The aim of the study is to describe the activities carried out in Italy by the ENEA Agency in order to define a new performance indicator for wastewater treatment service taking into account the appropriateness and efficiency of existing plants equipment and, consequently, evaluating economic incentives. The activity was carried out under the ENEA-MiSE (Italian Ministry of Economic Development) agreement.

1. Towards new urban wastewater treatment
Performance Indicators for life quality improvement:
experiences from Italy
S. De Gisi, L. Petta, P. Mulargia, R. Farina
The 7th IWA Specialist Conference on Efficient Use and Management of Water
“Water Efficient Strategies for Difficult Times”,
October 22th 2013, Paris, France

2. Framework
• Introduction
• Incentive mechanisms for efficiency
• The « Service Objectives » System
• The aim of the study
• Materials and methods
• Framework of the proposed methodology
• Results and discussions
• Assessment of the municipal WWTPs quality for each
District of Southern and Islands of Italy
• Evaluation of the wastewater treatment plants quality
coefficient (β)
• Mid-term incentives for S.11 indicator
• Conclusions
• References

3. Introduction
An Incentive mechanisms …
An incentive mechanism is aimed at motivating individuals or
government institutions in order to encourage better performances
in any specific service supply sector.
Water
Energy
Solid waste

4. Introduction
… and its characteristics
An incentive mechanism:
• must be based on appropriate indicators able to evaluate the
quality of sector/service investigated;
• should be publically accepted by stakeholders in order to have a
policy efficient (Kallbekken, 2013);
• should have transparency of the method and the ability to
earmark eventually revenues (Kallbekken and Aasen, 2010).

5. Introduction
Public acceptability of the incentive
• Kallbekken (2013) highlights
a trade-off between
acceptability of the incentive
(or in general, other price
instruments) and efficiency of
the policy.
• Generally, the most efficient
policies are often the least
acceptable.
Acceptability
of incentive
mechanism
Efficiency of
the policy
A new proposal for an incentive should consider the success
level of the current mechanism.
Where the mechanism is publicly accepted, a new procedure should
change as little as possible the current procedure architecture.

6. The “Service Objectives” system
Thematic areas
•
Education
Care for the
elderly and
children
The “Service
Objectives”
system
Integrated
water
service
Management
of municipal
solid wastes
Introduced in Italy by
the National Strategic
Framework 2007-2013
(Italian Law 296, 2006)
in compliance with the
EC Council Regulation
1083/2006, the
“Service Objectives”
system is an innovative
results-oriented
programme related to 4
thematic areas:
education, care for the
elderly and children,
management of
municipal solid wastes
and integrated water
service.

7. The “Service Objectives” system
Partnerships
Southern Italy and Islands districts

8. The “Service Objectives” system
The aim of the mechanism
THE
SYSTEM
GOAL
The system aims at improving the
quality of life of Southern Italy and
Islands citizens by means of an incentive
mechanism providing subsides for a
total amount of EUR 3 billion …
INDICATORS
Intermediate verification in 2009
Evaluation of the intermediate incentive
A
B
C
Publication of results
A = base value
B = intermediate value
2005
2007
2008
2009
2010
ISTAT data aquisition for the S.11 indicator
2013
C = target
… according to the achievement in
2013 of specific targets set for 11
service indicators related to as
many essential services.
An intermediate verification
in 2009 was also expected
in order to provide a first
incentive advance.

9. The “Service Objectives” system
The “Service Objectives” system
indicators
S.01
400
350
300
250
200
150
100
50
0
S.11
S.10
S.02
S.03
S.09
S.04
S.08
S.05
S.07
The S.11 indicator
S.06
(Values in Million of Euro)
The amount of potential
reward resources at 2013
for each indicator
(Values in Millions of euro)
S.11 = PEeff/PEtot [%]

10. The “Service Objectives” system
The current procedure for intermediate incentives evaluation
(1)
(2)
(3)
(4)
α coefficient = f (coverage degree of the sewage system)

11. The aim of the study
• The aim of the work is to define a new methodology for the
evaluation of the intermediate incentive in the wastewater
sector that takes into account (i) the urban wastewater
treatment demand as well as (ii) the quality of wastewater
treatment service.
• A simulation on the basis of data provided by ISTAT (with
reference to the time horizon 2005-2008-2013) for Southern
Italy and Islands districts was carried out in order to achieve
this goal.

12. Materials and methods
Framework of the proposed methodology
Phase 1
Definition of
alternatives
(Municipal
WWTPs)
Phase 2
Application of
MCA
Data input
Criteria
Criteria
definition
definition
Data
validation
Weights
definition
Alternative
definition
Application
of MCA
Phase 3
Wastewater
quality
evaluation for
each District
Phase 4
Evaluation of
β coefficient
Phase 5
Evaluation of
α coefficient
(quality)
(coverege degree
of sewage
system)
Evaluation
of β
coefficient
Evaluation
of α
coefficient
Phase 6
Evaluation of
intermediate
incentives
Definition
of the
WWTPs
indices for
each District
Evaluation
of WWTPs
quality
(IPDISTRICT)
Evaluation of
intermediate
incentives

13. Materials and methods
Proposed methodology: data input
Outlet WW
Flow-rate, BOD5, COD, Ntot, TSS, Ptot
WWTP Performance
Flow-rate, BOD5, COD, Ntot, TSS, Ptot
Influent WW
WWTP Flow-chart water line
1
2
3
4
5
ISTAT survey (2008)
Large WWTPs (PE > 50,000)
Values for each month of sampling, on
annual basis
9
Bar Screens
Grit Chamber
Primary Settling Tank
Denitrification
7
8
6
10
Oxidation / Nitrification Tank
Secondary Settling Tank
Biofiltration (BFs)
Disinfection
11
Primary Sludge
Return Activated Sludge
Secondary Sludge

14. Materials and methods
Proposed methodology: data validation
Data acquired
(concentrations, flow-rate,
Population equivalent,
Industrial percentage…) with
the ISTAT questionnaire are
true?
The WWTP’s flow-chart
is in line with that
present in the system?
Open questions ….

15. Example of use of aerial photos to validate the WWTP’s flow-chart
Secondary sedimentation processes
Primary treatment
Disinfection
Effluent
Inlet WW
Secondary treatment for the removal
of organic matter
Pre-treatment

16. Materials and methods
Proposed methodology: evaluation criteria
N.
C1
Criteria
N. Parameter
Technological C11 Primary and
equipment
secondary
treatment
C12
C13
C14
C2
Environmenta C211
l performance
of the plant
C212
C213
C214
C215
Part 1
Description and attribution of value to the parameter/sub-parameter
With reference to the current technological equipment installed in the WWTP, are there primary and secondary treatments units (for
the removal of the organic substance)? Three classes were considered with correspondent qualitative and quantitative judgments: Class
1: Yes, with primary and secondary treatment (0.666); Class 2: Yes, with only primary treatment (0.500); Class 3: No, there is neither
a primary nor a secondary treatment (0.166). TO MAXIMIZE
Treatments for
With reference to the current technological equipment installed in the WWTP, are there treatments units for nutrients removal
Nutrients removal (nitrogen and phosphorous)? Four classes were considered: Class 1: Yes, with nitrogen and phosphorus removal (0.875); Class 2: Yes,
(Nitrogen and
only with nitrogen removal (0.625); Class 3: Yes, only with phosphorus removal (0.375); Class 4: No treatment unit is installed
Phosphorus)
(0.125).
TO MAXIMIZE
Tertiary
With reference to the current technological equipment installed in the WWTP, are there tertiary treatment units? Four classes were
treatments
considered with correspondent qualitative and quantitative judgments: Class 1: Yes, with sand filtration and microfiltration (0.875);
Class 2: Yes, with only microfiltration (0.625); Class 3: Yes, with only sand filtration (0.375); Class 4: No treatment unit is installed
(0.125). TO MAXIMIZE
Disinfection
With reference to the current technological equipment installed in the WWTP, are there disinfection treatment units? Four classes were
considered: Class 1: Yes, with the use of technologies (UV, filter membranes, per acetic acid) more suitable with reference to the
minimization of the formation of DBPs (0.875); Class 2: Yes, with the use of ozone (0.625); Class 3: Yes, with the use of chlorine
compounds (chlorine gas, sodium hypochlorite) (0.375); Class 4: No treatment unit is installed (0.125). TO MAXIMIZE
BOD5
With reference to the nitrogen concentration values in the effluent, according to the Italian and European legislation, how many cases
of non-compliant samples of BOD5 have been observed for each month of sampling, on annual basis? Four classes were considered
with correspondent qualitative and quantitative judgments: Class 1: number of non-compliant samples between 0-25% (inclusive),
(0.875); Class 2: number of non-compliant samples between 25-50% (inclusive), (0.625); Class 3: number of non-compliant samples
between 50-75% (inclusive), (0.375); Class 4: Number of non-compliant samples between 75-100% (inclusive), (0.125). TO
MAXIMIZE
COD
As for parameter C211, how many cases of non-compliant samples with respect to COD have been observed? TO MAXIMIZE
TSS
As for parameter C211, how many cases of non-compliant samples with respect to TSS have been observed? TO MAXIMIZE
Total Nitrogen
As for parameter C211, how many cases of non-compliant samples with respect to Total Nitrogen have been observed? TO MAXIMIZE
Total Phosphorus As for parameter C211, how many cases of non-compliant samples with respect to Total Phosphorous have been observed? TO
MAXIMIZE

17. Materials and methods
Proposed methodology: evaluation criteria
N.
Criteria
N.
C221
C222
C223
C224
C225
C3
Treatment
capacity
C31
C32
C33
Part 2
Parameter
Description and attribution of value to the parameter/sub-parameter
Percentage removal
With reference to the percentage removal of Total Nitrogen (% NTOT), according to the Italian and European legislation, how many
of total nitrogen (%N) cases of non-compliant samples have been observed for each month of sampling on annual basis? Four classes were considered with
correspondent qualitative and quantitative judgments: Class 1: number of non-compliant samples between 0-25% (inclusive),
(0.875); Class 2: number of non-compliant samples between 25-50% (included), (0.625); Class 3: number of non-compliant samples
between 50-75% (inclusive), (0.375); Class 4: Number of non-compliant samples between 75-100% (inclusive), (0.125).
TO MAXIMIZE (a)
Percentage removal
As in the criteria C221, how many cases of non-compliant samples have been observed with reference to the percentage removal of
of total phosphorus
Total Phosphorous (% PTOT)? TO MAXIMIZE (a)
(%P)
Percentage removal
As in the criteria C221, how many cases of non-compliant samples have been observed with reference to the percentage removal of
of BOD5 (%BOD5)
BOD5 (% BOD5)? TO MAXIMIZE (b)
Percentage removal
As in the criteria C221, how many cases of non-compliant samples have been observed with reference to the percentage removal of
of COD (%COD)
the COD (% COD)? TO MAXIMIZE (b)
Percentage removal
As in the criteria C221, how many cases of non-compliant samples have been observed with reference to the percentage removal of
of TSS (%TSS)
TSS (% TSS)? TO MAXIMIZE (b)
Domestic wastewater With reference to the rate of domestic influent wastewater, the treatment capacity of the plant is defined as the ratio of the Total
Effective Domestic Population Equivalent (PEtot, eff) and Total Design Domestic Population Equivalent (PEtot, design). The lower is the
value the greater is the residual capacity of treatment of the plant. In this way the costs for possible future interventions relating to
structural adjustment are reduced.
TO MINIMIZE
Industrial wastewater As in the case of C31 criteria, but considering the rate of industrial influent wastewater.
TO MINIMIZE
Treatment of tanker
Treatment of tanker wastewater means the treatment of septic tanks or industrial plants, generally transported by tankers and then
wastewater
processed in the system (i.e. via a special section) before being mixed into the influent wastewater or sludge line. The purpose of
such pre-treatment is to avoid overloads and alterations of plant operation. With reference to the current technological equipment,
the plant is able to treat the tanker wastewater?
YES = 0.75; NO = 0.25.
TO MAXIMIZE

18. Materials and methods
Proposed methodology: weights vector
Criteria’s level with the PCT method
S-WWTPs priorities(a)
NS-WWTPs priorities(b)
1st
Local
Global
Local
Global
2nd
3rd
C1
0.333
0.333
0.333
0.333
C11
0.100
0.033
0.350
0.117
C12
0.350
0.117
0.350
0.117
C13
0.200
0.067
0.100
0.033
C14
0.350
0.117
0.200
0.067
0.500
0.500
0.500
0.500
0.667
0.334
0.667
0.334
C211
0.167
0.056
0.300
0.100
C212
0.167
0.056
0.300
0.100
C213
0.067
0.022
0.167
0.056
C214
0.300
0.100
0.167
0.056
C215
0.300
0.100
0.067
0.022
0.333
0.167
0.333
0.167
C221
0.500
0.083
C222
0.500
0.083
-
-
C223
-
0.069
-
0.417
0.069
C225
-
-
0.417
C224
0.167
0.028
0.167
0.167
0.167
0.167
C31
0.417
0.070
0.417
0.070
C32
0.417
0.070
0.417
0.070
C33
0.167
0.028
0.167
0.028
C2
C21
C22
C3
S-WWTPS
NS-WWTPS
SENSITIVE - WWTPS
C2 > C1 > C3;
C12 = C14 > C13 > C11;
C21 > C22;
C214 = C215 > C211 = C212 > C213;
C221 = C222;
C31 = C32 > C33;
(
NOT - SENSITIVE – WWTPS
C2 > C1 > C3;
C11 = C12 > C14 > C13;
C21 > C22;
C211 = C212 > C214 = C213 > C215;
C223 = C224 > C225;
C31 = C32 > C33.

19. Materials and methods
Proposed methodology: WWTPs performance indices

20. Materials and methods
Proposed methodology: β coefficient
IPDISTRICT quality classes
Definition of the wastewater treatment quality coefficient
(β) classes for municipal WWTPs.
β
•
The evaluation of the wastewater treatment quality coefficient (β) is carried out on
the basis of the values reported in Table as a function of the IPDISTRICT
Quality class
Class 1
Class 2
Class 3
Class 4
Class 5
(IPDISTRICT) Range values
0.800 – 1.000
0.600 – 0.800
0.400 – 0.600
0.200 – 0.400
0.000 – 0.200
(β) Values
β
1.0
0.9
0.8
0.7
0.6

21. Results and discussion
Mid-term
incentives
for S.11
indicator
3
GOAL
Evaluation
of the β
coefficient
Assessment of the
municipal WWTPs
quality for each
District of Southern
and Islands of Italy
2
1

22. Results and discussion
1) Assessment of the municipal WWTPs quality for
each District of Southern and Islands of Italy

23. Results and discussion
2) Evaluation of the β coefficient
•
It can be observed how Sardinia (1.000) can be considered like the best district in
terms of municipal WWTPs quality, but has to be underlined the advantage of the
limited number of WWTPs assessed (only one plant > 50,000 PE).
•
Whereas, the worst performances are related to the following districts: Abruzzo,
Basilicata and Molise (0.600).

24. Results and discussion
3) Mid-term incentives for S.11 indicator: comparison
between the current procedure and the proposal

25. Results and discussion
3) Mid-term incentives for S.11 indicator: the
management of the surplus
•
With reference to the amount of resources provided at the moment of the
intermediate verification (187.5 M€), the overall mid-term incentives at
2009 are of 166.10 M€ and 138.92 M€ respectively using the current
method and the new proposal. The surplus is of 27.18 M€.
•
The management of the surplus is an interesting element of this proposal
as it should be used to balance a potential decrease of acceptability by
stakeholders due to the introduction of the new mechanism. Consequently,
in order to increase the acceptability of the method as well as the competitiveness
of the districts, a re-distribution of such surplus has been expected in the proposed
methodology according to the following relation:
PDISTRICT = intermediate incentive at 2009 for the single district after the surplus re-distribution
= intermediate incentive at 2009 for the single district evaluated with the proposed procedure;
= intermediate incentives at 2009 evaluated with the proposed procedure;
= intermediate incentives at 2009 evaluated with the current procedure.

26. Results and discussion
3) Mid-term incentives for S.11 indicator after
surplus re-distribution

27. Policy Implications
Policy implications
•
The “Service Objectives” system presented in this study involves the
use of appropriate indicators aimed at monitoring the achievement of
specific objectives in a temporal period of about 10 years.
•
Its policy falls within the medium-term actions aimed at encouraging
each district in completing those infrastructures necessary for the
development of the territories.
•
Therefore, the “Service Objectives” mechanism is linked to the
policies of the individual regions and as such it could potentially affect
the programming of structural interventions in the field of public
works. This aspect is important and should be properly managed.
•
Potential conflicts could generate vertical fragmentations with
reference to the decision chain and as such the failure of the
mechanism.
•
Consequently, it is essential that all those actions that could
encourage the stakeholder’s acceptability are implemented.

28. Conclusions
The following outcomes can be pointed out:
•
data without uncertainty are required, such as those provided by an official
survey (as ISTAT in the case of Italy);
•
the lowest possible number of indices should be selected, so as to
adequately describe the wastewater treatment plants under study;
•
a proper wastewater treatment plants quality assessment at district-scale
(or other geopolitical area) necessarily requires a large number of plants to
be taken into account, also with reference to the small wastewater
treatment systems (<2000 PE);
•
in order to set an effective incentive mechanism, districts that do not
communicate data according to the fixed deadlines have to be penalized,
so as to force their commitment in providing the required information as
well;
•
in order to increase the public acceptability and consequently the success
of the policies, high importance must be given to the following aspects:
understanding of the method, budgetary transparency, public resources
allocation, more information about how revenues are spent.

29. References
•
Council Directive 91/271/EEC of 21 May 1991 concerning urban waste-water treatment.
•
Council Regulation (EC) n. 1083/2006 of 11 July 2006 laying down general provisions on the European
Regional Development Fund, the European Social Fund and the Cohesion Fund and repealing Regulation (EC) No
1260/1999.
•
D.Lgs. 152/2006. Decreto Legislativo 3 Aprile 2006, n. 152. “Norme in materia ambientale”, Gazzetta Ufficiale n.
88 del 14 Aprile 2006 – Supplemento Ordinario n. 96 (in Italian).
•
Delibera CIPE 82/2007, Regole di attuazione del meccanismo di incentivazione legato agli obiettivi di servizio del
QSN 2007-2013, Gazzetta Ufficiale n. 301 del 29/12/2007 (in Italian).
•
Hutchinson E., Kennedy P.W., Martinez C. 2010. Subsidies for the production of cleaner energy: When do they
cause emissions to rise?. B E J Econ Anal Pol. 10(1), 1-9.
•
ISTAT, 2008. Il Sistema delle Indagini sulle Acque. Anno 2008. ISTAT, Rome, Italy (in italian).
•
Kallbekken S. 2013. Public Acceptability of Incentive-Based Mechanisms. In: Shogren, J. (Ed.), Encyclopedia of
Energy, Natural Resource and Environmental Economics. Elsevier Inc., ISBN: 978-0-08-096452-2, pp. 306-312.
•
Legge 27 dicembre 2006, n. 296. “Disposizioni per la formazione del bilancio annuale e pluriennale dello Stato
(legge finanziaria 2007)”, Gazzetta Ufficiale n. 299 del 27 dicembre 2006 - Supplemento ordinario n. 244 (in
Italian).
•
Lienert J., Schnetzer F., Ingold K. 2013. Stakeholder analysis combined with social network analysis provides
fine-grained insights into water infrastructure planning processes. J Environ Manage. 125, 134-148.
•
MiSE (Italian Ministry of Economic Development) 2010. Stato di avanzamento degli Obiettivi di Servizio,
Istruttoria per l’attribuizione dei premi intermedi – Prima relazione al Comitato Nazionale per il Coordinamento e la
Sorveglianza della Politica Regionale Unitaria (in Italian).
•
Perotto E., Canziani R., Marchesi R., Butelli P. 2008. Environmental performance, indicators and measurement
uncertainty in EMS context: a case study. J Clean Prod. 16, 517-530.
•
Segerson K. 2013. Price Instruments. In: Shogren, J. (Ed.), Encyclopedia of Energy, Natural Resource and
Environmental Economics. Elsevier Inc., ISBN: 978-0-08-096452-2, pp. 185-192.

30. Sabino DE GISI, Ph.D.
sabino.degisi@enea.it
Luigi PETTA, Ph.D.
luigi.petta@enea.it
Pierpaolo MULARGIA, MSc
Pierpaolo.mulargia@enea.it
Roberto FARINA, MSc
roberto.farina@enea.it
Italian National Agency for the New Technology, Energy and Sustainable
Economic Development, Water Resource Management Lab.
Via Martiri di Monte Sole 4, 40129, Bologna (ITALY)