The electricity system vulnerability of selected European countries to climate change : A comparative analysis

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Water and Environmental Studies Department of Thematic Studies Linköping University

Master’s programme

Science for Sustainable Development Master’s Thesis, 30 ECTS credits

ISRN: LIU-TEMAV/MPSSD-A--13/008--SE

Linköpings Universitet

The electricity system vulnerability of

selected European countries to climate

change:

A comparative analysis

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

Water and Environmental Studies Department of Thematic Studies Linköping University

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

The electricity system vulnerability of

selected European countries to climate

change:

A comparative analysis

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List of Figures

1 Vulnerability Index Tree Diagram. . . 14

2 Possible heating and cooling country electricity system pathways. . . 17

3 Monthly consumption - Long term summer electricity consumption trend. . . . 18

4 Production and consumption by mean temperature - Spearman correlation ex-amples for heating and cooling values. . . 19

5 Production and consumption by mean temperature - Slope examples for heating and cooling values. . . 21

6 Actual Summer Temperature Increase Map (Scenario A2 1961-90 to 2070-99). 23 7 Actual Winter Temperature Increase Map (Scenario A2 1961-90 to 2070-99). . 23

8 Projected Air Conditioner Prevalence Map (2030). . . 24

9 Projected Air Conditioner Percent Difference Map (2005-2030). . . 24

10 Thermal Electricity Production Share Map. . . 26

11 Thermal Electricity Production Percent Change (2000-2011) Map. . . 26

12 Monthly production, consumption, imports and exports over time. . . 29

13 Monthly average production, consumption, imports, exports and mean temper-ature - Spearman correlation examples. . . 30

14 Monthly production, consumption, imports and exports over time - Production and consumption percentage discrepancy examples. . . 32

15 Ranked Vulnerability Index Map. . . 33

16 Mean temperature vs. the percent difference of electricity consumption from the annual average . . . 57

20 Monthly Electricity Production and Consumption Over Time (2000-2011) . . . 61

24 Mean monthly electricity production, consumption, imports, exports and mean temperature. . . 65

28 Mean monthly electricity production by source. . . 69

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List of Tables

1 Category 1: Production and Consumption Correlation to Mean Temperature

Ranked Index. Source: adapted from European Climate Assessment and Dataset

(2012) and IEA (2012). . . 20

2 Category 2: Production and Consumption and Mean Temperature Slope Ranked

Index. Source: adapted from European Climate Assessment and Dataset (2012)

and IEA (2012). . . 22

3 Category 3: Scenario A2 Temperature Increase 1961-90 to 2070-99 Ranked

Index. Source: adapted from Mitchell et al. (2002). . . 23

4 Category 4: Air Conditioner Prevalence Ranked Index. Note: No data was

available for CH or NO. Source: adapted from Adnot et al. (2008). . . 25

5 Category 5: Thermal Electricity Production Share. Source: adapted from IEA

(2012). . . 26

6 Category 6: Production and Consumption Summer and Winter Correlation and

Discrepancy Ranked Index. Source: adapted from IEA (2012). . . 28

7 Category 7: Import and Export Percentage Discrepancy Ranked Index. Source:

adapted from IEA (2012). . . 31

8 Ranked Vulnerability Index. . . 33

9 Category 1: Production and Consumption Correlation to Mean Temperature

Values. Source: adapted from European Climate Assessment and Dataset (2012)

and IEA (2012). . . 53

10 Category 2: Production and Consumption and Mean Temperature Slope Values.

Source: adapted from European Climate Assessment and Dataset (2012) and

IEA (2012). . . 54

11 Category 3: Scenario A2 Summer and Winter Temperature Increase (◦_{C). Source:}

adapted from Mitchell et al. (2002). . . 54

12 Category 4: Air Conditioner Prevalence (Per Capita). Note: No data was

avail-able for CH or NO. Source: adapted from Adnot et al. (2008). . . 55

13 Category 5: Thermal Electricity Production. Source: adapted from IEA (2012). 55

14 Category 6: Production and Consumption Summer and Winter Correlation and

Discrepancy. Source: adapted from IEA (2012). . . 56

15 Category 7: Import and Export Percentage Discrepancy. Source: adapted from

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Abstract

The electricity system is particularly susceptible to climate change due to the close in-terconnectedness 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 Eu-ropean 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 indica-tors. Overall, countries that either cannot currently meet their own electricity consumption demand with inland production (Luxembourg), or countries that experience and will expe-rience 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 qual-itative 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

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1 List of Abbreviations

◦_C _{DEGREES CELSIUS}

ECA EUROPEAN CLIMATE ASSESSMENT AND DATASET

EU EUROPEAN UNION

GDP GROSS DOMESTIC PRODUCT

GWh GIGAWATT HOURS

IEA INTERNATIONAL ENERGY ASSOCIATION

IPCC INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE

LED LIGHT-EMITTING DIODE

PV PHOTOVOLTAIC

1.1 Two-Letter Country Codes (ISO 3166-1 alpha-2)

AT AUSTRIA BE BELGIUM CH SWITZERLAND CZ CZECH REPUBLIC DE GERMANY DK DENMARK ES SPAIN FI FINLAND FR FRANCE

GB UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN

IRELAND GR GREECE HU HUNGARY IE IRELAND IT ITALY LU LUXEMBOURG NL NETHERLANDS NO NORWAY PL POLAND PT PORTUGAL SE SWEDEN SK SLOVAKIA

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2 Introduction

2.1 Problem Formulation

Overwhelming evidence indicates that the climate change in Europe will likely result in an in-crease in temperature as well as higher frequency of extreme events such as heat waves and droughts (Alcamo et al., 2007; R¨ubbelke and V¨ogele, 2011b). Due to the direct and close

rela-tionship between the electricity sector and climate, the changing climate will affect the entirety

of the electricity sector including production, imports and exports, distribution, and consump-tion 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

sig-nificant, 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). Cli-mate 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; Fl¨orke et al., 2011; Mimler et al., 2009). Further-more, 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; R¨ubbelke and V¨ogele, 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.

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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 compo-nents of the electricity system among each other?

• What is the influence of temperature, both directly and indirectly, on the electricity pro-duction, 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 Background Information

3.1 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

Commis-sion, 2009c; R¨ubbelke and V¨ogele, 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).

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3.2 The E

ffects 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; R¨ubbelke and V¨ogele, 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 technolo-gies 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

(Al-camo 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

experi-ence 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

sys-tem can be problematic for a variety of generation sources, however from a comparative out-look, thermal electricity production is most vulnerable (Rademaekers et al., 2011; R¨ubbelke and V¨ogele, 2011b; van Vliet et al., 2012; Wilbanks et al., 2008). Thermal electricity genera-tion (fossil fuels and nuclear among others) is sensitive to heat waves and lowered precipitagenera-tion due to their use of large volumes of cooling water, which, could be lacking in quantity and

ele-vated in temperature, and may decrease efficiency and plant output (Rademaekers et al., 2011;

R¨ubbelke and V¨ogele, 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

ele-vated temperatures, forcing a decrease in electricity production (R¨ubbelke and V¨ogele, 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 tem-perature 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

precipita-tion and increase water usage overall, which can be problematic for hydro electricity produc-tion(Rübbelke and Vögele, 2011a). Furthermore, some studies suggest that solar photovoltaic (PV) electricity production is also impeded by increased temperature that decreases cell ef-ficiency (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 mag-nitude and scale, on thermal electricity generation outweighs the impacts on other technologies (Rübbelke and Vögele, 2011a; Wilbanks et al., 2008).

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3.3 European Political Context

The European political context in terms of climate change vulnerability of the electricity sys-tem, or more generally the energy syssys-tem, 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 (Commis-sion 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 Communi-ties, 2009). The European Commission also published a Green Paper addressing the need for sustainable European energy security (Commission of the European Communities, 2006). Ex-plicitly 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 R¨ubbelke and V¨ogele (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

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vul-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

electric-ity demand on a European level. The study included only the effects of temperature on

electric-ity 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

demon-strate potential vulnerabilities. The one important influencing factor the direct effect of

temper-ature, 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 depen-dence, 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,

con-sumption, imports and exports, however each of these is also affected by qualitative factors that

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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; Olon-scheck et al., 2011; Rademaekers et al., 2011; R¨ubbelke and V¨ogele, 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 Rossell´o, 2011; Becken and Simmons, 2002).

4 Data and Methods

4.1 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 cat-egories: 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

con-sumption 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. Elec-tricity 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

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different source. The temperature increase projection data (in◦_{C) was available from the}

Tyn-dall 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 eco-nomic 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 con-ditioner 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-occ-arm], 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 nonresident 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

res-idential, 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 (Ad-not et al., 2008; EUROSTAT, 2012). Unfortunately, air conditioner stock data was unavailable for NO and CH, and those countries could therefore not be included.

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4.2 Methods

4.2.1 Climate Data Calculations and Population Weighting

The daily mean temperature data (European Climate Assessment and Dataset, 2012) was av-eraged 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 coun-tries 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.

Wi, j = popi, j Pnj i=1popi, j (1) Tmean, j = nj X i=1 Ti, j· Wi, j (2)

Wi, j: The relative population factor for grid cell i in country j.

popi, j: The population of grid cell i in country j.

i: A single grid cell (in1...nj

o ).

j: A single country.

nj: The number of grid cells in country j.

Tmean, j: The population weighted monthly mean temperature for the entire country j.

Ti, 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

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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.

∆E =" Emonth,year− ¯Eyear

¯

Eyear

#

(3) ∆E : The percent difference from the annual mean.

Emonth,year : The electricity production or consumption for a specific month and year.

¯

Eyear : 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):

ρ = Pi(xi− ¯x) (yi− ¯y)

q P

i(xi− ¯x)2Pi(yi − ¯y)2

(4)

ρ : Spearman’s rank correlation coefficient.

xi, yi : Ranked variables.

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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

coun-tries 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 cat-egory. 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.

Cx = Pf n=1vn f (5) I = Pk x=1 Pf n=1vn f k (6)

Cx: Category index value for category x.

x: A single category.

vf : Influencing factor index value.

f : The number of influencing factors in Category x.

I : The final ranked index value.

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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 vulnera-bility 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 poten-tially 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

cal-culated 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 sce-nario was chosen, despite having access to other scesce-narios as well, because it is a more extreme climate scenario and would better illustrate the relative temperature changes between the coun-tries. 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

calcula-tion 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.

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Hea$ng ‐ Produc$on

and Mean _{Temperature Correla$on}

Hea$ng ‐ Consump$on

Cooling – Produc$on

Cooling – Consump$on

Hea$ng ‐ Produc$on and Mean Temperature Slope Hea$ng ‐ Consump$on and Mean Temperature Slope Cooling – Produc$on and Mean Temperature Slope Cooling ‐ Consump$on and Mean Temperature Slope Summer ‐ Projected Temperature Increase Winter ‐ Projected Temperature Increase Thermal Produc$on Percent (2011) Thermal Produc$on Change (2000‐2011) Air Condi$oner Projec$on (2030) Air Condi$oner Percent Diﬀerence (2005‐2030) Summer ‐ Produc$on and Consump$on Correla$on Winter ‐ Produc$on and Consump$on Correla$on Category 1:

Produc$on, Consump$on and Mean Temperature Spearman Correla$on

Coeﬃcient Category 2: Produc$on, Consump$on and Mean Temperature Slope Category 3: Projected Temperature Increase _Category 4:

Air Condi$oner Prevalence Category 5:

Thermal Electricity

Produc$on Category 6:

Produc$on and Consump$on Category 7: _{Import and Export}

Projected Vulnerability Indicators and Categories Current Vulnerability Indicators and Categories

Final Ranked Vulnerability Index Winter Import and Export Discrepancy Summer Import and Export Discrepancy Winter Import and Export Discrepancy Summer ‐ Produc$on and Consump$on Discrepancy Winter ‐ Produc$on and Consump$on Discrepancy Figure 1: V ulnerability Inde x T ree Diagram (Blue = indicators that decrease vulnerability , Red = indicators that increase vulnerability)

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Category 4: Air Conditioner Prevalence The residential air conditioner stock for the ma-jority 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 vulner-ability. 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 magni-tude 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 consump-tion 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

cat-egory 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 expe-riencing 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

cat-egory includes the Spearman correlation coefficient as well as the discrepancy (between

pro-duction 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 pro-duction 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 consum-ing 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 cate-gories, all four indicators of the index values (summer and winter, correlation and discrepancy)

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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 con-sistent 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

sub-tracted 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 cal-culations were required.

5 Results

The vulnerability indicator tables containing the actual unranked values can be found in Ap-pendix A and the plots for all of the countries are available in ApAp-pendix B. This section only includes the ranked index indicator and category tables as well as the plots from selected coun-tries that were chosen to represent the vulnerability categories. The monthly production, con-sumption, 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 (Fig-ure 13 and Appendix B: Fig(Fig-ures 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, con-sumption, import and export plots in Appendix B (Figure 24 to Figure 27). The highest tem-peratures are in the months of June, July and August, while the lowest temtem-peratures 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 Coe

fficient

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

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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.

!"#$%&'(( !"#$%&'()%*'&+',(*-,'.% ()$%/01/%'#'2*,020*3%$'&()$4% ( )*++%&'%% 5'()%*'&+',(*-,'.%$"%)"*% ,'(2/%2""#0)1%*/,'./"#$4% !"#$%&'(% 50#$%&'()%*'&+',(*-,'.%()$% #"6',%'#'2*,020*3%$'&()$4% % )*++%&'(( 701/',%&'()%*'&+',(*-,'.% 6/02/%.-+(..%*/'%2""#0)1% */,'./"#$4% !#0&(*'!/()1'% !"#$%&'(% 89')%&0#$',%*'&+',(*-,'.%()$% #"6',%'#'2*,020*3%$'&()$4% % )*++%&'(( 89')%/01/',%*'&+',(*-,'.%()$% /01/',%'#'2*,020*3%$'&()$4% !"#$%&'%% 50#$',%&'()%*'&+',(*-,'.% ()$%#"6',%'#'2*,020*3%$'&()$4% % )*++%&'(( :'&+',(*-,'.%.-,+(..%2""#0)1% */,'./"#$%()$%/01/',%'#'2*,020*3% $'&()$4%

Heating Threshold Cooling Threshold

!#0&(*'!/()1'% Countries whose mean temperature does not currently reach the cooling threshold.

Countries whose mean temperature currently reaches the cooling threshold.

Figure 2: Possible heating and cooling country electricity system pathways.

In general there is a temperature and therefore geographic component inherent in this indica-tor, 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 cor-relation to temperature in this circumstance will become less vulnerable as the temperature increases, while countries with weaker correlations will not necessarily become more vulnera-ble, 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.

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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 5 10 15 20 25 30 −0.10 0.00 0.05 0.10 0.15 Mean Temperature (°C)

Consumption (% diff from A

v e .) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Jun. 2007−11 Jun. 1991−5 Jul. 2007−11 Jul. 1991−5 Aug. 2007−11 Aug. 1991−5 (a) ES (1991-2011) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 5 10 15 20 25 30 −0.1 0.0 0.1 0.2 Mean Temperature (°C)

v e .) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Jun. 2005−9 Jun. 1991−5 Jul. 2005−9 Jul. 1991−5 Aug. 2005−9 Aug. 1991−5 (b) GR (1991-2011) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 5 10 15 20 25 30 −0.2 −0.1 0.0 0.1 Mean Temperature (°C)

v e .) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● Jun. 2006−10 Jun. 1981−5 Jul. 2006−10 Jul. 1981−5 Aug. 2006−10 Aug. 1981−5 (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

The electricity system vulnerability of selected European countries to climate change : A comparative analysis



Linköpings Universitet

The electricity system vulnerability of

selected European countries to climate

change:

A comparative analysis



The electricity system vulnerability of

selected European countries to climate

change:

A comparative analysis

Contents

List of Figures

List of Tables

1

List of Abbreviations

1.1

Two-Letter Country Codes (ISO 3166-1 alpha-2)

2

Introduction

2.1

Problem Formulation

2.2

Aim

2.3

Research Questions

2.4

Structure of the Thesis

3

Background Information

3.1

Climate Change in Europe

3.2

The E

ffects 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

4

Data and Methods

4.1

Data Sources

4.2

Methods

5

Results

5.1

Mean Temperature

5.2

Category 1: Production, Consumption and Mean Temperature Spearman

Correlation Coe

fficient