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Water and Environmental Studies
Department of Thematic Studies
Linköping University
The electricity system vulnerability of
selected European countries to climate
change:
A comparative analysis
Daniel R. Klein
Master’s programme
Science for Sustainable Development
Master’s Thesis, 30 ECTS credits
ISRN: LIU-TEMAV/MPSSD-A--13/008--SE
Linköpings Universitet

Water and Environmental Studies
Department of Thematic Studies
Linköping University
The electricity system vulnerability of
selected European countries to climate
change:
A comparative analysis
Daniel R. Klein
Master’s programme
Science for Sustainable Development
Master’s Thesis, 30 ECTS credits
Supervisors:
Dr. Anders Hansson (Linköping University),
Prof. Dr. Jürgen P. Kropp (Potsdam Institute for Climate Impact Research)
2012
i
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© Daniel R. Klein
ii
Contents
1
List of Abbreviations
1.1 Two-Letter Country Codes (ISO 3166-1 alpha-2) . . . . . . . . . . . . . . . .
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Introduction
2.1 Problem Formulation .
2.2 Aim . . . . . . . . . .
2.3 Research Questions . .
2.4 Structure of the Thesis
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Background Information
3.1 Climate Change in Europe . . . . . . . . . . . . . . . .
3.2 The Effects of Climate Change on the Electricity System
3.3 European Political Context . . . . . . . . . . . . . . . .
3.4 Vulnerability . . . . . . . . . . . . . . . . . . . . . . .
3.5 State-of-the-Art . . . . . . . . . . . . . . . . . . . . . .
3.6 Influencing Factors . . . . . . . . . . . . . . . . . . . .
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Data and Methods
4.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Electricity Data . . . . . . . . . . . . . . . . . . . . .
4.1.2 Population Data . . . . . . . . . . . . . . . . . . . . .
4.1.3 Tourism Data . . . . . . . . . . . . . . . . . . . . . .
4.1.4 GDP Data . . . . . . . . . . . . . . . . . . . . . . . .
4.1.5 Air Conditioner Data . . . . . . . . . . . . . . . . . .
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Climate Data Calculations and Population Weighting .
4.2.2 Temperature Increase Calculations . . . . . . . . . . .
4.2.3 Tourism Data Calculations . . . . . . . . . . . . . . .
4.2.4 Percent Difference . . . . . . . . . . . . . . . . . . .
4.2.5 Heating and Cooling Temperature Thresholds . . . . .
4.2.6 Spearman’s Rank Correlation Coefficient Calculations
4.2.7 Slope Calculations . . . . . . . . . . . . . . . . . . .
4.2.8 Vulnerability Categories and Index . . . . . . . . . . .
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Results
5.1 Mean Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Category 1: Production, Consumption and Mean Temperature Spearman Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Category 2: Production, Consumption and Mean Temperature Slope . . . . . .
5.4 Category 3: Projected Temperature Increase . . . . . . . . . . . . . . . . . . .
5.5 Category 4: Air Conditioner Prevalence . . . . . . . . . . . . . . . . . . . . .
5.6 Category 5: Thermal Electricity Production Share . . . . . . . . . . . . . . . .
5.7 Category 6: Production and Consumption . . . . . . . . . . . . . . . . . . . .
5.7.1 Production and Consumption Spearman Correlation Coefficient . . . .
5.7.2 Percentage Discrepancy . . . . . . . . . . . . . . . . . . . . . . . . .
5.8 Category 7: Import and Export . . . . . . . . . . . . . . . . . . . . . . . . . .
5.9 Ranked Vulnerability Index . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5.10 Additional Data for Qualitative Analysis . . . . . . . . . . . . . . . . . . . . .
5.10.1 Monthly Electricity Production, Consumption, Imports and Exports Over
Time (2000-2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.10.2 Mean Monthly Electricity Production, Consumption, Imports, Exports,
and Mean Temperature . . . . . . . . . . . . . . . . . . . . . . . . . .
5.10.3 Electricity Production By Source . . . . . . . . . . . . . . . . . . . . .
5.10.4 Day Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.10.5 Heating Electricity Use . . . . . . . . . . . . . . . . . . . . . . . . . .
5.10.6 Tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
Discussion
6.1 Discussion of the Results . . . . . . . . . . . . . . . . . . .
6.1.1 Results Correlation with Existing Studies . . . . . .
6.1.2 Selected Index Countries . . . . . . . . . . . . . . .
6.1.3 Long Term Summer Electricity Consumption Trend
6.2 Discussion of the Methods and Limitations . . . . . . . . .
6.2.1 Methods . . . . . . . . . . . . . . . . . . . . . . .
6.2.2 Limitations . . . . . . . . . . . . . . . . . . . . . .
6.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . .
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7
Conclusions
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8
Acknowledgements
47
A Actual Category Indicator Tables
53
B Additional Results Figures
B.1 Electricity Production and Consumption by Mean Temperature . . . . . . . . .
B.2 Monthly Electricity Production, Consumption, Imports and Exports Over Time
(2000-2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.3 Mean Monthly Electricity Production, Consumption, Imports, Exports and Mean
Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.4 Mean Monthly Electricity Production Source . . . . . . . . . . . . . . . . . .
B.5 Monthly Electricity Production by Source Over Time (2000-2011) . . . . . . .
B.6 Electricity Production Source and Mean Temperature . . . . . . . . . . . . . .
B.7 Tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Figures
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Vulnerability Index Tree Diagram. . . . . . . . . . . . . . . . . . . . . . . . .
Possible heating and cooling country electricity system pathways. . . . . . . .
Monthly consumption - Long term summer electricity consumption trend. . . .
Production and consumption by mean temperature - Spearman correlation examples for heating and cooling values. . . . . . . . . . . . . . . . . . . . . . .
Production and consumption by mean temperature - Slope examples for heating
and cooling values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Actual Summer Temperature Increase Map (Scenario A2 1961-90 to 2070-99).
Actual Winter Temperature Increase Map (Scenario A2 1961-90 to 2070-99). .
Projected Air Conditioner Prevalence Map (2030). . . . . . . . . . . . . . . .
Projected Air Conditioner Percent Difference Map (2005-2030). . . . . . . . .
Thermal Electricity Production Share Map. . . . . . . . . . . . . . . . . . . .
Thermal Electricity Production Percent Change (2000-2011) Map. . . . . . . .
Monthly production, consumption, imports and exports over time. . . . . . . .
Monthly average production, consumption, imports, exports and mean temperature - Spearman correlation examples. . . . . . . . . . . . . . . . . . . . . . .
Monthly production, consumption, imports and exports over time - Production
and consumption percentage discrepancy examples. . . . . . . . . . . . . . . .
Ranked Vulnerability Index Map. . . . . . . . . . . . . . . . . . . . . . . . . .
Mean temperature vs. the percent difference of electricity consumption from
the annual average . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monthly Electricity Production and Consumption Over Time (2000-2011) . . .
Mean monthly electricity production, consumption, imports, exports and mean
temperature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mean monthly electricity production by source. . . . . . . . . . . . . . . . . .
Mean temperature vs. electricity production by source. . . . . . . . . . . . . .
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List of Tables
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Category 1: Production and Consumption Correlation to Mean Temperature
Ranked Index. Source: adapted from European Climate Assessment and Dataset
(2012) and IEA (2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Category 2: Production and Consumption and Mean Temperature Slope Ranked
Index. Source: adapted from European Climate Assessment and Dataset (2012)
and IEA (2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Category 3: Scenario A2 Temperature Increase 1961-90 to 2070-99 Ranked
Index. Source: adapted from Mitchell et al. (2002). . . . . . . . . . . . . . . .
Category 4: Air Conditioner Prevalence Ranked Index. Note: No data was
available for CH or NO. Source: adapted from Adnot et al. (2008). . . . . . . .
Category 5: Thermal Electricity Production Share. Source: adapted from IEA
(2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Category 6: Production and Consumption Summer and Winter Correlation and
Discrepancy Ranked Index. Source: adapted from IEA (2012). . . . . . . . . .
Category 7: Import and Export Percentage Discrepancy Ranked Index. Source:
adapted from IEA (2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ranked Vulnerability Index. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Category 1: Production and Consumption Correlation to Mean Temperature
Values. Source: adapted from European Climate Assessment and Dataset (2012)
and IEA (2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Category 2: Production and Consumption and Mean Temperature Slope Values.
Source: adapted from European Climate Assessment and Dataset (2012) and
IEA (2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Category 3: Scenario A2 Summer and Winter Temperature Increase (◦ C). Source:
adapted from Mitchell et al. (2002). . . . . . . . . . . . . . . . . . . . . . . .
Category 4: Air Conditioner Prevalence (Per Capita). Note: No data was available for CH or NO. Source: adapted from Adnot et al. (2008). . . . . . . . . .
Category 5: Thermal Electricity Production. Source: adapted from IEA (2012).
Category 6: Production and Consumption Summer and Winter Correlation and
Discrepancy. Source: adapted from IEA (2012). . . . . . . . . . . . . . . . . .
Category 7: Import and Export Percentage Discrepancy. Source: adapted from
IEA (2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
vi
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Abstract
The electricity system is particularly susceptible to climate change due to the close interconnectedness between not only electricity production and consumption to climate, but
also the interdependence of many European countries in terms of electricity imports and
exports. This study provides a country based relative analysis of a number of selected European countries’ electricity system vulnerability to climate change. Taking into account
a number of quantitative influencing factors, the vulnerability of each country is examined
both for the current system and using some projected data. Ultimately the result of the
analysis is a relative ranked vulnerability index based on a number of qualitative indicators. Overall, countries that either cannot currently meet their own electricity consumption
demand with inland production (Luxembourg), or countries that experience and will experience the warmest national mean temperatures, and are expected to see increases in their
summer electricity consumption are found to be the most vulnerable for example Greece
and Italy. Countries such as the Czech Republic, France and Norway that consistently
export surplus electricity and will experience decreases in winter electricity consumption
peaks were found to be the least vulnerable to climate change. The inclusion of some qualitative factors however may subject their future vulnerability to increase. The findings of
this study enable countries to identify the main factors that increase their electricity system
vulnerability and proceed with adaptation measures that are the most effective in decreasing
vulnerability.
Keywords: temperature change, thermal electricity production, air conditioners, heating
and cooling electricity consumption, electricity generation source
1
1
◦
List of Abbreviations
C
ECA
EU
GDP
GWh
IEA
IPCC
LED
PV
1.1
AT
BE
CH
CZ
DE
DK
ES
FI
FR
GB
GR
HU
IE
IT
LU
NL
NO
PL
PT
SE
SK
DEGREES CELSIUS
EUROPEAN CLIMATE ASSESSMENT AND DATASET
EUROPEAN UNION
GROSS DOMESTIC PRODUCT
GIGAWATT HOURS
INTERNATIONAL ENERGY ASSOCIATION
INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE
LIGHT-EMITTING DIODE
PHOTOVOLTAIC
Two-Letter Country Codes (ISO 3166-1 alpha-2)
AUSTRIA
BELGIUM
SWITZERLAND
CZECH REPUBLIC
GERMANY
DENMARK
SPAIN
FINLAND
FRANCE
UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN
IRELAND
GREECE
HUNGARY
IRELAND
ITALY
LUXEMBOURG
NETHERLANDS
NORWAY
POLAND
PORTUGAL
SWEDEN
SLOVAKIA
2
2
2.1
Introduction
Problem Formulation
Overwhelming evidence indicates that the climate change in Europe will likely result in an increase in temperature as well as higher frequency of extreme events such as heat waves and
droughts (Alcamo et al., 2007; Rübbelke and Vögele, 2011b). Due to the direct and close relationship between the electricity sector and climate, the changing climate will affect the entirety
of the electricity sector including production, imports and exports, distribution, and consumption in a negative way overall (McGregor et al., 2005; Michaelowa et al., 2010; Mimler et al.,
2009; The World Bank, 2008). The effect of climate on electricity demand is statistically significant, however not every country in Europe will be affected in the same way due to a variety
of factors that include not only temperature, but also different heating and cooling requirements
and the variety of sources used for electricity generation (Eskeland and Mideksa, 2010). Climate change is a broad concept with many aspects such as precipitation changes and sea level
rise for example, however this study focusses primarily on temperature increases.
There have been numerous studies focused on the effects of climate change on the energy
sector, and further studies have looked into electricity production, supply and consumption
specifically (Eskeland and Mideksa, 2010; Flörke et al., 2011; Mimler et al., 2009). Furthermore, European energy security, vulnerability and adaptation have been addressed both by
research and government reporting (Commission of the European Communities, 2006, 2009;
World Energy Council, 2008). The gap in the existing studies lies with the scope; the majority
of the studies already completed address the electricity sector from a single country perspective
(Rothstein and Parey, 2011) or very generally for the entirety of Europe or one region (van
Vliet et al., 2012), leaving it difficult to examine several countries comparatively. Furthermore,
studies that do include a larger scope geographically are limited in terms of influencing factors
(Eskeland and Mideksa, 2010; Rübbelke and Vögele, 2011b).
In light of the seemingly growing vulnerability of the European electricity sector, a country
based analysis of vulnerability of the electricity sector to climate related temperature changes
is a useful tool to help facilitate the adaptation of the electricity system to the changing climate.
As well, the comparative analysis of countries in terms of their vulnerability enables the easy
identification of countries that require immediate and more thorough adaptation measures to
be implemented. Due to the interconnectedness of the European electricity system, this study
will set its scope on a European scale, with data for each selected country. The 21 countries
included in the study are Austria (AT), Belgium (BE), Czech Republic (CZ), Denmark (DK),
Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Italy (IT),
Luxembourg (LU), The Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Slovak
Republic (SK), Spain (ES), Sweden (SE), Switzerland (CH) and the United Kingdom (GB). As
a result of constraints due to data availability, it was not possible to include additional countries
in the study.
2.2
Aim
The aim of this research project is to determine the relative electricity system vulnerability of
21 European countries to climate change using both quantitative and qualitative indicators, with
the goal of ultimately providing a comparative analysis of the countries based on a number of
influencing factors.
3
2.3
Research Questions
• What is the magnitude of the differences between the electricity system vulnerability of
European countries to climate change?
• What is the relationship between the electricity system and temperature and the components of the electricity system among each other?
• What is the influence of temperature, both directly and indirectly, on the electricity production, consumption, import and export of a country?
• Is it possible to discern geographic patterns of countries with similar vulnerabilities to
climate change?
2.4
Structure of the Thesis
The background information chapter of this thesis (Chapter 3) follows the introduction and
gives some context and basic grounding for the study. Chapter 3 also includes a summary of
existing work related to this thesis. The data and methodology section, which provide both
a description of the data sources (4.1) as well as an explanation of the methods used in the
study (4.2), is found in Chapter 4. In the 5th Chapter we introduce the results and findings
of the study which are divided by category. The following section (Chapter 6) is a discussion
of the results of the study, which includes a comparison with existing studies findings’ as well
as an analysis of selected countries (6.1.2). In addition, the methodology of the study and the
limitations are discussed (6.2) followed finally by potential future work (6.3). The report closes
with conclusions of the study in Chapter 7.
3
3.1
Background Information
Climate Change in Europe
There is ample evidence from the Intergovernmental Panel on Climate Change (IPCC), as well
as other sources, to suggest that in Europe and elsewhere in the world, the climate is changing
(Alcamo et al., 2007; Commission of the European Communities, 2009; European Commission, 2009c; Rübbelke and Vögele, 2011b). Regardless of mitigation efforts mean temperatures
are expected to increase for the majority of land areas, depending on geography and season,
and by some estimates continue to rise for many decades, if not centuries, due to anthropogenic
climate change (Alcamo et al., 2007; Rebetez et al., 2008; The World Bank, 2009).
Both positive and negative impacts of climate change will be experienced in varying degrees
in Europe depending on region and sector (Commission of the European Communities, 2009).
According to the IPCC, all countries are sensitive to climate change however, “the sensitivity of
Europe to climate change has a distinct north-south gradient, with many studies indicating that
southern Europe will be more severely affected than northern Europe” (Alcamo et al., 2007,
p.547). Warmer southern European countries are likely to experienced increases in temperature
and decreases in precipitation, while colder northern European regions will experience warmer
winters, and higher precipitation (Alcamo et al., 2007; Eskeland and Mideksa, 2010).
4
3.2
The Effects of Climate Change on the Electricity System
A number of studies examine the potential effects of climate change on the electricity system
in terms of temperature increases (Eskeland and Mideksa, 2010; Rothstein and Parey, 2011;
Rübbelke and Vögele, 2011b; van Vliet et al., 2012). There is reasonable certainty in the direct
effects of weather and climate on electricity production, consumption, supply and generation
technologies, and indirectly, imports and exports of electricity. Electricity generation technologies vary greatly in their sensitivity to climate change, however most if not all of the main
stream generation technologies such as coal, oil, nuclear, and hydro, will be affected in some
way by climate change related changes in weather or extreme events (Rademaekers et al., 2011).
Electricity consumption is also highly affected by climate, primarily due to the high percentage
of electricity used for heating and cooling, both in the residential sector and industrial sector
(Eskeland and Mideksa, 2010).
Some particularly problematic effects of climate change in terms of the electricity system
include the increased frequency of prolonged elevated temperatures, as well as an increase in
mean temperature (Alcamo et al., 2007; Rademaekers et al., 2011). As previously mentioned,
climate effects may differ by region, and countries in the traditionally warmer south of Europe
will be more highly affected by elevated temperatures, and face more serious consequences (Alcamo et al., 2007; Commission of the European Communities, 2009). On the consumption side
of the electricity system, as the temperature rises, regions that cool by air conditioner experience electricity consumption increases, this effect is also heighted in cities due to the heat island
effect (The World Bank, 2008).
The effects caused by heightened temperatures on the production side of the electricity system can be problematic for a variety of generation sources, however from a comparative outlook, thermal electricity production is most vulnerable (Rademaekers et al., 2011; Rübbelke
and Vögele, 2011b; van Vliet et al., 2012; Wilbanks et al., 2008). Thermal electricity generation (fossil fuels and nuclear among others) is sensitive to heat waves and lowered precipitation
due to their use of large volumes of cooling water, which, could be lacking in quantity and elevated in temperature, and may decrease efficiency and plant output (Rademaekers et al., 2011;
Rübbelke and Vögele, 2011a; van Vliet et al., 2012). Furthermore, legal requirements, which
differ by country and region, may prevent the discharge of cooling water during periods of elevated temperatures, forcing a decrease in electricity production (Rübbelke and Vögele, 2011b).
There is already a precedent for the effects of heat waves on thermal electricity generation, in
the summer of 2003, as well as 2006 and 2009, cooling water shortages and heightened temperature caused disruptions in thermal electricity generation (especially nuclear) in a number
of countries in Europe (Flörke et al., 2011; Rübbelke and Vögele, 2011b; van Vliet et al., 2012).
The effects of temperature increases due to climate change may lead to lowered precipitation and increase water usage overall, which can be problematic for hydro electricity production(Rübbelke and Vögele, 2011a). Furthermore, some studies suggest that solar photovoltaic
(PV) electricity production is also impeded by increased temperature that decreases cell efficiency (Crook et al., 2011; Rademaekers et al., 2011). While extreme increased temperature
events definitely impact a number of electricity generation sources, the impact, in terms of magnitude and scale, on thermal electricity generation outweighs the impacts on other technologies
(Rübbelke and Vögele, 2011a; Wilbanks et al., 2008).
5
3.3
European Political Context
The European political context in terms of climate change vulnerability of the electricity system, or more generally the energy system, to climate change is such that research on adaptation
has already begun. Based on the wide range of reports by not only the European Commission
itself, but other international bodies, it is possible to say that vulnerability and adaptation to
climate change are a pressing issue.
While a 2009 White paper report by the European Commission on adaptation to climate
change does not address the electricity system directly, it explicitly states the importance of
energy security and the potential risks of climate change to critical infrastructures (Commission of the European Communities, 2009). The report’s main goal is the creation of an action
framework to facilitate adaptation to climate change (Commission of the European Communities, 2009). The European Commission also published a Green Paper addressing the need for
sustainable European energy security (Commission of the European Communities, 2006). Explicitly integrated into the report is adaptation and the increase of energy security in the face of
climate change (Commission of the European Communities, 2006). The World Bank has also
published a report describing Europe and Central Asia’s vulnerabilities to climate change along
with a framework for adaptation plans (The World Bank, 2009). The report includes specific
components of the electricity production and consumption system, while identifying potential
vulnerabilities, ultimately introducing adaptation options (The World Bank, 2009).
3.4
Vulnerability
Vulnerability, as defined by Turner et al. “is the degree to which a system, subsystem, or system
component is likely to experience harm due to the exposure to a hazard, either a perturbation or
stress / stressor” (Turner et al., 2003, p.8074). Essentially, vulnerability is the susceptibility of
a system to threats or disturbances, the extent to which the system is impacted and the ability
of the system to cope with these disturbances (Holmgren, 2007). Disturbances to the electricity
system “can originate from natural disasters, adverse weather, technical failures, human errors,
labor conflicts, sabotage, terrorism and acts of war” (Holmgren, 2007, p.31). This study will
focus on the electricity system vulnerability to climate change.
3.5
State-of-the-Art
As previously mentioned, a number of studies have been conducted related to the topic of the
effects of climate change on the electricity system, the most relevant of which are summarized
here. A recent study by van Vliet et al. (2012) examines the vulnerability of the thermoelectric
electricity production to climate change for the United States and Europe. The study focuses
on the impacts of reduced river flows and increased river temperatures on thermal electricity
production that use river water for cooling. The results conclude that a significant negative
effect on electricity production will be seen, particularly in southern and southeastern Europe.
The study also suggests possible adaptation strategies for Europe and the United States to help
decrease their vulnerability, which include changing cooling infrastructure, or even a shift to
gas fired thermal power plants which are less water intensive than coal or nuclear.
A related study by Rübbelke and Vögele (2011b), looks at the effects of climate change
specifically on nuclear electricity generation which is then related to the energy system as a
whole. The study characterizes the European electricity system and identifies a number of vul6
nerabilities, citing principally the availability and temperature of cooling water used for nuclear
power plants. The authors go further in identifying particularly vulnerable countries in Europe
that rely heavily on nuclear electricity production or imports and offer a number of strategies to
deal with the potential shortage of electricity supply in Europe due to climate change.
Eskeland and Mideksa (2010) examined the relationship between temperature and electricity demand on a European level. The study included only the effects of temperature on electricity demand in a number of European countries, but included no other influencing factors. The
study suggests that the net effect of climate change on electricity demand is small, but increases
in summer electricity consumption and decreases in winter electricity consumption are likely,
which depends significantly on the geographic location and climate of a given country.
In the north and central parts of Europe, heating related energy demand and consumption
will decrease due to warmer winter temperatures over the next decades, and will predominate
over increases in cooling related electricity consumption (Alcamo et al., 2007; Eskeland and
Mideksa, 2010; Olonscheck et al., 2011). The opposite is true however for the south of Europe
where increases in cooling related electricity consumption will outweigh any heating decreases
(Alcamo et al., 2007; Eskeland and Mideksa, 2010). The south of Europe will be most affected,
specifically in the southern Iberian Peninsula (Spain), the Alps, the eastern Adriatic seaboard
(Italy), southern Greece, as well as Turkey, Cyprus and Malta (Alcamo et al., 2007; Eskeland
and Mideksa, 2010).
Rothstein and Parey (2011) published an analysis of climate change adaptation options for
the electricity sector in Germany and France. Most useful in the context of this study is their
identification of the impacts of weather and climate change on the electricity sector, however an
in-depth examination of adaptation options also included in the report. The study discusses the
impacts of climate change on both electricity production and consumption. On the production
side, cooling water requirements (in terms of quality, quantity and temperature) for thermal
electricity plants are examined, alongside water requirements for hydro electricity production
are analyzed among other potential climate related impacts. The effects of climate change on
electricity consumption are also discussed addressing seasonal consumption differences as well
as lighting and the 2003 European summer heat wave.
3.6
Influencing Factors
The influencing factors chosen for this study are by no means exhaustive, but were chosen as
being significant in terms of their impact on electricity consumption and their ability to demonstrate potential vulnerabilities. The one important influencing factor the direct effect of temperature, which, due to climate change has an increasingly large impact on the electricity system
as a whole (Eskeland and Mideksa, 2010; Mimler et al., 2009; Rübbelke and Vögele, 2011b;
van Vliet et al., 2012). The electricity production sources of each country included in the study
were also chosen as being an important indicator, especially in terms of vulnerability (Eskeland
and Mideksa, 2010; Mimler et al., 2009; Rübbelke and Vögele, 2011b). Electricity imports and
exports were considered in order to not only identify vulnerabilities related to import dependence, but also to help characterize the electricity system (Rübbelke and Vögele, 2011b). In
this study, the electricity system of each country was characterized by their production, consumption, imports and exports, however each of these is also affected by qualitative factors that
are external to temperature such as political and social influences.
7
The following additional influencing factors are more external from the electricity system,
but are nevertheless important to include. Cooling electricity consumption is mainly dependent
on air conditioner prevalence (Bertoldi and Atanasiu, 2009; Hekkenberg et al., 2009a; Olonscheck et al., 2011; Rademaekers et al., 2011; Rübbelke and Vögele, 2011a; van Vliet et al.,
2012; Wilbanks et al., 2008) and is affected by geographic differences between countries due to
seasonal changes in day length, which vary in magnitude by longitude, something which was
addressed qualitatively (Bertoldi and Atanasiu, 2009; Hekkenberg et al., 2009a; Kotchen and
Grant, 2008; Lapillonne et al., 2010). Finally, the qualitative effects of tourism, especially when,
due to tourism arrivals, population increases, have a small but potentially significant effect on
electricity consumption (Bakhat and Rosselló, 2011; Becken and Simmons, 2002).
4
4.1
Data and Methods
Data Sources
This study utilized a number of data sets obtained from different international and European
organizations. Temperature and electricity data were the principle components of the study,
however population, GDP, air conditioner prevalence as well as tourism data were also utilized.
4.1.1
Electricity Data
Monthly electricity data (in Gigawatt hours (GWh)) for the time period from January 2000 to
December 2011 was taken from the International Energy Agency (IEA) (IEA, 2012). The IEA
data was available for each country included in the study and was split into a number of categories: production by combustible fuels (which includes fossil fuels as well as combustible
renewables and wastes), production by nuclear, production by hydro (which includes pumped
storage), production by other sources (which includes geothermal, wind, solar, among others),
total production (the sum of the production by source), imports, exports and total supply (which
is determined by the following equation: production + imports - exports) (IEA, 2012).
The actual electricity consumption of a country is very difficult to determine, therefore the
electricity supplied to the grid (which is the only data available) is used as a proxy for consumption in this study. From this point forward in the report, in an effort to avoid confusion,
electricity supply will be referred to as electricity consumption, or simply consumption. Electricity transportation losses are not accounted for in the IEA data.
The monthly electricity data used for the long term electricity consumption plots for IT
came from the Italian electricity transmission system operator (Terna, 2012). The electricity
consumption data was available for the years 1981 to 2012, however only the values until the
year 2011 were used in the plots due to the temperature data availability. For the similar long
term plots for ES and GR, the 1991-2011 data was taken from the ENTSO-E (2011).
c
The mean daily temperature (in ◦ C) was obtained from the European Climate Assessment
and Dataset (ECA and D) for the years 1961 to 2011 (European Climate Assessment and
Dataset, 2012) and aggregated to monthly values. The data has a resolution of 0.25◦ x 0.25◦ and
comprises an area of 25N-75N x 40W-75E.
Temperature Increase Projection Data The projected temperature data was unavailable
from the ECA, which only gathers historical data, requiring the projected data to be from a
8
different source. The temperature increase projection data (in ◦ C) was available from the Tyndall Centre, which included data from 9 global climate models, all of which have been reviewed
by the IPCC (Mitchell et al., 2002). The data was a prediction of temperature changes between
the years 1961-90 and 2070-99 for A2 scenarios developed by the IPCC. The A2 scenario
predicts countries operating self reliantly with ongoing population increases and regional economic development (IPCC, 2000). We made use of two seasonal groups: winter data included
the months of December, January and February, and summer data included the months of June,
July and August.
4.1.2
Population Data
EUROSTAT provided both actual and projected population data which was used in the air conditioner weighting calculations, as well as the tourism data (EUROSTAT, 2012). Two yearly
population data sets were used, the actual population data (Population on 1 January by age and
sex) was used until the year 2010 as required, while the estimated projected population (not
forecasted) (1st January population by sex and 5-year age groups) was used for the years after
2010 (EUROSTAT, 2012).
The gridded population data set used in the climate population weighting calculations was
also provided by EUROSTAT (2006). The data included only 2006 population values for the
grid cells with a resolution of 1km2 , which covered the entirety of Europe.
4.1.3
Tourism Data
Two EUROSTAT data sets were used for the tourism indicator calculations and plots. The first
was the Arrivals in tourist accommodation establishments - national - monthly data [tour-occarm], which provided tourism arrivals to hotels, holiday and other short stay accomodation,
camping grounds, recreational vehicle parks and trailer parks from January 2000 to March
2011 (EUROSTAT, 2012). The data set provided arrivals of residents and non-residents, however only the non-resident arrivals were used. The other data set was the Number of trips holiday trips (4 or more overnight stays) - by month of departure - annual data [tour-dem-ttmd]
which provided data from January 2000 until December 2011 (EUROSTAT, 2012). The data
included the number of trips by residents over the age of 15 years, traveling to another country
(EUROSTAT, 2012).
4.1.4
GDP Data
Gross domestic product (GDP) data was also used for this study, the data set GDP and main
components - Current prices [nama-gdp-c] which gave an indication of GDP rise between
2000 and 2011 (EUROSTAT, 2012). The GDP data was utilized qualitatively in the discussion
section, but not quantitatively for any of the results.
4.1.5
Air Conditioner Data
Air conditioner stock data was available by country for the years 2005 with predictions for the
years 2010, 2015, 2020, 2025 and 2030, in a paper authored by Adnot et al. (2008). Total residential, office and retail air conditioner stock data was divided by the population (actual and
predicted from EUROSTAT) for each of the years where air conditioner data was available (Adnot et al., 2008; EUROSTAT, 2012). Unfortunately, air conditioner stock data was unavailable
for NO and CH, and those countries could therefore not be included.
9
4.2
4.2.1
Methods
Climate Data Calculations and Population Weighting
The daily mean temperature data (European Climate Assessment and Dataset, 2012) was averaged by month and weighted by population data (EUROSTAT, 2006) in order to account for
the fact that electricity consumption and to a somewhat less extent electricity production are not
distributed evenly across a country, but are often concentrated in areas where people live. This
aspect of electricity consumption and production is especially important to consider in countries such as NO or SE, where the majority of residents live in the warmer southern parts of the
country, but taking an average temperature for the whole country would result in a colder mean
temperature, something which is not expressly experienced by the majority of the population.
The population weighting of the temperature data was completed in ArcGIS (ESRI, 2011),
with the first step being the allocation of the grid cells for both the temperature and population
data sets into their respective countries. The weighting was then completed for each country
using equations (1) and (2) seen below.
popi, j
Wi, j = Pn j
i=1 popi, j
T mean, j =
nj
X
T i, j · Wi, j
(1)
(2)
i=1
Wi, j : The relative population factor for grid cell i in country j.
popi, j : The population of
n grido cell i in country j.
i : A single grid cell (i 1...n j ).
j : A single country.
n j : The number of grid cells in country j.
T mean, j : The population weighted monthly mean temperature for the entire country j.
T i, j : The mean monthly temperature for grid cell i in country j.
4.2.2
Temperature Increase Calculations
The temperature increase data required little calculation, however each country temperature
change projection included 9 values, one for each of 9 different global climate models, which
were averaged in an effort to acknowledge the differences between the projections.
4.2.3
Tourism Data Calculations
The tourism data for the years 2000 to 2010 was calculated using three EUROSTAT data sets:
tourism accommodation arrivals in tourist accommodation establishments, number of trips and
total population. The arrivals and trips data was divided by the total population, with the mean
of each month for the entire time period being used in the final plots.
4.2.4
Percent Difference
For a number of the data plots created for this study, the percent difference from the annual
mean of the electricity data was calculated. The percent difference calculation was necessary
in order to facilitate the comparison between countries as well as to eliminate or minimize the
overall increase in data values over the time period examined due to population growth and
10
GDP, which would bias our results. The calculation was an effort to isolate the temperature as
an influencing factor. The percent difference for all of the data was calculated using equation
(3) below.
"
#
Emonth,year − Ēyear
∆E =
(3)
Ēyear
∆E : The percent difference from the annual mean.
Emonth,year : The electricity production or consumption for a specific month and year.
Ēyear : The mean electricity production or consumption of all of the months in a
specific year.
4.2.5
Heating and Cooling Temperature Thresholds
Due to the non linear nature of the correlation between electricity production or consumption
and temperature, it was necessary to divide the data into three parts based on heating and cooling
temperature thresholds (Sailor and Muiqoz, 1997; Valor et al., 2001). In principle, the heating
and cooling thresholds represent the temperatures in between which, no heating or cooling is
required. Temperatures either below the heating threshold when heating is required, or above
the cooling threshold when cooling is required therefore bound this area. The thresholds used
in this study were decided based on existing studies. Temperature values less than or equal
to the heating threshold of 12◦ C were used as heating values (Matzarakis and Thomsen, 2007;
Prettenthaler and Gobiet, 2008), those greater than or equal to the cooling threshold of 21◦ C as
cooling values (Engle et al., 1992; Prek and Butala, 2010; Valor et al., 2001).
4.2.6
Spearman’s Rank Correlation Coefficient Calculations
Using the electricity production and consumption data (Figure 16 to Figure 19) with values that
correspond to temperatures below the heating threshold (12◦ C) and above the cooling threshold
(21◦ C), a Spearman correlation coefficient was calculated for each country, both for heating and
cooling. The majority of countries did not experience mean temperatures above the cooling
threshold and were excluded, and countries with fewer than 10 months over the entire time
frame with mean temperature values above the cooling threshold (10 data points) were excluded
as well for both this category and Category 2. The Spearman correlation coefficient provides
information on the influence of temperature on electricity production and consumption, and
gives good insight into the likely predictability of future production and consumption should the
mean temperatures in a given country change. The Spearman correlation coefficient was chosen
due to its properties which fit the data sets in questions. The Spearman correlation coefficient
describes non-parametric monotonic functions which describe the data and is frequently used
in a wide range of academic disciplines. There do exist a number of alternative possibilities in
therms of correlation calculations, however there is no compelling evidence in this case to use
a different method. The Spearman correlation coefficients were calculated using equation (4) in
R (R Development Core Team, 2012):
P
(xi − x̄) (yi − ȳ)
(4)
ρ= q i
P
2P
2
(x
(y
−
x̄)
−
ȳ)
i
i
i i
ρ : Spearman’s rank correlation coefficient.
xi , yi : Ranked variables.
x̄, ȳ : Variable mean.
11
4.2.7
Slope Calculations
Similar to the Spearman coefficient calculations, a slope was calculated for each country based
on the electricity production and consumption percent difference from the annual average data
and plots by mean temperature. Two slope calculations were completed for each country, one
for values below the heating threshold and one for values above the cooling threshold. The
slope values give an indication of the extent to which electricity production and consumption is
affected by temperature. The steeper the slope, the more drastically the electricity production
and consumption changes with each given temperature change when comparing the countries.
4.2.8
Vulnerability Categories and Index
A number of vulnerability indicators, which include both current and projected vulnerabilities,
were calculated in order to quantitatively generate a relative vulnerability index of each of the
countries included in the study. The methodology of each of the indicators is addressed below,
and ultimately the indicators were grouped into seven categories (presented below), which were
used in the final index value calculation. The absolute indicator values were not used in the
index calculation; instead each of the indicator values was normalized by the maximum value
in the group, giving a range of values. For indicators that have a potential positive effect on
vulnerability, the range from -1 to 0 is used. Similarly, for indicators that potentially have a
negative effect on vulnerability, the range from 0 to 1 is taken. For some indicators, the countries were first divided into more and less vulnerable groups and then divided by the maximum
of the new groups to differentiate between the increases and decreases of vulnerability.
The indicator values in each category were averaged to give an index value for each category. Countries that did not reach the cooling threshold were excluded from the calculation
in the correlation and slope categories. It is important to distinguish between categories that
provide projected vulnerability indicators (Categories 1-4) and categories that provide current
vulnerability indicators (Categories 5, 6 and 7), each of which will be discussed below. The final
category index values were averaged for each country, giving a final comparative vulnerability
index. The equation for the category and final ranked index calculations can be seen below in
equations (5) and (6) along with a diagram tree of the influencing factors and categories (Figure
1). The individual categories are briefly presented in the following paragraphs.
Pf
n=1 vn
Cx =
f
Pk
I=
Pf
n=1 vn
x=1
f
k
C x : Category index value for category x.
x : A single category.
v f : Influencing factor index value.
f : The number of influencing factors in Category x.
I : The final ranked index value.
k : The number of categories.
12
(5)
(6)
Category 1: Production, Consumption and Mean Temperature Spearman Correlation
Coefficient The Spearman correlation factor for production and consumption on the heating
side was calculated to give an additional indication and quantitative measure of the relationship
between electricity and temperature. The higher the correlation value is, the more linear and
less variable the effects of temperature are on consumption or production. Countries with strong
correlation to temperature on the heating side were determined to have less potential vulnerability due to the fact that as temperature increases, the winter peak decreases. The opposite
is true on the cooling side however, the group with strong correlation to temperature is potentially more vulnerable because as the temperature increases, so too does electricity production,
but more importantly consumption. The correlation values for the electricity production and
consumption and mean temperature for both heating and cooling were ranked separately (by
dividing by the maximum value for each indicator) and then all four of the indicator values
were averaged to determine the final category index value for each country.
Category 2: Production, Consumption and Mean Temperature Slope Slopes were calculated for both heating and cooling values for electricity production and consumption against
mean temperature. As with the previous category, the final index value for the category was
an average of all four ranked indicator components. Similar to the previous indicator as well,
countries with steep slope on the heating side were determined to be less potentially vulnerable,
while the opposite is true for the cooling side.
Category 3: Projected Temperature Increase The temperature increase category consists
of both summer and winter temperature increase values for the IPCC A2 scenario, again ranked
by the maximum values, and averaged to provide a final category index value. The A2 scenario was chosen, despite having access to other scenarios as well, because it is a more extreme
climate scenario and would better illustrate the relative temperature changes between the countries. The values were averages of 9 models and the variation of temperatures among the models
was greater than that between the averaged scenario values. Furthermore, the utilization of a
different scenario would have little effect on the relative values resulting from this category, and
primarily have an effect on the magnitude of the temperature increase. A comparative calculation was undertaken for the B2 IPCC scenario, which yielded an almost identical relative ranked
index result for the temperature changes.
Summer temperature increases were determined to increase vulnerability, while increases
in winter temperatures were deemed to decrease vulnerability. Warmer winters would decrease
electricity consumption used for heating, decreasing vulnerability, while hotter summers would
mean more air conditioner usage and therefore greater electricity consumption, thus increasing
vulnerability. The indicator provides a relative quantitative outlook of the differences between
the projected summer and winter temperature changes for each country. The final ranked index
values were calculated by the average of the summer and winter index values.
13
14
Hea$ng ‐ Consump$on and Mean Temperature Slope Hea$ng ‐ Produc$on and Mean Temperature Slope Winter ‐ Produc$on and Consump$on Correla$on Summer Import and Export Discrepancy Winter Import and Export Discrepancy Winter ‐ Produc$on and Consump$on Discrepancy Thermal Produc$on Change (2000‐2011) Thermal Produc$on Percent (2011) Summer ‐ Produc$on and Consump$on Discrepancy Air Condi$oner Percent Difference (2005‐2030) Cooling ‐ Consump$on and Mean Temperature Slope Cooling – Consump$on and Mean Temperature Correla$on Air Condi$oner Projec$on (2030) Summer ‐ Projected Temperature Increase Cooling – Produc$on and Mean Temperature Slope Cooling – Produc$on and Mean Temperature Correla$on Category 7: Import and Export Category 6: Produc$on and Consump$on Category 5: Thermal Electricity Produc$on Category 4: Air Condi$oner Prevalence Category 3: Projected Temperature Increase Category 2: Produc$on, Consump$on and Mean Temperature Slope Category 1: Produc$on, Consump$on and Mean Temperature Spearman Correla$on Coefficient Figure 1: Vulnerability Index Tree Diagram (Blue = indicators that decrease vulnerability, Red = indicators that increase vulnerability)
Current Vulnerability Indicators and Categories Summer ‐ Produc$on and Consump$on Correla$on Projected Vulnerability Indicators and Categories Winter ‐ Projected Temperature Increase Hea$ng ‐ Consump$on and Mean Temperature Correla$on Hea$ng ‐ Produc$on and Mean Temperature Correla$on Final Ranked Vulnerability Index Category 4: Air Conditioner Prevalence The residential air conditioner stock for the majority of countries was available, and was divided by the population to give an air conditioner
factor. Two air conditioner prevalence indicators were included in the category, one being the
projected air conditioner stock for the year 2030, and the other being the percent difference
between the 2005 and 2030 air conditioner stock. Air conditioners were considered to increase
vulnerability, and both indicators were determined to be important to characterize the vulnerability. The 2030 predictions give an indication of the future prevalence of air conditioners,
which will increase cooling electricity consumption and therefore also increase vulnerability.
The change in stock was also important to include in order to provide an indicator of the magnitude of the air conditioner stock change in comparison to the current situation. Countries with
larger increases in air conditioner stock were considered to have greater increases in terms of
vulnerability, due to the fact that the magnitude of change to the system and electricity consumption was greater. As with the other categories, the maximum ranked indicators were averaged
to provide the final category index value. It is important to note that the air conditioner factor is
a proxy for all electricity cooling, such as, for example, industrial cooling for which there is no
available data.
Category 5: Thermal Electricity Production Share The thermal electricity production category is divided into two influencing factors, the first being the current (2011) annual average
percentage of total electricity production that is generated by thermal sources (combustible fuels
and nuclear). Countries with higher percentages of thermal electricity production were deemed
to be more vulnerable based on the vulnerability of the sources. The second influencing factor
is the difference between the 2000 and 2011 percentage of thermal source electricity production
which was included in order to address changes in the system, most notable countries that are
actively increasing or decreasing their thermal electricity production over time. Countries experiencing decreases in the share of thermal electricity production have lower vulnerability than
those experiencing increases. The two ranked indicators were averaged to produce the category
index value.
Category 6: Production and Consumption The electricity production and consumption category includes the Spearman correlation coefficient as well as the discrepancy (between production and consumption) indicators. Both indicators were calculated for the summer (June,
July, August) and winter (December, January, February) months only, the same months as the
temperature change data. In terms, of vulnerability, stronger correlation between electricity production and consumption was determined to indicate lower vulnerability, as it implies a greater
ability to deal with changes in the electricity system. The values were ranked and divided by
the strongest correlation value.
The percentage discrepancy between electricity production and consumption was calculated
by simply dividing the production by consumption for each country, with the monthly values
over the entire time frame being averaged to give the final values. The discrepancy values
were then divided into two groups, net producing countries (with values >1) and net consuming countries (with values <1). The values were then ranked, as with the other indicators, but
each group was ranked separately, with net producing countries being ranked by diving by the
greatest net producer, and the consuming countries divided by the lowest consuming value. Net
producing countries were determined to be less vulnerable due to the fact that they can meet
their consumption demand with inland production on average. Finally, as with the other categories, all four indicators of the index values (summer and winter, correlation and discrepancy)
15
were averaged.
Category 7: Import and Export The summer and winter magnitude of imports or exports
were the two indicators included in Category 7. The seasonal values were used to remain consistent with the other indicators. For each country, the absolute export values for the summer
(June, July, August) and winter (December, January, February) months (2000-2011) were subtracted from the import values. The difference was then divided by total electricity production in
order to determine the extent to which a country is a net importer or exporter. Countries reliant
on electricity imports were determined to be vulnerable, while countries that are net exporters
were determined to be less vulnerable. As with the previous category, the maximum ranking
was completed after separating the countries into net importing and net exporting groups. Due
to the fact that the indicator index value is the only component of the category, no further calculations were required.
5
Results
The vulnerability indicator tables containing the actual unranked values can be found in Appendix A and the plots for all of the countries are available in Appendix B. This section only
includes the ranked index indicator and category tables as well as the plots from selected countries that were chosen to represent the vulnerability categories. The monthly production, consumption, import and export plots over time (Figures 12, 14 and Appendix B: Figures 20 to 23)
and the monthly average production, consumption, import, exports and temperature plots (Figure 13 and Appendix B: Figures 24 to 27) both present actual electricity values, and therefore
have different y-axis scales. This is due to the fact that there is a large difference in magnitude
between countries in terms of the electricity values, which would make the plots ineffective and
unreadable for the countries with smaller values as the variations between months or seasons
would be too small to see. All of the plots were created using R (R Development Core Team,
2012).
5.1
Mean Temperature
The mean monthly temperature for all of the countries examined in this study demonstrates a
typical European temperature curve and is included in the monthly average production, consumption, import and export plots in Appendix B (Figure 24 to Figure 27). The highest temperatures are in the months of June, July and August, while the lowest temperatures fall in
December, January and February. Of course there are variations in terms of magnitude and
range, with FI having the largest temperature range over the course of the year (just under 35 ◦ C
range), and PT and IE having the smallest (just under 17◦ C range). The other country of note
in terms of temperature is HU, which reaches the cooling temperature threshold consistently
enough to be considered in the cooling group, but is geographically in a different location than
the other four countries that border the Mediterranean Sea.
5.2
Category 1: Production, Consumption and Mean Temperature Spearman
Correlation Coefficient
The Spearman correlation coefficient indicator gives an idea of the variation of the data points as
well as the behavior of the electricity system in relation to temperature. The ranked index values
for each indicator (production and consumption for both the heating and cooling values), are
presented in Table 1, along with the total category index values for each country, which is the
16
average of all of the factor values. The heating and cooling influencing factors were considered
to be equal, meaning that a decrease in temperature and therefore electricity production or
consumption on the heating side, is equal to the same increase in temperature and production
or consumption on the cooling side. For the cooling values, only five countries reached the
cooling threshold, and were therefore included in the cooling indicators. Consequently, the
most vulnerable countries in this category are the countries that historically require summer
cooling due to their already warmer mean temperatures. Two possible effects of climate change,
as illustrated in the pathway diagram below (Figure 2), demonstrate the greater current and
predicted vulnerability of countries that use electricity for cooling now. The parabolic behavior
of the electricity consumption, meaning a high consumption both at low and high temperatures
and a low consumption in a comfort zone between, correlates with the findings of a number of
other studies (Guan, 2009; Hekkenberg et al., 2009b; Thatcher, 2007). The higher the mean
temperature in the countries analyzed, the more clearly the parabolic pattern. The long-term
increase of summer electricity consumption in countries that already reach the cooling threshold
is demonstrated for ES and GR for the years 1991-95 and 2006-11 and for IT for 1981-85 and
2005-10 (Figure 3). The natural rise in electricity consumption due to GDP and population
increase has minimal effects on the plots due to the use of the percent difference as opposed
to the actual consumption values. As the electricity consumption clearly increased without any
drastic changes in mean mean temperature, other factors must logically be the cause.
Countries whose mean temperature does not currently reach the cooling threshold.
Heating Threshold
!"#$%&'((
!"#$%&'()%*'&+',(*-,'.%
()$%/01/%'#'2*,020*3%$'&()$4%
(
)*++%&'%%
5'()%*'&+',(*-,'.%$"%)"*%
,'(2/%2""#0)1%*/,'./"#$4%
Cooling Threshold
!#0&(*'!/()1'%
!"#$%&'%%
50#$',%&'()%*'&+',(*-,'.%
()$%#"6',%'#'2*,020*3%$'&()$4%
%
)*++%&'((
:'&+',(*-,'.%.-,+(..%2""#0)1%
*/,'./"#$%()$%/01/',%'#'2*,020*3%
$'&()$4%
Countries whose mean temperature currently reaches the cooling threshold.
!"#$%&'(%
50#$%&'()%*'&+',(*-,'.%()$%
#"6',%'#'2*,020*3%$'&()$4%
%
)*++%&'((
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89')%&0#$',%*'&+',(*-,'.%()$%
#"6',%'#'2*,020*3%$'&()$4%
%
)*++%&'((
89')%/01/',%*'&+',(*-,'.%()$%
/01/',%'#'2*,020*3%$'&()$4%
Figure 2: Possible heating and cooling country electricity system pathways.
In general there is a temperature and therefore geographic component inherent in this indicator, and therefore four out of the five least vulnerable countries are in Scandinavia (FI, NO, SE,
DK), while the three most vulnerable countries are all on the Mediterranean Sea (GR, ES and
IT). The actual Spearman correlation coefficients for each indicator can be seen in Appendix A
(Table 9).
The correlation between electricity production or consumption and mean temperature for
the heating values gives an indication of potential vulnerability. Countries with a stronger correlation to temperature in this circumstance will become less vulnerable as the temperature
increases, while countries with weaker correlations will not necessarily become more vulnerable, however, their winter peaks will decrease only slightly if at all with temperature. A weaker
correlation with temperature also indicates that other factors have more influence on the system.
17
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Mean Temperature (°C)
Mean Temperature (°C)
(a) ES (1991-2011)
(b) GR (1991-2011)
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Jun. 1991−5
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Mean Temperature (°C)
(c) IT (1981-2010)
Figure 3: Monthly consumption - Long term summer electricity consumption trend of selected countries. Source: adapted from Terna (2012),
ENTSO-E (2011) and European Climate Assessment and Dataset (2012)
Examples of relatively strong, moderate and weak correlations to temperature for the heating
values can be seen in Figure 4 below. As previously mentioned, only five countries reach the
cooling threshold, and therefore only two examples are shown from the cooling values (Figure
4).
AT and CH are interesting cases for the heating indicators as they both have a weaker relative
correlation to temperature for the electricity production values, but have significantly stronger
correlation for their consumption values (Table 1). LU has a very weak correlation, with mean
temperature for the consumption values, but even more so for the production values which there
are a large number of anomalies. For the cooling indicators, GR is the only country with a strong
correlation to temperature for both production and consumption (Figure 4). IT is interesting due
to the drastically lower electricity production and consumption seen in August, which cause the
low correlation that is further evident in the monthly average electricity charts in Appendix
B (Figure 26). If the August data points were omitted from the correlation calculation, the
18
correlation for the cooling values would be much higher.
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DE
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−10 0
Mean Temperature (ºC)
10 20 30
Mean Temperature (ºC)
Figure 4: Production and consumption by mean temperature - Spearman correlation examples for heating (top row) and cooling (bottom row)
values as well as weaker to stronger correlation (left to right) (All countries: Appendix B: Figure 16 to Figure 19). Source: adapted from
European Climate Assessment and Dataset (2012) and IEA (2012).
19
Table 1: Category 1: Production and Consumption Correlation to Mean Temperature Ranked Index. Source: adapted from European Climate
Assessment and Dataset (2012) and IEA (2012).
Country
CH
LU
AT
IT
NL
ES
BE
IE
GR
PT
PL
DE
HU
CZ
SE
SK
GB
DK
FR
NO
FI
5.3
Heating
Production
Country
0.137
-0.257
-0.307
-0.454
-0.469
-0.566
-0.622
-0.625
-0.653
-0.658
-0.747
-0.752
-0.775
-0.823
-0.833
-0.852
-0.889
-0.922
-0.930
-0.932
-1.000
LU
IT
GR
NL
ES
HU
SK
DE
BE
IE
PT
PL
CH
DK
GB
AT
CZ
FR
FI
SE
NO
Consumption
-0.109
-0.325
-0.513
-0.535
-0.580
-0.584
-0.598
-0.618
-0.623
-0.625
-0.651
-0.701
-0.742
-0.813
-0.821
-0.826
-0.908
-0.947
-0.969
-0.990
-1.000
Country
Cooling
Production
Country
GR
ES
IT
PT
HU
1.000
0.490
0.295
0.009
-0.128
GR
HU
ES
IT
PT
Consumption
1.000
0.594
0.550
0.292
0.108
Country
Total
Average
GR
ES
IT
LU
HU
PT
CH
NL
AT
BE
IE
DE
PL
SK
GB
CZ
DK
SE
FR
NO
FI
0.209
-0.026
-0.048
-0.183
-0.223
-0.298
-0.303
-0.502
-0.566
-0.622
-0.625
-0.685
-0.724
-0.725
-0.855
-0.866
-0.868
-0.912
-0.939
-0.966
-0.985
Category 2: Production, Consumption and Mean Temperature Slope
The production or consumption and mean temperature slope gives an indication of the magnitude of the correlation between electricity and temperature. The steeper the slope of the data
values is, the greater the projected change in production per degree of temperature change. The
actual slope values for each country for the heating and cooling values (both for electricity
production and consumption) can be found in Appendix A (Table 10), while the ranked index
values are below in Table 2. Similar to the previous section, a steeper slope for the heating
values decreases vulnerability; while on the cooling side the opposite is true. This is due to
the fact that the magnitude of the production and consumption peak will increase or decrease
at a larger rate if the slope is steeper. Examples of countries with characteristically steep or flat
slopes for both the heating and cooling values can be seen in Figure 5 below.
The category index values indicate that again the countries that historically require summer
cooling are the most vulnerable, with the exception of PT, which is relatively much less vulnerable for this indicator. The Scandinavian countries are again amongst the less vulnerable
countries, however FR and GB are the least vulnerable (Table 2). IT is notable in that it has a
relatively flat slope on both the heating and cooling side for production and consumption. In
terms of absolute values, CH is a notable country in that it is the only country with a positive
production slope for the heating values, indicating a rise in electricity production with increasing temperature (Table 10). Similar, HU is the only country with a negative slope for the cooling
production values, meaning that as the temperatures increases, electricity production decreases
(Table 10).
20
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PL
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10 20 30
Mean Temperature (ºC)
Figure 5: Production and consumption by mean temperature - Slope examples for heating (top row) and cooling (bottom row) values as well as
flatter to steeper slopes (left to right) (All countries: Appendix B: Figure 16 to Figure 19). Source: adapted from European Climate Assessment
and Dataset (2012) and IEA (2012).
21
Table 2: Category 2: Production and Consumption and Mean Temperature Slope Ranked Index. Source: adapted from European Climate
Assessment and Dataset (2012) and IEA (2012).
Country
CH
AT
IT
BE
PL
DE
LU
NL
CZ
HU
SK
GR
SE
ES
FI
IE
NO
GB
FR
DK
PT
5.4
Heating
Production
Country
0.073
-0.116
-0.178
-0.221
-0.238
-0.242
-0.248
-0.25
-0.263
-0.291
-0.305
-0.358
-0.382
-0.383
-0.386
-0.432
-0.585
-0.632
-0.64
-0.861
-1
LU
IT
HU
DE
NL
PL
BE
AT
GR
SK
CH
FI
DK
CZ
ES
IE
SE
GB
NO
FR
PT
Consumption
-0.038
-0.11
-0.169
-0.221
-0.257
-0.268
-0.278
-0.31
-0.323
-0.342
-0.374
-0.378
-0.418
-0.427
-0.472
-0.493
-0.635
-0.706
-0.732
-0.932
-1
Country
Cooling
Production
Country
GR
ES
IT
PT
HU
1
0.3
0.197
0.053
-0.081
GR
ES
HU
PT
IT
Consumption
1
0.295
0.249
0.169
0.15
Country
Total
Average
GR
IT
ES
HU
LU
CH
AT
DE
BE
PL
NL
SK
CZ
FI
PT
IE
SE
DK
NO
GB
FR
0.33
0.015
-0.065
-0.073
-0.143
-0.151
-0.213
-0.232
-0.249
-0.253
-0.254
-0.324
-0.345
-0.382
-0.444
-0.462
-0.508
-0.64
-0.658
-0.669
-0.786
Category 3: Projected Temperature Increase
The projected temperature changes give a relative indication of the magnitude of temperature
increase expected for each country over the next decades. The changes are not the same for
each country and therefore it is important to include data that addressed the changes. Summer
temperature increases were determined to increase vulnerability, while winter increases lead to
less vulnerability. The actual temperature changes for summer and winter months can be seen
in Figures 6 and 7 below as well as in Appendix A (Table 11), while the ranked index, and
averaged categorical index values are presented below in Table 3.
Slightly more than half of the countries have greater projected summer temperature increases than winter increases (Table 3). There is a geographic trend to the temperature projections in that, countries that already have warmer climates will experience greater summer
temperature increases than in winter based on these predictions. Furthermore, the magnitude
of the difference between countries in terms of winter temperature changes is greater than for
summer. Ultimately, all but three countries, which happen to be typically colder countries climatically (FI, NO and SE), are considered less vulnerable according to the averaged category
ranked index values as they are expected to experience strong winter temperature increases but
only smaller increases for summer months. The geographical vulnerability trend of the actual
summer and winter temperature values is evident in the maps below (Figures 6 and 7) where a
clear north-south gradient is present.
22
FI
FI
SE
NO
SE
NO
DK
DK
GB
GB
IE
IE
PL
NL
BE
LU
PL
NL
BE
LU
DE
CZ
DE
CZ
SK
FR
AT
CH
SK
HU
FR
IT
PT
AT
CH
HU
IT
ES
PT
GR
2.5
7.5
ES
GR
2.5
Figure 6: Actual Summer Temperature Increase Map (◦ C) (Scenario A2 1961-90
to 2070-99). Note: Darker colors indicate higher vulnerability. Source: adapted
from Mitchell et al. (2002).
7.5
Figure 7: Actual Winter Temperature Increase Map (◦ C) (Scenario A2 1961-90
to 2070-99). Note: Darker colors indicate lower vulnerability. Source: adapted
from Mitchell et al. (2002).
Table 3: Category 3: Scenario A2 Temperature Increase 1961-90 to 2070-99 Ranked Index. Source: adapted from Mitchell et al. (2002).
Country
ES
HU
CH
AT
FR
GR
SK
IT
LU
CZ
PT
BE
PL
DE
FI
NL
SE
DK
NO
GB
IE
5.5
Summer (JJA)
1.000
0.953
0.952
0.909
0.903
0.885
0.885
0.866
0.842
0.826
0.815
0.793
0.792
0.781
0.763
0.710
0.710
0.683
0.656
0.621
0.543
Country
IE
PT
GB
ES
GR
FR
IT
BE
NL
LU
CH
DE
AT
DK
HU
CZ
SK
PL
NO
SE
FI
Winter (DJF)
-0.364
-0.389
-0.421
-0.432
-0.441
-0.467
-0.476
-0.512
-0.519
-0.527
-0.529
-0.580
-0.587
-0.604
-0.629
-0.631
-0.663
-0.719
-0.719
-0.814
-1.000
Country
Total Average
ES
GR
FR
PT
CH
IT
HU
AT
LU
BE
SK
DE
GB
CZ
NL
IE
DK
PL
NO
SE
FI
0.284
0.222
0.218
0.213
0.212
0.195
0.162
0.161
0.157
0.141
0.111
0.101
0.100
0.097
0.095
0.089
0.040
0.036
-0.032
-0.052
-0.119
Category 4: Air Conditioner Prevalence
The per capita air conditioner prevalence category ranked index combined the projected 2030
air conditioner data and the percentage increase between 2005 and 2030. The projected 2030
data gives an indication of the magnitude of potential warm weather electricity consumption in
the future, while the growth data provides information on the potential change from the current consumption. Vulnerability increases with higher values for either indicator, due to the
increasing effects of air conditioner use on electricity consumption. Overall, an upper limit of
23
air conditioner prevalence was observed in the data, meaning that while approaching a theoretical maximum number of air conditioners per capita, air conditioner growth slows. Therefore,
countries with high air conditioner prevalence now will not see as significant a rise in stock in
the future compared to some countries with relatively few at the current time, the best example
being GR. The ranked index values can be seen in Table 4 below, while the actual per capita
values can be found in Appendix A (Table 12). A map of the actual projected air conditioner
prevalence and projected air conditioner stock difference can be also seen below in Figures 8
and 9.
The countries that historically require summer cooling would logically be the most likely to
have the highest air conditioner prevalence due to their warmer temperatures. This is not true
in all cases however, PT being the exception with relatively few air conditioners, and limited
growth projected in the future. IT, GR and ES on the other hand are projected to have a large
stock by 2030, however with moderate or low growth (due to the observed maximum saturation
threshold). SK is a notable country due to the fact that they do not have particularly warm
temperature but have a large expected 2030 stock. Finally, in terms of air conditioner stock
growth, generally colder countries (FI, GB and SE), will experience the most growth, partly
due to their low current stock levels.
FI
FI
SE
NO
NO
DK
IE
DK
GB
NL
BE
LU
FR
CZ
AT
CH
IE
PL
DE
GB
NL
BE
LU
SK
HU
FR
IT
PT
0.015
ES
SE
PL
DE
CZ
AT
CH
SK
HU
IT
PT
GR
0.55
0.78
Figure 8: Projected Air Conditioner Prevalence Map (per capita, 2030). Note:
No data was available for CH or NO. Darker colors indicate higher vulnerability.
Source: adapted from Adnot et al. (2008).
24
ES
GR
3.66
Figure 9: Projected Air Conditioner Percent Difference Map (per capita, 20052030). Note: No data was available for CH or NO. Darker colors indicate higher
vulnerability. Source: adapted from Adnot et al. (2008).
Table 4: Category 4: Air Conditioner Prevalence Ranked Index. Note: No data was available for CH or NO. Source: adapted from Adnot et al.
(2008).
5.6
Country
Projection
(2030)
Country
Percent Difference
(2005-2030)
Country
Total Average
IT
GR
SK
ES
FR
NL
DK
BE
LU
GB
FI
HU
SE
PT
IE
AT
CZ
DE
PL
CH
NO
1.000
0.942
0.901
0.806
0.480
0.472
0.464
0.435
0.404
0.358
0.319
0.310
0.296
0.279
0.274
0.169
0.084
0.080
0.029
-
FI
SE
GB
AT
NL
FR
DK
IE
BE
LU
HU
IT
SK
PL
PT
ES
DE
CZ
GR
CH
NO
1.000
0.908
0.874
0.862
0.844
0.838
0.833
0.808
0.758
0.614
0.565
0.536
0.451
0.448
0.411
0.408
0.375
0.309
0.216
-
IT
SK
FI
FR
NL
DK
GB
ES
SE
BE
GR
IE
AT
LU
HU
PT
PL
DE
CZ
CH
NO
0.768
0.676
0.660
0.659
0.658
0.648
0.616
0.607
0.602
0.597
0.579
0.541
0.515
0.509
0.438
0.345
0.238
0.228
0.197
-
Category 5: Thermal Electricity Production Share
The thermal electricity production share provides information about the current vulnerability
of a countries’ inland electricity generation. Not all electricity production sources are equally
vulnerable to climate change and therefore countries with higher shares of production sources
that are susceptible to climate change related issues will face more problems. The actual percent share and percent difference (2000-2011) of thermal electricity production sources which
a number of studies identified as especially vulnerable to climate change (as outlined in the
Background Information and State-of-the-Art sections previously) can be found in Appendix
A (Table 13) and seen below in the maps in Figure 10 and Figure 11, while the ranked index
values can be seen below in Table 5.
The ranked index values show that LU and FR are the most vulnerable in terms of thermal
source share. LU is almost twice as vulnerable as FR, mostly due to the significant change over
the past decade, however both have high shares of thermal electricity production. DK and PT
are the least vulnerable, mainly due to their decline in thermal share over time. In terms of
actual values, the majority of countries (aside from SE, CH, AT and NO) produce more than
half of their electricity from thermal sources a number of which are almost exclusively reliant on
thermal production. HU, PL and NL produce greater than 95% of their inland electricity from
thermal sources and have the highest current (2011) indicator. All of the countries produce
greater than 40% of their electricity from thermal sources with the exception being NO which
produces almost no electricity from thermal sources (less than 4%). In terms of changes in the
percent share of thermal production over time, LU has by far experienced the greatest rise in
thermal use. A number of countries have decreased their share of thermal uses between 2000
and 2011, with DK, PT and IE experiencing the greatest decline.
25
FI
FI
SE
NO
SE
NO
DK
DK
IE
IE
GB
NL
BE
LU
NL
BE
LU
PL
DE
CZ
AT
CH
FR
GB
SK
FR
HU
PL
DE
CZ
AT
CH
SK
HU
IT
IT
PT
PT
ES
ES
GR
GR
!0.17
0.038
0.44
0.98
Figure 11: Thermal Electricity Production Percent Change (2000-2011) Map.
Note: Darker colors indicate higher vulnerability. Source: adapted from IEA
(2012).
Figure 10: Thermal Electricity Production Share Map (2011 %). Note: Darker
colors indicate higher vulnerability. Source: adapted from IEA (2012).
Table 5: Category 5: Thermal Electricity Production Share. Source: adapted from IEA (2012).
Country
HU
PL
NL
CZ
GB
BE
FR
GR
SK
DE
FI
IE
IT
DK
ES
LU
PT
SE
CH
AT
NO
5.7
Thermal Production
Percent (2011)
1.000
0.987
0.975
0.958
0.956
0.935
0.905
0.881
0.872
0.849
0.835
0.833
0.770
0.724
0.722
0.690
0.592
0.506
0.473
0.451
0.040
Country
LU
AT
SE
CH
NO
FI
FR
SK
PL
HU
CZ
NL
IT
GB
GR
BE
DE
ES
IE
PT
DK
Thermal Production Percent
Difference (2000-2011)
1.000
0.347
0.108
0.090
0.080
0.069
0.049
0.045
-0.039
-0.116
-0.186
-0.209
-0.214
-0.263
-0.294
-0.399
-0.634
-0.755
-0.815
-0.848
-1.000
Country
Total Average
LU
FR
PL
SK
FI
HU
AT
CZ
NL
GB
SE
GR
CH
IT
BE
DE
NO
IE
ES
PT
DK
0.845
0.477
0.474
0.459
0.452
0.442
0.399
0.386
0.383
0.347
0.307
0.294
0.281
0.278
0.268
0.107
0.060
0.009
-0.017
-0.128
-0.138
Category 6: Production and Consumption
Category 6 is useful in characterizing the current electricity system in each country by measuring the ability of a country to not only meet their electricity consumption demand by inland
production, but how well the system can react to changes in demand. Summer (June, July, August) and winter (December, January, February) indicators were utilized for both the correlation
between electricity production and consumption, but also the discrepancy between them. The
26
actual correlation and discrepancy values can be found in Appendix A (Table 14), while the
ranked index values, including the category index values, can be seen below in Table 6.
5.7.1
Production and Consumption Spearman Correlation Coefficient
A strong correlation between electricity production and consumption would mean that there is a
stronger relationship within the system meaning that the country has a greater flexibility within
the system and therefore lower vulnerability. It does not take into account the magnitude of
the difference between production and consumption, meaning that countries that produce much
more than they consume may have a low correlation value, but are obviously able to deal with
consumption fluctuations. This correlation value is most important for countries who meet their
consumption demand by inland production with few imports. The seasonal separation was done
in order to identify countries that are net electricity producers or consumers at different times
during the year.
Examples of weak, moderate and strong seasonal correlations between electricity production and consumption can be seen in Figure 12 below. It is important to note that the plots
include all months of the year over the entire time frame, however seasonal patterns are discernable. The entire time frame scope allows the identification of anomalies or unusual behavior or
events in the system, some specific cases will be discussed later in the report. In terms of the
indicators, ES and GB are notable countries for both the summer and winter indicators because
they have consistent strong correlation between electricity production and consumption. On the
other hand, SK and CH have a consistent weak correlation between production and consumption. The most interesting of the countries are those that vary in terms of correlation strength
between the summer and winter. AT is the best example of this as it has a relatively strong correlation between production and consumption during the summer months, but a much weaker
correlation for the winter. These phenomenon may also be seen in the monthly average charts in
Appendix B (Figure 24 to Figure 27), not only for the electricity production and consumption,
but also the electricity import and export data for Category 7. In order to examine the behavior
further in terms of variability between months and over time, monthly average maximum and
minimum plots were made, however they provide little additional information due to the fact
that they are easily misleading if a single monthly value is anomalous (this is also true for the
import and export data in Category 7).
27
Table 6: Category 6: Production and Consumption Summer and Winter Correlation and Discrepancy Ranked Index. Source: adapted from IEA
(2012).
Country
SK
CH
SE
DK
LU
NL
FR
FI
NO
HU
CZ
BE
AT
PT
DE
IE
PL
GR
GB
ES
IT
Correlation
Summer
Country
0.307
-0.187
-0.378
-0.400
-0.510
-0.537
-0.567
-0.576
-0.586
-0.653
-0.692
-0.697
-0.718
-0.829
-0.926
-0.934
-0.961
-0.962
-0.986
-0.994
-1.000
SK
CH
AT
SE
DK
HU
BE
NO
LU
FI
CZ
PT
NL
GR
FR
DE
PL
IT
GB
IE
ES
Winter
Country
-0.182
-0.339
-0.480
-0.573
-0.598
-0.699
-0.710
-0.713
-0.739
-0.751
-0.762
-0.809
-0.831
-0.863
-0.867
-0.874
-0.908
-0.932
-0.971
-0.984
-1.000
LU
FI
HU
NL
DK
IT
PT
GR
IE
BE
GB
DE
ES
SE
AT
PL
SK
NO
FR
CH
CZ
Discrepancy
Summer
Country
1.000
0.306
0.304
0.286
0.279
0.220
0.180
0.158
0.071
0.054
0.050
0.037
-0.031
-0.078
-0.109
-0.139
-0.312
-0.421
-0.659
-0.745
-1.000
28
LU
IT
AT
NL
FI
CH
HU
BE
PT
GR
SE
IE
GB
NO
ES
DE
PL
SK
FR
CZ
DK
Winter
Country
Total Average
1.000
0.267
0.240
0.214
0.214
0.187
0.170
0.153
0.099
0.057
0.055
0.048
0.027
-0.030
-0.032
-0.209
-0.279
-0.382
-0.449
-0.949
-1.000
LU
SK
FI
NL
HU
SE
AT
CH
BE
PT
IT
GR
DK
NO
IE
GB
DE
ES
PL
FR
CZ
0.188
-0.142
-0.202
-0.217
-0.220
-0.244
-0.267
-0.271
-0.300
-0.340
-0.361
-0.402
-0.430
-0.438
-0.450
-0.470
-0.493
-0.514
-0.572
-0.635
-0.851
6000
Imports
Exports
4000
6000
0
2000
Production
Consumption
2000
Imports
Exports
Electricity (GWh)
Production
Consumption
DK
0
Electricity (GWh)
10000
CH
2000
2003
2006
2009
2000
Year
2003
2006
2009
Year
GB
20000
40000
Imports
Exports
0
Electricity (GWh)
Production
Consumption
2000
2003
2006
2009
Year
Figure 12: Monthly production, consumption, imports and exports over time - Spearman correlation examples weak to strong correlation (top
left to bottom) (All countries: Appendix B: Figure 20 to Figure 23). Source: adapted from IEA (2012).
5.7.2
Percentage Discrepancy
The average percentage of electricity consumption met by inland production gives a quantitative indicator of the ability of a country to meet its own electricity consumption demand and
how the system is changing over time. This value identifies what is missing from the correlation
value in that it characterizes the system in terms of identifying countries that are net producers
or consumers and to what extent. The ranked index values for the indicators can be seen in
the same table as the correlation coefficients (Table 6), with the actual discrepancy values in
Appendix A (Table 14).
Example countries that are consistent net consumers or producers can be seen in the top row
of Figure 13 below, while two examples of clear seasonal electricity system differences can be
seen in the bottom row of the same figure. LU is the most extreme example of a net consuming
country, with less than half of its electricity consumption being met by inland production, which
29
is strikingly different from all of the other countries. CZ and FR are notable as well due to
their large production surplus that is consistent throughout the year. The majority of the other
countries however, experience seasonal differences and are for parts of the year net consumer
and other times net producers. AT, CH and DK both provide an interesting seasonal dynamic,
and are therefore good examples of a system in which production and consumption follow
different patterns seasonally (Figure 13, bottom row). AT and CH are net producers during a
number of spring and summer months, but are net consumers during the rest of the year. DK on
the other hand is the opposite, consuming more electricity than is produced during the warmest
months only.
1
3
5
7
9
1
3
5
7
9
30
10000
0
−10
2000
0
10
6000
20
30
0
−10
0
11
Production
Consumption
Imports
Exports
Mean Temp.
20
10
2500
Production
Consumption
Imports
Exports
Mean Temp.
1500
Mean Temperature (ºC)
CZ
500
30
−10
200
0
400
10
600
20
Production
Consumption
Imports
Exports
Mean Temp.
800
3500
IE
0
Electricity (GWh)
LU
11
1
3
5
7
9
11
Month
1
3
5
7
9
11
0
−10
1000
0
10
3000
20
5000
Production
Consumption
Imports
Exports
Mean Temp.
20
0
−10
0
2000
10
4000
6000
Production
Consumption
Imports
Exports
Mean Temp.
30
DK
30
8000
AT
1
3
5
7
9
11
Figure 13: Monthly average production, consumption, imports, exports and temperature - Percentage discrepancy examples for net consumers
to producers (left to right, top row) as well as seasonal variation (summer producers to summer consumers, left to right) (All countries:
Appendix B: Figure 24 to Figure 27). Source: adapted from IEA (2012).
30
5.8
Category 7: Import and Export
This indicator provides a quantitative description of whether a country is a net importer or exporter in both summer and winter, and to what extent. Net importing countries often do not have
the capacity to meet their electricity consumption demand by inland production and are reliant
on imports for part or all of the year. Thus, importing countries are more vulnerable, compared
to net exporting countries that have the ability to meet their consumption demand with a surplus. Import necessity is not in all cases a negative for an electricity system, for a number of
countries importing electricity from countries with abundant production resources could prove
cheaper than inland production based on the market and pricing. Should an exporting country
however, decide to cease exports, due to increasing inland consumption requirements for example, importing countries could face challenges in meeting consumption demand. In terms
of actual values (Appendix A - Table 15) less than half of the countries are net electricity exporters, with the rest being net electricity importers. The vast majority of the countries are very
close to a value of zero, meaning that the difference between imports and exports is very small
in comparison to electricity production. In terms of the ranked index (Table 7), most countries
have relatively low values in comparison with the extreme cases.
CZ and FR are the most notable net electricity exporters, with relative values far greater
than any other countries. LU on the other hand is the sole extreme net importer. Some countries
such as GB and IE export and import close to nothing throughout the entire year, meaning that
imports or exports do not play a large role in their electricity system. Furthermore, a number
of countries change between being net importers and exporters depending on the season, DK
being the most notable example due to high winter exports, but is a net importer during the
summer. CH on the other hand exports during summer months, but is then an importer during
the winter. Examples of net importers and exporters can be seen in Figure 14 below.
Table 7: Category 7: Import and Export Percentage
Discrepancy Ranked Index. Source: adapted from IEA (2012).
Country
Summer
Country
Winter
Country
Total Average
LU
DK
HU
NL
FI
IT
PT
GR
BE
GB
DE
IE
ES
SE
AT
PL
SK
NO
FR
CH
CZ
1.000
0.102
0.101
0.094
0.090
0.068
0.055
0.043
0.017
0.014
0.010
0.010
-0.036
-0.052
-0.105
-0.166
-0.203
-0.405
-0.707
-0.719
-1.000
LU
IT
AT
NL
FI
CH
HU
BE
PT
SE
GR
IE
GB
NO
ES
DE
SK
PL
FR
CZ
DK
1.000
0.106
0.098
0.085
0.084
0.080
0.066
0.058
0.039
0.022
0.020
0.011
0.009
-0.001
-0.037
-0.241
-0.301
-0.319
-0.493
-0.969
-1.000
LU
NL
IT
FI
HU
PT
BE
GR
GB
IE
AT
SE
ES
DE
NO
PL
SK
CH
DK
FR
CZ
1.000
0.090
0.087
0.087
0.084
0.047
0.037
0.032
0.012
0.010
-0.004
-0.015
-0.036
-0.115
-0.203
-0.243
-0.252
-0.319
-0.449
-0.600
-0.984
31
Production
Consumption
0
5000
600
200
Imports
Exports
15000
Imports
Exports
Electricity (GWh)
1000
Production
Consumption
NO
0
Electricity (GWh)
LU
2000
2003
2006
2009
2000
Year
2003
2006
2009
Year
FR
20000
60000
Imports
Exports
0
Electricity (GWh)
Production
Consumption
2000
2003
2006
2009
Year
Figure 14: Monthly production, consumption, imports and exports over time - Production and consumption percentage discrepancy examples,
net importer to net exporter (top left to bottom) (All countries: Appendix B: Figure 20 to Figure 23). Source: adapted from IEA (2012).
5.9
Ranked Vulnerability Index
The final ranked vulnerability index as described in the methods section is an average of the
category index values and is presented below in Figure 15 as a map and in Table 8. The index is therefore a relative indication of the vulnerability of each country to climate change that
weighs each of the seven included categories equally. There is a clear gradient of countries that,
including all of the indicators, are more or less vulnerable. It is important to note that the least
vulnerable country in the index is the least vulnerable relative to the other countries in the index,
but does not necessarily vulnerable in no way.
The vulnerability index is essentially a deductive measure of the potential of each country to
avoid major disruptions in the electricity system, and the ability to cope with such disruptions.
The relative vulnerability comparison is independent of political, social and other environmental
factors, which will probably have an impact on the future vulnerability of the electricity sys32
tem. Thermal electricity production is considered to be the most vulnerable production source,
for various reasons including technical and political reasons, however hydro and a number of
renewable sources are also susceptible to climate change which may increase or decrease vulnerability.
Table 8: Ranked Vulnerability Index.
LU is relatively the most vulnerable country,
by a significant margin, while GR, IT and HU are
incrementally less vulnerable than LU, but highest
on the list. For LU, GR and IT, this high relative
vulnerability is caused by the inability of these
countries to meet their electricity consumption demand with inland production, leading to a reliance
on electricity imports. NO, CZ and DK are the
least vulnerable in the index, due to the fact that
they can consistently produce more electricity than
they consume, export throughout the year and have
a very strong correlation between production and
consumption.
This means that their production
system is dynamic and has the ability to react in a
crisis.
Country
Mean Index
Value
LU
GR
IT
HU
NL
ES
AT
BE
SK
FI
PT
CH
SE
IE
GB
PL
DE
FR
DK
CZ
NO
0.339
0.180
0.133
0.087
0.036
0.033
0.004
-0.018
-0.028
-0.070
-0.086
-0.092
-0.117
-0.127
-0.132
-0.149
-0.156
-0.229
-0.262
-0.338
-0.373
FI
SE
NO
DK
GB
IE
PL
NL
DE
BE
LU
CZ
SK
AT
CH
FR
HU
IT
PT
ES
−0.21
−0.14
GR
−0.07
0
0.07
0.14
0.21
0.28
0.35
Figure 15: Ranked Vulnerability Index Map. Note: Darker colors indicate higher vulnerability.
33
5.10
Additional Data for Qualitative Analysis
A number of additional data is presented below in an effort to further characterize and examine
the electricity system of each country. The following sections include references to plots and
data sets of existing data (the plots can be found in Appendix B) that are not, strictly speaking,
results of this study. No significant calculation or manipulation of the data was conducted besides the monthly averaging required for some data sets, or at times the combination of two data
sets. This additional information is included in the results section because it was deemed to be
important and consequential in explaining or expanding the discussion of the original findings.
Due to the qualitative nature of the additional data in terms of their effect on vulnerability, these
factors (day length, electricity heating and tourism) were not included in the quantitative ranked
index, however they were included in the study to illustrate the effect of some additional influences on the electricity system. There are, of course, a large number of qualitative influences on
the electricity system which would affect vulnerability, however, due to a number of constraints
which are discussed in the limitations section, only those previously mentioned were included.
5.10.1
Monthly Electricity Production, Consumption, Imports and Exports Over Time
(2000-2011)
The monthly electricity production, consumption, import and export values were plotted over
the entire time frame of the data (2000-2011). The plots can be seen in Appendix B (Figure 20
to Figure 23), and are useful to identify a number of trends and anomalies in the data. Most
countries show an overall increase in both electricity production and consumption over time,
however most experience a slower increase, stagnation or even decrease over the past few years.
There are clear trends in the electricity system evident for some countries, for example NL,
where electricity production has been growing to meet demand during the past five years (Figure 22). The opposite can be seen for PT where consumption and production were for the early
part of the 2000s very close, but during the past few years, consumption has been increasing
at a faster rate (Figure 23). As well, the anomalies and large fluctuations seen in LU for the
years 2000 to 2002 (Figure 22) and CH for the year 2007 (Figure 20), can be easily identified
for further analysis.
Including the import and export data on the same plots highlights the specific anomalies
or event when the electricity system shifted or changed. Based on the intimate relationship
between production, consumption, imports and exports, the plots provide clear evidence of
behavioral shifts in the system. One notable example is HU during 2004 when production and
consumption were both slightly lower than expected, however the imports and exports for that
same period dropped significantly (Figure 21). FR, a consistent net exporter of electricity also
experienced a drop in exports and increase of imports for a short period during 2009 (Figure 21).
As well, ES which for the first part of the 2000s was a net importer, at some point became a net
exporter, however it is important to note that the magnitude of the import and export system in
ES is small compared to production and consumption (Figure 21). The most interesting example
however is LU, which has an electricity system that is not similar to any other of the countries
studied. Specifically, instead of production and consumption following similar patterns, the
production and exports share corresponding changes, while the consumption and imports are
linked closely. Furthermore, this is particularly interesting due to the large fluctuations of all
variables over time (Figure 22).
34
5.10.2
Mean Monthly Electricity Production, Consumption, Imports, Exports, and Mean
Temperature
In order to gain some additional perspective on the behavior of the electricity system as a whole
and its correlation to temperature, a number of plots were completed that include both electricity
production, consumption, imports, exports as well as temperature (Figure 24 to Figure 27). The
mean temperature aspect of the plots clearly demonstrates the typical temperature changes by
month for all countries. One interesting aspect of the monthly electricity data is that the majority
of the countries experience an increase in production and consumption from February to March,
which does not make sense in terms of the general trends, and will be discussed later.
5.10.3
Electricity Production By Source
Mean Monthly Electricity Production Source Plotting the electricity production separated
by source gives an qualitative indication of which countries vary their production sources by
season and thus temperature (Appendix B - Figure 28 to Figure 31). As well, due to the inherent vulnerability of certain electricity production sources, may indicate heightened electricity
system vulnerability, as was discussed for Category 5. There is no significantly evident pattern
shared by all countries in terms of the source data, however there are some notable trends for
some countries.
Many countries utilize combustible fuels as a way to meet peak demand (DK, FI, GR and
PT among others). AT and CH utilize hydro power heavily during the summer months, however
both do not experience consumption peaks during those same months.
Monthly Electricity Production Source Over Time (2000-2011) Plots of monthly electricity production by source for the period 2000-2011 can be seen in Appendix B (Figure 32 to
Figure 35). The plots are useful to give a qualitative idea of production source dynamics over
time as well as variation and anomalies for certain years or months. CZ for example shows an
increase in nuclear electricity production in the stead of combustible fuels over the time period.
Additionally, the increase for some countries over time in the use of other electricity production
sources can be seen (DE, DK, PT and ES), which is of note due to the fact that the majority of
other sources are renewable production sources.
Electricity Production Source and Mean Temperature The electricity production by source
was also plotted with mean temperature to give a further indication of the relationship between
them (Appendix B - Figure 36 to Figure 39). While it does not provide a drastically different
outlook compared to the monthly charts, the change in source by temperature specifically is
vital to help the characterization of seasonal electricity production. The plots very clearly show
the increased utilization of hydro and decreased use of nuclear in CH during periods of warmer
temperatures. For PT, the opposite can be seen; hydro (with some other sources) is utilized
increasingly as the temperature decreases and is used therefore to meet the winter consumption
demand. However, while the temperature increases in PT, combustible fuels are used almost
exclusively to meet the consumption peak at the warmest temperatures (hydro and other production sources decrease production). AT is a clear example of a country that utilizes hydro
increasingly as temperature increases, while decreasing combustible fuels.
35
5.10.4
Day Length
Day length, or importantly, the number of daylight hours, varies seasonally and geographically,
which has a potential significant effect on electricity consumption due to lighting requirements.
Lighting accounts for approximately 10% of household electricity use on average in European
countries, however the monthly variation of this consumption share is more important than the
average (Bertoldi and Atanasiu, 2009). For example citetKoroneos2007 demonstrate that for
GR, electricity consumption for lighting peaks in the months of January and December, and is
the lowest in the months of June and July. Their study reinforces the seasonal variation and
possible influence of lighting on electricity consumption, especially considering that GR is one
of the southernmost countries examined in this study, which means that most of the other countries experience much more variation of day length throughout the year.
One potential issue with the correlation is that day length is related to season, therefore
higher temperatures are experienced during periods of longer day length, making the isolation
of the day length effects on electricity consumption specifically difficult to determine. A further
challenge associated with the quantification of lighting electricity consumption is that energy
saving light bulbs and light-emitting diodes (LEDs) are readily available and are being used as
an increasingly large rate which therefore would alter the influence of lighting on the electricity
system (Bertoldi and Atanasiu, 2009). The seasonal effects of day length are further problematic due to the fact that countries experiencing significantly fewer daylight hours during winter
months, requiring more hours of lighting will experience the opposite effect during summer
months.
Day length is an influencing factor that is difficult to quantify. It is clear that electricity used
for heating is far more influential than lighting, and months of colder temperatures requiring
heating, also are the same months with the shortest days (Bertoldi and Atanasiu, 2009). To a
certain extent however, daylight has an effect on electricity consumption, unlike heating, lighting fuel is almost primarily electricity, and especially northern European countries experience
far more summer daylight hours than winter.
5.10.5
Heating Electricity Use
In terms of electricity consumption, the identification of electricity used specifically for heating purposes is an important tool for characterization. Data is not readily available for this
specifically, however one report presents residential electricity consumption for thermal uses by
household (Lapillonne et al., 2010). The overall findings of the report indicate that there exists
differences between countries that use electricity as a primary source for heating, and those that
use it less (Lapillonne et al., 2010). Perhaps most interesting and vital for this study is that there
is not a consistent geographical inclination to heat with electricity in certain regions, however
data was not available for all countries (Lapillonne et al., 2010).
SE, FI, FR and IE all use electricity as a primary source of heating (SE has by far the
greatest electricity consumption for thermal uses of all of the countries included) (Lapillonne
et al., 2010). On the other end of the spectrum, IT, PT and SK have a very small component of
their residential electricity consumption taken by thermal heating. It is tempting to assume that
countries requiring more heating due to colder climates would have higher values (which is true
in the case of FI and SE) however for a country such as SK, where the climate requires heating
in homes, the low thermal consumption of electricity could simply indicate alternative heating
36
sources.
5.10.6
Tourism
Many of the countries examined in this project experience large volumes of tourists at different
time periods in the year, depending on the country (Appendix B - Figure 40 to Figure 43). The
potential effects of the temporary increase or decrease in population on electricity consumption must be taken into account as a possible influencing factor. Preliminary data on arrivals
to tourism accommodation and the number of tourism trips taken by residents for each country
show the variation of tourism throughout the year, however in a number of cases the magnitude
of the arrivals and trips is very small.
There are no distinctive patterns in terms of groups, but a few countries show significant numbers of trips or arrivals. FI, FR, NO and SE have by far the greatest relative number of tourism arrivals during the summer months with significantly fewer trips during the
same months. The majority of the countries experience more tourism arrivals than departures
throughout the entire year, while some have patterns that vary by month (AT, BE, CZ, DK, NL).
LU is a unique case where the number of tourism trips is consistently more than the number of
arrivals for the whole year.
The effect of tourism is difficult to quantify in terms of its effect on the electricity system
of a country, partly due to lack of available data on the actual number of people arriving and
leaving over a given month. Theoretically as more people enter a country as tourists, electricity
consumption should increase, however this becomes complex due to the fact that lighting and
heating in a room, for example, would not necessarily increase if more people were there. The
majority of tourism trips or arrivals are during summer months, and would therefore affect
consumption during warmer temperatures, which for most countries would be at temperatures
when no heating or cooling is required. The majority of those same countries experience the
lowest electricity consumption during the summer as well. LU experiences a high number of
tourist trips during the summer, particularly in August, which could account to some extent for
the decrease in electricity consumption. NO however, despite a large number of tourism arrivals
in July, shows a definitive decrease in electricity consumption. Overall, the effects of tourism
seem far less influential than the effects of temperature.
6
Discussion
This section will attempt to identify and explain the underlying reasons for the relative vulnerabilities of the countries, and discuss the reasons certain countries are more vulnerable than
others, as well as examine these results in comparison with existing findings. Furthermore, additional qualitative influencing factors that have an effect on the vulnerability of a country to
climate change but were not included in the ranked index will be discussed. Due to the highly
complex nature of the electricity system in general, and its very pronounced subjectivity to
country specific conditions, explaining the behavior of the system is difficult and the findings
presented in this report are an attempt to break down and characterize the effect of some of
the most important influencing factors, but are by no means the entire picture (Schaeffer et al.,
2012).
37
6.1
Discussion of the Results
The vulnerability index (Table 8) gives a quantitative outlook of the relative vulnerabilities of
each country examined in this study, essentially describing the ability of each country to meet
its present and future electricity consumption needs, and the degree to which it is vulnerable to
climate change. It is important to note that a number of qualitative vulnerability factors are not
included in the index specifically, but are potentially very influential on the vulnerability of the
system.
6.1.1
Results Correlation with Existing Studies
The final ranked vulnerability index correlates well with a number of existing studies, however no previous work examines the electricity system in the same way or utilizes the same set
of indicators. The majority of other studies use qualitative indicators without country specific
findings and look specifically at the future, while this study also includes current vulnerability
indicators. The final ranked vulnerability index has a moderate north-south gradient, with two
of the three most vulnerable countries are in the south of Europe (GR and IT, with the exception
being LU which will be discussed in the following sections), while NO and CZ are the least
vulnerable. This trend correlates with a study by Alcamo et al. (2007) that indicates the sensitivity of southern European countries to climate change is greater than for northern European
Countries. The study is explicit in stating that regions in both ES and GR specifically will be
the most affected by climate change.
The index is further vetted by a study of Eskeland and Mideksa (2010) which identifies the
relative effects on heating and cooling due to climate change in Europe. The study concludes
that the climate change will induce less heating in northern European countries, while increasing cooling in southern European countries. Ultimately, the study identifies ES, GR and IT as
countries that will experience cooling increases that outweigh heating decreases due to climate
change. Thus correlating with the higher vulnerability ranking of those countries seen in our
final index.
A study by Gnansounou (2008), assesses the vulnerability of the energy sector as a whole
(including the electricity sector) on a country level in terms of a much wider scale that includes
the market and energy prices, fuel reserve availability, geopolitics and economic growth of some
emerging economies, among other factors. The vulnerability of the countries included in the
study is calculated based on a number of influencing factors that include energy intensity, oil and
gas import dependency, CO2 content of primary energy supply, electricity supply weaknesses
and non diversity in transport fuels. Despite the very different influencing factors considered
and wider range of countries considered, the final index of vulnerability presented in the study is
similar to the findings of this study. LU, GR and IT are considered to be very vulnerable, while
NO, FR and GB are considered relatively less vulnerable, which correlates well with our study.
Obviously, due to the examination of energy, as opposed to electricity sector, there are some
differences in the relative index ranking; however overall, there are a number of similarities as
well. One major difference to our study is that Gnansounou (2008) merely discusses climate
or climate change as an influencing factor in passing, and aside from an emissions perspective,
as in the costs associated with energy sector greenhouse gas emissions, climate is not readily
addressed.
In terms of the vulnerability of specific electricity production sources a number of stud38
ies qualitatively examine the effects of climate change on electricity production. Studies by
Rübbelke and Vögele (2011b) and van Vliet et al. (2012) examine the negative effects of climate change on the electricity production ability in Europe, specifically on the most vulnerable
electricity production source, thermoelectric generation. Southern and southeastern Europe are
identified as being particularly susceptible to climate change related problems which correlates
well with the index (van Vliet et al., 2012).
6.1.2
Selected Index Countries
LU LU is, by a wide margin, the most vulnerable country in terms of the ranked vulnerability
index. This can be primarily accounted for by the current vulnerability indicators (Categories 5
and 6), which demonstrate the very high reliance on imports. Moreover, LU is the most significant deviant in terms of electricity production and consumption percentage discrepancy. Inland
production in LU meets less than half of the consumption demand, making LU very reliant
on imports, for both summer and winter, from neighboring countries, which is a major factor
in terms of increasing vulnerability (IEA, 2009a). This is most likely due in large part to the
small size of the country as well as the high level of industrial electricity consumption (IEA,
2009a). However, LU experienced a drastic shift in its electricity system due to a 2002 capacity
increase (see Figure 34 in Appendix B) when gas-fired electricity production was introduced
which effectively increased production by 200% (European Commission, 2007). LU has one
of the highest per capita electricity consumption rates in the world, and is securely positioned
within the Central-West Europe electricity market which may account for the country putting
little emphasis on increasing inland production (IEA, 2009a).
Additionally, LU primarily utilizes combustible fuels as an electricity production source (see
Figure 34 in the Appendix B), which will likely experience climate change related decreases in
capacity during prolonged heat waves or droughts. Almost a third of the country’s production is
from hydro, which may help increase electricity security depending on future precipitation patterns, which in northern europe will likely be an increase, which would enhance hydro capacity
(IEA, 2007; Rübbelke and Vögele, 2011a; Semadeni, 2003). Between 2000 and 2011 however,
the share of thermal electricity production has increased by more than 40% (IEA, 2009c).
In terms of future vulnerability to climate change, LU does not stand out as being particularly vulnerable for categories 3 and 4 with moderate projected increases in mean temperature and air conditioner prevalence. Electricity production and consumption below the heating
threshold in LU on the other hand are neither particularly correlated or steep in terms of slope to
temperature (Categories 1 and 2), therefore, the effects of climate change will not improve the
situation by any significant margin in terms of electricity savings from heating, unlike in other,
mostly more northern, countries. Many of the countries examined in this project experience
large volumes of tourists at different time periods in the year, depending on the country (Appendix B - Figure 40 to Figure 43). The potential effects of the temporary increase or decrease
in population on electricity consumption must be taken into account as a possible influencing
factor. Tourism could act as a small factor in decreasing consumption during summer months
for LU where trips surpass arrivals during that period (EUROSTAT, 2012).
GR, IT As previously mentioned, two of the top three most vulnerable countries in terms of
the index are GR and IT, which all happen to be typically warm in comparison with the other
countries in the study and in the southern region of Europe. Unlike LU, these countries are con39
sidered more vulnerable in their index ranking due to the future likelihood of climate change
effects indicators. Due to the fact that they already reach the cooling threshold, they are the
most vulnerable in both categories 1 and 2. Furthermore, the expected temperature changes
due to climate change indicate a distinct warming during the summer that outweighs any winter
warming. IT is particularly vulnerable in terms of projected air conditioner prevalence, while
GR is less vulnerable in this category due to the low projected stock increase.
One of the most vulnerable countries in terms of the index is GR. This is primarily due to
the fact that no only is GR a net consumer of electricity in both summer and winter months,
but is a consistent net importer of electricity by a larger percentage. Furthermore, the temperatures in GR reach the cooling threshold after which electricity production and consumption are
highly correlated to temperature with a steep slope, meaning that as temperatures rise, so too
does electricity consumption. The reason for this increase in consumption during warmer temperatures is best explained by the high number of air conditioners in GR. The high number of
air conditioners is likely due to the comparatively warm mean temperatures. Furthermore, the
likelihood of GR experiencing more prolonged elevated temperature events historically, could
be a factor in that people would buy more air conditioners during that period as air conditioners
are often bought impulsively (Adnot et al., 2008).
The flat slope and low correlation between electricity production and consumption for the
cooling values experienced by IT are almost exclusively due to the behavior in the month of
August. The most compelling evidence is associated with tourism as IT has its vacation period
during August when a large number of people leave the country (Bosco et al., 2007). It is
important to note however, that there are a large number of variables associated with tourism
in general, and for IT specifically, the decrease may be due largely to the closing of businesses
and industry during the holidays (Bosco et al., 2007).
PT Of the four countries that reach the cooling temperature threshold consistently and are
geographically located on the Mediterranean Sea, PT behaves differently than the others. Due
to its current and projected mean temperatures, high air conditioner prevalence, similar to other
countries in the region would be expected, but is not the case. This low air conditioner stock
may be due to the air conditioner data, which was from 2005, the projections and more current
literature shows a significant increase in air conditioner prevalence over the past years (IEA,
2009b). PT is also ranked as less vulnerable due to its move away from thermal electricity
production over the past years, and the development and investment in renewable electricity
production sources (IEA, 2009b). PT is in the middle of the ranked index group, with only
moderate relative vulnerability.
PT utilizes imports to meet production shortfalls, however during the period after 2004,
there was much more reliance on imports to meet consumption demand. This is likely due in
large part to the opening of PT’s electricity market in the summer of 2004 (EIA, 2003). Imports
however seem to be decreasing in the last years as renewable production increases as part of an
ambitious renewables action plan (European Commission, 2009b).
CH CH is essentially in the middle of the ranked index for the categories and total values (CH
is the other country with no air conditioner data available however), but behaves uniquely, with
counterintuitive seasonal differences in the system in comparison to other countries. CH produces much more electricity than it requires to meet consumption demand in summer months,
40
which is due to their utilization of hydro which is seasonally based. The reservoirs are often
filled during periods of higher precipitation and spring glacier melting and stored until times of
need or ideal market conditions, and for CH hydro is utilized significantly more during warmer
months (Paul et al., 2007; Semadeni, 2003). Nuclear electricity production decreases drastically
during summer months, while hydro production increases, which indicates that nuclear is used
to help meet the winter peak while hydro is used for export. This seasonal shift of production
sources may add to the variability of the system, due to the fact that CH produces the most in
times when they can easily meet their own consumption needs, and thus they have no electricity
security issues for that period. Electricity production for CH varies greatly by year, however,
something which is primarily due to precipitation changes affecting hydro electricity production, something which further distances production from consumption (IEA, 2007).
Interestingly, CH has a number of electricity consumption anomalies, all of which occurred
during the year 2007. During that year there was an overall decrease in electricity consumption
compared to previous years due to warmer than average winter temperatures, but no drastic
events were found to warrant the extremely low consumption values (SFOE, 2008). 2005 was
also a notable year, during which, for a five month period, the largest nuclear power plant in CH
had to reduce its electricity generation (IEA, 2007).
FR FR is also considered to have very low vulnerability on the ranked index could mean
that they will face problems in the future due to their reliance on thermal electricity production. Over the past decade a number of extreme weather events, which are likely to increase
in frequency with climate change were problematic to the FR electricity system (Eskeland and
Mideksa, 2010; Rübbelke and Vögele, 2011b). The summers of 2003 and 2009 proved particularly problematic due to heat waves impacting cooling water for nuclear power plants in terms
of amount and temperature (Flörke et al., 2011; Rübbelke and Vögele, 2011b). In 2009, a third
of the nuclear electricity plants in FR were shut down due to the summer heat wave, forcing FR
to import electricity.
FR, which produces the vast majority of its electricity from nuclear sources, is very similar to CZ in that it produces far more electricity than demand, and exports consistently. FR is
striving for energy security and has an investment body which identifies electricity production
needs to aid in this endeavor (IEA, 2010a). Furthermore, and what might explain at least part of
the production surplus in FR is that while base load electricity consumption can easily be met,
peak consumption is ever increasing, which requires more production capacity (IEA, 2010a).
FR has experienced very few periods when during which imports exceed exports for an
extended duration, however this was the case in October 2009 due to a temporary maintenance
related shut down of some nuclear plants (IEA, 2010a). The regular maintenance schedule had
been postponed due to labor strikes earlier in the year, leading to a disruption in the normal
system (IEA, 2010a). There are a number of other instances when FR was forced to import
electricity, but for a much smaller time period.
CZ CZ produces far more electricity than it consumes and is a consistent net exporter of
electricity by a large margin, meaning that its current electricity system vulnerability is very
low. Furthermore, CZ is not expected to substantially increase its currently low air conditioner
stock in the future, and experiences only a moderate temperature increase, which is greater in
winter than summer. In terms of the relative quantitative indicators, CZ is not considered to be
41
very vulnerable to climate change in the future as well. Despite all of these factors, the actual
vulnerability of CZ, similar to FR, may be significantly higher due to their almost complete
reliance on combustible and nuclear electricity production (Category 5), which will probably
be negatively affected by climate change in the future. However, CZ has a large reserve of
domestic resources (primarily coal and uranium) that is easily accessible and readily used for
electricity production, which will maintain electricity security in the near future (IEA, 2010b).
NO The least vulnerable country in the final ranked index is NO with low vulnerability in the
majority of the categories. It is important to note however, that there was no air conditioner data
available for NO, and therefore, the index ranking value would be slightly higher if the data
would be available. That being said NO has the lowest vulnerability for both category 1 and
2, meaning that as the climate changes, electricity production and consumption will decrease
significantly during the winter. Furthermore, NO will benefit the most from the temperature
changes, with the winter rise in temperature being far greater than the summer increase. NO
relies almost exclusively on hydro electricity, and will experience greater precipitation in the
future due to climate changes, which will therefore be beneficial for electricity production in
the country (Alcamo et al., 2007). In addition, NO has almost no electricity production from
thermoelectric sources. Finally, NO is a net producer and exporter of electricity in both summer
and winter.
Universal Trends All of the countries, to differing degrees, show an increase in monthly electricity consumption from February to March, which for most countries is against the generally
decreasing electricity production and consumption trend in spring. This can be likely explained
by the 1 hour clock change for daylight savings time, usually done in March (European Parliament and Council, 2001). Daylight savings is designed to increase the number of daylight hours
and therefore reduce electricity consumption due to decreases in heating and lighting to some
degree, however studies show that for the first few weeks after the change in spring, consumption increases due to earlier wake up times which require more heating (Kellogg and Wolff,
2007; Kotchen and Grant, 2008).
The entire electricity production and consumption from 2000-2011 demonstrates the overall trend of increase for variables over time. This increase could be due to a number of factors,
most notable are a rise in GDP and rise in population (except DE and HU experienced no consistent population increase during the time period) (EUROSTAT, 2012). The time frame of only
11 years (due to data availability) does not provide enough for an obviously increasing trend
especially due to the decrease seen among most countries (with the exception of BE) around
2008/2009, which is most likely due to the global financial collapse (European Commission,
2009a).
6.1.3
Long Term Summer Electricity Consumption Trend
Aside from the current and predicted vulnerability of the countries included in this study, which
has been the focus thus far, the trajectories of certain electricity system components may further
increase vulnerability. The most compelling evidence for this is the increasing trend of summer
electricity consumption. Looking at selected warmer countries, using longer time periods than
the previous calculations clearly shows that over the past decades electricity consumption during
summer months has risen especially in July and August. It is clear that there is a parabolic
42
relationship between electricity production and consumption with temperature, however at this
point the majority of countries only experience the colder end of that curve. In the future,
however, already small increases in temperature may cause countries that at this point do not
reach the cooling threshold, to surpass it, leading to an increase in electricity consumption. Yet,
this increase can not be attributed totally to rising temperatures but indicates that people may
anticipate hotter summers in the future and use their acquired air conditioners disproportionately
often also with temperatures where it would normally not be necessary. Combining that finding
with the projected temperature and air conditioner data gives a glimpse of a potential future in
which more and more countries will take the trajectory of strongly increased summer electricity
cooling.
6.2
6.2.1
Discussion of the Methods and Limitations
Methods
The method of indexing utilized in this study was an attempt to quantify the relative vulnerability of each country to climate change, essentially determine a country’s ability to meet its
electricity consumption demand now and in the future. Considering the inability to include all
possible influencing factors, what were deemed to be the most important indicators were included, and there were a number of assumptions and methodological possibilities, the specific
ones chosen for this study will be discussed here.
In terms of the heating and cooling temperature thresholds, no definite or universal method
exists to determine the threshold values due to the large number of variables that changed for
each geographic location or country (Beenstock et al., 1999; Engle et al., 1992; Prek and Butala,
2010; Thevenard, 2011; Valor et al., 2001). The studies in literature used a number of differing
thresholds which varied from 10 to 25 degrees for heating and cooling (Beenstock et al., 1999;
Engle et al., 1992; Prek and Butala, 2010; Thevenard, 2011; Valor et al., 2001). The thresholds
vary by country due at least in part to the different climate and energetic building standards
(Olonscheck et al., 2011). It was important for comparisons sake however, to use the same
values for each country included in the study, and we therefore took values closer to the higher
and lower ends of the spectrum in order to ensure adequate coverage of all the countries.
Regarding the tourism data, despite the fact that the data was divided by population, tourism
arrivals and trips does not mean the number of people. Therefore, for example, if arrivals
increase to 0.4 for a given month, that does not indicate an increase in population by 40%,
however, a substantial increase in the number of people in the country must be considered, especially when the difference between trips and arrivals differs substantially.
For the sake of consistency, the projected temperature changes should have also been weighted
by population, however the data used was not gridded. Furthermore, the effects of population
weighting on the mean temperature data was not substantial, and only a small change was observed. Finally, both among the indicators for each category and the categories themselves,
each were weighted equally in terms of the determination of the final index values. Due to
the difficulty of determining the relative magnitude of the effects of each indicator or category
on the vulnerability of a country, equal weighting was reasonable due to a lack of information
enabling weighting factor calculation. In addition, the complexity of the electricity system in
general, but also the large differences between countries also required equal weighting.
43
6.2.2
Limitations
The major limitation of this study was the access and availability of data. The only available
monthly electricity data for a wide range of European countries included only the years 2000 to
2011, and did not include the entire continent (only the 21 countries included). The IEA (2012)
annual electricity data was available for every member state, however for an undisclosed reason,
the monthly data specifically was limited. Some data sets were found with longer time periods,
but only included a smaller selection of countries. As well, daily electricity data would have
been quite useful for examining specific extreme temperature related events and their effect on
the system, however no such data was found. This is particularly pertinent due to the 2003
summer heatwave in Europe which caused a number problems for some countries in terms of
meeting electricity demand and forced changes to the electricity system (Eskeland and Mideksa,
2010; Flörke et al., 2011; Rebetez et al., 2008; Rübbelke and Vögele, 2011a). This well documented disruption and extreme increases in electricity consumption and imports combined
with production and export decreases in some European countries were not found in our data
which is likely due to the monthly scope, while the heatwave was only weeks in scope (Eskeland and Mideksa, 2010; Flörke et al., 2011; Rebetez et al., 2008; Rübbelke and Vögele, 2011a).
Furthermore, there were limits to the import and export data available that included transfers between adjacent countries, but not information on the original exporter and final consumer
countries. An additional consideration for the import and export data is that fewer transfer connections for a country would mean that the potential for physical damages to the electricity
lines would be decreased by storms other events would be decreased but that disruption on one
of these few lines can have more severe impacts. Other data limitations included the lack of
availability of data, which provided a quantitative measure of the percentage of electricity consumption used for heating, cooling and lighting by month. One specific limitation was the air
conditioner data which was published in 2008, and therefore only the 2005 data values were
used, the others being projections, which means the air conditioner data may already be outdated. Moreover, the data only reveal information about the number of air conditioners that
exists in a country, and not how or when they are used. The assumption is then that countries
with a lot of air conditioners use them when the temperature exceeds the cooling threshold.
Furthermore, the air conditioner data did not provide values for CH and NO, meaning that the
air conditioner category index value was omitted from the final ranked vulnerability index calculations for those countries.
One key limitation was that despite the fact that all electricity generation sources are affected in some way by climate change, there is no relative quantitative data on the effects on
electricity production, therefore only the effects on thermal and nuclear electricity production
were considered significant and therefore vulnerable (Flörke et al., 2011; Rübbelke and Vögele,
2011a). Hydro electricity generation is also vulnerable to extreme events and changing precipitation patterns due to climate change, however research into the specific effects associated
with these phenomenon yields contradictory results (Rademaekers et al., 2011; Rübbelke and
Vögele, 2011a; The World Bank, 2008). Furthermore, the complex interaction between climate
and hydro electricity requires a detailed geographically specific analysis in order to quantitatively determine vulnerability.
Studies concerning the 2003 heat waves in Europe often cite thermal and nuclear power
plant output reductions specifically as being particularly problematic (Förster and Lilliestam,
2009; Rübbelke and Vögele, 2011a,b). Furthermore, and perhaps the most compiling evidence
44
of the increased vulnerability of combustible fuel heavy electricity production is the political and social objection to these emissions intensive and controversial electricity production
sources. Due to an increasing push for lower greenhouse gas emissions by a number of European countries as well as recent nuclear power disasters, nuclear and fossil fuel phase out plans
have been made. Most notably, DE has planned to close all of its nuclear power plants by 2022,
along with the remaining black coal mines by 2018 (Breidthardt, 2011; Dougherty, 2007). Ultimately, even a country with ample electricity production now, may be see their surplus diminish
as combustible fuel production diminished with environmental, social and political pressures,
something which will not be the case for hydro or renewable production sources.
The EU, as a whole has been undertaking extensive integration of renewable electricity production, which exceeded 20% in 2010, meeting a 2001 EU target (EWEA, 2012). A even more
ambitious EU renewable electricity target for 2030 has been requested by industry, with the
goal of decreasing emissions as well as improving energy security in the EU, both of which
require a move away from traditional thermal electricity production (EWEA, 2012). It is likely
that along with the planned electricity and energy targets for the EU, substantial electricity production changes will be undertaken in most countries in the upcoming years, unfortunately any
kind of future calculations or quantification in terms of the projected impacts of those changes
on the electricity system vulnerability would require extensive country specific analysis. This
analysis would be a useful extension of this study and improve the findings as well.
Electricity storage capacity could affect the vulnerability of the electricity system of a given
country strongly, but was not integrated in this study due to lack of adequate data (Semadeni,
2003). CH utilizes hydro electricity greatly and has a number of planned and existing hydro
pumped storage plants which, if integrated in this study would decrease their final index vulnerability ranking (Huber and Gutschi, 2010). Besides hydro pumped storage there are a number
of energy storage technologies including battery storage, compressed air systems, mechanical
storage and electro-magnetic storage among others which if available, bridge production and
consumption fluctuations (Naish et al., 2008).
As highlighted by this section and as previously mentioned, there are a large number of
influencing factors, both qualitative and quantitative, that have an effect on the vulnerability of
the electricity system. These factors however, are too many to include in a study of this size,
and are very difficult to incorporate into a study such as this. Besides the factors previously
mentioned in this section, the effects of innovations in the electricity system as well as efficiency
improvements, political decision making or social pressures are all potentially significant. The
study scope must be limited at some point, and therefore not all possible influencing factors
could be included.
6.3
Future Work
In terms of potential future work, there are many options and possibilities. This study could be
advanced in a variety of areas. Changing the focus slightly to the isolation of temperature dependent electricity consumption (primarily lighting, heating and cooling) would be very beneficial
in understanding how the system behaves in terms of temperature changes. The influence of
prices and the electricity market on the electricity system and its management became evident
during this study, and an economic based study would improve understanding of that aspect.
Furthermore, adding an economic perspective would add another level of complexity, but also
of potential understanding.
45
Using the current results, a prediction or projection of potential future vulnerabilities would
be a logical next step. Obviously, predictive work would require the addition of more variables
and influencing factors, including political and social influences, however scenarios would be
quite useful in a further identification of the countries that are likely to become more vulnerable
and why.
7
Conclusions
Assessing the vulnerability to climate change of European countries’ electricity system is a
complex issue with a wide variety and number of influencing factors. It is clear however that
many countries are not only susceptible to climate related stresses on the system, but that most
countries will become more vulnerable in the future. Of course, the future vulnerability of any
country is dependent not only on the existing electricity system and the climate projections,
but also political, economic and societal factors. This study attempted to provide an overall
outlook, incorporating a large number of influencing factors, of the vulnerability of many countries to climate change in a quantitative way whenever possible, something which has not been
previously undertaken. Ultimately, a ranked vulnerability index was presented, which provided
a quantitative relative indication of vulnerability amongst the countries included, which builds
on the more specific findings of existing studies. The index utilized indicating factors for both
the current electricity system as well as factors that incorporate projected data. Furthermore, a
number of qualitative influencing factors were discussed.
This study was successful in identifying the most vulnerable countries to climate change
with the use of a number of indicators related to the electricity system. We were able to discern a number of countries with similar characteristics in their electricity systems and ultimately
similar vulnerabilities. The country based analysis built upon existing vulnerability studies with
wider geographic scope, and the quantitative basis for the study is unique for the indicating factors examined.
The findings of this study may be useful in a number of ways. In terms of decreasing
vulnerability, policy makers, scientists and energy managers can examine the most important
influencing factors that increase vulnerability and focus their adaptation efforts on those areas.
Furthermore, due to the relative nature of the vulnerability index, countries with higher vulnerability can identify less vulnerable electricity systems as a guide to decreasing their vulnerability.
We feel that the findings of this study are an important first step towards a comprehensive analysis of the vulnerability of European countries to climate change.
46
8
Acknowledgements
This thesis project would not have been possible without the help and support of a large number
of people both from the Potsdam Institute for Climate Impact Research (PIK) and from the
University of Linköping. I would like to express my gratitude to Mady Olonscheck (PIK), with
whom I worked closely and who provided invaluable guidance not only for the science and
methodology of the study, but also for thesis writing in general. Her knowledge, patience and
supportive attitude cannot be overstated. I am indebted to Carsten Walther (PIK), with whom I
collaborated and worked closely; he assisted with some of the more technical data calculations
as well as provided some much-welcomed and invaluable expertise throughout the study. In
addition, I would like to express my gratitude to Prof. Dr. Jürgen P. Kropp (PIK) for overseeing
the project and his comments and supervision. I am grateful to the members of the PIK Climate
Change and Development working group who provided data provisions and calculations as
well as many valuable comments, discussions and remarks. Specifically for help on this project
I would like to thank: C. Matasci, C. Pape, Dr. D. Rybski, Dr. H. Förster, K. Voigt, L. Costa,
R. Lutz, and S. Kriewald. Finally, Dr. Anders Hansson (Linköping University), my university
supervisor, provided a number of excellent comments and guidance to the project and for this I
am most grateful.
47
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52
A
Actual Category Indicator Tables
Table 9: Category 1: Production and Consumption Correlation to Mean Temperature Values. Source: adapted from European Climate Assessment and Dataset (2012) and IEA (2012).
Country
CH
LU
AT
IT
NL
ES
BE
IE
GR
PT
PL
DE
HU
CZ
SE
SK
GB
DK
FR
NO
FI
Heating
Production
Country
0.120
-0.224
-0.268
-0.397
-0.409
-0.494
-0.543
-0.546
-0.570
-0.574
-0.652
-0.657
-0.676
-0.719
-0.728
-0.744
-0.777
-0.805
-0.812
-0.814
-0.873
LU
IT
GR
NL
ES
HU
SK
DE
BE
IE
PT
PL
CH
DK
GB
AT
CZ
FR
FI
SE
NO
Consumption
Country
-0.102
-0.305
-0.481
-0.502
-0.544
-0.547
-0.561
-0.580
-0.584
-0.587
-0.610
-0.658
-0.696
-0.763
-0.770
-0.774
-0.852
-0.889
-0.909
-0.929
-0.938
GR
ES
IT
PT
HU
53
Cooling
Production
Country
0.882
0.432
0.260
0.008
-0.113
GR
HU
ES
IT
PT
Consumption
0.828
0.491
0.455
0.242
0.089
Table 10: Category 2: Production and Consumption and Mean Temperature Slope Values. Source: adapted from European Climate Assessment
and Dataset (2012) and IEA (2012).
Country
CH
AT
IT
BE
PL
DE
LU
NL
CZ
HU
SK
GR
ES
SE
FI
IE
NO
GB
FR
DK
PT
Heating
Production
Country
0.0031
-0.0050
-0.0077
-0.0096
-0.0103
-0.0105
-0.0108
-0.0108
-0.0114
-0.0126
-0.0132
-0.0155
-0.0166
-0.0166
-0.0167
-0.0187
-0.0254
-0.0274
-0.0277
-0.0373
-0.0434
LU
IT
HU
DE
NL
PL
BE
AT
GR
SK
CH
FI
DK
CZ
ES
IE
SE
GB
NO
FR
PT
Consumption
Country
-0.0014
-0.0041
-0.0063
-0.0082
-0.0095
-0.0099
-0.0103
-0.0115
-0.0119
-0.0126
-0.0138
-0.0140
-0.0154
-0.0158
-0.0174
-0.0182
-0.0234
-0.0261
-0.027
-0.0344
-0.0369
GR
ES
IT
PT
HU
Cooling
Production
Country
0.0514
0.0154
0.0101
0.0027
-0.0042
GR
ES
HU
PT
IT
Consumption
0.0525
0.0155
0.0131
0.0089
0.0079
Table 11: Category 3: Scenario A2 Summer and Winter Temperature Increase (◦ C). Source: adapted from Mitchell et al. (2002).
Country
Summer
Country
Winter
ES
HU
CH
AT
FR
GR
SK
IT
LU
CZ
PT
BE
PL
DE
FI
NL
SE
DK
NO
GB
IE
4.976
4.740
4.737
4.522
4.491
4.406
4.402
4.309
4.189
4.108
4.056
3.946
3.939
3.886
3.796
3.531
3.530
3.399
3.264
3.088
2.702
FI
SE
NO
PL
SK
CZ
HU
DK
AT
DE
CH
LU
NL
BE
IT
FR
GR
ES
GB
PT
IE
7.081
5.761
5.093
5.089
4.698
4.469
4.453
4.278
4.153
4.103
3.743
3.733
3.674
3.626
3.369
3.307
3.120
3.057
2.981
2.757
2.579
54
Table 12: Category 4: Air Conditioner Prevalence (Per Capita). Note: No data was available for CH or NO. Source: adapted from Adnot et al.
(2008).
Country
Projection
(2030)
Country
IT
GR
SK
ES
FR
NL
DK
BE
LU
GB
FI
HU
SE
PT
IE
AT
CZ
DE
PL
CH
NO
0.521
0.491
0.469
0.420
0.250
0.246
0.242
0.227
0.211
0.186
0.166
0.162
0.154
0.146
0.143
0.088
0.044
0.042
0.015
-
FI
SE
GB
AT
NL
FR
DK
IE
BE
LU
HU
IT
SK
PL
PT
ES
DE
CZ
GR
CH
NO
Percentage Difference
(2005-2030)
3.651
3.316
3.189
3.145
3.082
3.061
3.040
2.948
2.766
2.242
2.062
1.956
1.645
1.635
1.501
1.491
1.368
1.128
0.790
-
Table 13: Category 5: Thermal Electricity Production. Source: adapted from IEA (2012).
Country
HU
PL
NL
CZ
GB
BE
FR
GR
SK
DE
FI
IE
IT
DK
ES
LU
PT
SE
CH
AT
NO
Thermal Production
Percent (2011)
0.975
0.963
0.951
0.935
0.932
0.912
0.883
0.860
0.851
0.828
0.814
0.812
0.751
0.707
0.704
0.673
0.577
0.494
0.461
0.440
0.039
Country
LU
AT
SE
CH
NO
FI
FR
SK
PL
HU
CZ
NL
IT
GB
GR
BE
DE
ES
IE
PT
DK
55
Thermal Production Percent
Difference (2000-2011)
0.435
0.151
0.047
0.039
0.035
0.030
0.021
0.020
-0.007
-0.020
-0.031
-0.035
-0.036
-0.044
-0.049
-0.067
-0.107
-0.127
-0.137
-0.143
-0.168
Table 14: Category 6: Production and Consumption Summer and Winter Correlation and Discrepancy. Source: adapted from IEA (2012).
Country
SK
CH
SE
DK
LU
NL
FR
FI
NO
HU
CZ
BE
AT
PT
DE
IE
PL
GR
GB
ES
IT
Correlation
Summer
Country
-0.301
0.183
0.370
0.391
0.499
0.525
0.554
0.563
0.573
0.639
0.677
0.682
0.703
0.811
0.906
0.913
0.940
0.941
0.965
0.973
0.978
SK
CH
AT
SE
DK
HU
BE
NO
LU
FI
CZ
PT
NL
GR
FR
DE
PL
IT
GB
IE
ES
Winter
Country
0.181
0.337
0.477
0.569
0.593
0.694
0.704
0.708
0.733
0.746
0.756
0.802
0.825
0.856
0.860
0.867
0.901
0.925
0.964
0.976
0.992
LU
FI
HU
NL
DK
IT
PT
GR
IE
BE
GB
DE
ES
SE
AT
PL
SK
NO
FR
CH
CZ
Discrepancy
Summer
Country
0.431
0.826
0.827
0.837
0.841
0.875
0.898
0.910
0.960
0.969
0.972
0.979
1.008
1.020
1.028
1.035
1.079
1.107
1.168
1.190
1.255
LU
IT
AT
NL
FI
CH
HU
BE
PT
GR
SE
IE
GB
NO
ES
DE
PL
SK
FR
CZ
DK
Winter
0.480
0.861
0.876
0.889
0.889
0.903
0.912
0.921
0.948
0.970
0.972
0.975
0.986
1.006
1.006
1.040
1.053
1.073
1.086
1.181
1.191
Table 15: Category 7: Import and Export Percentage Discrepancy. Source: adapted from IEA (2012).
Country
Summer
Country
Winter
LU
DK
HU
NL
FI
IT
PT
GR
BE
GB
DE
IE
ES
SE
AT
PL
SK
NO
FR
CH
CZ
2.100
0.215
0.212
0.198
0.190
0.144
0.116
0.090
0.036
0.029
0.022
0.021
-0.007
-0.011
-0.021
-0.033
-0.041
-0.081
-0.142
-0.144
-0.201
LU
IT
AT
NL
FI
CH
HU
BE
PT
SE
GR
IE
GB
NO
ES
DE
SK
PL
FR
CZ
DK
1.527
0.162
0.150
0.130
0.128
0.122
0.101
0.088
0.060
0.033
0.031
0.017
0.014
0.000
-0.006
-0.038
-0.047
-0.050
-0.077
-0.152
-0.157
56
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Production
Consumption
CH
Electricity (% diff from Ave.)
●
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Electricity (% diff from Ave.)
0.5
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Electricity (% diff from Ave.)
BE
●
−0.5
Electricity (% diff from Ave.)
AT
●
●
20
30
Mean Temperature (ºC)
Production
Consumption
−10
0
10
20
30
Mean Temperature (ºC)
Figure 16: Mean temperature vs. the percent difference of electricity consumption from the annual average, including the heating threshold
of 12 ◦ C and above the cooling threshold of 21 ◦ C (1/4). Source: adapted from European Climate Assessment and Dataset (2012) and IEA
(2012).
57
−10
0
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30
Production
Consumption
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−10
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30
GR
HU
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Production
Consumption
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10
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30
Mean Temperature (ºC)
Production
Consumption
−10
0
10
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0.5
GB
Electricity (% diff from Ave.)
Mean Temperature (ºC)
0.5
Mean Temperature (ºC)
Electricity (% diff from Ave.)
Mean Temperature (ºC)
0.0
−0.5
●
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20
FR
Electricity (% diff from Ave.)
Production
Consumption
−0.5
Electricity (% diff from Ave.)
0.5
●
●
Electricity (% diff from Ave.)
FI
●
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−0.5
Electricity (% diff from Ave.)
ES
●
●
20
30
Mean Temperature (ºC)
Production
Consumption
−10
0
10
20
30
Mean Temperature (ºC)
Figure 17: Mean temperature vs. the percent difference of electricity consumption from the annual average, including the heating threshold
of 12 ◦ C and above the cooling threshold of 21 ◦ C (2/4). Source: adapted from European Climate Assessment and Dataset (2012) and IEA
(2012).
58
Production
Consumption
−10
0
10
●
20
30
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0
10
0.5
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30
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Consumption
●
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0
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Consumption
10
●
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30
NO
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●
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Consumption
−10
0
10
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30
Mean Temperature (ºC)
Production
Consumption
−10
0
10
●
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0.5
NL
Electricity (% diff from Ave.)
Mean Temperature (ºC)
0.5
Mean Temperature (ºC)
Electricity (% diff from Ave.)
Mean Temperature (ºC)
0.0
−0.5
●
●
●
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●
−0.5
●
●
●
LU
Electricity (% diff from Ave.)
●
−0.5
Electricity (% diff from Ave.)
0.5
0.0
●●
●
● ●●●
●
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● ●●
●
Electricity (% diff from Ave.)
IT
●
●
−0.5
Electricity (% diff from Ave.)
IE
●
●
20
30
Mean Temperature (ºC)
Production
Consumption
−10
0
10
20
30
Mean Temperature (ºC)
Figure 18: Mean temperature vs. the percent difference of electricity consumption from the annual average, including the heating threshold
of 12 ◦ C and above the cooling threshold of 21 ◦ C (3/4). Source: adapted from European Climate Assessment and Dataset (2012) and IEA
(2012).
59
PT
SE
SK
●
−10
0
10
●
●
20
30
Mean Temperature (ºC)
Production
Consumption
−10
0
10
0.5
●
●
●
●
●
●
20
30
Mean Temperature (ºC)
●
●●
●●
●●
● ● ●● ●
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−0.5
Production
Consumption
Electricity (% diff from Ave.)
●
●
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−0.5
Electricity (% diff from Ave.)
0.5
●
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0.0
−0.5
Electricity (% diff from Ave.)
●
Production
Consumption
−10
0
10
●
20
30
Mean Temperature (ºC)
Figure 19: Mean temperature vs. the percent difference of electricity consumption from the annual average, including the heating threshold
of 12 ◦ C and above the cooling threshold of 21 ◦ C (4/4). Source: adapted from European Climate Assessment and Dataset (2012) and IEA
(2012).
60
B.2
Monthly Electricity Production, Consumption, Imports and Exports Over
Time (2000-2011)
2000
2003
2006
10000
Year
CH
CZ
2006
Imports
Exports
4000
Production
Consumption
2009
2000
2003
2006
Year
Year
DE
DK
2006
2009
Production
Consumption
5000
Imports
Exports
2000
Year
2009
0
Imports
Exports
10000
Year
0
Production
Consumption
2003
2009
Imports
Exports
0 2000
2003
2000
Electricity (GWh)
40000 80000
2000
Imports
Exports
4000
2009
4000
Production
Consumption
2006
Electricity (GWh)
10000
2003
Production
Consumption
0
Imports
Exports
0
Electricity (GWh)
2000
Electricity (GWh)
Electricity (GWh)
4000 8000
Production
Consumption
BE
0
Electricity (GWh)
AT
2003
2006
2009
Year
Figure 20: Monthly Electricity Production and Consumption Over Time (2000-2011) (1/4). Source: adapted from IEA (2012).
61
2009
FR
GB
2006
2003
Imports
Exports
20000
Production
Consumption
2009
2000
2003
2006
Year
GR
HU
2006
2009
5000
Year
Imports
Exports
Production
Consumption
2000
Year
2009
0
Imports
Exports
50000
Year
0
2000
2006
Year
4000
Production
Consumption
2003
Imports
Exports
2009
Imports
Exports
2000
2003
2000
Electricity (GWh)
10000
2000
Production
Consumption
0
Production
Consumption
2006
Electricity (GWh)
40000 80000
2003
0 4000 10000
Imports
Exports
0
Electricity (GWh)
2000
Electricity (GWh)
Electricity (GWh)
20000 40000
Production
Consumption
FI
0
Electricity (GWh)
ES
2003
2006
2009
Year
Figure 21: Monthly Electricity Production and Consumption Over Time (2000-2011) (2/4). Source: adapted from IEA (2012).
62
2003
2006
LU
NL
2006
Production
Consumption
2009
Imports
Exports
2000
2003
2006
Year
NO
PL
2006
2009
10000 20000
Year
Imports
Exports
Production
Consumption
2000
Year
2009
0 5000
Imports
Exports
15000
Year
0
2000
2003
Year
10000
Production
Consumption
2000
2009
Imports
Exports
0
2003
Imports
Exports
20000
2009
Electricity (GWh)
2000
Production
Consumption
0
Electricity (GWh)
4000
400 800
Production
Consumption
2006
Electricity (GWh)
2003
0
Electricity (GWh)
2000
Electricity (GWh)
Imports
Exports
2000
Production
Consumption
IT
0
Electricity (GWh)
IE
2003
2006
2009
Year
Figure 22: Monthly Electricity Production and Consumption Over Time (2000-2011) (3/4). Source: adapted from IEA (2012).
63
2000
2003
2006
2009
2000
Year
Imports
Exports
10000
Production
Consumption
0
Electricity (GWh)
Imports
Exports
4000
Production
Consumption
SE
0
Electricity (GWh)
PT
2003
2006
2009
Year
5000
Imports
Exports
2000
Production
Consumption
0
Electricity (GWh)
SK
2000
2003
2006
2009
Year
Figure 23: Monthly Electricity Production and Consumption Over Time (2000-2011) (4/4). Source: adapted from IEA (2012).
64
Mean Monthly Electricity Production, Consumption, Imports, Exports and
Mean Temperature
DE
DK
Month
12
1 3 5 7 9
Month
12
−10
1000
Electricity (GWh)
3000
5000
Production
Consumption
Imports
Exports
Mean Temp.
0
0
10
20
Mean Temperature (ºC)
Electricity (GWh)
20000 40000 60000
0
0
10
20
Mean Temperature (ºC)
−10
0
1 3 5 7 9
Production
Consumption
Imports
Exports
Mean Temp.
30
−10
CZ
30
Month
30
Month
12
30
1 3 5 7 9
Month
Production
Consumption
Imports
Exports
Mean Temp.
0
10
20
Mean Temperature (ºC)
8000
Electricity (GWh)
2000 4000 6000
12
0
10
20
Mean Temperature (ºC)
−10
1 3 5 7 9
Production
Consumption
Imports
Exports
Mean Temp.
0
30
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
2000
12
Electricity (GWh)
2000
6000
10000
1 3 5 7 9
CH
0
−10
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
Electricity (GWh)
6000
10000
30
BE
0
Electricity (GWh)
2000 4000 6000
8000
AT
−10
B.3
1 3 5 7 9
12
Month
Figure 24: Mean monthly electricity production, consumption, imports, exports and mean temperature (1/4). Source: adapted from IEA (2012).
65
Month
12
1 3 5 7 9
Month
12
Production
Consumption
Imports
Exports
Mean Temp.
−10
Electricity (GWh)
1000
3000
0
0
10
20
Mean Temperature (ºC)
−10
Electricity (GWh)
2000 4000 6000
0
0
10
20
Mean Temperature (ºC)
−10
0
1 3 5 7 9
5000
HU
30
GR
8000
GB
30
Month
12
30
1 3 5 7 9
Month
Production
Consumption
Imports
Exports
Mean Temp.
30
−10
0
12
Month
Production
Consumption
Imports
Exports
Mean Temp.
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
0
10
20
Mean Temperature (ºC)
1 3 5 7 9
Electricity (GWh)
20000 40000 60000
30
2000
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
−10
−10
12
Electricity (GWh)
10000
30000
50000
1 3 5 7 9
FR
0
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
Electricity (GWh)
6000
10000
30
FI
0 5000
Electricity (GWh)
15000
25000
35000
ES
1 3 5 7 9
12
Month
Figure 25: Mean monthly electricity production, consumption, imports, exports and mean temperature (2/4). Source: adapted from IEA (2012).
66
NO
PL
Month
12
1 3 5 7 9
Month
12
−10
Electricity (GWh)
5000
10000 15000
Production
Consumption
Imports
Exports
Mean Temp.
0
0
10
20
Mean Temperature (ºC)
−10
Electricity (GWh)
5000
10000 15000
0
0
10
20
Mean Temperature (ºC)
−10
1 3 5 7 9
30
NL
30
Month
12
30
1 3 5 7 9
Month
Production
Consumption
Imports
Exports
Mean Temp.
30
−10
0
12
Month
Production
Consumption
Imports
Exports
Mean Temp.
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
0
10
20
Mean Temperature (ºC)
1 3 5 7 9
Electricity (GWh)
200
400
600
800
30
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
−10
−10
0
12
0
Electricity (GWh)
5000
10000
15000
1 3 5 7 9
LU
0
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
Electricity (GWh)
10000
30000
30
IT
500
Electricity (GWh)
1500
2500
3500
IE
1 3 5 7 9
12
Month
Figure 26: Mean monthly electricity production, consumption, imports, exports and mean temperature (3/4). Source: adapted from IEA (2012).
67
Month
12
1 3 5 7 9
Month
12
30
−10
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
0
Electricity (GWh)
1000
2000
3000
30
20000
0
10
20
Mean Temperature (ºC)
−10
−10
1 3 5 7 9
Production
Consumption
Imports
Exports
Mean Temp.
Electricity (GWh)
5000 10000
0
10
20
Mean Temperature (ºC)
Production
Consumption
Imports
Exports
Mean Temp.
SK
0
30
SE
0 1000
Electricity (GWh)
3000
5000
PT
1 3 5 7 9
12
Month
Figure 27: Mean monthly electricity production, consumption, imports, exports and mean temperature (4/4). Source: adapted from IEA (2012).
68
Mean Monthly Electricity Production Source
Combustible Fuels
Nuclear
Hydro
Other
Combustible Fuels
Nuclear
Hydro
Other
Combustible Fuels
Nuclear
Hydro
Other
0
3000
0
1000
Production (GWh)
1000
3000
3000
0
1 2 3 4 5 6 7 8 9 1011
1 2 3 4 5 6 7 8 9 1011
Month
CZ
DE
DK
Combustible Fuels
Nuclear
Hydro
Other
Combustible Fuels
Nuclear
Hydro
Other
3000
2000
1000
Production (GWh)
30000
0
0
0 1000
10000
Production (GWh)
Combustible Fuels
Nuclear
Hydro
Other
4000
Month
50000
Month
3000
5000
7000
1 2 3 4 5 6 7 8 9 1011
Production (GWh)
5000
CH
5000
BE
Production (GWh)
AT
1000
Production (GWh)
5000
B.4
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 28: Mean monthly electricity production by source (1/4). Source: adapted from IEA (2012).
69
40000
0
0
1 2 3 4 5 6 7 8 9 1011
GR
HU
Combustible Fuels
Nuclear
Hydro
Other
Combustible Fuels
Nuclear
Hydro
Other
1 2 3 4 5 6 7 8 9 1011
Month
0
500 1000
5000
0 1000
10000
3000
Production (GWh)
Combustible Fuels
Nuclear
Hydro
Other
2000
GB
Production (GWh)
Month
7000
Month
20000
30000
1 2 3 4 5 6 7 8 9 1011
Month
0
Production (GWh)
40000
1 2 3 4 5 6 7 8 9 1011
Combustible Fuels
Nuclear
Hydro
Other
20000
Production (GWh)
60000
FR
1000
3000
5000
Combustible Fuels
Nuclear
Hydro
Other
Production (GWh)
20000
Combustible Fuels
Nuclear
Hydro
Other
10000
15000
FI
5000
Production (GWh)
ES
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 29: Mean monthly electricity production by source (2/4). Source: adapted from IEA (2012).
70
Combustible Fuels
Nuclear
Hydro
Other
Combustible Fuels
Nuclear
Hydro
Other
0
200
0
50 100
Production (GWh)
5000
15000
Production (GWh)
0
1 2 3 4 5 6 7 8 9 1011
1 2 3 4 5 6 7 8 9 1011
NO
PL
Combustible Fuels
Nuclear
Hydro
Other
Combustible Fuels
Nuclear
Hydro
Other
Combustible Fuels
Nuclear
Hydro
Other
Month
10000
0
5000
10000
0
5000
2000
0
1 2 3 4 5 6 7 8 9 1011
15000
NL
Production (GWh)
Month
15000
Month
Production (GWh)
Month
6000
10000
1 2 3 4 5 6 7 8 9 1011
Production (GWh)
300
Combustible Fuels
Nuclear
Hydro
Other
25000
LU
1500
2500
IT
500
Production (GWh)
IE
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 30: Mean monthly electricity production by source (3/4). Source: adapted from IEA (2012).
71
0
Month
1000
500
0
2000
1 2 3 4 5 6 7 8 9 1011
2000
Combustible Fuels
Nuclear
Hydro
Other
Production (GWh)
Combustible Fuels
Nuclear
Hydro
Other
2000 4000 6000 8000
Combustible Fuels
Nuclear
Hydro
Other
Production (GWh)
SK
3000
4000
SE
1000
Production (GWh)
PT
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 31: Mean monthly electricity production by source (4/4). Source: adapted from IEA (2012).
72
0
2006
2010
2012
2006
2008
2010
2012
2004
6000
Combustible Fuels
Nuclear
2000
2002
2004
2006
Year
DE
DK
2006
2008
2010
2012
Year
2010
2012
2010
2012
2010
2012
Hydro
Other
Year
Hydro
Other
2008
0 2000
Hydro
Other
20000
2002
5000
CZ
Combustible Fuels
Nuclear
2000
2006
CH
5000
2004
2004
Combustible Fuels
Nuclear
2008
Hydro
Other
2000
2002
2002
Year
0 2000
50000
2000
2000
Year
Combustible Fuels
Nuclear
5000
2008
Hydro
Other
0
2004
Electricity Production (GWh)
2002
Electricity Production (GWh)
2000
Combustible Fuels
Nuclear
2000
Hydro
Other
2000
5000
Combustible Fuels
Nuclear
BE
0
AT
Electricity Production (GWh)
Monthly Electricity Production by Source Over Time (2000-2011)
0
Electricity Production (GWh)
Electricity Production (GWh)
Electricity Production (GWh)
B.5
2000
2002
2004
2006
2008
Year
Figure 32: Monthly Electricity Production by Source Over Time (2000-2011) (1/4). Source: adapted from IEA (2012).
73
2006
2008
2010
2012
2004
4000
0
2004
2006
2006
2008
2010
2012
40000
2000
2002
2004
2006
Year
GR
HU
2006
2008
2010
2012
Year
2012
2010
2012
2010
2012
Hydro
Other
Year
2500
Combustible Fuels
Nuclear
2008
Hydro
Other
1000
Hydro
Other
2010
20000
Combustible Fuels
Nuclear
2008
0
Hydro
Other
4000
2002
2002
GB
Combustible Fuels
Nuclear
2000
2000
FR
20000
2004
Hydro
Other
Year
0
2002
Combustible Fuels
Nuclear
Year
Combustible Fuels
Nuclear
2000
FI
0
2004
Electricity Production (GWh)
2002
Electricity Production (GWh)
0
50000
2000
Electricity Production (GWh)
25000
Hydro
Other
10000
Combustible Fuels
Nuclear
0
Electricity Production (GWh)
Electricity Production (GWh)
Electricity Production (GWh)
ES
2000
2002
2004
2006
2008
Year
Figure 33: Monthly Electricity Production by Source Over Time (2000-2011) (2/4). Source: adapted from IEA (2012).
74
2006
2008
2010
2012
2004
15000 30000
0
2004
2006
2006
2008
2010
2012
Combustible Fuels
Nuclear
0 4000 10000
Hydro
Other
2000
2002
2004
2006
NO
PL
2006
2008
2010
2012
Year
20000
Year
2010
2012
Combustible Fuels
Nuclear
2008
2010
2012
2010
2012
Hydro
Other
10000
Hydro
Other
2008
Hydro
Other
Year
10000
2002
2002
NL
Combustible Fuels
Nuclear
2000
2000
LU
200
2004
Hydro
Other
Year
0
20000
2002
Combustible Fuels
Nuclear
Year
Combustible Fuels
Nuclear
2000
IT
0
2004
Electricity Production (GWh)
2002
Electricity Production (GWh)
0
400
2000
Electricity Production (GWh)
4000
Hydro
Other
2000
Combustible Fuels
Nuclear
0
Electricity Production (GWh)
Electricity Production (GWh)
Electricity Production (GWh)
IE
2000
2002
2004
2006
2008
Year
Figure 34: Monthly Electricity Production by Source Over Time (2000-2011) (3/4). Source: adapted from IEA (2012).
75
2000
2002
2004
2006
2008
2010
2012
10000
Hydro
Other
4000
Combustible Fuels
Nuclear
2000
2002
2004
2006
2008
2010
2012
Year
SK
Hydro
Other
1000
2000
Combustible Fuels
Nuclear
0
Electricity Production (GWh)
Year
SE
0
Hydro
Other
Electricity Production (GWh)
2000 4000
Combustible Fuels
Nuclear
0
Electricity Production (GWh)
PT
2000
2002
2004
2006
2008
2010
2012
Year
Figure 35: Group 3 - Monthly Electricity Production by Source Over Time (2000-2011) (4/4). Source: adapted from IEA (2012).
76
Electricity Production Source and Mean Temperature
20
●
Hydro
Other
●
● ●●●
●
●●●
●
● ●
●●
●
●
●●●
●
●●●
● ●
●
●●
●
●
● ●
●●
●
● ●●
●● ●● ●●
●●
●●
●
●
● ●●
●
●● ● ● ● ● ● ● ●
●● ●
● ●●
● ● ● ●●
●
●
●
●
●
●
●● ● ● ●
●●
●●
● ●
● ●●
●●
●●
● ●●●● ●● ●
●
● ●
●
●●
● ●●
●
● ●
●●● ●● ● ●●●
●● ●
●●
●
●
●
●●
●
●
●
● ●●●● ●
● ●
●
●●
●
●●
●
●
●
●
●●● ● ●● ● ● ●
●● ●
●● ●● ●
●●
●
●
●
●
●
●
●
●
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Figure 36: Mean temperature vs. electricity production by source (1/4). Source: adapted from European Climate Assessment and Dataset
(2012) and IEA (2012).
77
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Mean Temperature (ºC)
Figure 37: Mean temperature vs. electricity production by source (2/4). Source: adapted from European Climate Assessment and Dataset
(2012) and IEA (2012).
78
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●
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●
●
0
Hydro
Other
LU
Production (GWh)
●
●
●
30
−10
0
10
20
30
Mean Temperature (ºC)
Mean Temperature (ºC)
Mean Temperature (ºC)
NL
NO
PL
0
10
20
0
0
30
Mean Temperature (ºC)
●
Hydro
Other
●
●
● ●
●●
●
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● ●
●
●
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●
●●
●
●
−10
0
10
20
30
Mean Temperature (ºC)
Combustible Fuels
Nuclear
●
15000
●
10000
●
●
Combustible Fuels
Nuclear
●
●
●
Hydro
Other
●
●
●
●
●
●
● ●
● ●
● ● ●
● ● ●
● ●● ●● ●●●
●● ● ●
●● ●
●
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●
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● ●●
● ●
5000
●
0
●
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●
● ● ●
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●
●
●
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●●
●
●
●
●●
●
●
●●
●
−10
●
Production (GWh)
Hydro
Other
15000
●
2000
6000
●
●
●
10000
Combustible Fuels
Nuclear
5000
10000
●
Production (GWh)
●
Production (GWh)
●
25000
3000
Combustible Fuels
Nuclear
1000
Production (GWh)
●
15000
●
IT
100
IE
●●
●
●●
●
●●
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●
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●
●
●●●
●●
●●
●
●
−10
0
10
20
30
Mean Temperature (ºC)
Figure 38: Mean temperature vs. electricity production by source (3/4). Source: adapted from European Climate Assessment and Dataset
(2012) and IEA (2012).
79
−10
0
10
20
30
Mean Temperature (ºC)
●
Combustible Fuels
Nuclear
●
●
Hydro
Other
Combustible Fuels
Nuclear
●
●
−10
0
10
20
●
●
Hydro
Other
●
●
● ●
●
●
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● ● ●
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●
●
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●
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●
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● ● ●
● ●
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● ●
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●
●
●●
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●
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●
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●
●
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●
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●
●
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●
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●
●
●
●
●●
●
●
●●
500
1000
1500
●
0
0
Production (GWh)
1000
2000
3000
●
●
●
●
●
●
●
●●
●●
●●
●●
●
●●
●
●
● ● ●
●
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● ●
●
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●
● ●
●
●
●
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●
●
●
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●
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●
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● ●
●● ●
●●●
●
●
●
SK
Production (GWh)
Hydro
Other
8000
●
6000
●
4000
Combustible Fuels
Nuclear
2000
●
0
Production (GWh)
4000
●
SE
2000
PT
30
Mean Temperature (ºC)
●
●
●
●
●
●
●
●●●
●●
●
●
●
● ●
● ● ●
● ● ●
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● ●
●
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●
● ●
●
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●
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●
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●
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●
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●
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●
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●
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●
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●
●
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●
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●
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●
●
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●
●
●
●
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●
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●
●●
● ●
●
●●
●●
●
●●
●
● ●●
●
● ●●●● ●
●
●
●● ●
●
● ●● ●●
● ●
●● ●
● ●● ●●
●
●● ● ● ●
●
●● ●
●
● ●● ●
●
●
●
●
●
●
●
●
●
●●
●
● ●●
●●
●●
●●
●
●●
●●●●
● ●●
●
●
●●
●●
●●●
●●
●●●
● ●●
●●
●
●●●
●●
●●
●
●
●●
●●
●
●
●
●
−10
0
10
20
30
Mean Temperature (ºC)
Figure 39: Mean temperature vs. electricity production by source (4/4). Source: adapted from European Climate Assessment and Dataset
(2012) and IEA (2012).
80
Tourism
BE
0.4
0.3
Events / Pop
0.0
0.1
0.2
0.0
0.0
Arrivals
Trips
0.2
0.4
0.3
Arrivals
Trips
1 2 3 4 5 6 7 8 9 1011
1 2 3 4 5 6 7 8 9 1011
Month
Month
Month
CZ
DE
DK
Month
0.4
0.3
0.0
0.1
0.2
0.1
1 2 3 4 5 6 7 8 9 1011
Arrivals
Trips
0.2
0.3
Arrivals
Trips
0.0
0.0
0.1
0.2
Events / Pop
0.4
Arrivals
Trips
0.3
0.4
1 2 3 4 5 6 7 8 9 1011
Events / Pop
CH
0.1
0.2
Events / Pop
0.3
Arrivals
Trips
0.1
Events / Pop
0.4
AT
Events / Pop
B.7
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 40: Mean monthly tourism arrivals and trips (1/4). Source: adapted from EUROSTAT (2012).
81
FI
0.4
0.3
Events / Pop
0.0
0.1
0.2
0.0
0.0
Arrivals
Trips
0.2
0.4
0.3
Arrivals
Trips
1 2 3 4 5 6 7 8 9 1011
1 2 3 4 5 6 7 8 9 1011
Month
Month
Month
GB
GR
HU
1 2 3 4 5 6 7 8 9 1011
Month
0.4
0.1
0.2
0.3
Arrivals
Trips
0.0
0.1
0.2
Events / Pop
0.3
Arrivals
Trips
0.0
0.0
0.1
0.2
Events / Pop
0.4
Arrivals
Trips
0.3
0.4
1 2 3 4 5 6 7 8 9 1011
Events / Pop
FR
0.1
0.2
Events / Pop
0.3
Arrivals
Trips
0.1
Events / Pop
0.4
ES
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 41: Mean monthly tourism arrivals and trips (2/4). Source: adapted from EUROSTAT (2012).
82
IT
0.4
0.3
Events / Pop
0.0
0.1
0.2
0.0
0.0
Arrivals
Trips
0.2
0.4
0.3
Arrivals
Trips
1 2 3 4 5 6 7 8 9 1011
1 2 3 4 5 6 7 8 9 1011
Month
Month
Month
NL
NO
PL
1 2 3 4 5 6 7 8 9 1011
Month
0.4
0.1
0.2
0.3
Arrivals
Trips
0.0
0.1
0.2
Events / Pop
0.3
Arrivals
Trips
0.0
0.0
0.1
0.2
Events / Pop
0.4
Arrivals
Trips
0.3
0.4
1 2 3 4 5 6 7 8 9 1011
Events / Pop
LU
0.1
0.2
Events / Pop
0.3
Arrivals
Trips
0.1
Events / Pop
0.4
IE
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 42: Mean monthly tourism arrivals and trips (3/4). Source: adapted from EUROSTAT (2012).
83
SE
Month
0.4
0.3
Events / Pop
Arrivals
Trips
0.0
0.1
0.2
0.4
0.3
0.2
0.1
1 2 3 4 5 6 7 8 9 1011
SK
Arrivals
Trips
0.0
0.1
0.2
Events / Pop
0.3
Arrivals
Trips
0.0
Events / Pop
0.4
PT
1 2 3 4 5 6 7 8 9 1011
Month
1 2 3 4 5 6 7 8 9 1011
Month
Figure 43: Mean monthly tourism arrivals and trips (4/4). Source: adapted from EUROSTAT (2012).
84