Digitalisation of the European Water Sector to Foster the Green and Digital Transitions

Abstract

During the Digital Decade, the European Union (EU) is facing two important challenges: the green (and energy) transition and the digital transition, which are interconnected with one another. These transitions are of high relevance in several aspects of our life, e.g., in the industry, energy sector, transports, environmental management and our daily life. Digital technologies are particularly emerging also as multi-benefit solution in the water sector, as water is becoming more and more vulnerable to climate change (e.g., droughts and floods) and human activities (e.g., pollution and depletion). Within this context, in this study we assessed some of the several economic benefits that digital solutions can bring to the water sector, with a focus on leakage reduction in water distribution networks, reduction of combined sewer overflows and improvement of hydropower generation and operation. The benefits are calculated for each EU Member State and the UK, and then aggregated at the EU scale. Benefits were quantified in EUR 5.0, 0.14 and 1.7 billion per year (EUR 13.2 per person per year, on average), respectively, excluding environmental and social benefits, which may play a non-negligible role.

1. Introduction

The world has a huge growth potential with digital technologies. Digital solutions will create new opportunities for businesses, encourage trustworthy technology, foster an open and democratic society, enable a vibrant and sustainable economy, help fight climate change and achieve the green transition [1]. Digital technologies affect our lifestyle and business activities profoundly, as they encompass technical, economic, environmental, and social benefits. In the social sphere, digital infrastructure and rapid connectivity bring new opportunities. Digitalisation can connect people together independently of where they are physically located. Digitalisation can become a decisive enabler of rights and freedoms, allowing people to reach out beyond specific territories, social positions, or community groups, and opening new possibilities to learn, have fun, work, explore and fulfil ambitions, interact with public administrations, manage their finance and payments, make use of health care systems, automate transport systems, participate in democratic life, be entertained or meet and discuss with connected people anywhere in the world [1].

The relevance of digital technologies is highly recognized in the European Union (EU). During the Digital Decade, the EU is indeed facing two important challenges: the green transition and the digital transition. The European Commission is committed to delivering a Europe fit for the digital age, by empowering people, businesses and administrations with a new generation of technologies and digital solutions [1]. A fair digital transformation, that is devoted to the principles of data protection, transparency and data sparing, has the potential to increase the innovation and productivity of the EU economy, offering new opportunities for people and businesses. The digital transition will also contribute to reach the green objectives [2]. To achieve the ambitions set in the Digital Compass proposed by the Commission, the EU needs to step up investments in key digital technologies, including cyber security, cloud computing, artificial intelligence, data spaces, blockchain, quantum computing and semiconductors, as well as in relevant skills. To foster the digital transition, a 2020 estimate shows that additional investment of around EUR 125 billion are needed per year [2].

The digital transformation is of high relevance, especially in the EU water sector, which is currently facing several challenges. In the past 50 years, water demand has continuously increased on the back of a steadily growing population. The amount of water resources per capita has already dropped by 24% and water scarcity affects 17% of the EU [3]. About 40–60% of water is lost globally in terms of non-revenue water, an indicator for water that has been produced and lost before it reaches the customer. In the EU, the average water losses in water distribution networks (WDNs) are 23% of total water introduced in WDNs [4]. Therefore, there is now an established awareness on the importance of water conservation strategies, for example, by closing water loops, reducing water import and export, converting storm water and other potential hydric sources into a reliable and sustainable water supply, reducing water losses (e.g., in water distribution networks), enhancing water recycling and better managing multi-purpose reservoirs [5]. In this context, the digitalisation of the water sector could become a relevant enabling factor to mainstream water policy and unlock more effective actions [6]. Digital technologies delivered about EUR 300 billion in capital and operating expenditure savings in the global water industry in 2016–2020, targeting water segments such as the treatment of wastewater, distribution, customer management and metering of drinking water [7], for example, through real-time monitoring, modelling approaches to support decision making, or optimization measures in energy consumption. Closely related to the digital revolution is the emergence of new applications for Big Data technologies. Rapid advances in affordable sensors, high-resolution remote sensing, communication technologies, and social media are opening new opportunities for data collection, including real-time monitoring. Big data analytics combined with artificial intelligence (AI) and machine learning can better support data-based decisions with high accuracy and less computational cost [5].

However, digital solutions also come with challenges, especially in the water sector. Digitalisation is an emerging topic, but its benefits are not well-known in quantitative terms and in a systematic way, as several technologies have not reached yet the commercial stage [8]. Another hindering factor for the full use of the potential of digitalisation in the water sector is, amongst others, the lack of coherent governance conditions, such as respective technology guidance and standards for monitoring [6]. According to Stein et al. (2022) [6] the reasons for the still-lacking digitalisation within the water sector are manifold. The fragmentation of the water market makes the standardized implementation of digital solutions a challenge. In addition, there are inconsistent data management practices that lead to exchange and interoperability issues. Contractual lock-in of utilities to specific vendors and legacy systems often prevents the adoption of open-source solutions. In addition, existing systems are not necessarily compatible with IoT-oriented technical solutions (IoT: Internet of Things) and services because legacy systems do not have an internet connection or do not provide an API (application programming interface) or similar mechanisms to obtain the information without scraping. When smart sensors are implemented, the challenge remains that they must be robust and with low-maintenance needs, especially for the wastewater sector, in order to be economical. Another economic challenge is related to the market size of the water sector, because sensors need to be produced on a large scale. As in many other application areas, there is a knowledge gap in the water sector regarding digitalisation and cyber security, which also leads to uncertainty in the integration of digital solutions [6]. Furthermore, digital solutions are not sufficiently integrated into EU water policies. EU policies are missing a coherent terminology and clear definitions of digitalisation in the water sector. At the same time, they have different targets and different target audiences. As a result, users of water services and even providers of digital services in the water sector often either do not know or do not understand the relevant water policies, while policymakers do not know the real achievable benefits [6].

As a consequence, the appraisal of benefits is quite complex, and it encompasses environmental, economic and social benefits. Therefore, this exploratory study explores the market opportunities of digitalisation in the European Union (EU) water sector and seeks to quantify some economic benefits at the Member State level. This is a large-scale analysis (country scale) with the main aim of providing a screening-level assessment of the possible economic benefits of digitalisation to support the policy-making process. The investigated three water sectors are water distribution networks (WDN), combined sewer systems and hydropower plants.

2. Materials and Methods

Three water sectors are considered in this study:

• Drinking water distribution networks (WDN): WDN utilities account for 5% of the EU’s electricity consumption, 30–50% of local authority’s electricity consumption, and water produced and lost before reaching the customer is, on average, 23% of total water provided to WDNs in the EU [9].
• Wastewater combined systems: in 2019 alone, EU wastewater treatment and discharge plants emitted 27 million metric tons of CO₂ in the atmosphere [9]; combined sewer systems represent circa 50% of the EU sewer system and combined sewer overflow amounts to 5700 Mm³ per year (on average, in the last 30 years) [10].
• Hydropower plants: the hydropower sector represents 12% of the European electricity production and 16% of the global one (other renewables represent 10%, globally).

The calculations carried out in this study are mainly based on data available in the literature. These data are herein aggregated and elaborated to estimate benefits at the European Union (EU) scale, including the UK, even though it is not a EU Member State (MS) anymore. In the Results and Discussion section, the accuracy and sensitivity of results are discussed.

2.1. Water Distribution Networks

2.1.1. Role of Digitalisation

One of the main problems of WDNs is associated with water losses (leakages), mainly due to poor maintenance/management. Water losses occur during the transport and distribution of water in pipes and can be caused by leaks, pipe breaks and equipment failures. Through digital technologies, it is possible to monitor and detect these losses in real time, allowing the responsible team to act quickly to correct the problem. In addition, digitalisation allows the implementation of real-time water flow measurement systems, which allow supply system managers to monitor demand and water consumption in real time. These systems can be more accurate than traditional measurement systems, which often involve manually reading water meters. Another advantage of digitalisation is the use of data analysis systems and artificial intelligence, which allows for the identification of patterns of water consumption by different categories of users, which can be used to predict and avoid failures in the distribution system, in addition to an improved efficiency in system operation. In summary, digitalisation can be a valuable tool to reduce water losses in the water sector, making the distribution system more efficient and sustainable.

In recent years, digital twins (DTs) have been introduced to improve the monitoring, management and efficiency of the water sector. A DT allows live monitoring of system components and can analyse different scenarios and variables, such as pressure variation, operating devices such as pumps, the control of different valves and pressure-drop factors [11]. The use of DT is notorious for having the benefit of real-time data, since it is designed around a two-way flow of information/actions. By having better and constantly updated data from different components of a WDN, digital twins can improve the behaviour of the system, with greater potential to expand the system performance with several benefits [12,13,14,15].

2.1.2. Method

The percentage of potential reduction of leaks is very site-specific as it depends on several factors, such as the network configuration, number of pipe branches, type of joints/connections, type and state of reservoirs and tanks, water levels, pressure variation and head losses in the network, population density in the network, number of water entry points (which should be minimized whenever possible), pressure-reducing valves (PRVs), their location and type, inner diameters of the pipes, age of the system components and morphology and topography of the system [16]. Nevertheless, based on some key literature studies on the digitalization of WDNs to reduce leakages [11,12,13,14,15,16,17,18,19], an average percentage of reduction of 30% can be pointed out. However, it must be noted that, locally, the value can vary a lot: in some studies, it was possible to reduce 80% of leaks, while in others the reduction was only around 10%. Therefore, it is obvious that it is not possible to come out with a unique value of leakage reduction, and the potential of leakage reduction is very site-specific (see also Appendix A). Nevertheless, considering the explorative aim and preliminary screening level of this study, based on an expert judgement of the abovementioned references, the authors dare to point out an average value of 30% of possible leakage reduction in WDNs with digital implementation, representing a reasonable overall value for the EU context.

In order to calculate the benefits that digital solutions can entail if applied in WDNs, the following equation was used for each EU Member State (MS) (see Table 1):

$$B_{WDN} = C·W·(L-L_{un})·S$$

(1)

where:

B_WDN is the benefit expressed in EUR/year;

C is the price of drinking water for household, expressed in EUR/m³ [20];

W is the annual drinking water, before consumption (i.e., before leakages happen) in m³/year, based on internal data of water consumption, available at the Joint Research Centre (JRC), see, e.g., [21] (alternatively, Eurostat data could be used), and considering the population for each MS;

L is the WDN leakage expressed in %, based on internal data available at the Joint Research Centre (JRC), see, e.g., [3];

L_un is the unavoidable WDN leakage expressed in %, based on the equation presented by Ahopelto and Vahala [22] and using the value of WDN length (in km) from [23].

S is the reduction percentage (assuming, on average, 30%) of leakage that can be achieved by implementing digital solutions.

Table 1. Analysis and benefit for WDNs per year (Equation (1)).

Country		Annual Volume of Domestic Water (Mm3/y) W	Annual Cost of Domestic Water (Million EUR), Cd = C∙W	Leakages (Mm3/y) L = W(L − Lun)	Cost of Leakages (Million EUR) L∙C	Benefit (Million EUR) BWDN
AT	Austria	467.67	1636.86	155.89	545.62	114.39
BE	Belgium	391.52	1761.82	43.50	195.76	2.66
BG	Bulgaria	233.89	233.89	100.24	100.24	18.00
HR	Croatia	262.91	525.82	87.64	175.27	38.32
CY	Cyprus	93.81	164.16	31.27	54.72	13.99
CZ	Czech Republic	329.39	1119.94	109.80	373.31	58.93
DK	Denmark	276.37	2570.21	30.71	285.58	25.11
EE	Estonia	42.73	141.00	14.24	47.00	7.91
FI	Finland	350.70	2034.08	116.90	678.03	143.47
FR	France	3637.27	14,185.37	1212.42	4728.46	973.16
DE	Germany	3672.87	17,262.49	408.10	1918.05	164.59
EL	Greece	765.09	918.10	255.03	306.03	71.88
HU	Hungary	323.85	712.46	107.95	237.49	40.01
IE	Ireland	220.85	220.85	220.85	220.85	48.45
IT	Italy	3457.62	6915.25	2305.08	4610.17	1099.22
LV	Latvia	101.68	335.55	33.89	111.85	23.86
LT	Lithuania	69.71	230.05	23.24	76.68	9.79
LU	Luxembourg	31.00	186.02	3.44	20.67	1.97
MT	Malta	13.55	44.72	5.81	19.17	3.08
NL	Netherlands	797.81	4387.95	88.65	487.55	44.33
PL	Poland	1252.34	3381.32	313.09	845.33	119.26
PT	Portugal	582.99	1020.24	194.33	340.08	72.37
RO	Romania	821.82	1232.73	547.88	821.82	183.38
SK	Slovakia	144.25	360.63	61.82	154.55	24.81
SI	Slovenia	88.12	202.68	37.77	86.86	16.65
ES	Spain	2215.50	3987.89	949.50	1709.10	370.03
SE	Sweden	541.83	2384.07	180.61	794.69	167.88
UK	United Kingdom	3470.13	11,798.44	1487.20	5056.47	1106.64

Table 1. Analysis and benefit for WDNs per year (Equation (1)).

Country		Annual Volume of Domestic Water (Mm3/y) W	Annual Cost of Domestic Water (Million EUR), Cd = C∙W	Leakages (Mm3/y) L = W(L − Lun)	Cost of Leakages (Million EUR) L∙C	Benefit (Million EUR) BWDN
AT	Austria	467.67	1636.86	155.89	545.62	114.39
BE	Belgium	391.52	1761.82	43.50	195.76	2.66
BG	Bulgaria	233.89	233.89	100.24	100.24	18.00
HR	Croatia	262.91	525.82	87.64	175.27	38.32
CY	Cyprus	93.81	164.16	31.27	54.72	13.99
CZ	Czech Republic	329.39	1119.94	109.80	373.31	58.93
DK	Denmark	276.37	2570.21	30.71	285.58	25.11
EE	Estonia	42.73	141.00	14.24	47.00	7.91
FI	Finland	350.70	2034.08	116.90	678.03	143.47
FR	France	3637.27	14,185.37	1212.42	4728.46	973.16
DE	Germany	3672.87	17,262.49	408.10	1918.05	164.59
EL	Greece	765.09	918.10	255.03	306.03	71.88
HU	Hungary	323.85	712.46	107.95	237.49	40.01
IE	Ireland	220.85	220.85	220.85	220.85	48.45
IT	Italy	3457.62	6915.25	2305.08	4610.17	1099.22
LV	Latvia	101.68	335.55	33.89	111.85	23.86
LT	Lithuania	69.71	230.05	23.24	76.68	9.79
LU	Luxembourg	31.00	186.02	3.44	20.67	1.97
MT	Malta	13.55	44.72	5.81	19.17	3.08
NL	Netherlands	797.81	4387.95	88.65	487.55	44.33
PL	Poland	1252.34	3381.32	313.09	845.33	119.26
PT	Portugal	582.99	1020.24	194.33	340.08	72.37
RO	Romania	821.82	1232.73	547.88	821.82	183.38
SK	Slovakia	144.25	360.63	61.82	154.55	24.81
SI	Slovenia	88.12	202.68	37.77	86.86	16.65
ES	Spain	2215.50	3987.89	949.50	1709.10	370.03
SE	Sweden	541.83	2384.07	180.61	794.69	167.88
UK	United Kingdom	3470.13	11,798.44	1487.20	5056.47	1106.64

2.2. Wastewater Systems: Combined Sewer Overflows

2.2.1. Role of Digitalisation

Combined sewers are usually designed to collect the dry weather flow (DWF), consisting of sewage from households, industrial discharges and seepage of groundwater, into the sewers, together with urban runoff, and convey a certain amount of the combined flow to a wastewater treatment plant (WWTP). A WWTP generally receives a discharge of 4–6 times the average DWF in order to ensure the design pollution removal efficiency of the treatment process, although in some cases, it can be >6. When the sewer network discharge exceeds the conveyance capacity of the network, the overflow is released into the environment. Combined sewers are a widespread reality in the world and in the European Union. Pollution from combined sewer overflows (CSOs) exerts a significant pressure on the receiving water bodies and raises concern as a water management challenge [10].

Real-time control (RTC), by means of digital solutions (digital twins), is emerging as a water management strategy for the reduction of CSOs, as it allows for the better operation of hydraulic structures, such as gates and storage tanks within the sewer network. Van Der Werf et al. (2022) [24] reviewed RTC applications for CSO management by referring to a set of case studies. Based on these case studies, a regression analysis was carried out in [10], linking the volume reduction of CSO to the pre-intervention CSO volume. The regression equation was applied to each EU Member State (MS) to calculate the CSO reduction volume (m³/year) for each MS. The benefits of pollution removal were quantified by attributing a shadow price to the conventional pollutants conveyed by the CSO. A shadow price is the equivalent of the environmental damage avoided if these pollutants are removed or recovered. Therefore, this is an estimate of the environmental benefits.

2.2.2. Method

With the values discussed in [10], the shadow price of avoiding 1 m³ of DWF spill through CSO is Pr_DWF = ${\displaystyle \sum }_i{Pr}_{sh}^i{·C}_{\mathrm{DWF}}^i=$ 1.37 EUR and the shadow price for 1 m³ of avoided runoff (avoided volume of CSO minus avoided volume of DWF) is Pr_runoff = ${\displaystyle \sum }_i{Pr}_{sh}^i{·C}_{\mathrm{runoff}}^i=$ 0.005 EUR, where ${Pr}_{sh}^i$ is the shadow price of the pollutant i per unit mass, while $C_{\mathrm{DWF}}^i$ and $C_{\mathrm{runoff}}^i$ are its concentration in DWF and in runoff, respectively. The benefit associated with the avoided CSO is therefore (see

Table 2. Annual analysis and benefits for wastewater networks (Equation (2)). When the value is zero it is because the sewer system is separate and not combined; so, CSOs do not occur.

Country	CSO Volume (Mm3/y) VCSO	Economic Value of CSO (Million EUR) VCSO∙Shadow Price	Benefit (Million EUR) BCSO
AT	58.64	2.01	0.68
BE	259.13	27.01	7.73
BG	0.00	0.00	0.00
HR	86.05	6.24	1.86
CY	27.46	2.26	0.70
CZ	0.00	0.00	0.00
DK	104.01	10.84	3.14
EE	7.98	0.93	0.32
FI	26.88	2.78	0.95
FR	840.61	68.45	21.36
DE	772.94	35.43	11.63
EL	62.64	4.35	1.39
HU	42.22	4.24	1.41
IE	34.01	3.27	1.05
IT	1286.79	90.30	26.44
LV	5.62	0.66	0.25
LT	15.30	1.75	0.61
LU	41.92	4.05	1.13
MT	11.85	0.92	0.29
NL	135.38	6.49	2.24
PL	413.59	43.50	13.68
PT	163.99	10.59	3.15
RO	0.00	0.00	0.00
SK	6.16	0.55	0.22
SI	57.40	3.78	1.10
ES	91.86	7.52	2.76
SE	22.33	2.35	0.85
UK	1207.22	123.08	35.87

):

B_CSO = [V_CSO* − V_CSO − (V_DWF* − V_DWF)]C_runoff + (V_DWF* − V_DWF)C_DWF

(2)

where V_CSO is the annual CSO volume (m³), and V_DWF is the annual spilled volume of DWF (m³) under a given scenario, and V_DWF*, V_CSO* are the corresponding values in the baseline scenario (pre-intervention). For further details, see [10].

2.3. Hydropower Plants

2.3.1. Role of Digitalisation

The hydropower sector is highly interconnected with the environment and with our society, and it interacts with the hydrosphere, the biosphere, the lithosphere, the atmosphere and the anthroposphere. Thanks to its flexibility, it allows for the integration of volatile energy sources (wind and solar energy) and can also be hybridized with other energy technologies (e.g., floating photovoltaics and batteries). These are interconnected to each other in the so-called water–energy–food–ecosystem (WEFE) nexus.

Digital solutions can be implemented both for monitoring and enhancing the quality of the surrounding environment (e.g., water inflow and discharge, water temperature and quality, fish habitat, water levels, and stability of slopes), for improving the overall efficiency and supporting the operation and maintenance sector. Digitalisation is required to optimise operation, predict and detect possible future failures, reduce costs and increase resilience against physical and cyber threats. Therefore, digital solutions are emerging as multi-beneficial and sustainable solutions to increase hydropower energy generation, improve operation and mitigate impacts [8,25,26]. In [25,26], reviews of digital technologies were carried out, with a focus on energy generation and technical operation, and application to the EU context (see also Appendix B). In [8], a review of digital solutions for the real-time control of the environmental performance of hydropower plants was presented.

2.3.2. Method

In this study, we used the data reported in previous papers to estimate the economic benefits entailed by digital solutions in terms of efficiency increase, reservoir operation improvement and damage prevention. The Eurostat 2021 data were used as reference of the annual hydropower generation and installed capacity for each MS+UK. The average energy price from hydropower plants (C, EUR/kWh) was taken from the UNIDO report for each MS [27] (when not known, 0.10 EUR/kWh was used as the average price across the EU), and the following equations were implemented for each MS (see

Table 3. Annual analysis and benefits for the hydropower sector.

Country	E (GWh/y)	P (GW)	Eres (GWh/y)	Benefits (Equation (3)) (Million EUR)	Benefits (Equation (4)) (Million EUR)	Benefits (Equation (5)) (Million EUR)
AT	45,353.97	14.75	8918.23	46.49	0.37	45.71
BE	1314.60	1.43	266.90	1.35	0.04	1.37
BG	3320.26	3.13	2492.56	10.62	0.08	39.88
HR	5810.40	2.16	3455.10	5.96	0.05	17.71
CY	0.00	0.00	0.00	0.00	0.00	0.00
CZ	3436.96	2.28	2143.88	3.09	0.06	9.65
DK	17.06	0.01	17.06	0.01	0.00	0.07
EE	30.00	0.00	30.00	0.02	0.00	0.08
FI	15,883.34	3.26	15,883.34	16.28	0.08	81.40
FR	66,532.42	25.49	58,151.93	68.20	0.64	298.03
DE	24,876.00	10.88	975.00	22.39	0.27	4.39
EL	3440.30	3.42	2969.73	3.53	0.09	15.22
HU	244.00	0.06	0.00	0.20	0.00	0.00
IE	1224.22	0.51	932.66	1.25	0.01	4.78
IT	49,495.26	22.59	22,335.67	49.50	0.56	111.68
LV	2603.04	1.59	2.19	2.67	0.04	0.01
LT	1080.10	1.03	300.60	0.43	0.03	0.60
LU	1094.07	1.33	91.60	1.12	0.03	0.47
MT	0.00	0.00	0.00	0.00	0.00	0.00
NL	46.07	0.04	46.07	0.05	0.00	0.24
PL	2936.99	2.39	1876.92	3.01	0.06	9.62
PT	13,632.55	7.20	3306.54	12.95	0.18	15.71
RO	15,701.39	6.31	14,784.21	16.09	0.16	75.77
SK	4799.00	2.52	4517.00	4.92	0.06	23.15
SI	5224.74	1.30	0.00	5.36	0.03	0.00
ES	33,998.00	20.43	28,567.00	34.85	0.51	146.41
SE	72,440.00	16.48	72,290.00	74.25	0.41	370.49
UK	7691.24	4.77	5227.52	6.15	0.12	20.91

):

$$B_{eff}=E·1\%·C$$

(3)

where B_eff is the economic benefit entailed by an increased efficiency of 1% (e.g., better load share on turbines, better blade-opening and draft tube operation, Appendix B) and E is the annual energy generation (kWh).

$$B_{res}=E_{res}·5\%·C$$

(4)

where B_res is the economic benefit entailed by an improved reservoir operation of 5% (e.g., better inflow forecast, spill reduction, Appendix B), and E_res is the annual energy generation (kWh) for reservoir-type hydropower plants (excluding pumped-hydropower plants), from Eurostat Data, 2021.

$$B_s=\frac{25,000 \mathrm{EUR}}{1 \mathrm{GW}} P$$

(5)

where B_s is the economic benefit associated with less shutdown cycles of the power plants and P is the installed power in GW. The proportion implemented in Equation (5) is based on the cost savings over 8 months, due to the prevention of unplanned shutdowns, of 25,000 EUR to 100,000 EUR for a 1000 MW (i.e., 1 GW) plant (Appendix B, [26]). This benefit is highly questionable as it is not generalisable with high certainty. Nevertheless, it is negligible with respect to the others (two orders of magnitude lower).

All the benefits entailed on the environment are hard to quantify and were not assessed here, but a comprehensive review is presented by Quaranta et al. [8]. The UK is included in the results, even though it is not in the EU anymore.

3. Results and Discussion

3.1. EU Assessment

Table 1, Table 2 and Table 3 illustrate the economic benefits of digitalisation in drinking water distribution networks, wastewater systems and hydropower plants, respectively. Results are also shown in Figure 1. When results are aggregated at the European scale, they reach EUR 5.0, 0.14 and 1.7 billion per year, respectively. As a general view, the ratio of benefit to population ranges from 1.1 EUR/person/year (Belgium) to 59.1 EUR/person/year (Sweden), with an overall average EU aggregated value of 13.2 EUR/person/year (including the UK, even though it is not in the EU anymore), although most of the environmental and economic benefits could not be quantified here.

The greatest benefit is associated with the water distribution sector (Table 1): the average MS leakage loss is 23%, which is a great amount of water (6.3 billion m³, EU + UK) that is treated, transported and lost. This amount roughly corresponds to half of the annual saving of urban runoff that could be achieved by greening 35% of the EU impervious surface with a soil 30 cm thick [28]. Italy, France and the UK are the countries which would benefit most (around 1.0 billion EUR per each MS), as they are the MS with the highest amount of leakages. Germany is the country with the highest amount of domestic water consumption (3672 Mm³ per year), but water losses are only 10% (40% in Italy).

The MSs with the greatest CSO volume are Italy (1287 Mm³), UK (1207 Mm³) and France (840 Mm³) and, therefore, they are the countries which would benefit most from the digitalisation in wastewater systems (Table 2). The CSO economic value per m³ ranges between 0.05 and 0.12 EUR/m³, with the highest values in Latvia, Lithuania, and Estonia, due to the highest amount of DWF in the CSO [10]. It should be noted that the shadow prices used in this study were suggested for “chronic” pollution problems and may therefore underestimate the value of mitigating “acute” pollution arising from CSOs, where pollutants are released in relatively small amounts, but concentrated in time, as to disproportionally harm the ecosystems and/or hinder the recreational use or attractiveness of the receiving water bodies. Moreover, the shadow price for micropollutants is very low and unlikely to be representative of the real value of avoiding the release of chemicals of emerging concern present in CSOs [10].

For the hydropower sector, the countries which would benefit most are those with the highest hydropower installed capacity and generation, i.e., Sweden, France, Italy, and Austria, which realize 60% of the overall EU benefit (Table 3). The environmental benefits could be evenly relevant, but difficult to be quantified, e.g., on sediment management and fish migration [8].

3.2. Sensitivity and Accuracy of Results, and Limitations of the Study

The aim of this study is to perform a screening-level assessment to assess the relevance of digitalisation’s benefits at the European scale, to support the policy-making process. In their exploratory intent, the calculations made in this study are referred to hypothetical situations, and, as such, they cannot be validated and the accuracy of results cannot be quantified. Anyway, since the underlying assumptions are supported by several case studies and scientific references, and the input data are official data of European institutions, they can be considered accurate enough for the purpose of this European-scale assessment. Some comments on the accuracy and limitations of the achieved results are discussed below, with the aim of estimating a reasonable range within which results may vary. The discussion is divided into three main topics: input data, assumptions, cost and challenges, and future projections.

3.2.1. Input Data

• WDNs: cost of water, annual domestic water consumption and leakages. These are reported as real data and, therefore, can be considered the best available data to be used in this regional assessment.
• Wastewater systems: CSO data come from Quaranta et al. [10] which should be referred for more details on the accuracy and validation of the data and results. The model implemented in [10] suffers from uncertainties that can be only reduced through a more realistic representation of the catchments, which requires data usually not available at the large scale. Although data in [10], and those used here, represent a first attempt at modelling CSOs at the European scale, the hydrological model has proven to be reasonably realistic against independent evidence and a higher accuracy and finer detail can be arguably achieved only through specific studies at the local scale. The shadow price was assumed constant throughout Europe due to the unavailability of data at the EU scale, and more detailed prices could be used only when carrying out site-specific analyses. Therefore, also in this case, the used data can be considered the best available ones for the purpose of this study.
• Hydropower: data of the European hydropower fleet are official data of European institutions and can be considered accurate.

3.2.2. Assumptions

• WDNs: a total of 30% of leakage reduction was assumed. This value was chosen as the average value after a literature review (references mentioned in the manuscript) and based on the expert judgement of this paper. The value of leakage reduction is certainly very site-specific, but site-specific situations are out of the scope of this large-scale study. Results would change proportionally to this value. In general, this value ranges between 10% and 80%; therefore, excluding the extreme values, it may be stated that leakages may range within a factor of two (i.e., 15–60%), and, therefore, that the estimated results may range within this factor.
• CSOs: the assumptions are described in [10] and were mainly associated with the hydrological model and the CSO reduction. This reduction is about 20% and depends on the CSO volume in the reference scenario; the regression Equation used to estimate V_CSO (CSO volume after digitalisation implementation) was obtained by a meta-analysis of the studies reviewed in [24]. The error of the estimation of CSOs typically ranges between −50% and +50%, with only one case with an error of 100% (i.e., a factor of two). Therefore, also in this case, the results may vary within a factor of two.
• Hydropower: the made assumptions were the increased efficiency of +1% and the additional energy generation of +5% in reservoir-type hydropower plants. The assumed values come from a deep literature review and expert consultation published in [8,26]. The efficiency increase of 1% represents a recurrent value found in this context, ranging between 0.5% and 2%, while the increased generation from reservoir management improvement was set at +5%, although it may reach +10%. Therefore, the results here may also vary within a factor of two. The efficiency increase was assumed independent from the reservoir operation. The efficiency increase is generally due to a better share of load among the turbines and a better control of the electro-mechanical equipment, while better reservoir operation is mainly related to a better use of water and spill reduction.

3.2.3. Transversal Benefits, Costs and Challenges

On average in the EU, a 5% decrease in water distribution system leakage would save 313 million kWh of electricity annually. This is equal to the electricity usage of over 31,000 homes. It would also avoid the emission of approximately 225,000 metric tons of CO₂ [29]. The lost water from distribution systems over the world can meet the demand of 200 million people, and the energy consumption for treating 1 m³ of water is 0.6–12 kWh and varies depending on the source of water [30]. Digitalisation would also increase security of WDNs [31]. In Denmark, the entire water cycle of Aarhus’s wastewater treatment plant became energy neutral thanks to big digitalisation investments. Carbon footprint was also cut down by 35% through the installation of sensors, new variable speed drives and advanced process controls [32].

Digital technologies are subject to security attacks (physical attacks on sensors, cloning, data theft, high dependence to centralized servers). Other challenges are related to the integration of up-to-date advancements in the IT sector on existing and operating stations that currently use obsolete systems. However, data acquisition is not an easy task: for example, in the EU, the number of monitoring points found today in drinking water networks ranges from zero to about five per 100,000 inhabitants which is still extremely low [31]. Operational decision making, integrating lifetime and maintenance planning, as well as real-time inflow forecast with operation at liberalised power markets, are also important challenges, particularly concerning existing hydropower plants. In order to overcome the challenges, a unified operating centre can help water utility integrate and contextualise several types of data from various field infrastructures and different systems (instrumentation, sensors, power metering, telemetry and SCADA, Geographic Information System (GIS), simulation models, workflow, leakage management, and customer information systems). The data collected in the platform could be then used to provide operational insights and make smart decisions [29].

Benefits also entail an improved monitoring of water quality and quantity, which can help improve regulations [33]. Furthermore, the three examples from the water sector considered here are not the only ones to benefit from digitalisation. For instance, the agricultural sector can benefit from digital solutions, e.g., the improvement of the irrigation schedule or satellite monitoring [34,35,36]. Digitalisation can also be used to monitor water quality and quantity of freshwater systems and help prevent unwanted incidents [37]. The water industry (e.g., chemical industry, food and beverage) can also benefit from digitalisation [38].

Digital technologies also come with challenges and costs. It is important to keep in mind that they must not consume more energy than they save. At present, digital technologies account for between 8 and 10% of our energy consumption, and 2 and 4% of our greenhouse gas emissions [39]. Data usage and consumption also come with an environmental cost, which is associated with water and energy consumption in data centres, and to the rare earth material needed to produce the electronics. In 2020, it is estimated that the average European citizen used around 187 GB of data, which increased by 32.4% from 2021. Future projections to 2030 foresee an average increase of 0.8 m³/y per person up to water usage and 171 kWh/y per person due to data usage in the OECD Europe, which corresponds to EUR 19.7 per person per year (this is an overall cost, not only associated with the water sector), assuming an average electricity cost of 0.1 EUR/kWh and an average drinking water cost of 3.3 EUR/m³ in the EU [40]. Additional challenges are also scientific: more data are needed to feed more accurate models to estimate large-scale trends and benefits, e.g., [41]. Some specific limits on the benefits also exist, like unavoidable losses or site-specific constraints (see, e.g., Appendix C).

3.3. Future Projections

While domestic water consumption will remain quite stable in the future (internal JRC estimates), cost per cubic meter of water may increase. In this study, it was not possible to estimate such a trend because ad hoc economic/financial analyses should be performed and further studies could be carried out in this context. Anyway, an increase in water price would mean that the benefit of digitalisation would increase.

If no mitigation and prevention measure is implemented, CSOs will increase in the future due to the increase in population and extreme storm events. Therefore, also in this case, the benefits of digitalisation may be underestimated if future projections are considered. For this motivation the European Commission has recently revised the Urban Wastewater Treatment Directive in order to stimulate MSs to better deal with, and reduce, CSOs.

The energy generation from the European hydropower fleet is expected to increase to 430 TWh in 2050 in the EU27 (it was 374 TWh in 2021 for the EU27, excluding the UK, and 382 TWh including the UK). Digitalisation could contribute to fill this 46 TWh gap (+12.3%) by an increased generation of +1% in all hydropower plants (+3.82 TWh) and +5% in reservoir-type hydropower plants (+12.5 TWh); thus, a bulk increase of +4.3% for the EU. However, it must be noted this analysis is rather simplified, as it assumes that the hydrological, market and energy conditions would remain stable.

The analysis quantified the economic benefits coming from the technological improvement of the analysed water sectors, but did not consider most of the social and environmental benefits, which are hard to quantify. According to Stein et al. (2022) [6], these non-economic benefits include, for example, generating data and insights that can inform the design of more effective interventions in the form of policies and measures, and improve the transparency and efficiency of decision making in the context of integrated water resource management. Benefits also entail an improved monitoring of water quality and quantity, which can help improve regulations [33]. The results are clearly affected by future trends of, e.g., prices, water and energy markets, and climate change. Considering the increase in prices and water scarcity problems in the EU, such digitalisation benefits are expected to increase in the future. As the implemented equations are linear (except the ones that estimate CSOs, taken from [10]), new re-calculations can be carried out following the same methodology herein developed.

4. Conclusions

The green and digital transitions are two interconnected issues that are at the centre of the policy and strategic debate in the European Union. Digital technologies entail several economic, environmental and social benefits. In this contribution, a preliminary and screening-level assessment was carried out to quantify some economic benefits entailed by the application of digitalisation in the water sector, with a focus on water distribution networks, combined sewer systems and hydropower. Other sectors, e.g., the industrial and the agricultural ones, were not here analysed and would require specific studies. In their exploratory intent, the calculations made in this study are referred to hypothetical situations, and as such they cannot be validated. However, the underlying assumptions derive from several case studies and scientific studies.

The benefits are calculated for each EU Member State (MS), and then aggregated at the EU scale. The UK was also included. Benefits were quantified in EUR 5.0, 0.14 and 1.7 billion per year, respectively, excluding environmental and social benefits, which may play a non-negligible role, but which are very hard to estimate in a large-scale study. The MS with the highest monetary benefits are Italy and France (and the UK). The benefit to population ratio ranges from 1.1 EUR/person/year (Belgium) to 59.1 EUR/person/year (Sweden), with an overall average EU aggregated value of 13.2 EUR/person/year (including the UK). The benefits included leakage reduction in WDN, reduction of CSOs and improved hydropower (and reservoir) operation. The associated social and environmental benefits were not quantified by this study; nonetheless, we show that the quantified benefits can cover a large portion of the costs associated with data use.

However, digital solutions also present some challenges. First, their benefits are not well known in quantitative terms and in a systematic way, and, generally, only the technical benefits can be quantified, as performed in this study. The fragmentation of the water market makes the standardized implementation of digital solutions a challenge. In addition, there are inconsistent data management practices that lead to exchange and interoperability issues. Some systems are not always compatible with IoT solutions and services. As in many other application areas, there is a knowledge gap in the water sector regarding digitalisation and cyber security. EU policies are missing a coherent terminology and clear definitions of digitalisation in the water sector. At the same time, they have different targets and different target audiences. As a result, users of water services and even providers of digital services in the water sector often either do not know or do not understand the relevant water policies, while policymakers do not know the real achievable benefits. A more effective communication can strengthen the connection with society (e.g., consumers) while having a positive impact on the environment. Thus, data can make policies more tangible, easier to understand and more widely accepted.

These challenges should foster additional research on this topic, both to support policy-making strategies and technology implementation. Further research should be carried out to estimate the transversal benefits of digitalisation and to highlight how costs and benefits could be affected by a changing world, in particular by climate change, geo-political situations, energy crisis and water scarcity.