critical risk factors in ppp waste-to-energy incineration ... · international journal of...

International Journal of Architecture, Engineering and ConstructionVol 6, No 2, June 2017, 55-69

Critical Risk Factors in PPP Waste-to-Energy

Incineration Projects

Liguang Wang and Xueqing Zhang∗

Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology

Clear Water Bay, Kowloon, Hong Kong, China

Abstract: Municipal solid waste (MSW) is increasing rapidly due to the global economic growth and worldwide mass urbanization,creating serious environmental, economic and social problems. In China, public-private partnership (PPP) is regarded as aneffective mechanism to attract private capital to provide MSW treatment works and services, and hence a number of waste-to-energy (WTE) incineration projects have been developed. Various risks could occur in different stages of the PPP project deliveryprocess, causing problems or even leading to failure of a project. This paper first identified 21 risk factors in PPP WTE incinerationprojects through literature review and case studies. Then, through a questionnaire survey, the top five most critical risk factorswere found by statistically analyzing the significance of each factor. Next, factor analysis was conducted to determine the majorcommon dimensions of the failure reasons in PPP WTE incineration projects. After that, agreement analysis was performed toexplore the perspectives of academic researchers and industry experts in terms of the similarity and difference in the rankingof the risk factors. Finally, the causal relationships of the risk factors were discussed. Outputs of this research would facilitateboth public and private sectors to design effective preventive measures to successfully address the risks in PPP WTE incinerationprojects, and they could also be used as a reference for risk management in PPP projects of other sectors as well.

Keywords: Critical risk factor, risk management, factor analysis, waste-to-energy, public-private partnership

DOI: http://dx.doi.org/10.7492/IJAEC.2017.012

1 INTRODUCTION

Municipal solid waste (MSW) is increasing rapidly due to theglobal economic growth and worldwide mass urbanization, andaccording to the report published by the World Bank, there are1.3 billion tons of MSW produced per year at 2012 and willincrease to 2.2 billion tons by 2025 (World Bank 2012). Waste-to-Energy (WTE) incineration is an effective way to treat MSW(Rand et al. 2000; Zhao et al. 2016), decrease the volume ofMSW and generate electricity (Cheng and Hu 2010), which hasbeen widely used in many countries, such as Japan, China andDenmark (Ecke et al. 2001; Jung et al. 2004; Kleis and Dalager2004). The Public-Private Partnership (PPP) is regarded as aneffective mechanism to attract investment from the private sec-tor to provide infrastructure and public services with improvingefficiency in the delivery of such works and services, especiallyin emerging markets (Farquharson et al. 2011). In PPP ar-rangement, the private sector usually takes the responsibilitiesof financing, design, construction, and operation of the WTEincineration project, and also the private sector will repay loans,recover the initial cost, and receive reasonable profits by process-ing the MSW, generating and selling power within a concessionperiod (Akintoye et al. 2008; Martins et al. 2011).

A number of WTE incineration projects have been developedthrough PPP arrangement, such as the Dublin WTE project inIreland and the Cornwall energy recovery center project in UK,etc. In China, as of 2008, more than 70% of the WTE incinera-tion projects have been developed through the PPP arrangement(Chen et al. 2010; Song et al. 2013). Also, the Ministry of Fi-nance (MOF) in China had issued 752 pilot PPP projects in thesectors of waste, transportation, water, etc. in 2014, 2015 and2016, of which 36 are PPP WTE incineration projects. How-ever, due to the large construction cost, technical difficulties inconstruction and operation, and the long concession period com-monly associated with the PPP arrangement, many serious riskevents or even project failures have occurred in PPP WTE in-cineration projects (Song et al. 2013). For example, in BeijingLiulitun WTE incineration project in China, a large-scale pub-lic opposition happened because the project may have seriousimpact on nearby residents, cause environmental pollution andalso there was no sufficient public participation and transparentgovernment decision-making process when approving the project(Yi 2011).

Risk analysis is one of the most popular topics in previousstudies of PPPs (Castro et al. 2016; Zhang et al. 2016), and

*Corresponding author. Email: [email protected]

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http://dx.doi.org/10.7492/IJAEC.2016.019

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Wang and Zhang/International Journal of Architecture, Engineering and Construction 6 (2017) 55-69

many risk factors have been identified for the sectors of water,power and transportation considering the specific characteristicsof each type of infrastructure project (Ameyaw and Chan 2015;Cheung and Chan 2011). In previous studies, several key riskfactors have also been identified and analyzed for the sector ofPPP WTE incineration (Song et al. 2013; Xu et al. 2015),such as the government credit risk, technical risk, public oppo-sition and environmental pollution. Because, the appropriaterisk allocation is one of the critical successful factors for PPPsin infrastructure development (Zhang 2005a; Zhang 2005b), thecomprehensive risk identification is always the prerequisit of de-siging risk allocation mechanism and remedial measures.This paper is going to identify the critical risk factors for PPP

WTE incineration projects through a questionnaire survey, anda factor anallysis is going to be used to describe the interrelation-ships among risk factors and determine the major dimensions ofall risk factors, that will help to investigate the major dimen-sions of project failure reason, and also, the research findings areuseful to the public and private sectors to design efficient riskprevent measures.

2 IDENTIFICATION OF RISK FACTORS

2.1 Risk Factors for PPP Projects in General

As the risk management is one of the top research interests ofPPP study (Zhang et al. 2016), many risk factors for PPPprojects in general have been identified in previous studies, suchas the political risk (Wang and Tiong 2000), revenue risk (Shanet al. 2010) and technical risk (Soomro and Zhang 2011). Thecommon categories of risk factors had been given by the WorldBank, which includes design, construction and commissioning,operation, demand risk, etc. (World Bank 2014). Serval riskfactors may have similar significance in different types of PPPprojects. For example, the government intervention and pub-lic credit are ranked severe for the sectors of water, power andtransportation (Cheung and Chan 2011). However, many otherrisk factors should be carefully studied for each sector due to thespecific characteristics of each type of infrastructure project. Forinstance, the environmental pollution (Mills et al. 2006) publicopposition (Song et al. 2013) have drawn most focus for PPPWTE incineration projects.

2.2 Risk Factors for PPP WTE IncinerationProjects Identified in Previous Studies

Song et al. (2013) had investigated 10 risk factors for PPP WTEincineration projects in China based on case studies, and pro-vided response strategies for managing these risks by drawingexperience and learning lessons from several projects. Ouyangand Wu (2010) had studied relevant MSW management policiesand regulations in China, and suggested that the key risk factorsin PPP WTE incineration projects are environmental pollution,MSW supply risk and incompleteness of law. Xu et al. (2015)has identified five critical risk factors from analyzing risk eventsof 14 PPP WTE incineration plants through content analysis.These risk factors are insufficient waste supply, disposal of non-licensed waste, environmental risk, payment risk, and lack ofsupporting infrastructure.The writers have conducted a comprehensive literature re-

view to identify risk factors in PPP WTE incineration projects,including Xu et al. (2015), Wang and Tiong (2000), Ku-maraswamy and Zhang (2001), Grimsey and Lewis (2002), Millset al. (2006), Xu et al. (2006), Ng and Loosemore (2007), Xuand Li (2008), Qi et al. (2009), Ouyang and Wu (2010), Weberand Alfen (2010), Cheung and Chan (2011), Song et al. (2013),World Bank (2014), Zhang and Xiong (2015), Fayek and Omar(2016), Sha (2016), Song et al. (2016), and Yan et al. (2017). Atotal of 21 risk factors in PPP WTE incineration projects wereidentified through literature review. Table 1 lists the 21 riskfactors with description for each of them.

3 SIGNIFICANCE INDEXES OF RISKFACTORS

3.1 Questionnaire Survey on Relative Significanceof Risk Factors

The authors conducted an international questionnaire survey onexperts regarding the relative significance of these critical riskfactors in PPP WTE incineration project on a scale of 0-5 (with“0” being “not applicable,” “1” being “not important,” “2” be-ing “fairly important,” “3” being “important,” “4” being “veryimportant,” and “5” being “extremely important”). About 121questionnaires were sent out and 62 respondents returned com-plete questionnaires. All of the respondents had research or workexperience in PPP projects. Many of them were from organiza-tions with rich experience in PPP practice. 49 respondents camefrom the industry and 13 respondents were from the academia.Table 2 provides some details on respondents’ backgrounds.

3.2 Calculation of Significance Indexes

The following formula is developed by Zhang (Zhang 2005b) tocalculate the significance index of each critical success factor indeveloping PPP projects, and in this paper, it will be used tocalculate the significance index of each risk factor. The signifi-cance index could be used to reflect the expert’s judgments onthe significance of each risk factor. Normally the experts willgive such judgments based on their understanding on the riskoccurrence probability, potential loss or other aspects of the riskfactors. The higher significance index means that such risk fac-tor is more critical for PPP WTE incineration project in thisresearch.The Significance Index (SIi) is defined as

SIi=Ni0× 0+Ni1× 1+Ni2× 2+Ni3× 3+Ni4× 4+Ni5× 5

Ni0+Ni1+Ni2+Ni3+Ni4+Ni5(1)

where SIi= significance index for the ith risk; Ni0= number ofresponses as “not applicable” for the ith risk; Ni1= number ofresponses as “not important” for the ith risk; Ni2= number ofresponses as “fairly important” for the ith risk; Ni3= number ofresponses as “important” for the ith risk; Ni4= number of re-sponses as “very important” for the ith risk; and Ni5= numberof responses as “extremely important” for the ith risk.

3.3 Significance Indexes and Rank of Risk Factors

A consolidated summary of the responses from the academic sec-tor, the significance indexes, and rank of the risk factors based

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on academic responses are listed in Table 3. A consolidated sum-mary of the responses from the industry, the significance indexesand rank of the risk factors based on responses from the industryare in Table 4. The significance indexes, and rank of the riskfactors based on all responses are shown in Table 5.

4 AGREEMENT ANALYSIS

In previous section, the significance indexes were calculated sep-arately according to responses from industrial and academic sec-tors. There is a need to examine the level of agreement betweenrespondents from the industrial and academic sectors in the rat-ing of the significances of the risk factors. This is done by con-ducting a Mann Whitney U test to determine whether the meansignificance of each risk factor is equal across the different sec-tors. And, please read the book of SPSS for Windows Step ByStep: A simple Guide and Reference (George and Mallery 2010)for details. The hypotheses are as follows:

H0 = mean significance of each risk factor is equal between

two sectorsHa = mean significance of each risk factor is different between

two sectorsThe statistic of the Mann Whitney U test is U, which is com-

pared to a table of critical values based on the sample size ofeach group. If the value of U exceeds its critical value at somesignificance level (usually 0.05) it means that there is evidence toreject the null hypothesis and accept the alternative hypothesis(Zhang 2006). Table 6 shows the results of the Mann WhitneyU test for risk factors, and there are only two out of the 21 riskfactors are indicated as statistically different in terms of meansignificance at the 95% level of confidence between industrialand academic sectors. The two risk factors are “constructioncost overrun” and “unwillingness to pay”, and the respondentsfrom the academic sector always give a higher rating on the tworisks than the respondents from the industrial. According tothe agreement analysis, 90% of the risk factors are similarly se-lected by industrial and academic sectors indicates that all therespondents consider risk factors rather similarly in affecting thesuccess of a PPP project.

Table 1. Risk factors in PPP WTE incineration projects

No. Risk factor Description

1 Technical risk The selected technical solution is not suitable.2 Construction cost overrun The construction doesn’t complete within the construction budget.3 Delay in completion Delay in completion, such as construction delay.

4Design/construction/commissioningperformance risk

Unsatisfactory performance in quality, health, safety, etc. during design,construction and commissioning phase. The completion of constructionworks should be in a condition sufficient to merit release of the construc-tion contractor from delay liquidated damages liability.

5 Operating cost overrun Operating cost fluctuation. For instance, the cost of the fuel, power orlabors is much higher than expected.

6 Operational performance risk Performance risks associated with operation management and mainte-nance, such as low efficient operation, unreasonable maintenance, oper-ation incident or lack of raw material supply.

7 MSW supply risk Risks associated with the quantity and quality of MSW supply.8 Revenue risk Revenue from MSW treatment subsidy and power generation and selling

is much less than expected.9 Unwillingness to pay Tariff or revenues are not collected as expected because of the public’s

unwillingness to pay.10 Government decision-making risk Bureaucratic, opportunistic or corruption behavior of the government,

non-transparent decision-making process or lack of professional knowledge.11 Government credit risk Public agencies’ failing to fulfill their obligations in the concession con-

tract can negatively affect the project, such as serious deferred in subsidypayment, construction or operation suspended by the government.

12 Land acquisition and administrationapproval risk

Improper site selection, cost overrun or delay in acquiring the site, or delayin acquiring relevant project approvals from local government.

13 Private sector decision-making risk Opportunistic behavior of private sector, or lack of sound feasibility studywhile making investment decisions.

14 Private sector credit risk The risk of private sector default. Such as project bankruptcy, failing tofulfill the obligations of environmental protection on purpose.

15 Environmental pollution Environmental pollution caused by the project.16 Public opposition Public opposition. (e.g. Not-In-My-Back-Yard, NIMBY)17 Interest rate risk Changes in interest rate adversely affect the project outcomes.18 Currency exchange risk Changes in currency exchange rate adversely affect the project outcomes

or risk associated with currency convertibility.19 Inflation risk Changes in inflation adversely affect the project outcomes.20 Incompleteness of law or change in law Incompleteness of law or a change in general law or regulation adversely

affects the project, such as changes in general corporate taxation, in rulesgoverning currency convertibility, or repatriation of profits.

21 Force majeure External events beyond the control of the parties to the contract, such asnatural disasters, war or civil disturbance, negatively affect the project.

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Table 2. Respondent breakup details

Category Number of respondents Percentage

1. Based on working background(1) Academia 13 21%(2) Industry 49 79%2. Based on organization type(1) Research institute 13 21%(2) Government 12 19%(3) Investor 9 15%(4) Consultant/legal adviser 26 3%(5) Lender (e.g. bank) 2 42%3. Based on research/working experience in PPPs(1) Less than 2 years 2 3%(2) 2-4 years 37 59%(3) 5-10 years 21 34%(4) 11-15 years 1 2%(5) More than 15 years 1 2%4. Based on working experience in PPP practice(1) 1 or 2 projects 4 6%(2) 3-5 projects 23 37%(3) 6-10 projects 17 28%(4) 11-20 projects 8 13%(5) More than 20 projects 10 16%5. Based on working experience in PPP WTE practice(1) 1 or 2 projects 38 61%(2) 3-5 projects 24 39%(3) 6-10 projects 0 0%(4) 11-20 projects 0 0%(5) More than 20 projects 0 0%

Table 3. Summary of responses on significance indexes of risk factors from academia

Risk factorNumber of responses

Significance index Rank0 1 2 3 4 5

Public opposition 0 1 1 2 2 7 4 1Revenue risk 0 0 3 1 3 6 3.92 2Environmental pollution 0 1 1 1 5 5 3.92 3Construction cost overrun 0 0 0 3 8 2 3.92 4Government decision-making risk 0 0 2 3 4 4 3.77 5Land acquisition and administration approval risk 0 0 2 3 5 3 3.69 6Incompleteness of law or change in law 0 0 2 3 6 2 3.62 7Operating cost overrun 0 0 1 4 7 1 3.62 8Delay in completion 0 0 3 3 5 2 3.46 9Technical risk 0 0 2 5 4 2 3.46 10Unwillingness to pay 0 2 2 2 3 4 3.38 11Operational performance risk 0 0 2 5 5 1 3.38 12Government credit risk 0 1 3 3 3 3 3.31 13Private sector credit risk 0 1 3 3 6 0 3.08 14Municipal solid Waste supply risk 0 2 3 3 3 2 3 15Private sector decision-making risk 0 1 4 3 4 1 3 16Interest Rate Risk 0 1 4 5 1 2 2.92 17Design/construction/commissioning performance risk 0 2 1 6 4 0 2.92 18Force majeure 0 0 5 5 3 0 2.85 19Inflation risk 0 1 7 2 2 1 2.62 20Currency exchange risk 0 1 7 2 2 1 2.62 21

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Table 4. Summary of responses on significance indexes of risk factors from industry



Public opposition 0 0 2 10 16 21 4.14 1Land acquisition and administration approval risk 0 0 1 10 24 14 4.04 2Environmental pollution 0 1 4 7 19 18 4 3Government credit risk 0 0 6 13 16 14 3.78 4Revenue risk 0 1 3 14 24 7 3.67 5Government decision-making risk 0 1 6 17 12 13 3.61 6Technical risk 0 3 6 16 14 10 3.45 7Municipal solid waste supply risk 0 4 12 7 15 11 3.35 8Operating cost overrun 0 1 4 26 16 2 3.29 9Construction cost overrun 0 0 6 28 12 3 3.24 10Private sector credit risk 0 3 5 22 15 4 3.24 11Design/construction/commissioning performance risk 0 0 9 22 16 2 3.22 12Incompleteness of law or change in law 0 0 11 21 15 2 3.16 13Private sector decision-making risk 0 2 12 17 14 4 3.12 14Delay in completion 0 2 8 23 15 1 3.1 14Operational performance risk 0 2 9 28 9 1 2.96 16Interest rate risk 0 4 20 22 2 1 2.51 17Unwillingness to pay 2 8 14 17 6 2 2.47 18Force majeure 0 6 20 18 4 1 2.47 19Inflation risk 0 6 22 16 5 0 2.41 20Currency exchange risk 2 12 18 14 3 0 2.08 21

Table 5. Summary of all responses on significance indexes of risk factors



Public opposition 0 1 3 12 18 28 4.11 1Environmental pollution 0 2 5 8 24 23 3.98 2Land acquisition and administration approval risk 0 0 3 13 29 17 3.97 3Revenue risk 0 1 6 15 27 13 3.73 4Government credit risk 0 1 9 16 19 17 3.68 5Government decision-making risk 0 1 8 20 16 17 3.65 6Technical risk 0 3 8 21 18 12 3.45 7Construction cost overrun 0 0 6 31 20 5 3.39 8Operating cost overrun 0 1 5 30 23 3 3.35 9Municipal solid waste supply risk 0 6 15 10 18 13 3.27 10Incompleteness of law or change in law 0 0 13 24 21 4 3.26 11Private sector credit risk 0 4 8 25 21 4 3.21 12Delay in completion 0 2 11 26 20 3 3.18 13Design/construction/commissioning performance risk 0 2 10 28 20 2 3.16 14Private sector decision-making risk 0 1 8 20 16 17 3.1 15Operational performance risk 0 2 11 33 14 2 3.05 16Unwillingness to pay 2 10 16 19 9 6 2.66 17Interest rate risk 0 5 24 27 3 3 2.6 18Force majeure 0 6 25 23 7 1 2.55 19Inflation risk 0 7 29 18 7 1 2.45 20Currency exchange risk 2 13 25 16 5 1 2.19 21

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Table 6. Mann Whitney U Test results for risk factors between different sectors

No. Risk factorAsymp. significance (2-tailed)

(between industrial and academic sectors)

1 Technical risk 0.9212 Construction cost overrun 0.003a

3 Delay in completion 0.2444 Design/construction/commissioning

performance risk0.481

5 Operating cost overrun 0.1436 Operational performance risk 0.1057 MSW supply risk 0.3998 Revenue risk 0.2499 Unwillingness to pay 0.038a

10 Government decision-making risk 0.6411 Government credit risk 0.24412 Land acquisition and administration

approval risk0.278

13 Private sector decision-making risk 0.73314 Private sector credit risk 0.73515 Environmental pollution 0.98516 Public opposition 0.91217 Interest rate risk 0.27518 Currency exchange risk 0.18819 Inflation risk 0.74620 Incompleteness of law or change in law 0.121 Force majeure 0.17

aReject the null hypothesis and accept the alternative hypothesis.

5 FACTOR ANALYSIS OF RISK FACTORS

5.1 Adequacy for Factor Analysis

The survey data should be examined to see whether it is appro-priate to use factor analysis by conducting the Kaiser-Meyer-Olkin (KMO) test and/or the Barlett’s test of sphericity (Zhang2006). The two tests indicate the strength of the relationshipamong variables and provide a minimum standard that shouldbe passed before conducting factor analysis. The KMO measureof sampling adequacy is an index for comparing the magnitudesof the observed correlation coefficients to the magnitudes of thepartial correlation co-efficient. Its value should be greater than0.5 for a satisfactory factor analysis to proceed (Zhang 2006).The Bartlett’s test of sphericity examines the null hypothesisthat the correlation matrix is an identity matrix (that is, thevariables in the population correlation matrix are uncorrelated),which would indicate that the factor model is inappropriate.

5.2 Basic Steps of Factor Analysis

Factor analysis is a statistical approach that can be used toverify the conceptualization of a hypothesis by analyzing inter-relationships among a large number of variables and to explainthese variables in terms of their common underlying dimensionsby condensing the information contained in a number of originalvariables into a smaller set of dimensions with a minimum loss

of information. There are four basic steps for factor analysis:(1) generation of the correlation matrix; (2) extraction of initialfactors; (3) rotation and interpretation; and (4) construction ofscales or factor scores for further analyses. Please refer to Zellerand Carmines (1980) and Pett et al. (2003) for details on howto conduct a factor analysis (Zhang 2006).

5.3 Factor Analysis for All Risk Factors

Table 7 shows the results of the KMO and Bartlett’s tests forall of the 21 risk factors. The KMO measure is 0.681, indicatingthe data are adequate for factor analysis. The observed signifi-cance level of the Bartlett’s test of sphericity is 0.000, which issmall enough to reject the null hypothesis and supports a factoranalysis for the data. The two tests draw consistent conclusionsregarding whether a factor analysis is appropriate. Therefore, afactor analysis is conducted for all of the 21 risk factors.The principal components and orthogonal rotation are used

to extract highly correlated risk factors into a small number ofmajor components (dimensions). Figure 1 shows the scree plotof the factor analysis for all of the 21 risk factors. The screeplot graphs the eigenvalue against the number of components.Each successive component accounts for decreasing amounts ofthe total variance. Seven principal components are extracted byspecifying eigenvalues (i.e., the variances of the principal compo-nents) greater than 1. As shown in Table 8, the seven extractedcomponents cumulatively explain 73.899% of the total variance.

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Table 7. KMO and Bartlett’s Test for all risk factors

Kaiser-Meyer-Olkin measure of

sampling adequacy

Bartlett’s test of sphericity

Approximate chi square DOFa Significance

0.681 626.331 210 0aDOF=degree of freedom.

Table 8. Total variance explained by extracted major components (all risk factors)

Component

Initial eigenvalues Rotation sums

rotation of squared loadings

Total%of

Cumulative (%) Total%of

Cumulative (%)Variance Variance

1 6.045 28.787 28.787 2.963 14.111 14.111

2 2.333 11.111 39.899 2.673 12.727 26.838

3 1.799 8.566 48.465 2.423 11.536 38.374

4 1.728 8.229 56.694 2.168 10.322 48.696

5 1.429 6.804 63.498 1.86 8.858 57.554

6 1.161 5.526 69.024 1.827 8.699 66.254

7 1.024 4.875 73.899 1.605 7.645 73.899

Figure 1. Scree plot for factor analysis of all risk factors

Table 9 is the rotated component matrix, in which to makethe output easier to read absolute values less than 0.5 are sup-pressed. Each row of Table 9 contains component loadings, thecorrelations between each variable (risk factor), and the com-ponent. The component loadings indicate which risk factor be-longs to which component. The first component has the largestvariance and therefore can explain the problem most effectively.The second component is independent of the first component andcontains as much of the remaining information in all risk factorsas possible, and so on. As shown in Table 9, the risk factorsbelong to the first component usually occur due to the lack ofgovernment support to the project and then might cause projectfailure eventually. Therefore, after examination of the meaningsof the risk factors that belong to each component, the sevencomponents are renamed, respectively, “Reason I: lack of gov-ernment support”, “Reason II: unstable economic and financialsituation”, “Reason III: private sector’s opportunistic behavior”,

“Reason IV: low efficiency in cost management and fee control”,“Reason V: low performance in project quality management”,“Reason VI: lack of social and environmental management”, and“Reason VII: Force majeure and lack of technical capability”.

5.4 Factor Analysis for All Agreeable Risk Factors

As discussed in a previous section, two out of the 21 risk factorsare not agreeable among the different sectors. It is meaningfulto rerun the factor analysis using only the 19 agreeable risk fac-tors. Table 10 shows the results of the KMO and Bartlett’s tests.The KMO measure of 0.663 and the 0.000 significance level ofthe Bartlett’s test indicate that a factor analysis is appropriatefor the 19 agreeable risk factors. Figure 2 shows the scree plotof the factor analysis for the 19 agreeable risk factors.

Figure 2. Scree plot for factor analysis of all agreeablerisk factors

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Table 9. Rotated component matrix for all risk factors

No. Risk factorComponent

1 2 3 4 5 6 7

1 Government credit risk 0.839

2 Government decision-making risk 0.7

3 Revenue risk 0.695

4 Land acquisition and administration approval risk 0.624

5 Incompleteness of law or change in law 0.511

6 Interest rate risk 0.862

7 Currency exchange risk 0.846

8 Inflation risk 0.749

9 Private sector decision-making risk 0.884

10 Private sector credit risk 0.849

11 Construction cost overrun 0.714

12 Unwillingness to pay 0.671

13 Operating cost overrun 0.569

14 Design/Construction/Commissioning performance risk 0.815

15 Municipal solid waste supply risk 0.586 0.626

16 Operational performance risk 0.573

17 Public opposition 0.805

18 Environmental pollution 0.66

19 Delay in completion 0.557

20 Technical risk 0.828

21 Force majeure 0.646

Table 10. KMO and Bartlett’s Test for all agreeable risk factors

Kaiser-Meyer-Olkin measure of

sampling adequacy

Bartlett’s test of sphericity

Approximate chi square DOFa Significance

0.686 555.078 171 0aDOF=degree of freedom.

Six principal components are extracted by specifying a min-imum initial eigenvalue of 1. As shown in Table 11, the sixextracted components cumulatively explain 72.114% of the totalvariance. Table 12 shows the rotated component matrix (abso-lute values less than 0.45 are suppressed). After examination ofthe meanings of the risk factors that belong to each component,the six components are renamed respectively as “Reason I: lackof government support”, “Reason II: unstable economic and fi-nancial situation”, “Reason III: private sector’s opportunistic be-havior”, “Reason IV: low performance in project management”,“Reason V: lack of social and environmental management”, and“Reason VI: Force majeure and lack of technical capability”. Acomparison of the extracted components of the factor analysisusing all risk factors and only the agreeable risk factors is shownin Table 13.

In the sector of WTE incineration, the MSW supply is alwayscontrolled by the government, and that has directly influenceon project revenue. Therefore, it is reasonable that the MSWsupply risk and revenue risk belong to the first component. Therisk of delay in completion has strong interrelationship with the

public opposition as shown in Table 13, because the constructiondelay is always caused by the public opposition in WTE incin-eration project. Therefore, according to the factor analysis, therisk factor of delay in completion belongs to the fifth component.

6 ANALYSIS ON CRITICAL RISKFACTORS

6.1 Top Five-ranked Critical Risk Factors Basedon Questionnaire Survey

Figure 3 compares the top five-ranked risk factors based onresponses from the academia sector and the industrial sector.Among those significant risk factors, two risk factors seem tohave obvious difference in ranking based on the experts’ viewfrom the academia and industrial. The first one is construc-tion cost overrun, which is ranked fourth in significance by theexperts from the academic sector. However, the experts fromthe industry generally give a medium ranking on it. On onehand, the construction cost overrun is one of the most popu-

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lar risk factors in construction project and always encounteredin practice, so the experts from academia may consider it as acritical one. On the other hand, because the construction costoverrun always happens in practice, the experts from industrialusually have rich experiences and various methods of managingit, such as the effective management of claims and variations,or designing the tariff adjustment mechanism against the riskof construction cost overrun. Therefore, the experts from theindustrial may not rank this risk factor as the most critical oneconsidering the possible effective risk preventive measures.The other risk factor is government credit risk, which is more

emphasized by the experts from the industry than the ones fromthe academia. From the academic’s perspective, there were al-ready many studies on managing the government credit risk, andsuch risk could be managed under the specific conditions in the

PPP contract (Wang and Tiong 2000) or by purchasing the po-litical risk insurance. However, in practice, although there aresome preventive measures against this risk, the project still mayface huge financial loss due to the government default behaviors,such as delay in payment to the project (Song et al. 2013), sothe experts from the industrial always pay more attention to thegovernment credit risk.It is useful to measure the agreement in the ranking of these

risk factors between the industrial and academic sectors. Ok-pala and Aniekwu (1988) provided a quantitative method forrank agreement analysis. In this method, the “rank agreementfactor” (RAF) is used, which was also used by Zhang (2005b)to analyze the rank agreement of critical successful factors forPPP infrastructure development. The RAF shows the averageabsolute difference in the ranking of factors between two groups.

Table 11. Total variance explained by extracted major components (all agreeable risk factors)

Component

Initial eigenvalues Rotation sums

rotation of squared loadings

Total% of

Cumulative (%) Total% of

Cumulative (%)Variance Variance

1 5.668 29.83 29.83 2.931 15.428 15.428

2 2.16 11.37 41.2 2.719 14.309 29.737

3 1.753 9.226 50.426 2.538 13.36 43.097

4 1.69 8.895 59.321 2.168 11.412 54.51

5 1.332 7.011 66.332 1.76 9.264 63.774

6 1.099 5.782 72.114 1.585 8.34 72.114

Table 12. Rotated component matrix for all agreeable risk factors

No. Risk factorComponent

1 2 3 4 5 6

1 Government credit risk 0.833

2 Revenue risk 0.708

3 Government decision-making risk 0.69 0.462

4 Land acquisition and administration approval risk 0.631

5 Municipal solid waste supply risk 0.607 0.581

6 Incompleteness of law or change in law 0.491

7 Interest rate risk 0.849

8 Currency exchange risk 0.849

9 Inflation risk 0.772

10 Private sector credit risk 0.844

11 Private sector decision-making risk 0.844

12 Design/Construction/Commissioning performance risk 0.757

13 Operational performance risk 0.713

14 Operating cost overrun 0.526

15 Public opposition 0.846

16 Environmental pollution 0.718

17 Delay in completion 0.49

18 Technical risk 0.829

19 Force majeure 0.641

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ctor

ReasonI:Lackof

government

supp

ort

•Governm

entcred

itrisk

•Governm

entde

cision

-mak

ingrisk

•Revenue

risk

•Lan

dacqu

isitionan

dad

ministration

approval

risk

•Incompleten

essof

law

orchan

gein

law

ReasonI:Lackof

government

supp

ort

•Governm

entcred

itrisk

•Revenue

risk

•Governm

entde

cision

-mak

ingrisk

•Lan

dacqu

isitionan

dad

ministration

approval

risk

•Mun

icipal

solid

waste

supp

lyrisk

•Incompleten

essof

law

orchan

gein

law

ReasonII:U

nstableecon

omic

andfin

ancial

situation

•Interest

rate

risk

•Currencyexchan

gerisk

•Infla

tion

risk

ReasonII:U

nstableecon

omic

andfin

ancial

situation

•Interest

rate

risk

•Currencyexchan

gerisk

•Infla

tion

risk

ReasonIII:Private

sector’s

oppo

rtun

isticbe

havior

•Private

sector

decision

-mak

ingrisk

•Private

sector

cred

itrisk

ReasonIII:Private

sector’s

oppo

rtun

isticbe

havior

•Private

sector

decision

-mak

ingrisk

•Private

sector

cred

itrisk

ReasonIV

:Low

efficien

cyin

cost

man

agem

entan

dfeecontrol

•Con

structioncost

overrun

•Unw

illingn

essto

pay

•Ope

rating

cost

overrun

ReasonIV

:Low

performan

cein

projectman

agem

ent

•Design/

Con

struction/

Com

mission

ing

performan

cerisk

•Ope

ration

alpe

rforman

cerisk

•Ope

rating

cost

overrun

ReasonV:L

owpe

rforman

cein

projectqu

alityman

agem

ent

•Design/

Con

struction/

Com

mission

ing

performan

cerisk

•Mun

icipal

solid

waste

supp

lyrisk

•Ope

ration

alpe

rforman

cerisk

ReasonV:L

ackof

social

and

environm

entalm

anagem

ent

•Pub

licop

position

•Env

iron

mentalp

ollution

•Delay

incompletion

ReasonVI:Lackof

social

and

environm

entalm

anagem

ent

•Pub

licop

position

•Env

iron

mentalp

ollution

•Delay

incompletion

ReasonVI:Fo

rceMajeu

rean

d

lack

oftechnicalc

apab

ility

•Techn

ical

risk

•Fo

rcemajeu

re

ReasonVII:F

orce

majeu

rean

d

lack

oftechnicalc

apab

ility

•Techn

ical

risk

•Fo

rcemajeu

re

64


Figure 3. Comparison of risk ranking based on the responses from academic and industrial sector

For the two groups, let the rank of the ith item in group 1 beRi1 and that in group 2 be Ri2, N be the number of items, andj = N − i+ 1.The RAF is defined as

RAF =

∑Ni=1 |Ri1 −Ri2|

N(2)

The maximum rank agreement factor (RAFmax) is defined as

RAFmax =

∑Ni=1

∣∣Ri1 −Rj2

∣∣N

(3)

The percentage disagreement (PD) is defined as

PD =

∑Ni=1 |Ri1 −Ri2|∑Ni=1

∣∣Ri1 −Rj2

∣∣ × 100% (4)

The percentage agreement (PA) is defined as

PA = 1 - PD (5)

The higher the value of RAF, the lower the agreement will bebetween the two groups. A RAF of zero means perfect agree-ment, and a PA of greater than 55% can be considered as a goodagreement between the groups (Zhang 2005a).As shown in Table 14, the PA is 58.54% for the two groups

of respondents from academia and the industry. The results ofagreement analysis have shown that the experts from academiaand the industry have a good agreement on the ranking of riskfactors. Therefore, according to all responses as shown in Table5, of 21 risk factors, the top five most significant ones are: (1)

public opposition; (2) environmental pollution; (3) land acquisi-tion and administration approval risk; (4) revenue risk; and (5)government credit risk. Public opposition and environmentalpollution are always considered as the most critical risk factorsin PPP WTE incineration projects, which are also usually iden-tified as key risks in previous studies (Mills et al. 2006; Songet al. 2013). And these critical risk factors are belonging tothe component of “lack of government support” and componentof “lack of social and environmental management” as shown inTable 13.

6.2 Public Opposition

Public acceptance is considered most critical for the effective-ness of WTE incineration project implementation, which is dif-ferent from some other sectors. For example, the developmentof subway could increase the nearby housing price, which willbe welcomed by the nearby residents. However, due to the airpollution caused by the waste incineration, the development ofWTE incineration project will cause serious public oppositionfrom nearby residents and also have negative impact on nearbyhousing price (Huang 2012). Besides, the public opposition willcause other problems in PPP WTE incineration projects.

The public opposition might cause government default. For ex-ample, the Jiangsu Wujiang WTE incineration project in Chinawas suspended by the local government due to the strongly pub-lic opposition (Song et al. 2012). Also, from the private sec-tor’s perspective, the public opposition would also lead to a de-lay in land acquisition or administration approval. Such as in

65


Table 14. Rank agreement analysis of risk factors

Risk factor Rank Rank Agreement(academia) (industrial) analysis

Technical risk 10 7

RAF=3.24RAFmax=7.81PA=58.54%

Construction cost overrun 4 10Delay in completion 9 15Design/Construction/Commissioning performance risk 18 12Operating cost overrun 8 9Operational performance risk 12 16Municipal solid waste supply risk 15 8Revenue risk 2 5Unwillingness to pay 11 18Government decision-making risk 5 6Government credit risk 13 4Land acquisition and administration approval risk 6 2Private sector decision-making risk 16 14Private sector credit risk 14 11Environmental pollution 3 3Public opposition 1 1Interest rate risk 17 17Currency exchange risk 21 21Inflation risk 20 20Incompleteness of law or change in law 7 13Force majeure 19 19

Guangzhou Panyu project in China, the approval of environ-mental assessment was delay due to the public opposition, andeventually caused project suspended (Chen 2013; Song et al.2012).

6.3 Environmental Pollution

The incineration of MSW not only may produce emissions oftoxic air pollutants, but also may generate considerable volumesof solid residues, e.g., bottom ash, grate sifting, fly ash, and airpollution control (APC) residue, which are generated at differentpoints in the process of MSW incineration (Zhang et al. 2010).The environmental pollution from MSW incineration will causepublic opposition and impede the development of WTE inciner-ation project.In addition, during the operation of the WTE incineration

project, the extra cost will be spent on treatment of leachate,air pollution and fly-ash control to meet the relevant environ-mental standards if there was an environmental pollution inci-dent. Therefore, the environmental pollution might cause risksin operation phase, such as the operation cost overrun. Some-times private sectors even need to spend extra money on pub-lic relationship management while facing serious environmentalpollution. And also, the acid emissions would impact the oper-ation performance negatively because of equipment’s corrosion.For example, in Guangzhou Likeng WTE incineration project inChina, there was an operation incident happened which is theresult of equipment’s corrosion by acid emissions (Song et al.2010).

6.4 Land Acquisition and AdministrationApproval Risk

The risk of land acquisition and administration approval riskrefers to improper site selection, delay in acquiring the site and

relevant administration approval (World Bank 2014), which issignificant in PPP WTE incineration projects. Normally thesite of WTE incineration projects should be located near waterdownstream and downwind areas of the city. Otherwise a seri-ous environmental pollution might be caused (Chen 2013; Songet al. 2012).Because of the potential environmental pollution and public

opposition caused by WTE incineration project, the approval ofenvironmental impact assessment (EIA) is always delayed, whichmay cause delay in completion or even project suspended.

6.5 Revenue Risk

The subsidy of MSW treatment from local government and in-come of selling the generated electricity power are the main rev-enue of PPP WTE incineration project (Song et al. 2015). Aserious revenue risk will lead to financial loss of the project andeven cause project failure.Normally the price of grid-connected power will not be ad-

justed corresponding to the operation cost fluctuation, andmostly influenced by the policies issued by the government. Forexample, in 2012, the central government in China issued thenotice on improving the price policies of the municipal solidwaste incineration for power generation, which established awell-designed pricing mechanism of electricity generation forWTE incineration projects in China. The conversion coefficientfrom MSW to grid-connected power is temporarily determinedto be 280 kWh/ton and the price of grid-connected power is0.65 RMB/kWh (it is valid for the WTE incineration projectsapproved since January 1st, 2006), which is higher than the av-erage price of grid-connected power paid before in China (Songet al. 2013).On the other hand, the subsidy of MSW treatment could usu-

ally be adjusted under the conditions predefined in the PPPcontract to cover the operation cost inflation. Therefore, if the

66


PPP contract was not well designed or the government refusedto adjust the subsidy as defined in the contract, a serious revenuerisk will be encountered.

6.6 Government Credit Risk

The government credit risk is a kind of risk encountered withhigh occurrence frequency in PPP projects all over the world(Hainz and Kleimeier 2012; Qi et al. 2009; Wang and Tiong2000). Usually, the MSW treatment contracts in PPP WTE in-cineration projects are “Take-or-Pay” contracts, which mean thateven though the governments could not provide enough MSW,they still need to pay subsidy for MSW treatment as promised inthe contract. In PPPWTE incineration project, the governmentdefault usually results in public agencies’ not fully undertakingthe duties of paying for MSW treatment subsidy, which couldlead to revenue risk happening in some cases. Such situationscould be found in Shenzhen Nanshan WTE incineration projectsin China (Song et al. 2010).Although the government credit risk is quite important, Qi

et al. (2009) had pointed out that the government credit riskhappening was always caused by other risk factors, such as pub-lic opposition, opportunism of local government, moral hazardand opportunism of private sector during decision-making pro-cess, and incompleteness of law or change in law.

7 CONCLUSIONS AND FUTURE STUDY

MSW has tremendously grown due to the global economicgrowth and worldwide mass urbanization. WTE incinerationis one of the effective MSW treatment methods, which has beenwidely used in many countries. PPP is regarded as an effec-tive mechanism to attract investment from the private sectorto provide infrastructure and public services to improve effi-ciency in the delivery of such works and services. Consequently,a large number of WTE incineration projects have been devel-oped through the PPP arrangement. However, there are stillmany failures have been encountered in PPP WTE incinerationprojects due to a variety of risk factors, especially the criticalones.Through the literature review, we have identified 21 risk fac-

tors for PPP WTE incineration projects, the significance indexof each risk factor and the ranking has been given based ona questionnaire survey. Of all the 21 risk factors, the top fivemost significant ones are: (1) public opposition; (2) environmen-tal pollution; (3) land acquisition and administration approvalrisk; (4) revenue risk; and (5) government credit risk, which areconsidered as the critical risk factors.Factor analysis of these 21 risk factors has determined the

major common dimensions of the project failure reason in PPPWTE incineration project. The Mann Whitney U test showsthat the industrial and academic sectors consider risk factorsrather similarly, and the KMO test and Bartlett’s test confirmthe adequacy (at least for the agreeable risk factors) and qualityof the survey. Also, it is found that the top five critical risk fac-tors belong to the component of “lack of government support”and component of “lack of social and environmental manage-ment”, that would direct and concentrate the efforts of the gov-ernment and private sector in such aspects to achieve the projectsuccess in PPP WTE incineration project. The establishment

of the project failure reason dimensions and the detail analysisof each critical risk factor will be helpful to both of the publicand private sector to design preventive measures.

Several causal relationships between the critical risk factorshave been analyzed as well. For example, the environmentalpollution during the MSW incineration will cause public oppo-sition, and eventually might cause government default. Andalso, the government credit risk will cause revenue risk to theproject if the government refused to adjust the subsidy of MSWtreatment as defined in the PPP contract. Therefore, the riskof environmental pollution should be handed well in order toavoid the public opposition and the contract should be well de-signed to manage the government credit risk as the prevent mea-sure against the revenue risk. The casual relationships betweenthe risk factors will be investigated systematically in the futurestudy, and that will be helpful to better understand the risk fac-tors and design effective risk response strategies for PPP WTEincineration projects.

8 ACKNOWLEDGEMENT

This study is financially supported by the National Natural Sci-ence Foundation of China (Project Number: 71472052).

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