# Snowmaking's slippery slope: The effect of mountain reservoirs on water demand

Jonathan Cognard, Lucas Berard-Chenu, Yves Schaeffer, Hugues François

► To cite this version:

Jonathan Cognard, Lucas Berard-Chenu, Yves Schaeffer, Hugues François. Snowmaking's slippery slope: The effect of mountain reservoirs on water demand. Ecological Economics, 2025, 233, pp.108586. 10.1016/j.ecolecon.2025.108586. hal-04998301

HAL Id: hal-04998301

https://hal.science/hal-04998301v1

Submitted on 20 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Snowmaking’s slippery slope: The effect of mountain reservoirs on water demand

Jonathan Cognard<sup>a,b,*</sup>, Lucas Berard-Chenu<sup>a,c</sup>, Yves Schaeffer<sup>a</sup>, Hugues François<sup>a,b</sup>

## ARTICLE INFO

**Keywords:**

Water resources
Artificial snow
Rebound effect
Water use
Jevons paradox
Ski tourism

## ABSTRACT

Snowmaking is the primary adaptation of winter tourism to climate change and requires increased water use in winter. However, water withdrawals during this period coincide with the mountain low-flow period, which can potentially cause conflicts with other human uses and ecosystems. To address concerns about water availability, the number of reservoirs is increasing. Ski lift operators promote these reservoirs as environmentally beneficial under their 'ecological engagement' commitments, arguing that they reduce the impact of snowmaking by shifting some water withdrawals to periods outside the low-flow season. In theory, mountain reservoirs should therefore support both economic activity and environmental sustainability. Using econometric analysis of data from 35 ski resorts across nine seasons, we show that reservoirs significantly increase water withdrawals during the low-flow period. By employing maximum likelihood estimation of fixed-effects dynamic panel data models, we find that a 1 % increase in reservoir capacity leads to a 0.28 % short-term increase in low-flow withdrawals (0.4 % in the long term), ceteris paribus. These findings inform ongoing discussions to ensure that current socio-economic decisions do not lead to future water use conflicts in mountain socio-ecological systems.

## 1. Introduction

The winter ski tourism industry is a major economic activity in mountainous regions (Vanat, 2022) and faces growing threats from climate change (Steiger et al., 2017). Ski lift operators are experiencing a reduction in natural snow cover and are increasingly relying on snow-making as an adaptation strategy to climate change (Hock et al., 2019) for economic sustainability (Falk and Vanat, 2016). Demand for snow-making is expected to increase in the future (François et al., 2023), which implies a corresponding rise in water demand (Vorkauf et al., 2024). At the same time, the frequency of suitable cold periods for snow-making is declining as temperatures rise. Snow production is possible down to a maximum threshold of -2 °C (Olefs et al., 2020). The water footprint of snow-making raises sustainability concerns in mountain regions, with environmental impacts varying depending on the timing of water withdrawals. Most snow is produced at the beginning of the ski season, between November and February. When water is withdrawn directly from natural sources and used immediately to produce snow, these withdrawals coincide with a period of low water availability. This period, known in hydrology and water regulation policy as

the 'low-flow period', lasts from December to March under the nival hydrological regime (Bard et al., 2012). Water withdrawals during this time have the greatest environmental impact (Peyras et al., 2010). Moreover, water availability during this period may not be sufficient to meet the growing demand for snow-making (Vanham et al., 2009). In this context, withdrawals for snow-making can lead to conflicts with other human water uses and mountain ecosystems (Reynard, 2020).

Mountain reservoirs have become a subject of major public controversy, including accusations of water grabbing (Vorkauf et al., 2024). These reservoirs are particularly valuable for ski lift operators, as they ensure sufficient water availability for snow production during increasingly shorter snow-making windows (Gerbaux et al., 2020). In 2021, in the French Alps, Gerfand et al. (2024) estimated the number of reservoirs at 136, with a potential storage capacity of almost 7.3 million m³. The number of mountain reservoirs continues to grow and is expected to increase further in response to deteriorating natural snow conditions. The French Association of Ski-lift Operators promotes 16 'eco-engagements', including the use of water reservoirs for 'smoothing out withdrawals over time' (i.e. reducing water withdrawals during low-flow periods) (DSF, 2020). This is in line with Vanham et al. (2009), who

<figure><img src="image_3.png" /></figure>

<footnote><sup>a</sup> Univ. Grenoble Alpes, INRAE, LESSEM, F-38402 St-Martin-d'Hères, France</footnote>

<footnote><sup>b</sup> Labex ITTEM, Université Grenoble Alpes, Grenoble, France</footnote>

<footnote><sup>c</sup> ESTHUA, Université Angers, CNRS, ESO, SFR CONFLUENCES, Angers, France</footnote>

<footnote>* Corresponding author at: Univ. Grenoble Alpes, INRAE, LESSEM, F-38402 St-Martin-d'Hères, France.
E-mail address: jonathan.cognard@gmail.com (J. Cognard).</footnote>
suggest that reservoir construction can help prevent water withdrawals
during low-flow periods. Thus, reservoirs appear to offer a promising
means of re-organising water withdrawals for snowmaking, which
potentially enhances the eco-efficiency of the process, that is, achieving
a better balance between economic performance and environmental
impact (Figge et al., 2017; Simioni and da Silva, 2020). Eco-efficiency is
key to sustainability (Stergiou and Kounetas, 2022), including sustain-
able tourism (Gössling et al., 2005). However, eco-efficient measures
designed to reduce environmental impacts may sometimes trigger un-
intended socio-economic and behavioural responses (Freire-González,
2023). This phenomenon is commonly referred to as the ‘rebound ef-
fect’, wherein the anticipated environmental benefits of a new tech-
nology or organisational practice fail to fully materialise, or remain
smaller than expected, due to overlooked external factors (Hertwich,
2005, in Haddad et al., 2023). The rebound effect is defined as the ab-
solute or relative difference between the potential environmental ben-
efits (under ceteris paribus conditions) and the actual benefits achieved
 Font Vivanco et al., 2022). If the overall impact increases rather than
decreases, this is known as ‘backfire’ or the Jevons paradox. While
rebound effects have been widely documented in the field of energy
efficiency, they have also been studied in relation to water resources
(Freire-González, 2023), particularly in agricultural contexts (e.g. Ber-
bel et al., 2015; Pfeiffer and Lin, 2014; Song et al., 2018). The impact of
reservoirs on agricultural water demand has also been studied, high-
lighting the existence of a rebound effect linked to their use (Di Bal-
dassarre et al., 2018). Despite this, eco-efficiency and the possibility of a
rebound effect remain largely unexplored in the context of water use in
the ski tourism industry. Specifically, the impact of mountain reservoirs
on water demand has never been analysed, even though it is a critical
issue for the sustainability of mountain environments.

The literature has struggled to provide quantitative data on water use
for snowmaking (Reynard, 2001). However, recent studies have used
modelling outputs to quantify current and future seasonal water demand
for snowmaking and its potential impacts on other water uses (e.g.
Gerbaux et al., 2020; Vorkauf et al., 2024). Knowles et al. (2023) esti-
mated that Canadian ski resorts currently use 43.4 million m³ of water
per year for snowmaking, with projected increases ranging from 55 % to
97 % by 2050. Meanwhile, studies by Peyras et al. (2010) and Gerfand
et al. (2024) have highlighted some of the environmental risks associ-
ated with the creation of mountain reservoirs, including the possibility
that these reservoirs may function as ecological traps. Fait et al. (2020)
suggest that the creation of mountain reservoirs has ambivalent effects.
On the one hand, it often leads to the degradation of water resources by
damming rivers and draining wetlands. On the other hand, if well
planned and designed, reservoirs can enhance biodiversity in alpine
areas by providing habitats and stepping stones for species as they move
through their geographical range. Marnezy (2008) argues that using
existing infrastructure, such as hydroelectric dams, wherever possible is
a more economical and less environmentally damaging solution than
constructing new reservoirs specifically for snowmaking. There remains
a significant gap in the understanding of water use for snowmaking and
the effectiveness of measures aimed at reconciling economic and
ecological considerations, such as the construction of mountain reser-
voirs. Moreover, no research to date has assessed the effectiveness of
water reservoirs for snowmaking as a means of reducing withdrawals
during the low-flow period. This is a crucial issue for understanding
water sustainability in socio-ecological systems, particularly in the
context of climate change, as the evolving dynamics of supply and de-
mand are likely to lead to conflicts with other mountain water users.

This research aims to address this gap by investigating the impact of
mountain reservoirs on water demand for snowmaking, with a partic-
ular focus on withdrawals during the low-flow periods. The rest of the
paper is structured as follows: Section 2 presents the theoretical con-
siderations that informs the model under analysis. Section 3 describes
the data, econometric methodology and descriptive statistics. Section 4
presents the econometric results, followed by a discussion in Section 5.

Section 6 concludes with a summary of the main findings and their
implications.

## 2. Theoretical considerations

This section seeks to enhance the theoretical understanding of water
demand for snowmaking, providing insights into how reservoirs may
influence it.

The impact of mountain reservoirs on water demand is currently
unclear. While they are primarily built to ensure water availability
during the snowmaking period, an increase in annual withdrawals is
expected as water availability is a constraint on the economic activity of
many resorts (Hamilton et al., 2003). However, reservoirs enable the
desynchronisation of withdrawals from production and are therefore
promoted as a tool for mitigating environmental damage, particularly
during low-flow periods.

In addition to reservoirs, several other factors influence water de-
mand for snowmaking and must be considered. Since snowmaking
supplements natural snowfall, there may be an inverse relationship
between water demand and natural snow conditions. Vorkauf et al.
(2024) highlight this dynamic by showing that water demand increases
in winters with poor natural snow conditions. Weather factors also play
a role; for instance, high winds that prevent snow production may lead
to lower water demand. Furthermore, periods of insufficient water
availability, such as hydrological droughts, may restrict withdrawals.
Another key factor influencing water demand for snowmaking is the
amount of snow produced, which is likely related to the size of the
resort, as larger resorts have larger ski areas to cover. Water demand
may also be influenced by the behaviour of ski lift operators, who have
developed structured work routines to manage snowmaking systems
(Berard-Chenu et al., 2023). These routines may introduce an element of
inertia in water demand. The price of water is typically an important
factor in determining water demand (Reynaud, 2003; Schleich and
Hillenbrand, 2009). In France, a charge is levied on water withdrawals,
capped at 5.4 euro cents per m³ for economic uses. Given the economic
stakes associated with these withdrawals, this relatively low amount
does not appear to be a decisive factor in water demand for snowmaking.
However, the total cost of accessing water may vary depending on
topography (e.g. upstream/downstream) and the type of water source
(e.g. river, lake, dam or drinking water supply network). Other factors
may also affect water demand, such as the proximity of a resort to hy-
droelectric dams, which can influence both the availability and cost of
water for snowmaking (Marnezy, 2008).

Given these expectations regarding the determinants of water de-
mand for snowmaking, we have formulated several assumptions about
the relationship between mountain reservoirs and water demand, taking
potential confounding factors into account. These assumptions are pre-
sented in Fig. 1. Several of these variables are also likely to be related. In
France, for example, large resorts are often situated at higher altitudes,
where snow cover is more reliable, and they also tend to have the largest
mountain reservoirs.

The effects of these variables on water demand are likely to fluctuate
throughout the ski season, justifying an analysis beyond the annual scale

<figure><img src="image_4.png" /><figcaption>Fig. 1. Main variables influencing water demand for snowmaking.</figcaption></figure>
to better understand water demand processes and their implications. We identify key periods during the season that reflect both hydrological (low-flow periods in mountain streams) and economic (snowmaking management) considerations. The snowmaking period can be divided into three distinct phases (Spandre et al., 2016a, 2016b). The first phase runs from around 1 November to 15 December, during which ski lift operators aim to produce a base layer of 30 cm of snow to take advantage of future snowfall, regardless of existing natural snow cover. The second phase, from about 15 December to 28 February, is a 'consolidation' phase, in which operators maintain a total snow depth of 60 cm on the slopes by adjusting artificial snow production based on prevailing natural snow conditions. The third phase, from 1 March until the end of the ski season, sees no further snow production. Fig. 2 illustrates these different periods. The elements in orange represent management constraints associated with winter tourism and snowmaking operations. The ski season typically lasts from mid-December to mid-April, with two strategic periods for snow production, labelled (A) and (B). The blue line represents the mountain hydrological regime, while the blue arrow (2) indicates the low-flow period.

To conclude this section, we formulate three hypotheses about the effect of reservoirs on water demand:

**H1.** Water withdrawals for snowmaking during low-flow periods decrease with reservoir capacity.

**H2.** The decrease in water withdrawals is more pronounced in the second (B) than in the first (A) snowmaking management period.

**H3.** Annual water withdrawals for snowmaking increase with reservoir capacity.

The primary hypothesis to be tested is that reservoirs reduce water withdrawals during the low-flow period (H1), as such water withdrawals are undesirable during this time, and reservoirs allow for a decoupling between withdrawals and production. More specifically, a reduction in water demand is expected during the first (A) and second (B) snowmaking management periods, with a stronger decrease in the second phase (H2). Indeed, in parallel with production during the first phase (A), operators may refill partially emptied reservoirs to ensure sufficient water availability for future snow production in the event of

poor snow conditions. This suggests that withdrawals during the first snow management phase (A) may be more persistent than in the second phase (B), where demand is more dependent on existing snow cover. We also expect to observe a positive relationship between mountain reservoirs and annual water demand (H3). While this relationship may seem intuitive, since reservoirs enable ski lift operators to produce snow even when river water availability is low, it is still important to quantify its magnitude.

## 3. Materials and method

In this section, we present the data and describe the analytical approach.

### 3.1. Materials

For 35 ski resorts located in Savoie in the French Alps, covering nine seasons (2010/11 to 2011/12 and 2015/16 to 2021/22), we compiled a dataset that captures the variables relevant to the theoretical relationships outlined in Fig. 1. All data are measured at the resort level and originate from two main sources:

- Water demand for snowmaking is measured using monthly data on water withdrawals (m³) and reservoir capacity (m³) from the Savoie Snowmaking Observatory. This is a unique dataset in France, as data on water withdrawals for snowmaking at such a granular temporal scale are only available in Savoie. Government officials collect these data as part of their water use monitoring duties and verify data quality using their in-depth knowledge of the sector. This dataset also includes information on the total ski slope area covered by snowmaking (ha), which serves as an indicator of ski resort size. Notably, data were not available for the 2012/13 to 2014/15 seasons.

- Natural snow cover is measured using the natural snow reliability index, calculated according to Spandre et al. (2016a) and based on the SAFRAN-Crocus Resort modelling chain. This model provides a monthly index of natural snow conditions for each resort. However, data were unavailable for the 2020/21 and 2021/22 seasons.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Identified hydrological and snowmaking management periods.</figcaption></figure>
These variables are described in Table 1.

## 3.2. Descriptive statistics

Table 2 presents the descriptive statistics of the variables, aggregated annually (from July to June) to include the entire winter season. On average, a ski resort withdraws 212,113 m³ of water per year for snowmaking, with values ranging from 0 to more than 1 million m³. The minimum value of 0 corresponds to two resorts that did not use snow-making at the beginning of the study period. The standard deviation reflects the heterogeneity of annual water withdrawals across resorts and years. Reservoir capacity varies from 0 to 514,100 m³, with a mean of 80,258 m³ and a standard deviation of 115,186 m³, which highlights significant differences in storage capacity among resorts. At the beginning of the study period, 15 resorts had no reservoir, while by the end of the period, 12 resorts still lacked one. Appendix 1 shows the overall evolution of mountain reservoir construction and expansion, as well as the increase in total reservoir capacity. The snowmaking coverage area ranges from 0 (the same two resorts that did not use snowmaking initially) to 328 ha. The seasonal natural snow reliability index varies from 4 % to 85 %, with an average of 61 %. During the poor snow conditions of the 2010/11 season, a small, low-altitude resort recorded the minimum value for this index. It should be noted that the natural snow reliability index is unavailable for the 2020/21 and 2021/22 seasons, leading to fewer observations for this variable.

The evolution of each variable over time is presented in Appendix 2. Fig. 3 displays the total annual water withdrawals and the capacity of the reservoirs for ski resorts in Savoie. Annual water withdrawals range between 5.73 million and 8.69 million m³, without any clear trends over time. Despite the COVID-19 crisis, which affected ski resort operations following the French government's decision not to open ski lifts for the 2020/21 season, water withdrawals for that season remained within the range of other years. By contrast, there has been a clear upward trend in reservoir capacity. In 2010, ski resorts were capable of storing 2.22 million m³ of water, which increased to 3.54 million m³ in 2021. This trend indicates a growing dynamism in the construction of new reservoirs. For example, in the 2010/11 period, the ratio of storage capacity to withdrawal volume constituted approximately 28 % of the total water withdrawals. Eleven years later, in 2021/22, this figure had risen to approximately 50 %.

The percentage distribution of average monthly withdrawals is presented in Fig. 4. The first two snowmaking management phases (A & B) account for the majority of water withdrawals. Specifically, 57.6 % of

<table><caption>**Table 1**

Description of the variables.</caption><thead><tr><th>Variable</th><th>Description</th><th>Unit</th><th>Observation time step</th></tr></thead><tbody><tr><td>Water withdrawals (WW)</td><td>Measures the ski resort's water withdrawals from the natural environment (either for direct snowmaking or to fill a reservoir). This is closely linked to the concept of water demand for snowmaking.</td><td>m<sup>3</sup></td><td>Monthly</td></tr><tr><td>Reservoir capacities (RC)</td><td>Water storage capacity in the reservoirs of the ski resort. Represents the sum of the capacities of reservoirs.</td><td>m<sup>3</sup></td><td>Annual</td></tr><tr><td>Snowmaking-covered area of ski slopes (SCA)</td><td>Surface area of slopes equipped with snowmaking facilities. This also serves as a proxy for the size of the resort.</td><td>ha</td><td>Annual</td></tr><tr><td>Natural snow reliability (NSR)</td><td>Reliability of the natural snow cover. Measures the skiability of the ski resort under natural snow conditions.</td><td>%</td><td>Monthly</td></tr></tbody></table>

<table><caption>**Table 2**

Summary statistics of seasonal water withdrawals for snowmaking.</caption><thead><tr><th>Variable</th><th>Obs.</th><th>Mean</th><th>Std. dev.</th><th>Min.</th><th>Max.</th></tr></thead><tbody><tr><td>Seasonal water withdrawals (m<sup>3</sup>)</td><td>315</td><td>212,113</td><td>218,678</td><td>0</td><td>1,088,295</td></tr><tr><td>Reservoir capacities (m<sup>3</sup>)</td><td>315</td><td>80,258</td><td>115,186</td><td>0</td><td>514,100</td></tr><tr><td>Snowmaking-covered slopes (ha)</td><td>315</td><td>65</td><td>76</td><td>0</td><td>328</td></tr><tr><td>Annual natural snow reliability index (%)</td><td>245</td><td>61</td><td>16</td><td>4</td><td>85</td></tr></tbody></table>

withdrawals occur in November and December, while 22.5 % happen in January and February. The remaining 19.9 % of withdrawals are spread across the other eight months, suggesting that the reservoirs are being refilled during this period. During the low-flow period, ski lift operators made 57.2 % of their annual snowmaking withdrawals.

## 3.3. Methodology

We employed econometric methods to study the association between reservoirs and water demand. These methods provide robust estimates of the causal relationship between the variables while accounting for the influence of other variables on water withdrawals that could otherwise act as confounding factors. The use of panel data allows us to differentiate between individual effects (differences among ski resorts) and temporal effects (differences across periods). It also helps to reduce collinearity (redundancy of information that can affect estimation) between explanatory variables and offers a variety of estimators to select the most efficient and consistent one.

The standard static specification of a panel data model is:

$$y_{it} = X'_{it}\beta + \mu_{i} + v_{it} \quad (1)$$

where $y$ is the dependent variable, $\mu$ is the time-invariant individual effects, $X$ is the regressor matrix, $\beta$ the associated coefficient, and $v$ is the idiosyncratic error term. The subscript $i$ denotes the individual, and $t$ denotes time.

However, the static specification of panel data models does not account for the persistence of the dependent variable, which is often present in time series economic data. In our case, water withdrawals for snowmaking are likely to exhibit temporal persistence (see section 2.1). This persistence may be due to inertial effects, which can have major implications for interpreting regression coefficients and the validity of statistical tests. Therefore, it requires special consideration in the analysis of panel data. The dynamic specification addresses this limitation by including one or more lags of the dependent variable as regressors.

The typical econometric specification for a dynamic panel data model is:

$$y_{it} = \alpha y_{it-1} + X'_{it}\beta + \mu_{i} + v_{it} \quad (2)$$

where $y_{it-1}$ is the lagged dependent variable, and $\alpha$ is the associated coefficient.

It should be noted that temporal dummies are not included in (1) and (2). This would have enabled us to control for potential temporal effects. However, we do not believe there is a compelling reason for temporal effects common to all resorts to influence water withdrawals over the study period, except for unfavourable snow seasons, which we have already accounted for at the resort level. Moreover, since there is a temporal interruption in the observation period, we have focused the analysis on the post-interruption period. The index of natural snow conditions is also unavailable for the 2020/21 and 2021/2022 seasons, which is why the analysis has not been carried out for this period. Temporal dichotomies could have been useful for these two seasons to capture the impact of the pandemic and resort closures on water withdrawals, but this was not possible due to the data gap.

With the data presented in section 3.1, the dynamic specification is:
<figure><img src="image_6.png" /><figcaption>Fig. 3. Annual water withdrawals and reservoir capacity for all ski resorts in Savoie. Source: Authors’ elaboration based on data from the Savoie Snowmaking Observatory.</figcaption></figure>

$$ \log(WW_{it}) = \alpha \log(WW_{it-1}) + \beta_1 \log(RC_{it}) + \beta_2 \log(NSR_{it}) + \beta_3 \log(SCA_{it}) + \mu_i + v_{it} \quad (3) $$

We use logarithms to reduce the impact of extreme values and interpret the coefficients as elasticities. This common practice improves the reliability of the econometric analysis in the presence of heterogeneity or asymmetry.

Four variants of this model are analysed. The same variables are included, but the time scales differ to examine the four key periods identified in section 2.2. The first variant corresponds to the study of annual withdrawals. The second variant analyses withdrawals during the low-flow period (December to March). The third and fourth variants correspond to withdrawals during the snowmaking phases (A) and (B): November and December for phase (A) and January and February for phase (B). The monthly data are aggregated to match the periods presented in Fig. 2.

For a detailed description of panel data estimators, refer to Wooldridge (2015). This study focuses on the most efficient and consistent estimator given the limited temporal observations, which is the estimator proposed by Hsiao et al. (2002). This estimator maximizes the transformed likelihood function after a first-difference transformation of the model. The xtpdqml command in Stata software (Kripfganz and Schwarz, 2019) was used for the analysis. We applied the vce(robust) option in Stata to estimate robust standard errors that account for heteroskedasticity and intra-group correlation, thereby correcting potential biases due to non-constant error variance and other dependencies in panel data. As a robustness check, we used the Two-Step System-GMM approach, using the xtabond2 package (Roodman, 2009b). Collapse options were applied to address the problem of too many instruments (see Roodman, 2009a).

## 4. Results

This section presents the results of the panel data econometric analysis. We analyse the effect of reservoir capacity, the natural snow reliability index and the area of slopes equipped with snowmaking on water withdrawals. Four different periods are analysed: the annual period, the low-flow period (from November to March for typical nival hydrological regimes), the first phase of snowmaking management (November and December) and the second phase of snowmaking management (January and February). Table 3 shows the estimates obtained using the maximum likelihood fixed effects dynamic panel data model estimator for each of the four periods. Estimates obtained with the Two-Step System-GMM estimator, as well as from the maximum likelihood fixed effects dynamic panel data model estimator with time dummies, are available in the supplementary materials.

We observe that reservoir capacity has a positive and significant effect on annual water demand. This result confirms hypothesis H3, as reservoirs increase the annual water demand for snowmaking. The effect is also significant and positive during the first phase of snowmaking management (A) but not during the second phase (B). These results reject the first (H1) and second (H2) hypotheses, which proposed decreasing water demand for snowmaking during the low-flow period and phases (A) and (B) of snowmaking management.

The other results show that the lagged dependent variable is positive and significant for the annual, low-flow and first phase of snowmaking management periods. By contrast, it is not significant during the second phase of snowmaking management. The natural snow reliability index is significant and negative during the second phase of snowmaking management but not during the other periods. The area of slopes covered by snowmaking is only significant for the latest period, where the lagged dependent variable is insignificant. This suggests that the effect we
<figure><img src="image_7.png" /><figcaption>Fig. 4. Percentage of monthly withdrawals for all ski resorts in Savoie. Source: Authors’ elaboration based on data from the Savoie Snowmaking Observatory.</figcaption></figure>

**Table 3**

Estimates of the determinants of water withdrawals for snowmaking over four key periods using the maximum likelihood fixed effects dynamic panel data model estimator.

<table><thead><tr><th></th><th>Annual</th><th>Low-flow<br>(Dec.–<br>Mar.)</th><th>Snowmaking<br>management 1st<br>phase (Nov.–<br>Dec.)</th><th>Snowmaking<br>management<br>2nd phase (Jan.–<br>Feb.)</th></tr></thead><tbody><tr><td>Water<br>withdrawals<br>(First lag)</td><td>0.293**<br>(0.143)</td><td>0.671***<br>(0.224)</td><td>0.306**<br>(0.141)</td><td>0.00826<br>(0.145)</td></tr><tr><td>Mountain<br>reservoir<br>capacity</td><td>0.363***<br>(0.0888)</td><td>0.276***<br>(0.0657)</td><td>0.346***<br>(0.0749)</td><td>0.503<br>(0.508)</td></tr><tr><td>Slope area<br>covered by<br>snowmaking</td><td>-0.204<br>(0.265)</td><td>-0.148<br>(0.267)</td><td>-0.234<br>(0.484)</td><td>2.578***<br>(0.987)</td></tr><tr><td>Natural snow<br>cover<br>reliability<br>index</td><td>-0.245<br>(0.236)</td><td>-0.474*<br>(0.275)</td><td>-0.0169<br>(0.0431)</td><td>-3.263***<br>(1.027)</td></tr><tr><td>AR(1)</td><td>0.0229</td><td>0.00820</td><td>0.0868</td><td>0.281</td></tr><tr><td>No. of Obs.</td><td>80</td><td>79</td><td>75</td><td>66</td></tr></tbody></table>

intended to control is generally better explained by the snowmaking dynamics within each resort than by the area to be snowed.

Since we use the log-log transformation, we can interpret the coefficients in our model as short- and long-term elasticities, representing the percentage change in the dependent variable caused by a 1 % change

in the corresponding independent variable. A 1 % increase in reservoir capacity would, ceteris paribus, lead to a 0.363 % and 0.513 % increase in annual water withdrawals in the short and long term, respectively, and a 0.276 % and 0.398 % increase in water withdrawals during low-flow periods in the short and long term, respectively.

**5. Discussion**

The main finding is that mountain reservoirs increase water withdrawals during low-flow periods. In November and December (the first phase of snowmaking management), water withdrawals are persistent, increase with reservoir capacity and remain unaffected by natural snow conditions. There is no evidence that water withdrawals in January and February (the second phase of snowmaking management) decreased with the presence of mountain reservoirs. During this period, water withdrawals are not persistent and are primarily influenced by natural snow conditions.

Building on these results and drawing inspiration from Marnezy (2008), Fig. 5 provides a schematic illustration of the impact of mountain reservoirs on the intensity and timing of water demand for snowmaking. The blue curve represents water withdrawals without reservoirs, and the red curves illustrate withdrawals with reservoirs.

These results suggest that the eco-efficient use of water by mountain reservoirs contributes to a rebound effect in snowmaking water demand. While mountain reservoirs enhance eco-efficiency, they ultimately drive an increase in net water demand during low-flow periods. The analysis shows that reservoirs lead to a moderate rebound effect in January and February, where efficiency gains are offset by an approximately equal
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Schematic distribution of water withdrawals for snowmaking. Without reservoirs (blue) and with mountain reservoirs (red). Source: Authors' elaboration based on the results of the econometric analysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

increase in resource use, resulting in no significant difference in water withdrawals. By contrast, there is evidence of a 'backfire rebound effect' (Alcott, 2005) in November and December. In line with the Jevons paradox, the environmental impacts outweigh the eco-efficiency gains, which leads to a significant net increase in water withdrawals during the low-flow period.

This phenomenon highlights the behaviour of ski lift operators with regard to snowmaking. In the first phase of snowmaking management (November and December), these findings align with Spandre et al. (2016b), who argue that water demand for snowmaking remains consistent from year to year because it is used to produce a base layer regardless of natural snow conditions. This study demonstrates that the capacity of mountain reservoirs increases water withdrawals during this period. This increase can be explained by how water stored in the reservoirs could have accumulated earlier (e.g. during snowmelt from the previous ski season), but the reservoirs are refilled during this period. The risk aversion of lift operators concerning potential poor snow conditions (Köberl et al., 2021) may lead to increased demand during this phase. The underlying explanation for this increase lies in the difference between withdrawals with and without snowmaking reservoirs. Without reservoirs, withdrawals are direct and depend on both resource availability and cold conditions suitable for snowmaking. With reservoirs, withdrawals are decoupled from cold conditions, opening up the possibility of more withdrawals. This illustrates that lift operators adopt a maximisation approach to snowmaking rather than an optimisation approach. As a result, reservoirs enable greater water use for snowmaking, failing to fulfil their intended purpose of mitigating hydrological impacts. However, the relationship between reservoirs and water withdrawals may differ under other conditions. The observed increase in water withdrawals during the low-flow periods could be because the capacity of the reservoirs is lower than the annual water demand, prompting ski lift operators to refill them during the low-flow period. If the reservoirs do not need refilling, no withdrawals are expected during this period. This suggests that reservoirs may reduce water withdrawals during the low-flow period if the annual demand is saturated. Saturation occurs when sufficient water is available for ski resorts to produce the required amount of snow. It is worth noting that saturation may already have been reached for resorts located near hydroelectric dams, where the availability of water resources greatly exceeds the demand for snowmaking.

Our results contradict the argument put forward by the 'eco-engagement' of ski lift operators (DSF, 2020) that water reservoirs allow for the smoothing of withdrawals over time. The gap between the principles adopted, their implementation and the outcomes is similar to other sustainable development initiatives, such as the 'Sustainable Slopes Charter' by the National Ski Areas Association (NSAA) in the United States (George, 2003; Rivera and De Leon, 2004). Given the aim of the 16 'eco-engagements' promoted by ski areas, the findings of this

study suggest that the initiative encouraging the construction of water reservoirs could be considered a greenwashing strategy (de Freitas Netto et al., 2020). Further research on this topic could provide valuable insights into the communication strategies regarding environmental concerns in ski area management. In the meantime, it is clear that any communication about the role of water reservoirs for snowmaking should be accompanied by measures to control and monitor withdrawals to verify potential environmental gains.

However, as acknowledged by Hamilton et al. (2003), the availability of water for snowmaking is crucial for the success or failure of ski resorts. As a result, ski resorts are planning new reservoirs to adapt to climate change, which could lead to increased water demand in the future, even during low-flow periods. Given the evidence of a rebound effect, a parallel can be drawn between water use in agriculture and ski tourism. The solutions proposed to address the unintended consequences of irrigation improvements (Scott et al., 2014) could therefore inspire ski tourism stakeholders to better manage water demand for snowmaking. For example, policy mechanisms could be strengthened to ensure better control of water withdrawals, thereby contributing to the sustainability of water use in socio-ecological systems. Moreover, the risk of negative dependence on water resources should be investigated further. Di Baldassarre et al. (2018) found that, within irrigation systems, reservoirs can increase vulnerability to economic activity and water scarcity. This longer-term effect of reservoirs for snowmaking can be seen as a form of lock-in, resulting from a path-dependency effect that has already been studied in relation to snowmaking beyond water resource issues (Berard-Chenu et al., 2023).

This paper has several limitations. First, these results should be interpreted as a trend, meaning that there may be a few cases where mountain reservoirs reduce water withdrawals during low-flow periods. Second, the water withdrawal data were collected monthly, whereas the snow management periods identified in the literature are divided by mid-December, marking the end of the first snowmaking phase. Access to data on a finer temporal scale would allow for a more detailed analysis and more precise results. Third, the study only investigated ski resorts in Savoie, which represents only part of the ski tourism industry in the French Alps, and over a limited period. Despite the challenges in accessing data, we encourage further research with a larger number of ski resorts and a longer historical frame.

## 6. Conclusion

This study demonstrates that mountain reservoirs increase water demand for snowmaking, even during low-flow periods. A 1 % increase in reservoir capacity would lead to a short-term increase in low-flow withdrawals of 0.28 % (0.4 % in the long term), ceteris paribus. Despite improvements in eco-efficiency, the hydrological impact of snowmaking is greater with the construction of reservoirs. This results in an increased risk of water use conflicts within mountain socio-ecological systems during winter. Therefore, mountain reservoirs should not be regarded as a solution to mitigate these conflicts but rather as a factor that extends water pressure due to snowmaking. Decision-makers who authorise or fund reservoirs should be aware of these potential unintended effects. One area for improvement could be a contractual approach to monitoring water withdrawals, which would involve authorities that issue permits for reservoir construction, with restrictions on withdrawals during low-flow periods. Local knowledge of the area where these withdrawals occur can provide valuable insights into whether a rebound may arise and whether it could pose challenges for other human water users and ecosystems. This requires considering accessibility and cost factors related to the type of hydrological environment from which the water is drawn, such as rivers, natural lakes, mountain reservoirs, drinking water networks or hydroelectric dams, in determining eco-efficiency.

Further research is needed to fully understand the factors contributing to water challenges in mountain areas and to determine whether
snowmaking and associated reservoirs can be considered a form of maladaptation (Scott et al., 2024). A promising approach would be to examine water use from a social-ecological systems perspective. To evaluate the likelihood of conflicts, it will be essential to better understand the dynamics of snowmaking, other human water uses, ecosystem needs and resource availability in the context of climate change and its governance.

### CRediT authorship contribution statement

Jonathan Cognard: Writing – review & editing, Writing – original

draft, Methodology, Formal analysis, Conceptualization. **Lucas Berard-Chenu:** Writing – review & editing, Writing – original draft, Validation. **Yves Schaeffer:** Supervision. **Hugues François:** Supervision, Data curation, Funding acquisition.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A

1. Overall evolution of the number of mountain reservoir constructions or extensions, of the increase in reservoir capacity, and of the total capacity of reservoirs.

<table><thead><tr><th>Season</th><th>2010/11</th><th>2011/12</th><th>2015/16</th><th>2016/17</th><th>2017/18</th><th>2018/19</th><th>2019/20</th><th>2020/21</th><th>2021/22</th></tr></thead><tbody><tr><td>Number of new reservoir constructions or expansions</td><td></td><td>4</td><td>5</td><td>3</td><td>2</td><td>4</td><td>3</td><td>3</td><td>2</td></tr><tr><td>Increase in reservoir capacity<br>m<sup>3</sup></td><td></td><td>170,850</td><td>142,061</td><td>2490</td><td>103,600</td><td>267,274</td><td>264,434</td><td>148,000</td><td>216,604</td></tr><tr><td>Total mountain reservoir capacity<br>m<sup>3</sup></td><td>2,223,582</td><td>2,394,432</td><td>2,536,493</td><td>2,538,983</td><td>2,642,583</td><td>2,909,857</td><td>3,174,291</td><td>3,322,291</td><td>3,538,895</td></tr></tbody></table>

2. Evolution over time of the average of each variable

<table><thead><tr><th rowspan="2">Season</th><th colspan="4">Withdrawals per period (m³)</th><th rowspan="2">Water reservoir capacity (m³)</th><th rowspan="2">Slopes covered by snowmaking (ha)</th><th colspan="4">Natural snow reliability index per period (%)</th></tr><tr><th>Season</th><th>Low-flow</th><th>First snowmaking management</th><th>Second snowmaking management</th><th>Season</th><th>Low-flow</th><th>First snowmaking management</th><th>Second snowmaking management</th></tr></thead><tbody><tr><td>2010/11</td><td>208,844</td><td>127,141</td><td>121,017</td><td>51,910</td><td>63,531</td><td>65</td><td>44.63</td><td>58.77</td><td>20.53</td><td>61.86</td></tr><tr><td>2011/12</td><td>174,484</td><td>95,850</td><td>80,586</td><td>47,439</td><td>68,412</td><td>63</td><td>69.69</td><td>87.58</td><td>30.75</td><td>98.73</td></tr><tr><td>2015/16</td><td>224,947</td><td>138,038</td><td>110,466</td><td>68,224</td><td>72,471</td><td>62</td><td>59.73</td><td>68.38</td><td>4.63</td><td>84.55</td></tr><tr><td>2016/17</td><td>243,756</td><td>153,384</td><td>131,477</td><td>64,977</td><td>72,542</td><td>66</td><td>51.49</td><td>61.19</td><td>18.38</td><td>62.77</td></tr><tr><td>2017/18</td><td>218,933</td><td>97,644</td><td>150,041</td><td>21,335</td><td>75,502</td><td>69</td><td>75.36</td><td>90.38</td><td>32.80</td><td>98.68</td></tr><tr><td>2018/19</td><td>228,957</td><td>138,192</td><td>129,749</td><td>57,657</td><td>83,139</td><td>70</td><td>64.60</td><td>76.69</td><td>23.30</td><td>85.35</td></tr><tr><td>2019/20</td><td>232,545</td><td>109,269</td><td>124,558</td><td>48,683</td><td>90,694</td><td>72</td><td>63.26</td><td>80.63</td><td>30.20</td><td>87.32</td></tr><tr><td>2020/21</td><td>190,713</td><td>110,266</td><td>119,668</td><td>21,519</td><td>94,923</td><td>72</td><td></td><td></td><td></td><td></td></tr><tr><td>2021/22</td><td>221,071</td><td>121,421</td><td>131,310</td><td>47,140</td><td>101,111</td><td>73</td><td></td><td></td><td></td><td></td></tr></tbody></table>

## Appendix B. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolecon.2025.108586.

### Data availability

The authors do not have permission to share data.

### References

Alcott, B., 2005. Jevons' paradox. Ecol. Econ. 54 (1), 9–21. https://doi.org/10.1016/j.ecolecon.2005.03.020.

Bard, A., Renard, B., Lang, M., 2012. Tendances observées sur les régimes hydrologiques de l'Arc Alpin. La Houille Blanche 98 (1), 38–43. https://doi.org/10.1051/lhb/2012006.

Berard-Chenu, L., François, H., Morin, S., George, E., 2023. The deployment of snowmaking in the French ski tourism industry: a path development approach. Curr. Issue Tour. 26 (23), 3853–3870. https://doi.org/10.1080/13683500.2022.2151876.

Berbel, J., Gutiérrez-Martín, C., Rodríguez-Díaz, J.A., Camacho, E., Montesinos, P., 2015. Literature review on rebound effect of water saving measures and analysis of a Spanish case study. Water Resour. Manag. 29 (3), 663–678. https://doi.org/10.1007/s11269-014-0839-0.

de Freitas Netto, S.V., Sobral, M.F.F., Ribeiro, A.R.B., da Soares, G.R.L., 2020. Concepts and forms of greenwashing: a systematic review. Environ. Sci. Eur. 32 (1), 19. https://doi.org/10.1186/s12302-020-0300-3.

Di Baldassarre, G., Wanders, N., AghaKouchak, A., Kuil, L., Rangecroft, S., Veldkamp, T.I. E., Garcia, M., van Oel, P.R., Breinl, K., Van Loon, A.F., 2018. Water shortages
worsened by reservoir effects. Nature Sustain. 1 (11), 11. https://doi.org/10.1038/
s41893-018-0159-0. Art.

DSF, 2020. Domaines skiables et changement climatique : Les professionnels adoptent des engagements environnementaux pour préserver la montagne. https://www. domaines-skiables.fr/news/16-eco-engagements-pour-reserver-la-montagne/.

Fait, P., Demierre, E., Ilg, C., Oertli, B., 2020. Small mountain reservoirs in the Alps: new
habitats for alpine freshwater biodiversity? Aquat. Conserv. Mar. Freshwat. Ecosyst.
30 (4), 617–630. https://doi.org/10.1002/aqc.3306.

Falk, M., Vanat, L., 2016. Gains from investments in snowmaking facilities. Ecol. Econ. 130, 339–349. https://doi.org/10.1016/j.ecolecon.2016.08.003.

Figge, F., Givry, P., Canning, L., Franklin-Johnson, E., Thorpe, A., 2017. Eco-efficiency of virgin resources : a measure at the Interface between micro and macro levels. Ecol. Econ. 138, 12–21. https://doi.org/10.1016/j.ecolecon.2017.03.016.

Font Vivanco, D., Freire-González, J., Galvin, R., Santarius, T., Walnum, H.J., Makov, T., Sala, S., 2022. Rebound effect and sustainability science : a review. J. Ind. Ecol. 26 (4), 1543–1563. https://doi.org/10.1111/jiec.13295.

François, H., Samacoits, R., Bird, D.N., Köberl, J., Prettenthaler, F., Morin, S., 2023.
Climate change exacerbates snow-water-energy challenges for European ski tourism.
Nat. Clim. Chang. 13 (9). https://doi.org/10.1038/s41558-023-01759-5. Art. 9.

Freire-González, J., 2023. Rebound effect and the Jevons paradox (p. 453-459). Elgar Encycl. Ecol. Econ. 453-459, 2023. https://doi.org/10.4337/9781802200416.ch78.

George, A., 2003. Managing ski resorts : perceptions from the field regarding the
sustainable slopes charter. Manag. Leis. 8 (1), 41–46. https://doi.org/10.1080/
1360671032000075234.

Gerbaux, M., Spandre, P., François, H., George, E., Morin, S., 2020. Fiabilité de l'enneigement et disponibilité des ressources en eau pour la production de neige dans les domaines skiables du Département de l'Isère (France), en conditions climatiques actuelles et futures. J. Alpine Res. https://doi.org/10.4000/rga.6724. Art. 108-1.

Gerfand, B., Arthaud, F., Evette, A., Testi, B., Peyras, L., Gaucherand, S., 2024. Ecological quality of snowmaking reservoirs in the Alps and management perspectives. Aquatic Sci. Res. Across Bound. 87 (1), 9. https://doi.org/10.1007/s00027-024-01136-0.

Gössling, S., Peeters, P., Ceron, J.-P., Dubois, G., Patterson, T., Richardson, R.B., 2005.
The eco-efficiency of tourism. Ecol. Econ. 54 (4), 417–434. https://doi.org/10.1016/
j.ecolecon.2004.10.006.

Haddad, B.M., Solomon, B.D., (Éds.), 2023. Dictionary of Ecological Economics: Terms for the New Millennium. Edward Elgar Publishing. https://doi.org/10.4337/
9781788974912.

Hamilton, L.C., Rohall, D.E., Brown, B.C., Hayward, G.F., Keim, B.D., 2003. Warming winters and New Hampshire's lost ski areas : an integrated case study. Int. J. Sociol. Soc. Policy 23 (10), 52–73. https://doi.org/10.1108/01443303107903009.

Hertwich, E., 2005. Consumption and the rebound effect : an industrial ecology perspective. J. Ind. Ecol. 9, 85–98. https://doi.org/10.1162/1088198054084635.

Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y., Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U., Morin, S., Orlove, B., Steltzer, H., Allen, S., Arenson, L., Baneerjee, S., Barr, I., Zhang, Y., 2019. *High Mountain Areas* (p. 131–202). IPCC - Intergovernmental Panel on Climate Change. http://urn.kb.se/resolve?urn=urn:nbn:ue:uu:diva-414230.

Hsiao, C., Hashem Pesaran, M., Kamil Tahmiscioglu, A., 2002. Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods. J. Econ. 109 (1), 107–150. https://doi.org/10.1016/S0304-4076(01)00143-9.

Knowles, N., Scott, D., Steiger, R., 2023. Sustainability of snowmaking as climate change (mal)adaptation : an assessment of water, energy, and emissions in Canada's ski industry. Curr. Issue Tour. 0 (0), 1–18. https://doi.org/10.1080/
13683500.2023.2214358.

Köberl, J., François, H., Cognard, J., Carmagnola, C., Prettenthaler, F., Damm, A.,
Morin, S., 2021. The demand side of climate services for real-time snow management
in alpine ski resorts : some empirical insights and implications for climate services
development. Clim. Serv. 22, 100238. https://doi.org/10.1016/j.
cliser.2021.100238.

Kripfganz, S., Schwarz, C., 2019. Estimation of linear dynamic panel data models with time-invariant regressors. J. Appl. Econ. 34 (4), 526–546. https://doi.org/10.1002/
jae.2681.

Marnezy, A., 2008. Les barrages alpins. J. Alpine Res. https://doi.org/10.4000/rga.422.
Art. 96-1.

Olefs, M., Koch, R., Schöner, W., Marke, T., 2020. Changes in snow depth, snow cover duration, and potential snowmaking conditions in Austria, 1961–2020—a model based approach. Atmosphere 11 (12), 12. https://doi.org/10.3390/atmos11121330. Art.

Peyras, L., Mériaux, P., Degoutte, G., Evette, A., Laigle, D., Poulain, D., Deroo, L., Lefranc, M., 2010. Mountain reservoirs for snowmaking : a guideline for design, construction, monitoring and rehabilitation. In: IEC5 2010, 8th ICOLD European Club Symposium Dam Safety - Sustainability in a Changing Environment.

Pfeiffer, L., Lin, C.-Y.C., 2014. Does efficient irrigation technology lead to reduced groundwater extraction? Empirical evidence. J. Environ. Econ. Manag. 67 (2), 189–208. https://doi.org/10.1016/j.jeem.2013.12.002.

Reynard, E., 2001. Aménagement du territoire et gestion de l'eau dans les stations touristiques alpines. Le cas de Crans-Montana-Aminona (Valais, Suisse). Revue de Géographie Alpine 89 (3), 7–19. https://doi.org/10.3406/rga.2001.3045.

Reynard, E., 2020. Mountain tourism and water and snow management in climate change context. J. Alpine Res. https://doi.org/10.4000/rga.6816. Art. 108-1.

Reynaud, A., 2003. An econometric estimation of industrial water demand in France. Environ. Resour. Econ. 25 (2), 213-232. https://doi.org/10.1023/A:1023992322236.

Rivera, J., De Leon, P., 2004. Is greener whiter? Voluntary environmental performance of Western ski areas. Policy Stud. J. 32 (3), 417-437. https://doi.org/10.1111/j.1541-0072.2004.00073.x.

Roedman, D., 2009a. A note on the theme of too many instruments*. Oxf. Bull. Econ. Stat. 71 (1), 135-158. https://doi.org/10.1111/j.1468-0084.2008.00542.x.

Roedman, D., 2009b. How to do Xtabond2 : An introduction to difference and system GMM in stata. Stata J. 9 (1), 86–136. https://doi.org/10.1177/
1536867X0900900106.

Schleich, J., Hillenbrand, T., 2009. Determinants of residential water demand in Germany. Ecol. Econ. 68 (6), 1756-1769. https://doi.org/10.1016/j.ecolecon.2008.11.012.

Scott, C.A., Vicuña, S., Blanco-Gutiérrez, I., Meza, F., Varela-Ortega, C., 2014. Irrigation efficiency and water-policy implications for river basin resilience. Hydrol. Earth Syst. Sci. 18 (4), 1339–1348. https://doi.org/10.5194/hess-18-1339-2014.

Scott, D., Knowles, N., Steiger, R., 2024. Is snowmaking climate change maladaptation? J. Sustain. Tour. 32 (2), 282-303. https://doi.org/10.1080/09669582.2022.2137729.

Simioni, F.J., da Silva, F.A., 2020. Eco-efficiency : Concept, indicators and applications. In: Filho, W. Leal, Azul, A.M., Brandli, L., Ozuyar, P.G., Wall, T. (Eds.), Affordable and Clean Energy. Springer International Publishing, pp. 1–11. https://doi.org/10.1007/978-3-319-71057-0-63-1.

Song, J., Guo, Y., Wu, P., Sun, Sh., 2018. The agricultural water rebound effect in China. Ecol. Econ. 146, 497-506. https://doi.org/10.1016/j.ecolecon.2017.12.016.

Spandre, P., François, H., George-Marcelpoil, E., Morin, S., 2016a. Panel based assessment of snow management operations in French ski resorts. J. Outdoor Recreat. Tour. 16, 24-36. https://doi.org/10.1016/j.jort.2016.09.002.

Spandre, P., Morin, S., Lafaysse, M., Lejeune, Y., François, H., George, E., 2016b. Integration of snow management processes into a detailed snowpack model. Cold Reg. Sci. Technol. 125. https://doi.org/10.1016/j.coldregions.2016.01.002.

Steiger, R., Scott, D., Abegg, B., Pons, M., Aall, C., 2017. A critical review of climate change risk for ski tourism. Curr. Issue Tour. 22, 1–37. https://doi.org/10.1080/13683500.2017.1410110.

Stergiou, E., Kounetas, K., 2022. Heterogeneity, spillovers and eco-efficiency of European industries under different pollutants' scenarios. Is there a definite direction? Ecol. Econ. 195, 107377. https://doi.org/10.1016/j.ecolecon.2022.107377.

Vanat, L., 2022. 2022 international report on snow & mountain tourism. Laurent Vanat Consult. https://vanat.ch/RM-world-report-2022.pdl.

Vanham, D., Fleischhacker, E., Rauch, W., 2009. Impact of snowmaking on alpine water resources management under present and climate change conditions. Water Sci. Technol. 59 (9), 1793-1801. https://doi.org/10.2166/wst.2009.211.

Vorkauf, M., Steiger, R., Abegg, B., Hiltbrunner, E., 2024. Snowmaking in a warmer climate : an in-depth analysis of future water demands for the ski resort Andermatt-Sedrun-Disentis (Switzerland) in the twenty-first century. Int. J. Biometeorol. 68 (3), 565-579. https://doi.org/10.1007/s00484-022-02394-z.

Wooldridge, J.M., 2015. Introduction à l'économétrie : Une approche moderne. De Boeck.