Analyzing policy impact potential for municipal solid...

Resources, Conservation and Recycling 51 (2007) 418–434

Analyzing policy impact potential for municipalsolid waste management decision-making:

A case study of Taiwan

Jun-Pin Su a, Pei-Te Chiueh b,Ming-Lung Hung a, Hwong-Wen Ma a,∗

a Graduate Institute of Environmental Engineering, National Taiwan University,71 Chou-Shan Road, Taipei 106, Taiwan

b Research Center for Environmental Pollution Prevention and Control Technology,National Taiwan University, 71 Chou-Shan Road, Taipei 106, Taiwan

Received 26 June 2006; received in revised form 10 October 2006; accepted 18 October 2006Available online 16 November 2006

Abstract

In the past 20 years, municipal solid waste policies have changed in response to societal andenvironmental changes. Municipal solid waste policies in many countries become more complicatedand numerous. This paper reviews several models developed to support decision making in the areaof municipal solid waste management (MSWM). It has been discovered that many modern decision-making support systems are already partially considering social factor analysis in addition to expensesand benefits, environmental effects, technical issues, and management aspects. However, questionsare raised as to whether these analyses are sufficient and whether they can predict future possibleimpacts.

This research studies Taiwan’s major municipal solid waste policies in the past 10 years anddiscovers that there is still a great deal of uncertainty associated with policy implementation, evenwhen the effects of factors related to environmental, economic, social, technological, and managementaspects have been considered. The purpose of this study is to develop a decision-making modelof MSWM to resolve the insufficiencies in policy impact analysis used for decision-making. Thepolicy impact potential analysis method is developed to predict the possible impacts of a policy on

∗ Corresponding author. Tel.: +886 2 2363 0406; fax: +886 2 2392 8830.E-mail address: [email protected] (H.-W. Ma).

0921-3449/$ – see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.resconrec.2006.10.007

mailto:[email protected]

dx.doi.org/10.1016/j.resconrec.2006.10.007

J.-P. Su et al. / Resources, Conservation and Recycling 51 (2007) 418–434 419

particular alternatives; subsequently, a novel decision-making model for waste management is formed.A case study of fly ash management in Taiwan is presented to demonstrate the practicality of thismodel.© 2006 Elsevier B.V. All rights reserved.

Keywords: Municipal solid waste management; Decision making; Policy impact potential; Fly ash

1. Introduction

As municipal solid waste (MSW) problems become more complicated, waste policiesalso become more numerous and complex. In the past 30 years, MSW decision-making inmany countries has also undergone significant change. Earlier MSW management (MSWM)was installed primarily for deciding collection systems or for determining transportation ortransfer of solid waste. For example, in the 1970s, the goal of the MSWM model was simpleand narrow, aimed at optimizing waste collection routes for vehicles (Truitt et al., 1969)or transfer station sitting (Esmali, 1972; Helms and Clark, 1971). In 1980s, the focus wasextended to cover MSWM on the system level, minimizing the total economic cost (Hasitand Warner, 1981; Jenkins, 1982; Perlack and Willis, 1987).

After the 1990s, as MSW policies became more complicated, the factors to be consideredalso increased; hence, several MSWM models with deeper analysis emerged. The factorsconsidered in MSWM models were mainly economic (e.g., system cost and system ben-efit), environmental (air emission, water pollution) and technological (the maturity of thetechnology). Three models have played a major role in the decision making of MSWM: lifecycle assessment (LCA), multi objective programming (MOP) and multicriteria decisionmaking (MCDM). Many researchers used LCA to evaluate the environmental impact of thealternatives for MSWM (Barton et al., 1996; Eriksson et al., 2002; Finnveden, 1999; Powell,2000; Powell et al., 1996). Multiobjective programming is a popular method for solvingMSWM problems, such as locating sites and choosing management strategies (Alidi, 1996;Chang and Hwang, 1996; Chang and Wang, 1996; Chang and Wei, 1999). MCDM, whichis aimed at choosing the best among several alternatives by considering many criteria, isalso widely used. Many techniques are available for solving the environment problem withmultiple criteria, including the AHP method (Chiou and Tzeng, 2002; Haastrup et al., 1998;Tran et al., 2002), outranking methods (Brans and Vincke, 1985; Geldermann et al., 2000;Roy, 1991), and the TOPSIS method (Hwang and Yoon, 1981).

It was not until recently that societal acceptance and public participation became sig-nificant in the MSWM models. Morrissey and Browne (2004) proposed that a sustainableMSWM model should be not only environmentally effective and economically affordablebut also socially acceptable. At present, there are several studies in the literature on theintegration of social effects within MSWM models. The factors considered in social effectsanalysis include social welfare (Hernandez and Martin-Cejas, 2005), public acceptance(Cheng et al., 2002; Skordilis, 2004), social acceptability (Cavallaro and Ciraolo, 2005;Chung and Lo, 2003), social equity (Chung and Lo, 2003), political concerns (Cheng etal., 2002), cultural or heritage issues (Cheng et al., 2002; El-Naqa, 2005), and social cost(Hernandez and Martin-Cejas, 2005; Oliveira and Rosa, 2003). In addition, Hung (Hung et

420 J.-P. Su et al. / Resources, Conservation and Recycling 51 (2007) 418–434

al., 2007) also developed a consensus analysis model to supplement the decision-making byconsidering the shared views of stakeholders. Although with different targets, these studieswere concerned with incorporation of social analysis into stages of decision-making.

Whatever role social factors play within the decision-making model, their main purposeis two-fold: (1) respecting public opinions and social perspectives in decision-making and(2) decreasing the impacts of policy implementation. However, although the aforementionedanalysis methods take the factors lacking in past decision-making models into account, theystill cannot completely reflect the social impacts of a policy. It is not unusual that a decisionbased on the consideration of all the mentioned factors including social aspects fails to beimplemented because of public resistance. Relying on the analysis results of experts andscholars on partial social impact targets is not sufficient to provide an accurate and completepicture of the actual impacts of a policy’s implementation. The policy impact potential isneeded to be analyzed in order to understand the possible degree of resistance to policyimplementation and the risk of failure.

This study proposes a method of analyzing policy impact potential to understand theneglected but important factors having “policy impact potential”, and develops a decision-making model by combining the policy impact potential analysis (PIPA) with an MCDMmethod for MSWM.

The remainder of this paper is organized as follows. The issue of policy impact potentialis examined in Section 2. A decision model that combines an MCDM with the PIPA ispresented in Section 3. The fly ash management in Taipei City is used as a case study toillustrate the application of the model to MSWM in Section 4. Discussion of the results andthe final remarks regarding the benefits of the proposed model are presented in Sections 5and 6, respectively.

2. Exploring the policy impact potential

The PIPA method focuses on analyzing possible debates, conflicts, and considerations,as well as the possible increase in social expenses, in the implementation of policies. Thepast policy cases are investigated in order to identify the factors related to policy impact ofimplementation.

2.1. Summary and analysis of waste management policies in the past 5 years of Taiwan

This research summarizes Taiwan’s most significant waste-related cases in the past 5years, which includes the construction of incinerators, ash landfills, and the country’s onlyindustrial waste landfill site. It also analyzes the number of times these cases were reportedby the media during the planning stages and establishment stages of the policy. The mediareports can be divided into three kinds: positive, negative, and neutral, in terms of positiveor negative aspect of the case reported. The 13 subjects have 3 types of status as follows,and Table 1 summarizes the information about the cases.

(1) Announcement of cease in operations: includes three sites of ash and industrial wastesdisposal sites, and three incinerators.


Table 1Waste management facilities and media report analysis results

Issue Period Report times Report frequency(times/year)

Negativereporting rate

Results

Landfill A 2 May 2003–22October 2005

Positive 0 Positive 0 96.08 Type1Negative 49 Negative 20.28Neutral 2 Neutral 0.83

Landfill B 16 May 2003–17February 2006

Positive 2 Positive 0.73 91.38 Type 2Negative 159 Negative 57.75Neutral 13 Neutral 4.72

Landfill C 17 May 2004–21July 2004

Positive 0 Positive 0 88.89 Type 1Negative 16 Negative 89.85Neutral 2 Neutral 11.23

Landfill D 4 June 2003–6January 2006

Positive 0 Positive 0 96.51 Type1Negative 221 Negative 85.18Neutral 8 Neutral 3.08

Incinerator A 4 October2002–28 March2006


Incinerator B 29 October2002–28 May2004

Positive 11 Positive 7.01 76.44 Type1Negative 159 Negative 101.28Neutral 38 Neutral 24.21

Incinerator C 12 October2002–3 June 2004


Incinerator D 4 October 2002–4April 2006


Incinerator E 5 October2002–29 March2006


Incinerator F 31 March 2002–4April 20064


Incinerator G 12 October2002–13 October2004


Incinerator H 2 October1997–16November 2005


Incinerator I 26 February2002–7 April2006


Type 1: announcement of ceasing construction; Type 2: in debate; Type 3: successful finish.


(2) In debate: includes one industrial waste landfill site and three incinerators.(3) Successful finish: includes three incinerators.

2.2. Correlating success or failure of a policy with the media

Through the investigation of these cases, it is discovered that the implementation of thesepolicies and the media effectiveness (media report rates, negative news ratio) have a highcorrelation.

In the cases wherein policies were announced to cease operations or are still in debate, thenegative news rates are relatively high. Except for Incinerator F, with 56.11%, the negativenews rates of the remaining cases are all 60% or more. Three cases even reach 90%. Themedia strength of these cases averages 118.7 times a year.

On the other hand, in the three cases wherein the policies have been successfully imple-mented, the negative news rates are 7.66%, 3.20%, and 3.36%. The media presentations aremostly of the neutral kind. The average media strength is 56.89 times a year, half that ofthe debated or ceased cases.

This suggests that at a certain reporting rate, a higher negative news report rate wouldmean that the probability of failure in implementation of the waste treatment case is alsohigher. The correlation can be clearly observed in Fig. 1.

2.3. Defining “policy impact potential”

These cases have generally undergone feasibility studies and analysis, even includingsocial impact analysis. Then why are there such obvious failures in the implementation ofsome cases? Obviously, besides the traditional social impact analysis, there are still factorswith severe impact on a policy that have not yet been considered in policy planning.

Fig. 1. The correlation of failure in implementation of waste treatment facility construction and media reports.


Hence, it is very important for policy making and planning to determine how to define“policy impact potential” and how to conduct PIPA. Obviously, the media presentationis very effective in revealing the important points of the waste policy, and the evalu-ation of media reports can suitably explain societal concerns about the policy and itsimpacts.

The summary shows that in the implementation of a policy, a higher media report rateand a higher negative news ratio are associated with higher policy impact and resistance.Evidently, using PIPA in the initial stages of planning to adjust the current decision-makingsupport system is quite important. The evaluation of policy impact potential is conductedby identifying the policy impact criteria and quantifying the impact potential in this study;the details are described in Section 3.

3. Policy impact potential analysis and a decision-making model for municipalwaste management

An MCDM is combined with PIPA, in order to develop a decision-making model thatincorporates consideration of factors often neglected in traditional analysis but having sig-nificant influence on policy implementation for MSWM. Fig. 2 outlines the algorithm, andthe main steps are as follows.

3.1. Formulate the model for waste management problems

Real-world waste management problems require the consideration of numerous factors,including environmental, economical, and social aspects. Multiple criteria can be formulatedby obtaining the perspectives of various stakeholders, including government, experts, NGOs,business, etc.

3.2. Prioritize the alternatives

Many MCDM methods are used to prioritize the alternatives, and the AHP methoddeveloped by Saaty (1980) in 1980 is popular in decision-making methods. However,real-world waste management problems involve many stakeholders and different view-points for decision making; the traditional AHP method is thus insufficient. Buckley(1985) applies the fuzzy theory to the AHP method to avoid neglecting extreme val-ues. Applying the fuzzy AHP method involves five steps as follows (Hung et al.,2007).

3.2.1. Construct the hierarchical structure of waste problemsIn real MCDM problems, the process must be divided into distinct stages. First, based

on a general problem statement, the overall objective is set. Second, based on stake-holders’ perspectives, the problems can be classified into distinct aspects. Third, definingalternatives/strategies and criteria, a discrete MCDM problem comprising a finite set ofalternatives/strategies can be assessed in terms of multiple criteria.


Fig. 2. MSWM decision making process.

3.2.2. Calculate the criteria weightsThe criteria weights can be determined by the stakeholders. In this study,

the fuzzy weighting method is used to incorporate all the options of the stake-holders.


3.2.3. Determine the fuzzy performance of the alternatives for each criterionThe criteria can usually be divided into two categories: quantitative and qualitative. The

performance of alternatives for each criterion can be calculated as follows:

• Quantitative criteria:This investigation utilizes the triangular fuzzy number to express the performance for

quantitative criteria.• Qualitative criteria:

The linguistic variables are designed to express the words or sentences in a natural orartificial language (Vaillancourt and Waaub, 2002). Five levels are used to integrate thepreference of the experts. The fuzzy performance for the qualitative criteria is determinedby using a fuzzy triangular number.

3.2.4. Aggregate the fuzzy weights and fuzzy performanceThe simple additive method is utilized to aggregate the fuzzy weights and fuzzy perfor-

mance.

3.2.5. Rank the final score of the alternativesThe optimal index method is used to defuzzify the fuzzy numbers to prioritize the

alternatives.

3.3. Policy impact potential analysis (PIPA)

PIPA involves the identification of policy impact criteria and the quantitative analysis ofpolicy impact potential.

3.3.1. Identification of policy impact criteriaThe relationship between policy impact and media presentation has been confirmed in

Section 2. Hence, to quantify the policy impact potential, it is necessary to define the policyimpact analysis criteria. Experience and knowledge of the senior media reporters who havedeep understanding of and write the environmental news are elicited to select the criteriaof policy impact potential analysis.

Through interviews and questionnaires, the results show that “future risks,” “valueperspectives and belief conflicts,” “regional conflicts,” “local resistance,” “indirect envi-ronmental effects,” and “effects of concerned targets” are the six “policy impact potential”criteria of the waste management policy. These items are explained in Table 2.

3.3.2. Quantification of policy impact potentialTo quantify policy impacts potential, this research selects the ELECTRE method (Roy,

1991) to perform comparison of alternatives. The main reason for using the ELECTREmethod is due to its simplicity. Even if there is interdependence between criteria, the analysiscan still be applied. The ELECTRE method does not completely arrange all the cases inorder and can only do individual comparisons or select the better case group; the PIPAanalysis is aimed to assist the existing decision-making system and to avoid a high social


Table 2Policy impact potential analysis criteria

Criteria Descriptions

Future risks Concern about unknown technological uncertainty, such as nuclearwaste, electromagnetic field, and toxicity leaching of ash, of whichcompliance with regulatory standards does not resolve the public’sworry

Value perspectives, beliefs conflict Value perspectives, beliefs, and other non-technological factors,e.g., anti-energy depletion policy, religious and cultural conflicts

Regional conflicts Conflicts with area coalitions or race relationships to presentobstacle to regional cooperation of waste management

Local resistance The resistance of local communities and partiesIndirect environmental effects Although the effects may not be significant based on technical

assessment, the suspected effects may lead to headline newsEffects of particular symbolic targets Technological effects might not be evident, but due to existence of

particular symbolic targets, the public could become more skeptical,for example, the problems of incinerators against water treatmentplant, burial sites’ effects on particular reservoir, and effects ofdioxins from incinerations on particular agricultural industry

impact, so the ranking of all alternatives is not that significant. Hence, using this method inthe evaluation is suitable. The steps in calculating are explained below:

Step 1. Compute the normalized decision matrix RThe normalized decision matrix R can be calculated as follows:

R = ∣∣rij∣∣m×n(1)

rij = pij√∑mi=1p

2ij

(2)

where pij is the performance of alternative i for criteria j; m is the number ofalternatives; and n is the number of criteria.

Step 2. Calculate the weighted normalized decision matrix VThe weighted normalized decision matrix V = [vij]

m×ncan be calculated as

follows:

V = R × W (3)

W = [w1, w2, · · ·, wn] (4)

where W is the weights matrix of criteria.Step 3. Determine the concordance set Cij and discordance set Dij

Cij = {k∣∣vik ≥ vjk

}(5)

Dij = {k∣∣vik < vjk

}(6)

Step 4. Calculate the concordance matrix C

C = ∣∣cij

∣∣m×n

(7)


cij =∑

k ∈ cijwk∑n

k=1wk

(8)

Step 5. Calculate the discordance matrix D

D = ∣∣dij

∣∣m×n

(9)

dij =maxk ∈ Dij

{∣∣vik − vjk

∣∣}maxk ∈ J

{∣∣vik − vjk

∣∣} (10)

where J is the ordinal set of all criteria, J = {1, 2, . . . n}.Step 6. Determine the concordance dominance matrix F

The concordance dominance matrix F can be calculated as follows:

F = [fij]m×n

(11)

fij ={

1, if cij ≥ c̄

0, if cij < c̄(12)

c̄ =m∑

i = 1i�=j

m∑j = 1

j �=i

cij

m(m − 1)(13)

where c̄ is the average concordance index.Step 7. Determine the disconcordance dominance matrix GThe disconcordance dominance

matrix G can be calculated as follows:

G = [gij]m×n

(14)

gij ={

1, if dij ≥ d̄

0, if dij < d̄(15)

d̄ =m∑

i = 1i�=j

m∑j = 1

j �=i

dij

m(m − 1)(16)

where d̄ is the average disconcordance index.Step 8. Determine the aggregate dominance matrix E

E = ∣∣eij

∣∣m×n

(17)

eij = fij × gij (18)

Step 9. Eliminate the less favorable alternativesAfter the aggregate dominance matrix is determined, when eij = 1, it means alter-

native i is better than alternative j, which can be represented with Ai → Aj.


3.4. Communication and decision making

The decision maker can make decisions by considering both MCDM methods and PIPAmethods, not only to seek a compromise solution between the criteria, but also to enableconsideration of the possible impacts of the policy among all alternatives. This can helpdecision makers to resolve possible impacts during the decision-making stage.

4. An illustrating example

The newly developed decision-making model of waste management was used to evaluatethe fly ash management problem in Taipei City, Taiwan. The density of the population ofTaipei City is the highest in Taiwan. The MSW of Taipei is about 1400 t/day; the primarytreatment method is incineration, which produces about 30 t of daily fly ash. The fly ashis always solidified, and then landfilled. Because of the high population density of TaipeiCity and the difficulty of locating landfill sites, the Environmental Protection Bureau ofTaipei City wants to evaluate potential technologies to seek the suitable ways to reusefly ash.

The algorithm of finding the optimal fly ash management schemes based on the proposeddecision-making method is as follows.

4.1. Formulate the model for waste problems

This study considers five alternatives of fly ash treatment, including self-construction,external management (trusting enterprises to manage), area partnerships (cooperating withnearby city) and other methods (such as water washing or burning), and reusing of resources.We classify them as Alternatives 1–5, as explained below:

• Alternative 1: self-established reuse factory (e.g., water-washing and burning).• Alternative 2: self-established pre-treatment factory (water washing) and external

resource reuse (reuse through cement kilns outside the city).• Alternative 3: all-external treatment and resource reuse factory (both water washing and

reuse through cement kilns outside the city).• Alternative 4: area partnerships in establishment of a pre-treatment factory (water wash-

ing) and an external resource reuse factory (cement kiln).• Alternative 5: area partnerships in establishment of a reuse factory (water washing and

melting).

This study considers five objectives: environmental, economic, social, management, andtechnological factors. Environmental factors involve environmental issues, human health,resource consumption and ecological impacts. Economic factors include the cost and benefitof the waste management process and the marketing potential of the byproducts, resourcerecycling, and the issue of raising funds. Management factors include the degree of inde-pendence, implementation procedures and progress. Social factors comprise social justice,social welfare and social acceptability, while technological factors include land demandand technology maturity.


Fig. 3. The fuzzy AHP hierarchy of fly ash management problems.

4.2. Prioritize the alternatives

The fuzzy AHP approach is used to prioritize the alternatives and is described as follows:

(1) Build the hierarchical structure of the waste problems.The hierarchy of the fly ash management problem in Taipei City is shown in Fig. 3.

(2) Calculate the criteria weights.The criteria weights are determined by the questionnaires to reflect the opinion of the

stakeholders (including government, experts, and business). The priorities assigned toeach criterion by these stakeholders are integrated to develop the fuzzy criteria weights.

(3) Determine the performance of the alternatives for each criterion.The triangular fuzzy number is utilized to express the performance of quantitative

criteria (C3, C4, C6, C7, C12, C13, C14 and C16). The linguistic variables are usedto calculate the performance of the qualitative criteria (C1, C2, C5, C8, C9, C10, C11,C15 and C17).


Table 3The final score of fly ash management alternatives

The attitude ofdecision maker

Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5

Pessimistic 0.006 0.030 0.030 0.028 0.014Neutral 1.521 2.275 2.236 2.229 1.539Optimistic 3.036 4.520 4.442 4.430 3.064

(4) Aggregate the fuzzy weights and fuzzy performance and rank the final score of thealternatives.

The fuzzy weighting and fuzzy performance can be aggregated to obtain the finalscore of the alternatives. Table 3 shows the final result of fly ash management. The flyash management alternatives are ranked according to decision makers’ attitude.

When the decision maker’s approach is positive or neutral, the case order is as follows:

Alternative 2 � Alternative 3 � Alternative 4 � Alternative 5 � Alternative 1

When the decision maker’s approach is negative, the case order is as follows:

Alternative 4 � Alternative 3 � Alternative 2 � Alternative 5 � Alternative 1

(5) Sensitivity analysis.Sensitivity analysis is performed to further understand each alternative’s efficiency

value or significance value’s influence on the final evaluation. The evaluation methoddefined α-cut value. The results are shown in Table 4. It can be seen that the orderof all alternatives is almost the same. For example, under positive and neutral sit-uations, the rank is still Alternative 2 � Alternative 3 � Alternative 4 � Alternative5 � Alternative 1.

Table 4Sensitivity analysis results

Alternatives Attitude α-cut = 0 α-cut = 0.2 α-cut = 0.4 α-cut = 0.6 α-cut = 0.8 α-cut = 1

Alternative 1Pessimistic 0.006 0.020 0.047 0.092 0.160 0.256Neutral 1.521 1.083 0.745 0.500 0.340 0.256Optimistic 3.036 2.146 1.443 0.909 0.520 0.256






Fig. 4. The PIPA results of all alternatives.

4.3. PIPA

After selecting six policy impact potential criteria in the first stage, this research then usesthese six criteria with the five fly ash management alternatives in conducting the ELECTREmethod analysis through Eq. (1)–(18). Six experts participated in this research. They areall media persons deeply involved in environmental issues, with an average of 15 yearswriting about environmental news. Collectively, they have written a total of 1124 newsstories related to waste policies in the past 7 years.

ELECTRE’s case analysis results can be seen in Fig. 4. The policy impact potentials ofAlternative 1 and Alternative 2 are smaller. Alternative 3 and Alternative 5 rank the second.Finally, although there are no obvious differences in the degree of the impact potential,Alternative 4 has the biggest impact potential.

4.4. Communication and decision making

Based on the results of the MCDM, regardless of whether the decision-maker’s approachis positive, neutral, or negative, Alternative 2, Alternative 3, and Alternative 4 are all betterchoices, especially Alternative 2. When the decision maker’s approach is positive or neutral,Alternative 2 is always best.

According to the PIPA developed in this study, the policy impact potential of Alterna-tive 1 and Alternative 2 are smaller. Alternative 1 is the best in terms of the policy impactpotential analysis results; however, Alternative 1 is always the last choice through consid-eration of the expense and benefits, technological, environmental, social, and managementaspects, regardless of the decision-makers’ approach. Hence, combining the two analysismethods, it is suggested that Alternative 2 is the preferable choice. Alternative 3 ranks thesecond.

5. Discussion

The rank of the alternatives from MCDM is Alternative 2 � Alternative 3 � Alternative4 � Alternative 5 � Alternative 1. In contrast, based on PIPA, Alternative 1 has the smallest


policy impact and is considered the best choice, possibly because Alternative 1 (Self-established reuse factory) has a obviously lower impact regarding regional conflicts andlocal resistance. However, based on the results of the MDCM, Alternative 1 is rankedthe last choice, possibly because Alternative 1 has a higher land cost. Because PIPA pro-vides assessment of policy impact of implementation, it can complement the MCDM thatconsiders multiple criteria including the social influence factors. The traditional MCDMis not sufficient in making a decision that can be implemented successfully. Combiningfuzzy AHP and PIPA offers more complete information so as to select the proper pol-icy and reduce possibility of failure of implementation. Based on the results of MCDMand PIPA in the case study, Alternative 2 is the preferable choice through considera-tion of the expense and benefits, technological, environmental, social, and managementaspects.

The cases investigated in this study are mainly the construction of NIMBY facility, suchas landfills and incinerators. The experts involved in the PIPA analysis are experiencedreporters. They have high sensitivity to the pressure and concern that may impact the policy’simplementation. The PIPA method and the improved decision-making model can be appliedto different issues by selecting suitable experts.

6. Conclusion

This research demonstrates the discrepancy between policy planning and policy imple-menting by investigating and analyzing waste policies. It is found that many decisions basedon traditional policy analysis that has considered multiple criteria including social factorsstill encounter the public’s resistance and fail to be implemented. The PIPA is thereforedeveloped complement the traditional analysis by assessing the risk of failure of implemen-tation.

The decision-making model presented here provides a useful tool for aiding decisionmaking for real-world waste management problems. The PIPA can evaluate a policy’spotential impacts, helping decision makers to ponder potentially great obstacles in thefuture implementation of the policy. The decision-making model not only considers theeconomic, environmental and social factors at the same time but also address the risk con-trol in policy implementation. The PIPA method improves on the scientific decision-makingmodel by considering human aspects, thereby reducing risks and enabling sustainablewaste management. The decision-making model could also be applied to environmen-tal management of various issues besides waste management policy. Different types ofenvironmental policies would require different criteria for PIPA. Hence, careful selec-tion of the criteria and participation of suitable experts are essential to the credibility ofthe PIPA.

Acknowledgement

The authors would like to thank the Bureau of Environmental Protection of Taipei City,Taiwan for financially supporting this research.


References

Alidi AS. A multiobjective optimization model for the waste management of the petrochemical industry. ApplMath Modell 1996;20:925–33.

Barton JR, Dalley D, Patel VS. Life cycle assessment for waste management. Waste Manage 1996;16:35–50.Brans JP, Vincke PH. A preference ranking organization method—(the Promethee method for multiple criteria

decision-making). Manage Sci 1985;31:647–56.Buckley JJ. Fuzzy hierarchical analysis. Fuzzy Sets Syst 1985;17:233–47.Cavallaro F, Ciraolo L. A multi criteria approach to evaluate wind energy plants on an Italian island. Energy Pol

2005;33:235–44.Chang CT, Hwang JR. A multi objective programming approach to waste minimization in the utility systems of

chemical processes. Chem Eng Sci 1996;51:3951–65.Chang NB, Wang SF. Solid waste management system analysis by multi objective mixed integer programming

model. J Environ Manage 1996;48:17–43.Chang NB, Wei YL. Strategic planning of recycling drop-off stations and collection network by multi objective

programming. Environ Manage 1999;24:247–63.Cheng S, Chan CW, Huang GH. Using multiple criteria decision analysis for supporting decisions of solid waste

management. J Environ Sci Health Part A—Toxic/Hazard Subst Environ Eng 2002;37:975–90.Chiou HK, Tzeng GH. Fuzzy multiple-criteria decision-making approach for industrial green engineering. Environ

Manage 2002;30:816–30.Chung SS, Lo CWH. Evaluating sustainability in waste management: the case of construction and demolition,

chemical and clinical wastes in Hong Kong. Resources. Conserv Recycl 2003;37:119–45.El-Naqa A. Environmental impact assessment using rapid impact assessment matrix (RIAM) for Russeifa landfill,

Jordan. Environ Geol 2005;47:632–9.Eriksson O, Frostell B, Bjorklund A, Assefa G, Sundqvist JO, Granath J, et al. ORWARE—a simulation tool for

waste management. Resour Conserv Recycl 2002;36:287–307.Esmali H. Facility selection and haul optimisation model. J Sanit Eng Div—ASCE 1972:1005–21.Finnveden G. Methodological aspects of life cycle assessment of integrated solid waste management systems.

Resour Conserv Recycl 1999;26:173–87.Geldermann J, Spengler T, Rentz O. Fuzzy outranking for environmental assessment. Case study: iron and steel

making industry. Fuzzy Sets Syst 2000;115:45–65.Haastrup P, Maniezzo V, Mattarelli M, Mazzeo Rinaldi F, Mendes I, Paruccini M. A decision support system for

urban waste management. Eur J Operat Res 1998;109:330–41.Hasit Y, Warner DB. Regional solid-waste planning with Wrap. J Environ Eng Div—ASCE 1981;107:511–25.Helms BP, Clark RM. Locational models for solid waste management. J Urban Plann Dev Div—ASCE

1971;97:1–13.Hernandez MG, Martin-Cejas RR. Incentives towards sustainable management of the municipal solid waste on

islands. Sustain Dev 2005;13:13–24.Hung ML, Ma HW, Yang WF. A novel sustainable decision making model for municipal solid waste management.

Waste Manage 2007;27:209–19.Hwang, CL, Yoon, K. Multiple attribute decision making: methods and applications; 1981.Jenkins L. Parametric mixed integer programming—an application to solid-waste management. Manage Sci

1982;28:1270–84.Morrissey AJ, Browne J. Waste management models and their application to sustainable waste management. Waste

Manage 2004;24:297–308.Oliveira LB, Rosa LP. Brazilian waste potential: energy, environmental, social and economic benefits. Energy Pol

2003;31:1481–91.Perlack RD, Willis CE. Multiobjective decision-making in waste-disposal planning—closure. J Environ

Eng—ASCE 1987;113:666–7.Powell J. The potential for using life cycle inventory analysis in local authority waste management decision

making. J Environ Plann Manage 2000;43:351–67.Powell JC, Craighill AL, Parfitt JP, Turner RK. A lifecycle assessment and economic valuation of recycling. J

Environ Plann Manage 1996;39:97–112.


Roy B. The outranking approach and the foundations of ELECTRE methods. Theory Decis 1991;31:49–73.Saaty TL. The analytic hierarchy process: planning, priority setting resource allocation. McGraw-Hill; 1980.Skordilis A. Modelling of integrated solid waste management systems in an island. Resour Conserv Recycl

2004;41:243–54.Tran LT, Knight CG, O’Neill RV, Smith ER, Riitters KH, Wickham J. Fuzzy decision analysis for integrated

environmental vulnerability assessment of the Mid-Atlantic region. Environ Manage 2002;29:845–59.Truitt M, Liebnman J, Kruse C. Simulation model of urban refuse collection; 1969. pp. 289–298.Vaillancourt K, Waaub JP. Environmental site evaluation of waste management facilities embedded into EUGENE

model: a multicriteria approach. Eur J Operat Res 2002;139:436–48.

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