# Regional Resilience to Climate-Induced Water Scarcity: Evidence from Seine Aval

Martin Henseler, Helene Maisonnave, Enzo Salvatore

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Martin Henseler, Helene Maisonnave, Enzo Salvatore. Regional Resilience to Climate-Induced Water Scarcity: Evidence from Seine Aval. 2025. hal-05440805

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Regional Resilience to Climate-Induced Water Scarcity:
Evidence from Seine Aval

Martin Henseler<sup>a, c</sup> Helene Maisonnave<sup>b, c *</sup> Enzo Salvatore<sup>a</sup>

<sup>a</sup> Université Rouen Normandie, LERN UR 4702, Rouen, France

<sup>b</sup> Université Le Havre Normandie, EDEHN UR 7263, Le Havre, France

<sup>c</sup> Partnership for Economic Policy (PEP), Nairobi, Kenya

* Corresponding author: helene.maisonnave@univ-lehavre.fr

Acknowledgements

This research was supported by Région Normandie, project MoWat (Modélisation de la gestion intégrée des usages de l'eau – une approche micro-macro), and by the European Union's Horizon Europe research and innovation program, project InnWater (Promoting Social Innovation to Renew Multi-level and Cross-sector Water Governance, Grant No. 101086512).
**Abstract**

This study quantifies the economy-wide impacts of climate-induced groundwater scarcity in the Seine Aval region of Normandy using a regional computable general equilibrium model. Groundwater reductions disproportionately affect water-intensive sectors. Direct output losses are moderate, but indirect effects, including higher water costs, and increased unemployment, amplify social vulnerability. Adaptation strategies targeting demand-side behaviour (household water savings) and supply-side efficiency (network leakage reduction) substantially mitigate negative effects, though neither fully offsets losses. Combined measures and integrated upstream-downstream water management are essential for improving resilience. Results underscore that groundwater scarcity represents both an environmental and socio-economic risk, with implications for sustainable development, employment, and equity in regional planning.

**Keywords:** regional computable general equilibrium, Normandy, water governance, economic modelling, policy scenarios, impact assessment

**JEL Code:** C68, Q25
# Regional Resilience to Climate-Induced Water Scarcity: Evidence from Seine Aval

## 1 Introduction

Global climate change generates heterogeneous regional impacts on water availability, increasing risks of scarcity, conflicts, and economic losses, thereby necessitating sustainable water management and governance (IPCC 1997, 2022; Stern 2006). Water availability and use are strongly shaped by geographical conditions and economic structures, which produce region-specific vulnerabilities to water scarcity, usage conflicts, and flood risks. These differences require tailored regional adaptation strategies, particularly in water resources management. In this context, the European Water Resilience Strategy (EWRS) (European Commission 2025) emphasizes sustainable water governance that accounts for whole-of-society involvement, intersectoral linkages, and regional heterogeneity. The strategy underlines that Member States and their regional and local authorities are best positioned to manage water resources due to their contextual knowledge, as illustrated by initiatives such as Interreg programmes. Achieving EWRS objectives requires reducing water demand across all sectors through efficiency improvements, water reuse, and smart metering technologies. From an analytical perspective, the evaluation of water policies is most effective within regional or river-basin-level computable general equilibrium (CGE) frameworks, as these better capture hydrological specificities, sectoral water demands, and localized economic interactions than national-scale models (European Commission 2025).

The urgency of sustainable water governance has become increasingly evident as climate change-induced water stress now affects a growing number of regions, including those historically considered water-abundant. In France, water-related risks have become systemic and are now perceptible across all territories, intensifying trade-offs between uses, basins, and seasons and elevating water to a central public policy concern (Ministère de la Transition écologique et de la Cohésion des territoires 2023; Bednar-Friedl et al. 2022; Cour des Comptes 2023; Dayon et al. 2018). The exceptionally hot and dry summer of 2022 revealed the magnitude of tensions over water resources and the limits of local adaptive capacity in the face of increasingly frequent and intense climatic extremes (Météo-France 2022; Sorel et al. 2023; Tripathy and Mishra 2023; Bevacqua et al. 2024; Barthélemy et al. 2025). While southern France has traditionally been more drought-prone, northern regions such as the Paris Basin and Artois-Picardie are also highly vulnerable, as variability in chalk aquifer levels conditions the persistence of groundwater droughts (Slimani et al. 2009; El Janyani et al. 2012; Baulon et al. 2022, 2025).

The Seine-Aval region relies heavily on shallow, river-connected alluvial aquifers, which are highly sensitive to groundwater fluctuations and climate-induced changes in river flows (Chabart et al. 1999; BRGM 2017). These pressures are further amplified by upstream abstractions from deeper aquifers (Flipo et al. 2021). As a result, groundwater stress in the Seine-Aval region is driven by both local and upstream water use, highlighting the need for integrated river basin management to ensure water security under changing climatic conditions (Agence de l'eau Seine-Normandie 2022; Sandre 2023).

In this paper, we assess the regional economic impacts of groundwater scarcity and water governance policies aimed at enhancing water resilience to climate change. To this end, we develop a river-basin-level CGE model for the Seine-Aval basin in Normandy, north-western France.

At the European scale, only a limited number of large-scale quantitative studies estimate the economic losses associated with droughts across the European Union (Cammalleri et al. 2020; European Commission 2025). Existing regional multisectoral analyses estimate losses of 1–2% of regional GDP in the most exposed areas (Motta et al. 2025). Water agencies
anticipate that the impacts of climate change will materialize primarily at territorial and sectoral levels. Consequently, they rely on regional climate and hydrological information to support the design of measures for water conservation, enhanced groundwater recharge, and the assessment of risks related to water-use conflicts as key structural objectives for the coming decades (AESN 2023; Andrianarisoa et al. 2025).

A range of regional multidisciplinary research programmes has investigated climatic and hydrological impacts in the Seine basin, particularly in Normandy. Climate scenarios and hydrological models quantify the effects of anthropogenic climate change on flow regimes and hydrological extremes, including floods and low flows (Ducharne et al. 2009). Field measurements and modelling efforts have enhanced the integrated understanding of the Seine system, encompassing its hydrographic network, watershed, and socio-economic interactions (Habets et al. 2011). More recently, coupled hydrological–hydrogeological models have provided detailed spatial analyses of hydrological dynamics, including water and material fluxes and interactions between surface waters, the unsaturated zone, and aquifers (Flipo et al. 2022; Boé et al. 2023). Despite increasingly precise climate and hydrological projections, the literature remains limited and highly uncertain regarding regional economic impacts, particularly in the water sector (Watkiss et al. 2007).

Several studies address water-related research questions at the regional scale using CGE models to analyse economy-wide impacts at the river-basin level. A recent systematic literature review by Henseler et al. (2025), covering 157 journal articles, identifies only 20% of studies conducted at the spatial resolution of a river basin. Moreover, most of these focus on countries facing acute water scarcity. European regions are underrepresented and are largely confined to southern Europe, such as Spain and Portugal, where water stress has been historically prevalent. Studies on European regions not traditionally exposed to water stress are notably absent.

As a highly industrialised region with complex ecosystems, the Seine River basin has already been analysed using economic modelling frameworks. Cordier et al. (2011, 2017) and Pantet (2020) develop input–output models for the Seine estuary that focus on water quality and ecological impacts. These models allow for the visualization of interconnections between water resources and economic uses. However, compared with CGE models, input–output frameworks are limited in their capacity to simulate adaptation policies and behavioural responses to climate change impacts. CGE models have therefore been identified as a suitable methodological approach to analyse climate change impacts in the Seine estuary (Briand 2009).

This paper contributes to the literature by developing a CGE model for the Seine-Aval region to analyse the economy-wide impacts of climate change and potential adaptation policies aimed at improving water resilience. Specifically, we:

(i) design and simulate climate change scenarios to identify region-specific economic impacts of climate-induced water stress;

(ii) assess the effectiveness of water resilience strategies, including reduced water consumption by households and improvements in water distribution networks to reduce leakage; and

(iii) draw conclusions regarding climate change impacts and adaptation measures in the Seine-Aval river basin district.
# 2 Methodology

## 2.1 Data and study region

### 2.1.1 Economy

The Seine-Aval basin is the downstream section of the Seine River and its coastal waterways in Normandy. It is a sub-basin of the larger Seine-Normandie basin, which is home to nearly 18 million inhabitants. Among the six hydrographic basins of metropolitan France, the Seine-Normandie basin is the most heavily impacted by human activities. Its high anthropic pressure results from intensive agriculture, industry, and the development of the Paris metropolitan area, the economic and social centre of France. In this long-settled, deforested, and industrialized basin, human control over water and material fluxes often exceeds natural regulatory mechanisms (Flipo et al. 2021).

Covering roughly 18,700 km² and inhabited by 2.4 million people (~13% of the basin population), the Seine-Aval sub-basin is highly urbanized and industrialized, with concentrated port, logistics, and industrial activities along the Seine corridor. The estuary, extending from the Poses dam to the English Channel between Le Havre and Honfleur, is a heavily engineered system facing challenges in water quality, sediment transport, and ecosystem preservation while simultaneously supporting one of France's major industrial and employment areas (Flipo et al. 2021).

For the macroeconomic representation of Seine-Aval, we use the Social Accounting Matrix (SAM) of the Haute-Normandie region provided by García-Rodríguez et al. (2023, 2025). The SAM represents the economic situation in the base year 2017 and covers the two departments Seine-Maritime and Eure, encompassing the majority of the Seine-Aval region, providing a sufficient approximation as a regional CGE model database. Macroeconomic data show that water-intensive industries contribute approximately 25% of regional value added and represent 24% of total labour demand, highlighting their significance as employers and providers of household income. We extend the SAM by including production factors for ground- and surface water, and by incorporating water and sanitation activities and services.

<figure><img src="image_2.png" /><figcaption>Figure 1: The river basin Seine in blue and Seine-Aval (deep sky blue) in France.</figcaption></figure>

Source: Henseler (2025a)

<caption>Figure 2: The Seine-Aval river basin (deep sky blue) and the region Haute-Normandie comprising the department Seine-Maritime and Eure (delineated in red).</caption>

Source: Henseler (2025a)
### 2.1.2 Hydrology

Economic activities in Seine-Aval, including public drinking water supply, industrial production, and, to a lesser extent, agricultural irrigation, rely heavily on groundwater abstraction. More than 80% of withdrawals originate from aquifers classified as Zones de Répartition des Eaux (ZRE), where long-term imbalances between water availability and demand are officially recognised. These structurally water-deficient systems are subject to reinforced quantitative management, with stricter abstraction rules aimed at preventing quantitative stress and indirectly protecting water quality and ecosystem functioning (Agence de l'eau Seine-Normandie 2022; Sandre 2023; BRGM 2025). The high reliance on ZRE aquifers underscores the region's vulnerability to groundwater stress, particularly during summer periods characterised by high water demand, low recharge, and elevated evapotranspiration.

Shallow alluvial aquifers along the Seine, hydraulically well connected to the river, dominate regional groundwater abstraction due to their high yield and accessibility (Chabart et al. 1999). Recharge in these aquifers during dry periods largely depends on river levels and river-aquifer exchanges (BRGM 2019). Groundwater contributions to river base flow are essential for maintaining river discharge and ecological conditions during low-flow periods (Abhervé et al. 2023). Declining groundwater levels reduce base flow, simultaneously affecting river discharge and aquifer storage.

Hydraulic connectivity links the aquifers in Seine-Aval to deeper, upstream aquifers in the Paris basin, including the Beauce, Champigny, and Albien-Néocomien formations. These aquifers recharge slowly and are located further from the river channel, but they contribute significantly to the Seine's base flow over long timescales. Summer abstractions from upstream aquifers, particularly to meet the demands of the densely populated Paris region, can lower groundwater levels and reduce downstream contributions to base flow (Flipo et al. 2021; Baulon et al. 2025; Texier et al. 2024). Consequently, downstream aquifers in Seine-Aval experience coupled stress from both local and upstream water use.

The mechanisms described above primarily apply to shallow, river-connected aquifers, which account for the majority of abstraction. Groundwater bodies located farther from the river or in deeper formations contribute less to total withdrawals and respond more slowly to hydrological fluctuations. While fluvial aquifers dominate the region's water supply and low-flow dynamics, these observations cannot be generalized to all groundwater bodies in Seine-Aval.

Overall, groundwater vulnerability in Seine-Aval reflects both local abstraction and upstream pressures, underscoring the need for integrated river basin management to ensure long-term water security under changing climatic conditions (Flipo et al. 2021; Agence de l'eau Seine-Normandie 2022; SDAGE Seine-Normandie 2022).

### 2.1.3 Water use

Figure 3 presents raw water sources (left) as ground and surface water, and their use by economic sectors (right). Most withdrawals come from groundwater sources. Piped water production by the water sector constitutes the dominant share of total water use, drawing almost exclusively from groundwater. Industrial water use (e.g., manufacturing) accounts for approximately 20% of total withdrawals, indicating the high relevance of water for these sectors. Agricultural irrigation is marginal, reflecting the reliance of Normandy's climatic conditions and agricultural systems primarily on precipitation rather than artificial irrigation.
<figure><img src="image_3.png" /><figcaption>Figure 3: Water extraction and use in Seine-Aval</figcaption></figure>

Notes: Sankey charts represent 2020 water quantities for illustration; modelling uses 2017 base-year data.

Source: Henseler (2025a), based on BNPE (2024)

### 2.1.4 Extraction cost

To assess the economic impacts of climate change-induced water stress and adaptation measures, we extend the macroeconomic SAM by including water as a production factor and a service consumed by households and industries. Water is differentiated into surface water, groundwater outside ZRE, and groundwater within ZRE. The corresponding monetary flows are introduced into the SAM according to these three sources.

Water quantities are converted to monetary amounts using the withdrawal-fee rates set by the Seine-Normandie Water Agency (10th program, 2013–2018) (Seine-Normandie 2017). Rates (in c€/m³) vary by use (piped supply, irrigation, other industrial uses), water source (surface, groundwater, ZRE), and special cases (e.g., industrial cooling with >99% return). The ZRE tariff applies only to groundwater withdrawals within ZRE; other cases use non-ZRE rates. For public water supply, groundwater outside ZRE is valued at 6 c€/m³, and groundwater within ZRE at 8.2 c€/m³. For irrigation, gravity-fed systems are marginal nationally (~4% of irrigated area in 2020; Scotti & Loubier 2024), so non-gravity rates are applied: 1.8 c€/m³ for surface water, 2.3 c€/m³ for groundwater outside ZRE, and 3.5 c€/m³ for groundwater within ZRE.

For other industrial uses, two scenarios are distinguished based on BNPE-declared use. For cooling with >99% return, a specific schedule of 0.26 c€/m³ for surface water is applied. Otherwise, the general “other economic uses” schedule is used: 1.2 c€/m³ for surface water, 3 c€/m³ for groundwater outside ZRE, and 4.2 c€/m³ for ZRE groundwater.
<table><caption>Table 1: Withdrawal fee rates, 10th program, applicable in 2017, by use and zone (c€/m³).</caption><thead><tr><th rowspan="2"></th><th>Surface water</th><th colspan="2">Ground water</th></tr><tr><th></th><th>Outside ZRE</th><th>Water allocation<br>zone (ZRE)</th></tr></thead><tbody><tr><td>Piped water production</td><td>3.8</td><td>6</td><td>8.2</td></tr><tr><td>Irrigation (non gravity-fed)</td><td>1.8</td><td>2.3</td><td>3.5</td></tr><tr><td>Irrigation (gravity-fed)</td><td>0.14</td><td>0.16</td><td>0.19</td></tr><tr><td>Industrial cooling</td><td>0.26</td><td>0.34</td><td>0.52</td></tr><tr><td>Other economic use</td><td>1.2</td><td>3</td><td>4.2</td></tr></tbody></table>

Source: AESN (2025 a, b)

## 2.2 Model

To analyse the economic impacts of climate change-induced water scarcity and adaptation policies for water resilience, we develop a regional CGE model for the Seine-Aval river basin. The model is based on the comparative static single-country model PEP-1-1 (Decaluwé et al. 2013) and calibrated to the water-extended SAM of the Haute-Normandie region, developed by García-Rodríguez et al. (2023, 2025).

The economy is represented as a small open regional economy, comprising a representative household, a government, the rest of the world, and 12 production sectors, including a water sector supplying piped water for households and other sectors. The water sector and other sectors use ground and surface water as production factors. Figure 4 presents the water-using production activities as a production tree.

A two-level nested production function is specified. Water is introduced as an explicit factor, distinct from labour and capital, and disaggregated between surface water and groundwater, both inside and outside ZRE zones. This structure directly links recharge constraints (through different water-source stocks), the valuation of abstracted volumes via extraction rates, and intersectoral interactions through water withdrawals.

At the first level, output combines a value-added aggregate and an intermediate consumption aggregate using a Leontief function. Value added is then formed as a combination of a water composite and a non-water composite (labour and capital) using a quasi-Leontief CES function:

$$VA_j = B_j^{VA} (\beta_j^{VA} WDC_j^{-\rho_j^{VA}} + (1 - \beta_j^{VA}) NWAT_j^{-\rho_j^{VA}})^{-1/\rho_j^{VA}},$$

where (WDC_j) represents the composite of raw water withdrawals and (NWAT_j) the labour-capital composite. The elasticity of substitution between water and non-water factors is set at a very low value $\sigma_j^{VA} = 0.15$.
<figure><img src="image_4.png" /><figcaption>Figure 4: Production tree of water demanding activities</figcaption></figure>

Notes: symbols in equations: Value Added = VA, Raw Water = WDC, Surface Water = SWDC, Ground Water = GWDC, Not ZRE = GWD, ZRE = ZRE, Primary Factors = NWAT, Capital = KDC, Labour = LDC.

This specification allows water to act as a complementary input in the short-term production system (e.g., irrigated agriculture, piped-water supply, industrial uses) while maintaining numerical solvability. A standard CES structure with higher elasticity would allow stronger substitution, reducing estimated economic costs of scarcity, whereas a pure Leontief function would impose rigid constraints. The quasi-Leontief approach offers a compromise: nearly fixed factor combinations with limited adaptation, reflecting technological rigidity conservatively (Bardazzi et al. 2024).

This approach follows common practice in CGE models applied to water, where water is treated as a complementary input to other production factors. Studies often combine water with other inputs through Leontief nodes or CES functions with low substitution elasticities, either at the top of the production function (Berrittella et al. 2007; van Heerden et al. 2008; Nechifor & Winning 2018) or within specific blocks (Gomez et al. 2004; Solís & Zhu 2015; Faust et al. 2015; Wu et al. 2014; Philip et al. 2014; Luckmann et al. 2014; Henseler 2025b; Sun et al. 2021), or in fixed proportions per unit of output with limited substitution between water and other factors (Vargas et al. 2018; Touitou et al. 2020; Briand et al. 2023).

Within the non-water factor composite, labour and capital are modelled using a standard CES function. For sectors without observed water withdrawals (construction, information and communication, finance, and real estate), the water composite is zero, and value added reduces to a CES between labour and capital:

$$NWAT_j = B_j^{NWAT} (\beta_j^{NWAT} LDC_j^{-\rho_j^{NWAT}} + (1 - \beta_j^{NWAT}) KDC_j^{-\rho_j^{NWAT}})^{-1/\rho_j^{NWAT}},$$

where $LDC_j$ represents the labour composite and $KDC_j$ the capital composite. The elasticity of substitution between these factors is set at $\sigma_j^{NWAT} = 0.8$.

Water withdrawals are modelled as a nested composite consistent with the SAM water account. For each water-withdrawing sector, a first-level CES function distinguishes surface water and groundwater, allowing partial substitution:

$$WDC_j = B_j^{WDC} ([\beta_j^{WDC} SWDC_j^{-\rho_j^{WDC}} + (1 - \beta_j^{WDC}) GWDC_j^{-\rho_j^{WDC}}])^{-1/\rho_j^{WDC}},$$
where $SWDC_j$ represents the surface-water composite and $GWDC_j$ the groundwater. The substitution elasticity is $\sigma_j^{WDC} = 0.2$. In this regional economy, surface water represents a small share relative to groundwater (14.2% for industrial uses, 0.01% for irrigation), without ignoring technical, regulatory, or spatial constraints that limit substitution, particularly in the short term.

A second level, specific to groundwater, divides withdrawals between aquifers inside and outside ZRE zones:

$$GWDC_j = B_j^{GWD} \left[ \sum_{g:GWDO_{g,j}} \beta_{g,j}^{GWD} GWD_{g,j}^{-\rho_j^{GWD}} \right]^{-1/\rho_j^{GWD}},$$

where $GWDC_j$ represents groundwater demand by type. Elasticity between these sources is assumed low $\sigma_j^{WDC} = 0.8$, reflecting the small share of non-ZRE areas and transport-cost constraints for transport between location of withdrawal and use.

In addition to the water extensions, the PEP-1-1 model is augmented with a wage curve to represent the labour market. Following Blanchflower & Oswald (1995), unemployment is modelled with a negative slope between unemployment rates and wages. The unemployment rate is set at 14.8%, according to regional statistics for the reference period.

Macro closure rules define a small country economy with exogenous world prices, fixed factor stocks, and endogenous government spending. The nominal exchange rate is used as the numeraire.

# 3 Scenarios and Results

## 3.1 Scenarios

### 3.1.1 Groundwater Scarcity

Climate change, through anomalous precipitation patterns and increased evaporation, reduces groundwater recharge while simultaneously increasing water demand. The Groundwater Scarcity scenario simulates a reduction in groundwater supply driven by hydro-climatic effects in Seine-Aval and upstream Seine regions, as described in Section 2.1.2. We assume a 30% decrease in groundwater availability to represent climatic impacts on supply.

At the national scale, renewable water resources in France have decreased by approximately 14% over the past fifteen years due to combined effects of climate change and evolving water use patterns (Arambourou et al. 2025). The 30% reduction represents a significant pressure in Seine-Aval, potentially creating water crises and trade-offs among competing uses. Climate projections estimate reductions ranging from -10% to -30%, with substantial uncertainty and increased risk of restrictions when winter recharge is affected downstream (Habets et al. 2013; AESN 2023; Lanini et al. 2019; Kazmierczak et al. 2019).

While this magnitude is roughly twice historical decreases, it aligns with mid- and long-term modelling projections (2040–2060 and 2080–2100), which estimate deficits of -27.6% to -33.6% relative to the 1980–2000 reference period (Ducharme et al. 2009). The Groundwater Scarcity scenario serves as the baseline for analysing economic impacts and estimating the macroeconomic cost of inaction, as well as a reference for the adaptation scenarios. Technically, the reduction is implemented by decreasing the groundwater factor stock by 30%.
### 3.1.2 Water-Saving

Private consumption of piped water represents the largest share of total water demand. Reducing consumption through water-saving behaviour offers substantial potential. Increasing consumer awareness is an intermediate target of the EWRS, aiming for structural reductions in demand and reassessment of consumption priorities across sectors (European Commission 2025, Flipo 2024; Habets 2025; Feuillette et al. 2025). OECD (2025) estimates potential water savings in Paris's metropolitan area at -9.5% to +32.5% through awareness campaigns and 4-20% via water-saving kits. AESN (2025) targets a ~14% reduction in total potable water withdrawals by 2030, including private use. In this scenario, we assume households in Seine-Aval reduce consumption by 10%, below the official target of 14%.

Technically, we implement this reduction by scaling the minimum consumption in the Stone-Geary household demand function (Decaluwé et al. 2013). The costs of awareness campaigns are not included, acknowledging that such measures require public spending to fund information campaigns for increasing consumer's awareness.

### 3.1.3 Network Performance

Across EU member states, network leakage ranges from 8% to 57%, representing significant potential to reduce water extraction. The EWRS encourages member states to reduce leakage through national action plans by 2030 (European Commission 2025). In the Seine-Normandie basin, rehabilitation of drinking water networks typically improves efficiency by 1-3 percentage points within a trajectory targeting a 14% reduction in abstractions (AESN 2023). Local examples show larger gains are possible: between 2010 and 2014, Rouen Normandy Metropolis improved network efficiency by 8.6%, while reducing subscriber consumption and abstractions (AESN 2023). Comparison of water distribution efficiency between Seine-Normandie and Seine-Aval shows that, between 2020 and 2022, Seine-Aval networks experienced 9-10 percentage points higher leakage. We simulate a 5 percentage-point reduction in network losses, closing roughly half the gap (for details see Appendix A.1).

Technically, network performance improvements are applied as a multiplier in the value-added function of the water distribution sector (Chitiga et al. 2016; Estache 2012; Adam & Bevan 2006). To represent the costs for fixing the water pipes, we allocate an amount of 10% of annual regional infrastructure spending. While this may appear high, the static framework simulates costs in a single period; in reality, the network modernisation last multiple years. Thus, the total cost would be annualised. Costs are derived based on estimated pipeline length (for details see Appendix A.2)

<table><caption>Table 2: Overview of the climate change and adaption scenarios</caption><thead><tr><th></th><th>Groundwater scarcity</th><th>Water saving</th><th>Network performance</th></tr></thead><tbody><tr><td>Ground water supply</td><td>-30% decrease of groundwater</td><td>-30% decrease of groundwater</td><td>-30% decrease of groundwater</td></tr><tr><td>Water demand</td><td></td><td>-10% a decrease in minimum consumption),</td><td></td></tr><tr><td>Costs of measure</td><td></td><td></td><td>Spending in reparation of water pipes accounting for 10% of the governmental annual spending for infrastructure</td></tr></tbody></table>
<table><tr><td>Piped water supply</td><td>-5 percentage points reduction of network losses by repairing water pipes</td></tr></table>

## 3.2 Results

Water-intensive sectors, such as drinking water supply, industrial production, and irrigation, exhibit particularly strong dependencies on, and impacts from, water availability. Table 3 and Figures 5–7 present the economic effects of the Groundwater Scarcity scenario and the associated adaptation measures, expressed as percentage changes relative to the reference scenario, which assumes no influence of climate change or policy interventions. The adaptation scenario results thus reflect both the economic consequences of climate change–induced water scarcity and the effects of the implemented adaptation measures.

### 3.2.1 Ground Water Scarcity

Climate-induced groundwater scarcity negatively affects the overall economic performance of the Seine-Aval region. Gross domestic product (GDP) declines by approximately 0.7% (Table 3), primarily due to reduced output in sectors that rely on groundwater as a production input. Although production losses are relatively small across most sectors (Figure 6, blue bars), they generate substantial labour displacement, increasing regional unemployment by more than 5%. Lower production levels reduce the need for government subsidies, which in turn allows for higher investment levels (Table 3).

Sectoral responses are largely determined by dependence on water inputs. Agricultural production in Normandy is predominantly rain-fed, requiring only limited irrigation water. Consequently, irrigation represents a minor share of production costs, and the impact of groundwater scarcity on agricultural output remains below 1%. Reduced groundwater availability is partially offset by increased surface water extraction. Under climate change, however, decreased groundwater recharge and lower river levels may coincide with reduced precipitation, amplifying potential impacts.

Industrial production depends on groundwater, surface water, and piped water. Groundwater scarcity is partially mitigated by increased surface water extraction (+5%). Nevertheless, higher piped water prices raise production costs, resulting in reduced industrial output and lower labour and capital demand. Service sectors also rely on groundwater, surface water, and piped water, though their absolute consumption is small relative to water-intensive sectors. They are nonetheless affected by rising piped water prices. Overall, contraction in industrial activity contributes to labour shedding and reduced wage income, amplifying negative societal impacts.

The water sector is most severely affected, with production declining by approximately 4% and water demand falling by over 25% (Figure 7). While other sectors increase their relative demand for alternative sources, absolute groundwater demand in these sectors is smaller, explaining the larger percentage changes in the water sector. Groundwater prices rise sharply, reflecting higher extraction costs, while surface water prices remain largely unaffected. Surface water partially substitutes for reduced groundwater use, but higher groundwater costs increase overall production expenses.

Water production relies almost exclusively on groundwater, limiting substitution possibilities. The sector increases the use of other production factors, particularly capital, indicating higher costs associated with accessing raw water under scarcity conditions. Higher groundwater extraction costs propagate throughout the economy, leading to increased consumer prices (Table 3) and reduced private consumption. These demand-side effects further reduce GDP.
Although sectoral production declines are generally below 3%, the combination of job losses, reduced household income, and higher consumer prices can exacerbate social inequalities. Secondary effects may therefore drive broader socioeconomic changes, even when direct sectoral impacts appear moderate.

The Seine-Aval region is modelled as a single, country-like economy engaged in international trade with neighbouring regions and the rest of the world. In reality, the region participates in a dense network of interregional economic exchanges. Assuming limited trade and restricted mobility of production factors likely overestimates economic impacts. More fluid exchanges with neighbouring regions would probably mitigate the effects of groundwater scarcity, suggesting that our results represent an upper-bound estimate of potential losses.

<table><caption>Table 3: Impact on macroeconomic indicators (% changes relative to the reference scenario)</caption><thead><tr><th></th><th>Groundwater scarcity</th><th>Water Saving</th><th>Network Performance</th></tr></thead><tbody><tr><td>Gross domestic product</td><td>-0.67</td><td>-0.20</td><td>-0.31</td></tr><tr><td>Investment</td><td>1.31</td><td>0.26</td><td>-3.38</td></tr><tr><td>Governmental spending</td><td>0.00</td><td>0.00</td><td>3.00</td></tr><tr><td>Private consumptions</td><td>-1.17</td><td>-0.32</td><td>-0.52</td></tr><tr><td>Producer price index</td><td>1.14</td><td>0.31</td><td>0.48</td></tr><tr><td>Unemployment</td><td>5.67</td><td>1.54</td><td>2.55</td></tr></tbody></table>

### 3.2.2 Water Saving

Simulating groundwater scarcity in combination with improved water-saving behaviour substantially mitigates negative economic impacts. Under this adaptation scenario, GDP declines by only 0.2% and unemployment increases by only 1.5%. Reductions in sectoral output, consumer prices, and private consumption are markedly smaller than in the scenario without adaptation. A 10% decrease in household water consumption significantly reduces groundwater demand from the water sector. Private piped water use falls by approximately 12%, enabling suppliers to adjust output accordingly. Consequently, groundwater extraction by the water sector declines by around 30% (Figure 7), alleviating pressure on the resource. The resulting increase in groundwater prices as a production input is therefore less pronounced than in the absence of adaptation. In total the negative impacts on sectors, prices, employment and society are milder than in the scenario without water saving.

These results indicate that promoting water-saving behaviour among consumers is an effective strategy to reduce the economic impacts of groundwater scarcity. The simulation does not account for the costs of information or awareness campaigns aimed at encouraging water conservation; including such expenditures as public spending would likely slightly decrease the GDP.

### 3.2.3 Network Performance

The adaptation measure aimed at improving water distribution networks by reducing leakage by five percentage points also mitigates negative economic impacts. Under this scenario, GDP declines by only 0.3%, and unemployment rises by 2.6%. Costs associated with network repairs are modelled as public spending, increasing by 3%. This expenditure is financed through a reallocation of the government budget, resulting in a 3.4% reduction in investment in other public projects, which exerts a modest negative effect on GDP.

Compared to the groundwater scarcity scenario without adaptation, effects on consumer prices, private consumption, and employment are reduced, although they remain less favourable than in the water-saving behaviour scenario. Sectoral production in water-intensive
activities remains broadly comparable to the baseline scarcity scenario. In contrast, output in sectors supporting network repairs, most notably construction, increases by approximately 2%. Groundwater demand declines at a level between that observed in the groundwater scarcity and water-saving scenarios (Figure 7). Social impacts, including higher prices and unemployment, are mitigated relative to the scenario without adaptation.

The simulation assumes that network repair investments are undertaken within a single period, reflecting the comparative-static nature of the CGE model. In practice, achieving a five-percentage-point reduction in leakage would likely require investments spread over several years. A dynamic analysis incorporating annualized expenditures and gradual efficiency improvements would likely yield more favourable outcomes, with smaller GDP losses.

<figure><img src="image_5.png" /><figcaption>Figure 5: Impact on production in percentage deviation from base</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 6: Impact on private consumption in percentage deviation from base</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 7: Impact on ground water demand in percentage deviation from base</figcaption></figure>

Notes: agri = agriculture, oind = other industries, wadi = water production and distribution sector, manu = manufacturing, admi = public services

## 4 Conclusions

The analysis demonstrates that groundwater scarcity in Seine Aval constitutes a multidimensional challenge with both economic and social consequences. Sectoral production is moderately affected, as output can continue under higher water costs; however, indirect effects propagate through labour markets and consumer prices, generating notable social impacts. Unemployment increases and consumption declines, highlighting the potential for elevated poverty and inequality. Water-intensive sectors, particularly piped water supply and industrial production, are most sensitive reduced precipitation could amplify stronger impacts under future scenarios. The water sector itself experiences the largest production decline, reflecting limited substitution possibilities and higher extraction costs.

Adaptation measures provide meaningful mitigation. Household water-saving behaviour, reducing minimum consumption by 10%, lowers groundwater extraction by roughly 30%, easing pressure on resources and moderating price increases. Network leakage reductions of five percentage points similarly limit groundwater demand and social impacts, though implementation requires public investment and temporary reductions in other governmental expenditures. Neither strategy alone fully offsets losses, emphasizing the importance of combined demand- and supply-side interventions, consistent with the European Water Resilience Strategy and regional water plans.
The study also highlights the critical role of integrated water governance. Groundwater stress in Seine Aval is influenced not only by local abstraction but also by upstream river basin management, underscoring the necessity of coordinated policy across spatial scales. Regional CGE modelling proves essential for capturing these interdependencies, linking hydrological constraints to sectoral outputs, prices, and socio-economic outcomes.

Finally, the Water-CGE framework provides a robust foundation for policy analysis and future research. While the current model is comparative-static and represents an upper-bound estimate of economic losses, it demonstrates the capacity of regional CGE approaches to inform water governance, stakeholder engagement, and climate adaptation planning. Future refinements could incorporate dynamic investment pathways, interregional trade linkages, and uncertainty analysis to enhance realism. Overall, this study illustrates that addressing groundwater scarcity requires coordinated, multi-sectoral strategies that integrate economic, social, and environmental objectives to enhance regional resilience under climate change in line with the “whole-of-society” approach promoted by the European Water Resilience Strategy (EWRS).
# 5 References

Abhervé R, et al. (2023) Calibration of groundwater seepage against stream network distribution to assess catchment hydraulic properties. *Hydrology and Earth System Sciences* 27:3221–3239. https://doi.org/10.5194/hess-27-3221-2023

Adam CS, Bevan DL (2006) Aid and the supply side: public investment, export performance, and Dutch disease in low-income countries. *World Bank Economic Review* 20(2):261–290

AESN (2018) 10ème Programme (2013 – 2018). Version : 12 janvier 2018 (délibération n° CA 18-11). 9. 89. Agence de l’Eau Seine-Normandie (AESN). Tableau 1 Taux de redevances par année d’activité de 2013 à 2018. URL: https://economiev2.eaufrance.fr/sites/default/files/2020-10/doc383-10ander_2018.pdf [2025-12-31]

AESN (2023) *Stratégie d'adaptation au changement climatique sur le bassin Seine-Normandie*. Agence de l'Eau Seine-Normandie (AESN), Paris

AESN (2025). *Quelle répartition de la consommation d'eau potable entre les différents usagers sur le bassin Seine-Normandie*. Agence de l'Eau Seine-Normandie (AESN), 85 pp. Available at: https://economiev2.eaufrance.fr/sites/default/files/2025-07/Repartition_consommation_eau_potable_s SUR_bassin_SN.pdf (Accessed: 24 Dec 2025). (Économie Eaufrance)

AESN (2022) *Schéma directeur d'aménagement et de gestion des eaux (SDAGE) 2022-2027 – Bassin Seine-Normandie*. Agence de l'Eau Seine-Normandie (AESN), Paris

Andrianarisoa K, et al (2025) *Avis du conseil scientifique du comité de bassin Seine-Normandie sur la sobriété en eau*. Comité de bassin Seine-Normandie

Arambourou H, Ferrière S (2025) Quelle évolution de la demande en eau d'ici 2050 ? *La note d'analyse de France Stratégie* 148:1–15

Arambourou, H., Ferrière, S. & Gaillot, A., 2025. Confrontation entre la ressource en eau et la demande à l'horizon 2050., s.l.: s.n.

Bardazzi, E., Standardi, G., Bosello, F. & Key Hernández, R. E., 2024. Toward the full implementation of the water-energy-food nexus in computable general equilibrium modelling: methods and macroeconomic implications.. *Economic Systems Research*, 36(3), pp. 422-450.

Barthélemy, S., Bonan, B., Tomas-Burguera, M., Grandjean, G., Bernardie, S., Naulin, J. P., et al. (2025). Analysis of past and future droughts causing clay shrinkage in France. *Hydrology and Earth System Sciences*, 29(11), 2321-2337.

Baulon L, et al (2022) Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin. *Hydrology and Earth System Sciences* 26:2829–2854

Baulon L, et al (2025) Large-scale climate drivers of groundwater level variations in northern France over the last century. *Journal of Hydrology* 656:132937

Bednar-Friedl B, et al (2022) Europe. In: *Climate Change 2022: Impacts, Adaptation and Vulnerability*. Cambridge University Press, Cambridge, pp 1817–1927

Berrittella M, et al (2007) The economic impact of restricted water supply: a computable general equilibrium analysis. *Water Research* 41(8):1799–1813

Bevacqua E, et al (2024) Direct and lagged climate change effects intensified the 2022 European drought. *Nature Geoscience* 17(11):1100–1107

Blanchflower DG, Oswald AJ (1995) An introduction to the wage curve. *Journal of Economic Perspectives* 9(3):153–167

BNPE (2024) *Banque nationale des prélèvements en eau*. Eaufrance

Boé J, Gallois N, Deman J, Flipo N (2023) Impacts du changement climatique sur le fonctionnement hydrologique du bassin de la Seine. Programme Interdisciplinaire de Recherche sur l’Eau et l’Environnement du Bassin de la Seine
BRGM (2017) *Interactions entre aquifères régionaux et les systèmes hydrauliques de l'estuaire de la Seine*. BRGM Report RP-67022-FR, Orléans

BRGM (2019) *Système d'information pour la gestion des eaux souterraines (SIGES)*. BRGM
Report RP-68627-FR, Orléans

BRGM (2025) *Groundwater abstraction, drought and flooding: frequently asked questions*.
Bureau de Recherches Géologiques et Minières, Orléans

Briand A (2009) *Impacts économiques du changement climatique dans l'estuaire de la Seine*.
Doctoral dissertation, Seine-Aval

Briand A, et al (2023) Assessing the macroeconomic effects of water scarcity in South Africa using a CGE model. *Environmental Modeling & Assessment* 28(2):259-272

Cammalleri C, et al (2020) *Global warming and drought impacts in the EU*. Publications Office of the European Union, Luxembourg

Chabart M, et al (1999) Groundwater circulations between alluvial aquifer and underlying Senonian chalk in the Seine valley. *Physics and Chemistry of the Earth* 24(1-2):151-154

Chitiga M, Mabugu R, Maisonnave H (2016) Analysing job creation effects of scaling up infrastructure spending in South Africa. *Development Southern Africa* 33(2):186-202

Cordier M, Pérez Agúndez JA, O’Connor M, Rochette S, Hecq W (2011) Quantification of interdependencies between economic systems and ecosystem services: an input–output model applied to the Seine estuary. *Ecological Economics* 70(9):1660–1671

Cordier M, Uehara T, Weih J, Hamaide B (2017) An input–output economic model integrated within a system dynamics ecological model. *Ecological Economics* 140:46–57

Cour des Comptes (2023) *La gestion quantitative de l'eau en période de changement climatique*. Cour des Comptes, Paris

Dayon G, Boé J, Martin E, Gailhard J (2018) Impacts of climate change on the hydrological cycle over France. *Comptes Rendus Géoscience* 350(4):141-153

Decaluwé B, Lemelin A, Robichaud V, Maisonnave H (2013) *PEP-1-1: The PEP standard single-country static CGE model*. Université Laval

Ducharne A, et al (2009) *Projet RExHySS – Description des modèles hydrologiques*. CNRS, Paris

El Janyani S, et al (2012) Hydrological responses of the chalk aquifer to the regional climatic signal. *Journal of Hydrology* 464:485-493

Estache, A et al. 2012. The Impact of Infrastructure Spending in Sub-Saharan Africa: A CGE Modeling Approach. Economics Research International, 2012(1), p. 875287.

European Commission (2025) *European Water Resilience Strategy*. COM(2025) 280 final, Brussels

Faust AK, Gonseth C, Vielle M (2015) The economic impact of climate-driven changes in water availability in Switzerland. *Water Policy* 17(5):848–864

Feuillette S, et al (2025) *Quelle sobriété en eau?*. Paris

Flipo A (2024) Ce que la sobriété veut dire: pratiques et représentations de la sobriété en Europe. *Lien social et politiques* 93:379-398

Flipo N, Gallois N, Labarthe B, et al (2021) Pluri-annual water budget on the Seine basin. In: Labadie P, Lestel L (eds) *The Seine River Basin*. Springer, Cham, pp 59–89

Flipo N, Gallois N, Schuite J (2022) Regional coupled surface–subsurface hydrological model fitting. *Geoscientific Model Development*

García-Rodríguez A, et al (2023) A NUTS-2 EU interregional system of social accounting matrices for 2017. *JRC Working Papers on Territorial Modelling and Analysis* 01/2023

García-Rodríguez A, et al (2025) Constructing interregional social accounting matrices for the EU. *Spatial Economic Analysis*
Gomez CM, Tirado D, Rey-Maquieira J (2004) Water exchanges versus water works: insights from a CGE model for the Balearic Islands. *Water Resources Research* 40(10)

Habets F (2025) Combattre le manque d'eau: la sobriété obligée et impensée. *Raison présente* 235(3):36-45

Habets F, et al (2011) *Impact du changement climatique sur les ressources en eau du bassin de la Seine*. Paris

Habets F, et al (2013) Impact of climate change on the hydrogeology of two basins in northern France. *Climatic Change* 121(4):771-785

Henseler M (2025a) *Physical water accounts for WEFE nexus CGE models*. EU Horizon InnWater Project

Henseler M (2025b) *Modelling cross-sectoral interactions with water at river basin level*. EU Horizon InnWater Project

Henseler M, Maisonnave H, Salvatore E (2025) How water flows through CGE models: a systematic review. *HAL Working Paper*.

IPCC (1997) *The regional impacts of climate change*. Cambridge University Press, Cambridge

IPCC (2022) *Climate change 2022: impacts, adaptation and vulnerability*. Cambridge University Press, Cambridge

Kazmierczak L, Laignel B, Charrier F (2019) *La ressource en eau et le risque inondation dans la Métropole Rouen Normandie*. Métropole Rouen Normandie

Lanini S, et al (2019) Recharge des aquifères à l'échelle de la France. *Colloque SHF-UNESCO*

Luckmann, J. et al., 2014. An integrated economic model of multiple types and uses of water. *Water Resources Research*, 50(5), pp. 3875-3892.

Météo-France (2022) *Bilan climatique de l'été 2022*. Météo-France, Paris

Ministère de la Transition écologique et de la Cohésion des territoires (2023) *Plan d'action pour une gestion résiliente et concertée de l'eau*. Paris

Motta C, et al (2025) Assessing the economic impact of droughts in Europe at regional scale. *Journal of Hydrology: Regional Studies* 55:102296

Nechifor V, Winning M (2018) Global economic and food security impacts of demand-driven water scarcity. *Water* 10(10):1442

OECD (2025) *Adapting the Paris metropolitan area to a water-scarce future*. OECD Publishing, Paris

Pantet J (2020) *Utilisation de l'estuaire de la Seine par l'économie régionale*. Doctoral dissertation, Normandie Université

Philip JM, Sánchez-Chóliz J, Sarasa C (2014) Water and production reallocation in the Spanish agri-food system. *Economic Systems Research* 26(2):278-299

Sandre (2023) *Zones de répartition des eaux (ZRE): définitions et usages*. Eaufrance

Scotti A, Loubier S (2024) Vers une augmentation structurelle de l'irrigation ? *Sciences Eaux & Territoires* 45(2):74-83

Slimani S, et al (2009) Combined climatic and geological forcings on piezometric levels. *Hydrogeology Journal* 17(8):1823-1832

Solís AF, Zhu X (2015) Water markets: insights from an applied CGE model. *Water Resources Management* 29(12):4335-4356

Sorel M, Mittelberger S, Berne C, Ribes A (2023) Bilan climatique de l'année 2022. *La Météorologie* 120:2-4

Stern N (2006) *The economics of climate change: the Stern review*. Cambridge University Press, Cambridge

Sun, Y., Zhi, Y. & Zhao, Y., 2021. Indirect effects of carbon taxes on water conservation: A water footprint analysis for China. *Journal of Environmental Management*, Band 279, p. 111747.
Texier J, et al (2024) Groundwater-surface water exchanges in an alluvial plain subjected to pumping. *Journal of Hydrology: Regional Studies* 56:101995

Touitou M, Laib Y, Boudeghdegh A (2020) Management of water resources under climatic shocks. *Environmental & Socio-economic Studies* 8(1):48–55

Tripathy, K. P. & Mishra, A. K., 2023. How unusual is the 2022 European compound drought and heatwave event?.. Geophysical Research Letters, 50(15), p. e2023GL105453..

van Heerden, J. H., Blignaut, J. & Horridge, M., 2008. Integrated water and economic modelling of the impacts of water market instruments on the South African economy. *Ecological economics*, 66(1), pp. 105-116.

Vargas R, et al (2018) Climate risk and food availability in Guatemala. *Environment and Development Economics* 23(5):558–579

Watkiss P, et al (2007) *Climate change: the cost of inaction and the cost of adaptation.*
London

Wu F, et al (2014) Evaluating impacts of industrial transformation on water consumption.
*Sustainability* 6(11):8283–8296
# 6 Appendix

## A.1.1 Network performance in Seine-Normandie and Seine-Aval

We benchmark network performance by comparing the Seine-Aval sub-region with the overall Seine-Normandie region, using Datagouv (2025) data. Seine-Aval is represented by the combined networks of Seine-Maritime and Eure. Table A.1-1 shows that the gap in network performance has widened since 2017. Between 2020 and 2022, Seine-Normandie improved by 10 percentage points, reaching 88%, while Seine-Aval increased by only 4 percentage points to 78%, resulting in a 10-percentage-point gap. This indicates that network improvement measures have been more effective at the regional level than in Seine-Aval. A 5-percentage-point improvement in Seine-Aval is considered a plausible target to reduce this gap.

<table><caption>Table A.1-1: Comparison of net performance between Seine-Normandie and Seine-Aval</caption><thead><tr><th></th><th>2017</th><th>2018</th><th>2019</th><th>2020</th><th>2021</th><th>2022</th></tr></thead><tbody><tr><td>Seine-Normandie</td><td>77.5</td><td>84.2</td><td>84.4</td><td>87.4</td><td>86.8</td><td>87.5</td></tr><tr><td>Seine-Aval</td><td>74.2</td><td>79.7</td><td>78.0</td><td>78.0</td><td>77.7</td><td>78.1</td></tr><tr><td>Difference</td><td>3.3</td><td>4.5</td><td>6.4</td><td>9.4</td><td>9.1</td><td>9.5</td></tr></tbody></table>

Source: Own computation based on data by Datagouv (2025)

## A.1.2 References

Datagouv (2025) Réseaux de distribution en eau potable : performances. https://www.data.gouv.fr/datasets/reseaux-de-distribution-en-eau-potable-performances#/resources/2b1bc663-7d7e-4d10-a09a-8dc65b2ebf77

## A.2 Network modernisation cost

To obtain an order-of-magnitude estimate of the cost of pipe renewal in the Seine-Aval basin, we apply an approximate approach consistent with regional statistical data and engineering-oriented planning methods documented in the literature (e.g. Xu et al., 2014; Ahopelto and Vahala, 2020; Ghobadian and Mohammadi, 2023). This approximation is considered suitable for application in a CGE modelling context, where the magnitude of effects is more relevant than precise engineering estimates. Nevertheless, the resulting values are subject to uncertainty when compared with detailed engineering cost assessments.

We compute the total cost of network modernisation ($C_{mod}$) as the product of the leakage-relevant network length ($L_{leak}$), the targeted reduction in leakage expressed in percentage points ($\Delta pp_{leak}$), the pipe renewal intensity ($r_{pipe}$), and the per-unit renewal cost ($C_{ren}$):

$$C_{mod} = L_{leak} \times \Delta pp_{leak} \times r_{pipe} \times C_{ren}$$

and

$$L_{leak} = POP_{netw} \times length_{leak}$$

and

$$length_{leak} = length_{pop} \times prop_{leak}$$

with:

$C_{mod}$ : Total modernisation cost in [€],

$L_{leak}$ : Leakage relevant network length in [m],

$\Delta pp_{leak}$ : Target leakage reduction in [percentage points],
$r_{pipe}$ : Renewal intensity, defined as the share of leakage-relevant network renewed per percentage point of leakage reduction [percent] ,

$C_{ren}$ : Per unit renewal cost in [€ / m] ,

$POP_{netw}$: Population served by the network [inhabitants] ,

$length_{leak}$: Average network length per inhabitants considered as leakage relevant in [m / inhabitant] ,

$length_{pop}$ : Average network length per inhabitant in [m / inhabitant] ,

*prop*<sub>leak</sub> : Proportion of the network considered as leakage relevant in [percent] .

A.2.1 Average network length (*length*<sub>pop</sub>)

We estimate the average network length per inhabitant for France using national data on
total drinking water distribution length and population served in 2017. In the absence of
readily available region-specific data (e.g. for Normandy or Seine-Maritime), national
statistics are used as a proxy.

In 2017, the total length of drinking water distribution pipes in France was approximately
875,000 km, while the legal population amounted to around 67 million inhabitants (INSEE,
2019a). This corresponds to an average of roughly 13 m of utility-managed drinking water
network per inhabitant.

This estimate is consistent with national statistics reported by OFB/INRAE and the
SISPEA database (OFB/INRAE, 2021; Eaufrance, 2021). However, the value should be
regarded as a lower bound, as some private or auxiliary components may not be fully captured
in official statistics. To address potential underestimation, we consider alternative
assumptions of 18 m and 21 m per inhabitant, corresponding to cases in which the reported
network length represents only 70% or 60% of the actual network.

### A.2.2 Average leakage relevant network length (*length*<sub>leak</sub> & *prop*<sub>leak</sub>)

The total network length is assumed to be distributed across five main components commonly identified in European drinking water systems: transmission mains, primary and secondary distribution mains, small-diameter tertiary mains, service connections, and private pipes (Table A.2-1). Empirical evidence indicates that service connections exhibit a particularly high leakage propensity due to their high joint density, variable materials, and exposure to external stresses.

Based on mid-range estimates reported in the literature, we define 30% of the total network length as leakage-relevant proportion for modelling purposes, consistent with planning-oriented studies and operational guidelines (Lambert and Hirner, 2000; European Commission, 2015; Serafeim et al., 2024). Applying this proportion (*prop*<sub>leak</sub>) to the baseline value of 13 m per inhabitant yields an average leakage-relevant length (*length*<sub>leak</sub>) of approximately 4 m per inhabitant. Under the alternative assumptions of 18 m and 21 m per inhabitant, the corresponding leakage-relevant lengths amount to approximately 5 m and 6 m per inhabitant, respectively.
<table><caption>Table A.2-1: Net-work components and leakage occurrence</caption><thead><tr><td>Component</td><td>Typical Share of Pipe Length</td><td>Leakage Occurrence Tendency</td><td>Description &amp; Leakage Risk Drivers</td><td>Responsible Institution</td><td>Source, references for</td></tr></thead><tbody><tr><td>Transmission mains</td><td>&lt;5–10 %</td><td>Low frequency / moderate volume</td><td>Large-diameter pipelines connecting treatment plants to distribution zones. Few joints, long pipes → rare leaks but high consequence.</td><td>Water utility (public or regional operator) or large municipal entity; often specialized maintenance teams</td><td>Pilcher et al (2008) for water models and water loss manuals</td></tr><tr><td>Primary &amp; secondary distribution mains</td><td>30–40 %</td><td>Moderate-high</td><td>Main pipes distributing water inside zones before reaching local networks. Intermediate diameters → more joints/fittings → moderate leakage risk.</td><td>Local water utility / municipal operator</td><td>European Union (2015) for EU case studies</td></tr><tr><td>Small-diameter tertiary mains</td><td>15–25 %</td><td>High</td><td>Street-level pipes feeding small clusters of consumers. Many joints, elbows, pressure changes → frequent leaks.</td><td>Local water utility / municipal operator; sometimes decentralized maintenance teams</td><td>European Union (2015) for case studies showing higher leak frequency in tertiary main</td></tr><tr><td>Service connections (branchements)</td><td>25–40 %</td><td>Very high</td><td>Pipes connecting mains to customer meters/curb stops. High joint density, variable materials, external stresses.</td><td>Shared responsibility: Utility responsible up to meter; customer / property owner responsible beyond meter (in some countries, the utility may cover private branch repairs if under contract or law)</td><td>Serafeim et al. (2024)</td></tr><tr><td>Private pipes (post-meter)</td><td>0–15 % (often excluded)</td><td>Variable — not utility-managed</td><td>Pipes beyond the utility meter, e.g., household plumbing. Not counted in national statistics, variable quality.</td><td>Property owner / private entity</td><td>Water system component definitions; generally excluded from utility statistics<br/>National Academies of Sciences, Engineering, and Medicine. 2006.</td></tr></tbody></table>
<table><caption>Table A.2-2: Overview information and data</caption><thead><tr><td>Source</td><td>Data information</td></tr></thead><tbody><tr><td>OFB / INRAE (2021)</td><td>National network length data for France, total length and breakdown by component.</td></tr><tr><td>Eaufrance (2021).</td><td>National network length data for France, total length and breakdown by component.</td></tr><tr><td>Lambert & Hirner (2000)</td><td>Component-level breakdown of leakage risk (mains, service connections), justifying why certain components are more leakage-prone.</td></tr><tr><td>European Commission (2016).</td><td>Details on distribution network types, leakage risk by component, and operational responsibility in European utilities.</td></tr><tr><td>Serafeim et al. (2024)</td><td>Data on relative leakage contributions of mains vs service connections and confirms high-risk components.</td></tr><tr><td>Pilcher et al (2008)</td><td>General descriptions of network components, responsibilities, and typical lengths.</td></tr><tr><td>National Academies Press (2006)</td><td>Definitions of private pipes vs utility-managed components, including responsibility breakdown.</td></tr></tbody></table>

### A.2.3 Total leakage-relevant network length in Seine-Aval ($L_{leak}$)

To estimate the total leakage-relevant network length in the Seine-Aval study region, we use population data for the departments of Eure and Seine-Maritime, which together correspond to the former Haute-Normandie region. This area is considered a reasonable proxy for Seine-Aval. In 2017, Eure and Seine-Maritime had populations of 601,843 and 1,254,378 inhabitants, respectively (INSEE, 2019b), yielding a total of 1,856,221 inhabitants.

Multiplying the population by the leakage-relevant network length per inhabitant results in a total leakage-relevant network length of approximately 7,425 km under the baseline assumption. Under the alternative assumptions, the corresponding values amount to roughly 9,281 km and 11,137 km.

### A.2.4 Per-unit renewal costs ($C_{ren}$)

Observed empirical data on pipe renewal costs in France indicate values ranging from 126 to 435 €/m, depending on network characteristics and settlement structure (Agence de l'eau Rhône-Méditerranée-Corse, 2017). These figures are backward-looking and reflect historical investment conditions.

For scenario development, we therefore adopt conservative planning benchmarks of 800, 1,000 and 1,200 €/m to represent low-, medium- and high-complexity renewal contexts (Table A.2-3). These values are intended to capture forward-looking uncertainty, increasing technical complexity, and risk associated with future renewal programmes. Adopting planning benchmarks above historically observed unit costs is consistent with audit findings highlighting systematic underinvestment in drinking water infrastructure and the need for higher annual renewal expenditure (Cour des comptes, 2015).
<table><caption>Table A.2-3: Unit costs for drinking water pipe renewal in France: observed data and planning benchmarks</caption><thead><tr><td>Category</td><td>Statistic / Scenario</td><td>Unit cost (€ / m, excl. VAT)</td><td>Data</td><td>Source</td></tr></thead><tbody><tr><td rowspan="2">Observed empirical costs</td><td>Rural</td><td>126-224 €/m</td><td>Empirical</td><td>Agence de l'eau RMC (2017), p. 5</td></tr><tr><td>Urban</td><td>162-435 €/m</td><td>Empirical</td><td>Agence de l'eau RMC (2017), p. 5</td></tr><tr><td rowspan="3">Derived planning benchmarks</td><td>Lower-bound planning cost</td><td>800 €/m</td><td>Planning</td><td>Derived</td></tr><tr><td>Mid (reference) planning cost</td><td>1,000 €/m</td><td>Planning</td><td>Derived</td></tr><tr><td>Upper-bound planning cost</td><td>1,200 €/m</td><td>Planning</td><td>Derived</td></tr></tbody></table>

### A.2.5 Renewal intensity ($r_{pipe}$)

The renewal intensity parameter ($r_{pipe}$) links the targeted reduction in leakage to the extent of structural intervention required in the network. It represents the share of leakage-relevant pipe length that must be structurally repaired or renewed to achieve a one percentage point reduction in leakage. Importantly, $r_{pipe}$ does not describe the share of the total network renewed, but rather the intensity of structural intervention per unit of leakage reduction.

Leakage reduction can be achieved through a combination of operational measures, such as pressure management and leak detection, and structural measures, such as pipe repair or replacement. Empirical studies show that leakage responds to operating pressure according to a power-law relationship, with reported pressure-leakage elasticities typically ranging between 0.5 and 1.5 depending on pipe materials and network conditions (Thornton, 2004; Abdel-Magid et al., 2017; Mosetlhe et al., 2021). As low-cost operational measures are progressively exhausted, additional leakage reductions increasingly require structural interventions.

In the modelling framework, this relationship is captured in reduced form by $r_{pipe}$. For the scenario analysis, $r_{pipe}$ is set to 0.01, implying that a one percentage point reduction in leakage requires structural repair or renewal of 1% of the leakage-relevant network. This value should be interpreted as a planning-level approximation rather than a physical law. The parameter is held constant across cost scenarios in order to isolate the effect of alternative unit-cost assumptions.

### A.2.6 Total modernisation cost ($C_{mod}$)

Total modernisation costs are computed using the equation introduced above. The target leakage reduction is set to 5 percentage points across all scenarios. Unit renewal costs, average network lengths, and leakage-relevant shares are varied to reflect uncertainty regarding network characteristics and potential underestimation of total network length.

The resulting cost estimates range from approximately €297 million to €668 million (Table A.2-4). For the simulations, we use the median value of around €445 million and adopt a rounded investment level of €450 million as an order-of-magnitude estimate of the cost required to reduce leakage by five percentage points in the Seine-Aval region.
<table><caption>Table A.2-4: Estimated pipe renewal costs in Seine-Aval for different ranges</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Unit</th><th colspan="3">No underestimation of total network length</th><th colspan="3">Underestimation of total network length by 30%</th><th colspan="3">Underestimation of total network length by 40%</th></tr><tr><th>low</th><th>mid</th><th>high</th><th>low</th><th>mid</th><th>high</th><th>low</th><th>mid</th><th>high</th></tr></thead><tbody><tr><td>Average network length per inhabitants (length<sub>pop</sub>)</td><td>[m / inhabitant]</td><td>13</td><td>13</td><td>13</td><td>18</td><td>18</td><td>18</td><td>21</td><td>21</td><td>21</td></tr><tr><td>Average leakage relevant network length (length<sub>leak</sub>)</td><td>[m / inhabitant]</td><td>4</td><td>4</td><td>4</td><td>5</td><td>5</td><td>5</td><td>6</td><td>6</td><td>6</td></tr><tr><td>Population served (POP<sub>netw</sub>)</td><td>[millions]</td><td>1.8</td><td>1.8</td><td>1.8</td><td>1.8</td><td>1.8</td><td>1.8</td><td>1.8</td><td>1.8</td><td>1.8</td></tr><tr><td>Per unit renewal cost (C<sub>ren</sub>)</td><td>[€ / m]</td><td>800</td><td>1000</td><td>1200</td><td>800</td><td>1000</td><td>1200</td><td>800</td><td>1000</td><td>1200</td></tr><tr><td>Pipe renewal intensity (r<sub>pipe</sub>)</td><td>[index]</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td></tr><tr><td>Target leakage reduction (Δpp<sub>leak</sub>)</td><td>[pp]</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td></tr><tr><td>Total modernisation cost (C<sub>mod</sub>)</td><td>[million €]</td><td>297</td><td>371</td><td>445</td><td>371</td><td>464</td><td>557</td><td>445</td><td>557</td><td>668</td></tr></tbody></table>

### A.2.7 Plausibility check

To assess the plausibility of this estimate, we benchmark the €450 million investment against the annual budget of the Agence de l'Eau Seine-Normandie (AESN). Under the 12th intervention programme "Eau, climat & biodiversité" (2025-2030), AESN allocates approximately €610 million per year to measures related to water-use efficiency, drinking water protection, and infrastructure upgrading across the entire Seine-Normandie basin (AESN, 2025).

Relative to this basin-wide envelope, an investment of €450 million concentrated in Seine-Aval—one of six sub-basins—would represent a substantial share of annual expenditure. While such a cost would not be compatible with a single-year budget, it would correspond to
roughly 15% of the programme's five-year funding envelope. This comparison suggests that the estimated investment is large but plausible when considered as part of a multi-year intervention programme.

### A.2.8 References

Abdel-Magid IM, El-Sayed A, El-Ghandour M (2017) Pressure control for minimizing leakage in water distribution systems. *Alexandria Engineering Journal* 56(4):601–612.
https://doi.org/10.1016/j.aej.2017.05.001

Agence de l'eau Rhône-Méditerranée-Corse (2017) *Renouvellement et réduction des fuites des canalisations d'eau potable : étude des coûts*. Observatoire des coûts – Eau potable, Lyon.

Agence de l'Eau Seine-Normandie (AESN) (2022) *SDAGE 2022-2027 du bassin de la Seine et des cours d'eau côtiers normands*. Agence de l'Eau Seine-Normandie, Paris.
https://www.eau-seine-normandie.fr/domaines-d-action/sdage

Agence de l'Eau Seine-Normandie (AESN) (2025) *Plaquette de présentation du 12e programme*. Agence de l'Eau Seine-Normandie, Paris. https://www.eau-seine-normandie.fr/actualites/plaquette-presentation-12e-programme

Ahopelto S, Vahala R (2020) Cost-benefit analysis of leakage reduction methods in water supply networks. *Water* 12:195. https://doi.org/10.3390/w12010195

Cour des comptes (2015) *Les réseaux d'eau potable : des progrès à confirmer*. Rapport public annuel, Cour des comptes, Paris.

Eaufrance (2021) Longueur de conduites du réseau de distribution d'eau potable en 2017. https://www.eaufrance.fr/chiffres-cles/longueur-de-conduites-du-reseau-de-distribution-deau-potable-en-2017

European Commission (2015) *Good practices on leakage management*. WFD CIS Working Group PoM. Publications Office of the European Union, Luxembourg.
https://doi.org/10.2779/102151

Ghobadian R, Mohammadi K (2023) Optimal design and cost analysis of water distribution networks based on pressure-dependent leakage using NSGA-II. *Applied Water Science* 13:92. https://doi.org/10.1007/s13201-023-01856-0

INSEE (2019a) *Populations légales 2017*. Institut national de la statistique et des études économiques, Paris.

INSEE (2019b) *Populations légales 2017 – Départements de Seine-Maritime et de l’Eure*. Institut national de la statistique et des études économiques, Paris.

Lambert A, Hirner W (2000) *Losses from water supply systems: standard terminology and recommended performance measures*. IWA Blue Pages, International Water Association.

Moselthe TC, Hamam Y, Du S (2021) Appraising the impact of pressure control on leakage flow in water distribution networks. *Water* 13(19):2617.
https://doi.org/10.3390/w13192617

National Academies of Sciences, Engineering, and Medicine (2006) *Drinking water distribution systems: assessing and reducing risks*. National Academies Press, Washington, DC. https://doi.org/10.17226/10954

OFB, INRAE (2021) *Mieux connaître le réseau d'eau potable français – Synthèses Eaufrance*. Office français de la biodiversité / INRAE.
https://www.eaufrance.fr/sites/default/files/2021-10/MemoSurLeau_Maquette_VersionFinale_compresse.pdf

Pilcher R et al (2008) *PROWAT basic water loss book: a guide to the water loss reduction strategy and application, Part 1*. https://www.pseau.org/outils/ouvrages/prowat basic water loss book 1 2008.pdf
Serafeim AV et al (2024) Leakage in European water distribution networks: component
contribution and risk. Water 16(11):1534. https://doi.org/10.3390/w16111534

Thornton J (2004) Managing leakage by managing pressure: a practical approach. *Water21*, International Water Association, April issue.

https://www.geocities.ws/kikory2004/37_Water21_3rd_Article_Pressure.pdf

(accessed 2 January 2026)

Xu Q et al (2014) Review on water leakage control in distribution networks and the associated environmental benefits. *Journal of Environmental Sciences* 26:955–961.
https://doi.org/10.1016/S1001-0742(13)60562-9