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The Impact of Climate Change on the Pattern of
Demand for Bottled Water and Non-Alcoholic
Beverages
S. Mirasgedis,
1
*E. Georgopoulou,
1
Y. Sarafidis,
1
K. Papagiannaki
1
and D. P. Lalas
2
1
National Observatory of Athens, Athens, Greece
2
FACE
3
TS S.A., Athens, Greece
ABSTRACT
To date, the majority of the research literature on the impacts of climate change has addressed
the negative aspect, i.e. the risks associated with a future permanent modification of climate.
Potential opportunities have received much less attention and are rarely transformed into
monetary values. Furthermore, manufacturing is one of the economic sectors where the influ-
ence of climate change remains practically unknown, although the economic performance
of some industrial activities depends directly on climatic conditions –bottled water and
non-alcoholic beverages (i.e. soft drinks and fruit juices) are among these. This paper aims
to explore the link between weather and product sales in these sectors, and estimate in
quantitative terms the potential impact of future climate change on their revenues. Historic
data were explored through statistical analysis and appropriate regression models were
developed. Models were applied for the historic (1961–90) and future climate (2021–50)
and the difference in sales forms the expected quantified impact of climate change. The
results indicate that significant opportunities may arise for some sectors from modifica-
tions in climate, provided their production infrastructure can meet the expected demand
and their management strategies can successfully adapt to altered climatic conditions.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.
Received 7 September 2012; revised 18 December 2012; accepted 10 January 2013
Keywords: climate change; impacts; industry; Greece; water; beverage
Introduction
ANUMBER OF STUDIES HAVE INVESTIGATED IN QUANTITATIVE TERMS THE RELATIONSHIP BETWEEN TEMPERATURE OR
other meteorological parameters and the level of economic activity at either the sectoral (see for example
Bahng and Kincade, 2012) or the whole economy level (e.g. Xu and Sun, 2011). In that respect, although
a lot of research on the adverse effects of future climate change on physical and economic systems has
been carried out and published, not much has been said to date on the economic opportunities that may arise from
such a change. The consumption of number of products especially in the manufacturing sector is greatly influenced
by weather conditions and thus their sales will probably be favorably affected by a future modification in climate.
*Correspondence to: S. Mirasgedis, National Observatory of Athens, Lofos Nimfon, Thesion, Athens 11810, Greece. E-mail: seba@noa.gr
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment
Business Strategy and the Environment
Bus. Strat. Env. (2013)
Published online in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/bse.1782
Thus, while for power companies, households and most activities in the tertiary sector, heat waves represent a clear
risk, for example by increasing further peak electricity demand for space cooling, for others, including manufac-
turers of beer, ice cream, soft drinks, juices, and bottled mineral water, these weather conditions are highly benefi-
cial as high temperatures (usually during summer) are in most of cases associated with increased product sales.
The importance of weatherand climateon business activities and the need to develop appropriate methods and tools
for companies to deal with the potential risks but also opportunities associatedwith climate change have been identified
and emphasized recentlyin several studies. Winn et al. (2011) have presented a conceptual framework for organizations
and businesses to deal with climate change that takes in account the management of sustainability, crisis, risk, resil-
ience and adaptive organizational change. Weinhofer and Busch (2012) in an exploratory case study in 11 electric utilities
showed that while climate change is perceived as a material issue for their business, the implemented climate risk man-
agement follows the same approach used for other business risks because of incomplete knowledge of the effects of
climate change. Wedawatta et al. (2011) explored the vulnerability of construction small and medium sized enterprises
(SMEs) in the UK to weather extremes and potential responses and identified a lack of appropriate coping strategies.
Acknowledging the importance of weather for their activity, a number of companies and industry associations
have also analyzed the annual variation of sales in relation to climatic variables and include this kind of analysis into
their annual reports at either company or sectoral level (e.g. British Soft Drinks Association, 2008; BCI, 2009).
For example, in the UK Soft Drinks Report for the year 2008, the section analyzing the weather conditions dur-
ing that year starts as follows: ‘It was a promising start. In the absence of April showers, the UK basked in a balmy
sun-filled month, fuelling expectations of heat wave conditions to come’. Similarly, the Irish Soft Drinks Report for
the same year states that ‘...May set expectations soaring with the lowest rainfall and highest temperatures recorded
for the five years from 2003’.
With regard to soft drinks in particular, the weather is considered a decisive factor for the industry’s sales and the
correlation between sales and temperature has been demonstrated in several studies (see for example Murray et al.,
2010 and Dubé, 2004). An additional critical parameter is precipitation; as soft drinks are usually consumed out-
doors during the summer, continuous or frequent rain events will decrease the number of people out and about.
Sunshine duration may also represent a complementary variable that may be examined (see for example British Soft
Drinks Association, 2009; BCI, 2009).
A large seasonal increase in consumption as a result of weather conditions may represent not only an opportunity
but also a risk for a company if it is not well-prepared for this increase. For example, the existing production capacity
and safety stock of a company may not be sufficient to satisfy the higher demand, resulting in a temporal loss of
market share that may have further consequences (WTI, 2009).
It should also be noted that reports on soft drinks sales nationally reveal that although favorable weather condi-
tions during the summer significantly affect seasonal consumption, their effect on the total annual consumption is
much less pronounced or even negligible, as non-climatic factors (e.g. household expenditure, availability of new or
specifically targeted products) seem to play a more determining role. For example, although the summer of 2008 in
Ireland was very rainy compared with 2006 (August 2008, precipitation 150 mm; August 2006, 60 mm), the
annual consumption of soft drinks was more or less the same (2008, 858 million liters; 2006, 839 million liters).
Thus, in order to better estimate risks and opportunities associated with weather conditions, it seems important
to develop a better understanding of the role of weather on sales by developing a quantitative model that can forecast
the level of sales/ consumption of beverages as a function of climatic as well as non-climatic parameters. Although
large manufacturers may have developed such models for their own use, the information published on this issue is
scant. Regression analysis seems to be the main methodological tool applied in order to explore the link between
weather variables and consumption/sales. Typically, correlation is determined on a month-by-month basis. In the
cases of other beverage products (e.g. beer), this was based on a temperature threshold (usually 15 C) above which
an increase in consumption is triggered (Blom, 2009). Thus, an index called ‘beverage degree days’(BDD) was for-
mulated on the basis of maximum temperature and used similar to the way heating degree days (HDD) or cooling
degree days (CDD) are used in energy analysis. By developing a linear regression model involving BDD for a large
Norwegian brewery, Blom (2009) found that the determination coefficient (R
2
) of the model was 64%, i.e. 64% of
the changes in sales are associated with changes in accumulated BDDs. In addition, the model developed showed
that a 1 C of increase in maximum temperature during the summer season (May–September) results in a 3.4%
increase in the industry’s sales during this season.
S. Mirasgedis et al.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Statistical analysis needs to treat different product families separately since the influence of weather conditions
on sales is not the same. For example, in a recent study (Ramanathan and Muyldermans, 2010) analyzing and quan-
tifying the relative influence of a number of factors on the sales of a large UK soft drinks company, temperature was
found to be an important factor for the 2 liter and 500 ml bottles but not for the 330 ml cans. Such differences are
associated not only with market factors such as where each product family is sold (supermarkets, outdoor stores,
etc.) but also with regional/ national particularities (e.g. types and locations of tourism activities).
It also needs to examine the suitability of various measures of the same variable, as for example maximum, mean
or apparent temperature (which incorporates somewhat the effect of humidity), but also the absolute level of average
local temperatures which varies substantially from location to location.
In view of the above, this paper aims to explore and quantify the link between climatic conditions and the sales of
bottled water and other non-alcoholic beverages. Alcoholic beverages have not been included as their sales are af-
fected by social factors that cannot be easily quantified and disaggregated. For the purposes of this study, a southern
European country (Greece) where high temperatures are often reached and their effects should be clearly felt has
been chosen. A second reason for choosing a southern country is the recent increase of heat waves there, occur-
rences of which are expected to become the norm in the future (Hansen et al., 2012), based on all predictions carried
out by numerous research groups worldwide of impending changes in climate. The possible influence of climate
change on sales affects long-term business development planning, especially in choices of location of plants and
warehouses. To address these aspects, one needs to first develop appropriate methodology to specify relevant forms
and their annual variations of the climatic variables affecting consumption over decades in the future. This method-
ology can also be useful in similar studies of the impact of climate change on other consumer goods, thus providing
useful information for relevant business risks and related costs.
The structure of this paper is as follows: the next section presents the methodological framework developed for
estimating the dependence of bottled water and other non-alcoholic beverage sales on weather variables, then we
describe the data used in the study. Following this we address the possible future implications on demand for
these products in Greece under climate change. Finally, we summarize the main findings of the study and draw
our conclusions.
Methodological Framework
Overview
The methodological framework developed and implemented in this study comprises three basic stages. First, a
demand model for sales is constructed that aims to quantify the extent to which the demand for several products
classified under the beverage sector (e.g. juices, soft drinks, bottled mineral water and carbonated water) is a func-
tion of climatic and socio-economic factors. For this, a statistical approach (multiple regression models) based on
historical monthly data for the last 10 years (i.e. 2002–11) for Greece is applied. Second, the results from a regional
climate model are used to specify the changes in climatic conditions in Greece in the upcoming decades (i.e. the
period 2021–50) compared to historic climate (1961–90). The A1B scenario of the Intergovernmental Panel for
Climate Change (IPCC) Special Report on Emission Scenarios (SRES) has been chosen for the present analysis,
which is considered as a scenario of medium climate change ‘intensity’and matches with all EU policies on
emission restrictions until 2020. Third, the regression models developed previously are used to estimate future
demand of the examined products in Greece that can be attributed to climate change. Details for the first two
parts follow.
The Demand Model
In the context of this study, several multiple regression models have been developed and tested in order to quantify
the influence of various climatic and socio-economic factors on the monthly demand for various products of the
beverage sector in Greece.
Climate Change and Demand for Bottled Water and Non-Alcoholic Beverages
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Several factors, internal and external parameters to the industry (Agnew and Thornes, 1995), influence the sales
of specific products of the beverages industry in a region. The former comprise the product prices (in general, as the
prices go down the demand goes up), the total amount of money spent in advertising campaigns, the in-store
promotions, the availability of competitive products, the level of development of distribution networks, etc. The
latter include the number of inhabitants and visitors (defining the size of the market), general economic condi-
tions influencing the available income for spending, consumer preferences and social trends. They also comprise
weather variability (e.g. one is more likely to buy a soft drink in hot weather), holiday periods, other special conditions
(e.g. bottled water sales are higher in areas with degraded water resources), etc.
The development of analytical models for simulating the demand for specific beverages in relation to the above-
mentioned parameters requires a significant amount of data in appropriate temporal analysis, which, in general are
not always available. In order to deal with this difficulty, for each product examined in the context of this analysis we
have developed and tested several models, which incorporate a limited number of robust economic drivers, as well
as several meteorological parameters, as explanatory variables. The models finally selected, incorporate only the
statistically significant independent parameters according to the results of the statistical analysis.
Regression models developed in the context of this study in order to simulate the demand of bottled water and
non-alcoholic beverages have the following general form:
log DEt
ðÞ¼cþX
i
ailog PMi;t
þX
j
bjlog SMj;t
þX
k
dklog Ek;t
þft þX
4
l¼2
plSlt þqSEtþet(1)
where c,a
i
,b
j
,d
k
,f,p
l
(lfrom 2 to 4) and qare the coefficients to be estimated from the regression analysis and e
t
is
the residual term.
The dependent variable (total amount of sales of a specific product in month t) appears in the above equation in
logarithmic form in order to reduce the serial correlation and the heteroscedasticity of the models developed
(Makridakis et al., 1998). Thus, possible loss of forecast accuracy, which is sometimes caused when the independent
variables of a forecast model take in the future values that differ substantially from those in the past on which the
regression of the model has been based, is avoided. We have examined four products, namely juices, soft drinks,
mineral bottled water and carbonated water, considering the total volume of sales, as well as the demand per product
and packaging size.
The explanatory variables used in the model comprise:
•A number of primary meteorological parameters (PM
i,t
), namely the maximum, mean and minimum temperature
(T
max
,T
mean
and T
min
correspondingly), precipitation (Pr), sunshine duration (SD) and relative humidity (RH).
•Secondary meteorological parameters (SM
j,t
) aiming at describing in a more effective way the meteorological
parameters which influence consumer preferences and behavior. To this end, we have used the apparent temper-
ature (T
app
), which is estimated on a monthly basis from the corresponding values of maximum temperature and
relative humidity using the following formula (Steadman, 1984):
Tapp ¼1:3þ0:92Tmax þ2:2e
e¼RH
100 6:105 exp 17:27Tmax
237:7þTmax
101(2)
•A number of selected economic variables (E
k,t
) aiming at describing the exogenous economic environment.
To this end, we have used the number of overnight stays during month t, which serve as a measure of the tourist
activity in the region, and the household expenditure in month twhich serve as a measure of the overall economic
environment and the willingness of the permanent population to spend money on consumer goods.
•The time (t) in months aiming at taking into account the potential long-term trends in the monthly sales of the
product in question. This trend can be related to social, economic or demographic factors that result in a systematic
change to the total volume of sales in case the analysis undertaken on a sub-sectoral level or to a variety of parameters
which influence the market share of a specific enterprise in case the analysis is undertaken at a company level.
S. Mirasgedis et al.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
•Three dummy variables (S
l,t
), which aim at representing the potential seasonal variability in the sales of each prod-
uct that does not relate to specific weather conditions. An index ltaking values within the interval [2,4] has been
used, to represent three seasons of the year (i.e. l= 2 for spring, l= 3 for summer and l= 4 for autumn). The con-
sumption during winter is the baseline consumption in the model. It should be noted that any season of the year
could have been selected as the baseline season for the model without affecting the results of the analysis. Each
dummy variable has only two allowable values, 0 or 1. The variable S
l,t
takes the value of 1 if the t-observation
belongs to season I and 0 otherwise. We use one dummy variable less than the number of periods (four seasons
per year) to avoid multi-collinearity problems (Makridakis et al., 1998).
•One dummy variable (SE
t
), with a view to cover months of the year when special events (e.g. holidays) influence
the demand for a product. This variable takes the value of 1 if the month tincludes special events such as Christmas
and the mid-August holiday period and 0 otherwise.
Again, a logarithmic form was preferred in order to express the meteorological and economic parameters incor-
porated in the models developed, in order to avoid serial correlation and heteroscedasticity. Furthermore, it should
be pointed out that these models, though able to produce (given the simplifications adopted and the uncertainty in
the future inputs) only approximate estimates of the future long-term demand of bottled water and non-alcoholic
beverages, can provide considerable insight on the potential changes attributable to weather variability.
Estimation of Future Regional Climate
Projections of anthropogenic emissions of greenhouse gases (GHGs) and other constituents are necessary to predict
future climate change. Since the long-term evolution of GHG emissions is characterized by large uncertainties, a
range of emissions scenarios has been generated by the IPCC on the basis of explicitly different packages of
socio-economic and technological conditions (‘storylines’). Four qualitative storylines are usually considered that
yield four sets of scenarios called ‘families’: A1, A2, B1 and B2. They cover in a consistent way a wide range of
key future characteristics such as demographic change, economic development and technological change, resulting
in a substantial variation of GHG emissions (IPCC, 2000).
In the present analysis, the A1B scenario of the IPCC/SRES has been chosen, as it is considered a scenario of
medium climate change ‘intensity’and is consistent with EU policies on emission restrictions until 2020. In order
to specify the changes of climatic conditions in Greece in the upcoming decades (i.e. during 2021–50) compared
with historic climate (1961–90) the results of a regional climate model (RCM), namely the RACMO2 climatic model
developed by the Royal Dutch Meteorological Institute (Meijgaard et al., 2008), utilizing the A1B scenario emissions
pathway have been used.
The use of a RCM rather than the use of a global climate model (GCM) has been dictated by the considerable
variation in terrain characteristics (long coastlines in close proximity to mountainous regions as is the case in the
Mediterranean) which results in considerable spatial climatic variations that cannot be discernible with the low
resolution (a few hundred kilometers) of GCMs (CCSP, 2008).
In an attempt to construct comparable results on a monthly basis for a 30-year period, it was decided that
simple mean values of meteorological parameters for each month averaged over the whole period would not
capture the true variability present in both the past and the future 30-year period. Instead, the well-
established approach for long-term analysis of making use of a typical meteorological year (TMY) was preferred,
which, however, would be a special purpose TMY taking into account the ranking of the variables as identified in
the demand model.
The development of the special purpose TMY is based on the selection, following statistical criteria, of actual sin-
gle months from a multi-year database that are then merged into a TMY (Argiriou et al., 1999). Among the different
methods available for TMY generation, the Sandia National Laboratories method using Filkenstein–Schafer (FS)
statistical method (Hall et al., 1978) is selected, as it is one of the most common methodologies used for generating
a TMY (Argiriou et al., 1999; Sawaqed et al., 2005; Skeiker, 2007; Jiang, 2010). The daily values computed by the
RACMO2 climatic model are utilized for the selection of a typical month on the basis of seven daily meteorological
parameters, namely the maximum, mean and minimum temperature, precipitation, sunshine duration and relative
humidity. The procedure is as follows:
Climate Change and Demand for Bottled Water and Non-Alcoholic Beverages
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Step 1: For each meteorological parameter mentioned above and each year of the selected 30-year period (i.e. 1961–90
or 2021–50), the daily values of the parameter during each month of the year are sorted (short-term sorting) in increas-
ing order (x
1
,x
2
,...,x
n
). In addition, for the same meteorological parameter and each month of the year, the daily
values of the parameter during that month and all years of the selected 30-year period are sorted in increasing order
(long-term sorting). Next, for each daily variable xin the sorting, the cumulative distribution function CDF
n
(x)is
calculated with the following function:
CDFnxðÞ¼
0forx<x1
k0:5
nfor xk≤x<xkþ1
1forx≥xn
8
>
<
>
:
(3)
where CDF
n
(x) is the value of the cumulative distribution function for daily variable x,nis the total number of sorted
elements and kis the rank order number. From its definition, CDF
n
(x) is a monotonically increasing step function
with steps of size 1/noccurring at x
i
and is bounded by 0 and 1. The calculation of CDF
n
(x) is performed both over
short-term and long-term sorting.
Step 2: The FS statistics for each month mof a specific year yare calculated according to the following equation:
FSxy;mðÞ¼
1
NX
N
i¼1
CDFmxi
ðÞCDFy;mxi
ðÞ
(4)
where FS is the FS statistics, CDF
m
is the long-term (30 years in this study) and CDF
y,m
is the short-term (for the year y)
cumulative distribution function ofthe daily variable xfor month m,x
i
is a sorted element in a set of Nobservations, and
Nis the number of daily data for month m.
Step 3:AweightingfactorWF
x
is applied to the FS statistics for each meteorological parameter x(FS
x
). In this way, a
weighted sum, WS, is derived for each month of each year in the selected 30-year period (1961–90 or 2021–50):
WS y;mðÞ¼
1
mX
m
i¼1
WFxFSxy;mðÞ (5)
where WF
x
are the weighting factors, one for each variable, and mis the number of meteorological parameters consid-
ered (seven in this study). For each month of the year, 5 months having the smallest weighted sum of the FS statistics of
the seven meteorological parameters are selected.
Step 4:Tofinally select the typical meteorological months from the five candidate months identified at the end of Step 3,
the simpler selection process introduced by Pissimanis et al. (1988), which uses the root mean square difference
(RMSD) of the key parameter (solar radiation in the original method) as the selection criterion, is applied:
RMSD ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
N
i¼1
KPy;m;iKPm
N
v
u
u
u
u
t(6)
where Nis the number of daily readings for the month, KP
y,m,i
is the daily values of the key meteorological parameter of
year y, month mand day i,andKP
m
is the long term mean daily values of the key meteorological parameter.
S. Mirasgedis et al.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Data
The food and beverage industry constitutes one of the most significant and dynamic manufacturing sectors of the
Greek economy. In 2009, the sector accounted for 25.3% of the total turnover of manufacturing, produced 31%
of the relevant overall gross added value and employed approximately 22.9% of workers in industry. The sub-
sectors of bottled water and soft drinks (NACE Rev.2 code 1107) and juices (NACE Rev.2 code 1032) account for
15% of the total sales in the food and beverage industry.
The regression models presented in this paper were developed utilizing historical data for sales for specific
beverage products provided by two large Greek enterprises, covering a 10-year period from 2002 to 2011. These
companies account for a significant percentage of the market for bottled water and non-alcoholic beverages and
therefore can be considered to be a ‘guide’for the behavior of the whole sector against weather and future climate
change. More specifically, the company that provided sales data for bottled mineral water and carbonated water has a
large distributional network covering almost all the Greek territory. The second company that provided data for
juices and soft drinks covers mainly the metropolitan area of the capital of Greece (i.e. Athens, where approximately
40% of the permanent population as well as a large number of visitors reside). The data set used comprises monthly
sales data (in volume), disaggregated per product and type of selling pack (e.g. 500 ml bottle, 330 ml can etc.).
Figure 1 shows the sales profile of the four products examined (bottled mineral water, carbonated water, juices
and soft drinks) over the 10-year period (2002–11). As shown, the sales of all beverages in question exhibit a signif-
icant seasonal variation. Specifically, the demand for bottled mineral water and carbonated water increases signifi-
cantly during the summer. The sale of juices follows a similar profile; however, the demand first peaks at the end of
spring/early summer, then declines and recovers temporarily in September. As the data on juices derive from a
company mainly active in the Athens area, the above-mentioned seasonal sales’profile can be explained by the fact
that many permanent residents leave the capital during the summer vacation period (particularly in August). The
sales of soft drinks follow a trend similar to that of juices, with an additional peak in December probably due to
the Christmas holiday. Figure 1 also shows that for all products examined, the seasonal and hence weather-
sensitive effect is superimposed on a long-term increase in sales. This is particularly noticeable for juices and soft
Figure 1. Monthly demand for bottled mineral water and other non-alcoholic beverages in Greece (2002–11)
Climate Change and Demand for Bottled Water and Non-Alcoholic Beverages
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
drinks and can possibly be attributed either to a substantial expansion of these two sub-sectors during the reference
period or/and a dynamic development of the specific company, which provided the data on sales.
Necessary historical weather data for the period 2002–11 were derived from a first-class meteorological station
(so classified according to the criteria set by the World Meteorological Organization) located in the center of Athens.
Since almost half of the total Greek population lives in the greater Athens area, and consequently consumes a
relevant portion of bottled mineral water and non-alcoholic beverages, the meteorological data utilized constitute
a representative basis for analyzing the behavior of consumers in Greece in response to weather conditions.
The experience of many companies worldwide indicates that several weather elements can influence the demand
for specific products. Thus, in the context of this analysis we have tested the influence of maximum, mean and
minimum temperature, precipitation, sunshine duration and relative humidity on the sales of bottled water and
non-alcoholic beverages. The monthly values of these parameters are the average (for temperature, relative humidity
and sunshine duration) or the sum (for precipitation) of the corresponding daily figures.
To investigate the impact of climate change for the period 2021–50, the daily meteorological data at the same
location were derived by interpolating the results provided by the regional climate model for the four nearer grid points
of the computational grid of the model (grid analysis 25 km25 km). Then, a typical meteorological year for the period
2021–50 was formulated on the basis of the approach presented in Estimation of Future Regional Climate and finally
the monthly values of the selected parameters were calculated from the corresponding daily figures.
The socio-economic parameters included in the developed regression models comprise private consumption of
households and the number of overnight tourist stays; the relevant data derived from the National Statistical Service
of Greece. The number of overnight tourist stays was available on a monthly basis for the entire period under exam-
ination. Data on the private consumption of households were available on a quarterly basis. To disaggregate them on
a monthly basis, first it was assumed that consumption is equally spread within each 3-month period. On the
assumption that the monthly values obtained stand for the private consumption at the second month of each quarter,
the consumption of the third month of the quarter nand the first month of the quarter n+ 1 derived from a linear
interpolation of the consumption estimated for the second month of the quarters nand n+1.
Results
Sensitivity of Product Sales to Weather Conditions
Using n= 114 monthly observations (covering the period from January 2002 up to June 2011) several multiple re-
gression models were developed, correlating the volume of sales for each product category in question with a num-
ber of explanatory variables presented in The Demand Model. Table 1 shows, for each product category examined,
the models presenting the highest adjusted coefficient of determination (R
2
), the significant independent variables
included and other indices related to the performance of the models. It is worth mentioning that the selected models
present a satisfactory to very high predictive power, with R
2
ranging from 70 to 90%, with somewhat lower values
(c. 55%) for small containers of less than 500 ml, meaning that the independent variables included in each model
explain a corresponding percentage of sales fluctuations for the relevant product category. According to the results
of the analysis, in all models the private consumption of households is statistically very significant. In some models,
additional economic parameters are statistically significant, namely the overnight stays of tourists as well as the
linear time variation (see for example the equations modeling the sales of soft drinks and juices), identifying a long-
term development of the corresponding markets and/or the activity of the specific company which provided the relevant
sales data. In addition, some models selected incorporate as statistically significant independent variables one or more
weather parameters, namely the mean monthly maximum temperature, the mean relative humidity or the mean
apparent temperature. Finally, for all the products examined, the corresponding models include a number of dummy
variables, identifying a seasonal or monthly variation of product sales not attributed to weather variability.
Despite the fact that the multiple regression models developed in the context of this study present a satisfactory to
high R
2
, many of them still show large serial correlation, violating one of the basic assumptions of the regression
analysis. The Durbin–Watson statistic is widely used for testing the serial correlation or the independence of
S. Mirasgedis et al.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
residuals at lag 1 in regression models; it ranges in value from 0 to 4, with an intermediate value of 2, which
indicates the absence of autocorrelation at lag 1. As clearly depicted in Table 1, for several models developed in
the context of this study the Durbin–Watson statistic deviates significantly from this central value, which casts doubt
on the validity of the F- and t-tests and confidence intervals (Makridakis et al., 1998). One of the common methods
for reducing the serial correlation observed in econometric regression is the incorporation of an autoregressive
structure in the error term (Makridakis et al., 1998).
In the context of this paper, autoregressive structures were incorporated in all models presenting a Durbin–Watson
index higher than 2.3 or lower than 1.7 (facts that show a significant serial correlation problem in the corresponding
models). All the other independent parameters remained unchanged. Tables 2–5presentthefinal models selected
for each product category and the corresponding estimated coefficients. Notably, the incorporation of autoregressive
structures in the models resulted in increased R
2
values, indicating an improvement of their predictive power. The
inclusion of autoregressive structures in the models examined does not affect significantly the importance of weather
parameters. In all cases of products examined, the maximum ambient temperature is a statistically significant inde-
pendent variable, affecting positively the volume of product sales. The humidity, either directly or in combined form
(T
app
) has been included only in a limited number of models and seems to be less significant. As regards the economic
parameters, the inclusion of autoregressive structures seems to weaken the significance of the private consumption of
households in some models. This is reasonable as the autoregressive structures reflect in a way the economic activity.
The additional presence of tourists in the models does not seem to influence sales either of water (except for carbon-
ated water in large containers) or of the other non-alcoholic beverages. Possible explanations are the relatively small
percentage of their numbers in comparison to the total population in the Athens area and the similarity of their choice
behavior to the local residents.
As the maximum temperature was found to be the meteorological parameter with the most significant influence
on the level of sales of non-alcoholic beverages, Figure 2 shows the changes in total sales for all products examined
attributable to a uniform increase of the mean monthly maximum temperature by 1 C for all months of the year.
For the case of soft drinks in large containers where T
app
is included instead of T
max
, the value of T
app
to be used in
the demand equations resulted from Eq. (2) with a 1 C increase in T
max
and the relative humidity constant (RH).
Model Significant Independent Variables Adjusted R
2
Significance FDurbin–Watson
Bottled mineral water
Total volume of sales T
max
, PC, S
2
,S
3
0.724 8.1 10
–29
2.397
Sales in packs <500 ml T
max
, PC, S
2
,S
3
0.538 2.1 10
–17
2.227
Sales in packs >500 ml T
max
, PC, S
2
,S
3
0.717 3.2 10
–28
2.475
Carbonated water
Total volume of sales T
max
, PC, S
2
,S
3
0.733 1.5 10
–29
2.206
Sales in packs <500 ml T
max
, PC, S
2
,S
3
0.722 1.2 10
–28
2.075
Sales in packs >500 ml T
max
, PC, TRSM, S
4
0.759 7.9 10
–32
2.208
Soft drinks
Total volume of sales T
app
, PC, t,M
Aug
,M
Dec
0.706 3.4 10
–28
0.755
Sales in packs <500 ml T
max
, RH, PC, t,M
Aug
,M
Dec
0.587 8.5 10
–20
0.545
Sales in packs >500 ml T
app
, PC, t,M
Aug
,M
Dec
0.730 3.4 10
–30
0.889
Juices
Total volume of sales T
max
, RH, PC, t,M
Aug
0.901 1.3 10
–53
1.390
Sales in packs <500 ml T
max
, PC, t,M
Aug
0.847 2.1 10
–44
1.120
Sales in packs >500 ml T
max
, RH, PC, t,M
Aug
0.894 5.3 10
–52
1.562
Table 1. Independent variables and overall performance of multiple regression models selected for analyzing the sales of bottled
mineral water and non-alcoholic beverages. All models are structured on a monthly basis, incorporating the following independent
variables: T
max
, mean maximum temperature; RH, mean relative humidity; T
app
, mean apparent temperature; PC, private
consumption of households; TRSM, number of tourist overnight stays; S
2
,S
3
and S
4
, dummy variables indicating the season of the
year (i.e. 2 for spring, 3 for summer and 4 for autumn); M
Aug
and M
Dec
, dummy variables indicating correspondingly the months
August and December; t, time in months showing the long-term trends in sales
Climate Change and Demand for Bottled Water and Non-Alcoholic Beverages
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
As a general remark, the results obtained indicate that as the temperature increases, the demand for bottled mineral
water and other non-alcoholic drinks tends to be higher; on an annual basis this increase ranges from approximately
0.9% for bottled mineral water and soft drinks up to 2.2 and 2.3% for carbonated water and juices, respectively. Yet,
the estimated changes attributable to a +1 C increase in the mean monthly maximum temperature are seasonally not
uniform. Specifically, on a percentage basis, the projected increases are more significant in the winter period (i.e.
December to February), which is above 1.2% for bottled mineral water and soft drinks, reaching up to 3.7% for carbon-
ated water and 4% for juices. On the other hand, the level of sales during the summer period (i.e. May to September)
seems to be less affected by an increase in mean monthly maximum temperature by 1 C and ranges between 0.5 and
0.7% for bottled mineral water, 1.3 and 1.8% for carbonated water, 0.4 and 0.7% for soft drinks and 0.8 and 3.1% for juices.
Variable
Total volume of sales Sales in packs <500 ml Sales in packs >500 ml
Coefficient Sig. Coefficient Sig. Coefficient Sig.
c2.409 0.016 4.171 0.000 6.364 0.000
T
max
0.427 0.000 0.462 0.000 0.432 0.002
RH
T
app
PC 1.825 0.000 2.235 0.000 0.671 0.007
TRSM 0.153 0.002
t
S
2
0.106 0.000 0.099 0.000
S
3
0.150 0.000 0.130 0.000
S
4
0.200 0.000
M
Aug
M
Dec
R
2
0.733 0.722 0.759
Table 3. Values of the independent coefficients of the final models selected for analyzing the sales of carbonated water. See text
and Table 1 for definitions of variables; c, constant of the model
Variable
Total volume of sales Sales in packs <500 ml Sales in packs >500 ml
Coefficient Sig. Coefficient Sig. Coefficient Sig.
c1.659 0.028 0.089 0.949 1.770 0.017
T
max
0.163 0.011 0.206 0.061 0.144 0.023
RH
T
app
PC 1.227 0.000 1.442 0.000 1.177 0.000
TRSM
t
S
2
0.116 0.000 0.125 0.000 0.111 0.000
S
3
0.240 0.000 0.212 0.000 0.251 0.000
S
4
M
Aug
M
Dec
AR (1) 0.213 0.033 0.251 0.011
R
2
0.746 0.538 0.744
Table 2. Values of the independent coefficients of the final models selected for analyzing the sales of bottled mineral water. See text
and Table 1 for definitions of variables; c, constant of the model; AR(1), autoregressive structure at lag 1
S. Mirasgedis et al.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
A possible explanation for this differentiation is that the consumption of bottled mineral water and non-alcoholic bev-
erages during the summer period is alreadyhigh and therefore there is not much room for a substantial further growth
in sales as a result of higher temperatures. Also, at extremely high temperatures people limit their outdoor activities,
remaining at home.
Variable
Total volume of sales Sales in packs <500 ml Sales in packs >500 ml
Coefficient Sig. Coefficient Sig. Coefficient Sig.
c9.763 0.000 8.208 0.000 10.399 0.000
T
max
0.243 0.000
RH 0.077 0.171
T
app
0.164 0.000 0.136 0.000
PC 0.214 0.472 0.021 0.924 0.391 0.204
TRSM
t0.001 0.000 0.001 0.203 0.001 0.000
S
2
S
3
S
4
M
Aug
0.059 0.000 0.052 0.000 0.061 0.000
M
Dec
0.199 0.000 0.103 0.000 0.218 0.000
AR (1) 0.733 0.000 0.638 0.000 0.664 0.000
AR (2) 0.186 0.095
AR (3) 0.474 0.000
R
2
0.846 0.881 0.838
Table 4. Values of the independent coefficients of the final models selected for analyzing the sales of soft drinks. See text and Table 1
for definitions of variables; c, constant of the model; AR(1) - AR(3), autoregressive structures at lag 1–3
Variable
Total volume of sales Sales in packs <500 ml Sales in packs >500 ml
Coefficient Sig. Coefficient Sig. Coefficient Sig.
c9.325 0.000 9.186 0.000 9.562 0.000
T
max
0.401 0.000 0.254 0.000 0.420 0.000
RH 0.108 0.079 0.179 0.005
T
app
PC 0.227 0.199 0.385 0.166 0.281 0.132
TRSM
t0.002 0.000 0.003 0.000 0.002 0.000
S
2
S
3
S
4
M
Aug
0.096 0.000 0.137 0.000 0.087 0.000
M
Dec
AR (1) 0.219 0.035 0.382 0.001 0.140 0.150
AR (2) 0.057 0.566 0.157 0.136 0.101 0.274
AR (3) 0.391 0.000 0.339 0.001
R
2
0.923 0.886 0.916
Table 5. Values of the independent coefficients of the final models selected for analyzing the sales of juices. See text and Table 1 for
definitions of variables; c, constant of the model; AR(1) - AR(3), autoregressive structures at lag 1–3
Climate Change and Demand for Bottled Water and Non-Alcoholic Beverages
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Climate Projections for Greece
As already mentioned, changes in climatic conditions in Greece during the upcoming decades (i.e. 2021–50) com-
pared with historic climate (1961–90) are examined in the context of the A1B Scenario of the IPCC. The data set
developed for the historic and future climate comprises daily values for the selected meteorological parameters
for a 30-year period (1961–90 and 2021–50). TMY are defined on the basis of the above-mentioned data sets
according to the Sandia National Laboratories method.
In view of the results of the previous section, after the FS statistics were computed for all 30 years of the historical
(1961–90) and future (2021–50) periods as described in Step 2 of the TMY methodology above, the following
weighting factors (Table 6) have been utilized to compute the weighted sum of FS statistics (Step 3 of the TMY
methodology). The weighting factors are in line with the sales sensitivity for non-alcoholic beverages to the meteo-
rological parameters identified in the regression analysis. The regression analysis shows that maximum tempera-
ture and in some cases relative humidity are the most relevant meteorological parameters. Therefore, weights
assigned to temperature-related parameters were set to 50%, for relative humidity to 30% while the remaining
20% accounts for the other parameters (sunshine duration, wind speed and precipitation).
As mentioned, the final selection of typical months is based on the simpler process introduced by Pissimanis
et al. (1988). Since maximum temperature represents the key determining meteorological parameter, the RMSD
of maximum temperature is calculated for the five candidate years (Table 7). Years finally selected per month are
presented in bold.
Figure 2. Estimated change of sales of bottled mineral water and non-alcoholic beverages as a result of a +1 C change in the
average monthly maximum temperature
Maximum
temperature
Minimum
temperature
Mean
temperature
Precipitation Relative
humidity
Wind
speed
Sunshine
duration
Weight 30% 10% 10% 10% 30% 5% 5%
Table 6. Weighting factors used for the development of the typical meteorological years for historic and future climate
S. Mirasgedis et al.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Figure 3 presents the monthly values of maximum temperature and relative humidity for the historic and future
climate calculated from the corresponding daily values of the TMYs developed. On average, mean annual maximum
temperature is expected to increase under the future climate by 1.4 C compared with the historic climate. On a
monthly basis, temperature increases are more pronounced during autumn periods and particularly in October,
when the maximum temperature is projected to increase by 3.0 C. During winter, spring and summer, maximum
monthly temperature increases, on average, by 0.8, 1.5 and 1.5 C, respectively. Changes of minimum and mean
temperature between historic and future climate follow a similar profile, while minor changes are observed for
the remaining parameters (e.g. relative humidity shown in Figure 3b).
Impacts of Climate Change on Product Sales
The empirical relationships developed using historic data in order to estimate the effect of weather conditions on the
demand for bottled water and the other non-alcoholic beverages in question have been utilized to estimate the
potential implications of future climate change for the level of product sales.
Table 8 presents the projected future monthly and annual changes in sales of bottled mineral water and other
non-alcoholic beverages attributable to the anticipated climate change in Greece for the period 2021–50 compared
to sales under the historic climate (1961–90). For all products examined, future climate (as simulated under the A1B
global GHG emissions scenario) will result in an increase in annual sales of all products examined. Specifically, the
total volume of sales is projected to increase by 1.1% for bottled mineral water, 2.9% for carbonated water, 1.2% for
soft drinks and 3.1% for juices. On a seasonal basis, the most significant increases in demand are expected in
autumn for bottled mineral water, carbonated water and soft drinks, and in spring for juices. On the other hand,
the total volume of sales in summer seems to be less affected by climate change.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Historic climate
3.2371 2.5820 3.5492 3.4458 2.6304 2.4000 3.6287 2.9602 3.1059 4.0684 2.3279 3.2379
1984 1982 1977 1988 1984 1988 1977 1986 1977 1970 1965 1974
2.5232 3.0443 3.3555 2.9314 3.1291 3.1661 2.9129 3.6161 2.5628 3.9097 2.6612 2.5408
1964 1983 1976 1986 1977 1981 1967 1963 1969 1975 1969 1978
3.4405 2.8763 2.3954 3.4494 2.7303 3.2687 2.8282 2.8647 3.4863 3.8832 1.8644 2.2017
1972 1988 1969 1990 1966 1975 1974 1987 1967 1980 1987 1971
2.6263 2.9217 3.3276 3.3719 3.3759 2.8757 2.7767 2.7039 2.6516 3.0020 2.6280 2.3136
1961 1987 1963 1968 1979 1984 1985 1964 1975 1976 1961 1986
4.0935 1.8709 3.7459 2.9051 2.6128 1.8589 2.4831 3.5020 3.6046 2.8898 2.6772 4.5677
1978 1973 1982 1976 1986 1990 1975 1972 1963 1965 1968 1984
Future climate
2.7317 3.1920 3.0589 1.9417 1.7007 3.0831 3.2969 1.9610 2.7650 2.0328 2.6879 4.0741
2047 2048 2025 2045 2036 2030 2040 2036 2041 2046 2031 2048
2.7038 4.2281 3.5996 4.5810 3.0275 2.8154 2.5816 2.9921 2.0654 2.1960 2.2300 3.3410
2039 2021 2034 2022 2041 2024 2033 2041 2047 2047 2044 2038
3.2718 4.2537 3.0287 2.4963 4.0578 2.6121 2.5964 3.8993 2.7687 2.4307 1.9985 3.1612
2025 2047 2036 2021 2046 2045 2039 2034 2027 2036 2036 2025
2.2027 2.7438 3.0285 3.6412 1.8015 2.8977 2.3518 2.7192 3.4011 2.0899 3.7143 1.8509
2036 2040 2028 2042 2050 2032 2048 2039 2028 2030 2049 2024
2.3067 3.1585 3.7659 2.8572 3.3510 2.2411 3.4347 2.5085 2.4827 4.1670 2.7890 2.6672
2048 2033 2031 2037 2039 2042 2025 2033 2023 2045 2024 2029
Table 7. Root mean square difference values for maximum temperature in historic and future climate and years selected per month
Climate Change and Demand for Bottled Water and Non-Alcoholic Beverages
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Regarding the behavior of different product types, the results of the analysis show that the sales of bottled mineral
water and soft drinks available in packages of less than 500 ml are most sensitive to weather conditions; this leads to
a higher expected growth of sales under future climate than for larger packages of the same products. This trend can
be explained by the fact that future climate is very likely to favor more outdoor activities, while the expected higher
temperatures will increase the demand for bottled mineral water and soft drinks in individual packs. Consumption
of bottled mineral water and soft dinks in family packages is also positively related to temperature increases, but to a
lesser extent than individual packs. On the other hand, the increased sales of carbonated water under the future cli-
mate seem to be irrelevant to the type of packaging. Finally, sales of juices in family packs seem to be more sensitive
to weather conditions compared with individual packs. As juice consumption mainly takes place within the home,
future climatic conditions will result in increased consumption of juices mainly in the form of family packs.
Some Conclusions
In this study, the potential impacts of the upcoming climate change in 2021–50 on consumer goods sales, and
specifically on the sales of bottled water and other non-alcoholic beverages, has been investigated and applied
to Greece as a case study. To this end, a methodological framework was developed comprising demand equations
derived from long-term existing data that incorporate economic, social and climate parameters, and a special pur-
pose TMY for future and historic climate constructed from the outcome of a high-spatial-resolution regional cli-
mate model taking into account the significance of various meteorological parameters as per the demand
equations. The use of special purpose TMY and the estimation of monthly values from the corresponding daily
Figure 3. Monthly maximum temperature (a) and relative humidity (b) under historic and future climate on the basis of the typical
meteorological year developed
S. Mirasgedis et al.
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
Bottled mineral water Carbonated water Soft drinks Juices
Total
volume
of sales
Sales in
packs
<500 ml
Sales in
packs
>500 ml
Total
volume
of sales
Sales in
packs
<500 ml
Sales in
packs
>500 ml
Total
volume
of sales
Sales in
packs
<500 ml
Sales in
packs
>500 ml
Total
volume
of sales
Sales in
packs
<500 ml
Sales in
packs
>500 ml
Jan 0.8 1.0 0.7 2.1 2.2 2.1 0.7 1.6 0.5 2.6 1.2 3.1
Feb 2.0 2.5 1.8 5.3 5.8 5.4 2.1 3.0 1.8 5.0 3.1 5.2
Mar 1.4 1.8 1.2 3.7 4.0 3.7 1.4 2.4 1.2 4.0 2.2 4.4
Apr 1.3 1.6 1.1 3.4 3.7 3.4 1.3 2.2 1.1 3.5 2.0 3.9
May 1.1 1.4 1.0 2.9 3.1 2.9 0.9 2.7 0.7 4.2 1.7 5.3
Jun 0.7 0.9 0.6 1.8 1.9 1.8 0.9 0.6 0.7 1.2 1.1 0.9
Jul 0.8 1.0 0.7 2.1 2.2 2.1 1.0 0.9 0.8 1.6 1.2 1.4
Aug 0.9 1.1 0.8 2.3 2.5 2.3 0.8 1.9 0.7 3.0 1.4 3.7
Sep 1.3 1.7 1.2 3.6 3.9 3.6 1.6 1.9 1.3 3.2 2.1 3.3
Oct 2.3 2.9 2.0 6.2 6.7 6.3 2.6 3.6 2.1 6.0 3.6 6.4
Nov 0.6 0.8 0.6 1.6 1.8 1.7 0.6 1.1 0.5 1.8 1.0 2.0
Dec 0.4 0.5 0.4 1.1 1.2 1.1 0.4 0.6 0.4 1.0 0.6 1.1
Total 1.1 1.4 1.0 2.9 3.1 2.9 1.2 1.9 1.0 3.1 1.8 3.3
Table 8. Estimated percentage increase of sales of bottled mineral water and non-alcoholic beverages attributable solely to climate change under A1B scenario for the
period 2021–50 compared with historic climate (i.e. 1961–90)
Climate Change and Demand for Bottled Water and Non-Alcoholic Beverages
Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. (2013)
DOI: 10.1002/bse
figures rather than using average monthly values for the entire period under consideration also enhanced the
accuracy of the results.
The results of the analysis clearly show that future sales for bottled water and non-alcoholic beverages in Greece for
the period 2021–50 could increase considerably under the future warmer climate associated with the A1B global GHG
emissions scenario of IPCC. Specifically, on an annual basis, the increase in the total sales volume attributed solely to
climate was estimated at 1.1% for bottled mineral water, 2.9% for carbonated water, 1.2% for soft drinks and 3.1% for
juices. It isalso worthmentioning that for all products examined these changes are not seasonally uniform, and in some
months exceed 6%. The increase is more pronounced in autumn and spring rather than summer, possibly because dur-
ing summer the consumption of bottled water and non-alcoholic beverages is already high and therefore there is not
much room for substantial further growth in sales. The expected rate of sales change depends also on the type of bev-
erage packaging. Thus, the sales of bottled mineral water and soft drinks in smaller packaging (i.e. 330 ml cans) seem to
be more weather-sensitive compared with family packs, while the opposite was found for juices. From the results of the
analysis itis obvious that future climate change is expected toaffect positively the demand for bottled mineral water and
non-alcoholic beverages, creating new business opportunities. Compared with historic climate (1961–90), future
warming in Greece during 2021–50 under the A1B global GHG emissions scenario is expected to average over 2C
and could result in an increase due to solely climatic conditions of the present (2010) annual turnover by €3.0 million
for bottled mineral water, €0.4 million for carbonated water, €6.8 million for soft drinks and €8.5 million for juices.
A large number of retail sales as well as other commercial activities will be influenced, mostly adversely, by climate
change. Others, such as the water and soft drinks industry, could be affected positively. It is important to continue exam-
ining and quantifying theeffect of climate changein all business activities, above and beyond the clearly important ones
of energy, if we want to provide better estimates of the impact in economic terms of climate change, to be juxtaposed
with the cost of mitigation. To this end, it is important for enterprises and business associations to develop:
•capacity for understanding the diverse impacts of climate change on the economy at both micro- and macro-level.
The identification of the climatic parameters that influence a specific activity, the nature of impacts, their severity,
the temporal and spatial scale at which they will occur, the way they influence businesses (directly or indirectly),
the uncertainties associated with climate research, etc., are important aspects that businesses need to understand
before developing adaptation strategies as well as short-to-medium business plans.
•analytical historic data collection and recording capacity at company level and appropriate tools that will allow
investigation of the potential impacts of climatic parameters on businesses in quantitative terms and at an appro-
priate level of analysis. In this context, the existence of short term or even daily data regarding the level of sales for
different products per size of package, region of sale, etc., together with local meteorological parameters as well as
other economic parameters (e.g. expenditures in advertising campaigns) could provide a sound basis for improv-
ing and taking advantage of models aiming at quantifying the influence of various parameters on the demand for
the products in question. This could result in adjusting the levels of production for specific products, enhancing
the stocks, implementing appropriate pricing policies, production of new products, etc.
Acknowledgement
The authors would like to thank the two (anonymous) Greek companies which provided the sales data for bottled mineral water
and non-alcoholic beverages.
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