et al., 2006; Wang et al., 2009), GIS and fuzzy systems (Chang et al., 2008; Gemitzi et al., 2007; Lofti et al., 2007), GIS and factor spatial analysis (Biotto et al., 2009; Kao & Lin, 1996), as well as GIS-based integrated methods (Hatzichristos & Giaoutzi 2006; Gómez-Delgado & Tarantola 2006; Kontos et al., 2003, 2005; Zamorano et al., 2008). A large fraction of these applications produce binary outputs while most recent ones aim at evaluating a ”suitability index” as a tool for ranking of the most suitable areas (Kontos et al., 2005). The main steps of a typical GIS – based landfill allocation model (fig.1) are as following. a. Conceptualisation of the evaluation criteria and the hierarchy of the landfill allocation problem. This step is dedicated to the selection of the criteria related to the problem under investigation. b. Creation of the spatial database. Here, the development of GIS layers for the modelling is implemented. These layers correspond to the primary variables. Integrated Waste Management – Volume I 420 c. Construction of the criteria – layers within the GIS environment. Criteria maps are primary or secondary variables. d. Standardisation of the criteria – layers. This step includes reclassification of the layers in order to use a common scale of measurement. Most often, the ordinal scale is used. e. Estimation of the relative importance for the criteria. This estimation is implemented by weighting, e.g. with the use of Analytic Hierarchy Process (AHP) and pair wise comparison between variables. f. Calculation of the suitability index. A standard procedure for this step is the weighted overlay of the standardised criteria/layers. g. Zoning of the area under investigation is the next phase of the modelling. This classification action is based on the suitability index and reveals the most suitable areas for the application. h. Sensitivity analysis and validation of the model. i. Final selection – land evaluation. GIS Spatial database development (organize layers- primary variables within GIS context) Construct criteria in the form of GIS layers (primary or secondary variables) Estimation of the relative importance of the criteria (weighting, e.g. AHP method and pair wise comparison) Standardization of the criteria-layers (reclass- ification, common scale of measurement) Calculation of the suitability index (weighted overlay of the criteria) Classification – spatial clustering according to the suitability index (selection of the most suitable areas) Sensitivit y anal y sis – validation of the model Final lan d ev al u ati o n - se l ec ti o n Conceptualize of the evaluation criteria and the hierarchy of the landfill allocation problem Fig. 1. Landfill site selection. A GIS approach. [...]... 5 Conclusions GIS technology supports the optimisation of municipal solid waste management as it provides an efficient context for data capture, analysis and presentation Two main 432 Integrated Waste Management – Volume I categories of GIS-based waste management applications can be identified in the international literature In the first, GIS is used for the selection of waste disposal landfills, and... efficiency of WC&T in the study area via: (a) the reallocation of waste collection bins; and (b) the optimisation of vehicle routing in terms of distance and time travelled, via GIS routing The outputs of various different scenarios examined are finally compared with the empirical routing, which is the current vehicle routing practice Benefits are assessed in terms of minimising collection time, distance... often, especially in developing countries, the research team has to acquire this information with field work c Installation of a modern GIS facility within the municipality enriched with network analysis functions Advanced training of the staff is a very important factor for the efficient operation of this system d Validation of the outputs from GIS-based modelling in order to ensure the applicability of... of GIS supported waste management applications is related to waste collection There are several applications for route optimisation, reallocation of waste bins and complete redesign of the collection sectors The main aim of these applications is to reduce the collection distance and/or time of the collection vehicle fleet The implementation of GIS-based modelling for waste collection optimisation in... allowing rescheduling it is possible to significantly increase the improvement rate Karadimas & Loumos (2008) proposed a method for the estimation of municipal solid waste generation, optimal waste collection and calculation of the optimal number of waste bins and their allocation This method uses a spatial Geodatabase, integrated in a GIS environment and was tested in a part of the municipality of Athens,... reallocation of the waste bins, their total number was reduced by more than 30% This reduction had a direct positive impact on collection time and distance Chalkias & Lasaridi (2009) developed a model in ArcGIS Network Analyst in order to improve the efficiency of waste collection and transport in the Municipality of Nikea, Athens, Greece, via the reallocation of waste collection bins and the optimisation... procedure is the Inventory Analysis (compilation and quantification of inputs and outputs), which is the most important phase of the activity because it allows for the acquisition of all the information which is useful in compiling and quantifying the flows of matter and energy in input and output from each phase for the quantification of emissions Data about the Inventory Analysis of the WISARD procedure... implemented in this study comprised of three general steps (Fig 4) Step 1 establishes the spatial database of the study area as described previously Step 2 is dedicated on the reallocation of waste collection bins with the use of GIS spatial analysis functions Finally, Step 3 consists of the Benefits from GIS Based Modelling for Municipal Solid Waste Management 427 waste collection routing optimisation for minimum... countries with different socioeconomic conditions and technological background shows that significant savings could be achieved in most setups The optimisation of routing has a direct positive impact on cost savings (reduction of fuel consumption and maintenance costs) as well as significant environmental impacts due to the lower levels of sound pollution within the urban environment and the reduction... environment with the use of the proper spatial analysis functions The allocation of waste collection bins in their newly proposed locations was based on the following criteria /restrictions: i On the basis of the population density and the type of buildings in the study area, bins of 1100L capacity were considered preferable, in order to minimise the number of required bins and vehicle stops This is the typical . www.protezionecivile.it/cms/attach/editor/rischi- nucleare/Sintesi_dei_risultati_e_indicazioni_preliminari.pdf [Accessed]. Winpenny , J. T. 1991. Values for the Environment. A Guide for Economic Appraisal,. national waste management policies and legislation in many parts of the world are becoming increasingly demanding for the providers of these services, namely municipalities and their associations,. analysis functions. Finally, Step 3 consists of the Benefits from GIS Based Modelling for Municipal Solid Waste Management 427 waste collection routing optimisation for minimum time, distance,