Door to Door Collection and Disposal Alternatives in Italy:
Analysis of an Average Cost Function
Abstract
This paper analyses empirically the impact of the collection method and of the
disposal characteristics of residual waste average costs per inhabitant, in a sample of 534
Italian municipalities from 2004 to 2010. The purpose of this work is to provide answers to
very actual questions as: is the door to door collection more expensive? Is it better to bury
residual waste or to incinerate it? Given a certain amount of garbage, is there an optimal
quantity to be incinerated? Moreover, given that many communities use different dumpsites at
the same time, which is the ideal number of disposal sites for unsorted waste? In order to
answer these questions, I consider an average cost function, with cost per inhabitant as a
dependent variable, using a translogarithmic specification and I estimate it with the SUR
method.
The results indicate that cities should use the door to door system in order to collect
waste and to increase recycling, bring the residual rubbish in only one disposal site, which has
to be preferably a WTE plant, located as close as possible to the place where garbage is
produced. The law requirement of 65% of differentiation does not bring about an increase in
the service costs; this target can be reached by all municipalities, no matter the size, while
higher levels of sorting are affordable only for cities with less than 50,000 inhabitants. If
door-to-door collection is implemented, the set-up cost is amortized only if the recycling
share increases. Moreover, the gap between the North and the South of the country for what
concerns incineration should be filled and policy makers should provide a Waste to Energy
plant development on a national scale, especially nearby large cities of the South. At the
regional level, instead, disposal should be planned accordance with the European principles of
proximity in order to ensure self-sufficiency of disposal sites.
1
1. Introduction
Differentiated collection is a system of waste collection that groups garbage based on
the type of material and it represents the first step in order to recycle. Whether waste is
collected using street bins or it is divided within homes and recovered through the domestic
collection, paper, glass, plastic, aluminum and iron materials collected separately allow one to
have clean material fractions that, once purified by the presence of other materials, can be
used in industrial production as secondary raw material. Separate collection, therefore,
responds to two problems related to the exponential increase of the production of waste: the
consumption of raw materials (decreased precisely through recycling) and the reduction in the
amount of mixed waste to be disposed of in landfills or incinerators.
The European Community took charge of these issues in the last two decades and has
been the driving force for a better waste management in member countries and the Italian
legislator has complied to the principles ratified by the European Union. Thus, on the one
hand, the national legislation, implementing the main European Directive, the Waste
Framework Directive 2008/98/EC, identifies specific objectives to be achieved regarding the
recyclables collection, with a minimum of 35% in 2006, 45% to be reached by 2008 and 65%
by 20121. On the other hand, the Italian legislator, inspired by the European directive on the
reduction of the use of landfills 1999/31/EC, imposes strict limits on the reduction of
biodegradable waste going to landfill. Finally, the Directive on packaging and packaging
waste, 1994/62/EC, aims at recovering materials and energy by at least 60% by weight of
packaging waste and to the recycling of 55% of these materials within 2008. Despite this
ambitious objectives established by law, the country is traveling at three speeds: the North is
close to the target, the Center only in very few areas is close to the Northern standard and the
South in which, with few exceptions, the separate collection and the reduction of landfill
disposal is a chimera.
In Italy, the method of collection is able to heavily influence the recycling share, as it
might be seen in Table 4 and 5 at the end of this paper. Municipalities use two main collection
1
Nowadays, the new deadline for the achievement of the 65% of RS is 31 December 2020, as established by the
article 18 of the draft the law decree entitled "Provisions on environmental measures to promote green economy
and for containment of the overuse of natural resources”. “The shift of the terms - may be read in this decree - it
is necessary to adapt the legislative data to real data and to prevent municipalities to incur into penalties related
to the failure to achieve these goals in the current terms of the law." The article then introduces a measure in
order to penalize the municipalities that do not achieve the minimum targets for collection in time, given by an
additional special tribute to the landfill, the proceeds of which will be fully used by the regions to support
financially "incentives aimed at encouraging the market of recycling and recycled products".
2
procedures: big garbage bins and, where available, also recyclables bins placed on the public
soil, usually on the pavement (in what follows “street bins”), and door to door (dtd) collection
of both recyclables and unsorted waste. The use of the term “door to door” is an Italian
particularity and, in recent years, borrowed by other European countries. In the UK and US,
the closest thing to this kind of collection is the kerbside collection, with or without
differentiation of waste (see Hirsch, 1965; Stevens, 1978; Domberger, 1986; Dubin and
Navarro, 1988; Reeves and Barrow, 2000 and Bohm et al., 2010). The alternative to kerbside
in the literature is the backyard collection, which is a more labour intensive method (see
Domberger, 1986 for definition). The Italian use of big waste bins, instead, needs a much
lower use of labour than door-to-door. The latter method, which is considered normal in other
countries, was introduced in Italy for the first time during the Nineties with the only target to
collect differentiated waste and to increase the sorting of recyclable materials and has become
widespread in the last decade in order to respect the recycling shares established by law
152/2006. The transition from the street bins collection system (with or without recyclable
collection) to dtd collection is made with the sole purpose of collecting higher amounts of
separated waste to be sent for recycling2. Unlike what happens in other countries, in Italy
there is no kerbside collection of only mixed waste. There are other collection methods that
increase the differentiated share (for example, the use of locking mechanisms on the bins,
which are accessed with a key associated with each user) but nowadays they are very rarely
used or are in an experimental stage and thus not worthy to be taken into account.
Another advantage of dtd is the MSW reduction and this will be clear throughout the
paper. Indeed, the change in the collection system ensures that the waste illegally conferred
on the road bins freely accessible find the right destination (flows from commercial activities
dropped illicitly in the first available dumpster in order to avoid paying garbage fee disappear,
as well as waste imported from commuters). Moreover, when the collection system is
changed, also the assimilated waste norms are regulated and quantitative and qualitative limits
are introduced, therefore contributing in reducing the waste collected within the urban waste.
It is important to highlight that the overall waste quantity produced at a province level do not
vary. Citizens are induced to be more responsible in buying products with less packaging,
especially when introducing waste reducing policies as the pay as you throw tariff, but this
2
In Anglo- Saxon countries, however, the transition to dtd from the backdoor collectionis done with the sole
objective of reducing the cost of labour, offering service which is considered inferior. In Italy happens the
opposite: a system of low use of labor is abandoned against a more labor intensive one. The goals that the two
methods, in fact, are different.
3
occurs in a small variation in quantity. The more plausible explanation for the waste reduction
is the shift of the garbage from the circuit of municipal waste to the special waste one. In
short, waste do not disappear but it finds another disposable solution and it’s accounted
separately from the municipal one.
Given that other countries use kerbside collection since many decades, which does not
automatically imply differentiation, scholars use both the collection method and the
percentage of collection of certain waste fractions as explanatory variables in the cost of
service when dealing with the estimation of cost functions. Domberger et al. (1986),
Szymanski and Wilkins (1993), Szymanski (1996) introduced in the model a dummy for the
amount of paper and bottles collected and their impact on costs is positive. Dijkgraaf and
Gradus (2003, 2007) take into account the percentage of glass, paper and organic fraction
while only Bel and Costas (2006) and Bel and Fageda (2009) use the percentage of recycling
in their research. In this chapter, instead, I use the door-to-door collection in order to assess
the impact on costs of this method of collection which, as already stated, is the main
instrument that Italian municipalities have in order to increase the recycling share and at the
same time to reduce the use of landfills. Indirectly we have the impact on service costs of
increasing the recycling share. This variable is used here for the first time. Italian studies
estimating MSW cost functions in Italy are few (Antonioli et al., 2002; Abrate et al.,
2012,2014) and, compared to them, I make an important step forward in filling the gap with
the existing literature by using a different specification of the cost function and new
independent varibles.
The dtd system requires more resources than the street bins usage (and therefore one
might expect an increase in the garbage service cost) but at the same time it diverts waste
from landfill disposal so costs are reduced. It is therefore very interesting to assess the overall
effect on costs of this compulsory choice for municipalities in order to adequate to the law
limits. But not all the waste can be recycled thus it has to be burned or buried. In this paper I
also want to evaluate how disposal characteristics affect the costs paid by citizens. Most
papers measure the effect of the presence of a landfill (or an incinerator) in the municipality in
order to take into account the impact of transportation on the garbage fees. Here I make an
important step forward. I first introduce in my cost model the dummies used in the previous
literature, showing that other variables might be more adequate in describing disposal features
and the possibilities that a municipalities has in order get rid of residual waste. Thus, given
that many communities use more than one dumpsite, I use the number of alternative
destinations for unsorted rubbish as a dependent variable. Moreover, the km driven by trucks
4
in order to dispose waste and finally the percentage of incineration will be part of the model.
Notice that the use of these variables represents an innovation in formulating the
characteristics of the service as they have never been used, especially together, in the
literature reviewed here.
The estimation results allow me to make some general considerations on the policies
to be taken at regional or national level on the organization of waste disposal. Furthermore,
with regard to the use of door-to-door, I investigate if the fears that often managers and
mayors have are founded. In fact, the most widespread critique in introducing this collection
method that internalizes the costs of waste separation and optimizes the quality of the various
fractions, is an increase in service costs (and hence in rates) which upsets consumers and
induces a loss of electoral support.
From a methodological point of view, I first estimate an average cost function with a
translogarithmic specification and using SUR introduced by Zellner (1962). I bring a
substantial innovation with respect to previous studies as I use the average cost per capita as
dependent variable in order to better estimate the impact of door-to-door collection. The work
is organized as follows. In the next section I show the literature regarding the main topics of
this work (the method of collection and the disposal characteristics) while in the third section
I go into the details of the cost model used in this analysis. In the fourth paragraph I describe
the data set constructed for the purpose of this research and I present some descriptive
statistics. Finally, in the fifth and sixth section I go respectively through the estimation
procedure and the results while the seventh paragraph summarizes the main findings of this
research.
2. Empirical background
The empirical literature that studies the cost functions of the waste sector takes its
roots 50 years ago, in 1965, when Hirsch publishes a first important contribution to the study
of the costs of municipal waste. This pioneering model opened the way to a long series of
research on the topic with different aims and methods. Regardless of the goal each paper, all
the studies look for methods to improve the efficiency in waste management in order to
reduce the costs borne by citizens. In what follows, I present the main outcomes of the
empirical studies that deal with service characteristics such as the collection method and
disposal alternatives. A major – although far from surprising – conclusion of these studies is
5
that a higher level of service (i.e., more frequent collection, pickup location more distant from
the curb) is more costly than a lower level of service.
2.1.
Literature on method of collection
Since the seminal research of Hirsch (1965) the location of the collection point has
been considered as a factor that can substantially influence the service cost. Stevens (1978),
Domberger et al. (1986), Dubin and Navarro (1988), Szymanski and Wilkins (1993),
Szymanski (1996) have considered different aspects of the kerbside system, with or without
the collection of separated waste, which are distinguished by the level of service provided. In
particular they contrast kerbside collection (in which the household brings itself on the
pavement the garbage bins to be emptied) to the rear of house collection3 (in which the bins
are left inside the yard and the operator provides to their movement and emptying). Since the
collection methods considered in the literature are different from those existing in Italy, it is
difficult to make comparisons with previous work for the impact of this variable. A clear
result in the literature is that the higher the level of service the higher the cost. Surely, dtd
collection corresponds to a better service, but at the same time it typically brings about an
increase of the differentiated fractions, that will be recycled, so the costs of landfilling
decreases.
Hirsch (1965) makes a cross section analysis of the 1960 data for 24 cities in St. Louis
and uses pick-up location as a cost determinant. He uses the kerbside collection as alternative
to the rear of house one, without taking into account the possibility to collect different
materials. He finds that picking up garbage from the rear doubles the costs with respect to the
curb. Collins and Downes (1977), in order to analyse on one hand the cost advantages of
cooperating with neighbouring communities and on the other the costs effects of providing
garbage collection directly through a municipally operated system, consider two main
alternative locations for the actual pickup of garbage at each residence. They are the street
kerb, requiring the residents to carry the garbage out from the house each pickup day, or at the
building line, assuming that that the latter is more expensive than kerb pickup because of the
additional crew time required. Their findings highlight that the third most important
characteristic influencing collection costs per month in an upward direction is, indeed,
building line pickup.
3
This option, also called backdoor or point of storage collection requires to the operatives to collect rubbish
from the residents normal storage point and return the container back to the storage point. This is arguably a
higher level of service as the residents play little or no part in the collection process.
6
Also Stevens (1978), while addressing the topic of impact of market structure on costs
using data of 340 US enterprises for the years 1974 and 1975, includes the collection method
among the explanatory variables. She uses the percentage of households serviced by the firm
at backyard collection points in the model and finds that an increase in this percentage, on
average, resulted in a cost increase of 29%-34%, arguing that this result is reasonable, as
backyard pickup requires more additional time.
The study of Domberger et al. (1986) is much more complete than the ones seen above
as it specifies five different collection alternatives while analysing the effect of the tendering
process on refuse collection costs. The essential difference between the various methods lies
in whether the customer has to carry his dustbin to and from the kerbside or whether this is
done by employees. As previously in the literature, they show that backdoor method of
collection is the most expensive method. The effect of a change from backdoor collection to
kerbside collection is to reduce total cost by 23 per cent.
In line with the previous paper, Szymansi and Wilkins (1993), Szymanski (1996) and
Bello and Szymanski (1996) use the same model and dataset of Domberger et al. (1986) in
order to analyse competitive tendering in sanitation services in the UK. To do this they have
assembled data on refuse collection services over the 10 years period 1984-93. The authors
identify three main options regarding designated pick-up points: kerbside, front of property
and point of storage (backdoor). As in Domberger et al. (1986), kerbside collection
significantly reduces costs and this explains the secular decline in back-door collection and
the increase in kerbside/front of property collection, which allow for a 15% savings in citizens
fees.
2.2.
Studies on the method of disposal
While early studies broadly focus on the collection method, the more recent ones take
into consideration some disposal characteristics. The transportation cost between the
municipality where collection takes place and the disposal site represents an important part of
the service’s cost. To analyse this aspect, most scholars in the last two decades consider a
dummy variable in order to show the existence of a landfill in the municipality or an
incineration plant in the community. The presence of a disposal facility within the municipal
district, in fact, may enable the cost of transporting waste from collection points to the
disposal site to be reduced. These dummies are used in the absence of precise measurements
7
of the distance traveled by trucks and therefore the presence of a dump site in the city
becomes the best approximation of transportation costs.
Domberger et al. (1986), Szymanski (1996), Szymanski and Wilkins (1996) use a
distance variable among the explanatory variables of their cost model finding that the greater
is the distance to the disposal point where the collection vehicle is emptied the higher the cost.
Callan and Thomas (2001) model the relationship between recycling and disposal activity and
investigate the extent of product-specific scale economies and overall scope economies in this
market for 110 of the 351 Massachusetts cities for year 1997. They include a landfill variable
in the disposal cost function, since the presence of a landfill in the town should result in lower
MSW transportation costs. The landfill parameter is negative, as expected, and this implies
that communities that have their own local landfill incur lower disposal costs.
Bel and Costas (2006) include the landfill in the municipality dummy in the cost
function which has the main purpose to verify if the private production bring savings in the
MSW collection. They argue that the transportation cost between the municipality where
collection takes place and the disposal site represents an important part of the service’s cost
and, indeed, they demonstrate that having a landfill within the municipality has cost-reducing
effects. Bel and Fageda (2009) take into consideration the WTE plants. Their paper analyses
the factors that determine solid waste service costs, using information derived from a survey
conducted in a sample of Galician municipalities and they use a dummy variable approach to
state the impact of an incineration plant within the municipal district. Authors say that this
event may enable the cost of transporting waste from collection points to the incineration site
to be lower and they expect the coefficient associated with this variable to be negative. As
previously seen in the literature, the presence of a WTE plant within the municipal district
leads to significant savings.
Finally, it is worth remembering Bae (2010). His paper examines the effects of
different institutional arrangements and characteristics on cost savings, efficiency gains and
productivity of delivering municipal solid waste services for North Carolina municipalities.
Among the control variables, he includes in the estimation whether a sanitary landfill facility
is operated by the municipality, private company, county or regional entity. The author
concludes that when the municipality collects residential solid waste, implements a recycling
and reuse program, and operates a sanitary landfill facility, it may be able to reduce costs of
solid waste collection, recycling, and disposal through economies of scope. Moreover, he uses
the distance to a landfill site, but this variable seems to have no effect on cost.
8
The present model considers new and unique disposal characteristics. Instead of the
presence of a landfill or incinerator in the municipality, a measure of distance is preferred as it
gives more accurate information. Moreover, I consider how costs change with the WTE plant
usage. Some municipalities bury all the unsorted garbage, others send it to an incinerator
while a third category use both solutions. If a municipality choses to incinerate residual waste,
I investigate how cost changes with the quantity of incinerated waste. Finally, I consider the
impact of the number of disposal sites on the cost of service delivering. Both the percentage
of incineration and the number of dump-sites are here used for the very first time.
3. Average cost function
For the specification of the cost model I considered a municipality which collects,
recycles and disposes solid waste, using three inputs, labour (L), capital (K) and energy (E).
Output of solid waste management reflects both the annual collection of recycling and
disposables materials (measured in kg per year). If it is assumed that the municipality
minimizes cost and that the isoquants are convex, cost function can be written as4:
𝐶 = 𝑔((𝑄; 𝑧1 , 𝑧2 , … , 𝑧𝑚 ); 𝑃𝐿 , 𝑃𝐾 , 𝑃𝐹 )
[1]
where:
𝐶 = gross cost of waste collection and disposal for the municipality
𝑄 = total amount of recyclable and disposable waste waste
𝑧𝑖 = service characteristics, local factors etc.
𝑃𝐿 = price of labour
𝑃𝐾 = price of capital
𝑃𝐹 = price of fuel
In this paper I estimate an average cost function and cost per inhabitant5 (€/inh) is
taken as the explained variable. The choice of expressing the cost data on the inhabitants (or
on any other measure of population) instead of the tons of waste collected is made in order to
avoid biased assessments related to different per capita production of waste in the various
municipalities. Expressing average cost in terms of €/ton creates distortions because it does
4
See the previous chapter for derivation.
According to Bel and Warner (2008), among the 18 empirical studies on solid waste collection that they
reviewed, nine studies employed different measures of average cost as dependent variable, while 8 studies
employed total costs. Different measures of average cost include cost per ton collected (Hirsch, 1965; Ohlsson,
2003), cost per collection unit and cost per employee (Szymanski and Wilkins, 1993), cost per household
(Szymanski, 1996), and cost per yard of garbage collected (Dubin and Navarro, 1988).
5
9
not take into account the different total waste intercepted by the alternative collection
systems. This parameter, in fact, increases as the amount of waste collected decreases. This
effect does not make it obvious, but on the contrary reverses, the benefit resulting from the
reduction in total waste intercepted in the application of policies to reduce waste or in the
transition from a street bins collection system to the door-to-door one6. Surely the estimation
of AC in €/inh per year is more fair and effective. This takes automatically into account the
benefits resulting from the reduction of overall waste production, and is also better suited to
the cost of the service paid by the citizen with the waste tariff (especially if there are in force
variable tariff schemes that take into account, for example, the number of family members).
The translog average cost function is written and estimated in the same manner as the
total cost function used in the previous chapter except that:

average cost replaces total cost as the dependent variable;

the two outputs of the cost function (undifferentiated and differentiated waste)
become a single output: the garbage per capita produced by citizens. The
normalization on population reduces multicollinearity as population and total
waste both exhibit a very high correlation and this might lead to inconsistent
estimates;

the municipality population enters in the cost function as a control variable.
First of all, it is necessary to select the appropriate function g in equation [1]. It is
necessary to select a suitable expression, which imposes the minimum set of limitations to the
characteristics of the production function. There are a number of functions with these
characteristics available today, such as the transcendental logarithmic function (TLF), the
Leontief generalized function, the generalized quadratic function and others. A common
characteristic of all these functions is that they can provide a certain approach to the
formulation of a cost function. For this empirical analysis, I rely on a version of the translog
cost function, which is based on the functional form initially proposed by Christensen et al.
[1973]. The transcendental logarithmic cost function I intend to use for the present work can
be written as follows:
𝐴𝐶 ∗
𝑃
𝑃
𝑃
𝑃
ln (𝑃 ) = β0 + β𝑄 ln 𝑄𝑝𝑐 + β𝐿 ln (𝑃 𝐿 ) + β𝐾 ln ( 𝑃𝐾) + β𝐿𝐾 ln (𝑃 𝐿 ) ln ( 𝑃𝐾 ) +
𝐹
𝐹
𝑃𝐿
𝑃𝐾
1
𝐹
𝐹
2
β𝑄𝐿 ln 𝑄𝑝𝑐 ln (𝑃 ) + β𝑄𝐾 ln 𝑄𝑝𝑐 ln ( 𝑃 ) +
6
𝐹
𝐹
2
𝐹
2
1
𝑃𝐿
β𝑄𝑄 (ln 𝑄𝑝𝑐 ) + 2 β𝐿𝐿 (ln (𝑃 )) +
𝐹
Lombardy Regional Program of Waste Management approved by DGR n. X of 20/06/2014.
10
1
2
𝑃
2
β𝐾𝐾 (ln ( 𝐾)) + β1 (𝑑𝑡𝑑) + β2 (ln 𝑟𝑠_𝑑𝑡𝑑) + β3 (ln 𝑝𝑜𝑝) + β4 (𝑛_𝑑𝑒𝑠𝑡) + β5 (%_𝑖𝑛𝑐) +
𝑃𝐹
β6 (𝑑𝑖𝑠𝑡) + β7 (𝑡𝑜𝑢𝑟) + β8 (𝑠𝑜𝑐𝑖𝑎𝑙_𝑐𝑜𝑜𝑝) + β9 (ln 𝑎𝑙𝑡) + β10 (𝑛𝑜𝑟𝑡ℎ) + β11 (𝑐𝑒𝑛𝑡𝑒𝑟) +
β12 (𝑠𝑚𝑎𝑙𝑙) + β13 (𝑚𝑒𝑑𝑖𝑢𝑚) + β𝑖 (𝑦𝑒𝑎𝑟𝑖 )
[2]
where dtd is a dummy variable indicating whether the municipality has a door to door
collection system, rs_dtd is an interaction between recycling share and dtd dummy that shows
how dtd costs change with the recycling share. N_dest indicates the number of disposal sites
for undifferentiated waste, %_inc represents the percentage of residual waste disposed in a
WTE plant while the dist variable specifies the distance trucks have to travel in order to
dispose the unsorted garbage. Also local factors are used in order to better understand costs.
In fact, tour is an index of the presence of tourists, alt indicates the altitude of a city while
social_coop is a dummy showing the presence of a social cooperative in the province. Finally,
yearly (yeari), macro-regional (north, center) and dimensional dummies (small, medium) are
employed in order to control for unexplained variations across years and different parts of
Italy. Note that by normalizing total cost and input prices by one of the input prices, I impose
the theoretical condition that the cost function is linearly homogeneous in input prices.
One way of estimating the parameters in the total cost function is to estimate the cost
function directly using ordinary least squares. This approach generates consistent and
unbiased estimates. However, the efficiency of the estimates can be improved by using a
system of seemingly unrelated equations. The system consists of the average cost function
[2], as well as n equations, each associated with the demand for a factor of production, which
are derived by applying Shephard’s lemma to the cost function. Thanks to this joint
estimation, new statistical information is introduced without increasing the number of
coefficients of the system, improving the efficiency of the estimation procedure.
𝜕 ln 𝑇𝐶 𝜕𝑇𝐶 𝑃𝑖
𝑃𝑖
=
= 𝑋𝑖
= 𝑆𝑖 , (𝑖 = 𝐿, 𝐾, 𝐹)
𝜕 ln 𝑃𝑖
𝜕𝑃𝑖 𝑇𝐶
𝑇𝐶
[3]
where Xi is the optimal factor demand and Si is the relative share.
Hence, from equation [2] the following shares are derived:
𝑆𝐿 =
𝜕 ln 𝑇𝐶
𝑃𝐾
𝑃𝐿
= β𝐿 + β𝐿𝐾 ln ( ) + β𝐿𝐿 ln ( ) + β𝐷𝐿 ln(𝑌𝐷 ) + β𝑅𝐿 ln(𝑌𝑅 )
𝜕 ln 𝑃𝐿
𝑃𝐹
𝑃𝐹
[4]
𝑆𝐾 =
𝜕 ln 𝑇𝐶
𝑃𝐿
𝑃𝐾
= β𝐾 + β𝐾𝐿 ln ( ) + β𝐾𝐾 ln ( ) + β𝐷𝐾 ln(𝑌𝐷 ) + β𝑅𝐾 ln(𝑌𝑅 )
𝜕 ln 𝑃𝐾
𝑃𝐹
𝑃𝐹
[5]
11
Because the cost shares add up to one, error terms are correlated across the cost share
equations and the covariance matrix is singular, it is necessary to delete one share equation. In
this paper, the share equation of fuel7 was deleted8 (Bae, 2010). Our system, therefore, has
three equations: the translog cost function [2] and the share equations of labour [4] and capital
[5] and I estimate it using Zellner’s seemingly unrelated regression (SUR) method, to increase
efficiency (Zellner, 1962).
4. Data and variables
This paragraph describes the variables used, paying particular attention to door-todoor collection and to disposal characteristics. More information on the overall dataset
construction can be found in the previous chapter of this dissertation. I here analyse 534
Italian Municipalities providing waste disposal and recycling services over a period of seven
years, from 2004 to 2010. The total number of this balanced panel consists of 3,738 pooled
observations.
4.1.
Data sources
As already mentioned, this database was constructed ad hoc and to the best of our
knowledge is the largest one used to estimate the phenomena we consider. Abrate et al. (2012,
2014) used data of 529 Italian municipalities for a period of three years (2004-2006) and their
work is an important step forward in the cost function estimation. I have updated and
expanded their dataset with other variables that could better describe the characteristics of the
waste management which have a strong impact on cost. These are: the presence of door-todoor collection, the method of waste disposal, the distance covered in order to reach the site
of the final disposal, tourism information and other local factors as the presence of social
cooperatives in a given area.
Data on costs and output were obtained from the annul declarations concerning MSW
collection, transportation, treatment, preparation for recycling and disposal, which firms must
submit yearly to the local Chamber of commerce. Information on input prices were drawn
7
The input whose share equation we drop from the estimating system is the same of the price by which we
normalize.
8
When either the share equation capital or the share equation of labour is deleted, there is no difference in
empirical results.
12
from institutional websites of the municipalities, telephone interviews made afterwards in
order to complete the missing data and from the Aida – Bureau van Dijk database containing
the restated financial statements of more than 700,000 Italian corporations. Finally,
demographical and service characteristics were obtained from a variety of sources, presented
below. In what follows I describe all the sources and the variables in a detailed manner and I
conclude with a summarizing table with all the data.
4.1.1. Main Sources
The most important source of data for this paper in the Modello Unico di
Dichiarazione Ambientale, (MUD), a document that municipalities submit annually to
Ecocerved, a company of the Italian Chambers of Commerce operating in the field of
information systems for the environment. It contains information on the quality and quantity
of waste produced, stored, transported, prepared for recycling and disposed during the
previous year. This model also contains indications regarding the subjects to which waste has
been given in order to be disposed of or recovered and allows the traceability of all waste
produced in Italy. Municipalities have to fill it for what concerns the MSW produced on their
territory by residents, tourists, workers or for non-hazardous commercial waste assimilated to
urban.
The following data9 were extracted from the MUD:

Year;

Municipality;

Total municipal waste collected;

Quantity of waste, divided by EWC code 200301 (residual municipal waste nondifferentiable further), code 200303 (street sweeping waste) and code 200307
(bulky waste)10;

Costs and revenues;

Municipality of destination of undifferentiated waste;

Number of disposal sites;
From the National Institute of Statistics (ISTAT11), which carries out every ten years
the census of population and housing for each of the 8,057 Italian municipalities, I extracted
9
Please refer to the previous chapter for the data cleaning procedure.
As we will see in the description of the variables used for the estimation, these three categories, along with the
cemetery waste, form the class of undifferentiated waste. The amount of recyclable waste was obtained by
difference with the total amount collected.
10
13
information on the number of inhabitants for the 534 municipalities in the dataset. Also
geographical data regarding surface and altitude were obtained from Istat.
As for factor prices, I first contacted the technical offices of municipalities or used the
institutional websites of the city hall in order identify the company in charge of waste
collection. Once I found the name of the main company responsible of the service, both
municipal and private utilities, I carried out a search on AIDA12 and downloaded their
financial statements from which it was possible to trace the average cost of labour and capital
used in production. These companies carry out the collection and disposal of waste in many
other cities, or they take care of other services related to the environment. I have assumed that
the input costs are the same across services and across areas. Moreover, the same municipality
can have more than one supplier but for simplicity, for what concerns this paper I only
considered the factor prices of the main contractor of the service. Similarly, if the reference
company has contracted out the service, I only considered its costs and not those of the
contractors. Given that contracts should be regulated by national agreements, this does not
seem to entail a major distortion.
4.1.2. Other sources
Data on fuel prices were taken from the websites of the local Chambers of
Commerce. In few regions the price of automotive fuel is available only upon payment and in
those cases, in order to overcome the problem, I used the available data of the closest region.
The same problem arose for some provinces, especially in the South of the country, where I
substituted the missing information with the average price of the provinces in the same
region.
Another interesting variable that I surveyed is the destination of residual waste. As
highlighted previously, Ecocerved provided the names of the municipalities where garbage
was buried or burned. It was not possible to distinguish between the two alternatives from the
Ecocerved data. Therefore, the first step in order to calculate the distances to reach the
disposal site of undifferentiated waste was the identification of the WTE plants and the
starting date of their activity, through the website of Federambiente13. I therefore assumed
11
Unlike Ecocerved data, the census is available for free at www.istat.it.
https://aida.bvdinfo.com
13
Federambiente is an association that brings together companies, corporations and consortia providing services
to the environment or applying the national collective labour contract in the industry. Federambiente is a trade
union that works to ensure member companies the services and the support necessary for an integrated
12
14
that the destination of residual waste is a WTE plant if in the destination municipality there is
an incinerator as it is reasonable to take advantage of the presence of a WTE where it exists14.
By exclusion, all other destinations have been considered landfills. Through Google maps15 I
then calculated the distances between the garbage collection point and its final disposal
destination, taking the city centre as a proxy both for the departure and the arrival, except for
incinerators where the exact location of a plant was available. For distances under 50 km I
considered likely the use of secondary roads while if the distance from a municipality to the
disposal site exceeded 50 km I considered reasonable the use of highways.
An initial research of the starting date of the door-to-door was performed on
municipality institutional websites, where in some cases there are special sections with
references to the waste related services. Secondly, I placed direct enquiries with the city hall.
Finally, for those municipalities who didn’t reply, I used less formal research and the starting
year of the door-to-door collection has been inferred from articles in local newspapers found
on the internet. In a few cases, corresponding to approximately 5% of the sample, it was not
possible to find out the way garbage was collected. I assumed that cities use the traditional
garbage bin when the percentage of differentiated collection was stable over time while, in
those municipalities that from year to year showed a sudden increase in the share of
recyclables collection, the year of increase was considered as the starting date of door-to-door.
Data on tourism have been taken from the National Observatory of Tourism
(ONT)16 and in particular I considered an indicator of the density of accommodation in terms
in terms of number of beds per square km. I considered all the facilities in the area, not only
hotels: camps and villages, hostels, holiday homes, cottages, lodges and bed and breakfast.
The accommodation capacity is certainly not an indication of successful touristic flow for a
given municipality in a given year but, in the absence of other data, it is a good approximation
of the impact of tourism on an area and allows a comparison between the various territories.
The ONT contains data up to 2008 and thus for the years 2009 and 2010 I used in the 2008
data.
management of the waste cycle and comparison with a competitive market-oriented quality.
(www.federambiente.it)
14
The European Union defines a hierarchy of priorities according to which each country should focus on the
prevention of waste, then on the recycling and eventually on other forms of indirect exploitation of waste, while
landfill becomes a residual solution (Directives No. 91/156/EEC and 2008/98/EEC).
15
https://www.google.it/maps
16
http://www.ontit.it
15
The last variable I considered in my dataset is the presence of Social Cooperatives17
that deal with the collection of municipal solid waste. I obtained data about their presence in a
given area from the database of LegaCoop18, the main association that deals with
representation, protection, assistance and supervision of the member cooperatives. The cost of
labour in social cooperatives, established by National Collective Bargaining Agreement, is
lower than other contracts, resulting in significant savings for the clients19.
4.2.
Variables description
It may be useful to organize variables in five categories: output data, factor prices,
local factors, collection method and disposal characteristics.
4.2.1. The dependent variable
The dependent variable I consider is the average cost per inhabitant, equal to the
total cost of the service20 divided the population in the municipality. In the first model,
however, I show the estimation of AC in €/ton (or any quantity unit), an exercise that is often
misleading because in the transition from street bins to the dtd system it gives the false
impression that the costs will increase considerably. In fact, when a municipality switches to
dtd collection, the volume of waste often decreases due to the concurrence of the following
factors:

flows illicitly conferred by commercial activities (in order to avoid paying garbage
fee) that find a large dumpster available on road, disappear;

waste imported from commuters who come from areas where there is already in
place a system of curbside collection (and bins were removed from the road),
disappear;
17
Social cooperatives are a special type of cooperatives who manage health, education and social services or a
variety of other activities (agricultural, industrial, commercial, trade, services) by allocating a portion of the jobs
created, at least 30%, to people who have difficulty in entering the world of work as physically or mentally
disabled, drug addicted, alcoholics, people under psychiatric treatment. (Law 381/91).
18
www.lagacoop.coop
19
http://fpcgil.it/
20
Total cost is the sum of labour, capital and fuel cost borne by the municipalities. As mentioned previously, it
includes both not differentiated waste cycle costs (sweeping and cleaning of public roads, collection and
transportation, treatment and disposal) and recyclables costs (collection, treatment and preparation for recycling)
in addition to the aforementioned common and capital cost.
16

the collection of assimilated waste is regulated and quantitative and qualitative
limits are introduced;

citizens are induced to be more responsible because of waste reducing policies in
the presence of a pay as you throw tariff.
For these reasons, in order to better estimate the impact of door-to-door collection, I
decided to use the average cost per capita. This variable too presents some biases as the
calculation only considers resident population which is not always linearly related to the
number of users served by the collection service (eg. touristic flows, non-domestic users,
etc.). The parameter €/inh (which consider only residents), in fact, penalizes municipalities
with more touristic arrivals and those with many non-domestic users, for whom, however, the
municipality provides a service. An improvement of the parameter can be obtained by
dividing the total cost by the number of households but it was impossible to calculate the
number of domestic and non-domestic users since it is not compulsory to declare them in the
Mud and at the same time, the rules of assimilation are not the same across municipalities. For
what concerns touristic presence, a touristic index was introduced in order to obtain more
homogeneous and comparable results.
4.2.2. Independent variables
Waste quantity
The output I consider is the per capita quantity of waste (QPC). This is given by the
sum of unsorted and residual waste, which represents the garbage sent to a disposal site, and
differentiated waste (mainly paper, glass, plastic, aluminium) which will be sent to recycling.
I will therefore divide total waste by the number of residents in each municipality and obtain a
per capita annual production, expressed in kilograms. I expect to find a positive relationship
between the volume of waste generated and total costs. Therefore, the coefficient associated
with this variable should be positive.
Factor prices
The productive factors considered are: labour, capital and fuel:
17

Pl: average labour price in the company that manages the waste services in the
municipality represented by the ratio of the total salary expenses to the number of
employees;

Pk: average capital price in the company that manages the waste services in the
municipality determined as depreciation cost divided by the capital cost;

Pf: average fuel price during the year.
I obviously expect positive coefficients for all factor prices. In order to estimate a
translog cost function, it is also necessary to consider the cost share of labour and capital,
obtained respectively from expenditures on solid waste salaries and capital divided by total
solid waste expenditures.
Local Factors
There are many factors that differentiate one city from another and which affect the
MSW cost. The most important is certainly the population (POP) of the municipality as
residents are the main producers of waste. As an explanatory element of variability between
municipalities I have also considered the altitude (ALT) measured in meters above sea level
because I expect a greater difficulty for the trucks to reach and collect waste in those
municipalities situated on a hill or in the mountain. In fact, some areas located at high altitude
don’t have a widespread road network or are more difficult to be reached.
As introduced above, also the level of touristic activity in the municipality is
potentially very important. There are only few examples of the use of this variable into waste
management cost function estimation (Bel and Costas, 2006; Bel and Fageda, 2009).
However, the importance of this activity in the Italian geographical context makes its
inclusion advisable in this empirical analysis for several reasons. First of all, the volumes
produced in terms of kg per resident inhabitant are greater in areas with high touristic flows.
Moreover peak seasons require additional efforts, i.e. changes in frequency, using more
inputs, etc. Thus, tourism intensity should bring about higher costs. I take as a measure for
tourism (TOUR) the supply of accommodation facilities in terms of beds/km2, including all
the facilities in the area, not only hotels: camps and villages, hostels, holiday homes, cottages,
lodges and bed and breakfast. This does not accurately indicates the actual number of tourists
in a given municipality for a particular year but captures the trend of tourists in previous
years. In fact, the supply of beds in touristic facilities varies annually according to the flows
18
of previous years and reflects what managers of accommodations expect for the current
season. I suppose a positive influence of this variable on the cost.
Another Italian feature is the presence, in some provinces, of social cooperatives
dealing with waste collection. These cooperatives can either compete with other operators for
a waste management tender, or manage the collection of garbage in subcontracting for the
main manager. Moreover, they certainly have a lower labour cost and in the areas where they
operate I expect competition to reduce costs. The variable SOCIAL_COOP, in fact, is a
dummy that takes the value 1 when in the province there are more than three social
cooperatives operating in the field and 0 otherwise.
In order to take into account regional differences, I considere here the geographical
distribution commonly used in Italy between North, Center and South. The dummy NORTH,
then, takes the value of 1 if the municipality considered belongs to one of the eight northern
regions (Emilia-Romagna, Friuli-Venezia Giulia, Liguria, Lombardy, Piedmont, Trentino
Alto Adige, Valle d'Aosta and Veneto) and zero otherwise. The dummy CENTER takes the
value 1 if the municipality belongs to one of the regions of central Italy (Lazio, Marche,
Tuscany and Umbria). The reference area is considered the SOUTH, including the islands,
with 8 regions (Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sardinia and Sicily).
In addition to the areas of belonging,
I have also taken into consideration size dummies. A city is SMALL if it has a
population of less than 12,000 inhabitants, it is MEDIUM if the number of residents are
between 12000 and 50000 and while it is considered BIG if the resident population is greater
than 50000. As it will be clear soon after, the dimensional categories are chosen in such a way
to reflect cities with similar features with respect to recycling share and disposal
characteristics.
Collection method
The collection method is a very important variable in determining both the cost of the
service and the performance of recycling. As it has been mentioned several times in this work,
the collection through road bins permit the achievement of very low rates of recycling so
many municipalities introduce door to door collection to comply with national legislation.
Although this type of collection has reached high diffusion, many municipalities are still
reluctant to adopt it because they fear an increase in costs. The dtd, in fact, is a laborintensive method of collection. It is therefore very interesting to see its impact on the
19
economic costs of the service. DTD is a dummy variable that takes the value 1 if the
municipality makes the dtd collection and 0 if street bins are used. A clear result in the
literature is that the higher the level of service the more cost increases. Surely, the dtd
collection corresponds to a higher service but at the same time results in an increase of the
differentiated fractions, that will be recycled, so the costs of landfilling decreases. It is
difficult to predict the net effect on costs.
I also consider the interaction between the collection method and the recycling share
(RS_DTD) as an explanatory variable as is interesting to verify the impact on dtd when the
recycling share increases. For reasons that will be clear below, I expect dtd costs to decrease
when the quantity of separated waste grows.
Disposal characteristics
Once the waste is collected separately and prepared for recycling, Conai withdraws the
various fractions upon the payment of a fee in order to sell them to companies as secondary
raw materials. The different material fractions, therefore, are resources and become a return to
the town. The unsorted garbage, however, is under the responsibility of municipality until
disposal, which entails the payment of a fee that varies depending on the method of disposal
(landfill or incineration) and on the location of the site. European laws set the goal to
minimize the use of landfills, placing this disposal solution at the bottom in the waste
hierarchy. All the countries, in fact, have to favor – in order – reuse, recovery, recycling, and
finally the energy recovery through incineration. Despite this, many municipalities still use
landfills, where prices have continuously risen in recent years due to lack of space, especially
in urban areas with high population density. This increase in the price of landfilling has
reversed the past trend and made incinerators more competitive. Unfortunately, many areas
still lack these facilities. Thus, it becomes interesting to understand the impact of the disposal
method on the total cost of the management of municipal solid waste.
The variables I introduce are the following. INC is a dummy variable that takes the
value of 1 if a city disposes its undifferentiated rubbish in an incinerator, with or without
energy recovery, and 0 if it uses a landfill. Given that WTE plants are still not widespread, it
is equally important to understand how total costs vary with the percentage of incineration
(%_INC), which is a purely empirical question. None of the studies mentioned above have
analyzed this phenomenon so that I do not have a precise idea of result to expect.
Another variable, which appears here for the first time, is the number of disposal
sites used on average to dispose the garbage (N_DEST). In principle, one might expect that
20
each municipality uses a single disposal facility, but this is not what one observes in the
Italian case. Yet many of the municipalities in our dataset use regularly more than one
alternative destination - possibly to diversify the risk that a specific landfill may be unable to
receive more waste – so it seems relevant to analyze how this choice affects the cost of the
service.
Last but not least, the transportation cost between the collection point and the disposal
site represents an important part of the service’s costs. In the existing literature various
dummies are used in the absence of precise measurements of the distance traveled by trucks
and therefore the presence of a dump site in the city becomes the best approximation of the
distance to disposal. In this work, however, I calculate the actual kilometers traveled by
vehicles (DIST) that collect the waste to the nearest disposal site. I expect the cost to increase
with the distance. In one of the models estimated, I also consider the presence of a disposal
site within the municipality (MUN_DISP) as a control variable because scholars used it in
previous studies. For reasons that will be clear in a while, this variable was dropped as it is an
approximation of the distance. Obviously, the presence of a WTE plant or of a landfill within
the city perimeter should lower transportation cost. Thus, the precise number of km made by
trucks to reach the closest disposal site is a more precise measure and it was preferred in the
cost function estimation. Finally, six yearly dummies (Y_2005 to Y_2010) are included to
control for possible unexplained changes over time.
4.3.
Descriptive statistics
The dataset we have used refers to a balanced panel of 534 Italian Municipalities
providing waste disposal and recycling services. Data are representative of the 20 Italian
regions over a period of 7 years for a total number of 3,738 pooled observations. The sample
covers 35% of the Italian population, equally distributed between North, Center and South,
and 37% of the total municipal solid waste produced by citizens. It includes 67% of those
municipalities with a population over 250,000 and 51% of municipalities with a population
between 60,000 and 250,000. It also comprehends 41% of the municipalities which have a
population between 20,000 and 60,000 inhabitants. Finally, the sample has only 4.5% of
Italian towns with less than 20,000 inhabitants, as they are the majority in our country (85%).
Although in percentage terms it seems that municipalities with fewer than 20,000 inhabitants
are under-represented, in absolute value the number of observations in this category are many.
21
Table 2 summarizes the characteristics of our average municipality while Table 3
presents the sample composition for different size categories. The average municipality of the
sample has 39,197 inhabitants, that live in an area of 85 km2 with a density of 900 inh/ km2
and produces 22,460 tons of waste. Each inhabitant produces 574 kg per year of which 415
kilos are undifferentiated and disposable garbage while 159 kg are differentiated, the
recycling share on a national level being almost 31%. The unsorted rubbish is incinerated only
for 21.5% of the total amount. Trucks drive, on average, 25 km in order to reach the closest
dumpsite and bring the waste in 1.7 alternative sites. Turning now towards the geographical
breakdown of the dataset, 44% of observations refer to municipalities localized in Northern
Italy and 56% in the Center and South of the country. Municipalities can be aggregated also
on a dimensional basis in order for cities with similar characteristics in terms of recycling
share and disposal features to be analysed together. We can identify three subsamples.
I label as small municipalities the ones that have less than 12,000 inhabitants and they
represent 28.6% of the sample. What distinguishes this category in a quite low recycling share
(26%) and dtd collection (introduced in 25% of cities), always higher in the North than in the
rest of the country, and a very reduced use of the WTE plants, with a striking 2.3% in the
Central and Southern Italy compared to the 28.3% reached by the Northern communities.
Another particular feature of this subsample is the low population density, about 349 inh/km2,
and a distance to reach the dumpsite 10 km above the average. Finally, only 10.3% of the
residual garbage is burned.
Medium size communities, which account for the 58.8% of the data, have between
12,000 and 50,000 residents and both the highest RS and dtd implementation share. In fact,
overall, cities in this category recycle 34% of waste and use the domestic collection in 36.6%
of cases. The North has the primacy in virtuosity as it reaches 59% of differentiation and
almost 50% of dtd collection. About 50% of unsorted waste goes to an incineration plant in
the North while landfills are intensively used in the rest of the country. This subsample has
the highest presence of social cooperatives (62.3%), trucks drive on average 23 km to reach
1.7 disposal sites. The population density is three times higher than in the small cities wile
waste per capita is still below the sample average.
Large municipalities, with more than 50,000 residents, comprehend the metropolitan
areas of Milan and Rome with 1.3 and 2.7 millions of inhabitants respectively. It might seem
a very broad class but cities in this subsample, 12.6% of the total, share very similar features
for what concern the variables of interest in this research. Differentiated collection drops to
the small ones level while only one fifth of this group uses dtd collection. WTE plants use
22
also decreases to 18.8% as well as the disposal distance (17 km). Cities use two alternative
dumpsites. The average municipality of this category has 185,133 residents, a very high
population density (1514 inh/km2) and a substantial production of waste per capita, reaching
585 kg.
Looking now to Table 4, we can see the different capacity in waste interception of the
two alternative systems of collection and the different performances in terms of separate
collection. When a municipality introduces door to door collection, the recycling share goes
beyond 53.3% compared to the 21% of the street bins collection. This is still below the limit
of 65% - set by the law – but notice that in this sample we have cities where the dtd is only at
the beginning (therefore sorting is still very low) and communities with more than one decade
of experience and very high performances. Moreover, when introducing this method of
collection, the North of the country has almost always better results than other regions.
Turning our attention to the MSW production, when street bins are present on the public soil,
the quantity collected by the municipalities is 18% higher than under the dtd method. As
already mentioned, this does not mean that citizens produce suddenly less garbage but many
other factors occur. Finally, Table 5 show how the dtd collection and the RS varied from 2004
to 2010 in the 534 cities of this sample. In 2004, only 20% of municipalities used dtd while
half of the sample introduced it by 2010. Obviously, the separated collection too is increasing,
going from 25.3% in 2004 to 38% in 2010.
5. Estimation procedure
In this paper I discuss seven models using cross-section data. The coefficients are
obtained from the estimation of seven translog average cost functions, along with their share
equations for capital and labour, using Zellner’s SUR estimation method. In order to ensure
the cost functions to be linearly homogeneous in input prices, I normalize total cost and input
prices by the price of fuel. Moreover, before the estimation, I standardize all the right-hand
side variables on their respective sample average values. Below a brief presentation of the
models described in the next section.
a)
Average cost per ton estimation: in order to show continuity with part of the
waste literature, Model 1 in Table 6 estimates the average cost per ton of waste, using total
quantity of garbage collected as the output. Since the focus of this paper is on both the
collection method and the disposal features, I have enriched the very parsimonious models of
23
the previous chapter with many control variables, including among them local factors and
service characteristics to better contextualize the environment in which each municipality
operates. This model presents an inaccurate impact of the door to door collection because, as
already seen, when this system is introduced the quantity of waste collected diminishes while
cost are constant or vary for the combination of many other factors. The net effect is a
distorted outcome for this method of collection as the quantity of waste is not endogenous.
b)
Average cost function estimation: the second set of models (from 2 to 4, Table
6) presents an original contribution to the literature, considering the average cost per
inhabitant as the dependent variable and estimating the cost function as a system together with
the capital and labour price share equations. In order not to introduce discontinuities, model 2
has the same control variables as model 1, but has average cost per inhabitant instead of the
average cost per ton on the left hand side of the function while the quantity of waste is now
expressed in per capita terms. As it will be clear in a moment, the DTD coefficient changes
considerably and allows for a striking interpretation. Model 3 introduces the percentage of
incineration (%_INC) and the km made by trucks in order to reach the disposal site (DIST)
among explanatory variables while model 2 uses dummy variables for the presence of a dump
site in the municipality (MUN_DISP) and for the use of WTE plants (INC). Model 3 is
preferred as it uses measures that are more precise. Model 4, which I consider the best choice
and will allow further analysis, enriches the previous ones with one more variable, in order to
better understand the impact of the collection method costs depending on the level of separate
collection reached (RS_DTD).
c)
Average cost function estimation for dimensional subsamples: for robustness
check and in order to contribute to the ongoing debate, I undertake a third set of regressions
(Table 7). I compare the results of the selected model, which pools together municipalities of
different sizes, with other 3 models (5 to 7), estimating different cost functions for small,
medium and big municipalities. Geographical subsamples were also considered but the results
are not reported here as there were not big differences in the coefficients. Since the Seventies,
thanks to Stevens (1978), the issue of estimating a single model for all the sample, possibly
with size dummies, rather than different models for various class sizes is considered. Several
previous studies have shown that the estimation should be made for subsamples of
municipalities according to population bands, since the differences between large and small
municipalities may be relevant (Bel, 2006; Bel & Costas, 2006; Dubin & Navarro, 1988;
24
Stevens, 1978). In this study, as for the method of collection, the estimation of different cost
function on dimensional basis it is more relevant than pooling data in a unique sample and the
same holds for the disposal characteristics. Comparing the results of the Small, Medium and
Big subsamples, interesting results emerge.
6. Results
6.1.
Average cost per ton estimation
The estimated coefficients in Table 1 are all statistically significant (at a 1% level) but
the sign taken by coefficients is in some cases not consistent with expectations. Total cost
increases with output and input cost shares, implying that the cost function grows
monotonically in input prices. The explanatory power of the model is not very high but
remains in line with studies taking average cost per ton as dependent variable. The cost
elasticity with respect to output is negative. A 1% increase in collected waste brings, not
surprisingly, a decrease in average cost by approximately 0.28%. Keeping total costs fixed,
the more we increase output the higher the denominator and the lower average cost per ton.
Turning to the characteristics of the service, the DTD coefficient is positive: the
introduction of the door-to-door collection method, all the other factors fixed, brings a 5.9%
of increase in costs. This might seem obvious because, as already pointed out, this system
needs more additional labour.
As for disposal, increasing the number of destination for the residual waste above the
average value of the sample (1.7) increases cost. This is in line with expectations as usually
all municipality have one dumpsite which is closer than others and it seems desirable to
dispose garbage as close as possible to the point of production. At the same time, if the
entrance fees of the neighbouring landfill or WTE plant are high, one might think it is useful
to have more disposal opportunities near the city where waste is collected. N_DEST, indeed,
bring a 2% increase in service costs, meaning that transportation costs more than compensate
the differences in the entry price between various dumpsites.
Related to N_DEST, the DIST variable supports the previous conclusions: the more
kilometres trucks travel in order to dispose waste, the more costs grow. Each extra km above
the average (25) increases average cost per ton by 0.17%. It might seem a reasonable amount
but consider that some municipalities make even 100 km to reach a landfill and this would
mean a 17% increase in their costs. Another interesting characteristic is role of the percentage
25
of incineration of each municipality. Overall, cities bring to a WTE plant 21.5% of their
unsorted and residual rubbish, with huge differences between regions. Quite unexpectedly,
%_INC is negative and accounts for 0.05% of cost variation. The more disposable waste is
incinerated, the more municipality save. The alternative way to get rid of undifferentiated
waste is to bury it. Therefore, the negative sign in front of the incineration coefficient may be
explained by the fact that at the moment, in Italy, WTE plants have more competitive fees
compared to landfills. Moreover, this is particularly true for the North of Italy as this macro
region it the one who incinerates more, while the rest of the country uses only marginally this
way of disposal.
Other local factors considered here are the tourism index and the presence of social
cooperatives in the province. The impact on the TOUR variable on costs is very low, despite
expectations. This could be because the index used here if not an effective measure of the
touristic presence on a given territory. The supply in terms of beds/km2 might be very
different and not correlated to the number of visitors in the touristic cities. I suppose that
owners of accommodation do not vary the beds offer as soon as touristic presence varies
unless this new positive or negative variation become stable and confirmed with year passing.
Very important, instead, is the impact of SOCIAL_COOP. If in the province where a city is
located there are more than three social cooperatives, the average cost per ton of collected,
recycled and disposed waste will be lower by 5.5%. This particular form of cooperatives,
present in half of the Italian provinces, benefits of a privileged taxation regime because it
employs 30% of disadvantaged workforce. The labour cost is, therefore, lower than in other
firms (and labour accounts for 43% of the total cost of the service) and they can make lower
offers when participating in a tender. The benefit of strengthening their presence on a territory
becomes twofold: on the one hand, they provide jobs to people who otherwise would have
difficulty in finding one, and on the other one they are excellent competitors and bring about a
reduction in the cost of the waste collection service.
he NORTH and the CENTER dummy have negative coefficients. A city of the North
has a cost which is about 14% lower than the South while the Center part of the country saves
4% with respect to the reference area. On the other hand, the municipalities on the Center
Italy spend around 10% more than the Northern ones. For what concerns the dimension of the
cities, it can be observed that small and medium municipalities have lower costs that the
bigger ones. SMALL dummy is not significant while MEDIUM communities indeed, save
4.5% with respect to the South. Being a small city in the Center allow big cost advantages,
especially at high altitudes. Finally, the time dummies’ coefficients tell us that from in 2005
26
and 2006 costs were lower than in 2004, between 5.2% and 7.7%. No significant deviation
occurred in 2007 and 2008. Since 2009, conversely, there is a positive variation in contract
prices with respect to 2004, which is higher in 2009 (around 11%) than in 2010 (7%). This is
a surprising result and it substantially says that cost increased a lot after the beginning of the
economic crisis, while the waste produced remained stable.
6.2.
Average cost per inhabitant estimation
In Table 6, from model 2 to 4, I consider the cost per inhabitant as a dependent
variable. Most of the variables considered are significant at 1% level. The explanatory power
of the cost function is higher than in model 1 and varies between 0.43% (model 2) and 0.45%,
when the interaction RS_DTD is added among the control variables (model 5). This result for
R-squared in average cost function is similar to that of Dubin and Navarro (1988) and higher
than the one in Ohlsson (2003) and Bel and Costas (2006). The cost elasticity with respect to
quantity per capita, which measures the percentage increase in cost due to a 1% increase
produced waste, is positive: the Qpc coefficient is around 0.09. Factor prices too have a
positive impact on cost. We will now turn our attention to the other explanatory variables.
For what concerns the DTD variable, this is found to be negative, just the opposite of
the previous estimations, meaning that the introduction of this collection method decreases
cost. This result is striking as it seems to reject the fears of service managers that a more
demanding system in terms of labour costs will be a burden on the bills of citizens. It is clear
from Table 4 that where door to door collection is in use, the recycling share is considerably
higher (53.3% compared to 28% on the overall sample). Even though the revenues from the
sale of differentiated materials are not incorporated, when recycling collection increases,
unsorted garbage is subtracted to landfill or incinerators, so that the reduction observed in
costs can be easily explained in terms of lower disposal fees incurred. On the other hand, a
more intensive separate collection requires the adoption of more demanding methods, which
involve a high use of labour and this reduces the resources that are saved from the nondisposal. Given labour cost, cities who want to increase the RS save more money if the
disposal fees are very high with respect to those municipalities that previously paid less for
WTE plants and landfills. The net effect of those two forces is the negative coefficient that we
observe in Table 6 and this confirms that disposal fees are becoming so high that reducing the
undifferentiated waste saves resources.
27
A very interesting result, instead, is presented in model 5. Apparently, when
introducing the coefficient of the interaction between recycling share and door-to-door
collection (RS_DTD), the cost of door-to-door becomes positive and very high. Now RS_DTD
and DTD have to be read and interpreted together. The DTD coefficient tells us the impact of
this system if recycling share would not vary. If we change collection method without any
benefit in terms of separate collection, a municipality spends as previously in disposal and it
has higher costs, both for capital and labour. The new system increases average cost by
10.8%. But if the share of recycling increases, the cost of having a dtd system decreases by
0.32% per each percentage point of differentiation growth. Therefore, if the average
municipality in our sample, which has 39,107 inhabitants, passes from 31% of RS to the law
requirements, won’t have any change in costs and this result stays valid even without the
revenues from recyclables selling. For this extra piece of information model 4 is preferred to
all the others.
As for disposal characteristics, we notice that the variation of the dependent variable
used in the model didn’t change the signs of the associated coefficients. There are, instead,
little differences in the magnitude of betas between cost functions. In model 2 and 3 I consider
two alternative specifications of disposal features. Model 2 uses MUN_DISP as alterative to
the punctual distance to the disposal site. If a city hosts a landfill or a WTE plant, costs
decrease by 2.5%. This result is in line with expectations because having a city dumpsite
implies having lower transportation costs. In literature, this was argued with the presence of
scope economies for the municipality if the same firm or public subject ran both waste
collection and disposal. This is not our case because the property of disposal sites is usually
different from the company managing the collection. Vertically integrated companies are very
rare in Italy and they operate in the North of the country. The DIST variable was preferred to
MUN_DISP because it is more accurate and it gives a precise dimension of cost variation with
the change in distance. When using both variables in the same estimation, MUN_DISP
becomes not significant as all the variation is caught by DIST, which has the same magnitude
as in the first model.
Again, model 3 presents an alternative specification of the incineration variable as INC
indicates the use of a WTE plant for residual waste. The negative coefficient states that using
an incinerator (rather than a landfill) lowers costs by 5.4%. As previously, when both
variables were used in the same cost function only %_INC was found significant and the
dummy was dropped. N_DEST now becomes slightly higher, with an impact of 2% on costs
when increasing the number of destinations of undifferentiated garbage. The TOUR
28
coefficient remains very small, the ALT impact is still negative while the social cooperatives
in the province, other factors being equal, bring lower savings to citizens as SOCIAL_COOP
accounts now by 4%.
Also the geographical dummies’ impact decrease consistently: the NORTH coefficient
diminishes to 0.028 while the CENTER municipalities show higher fees per inhabitant with
respect to the South ones. The size dummies remain significant and indicate differences
between the categories considered. For this reasons, in the next paragraph I make separate
estimations for the small, medium and big cities in order to check the robustness of these
results. While the results shown above are robust across models, except for the collection
method, in the next paragraph I concentrate on model 4.
6.3.
The role of city size
All models presented in the Table 7 show a moderate explanatory power that varies
between 30% for the Big subsample to 47% for the Small one and most of the coefficients are
significant. For what concerns the main variable of interest of this paper, the door-to-door
collection, the impact on average cost per inhabitant of the DTD coefficient changes
considerably for municipalities having different dimensions. When municipalities with less
than 12,000 inhabitants (model 5) change the collection method, keeping fixed the separate
collection, they have a 23.55% increase on average costs, compared to 6.43% in the medium
ones (model 6) while large cities, with more than 50,000 residents, have negative variation of
costs with the dtd introduction: -12.65% (model 7). We observe the same differences in the
RS_DTD coefficient, negative and larger for small communities, becoming then positive in
big cities. For less than 50,000 residents, increasing the RS decreases the dtd cost while in the
big subsample the opposite occurs. It is interesting to turn our attention at Tables 8a and 8b,
which translate the beta coefficients in percentage and per capita changes of cost per
inhabitant and allows us to better understand the monetary impact of this collection method.
Passing from the average differentiation share of 26% to the 65% level that has to be reached
by law, small municipalities have a slight cost reduction of about 1.2€. The same holds for
medium and large cities, where a 65% level of differentiation bring even slight savings in
costs. Once this level is exceeded, the average cost per inhabitant decreases consistently for
small and medium group and rises in big communities.
These substantial differences may be easily understood if we think at the different
characteristics of those cities. In fact, the small cities have, on average, a very low housing
29
and population density, with 349 inhabitants per km2 and the organization of this method of
waste collection it is much easier and straightforward. There are more single-family homes,
spread on a bigger territory, with larger streets, less congestion problems. Transportation costs
are low, the crew employed in the collection of different materials is more efficient, allowing
for the reduction of the downtime. At the same time, if sorting would not vary, the
morphology of these cities would result in a higher starting cost for the domestic collection.
When population grows and we move to the next dimensional band, density becomes tree
times higher. Even though the number of medium municipalities using the door-to-door
collection is higher (36%) with respect to small cities (25%), the average cost reduction when
introducing this system decreases, indicating more difficulties in organizing the service,
quantity per capita being almost the same.
Large municipalities, instead, behave very differently. Even without any variation in
the RS, the reorganization of the service, which helps in the disappearance of both illegal
dumping and waste tourism, brings high savings which account for 19€ for each citizen. The
higher the share of differentiated waste, on the other hand, the lower the savings. In fact, our
average municipality of this sample, having 185,133 inhabitants and a density of 1,513
inhabitants per km2, is very affected by congestion problems and many other difficulties in
organizing the dtd as the high presence of condominiums, a higher area, a higher waste
production. Thus, reaching 65% of differentiation would have a null impact on garbage costs
while going above that RS has big disadvantages.
The disposal characteristics, too, show some variation on a dimensional basis. Small
cities, on average, dispose the undifferentiated waste in a lower number of sites (1.5 with
respect to 1.7 for medium ones and 2 for big cities) and in fact, for them, costs would rise by
2.1% for every extra destination. Table 19a quantifies in 2.4€/inh the increase in costs for
passing from 1.5 to 2.5 destinations. Medium communities have a similar cost variation
while the N_DEST variable is negative and not significant for the big subsample suggesting
that the higher the alternative sites, the lower the cost for citizens. On the other hand, the
distance of a dumpsite becomes a problem in municipalities with more than 50,000
inhabitants as the cost variation is 0.4% per kilometer. Driving 10 more km with respect of
the sample average of 17 km, rises average cost by 4.2€/inh. The average distance made by
trucks in order to dispose residual garbage rises in smaller municipalities, as it might be seen
from Table 3, and the DIST coefficient turns out to be lower for less than 12,000 inhabitants
(0.15€/inh/km). The %_INC coefficient has a similar impact on costs in the big and medium
subsamples, respectively -0.09 €/inh and -0.08€/inh per each percentage point of incineration
30
increase, while it is higher in small cities. Thus, if we increase the use of WTE plants from the
sample average of 10% to 100%, cities up to 12,000 inhabitants would lower costs by 11.7€.
7. Concluding remarks
Article 205 of Legislative Decree 152/2006 determines the minimum targets of
separate collection that must be guaranteed within each Optimal Territorial Area (ATO). By
2008 all municipalities had to reach 45% of differentiation while by 2012 the level of sorting
should have been 65%. Very few areas have complied with the provisions of law, especially
in the North of the country. The majority of Italian municipalities tied to close the gap with
respect to the most virtuous ones and to implement best practices in order to increase the
sorting of municipal waste. The most common method has been the adoption of the door-todoor collection.
Although this system has achieved broad diffusion, many mayors fear the transition
from the street bins collection to the domestic one because of a possible increase in waste fees
and a subsequent loss of electoral support. In this article I wanted to verify if this critique was
founded thus I assessed the effective impact of the dtd on the service costs. The second
purpose of this research was to evaluate the effect of the various characteristics of disposal. If
the fractions collected separately take the path of recycling and become revenues for
municipalities, the unsorted garbage or the residuals from recycling must be disposed,
therefore the managers have to pay in order to bring rubbish in a landfill or a WTE plant. I
here replied to questions as: it is better to burry residual waste or to incinerate it? Given a
certain amount of garbage, is there an optimal quantity to be incinerated? Moreover, given
that many communities use contemporaneously different dumpsites, which is the ideal
number of disposal sites?
In order to reply to the previous questions, I estimated several average cost functions
with a translogarithmic specification and using the SUR method introduced by Zellner. The
dataset is a representative sample of 534 Italian municipalities providing waste disposal and
recycling services over a period of seven years, from 2004 to 2010, having a total number of
3,738 pooled observations. For a better estimation of the impact of door-to-door collection, I
decided to use the average cost per capita as the dependent variable. On the right hand side of
the model, among the service characteristics, I used the collection method, the number of
alternative destinations for unsorted rubbish, the km drove by trucks in order to dispose waste
and finally the percentage of incineration for each municipality. All these variables represent
31
a huge innovation in formulating the characteristics of the service and they have never been
used, especially together, in the revised works.
As for the estimation of the impact of the door-to-door collection, I find it is more
correct the analysis for dimensional subsamples. The results present striking differences
between cities of different sizes. When small and medium municipalities change the
collection method, without varying the share of differentiate collection, they have a 23.55%
and 6.43% increase on average costs respectively, while large cities, with more than 50,000
residents, benefit from cost savings which account for 19€ for each citizen, even if separate
collection is fixed. Thus, in cities with less than 50,000 inhabitants, the starting cost of
separate collection is high but decreases quickly if the recycling share increases. In big
municipalities, on the other hand, even without the variation of the RS, the reorganization of
the collection method and the disappearance of illegal dumping and waste tourism that
follows the dtd introduction, bring high advantages in terms of citizens’ fees, but the higher
the differentiation, the higher the cost incurred.
Passing from the average differentiation share of each subsample to the 65% level that
has to be reached by law, brings slight savings in service costs, regardless the size of the city.
Once this level is exceeded, average cost per inhabitant reduces consistently for the small and
the medium group (allowing savings up to 12€/inh for an 80% level of separate collection)
and rises consistently in big communities. Our average big municipality, having 185,133
residents and a density of 1,513 inh/km2, is very affected by congestion problems and many
other difficulties in organizing the dtd as the high presence of condominiums, a higher area,
and a higher waste production. Thus, reaching 65% of differentiation would have a null
impact on garbage fees while going above that RS has big disadvantages in terms of waste
tariffs.
Even though the revenues from the sale of differentiated materials are not
incorporated, when recycling collection increases, unsorted garbage is subtracted to landfill or
incinerators therefore the reduction observed in costs for cities with less than 50,000
inhabitants can be explained in terms of lower disposal fees incurred, that
more than
compensates the rise in labour costs. Keeping fixed the labour cost, cities who want to
increase the RS save more money if the disposal fees are very high with respect to those
municipalities that previously paid less for WTE plants and landfills. The net effect of those
two forces confirms that disposal fees are increasing so much that reducing the
undifferentiated waste brigs high benefits in saving resources for small and medium cities
32
while large cities suffer from big difficulties in organizing the dtd collection, no matter the
savings reached lowering the quantity disposed.
Also for what concerns disposal characteristics we observe interesting variations on a
dimensional basis. Small and medium cities have to spend 2.4€/inh for any extra destination
with respect to the average, while big communities don’t have a significant difference with
the variation of the number of dumpsites. On the other side, the distance becomes a problem
in municipalities with more than 50,000 inhabitants, as the cost variation is 0.4% per
kilometer. Driving 10 more km with respect of the sample average of 17 km, rises their
average cost by 4.2€/inh. For municipalities with a population lower than 50,000 residents,
the average distance make by trucks in order to dispose residual garbage can rise as the
impact of transportation costs is halved with respect to big ones. Finally, the more disposable
waste is incinerated, the more municipalities save, especially if they have less than 12,000
inhabitants, meaning that at the moment, in Italy, WTE plants have more competitive fees
compared to landfills. Thus, if we increase the use of WTE plants from the sample average of
10% to 100%, small cities would lower costs by 11.7€.
Summarizing, the optimal city uses the door to door system in order to collect waste
and to increase recycling, brings the residual rubbish in only one disposal site, which has to be
preferably a WTE plant, located as close as possible to the place where garbage is produced.
The law requirement of 65% of differentiation do not involve an increase in the service costs
and it can be reached by all the municipalities, no matter the size, while higher levels of
sorting are affordable only for cities with less than 50,000 inhabitants. If dtd collection is
implemented, the starting cost is amortized only if the recycling share increases.
Disposal however, has very different features if we consider that in the Northern Italy
and Tuscany are concentrated 75.5% of incinerators while in the rest of the country there is a
small number of WTE plants, making it necessary the use of landfills. The gap between the
North and the South of the country should be filled and policy makers should provide a WTE
plant development on a national scale, especially nearby large cities. At the regional level,
instead, disposal should be planned in accordance with the European principles of proximity
in order to ensure self-sufficiency of disposal sites.
This paper makes an important step forward in the waste literature and tries to provide
answers to very actual questions. Despite this, there is plenty of room for improvements in the
estimation. First, it would be interesting to introduce the revenues from the sale of materials
collected separately and from the energy obtained by incinerating waste. Secondly, the
characteristics of the service should be better articulated adding information such as
33
frequency of dtd collection, the number of non-domestic users served and the actual presence
of tourists in a municipality. Also the years of use of this method of collection would allow to
understand the effect of the organizational know-how, acquired with experience. Finally,
expressing the average cost per inhabitant presents some biases due to the calculation of the
only permanent residents which are not always linearly related to the number of users served
by the collection service (eg. touristic flows, non-domestic users, etc.). The parameter €/inh
(which consider only residents), in fact, penalizes municipalities with more touristic arrivals
and those with many non-domestic users, for whom, however, the municipality provides a
service. An improvement of the parameter can be obtained by dividing the total cost by the
number of households. Now, unfortunately, it is not straightforward to calculate the number
of domestic and non-domestic users since there isn’t an obligation to declare them in the Mud
and, at the same time, there are no unique rules of assimilation throughout the various
municipalities.
34
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Websites
https://aida.bdvinfo.com
www.isprambiente.gov.it
www.istat.it
http://www.mudcomuni.it
Abbreviations
DTD = door to door collection;
ISTAT = National Institute of Statistics;
MSW = Municipal Solid Waste;
MUD = Modello Unico di Dichiarazione Ambientale (annual declaration concerning the
MSW collection);
RS = Recycling Share;
WTE = Waste to Energy plant.
37
Tables and Figures
Table 1: Variables description and sources
Variable
Unit
Log or
level
Description
Source
Cost
TC
€
Log
AC/t
€/ton
Log
Total gross cost of waste collected and disposed. Includes
both not differentiated waste costs (sweeping, cleaning,
collection and transportation, treatment and disposal) and
recyclables costs (collection, treatment and preparation for
recycling).
Average cost of waste collected and disposed per ton.
AC/inh
€/inh
Log
Average cost of waste collected and disposed per inhabitant.
MUD
YR
Tons
Log
MUD
YD
Tons
Log
Qpc
Kg
Log
Differentiated waste collected in order to be sent to recycling.
Unsorted and residual waste sent to disposal (landfill or
WTE).
Total waste collected in the municipality.
Pl
€
Log
Average wage.
AIDA
Pk
€
Log
Average capital price.
AIDA
Pf
€
Log
Average fuel price.
Chambers of
commerce
Alt
Meters
Log
Municipality altitude above the sea level.
ISTAT
Tour
Beds/km2
Level
The offer of accommodation facilities of the municipality
(hotels, camps, villages, hostels, holiday homes, B&B).
Observatory
of Tourism
Social_coop
Dummy
Level
Dummy variable: 1= more than 3 social cooperatives in the
province; =0 otherwise.
LegaCoop
North
Dummy
Level
Center
Dummy
Level
Dummy variable: 1= northern area municipality; =0
otherwise.
Dummy variable: 1= central area municipality; =0 otherwise.
Small
Dummy
Level
Dummy variable: 1= below 12000 inhabitats; =0 otherwise.
Medium
Dummy
Level
Dummy variable: 1= 12000 <inhabitants<50000; =0 otherwise.
MUD
MUD
Quantity
MUD
MUD
Factor prices
Local factors
ISTAT
ISTAT
Collection method
Dtd
Dummy
Rs_dtd
Level
Dummy variable: 1= door to door collection method; =0
street bins.
Interaction between dtd collection and recycling share.
Level
Mixed
Mixed
Disposal characteristics
Inc
Dummy
Level
Dummy variable: 1= waste incinerated; =0 landfill disposal.
MUD
%_inc
Percentage
Level
Percentage of waste sent to WTE plants
MUD
N_dest
Number
Level
Number of final disposal destinations for each municipality
MUD
Mun_disp
Dummy
Level
Dummy variable: 1= municipality disposal; =0 outside.
MUD
Dist
Km
Level
Distance made by trucks in order to reach the disposal site,
MUD/
38
weighted by the quantity disposed
Google Maps
Table 2: Summary statistics
Variable name
Mean
Std. Dev
Min
Max
Total Cost (thousands)
5787.61
27300.00
65.63
659000.00
16251.11
6208.44
71618.54
19795.26
225.29
0.00
1497029.00
394446.20
0.10
37209.34
0.02
6011.46
0.02
19200.00
0.17
62613.00
0.06
0.43
0.04
0.13
0.00
0.08
0.18
0.73
39106.99
183.60
85.03
900.79
30.90
25.34
21.51
1.70
40.58
139611.70
176.97
248.09
1226.95
21.31
26.89
39.92
1.25
222.41
1042.00
0.00
248.09
2.34
0.00
2.26
0.00
1.00
0.00
2711491.00
1154.00
5325.00
9441.50
88.95
274.00
100.00
16.00
4763.91
Output
Waste disposed (tons): YD
Waste recycled (tons): YR
Input prices
Capital: PK
Labour (euro): PL
Cost shares
Capital (%)
Labour (%)
Other variables
Population
Altitude
Area (Km2)
Density (inh/km2)
Recycling Share (%)
Distance to disposal site (Km)
Incineration (%)
N° of disposal sites
Tourism index (beds/km2)
39
Table 3a: Sample breakdown by dimensional subsamples and area
Municipality dimension
Small
Medium
Big
N
C/S
Tot
N
C/S
Tot
N
C/S
Tot
47
106
153
160
154
314
27
40
67
43.9% 18.0% 26.1% 49.6% 18.1% 33.9% 40.0% 20.1% 27.8%
40.5% 18.0% 25.0% 59.0% 13.3% 36.6% 32.3% 11.4% 19.7%
28.3% 2.3% 10.3% 48.2% 6.0% 27.5% 35.4% 7.8% 18.8%
23.6
39.0
34.3
19.2
26.6
22.8
11.0
20.6
16.8
1.5
1.6
1.5
1.6
1.9
1.7
2.0
2.2
2.1
Area
N. of municipalities
Recycling Share
Dtd collection
Incineration
Disposal (km)
N° of disposal sites
Table 3b: Sample breakdown by dimensional subsamples
Municipality dimension
N. of municipalities
Inhabitants
Surface
Density (inh/km2)
Waste per capita (kg)
Social cooperatives
Small
Medium
Big
<12,000 inh
153
8348
50
349
514
40.5%
12,000-50,000 inh
314
23116
83
1038
529
62.3%
>50,000 inh
67
185133
175
1514
585
49.8%
Table 4: Variation of RS and waste per capita with door-to-door collection
Recycling Share
Waste/per capita (kg)
Collection
method
Small
Medium
Big
Tot
Small
Medium
Big
Tot
Door-to-door
Street bins
53.0%
17.1%
54.5%
22.0%
43.5%
24.0%
53.3%
20.8%
407
550
473
561
545
595
464
562
Table 5: Variation in differentiate collection and dtd collection from2004 to 2010
Year
RS
DTD
2004
2005
2006
2007
2008
2009
2010
25.3%
26.5%
27.4%
30.0%
33.2%
36.0%
38.0%
20.2%
22.5%
25.1%
29.6%
34.5%
39.9%
47.4%
40
Table 6: Translog estimation of Average Cost Functions
1
2
3
4
AC per ton
AC per inhabitant
AC per inhabitant
AC per inhabitant
All sample
All sample
All sample
All sample
Model n.
Regressors
Estimates
SE
Estimates
SE
Estimates
SE
Estimates
SE
Constant
-0.7880***
0.103
-0.6598***
0.103
-0.5723***
0.030
-0.6598***
0.103
dtd
0.0573***
0.011
-0.0616***
0.010
-0.0638***
0.010
0.1022***
0.027
rs_dtd
-
-
-
-
-
-
-0.0032***
0.001
mun_disp
-
-
-
-
-0.0250**
0.013
-
-
inc
-
-
-
-
-0.0527***
0.011
-
-
n_dest
0.0199***
0.003
0.0200***
0.003
0.0253***
0.003
0.0191***
0.003
%_inc
-0.0005***
0.000
-0.0006***
0.000
-
-
-0.0005***
0.000
dist
0.0017***
0.000
0.0017***
0.000
-
-
0.0017***
0.000
tour
0.0002***
0.000
0.0003***
0.000
0.0003***
0.000
0.0003***
0.000
social_coop
-0.0532***
0.008
-0.0440***
0.008
-0.0456***
0.008
-0.0400***
0.008
-0.0057*
0.003
-0.0383***
0.003
-0.0386***
0.003
-0.0389***
0.003
lnpop
0.0863***
0.009
0.0850***
0.009
0.0810***
0.009
0.0825***
0.009
north
-0.1308***
0.011
-0.0426***
0.011
-0.0597***
0.011
-0.0282***
0.011
center
-0.0378***
0.011
0.0547***
0.011
0.0429***
0.011
0.0542***
0.011
small
-0.0162
0.024
-0.0802***
0.024
-0.0726***
0.027
-0.0800***
0.024
medium
-0.0447**
0.018
-0.0676***
0.018
-0.0702***
0.019
-0.0656***
0.018
y_2005
-0.0507***
0.014
-0.0393***
0.014
-0.0358**
0.015
-0.0374***
0.014
y_2006
-0.0747***
0.014
-0.0503***
0.015
-0.0463***
0.015
-0.0488***
0.015
y_2007
-0.0446
0.014
-0.0114
0.015
-0.0034
0.015
-0.0105
0.015
y_2008
-0.0657
0.014
-0.0220
0.015
-0.0177
0.015
-0.0202***
0.015
y_2009
0.1039***
0.014
0.1506***
0.015
0.1554***
0.015
0.1534***
0.015
y_2010
0.0677***
0.014
0.1310***
0.015
0.1369***
0.015
0.1333***
0.015
lnQpc/tot
-0.2832***
0.018
0.0896***
0.004
0.0901***
0.004
0.0860***
0.004
lnQpc2
lnPL
0.1430**
0.057
0.2563**
0.059
0.27354***
0.060
0.2684**
0.059
0.4278***
0.002
0.4283***
0.002
0.4280***
0.002
0.4281***
0.002
lnPK
0.0593***
0.001
0.0591***
0.001
0.0590***
0.001
0.0591***
0.001
lnPL2
-0.1956***
0.010
-0.1896***
0.010
-0.1886***
0.010
-0.1896***
0.010
lnPK2
0.0163***
0.002
0.0180***
0.005
0.0178***
0.005
0.0180***
0.005
lnPLlnPK
0.0444***
0.003
0.0286***
0.005
0.0283***
0.005
0.0291***
0.005
lnQpclnPL
-0.0492***
0.007
-0.0576*
0.007
-0.0591***
0.007
-0.0581***
0.007
lnQpclnPK
0.0230***
0.002
0.0968***
0.054
0.0534
0.055
0.1065**
0.054
lnalt
Obs
3738
3738
3738
3738
R2
0.31
0.43
0.43
0.45
*** Significant at 1% level in a two tailed test. ** Significant at 5 percent level. *Significant at 10 % level. All
regressors are normalized on their sample mean values.
41
Table 7: Translog estimates of Model 4 for dimensional subsamples
5
6
7
Small
Medium
Big
Model n.
Regressors
Estimates
SE
Estimates
SE
Estimates
SE
-0.3083
0.215
-1.0111***
0.131
-0.2323
0.155
dtd
0.2167***
0.054
0.0650*
0.054
-0.1392**
0.063
rs_dtd
-0.0065***
0.001
-0.0029***
0.001
0.0035***
0.001
n_dest
0.0210**
0.010
0.0210***
0.004
-0.0024
0.010
%_inc
-0.0012***
0.000
-0.0007***
0.000
-0.0006**
0.000
dist
0.0013***
0.000
0.0018***
0.000
0.0040***
0.000
tour
0.0001***
0.000
0.0001***
0.000
0.0009***
0.000
-0.0237
0.018
-0.0543***
0.010
0.0958***
0.020
lnalt
-0.0847**
0.007
-0.0254***
0.004
-0.0130**
0.006
lnpop
0.0680***
0.023
0.1095***
0.013
0.0131
0.013
y_2005
-0.0145
0.030
-0.0411**
0.018
-0.0457
0.033
y_2006
-0.0330
0.030
-0.0516***
0.018
-0.0496
0.033
y_2007
0.0220
0.031
-0.0211
0.018
-0.0091
0.033
y_2008
0.0225
0.031
-0.0322*
0.018
-0.0166
0.034
y_2009
0.2272***
0.031
0.1341***
0.018
0.1058***
0.034
y_2010
0.2053***
0.031
0.1182***
0.018
0.1061***
0.034
lnQpc
0.0769***
0.007
0.0735***
0.005
0.0800***
0.012
lnQpc2
0.5344***
0.109
0.5962***
0.080
-1.5900***
0.442
lnPL
0.4475***
0.004
0.4200***
0.003
0.4213***
0.005
lnPK
0.0550***
0.003
0.0600***
0.001
0.0643***
0.002
lnPL2
-0.2064***
0.018
-0.1683***
0.013
-0.2395***
0.027
Constant
social_coop
lnPK2
0.0160
0.012
0.0213***
0.005
-0.0043
0.008
lnPLlnPK
0.0369***
0.011
0.0197***
0.005
0.0777***
0.010
lnQpclnPL
-0.0330***
0.010
-0.0682***
0.010
-0.1039***
0.030
lnQpclnPK
-0.1133
0.101
-0.2532***
0.101
-0.3553
0.233
Obs
1071
2198
469
R2
0.47
0.40
0.30
42
Table 8a: Percentage variation in costs with door-to-door collection
Recycling
share
26%
28%
30%
34%
40%
50%
60%
65%
70%
80%
90%
Small
Medium
Big
23.55
-
-12.65
21.61
-12.30
6.43
-
14.48
4.98
-8.80
8.65
2.09
-5.30
2.17
-50.46
-1.80
-1.08
-2.24
-0.04
-4.32
-3.69
1.71
-10.80
-6.58
5.21
-17.28
-9.47
8.71
Table 8b: Variation in average cost per inhabitant with door-to-door collection (euro)
Recycling
share
26%
28%
30%
34%
40%
50%
60%
65%
70%
80%
90%
Small
Medium
Big
€26.4
-
-€19.1
€24.2
-€18.6
€7.5
-
€16.2
€5.8
-€13.3
€9.7
€2.5
-€8.0
€2.4
-€59.1
-€2.7
-€1.2
-€2.6
-€0.1
-€4.8
-€4.3
€2.6
-€12.1
-€7.7
€7.9
-€19.3
-€11.1
€ 13.2
Table 9a: Results disposal small municipalities
Variable
Waste to energy share (%)
Distance to disposal site (km)
Number of disposal destinations
β
% Variation
€/inh/year
-0.0012
0.0013
0.021
-0.12
0.13
2.12
-0.13
0.15
2.38
β
% Variation
€/inh/year
-0.0007
0.0018
0.021
-0.07
0.18
2.12
-0.08
0.21
2.49
β
% Variation
€/inh/year
-0.0006
0.0028
-0.0024
-0.06
0.28
-0.24
-0.09
0.42
-0.36
Table 9b: Results disposal medium municipalities
Variable
Waste to energy share (%)
Distance to disposal site (km)
Number of disposal destinations
Table 9c: Results disposal big municipalities
Variable
Waste to energy share (%)
Distance to disposal site (km)
Number of disposal destinations
43
44