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\begin{document}

\title{Assessing land consumption impact on ecosystem services provision: an
insight on biophysical and economic dimension of loss of erosion control
in Italy}



\author[1]{Francesca Assennato}%
\author[1]{Marco D'Antona}%
\author[1]{Marco Di Leginio}%
\author[1]{Andrea Strollo}%
\author[1]{Michele Munafò}%
\affil[1]{ISPRA}%


\vspace{-1em}



  
  \date{June 08, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
While much research addresses soil erosion rates and erosion control
costs under different land use conditions, few studies address the issue
of the impact due to land consumption. The study presents results from
the national assessment of ecosystem services carried out by ISPRA in
the framework of land consumption monitoring. The purpose is to provide
information on the impact of land consumption and loss of benefits for
the community to support land protection policies. The study is based on
the high-resolution land cover products for Italy produced by ISPRA
using data from the Copernicus-Land Monitoring Services programme.
Accordingly with main research on the topic, RUSLE formula and transport
function is adopted, applying InVEST model. The avoided erosion change
between two reference years is assumed to represent the loss in erosion
control service due to land consumption. The economic dimension is
evaluated by cost of service replacement, with values available in
literature applied to biophysical results. Inputs to the model are the
two different land cover scenario for 2012 and 2018 available from
ISPRA, while other parameters are derived from JRC studies. Results
indicate that the land consumption growth in Italy between 2012 and
2018, evaluated in 31.500 ha is associated to an economic loss that is
over 2,4 billion \textbackslash{}euro/year, with erosion control
accounting for an average value over 135 M\textbackslash{}euro/year. The
study demonstrates the importance of valuing erosion control to support
a sustainable decision-making upon land use.%
\end{abstract}%



\sloppy


\emph{Keywords: land consumption, soil erosion, ecosystem services,
economic loss, decision making}

\textbf{Introduction}

Soil erosion is a natural phenomenon which, through the removal of the
topsoil which is richer in organic carbon, contributes to the changing
of the earth's surface. The magnitude of this process depends on many
factors but it is common understanding that a land in good condition
offers protection from erosion, through the maintenance of soil
functionality.

Soil erosion is a natural phenomenon of removal of the surface part of
the soil with a rate higher than its formation, contributing to the
modelling of the earth's surface and shaping the physical landscape of
today.

The extent of this phenomenon depends on various factors, including
geology, pedology, morphology and vegetation characteristics of the
territory and to climatic conditions. Erosion involves the loss of the
richest part of organic substance, with a significant reduction in
productivity and in the case of shallow soils also the irreversible loss
of the entire productive layer (ISPRA, 2015).

Soil has a very slow formation rate and any soil loss greater than 1 t /
ha / year can be considered irreversible over a period of 50-100 years.
In Europe, losses due to extreme precipitation events can occur once
every two or three years with losses of over 100 t/ha (Panagos et al.,
2015c).

The relevance of the phenomenon lead to the inclusion of erosion within
the eight threats listed in the European Commission's Thematic Soil
Strategy (2006).

Erosion protection is therefore classified in the as one of the main
ecosystem services offered by nature to human being, classified among
the services of Regulation \& Maintenance.

Although the phenomenon of erosion is a natural process, this can
undergo an acceleration from anthropic activities, mainly agriculture
and urbanization, which remove the vegetation cover and expose the soil
to agents of erosion, mainly in our latitudes, rainfall and surface
water. A soil in good condition is less erodible, thus not degraded
land, which preserves soil functionality, offers protection from erosion
as a regulation service.

Following the main literature on ecosystem services, the adopted
approach refers to the cascade model (Haines-Young et al. 2010). Recent
extensions of this scheme differentiate both the biophysical structure
and the processes / functions from the services, intended as flows of
interactions and ecological information. In particular, goods are
''means'' through which the ecosystem is used while the benefit is
intended as the product of the ecosystem service that produces a change
in the quality of life for the human being (La Notte, 2017).

According to estimates at European level carried out by the Joint
Research Centre of the European Commission, the area affected by the
phenomenon in the EU-27 is 1.3 million Km2, 20\% of which is affected by
soil loss with value more than 10 tonnes/ha/year and a total value of
970 million t/year (Panagos et al. 2015d). Estimation is based on
universal equation USLE in its revised version (RUSLE, Renard et al.,
1997). The latest result (Borrelli et al 2018) with the distributed
WaTEM / SEDEM model, which quantifies the potential path and the
transport of soil sediments due to water erosion on a European scale,
indicate that the loss of soil in river systems is approximately 15\% of
the gross erosion previously expected from RUSLE application in 2015.
Most of the gross erosion occurs in agricultural land (93.5\%), while on
the contrary, forest areas and other semi-natural areas suffer a global
sediment surplus which is driven by a new deposition of sediments eroded
by agricultural soil. In particular, the sediment transport coefficient
(SDR).

Among the 28 member states, Italy presents the highest soil loss rate
with values of 8.77 t/ha/year against an average of 2.46 t/ha/year in EU
countries, due to the combination of steep slopes and the high values in
the rainfall erosivity, resulting from intense precipitation after long
periods of drought.

It must be pointed out that erosion level threshold up to which is
considered not sustainable, according to US Soil Conservation Service
(USDA) is 11,2 t/ha/year. Looking at Italian condition, this threshold
could be considered tolerable (corresponding to about 1 mm/year) for
very deep soils and easily workable substrates, that can be improved
with fertilizations and organic matter inputs, such as those of the
mountain-hilly agricultural areas of Emilia Romagna region. On the
contrary, for thin and highly erodible soils, characterizing large
sectors of the Italian territory especially in the southern regions, the
tolerability threshold drops to 2 t/ha/year (McCormack, 1982).

The natural protection from erosion, depending on the protective
capacity of vegetation, and is strongly linked to land-use/land cover
changes and to the effective functionality of soils. The different forms
of soil degradation (e.g. sealing, compaction, intensive farming) entail
a reduction in water infiltration capacity, a modification of the
natural drainage pattern and the alteration of plant coverings a
consequent increase in surface water outflows even at high solid load.

Since 2016, ISPRA produces the evaluation of changes in the main
ecosystem services provided by soil, as resulting from the increase in
land consumption. This biophysical and economic loss estimation has the
aim of improving knowledge base on processes and economic dimension,
providing indicators useful to support the public policies for soil
protection and reduction of land consumption. The economic
quantification of lost services is provided to highlight the largely
hidden value of what is lost and is not intended as a way to build a
market for non-renewable resources through trade and compensation.

In the Italian case, a system of small and large interests around the
urbanization of soils has long been in place, such as to determine the
continuous loss of fertile soil for new urban interventions. Economic
quantification in this context should help the reverse of the current
process of agricultural soils loss due to lower costs of transforming
those soils than intervening on degraded areas (i.e. brownfields) or on
existing infrastructures and urban abandoned settlements, if anything in
disuse. Biophysical and economic quantification of ecosystem services
should have this basic objective.

The aim of the paper is to present the economic assessment of the change
in the ecosystem service ``erosion control'' resulting from the increase
of soil consumption from 2012 to 2018 in Italy. Here we present data
updated to the latest land consumption data for 2018 (Munaf\selectlanguage{ngerman}ò, 2019).

\textbf{Materials and methods}

According to the scheme proposed by MAES project (La Notte et al., 2017)
for ecosystem services assessment, the erosion control service is
defined as ``the reduction in the loss of soil by virtue of the
stabilising effects of the vegetation that mitigates the potential
damage of human use to the environment''. This refers to scheme proposed
within the CICES classification V. 5.1 (Haines-Young and Potschin 2018):
Regulation \& Maintenance (Biotic)/ Regulation of baseline flows and
extreme event (code 2.2.1.1).

The magnitude of water erosion depends on many factors, mainly dependent
on the protective capacity of vegetation cover in different land
use/land cover conditions. The different forms of soil degradation (even
partial sealing, compaction, intensive agriculture, etc.) entail the
reduction of the infiltration capacity of the water, a modification of
the natural drainage network and the alteration of the plant coverings,
a consequent increase in outflows surface water even with high solid
load. In general, the lack of water retention by the soil leads to an
increase in floods and erosive phenomena (European Commission, 2012).

The variation of the retention capacity through the comparison between
the steady state of land use/cover and soil properties with different
scenarios, can be considered as an indicator of the ecosystem service
change in biophysical terms, to be further examined for economic value.

The complexity of phenomenon, with the high number of involved
parameters to be consider lead to consider simplified models, such as
the most popular formula, the Universal Soil Loss Equation (USLE) ,
which assesses the potential soil erosion and whose parameters are also
available at European level from JRC (Panagos et al., 2014; 2015a;
2015b; 2015c).

An ideal model would describe all the individual processes on the basis
of hydraulics, hydrology and sediment transport theory equations,
providing the basin response in terms of volume of sediments passing
through the closing section for a given input rainfall
(Gianinetto\emph{et alii} , 2019).

The very limited number of parameters and the simplicity of RUSLE make
the outputs extremely sensitive to the input data and no information on
the forecasts of the quantities of sediments reaching water bodies are
given by this empirical model. The possibility of applying this model to
individual basins, specifically characterizing rainfall and its erosion
capacity with the availability of specific data on soil characteristics,
could allow for a better response in the future.

Through the USLE, or the RUSLE (Revised Universal Soil Loss Equation)
revision of the USLE equation adapted to complex topographical
environments, the loss of soil (potential) from the single portion of
territory is calculated, depending on the characteristics related to
topography, soil properties, precipitation, land use/land cover,
vegetation and soil conservation practices.

The loss thus calculated represents the share of sediment which, removed
from the ground, can be transported by water. In turn, these sediments
can be redeposited along the path or reach the receptor of the outflow
of the basin. The share of soil actually lost is assessed by multiplying
the USLE result by the entire basin with a Sediment Delivery Ratio (SDR)
which represents the share of the removed sediment that actually reaches
the water bodies. The result is an erosion value per unit area.

The amount of soil loss is not in itself the measure of the ecosystem
service ``erosion control'' provided by soil, since it represents the
''equilibrium'' situation in the given conditions of land use and soil
characteristics. However, its variation can be used as an indicator of
this capacity: the increase in soil lost between two scenarios
(different time periods or land use change) represents the variation in
the holding capacity and therefore the variation in the service. The
absolute value of soil loss calculated in this way can also be used for
comparisons between different areas, or as an indicator of the different
sensitivity to the phenomenon of erosion of one territory compared to
the other.

The European figure was recently updated with the addition of a
transport formula by the JRC (Borrelli et al. 2018) is, published with a
resolution of 100 m but also available at 25 m on request. The model
calculates the amount of eroded sediment for each pixel, and then
estimates the portion that actually reaches the water bodies.

Also InVEST's \emph{Integrated Valuation of Ecosystem Services and
tradeoffs} suite of models (Sharp et al. 2016) adopted in this study
uses the revised formula RUSLE (Revised Universal Soil Loss Equation)
weighted by the SDR factor in a transportation modelas illustrated in
figure 1. The sediment retention capacity or reduction in soil loss
provided by soil cover is calculated as avoided water erosion for each
land cover scenario (2012 and 2018). Avoided erosion is expressed in
tons/pixel, by the current land use compared to bare soil.

\emph{Model description}

Input to the model are the digital elevation model and the land use/land
cover maps produced by ISPRA, while the values of the other parameters
were derived from JRC studies.

The amount of soil lost annually in a given pixel i is given by the
formula (Sharp et al., 2016):

\begin{equation}
\text{usle}_{i}=\ R_{i}\bullet K_{i}\bullet\text{LS}_{i}\bullet C_{i}\bullet P_{i}\nonumber \\
 \end{equation}

where

* ?? is rainfall erosivity (units: MJ \selectlanguage{english}mm (ha \selectlanguage{english}hr) -1), derived from
JRC with a 500 m resolution;

* ?? is soil erodibility (units: ton \selectlanguage{english}ha \selectlanguage{english}hr(MJ \selectlanguage{english}ha \selectlanguage{english}mm) -1),
derived from JRC with a 500 m resolution;

* ??? is a slope length-gradient factor (non-dimensional), directly
calculated by model on the basis of DEM in input;

* ?? is a crop-management factor (non-dimensional), derived from JRC and
adapted to Corine 2006 cover classes, considering zero for artificial
areas;

* and ?? is a support practice factor, derived from JRC and adapted to
Corine 2006 cover classes, considering zero for artificial areas.

The SDR index is instead calculated starting from the IC connectivity
index:

\begin{equation}
IC=\ \operatorname{}\left(\frac{D_{\text{up}}}{D_{\text{dn}}}\right)\nonumber \\
 \end{equation}

which is a function of the upstream area (D\_up) and the flow path
towards the water body (D\_dn) of the cell considered. The two
components are estimated by (Sharp et al., 2016):

\begin{equation}
D_{\text{up}}=\ \overset{\overline{}}{C}\overset{\overline{}}{S}\sqrt{A}\nonumber \\
 \end{equation}

Where C~ \selectlanguage{english}and S~ \selectlanguage{english}are the average contributions respectively of the land
cover factor and the gradient of the pixels belonging to the upstream
surface and A is the upslope contributing area. The downslope component
is given by

\begin{equation}
D_{\text{dn}}=\ \sum_{i}\frac{d_{i}}{C_{i}S_{i}}\nonumber \\
 \end{equation}

where d\_i is the length from the same cell according to the steepest
downslope direction to the water body.

The value of the sediment transport coefficient is then calculated as
(Sharp et al., 2016):

\begin{equation}
\text{SDR}_{i}=\ \frac{\text{SDR}_{\max}}{1+exp\left(\frac{\text{IC}_{0}-\text{IC}_{i}}{k}\right)}\nonumber \\
 \end{equation}

The maximum value of the SDR\textsubscript{max} index is set at 0.8,
while \(\text{IC}_{0}\) and k are calibration parameters.

The final sediment load (in Mg \selectlanguage{ngerman}· ha\textsuperscript{-1} ·
yr\textsuperscript{-1}) will be given by:

\begin{equation}
E_{i}=\ \text{usle}_{i}\bullet\text{SDR}_{i}\nonumber \\
 \end{equation}

The model also provides a quantitative estimate of the detention
service, using as a reference point a hypothetical scenario in which all
uses and ground coverings are cancelled on bare ground: the value of the
conservation service is therefore based on the difference between the
removal sediment from this bare basin and that of the scenario of
interest.

The value of the soil retained in tons per hectare (in Mg \selectlanguage{ngerman}· ha-1 · yr-1)
is given by the formula:

\begin{equation}
{\text{\ Avoided\ erosion}_{i}=R}_{i}\bullet K_{i}\bullet\text{LS}_{i}\bullet{(1-C}_{i}\bullet P_{i})*\text{SDR}_{i}\nonumber \\
 \end{equation}

which represents the loss of soil avoided by current land use compared
to bare (maximum) soil, weighted by the SDR factor.

The difference in avoided erosion values between the two scenario is
assumed to represent the variation in ecosystem service provision caused
by the consumption of soil in the reference period.

The main inputs to the study are high-resolution complete land cover map
of Italy with 10 m resolution is available for 2012 and the land
consumption map, with detail only for cover changing from
natural/agricultural and forest or semi-natural areas to artificial, is
available for each year from 2012 and the last one if for 2018 (Munaf\selectlanguage{ngerman}ò,
2019). Those maps are produced by ISPRA on the basis of data from the
Copernicus-Land Monitoring Services programme and used as reference for
changing scenarios.

The slope length and steepness factor (LS-factor) is calculated using a
high-resolution (25 m) Digital Elevation Model (DEM) for the whole
European Union and available in raster format
(https://esdac.jrc.ec.europa.eu/themes/slope-length-and-steepness-factor-ls-factor).

With the exception of the digital elevation model, also available from
ISPRA, the required input data (the parameters of the RUSLE formula) are
obtained from the Joint Research Center studies, available in raster
format (http://esdac.jrc.ec.europa.eu/).

To simplify and speed up the processes, the rainfall erosivity
(R--actor, Panagos et al., 2015a) with resolution of 500 m, has been
resampled with the resolution of the digital elevation model and aligned
to it. Same resolution and same procedure for the soil erodibility
factor (K-factor, Panagos et al., 2014), which however is not defined in
densely urbanized areas. Due to the difference in resolution between the
soil erodibility raster has been ''filled'' by proximity with the
closest pixel value, thus managing to obtain an output in all areas
affected by soil erosion. The land cover management factors (C-factor,
Panagos et al., 2015b) and the effect of the support practices (P
factor, Panagos et al., 2015c) have a resolution of 250m. These two data
must be entered in the model not in raster format but with
non-dimensional values according to land use and land cover classes. To
extrapolate these values, spatial analyses were performed to calculate
the average of the land use and land cover classes of the CORINE Land
Cover 2006, on which the starting data are based (Panagos et al., 2015b;
Panagos et al., 2015c). The average values of C-factor and P-factor of
the artificial surfaces were forced to 0 to consider the absence of the
soil erodibility in such areas.

USLE formula parameters considered are:

\begin{itemize}
\tightlist
\item
  R factor: high temporal resolution data from a series of stations
  distributed homogeneously; for Italy, there are 251 stations that
  covered the 2002-2011 interval with an average time resolution of 30
  minutes;
\item
  K factor: data from LUCAS surveys (around 1,300 points in Italy);
\item
  \selectlanguage{english}?\selectlanguage{english}?\selectlanguage{english}? is a slope length-gradient factor (non-dimensional), directly
  calculated by model on the basis of DEM;
\item
  \selectlanguage{english}?\selectlanguage{english}? is a crop-management factor (non-dimensional), derived from JRC and
  adapted to Corine 2006 cover classes, considering zero for artificial
  areas; EUROSTAT data for agricultural areas (type of crop and
  agricultural practices adopted such as reduced processing / no
  processing, cover crops, etc.); Copernicus and Corine Land Cover data
  for non-agricultural areas;
\item
  and \selectlanguage{english}?\selectlanguage{english}? is a support practice factor, derived from JRC and adapted to
  Corine 2006 cover classes, considering zero for artificial areas.
\item
  Factor P: data from the GAEC database (Good Agricoltural Environmental
  Conditions) and from the LUCAS samplings (anti-erosive practices
  observable directly in ground surveys - dry stone walls, hedges,
  etc.).
\end{itemize}

Other inputs are:

\begin{itemize}
\tightlist
\item
  Watershed boundary national map (ISPRA), available at
  http://www.sinanet.isprambiente.it/it/sia-ispra/download-mais);
\item
  DEM with 10 m resolution (ISPRA, available at
  http://www.sinanet.isprambiente.it/it/sia-ispra/download-mais);
\item
  Land cover high resolution national map 2012 at 20 m (ISPRA) ;
\item
  Land consumption map 2012 and 2018 with 10 m resolution (Munafo,
  2019).
\end{itemize}

Among the main limitations of the model is its dependence on the USLE
formula. It is known that USLE is an empirical equation developed for
United States, with limited performance in other areas with different
morphological condition and climate. It is therefore possible to improve
the result by specifying different values of the inputs R, K, P and in
particular C, to take into account the results of local studies (Sougnez
et al., 2011).

This equation is widely used but is limited in scope, since it only
considers the effects of distributed type of rainfall, i.e. surface flow
with laminar flow, while neglecting other sources of erosion (currents
channeled in streams or ditches, streambank and gullies erosion and mass
movement) and also ignores the dynamic effects of the rains. Therefore,
when comparing the scenarios, the value obtained from the model must be
considered as the lower limit of the estimate of the total impact.

Additional limitation is referred to effects of changes in land cover
from natural to artificial, that determine significant changes in the
peak flow rate during rain events and can therefore affect the extent of
canalization and bank erosion. Neglect these components in the total
calculation of the sediments removed (or retained) leads to further
underestimation. The JRC study using on the WaTEM / SEDEM model also
reaches the same conclusion. The conditions of the Italian territory,
largely subject to erosive phenomena and with territorial variability
difficult to mediate, pushes to explore the possibility of perfecting
the application of the model with more detailed information on local
conditions. Results for sediment transport seems to have high
sensibility to slope- The resolution of DEM adopted is thus a critical
point, there is a risk of over representation of slope factor, if
compared to other usle factor.

The economic valuation of soils and its ecosystem services is a
relatively new and still not well developed as suggested by a number of
recently published review articles (Van der Meulen, 2018).
Theoretically, land prices should reflect, among other factors, also the
value of soil ecosystem services but this is far from real-world
application. In order to estimate the economic value of soil ecosystem
services, market price proxies or cost-based methods are used, both with
limitations, but could be helpful as a first estimate.

Cost-based methods are the simplest method class with low data demands,
even inconsistent with economic theory. Costs of
replacing/restoring/avoiding loss of a given ecosystem service are used
as proxy of its economic value and are applied the most to estimate
economic value of regulating ecosystem services.

The economic value used in the study, mainly cost of replacing or
avoided loss, refers to an extensive literature review (Annex to ISPRA,
2018) that shows a large variability of the economic values connected to
erosion control.

Main studies concerning this ecosystem service report an economic value
for the erosion control between 22 and 235 \textbackslash{}euro/ha/year
referred to 2003 (Van der Meulen \emph{et al} . 2018). Those values, if
related to the JRC-EC data of the average erosion of 2.46 t/ha/year
(Panagos\emph{et al} . 2015 d), allows to estimate an interval for the
economic cost per tonne of soil loss between 8.94 and 95.53
\textbackslash{}euro/t (2003). It must be pointed out that these studies
refer to replacement or market costs, not including the social component
of the cost, thus leading to an underestimation of the problem.

The above-mentioned economic value, by revaluation to year 2014 in 11,01
and 117,6 \textbackslash{}euro/t, is applied to the output of the InVEST
model expressed in t/ha, resulting in the economic value of change in
ecosystem service in \textbackslash{}euro/ha.

\textbf{Results}

In the period (2012-2018) considered for the study, land consumption
increased in Italy of 31.500 ha (Munafo, 2019) with a high variability
along the peninsula. The analysis of the flow of ecosystem services
shows that the economic impact of soil consumption in Italy produces
annual losses which are confirmed to be very high. The increase in
surface runoff produced by the consumption of soil (estimated at over
200 million m3 / year) is the most significant effect.

Land consumption is associated to a decrease of the annual service flow
of erosion control service with an average value estimated in of 135
M\textbackslash{}euro/year, following only water flow regulation, which
is largely over the others (table1).

The geographical distribution of erosion control loss and land
consumption density between 2012-2018 (figure 2 and figure 3) shows the
high variability between different conditions in Italy. Values are
represented for each municipality, respectively the maximum economic
loss in \textbackslash{}euro/year (average of maximum values in the
municipal area) and the density of land consumption in
m\textsuperscript{2}/ha.

As it is expected, the major economic losses are concentrated in the
areas most subject to erosion, corresponding to the higher slope, where
even a small increasing in land consumption results in relevant economic
impact due to loss of erosion control. In Italian small municipalities
of piedmont and hilly areas, where erosion control is more needed, land
consumption has increased in some cases with a higher density than in
large urbanized areas, demonstrating the urgency of a proper
consideration of land consumption effects.

In the areas in which the highest rate of land consumption, concentrated
in lowland areas, the loss of the ecosystem service linked to erosion is
rather limited. Conversely, in mountain areas where land consumption is
rather low and in some coastal areas being almost high coasts, the cost
due to the loss of the ecosystem service is much higher. This indicates
the sensibility of sloped areas, where even a small increasing in land
consumption determines an increase in the erosive phenomenon with a
significant economic impact.

\textbf{Discussion}

The study is a first attempt to assess the economic value of ecosystem
services loss produced by land consumption at a national level. The main
limitations of this approach applied to erosion control are related to
the variability in economic values published in the literature and to
specific limitations of biophysical model considered. The USLE equation
(Renard \emph{et al} . 1997) is widely used but is limited in scope,
only representing rill/inter-rill erosion processes. Other sources of
sediment are not considered, including gully erosion, streambank
erosion, and mass erosion. In addition, as an empirical equation
developed in the United States, the USLE has shown limited performance
in other areas even when focusing on sheet and rill erosion. Based on
local knowledge, users may modify the soil loss equation implemented in
the model by altering the R, K, C, P inputs to reflect findings from
local studies (Sougnez \emph{et al} . 2011).

The sediment retention index computed by InVEST model underestimates
retention, since it does not account for the retention from upstream
sediment flowing through the given pixel. In some situations, index
values may be counter-intuitive: for example, urban pixels may have a
higher index than forest pixels if they are highly connected to the
stream. In other terms, the SDR (second factor) can be high for these
pixels, compensating for a lower service of avoided soil loss (the first
factor): this suggests that the urban environment is already providing a
service of reduced soil loss compared to an area of bare soil
(Sharp\emph{et al.} 2016). Therefore, the output should not be
interpreted quantitatively but as an index able to describe a complex
phenomenon.

The ecosystem service flow analysis indicates that the level of economic
loss related to the Land consumption in Italy is quite high (ISPRA,
2018), providing useful indications to the administrations to manage the
phenomenon. Economic valuation demonstrates the importance of ecosystem
services for human well-being and can be helpful supporting land use
decision-making. Only a small share of the relevant environmental
effects is usually accounted for in decision-making processes. Many
ecosystem services are public goods not valuable in market terms and the
use itself can be a source of externalities, without market valuation.

The ''hidden costs'' (European Commission, 2012) of land consumption,
therefore, could be far greater than the values reported.

The views expressed in this information product are those of the
author(s) and do not necessarily reflect the views or policies of ISPRA.

Authors declare that they have no conflict of interest in the subject
matter or materials discussed in this manuscript.

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Table 1: Variation in the flow of regulating and maintenance ecosystem
services due to the soil consumption between 2012 and 2018 in Italy.
Source: ISPRA processing\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Ecosystem Services & Minimum value of service
loss{[}\textbackslash{}euro/year{]} & Average value of service
loss{[}\textbackslash{}euro/year{]} & Maximum value of service
loss{[}\textbackslash{}euro/year{]}\tabularnewline
\midrule
\endhead
Carbon sequestration & -118.815 & -373.106 & -627.397\tabularnewline
Habitat quality & -14.787.820 & -14.787.820 & -14.787.820\tabularnewline
Crop production & -109.097.041 & -109.097.041 &
-109.097.041\tabularnewline
Timber production & -29.110.714 & -29.110.714 &
-29.110.714\tabularnewline
Crop pollination & -5.252.736 & -6.133.071 & -7.013.406\tabularnewline
Regulation of local climate/temperature & -2.858.857 & -7.147.143 &
-11.435.429\tabularnewline
Removal of particulate and ozone & -1.521.157 & -3.080.862 &
-4.640.568\tabularnewline
Control of erosion & \textbf{-21.355.485} & \textbf{-135.004.727} &
\textbf{-248.653.968}\tabularnewline
Water availability & -2.247.613 & -28.095.161 &
-53.942.709\tabularnewline
Water flow regulation & -1.925.993.347 & -2.085.208.798 &
-2.244.424.248\tabularnewline
Water purification & -286.840 & -38.402.822 & -76.518.805\tabularnewline
Total & \textbf{-2.112.630.426} & \textbf{-2.456.441.265} &
\textbf{-2.800.252.105}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
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