Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 Upphovsrätt Detta dokument hålls tillgängligt på Internet – eller dess framtida ersättare – under 25 år från publiceringsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervisning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säkerheten och tillgängligheten finns lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsmannens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet – or its possible replacement – for a period of 25 years starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to download, or to print out single copies for his/her own use and to use it unchanged for noncommercial research and educational purpose. Subsequent transfers of copyright cannot revoke this permission. All other uses of the document are conditional upon the consent of the copyright owner. The publisher has taken technical and administrative measures to assure authenticity, security and accessibility. According to intellectual property law the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement. For additional information about Linköping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its www home page: http://www.ep.liu.se/. © Daniel R. Klein ii Contents 1 List of Abbreviations 1.1 Two-Letter Country Codes (ISO 3166-1 alpha-2) . . . . . . . . . . . . . . . . 2 2 2 Introduction 2.1 Problem Formulation . 2.2 Aim . . . . . . . . . . 2.3 Research Questions . . 2.4 Structure of the Thesis 3 4 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3 3 4 4 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4 5 6 6 6 7 . . . . . . . . . . . . . . . 8 8 8 9 9 9 9 10 10 10 10 10 11 11 12 12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 16 16 16 20 22 23 25 26 27 29 31 32 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 34 35 35 36 36 37 37 38 38 39 42 43 43 44 45 7 Conclusions 46 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 57 iv 61 65 69 73 77 81 List of Figures 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 20 24 28 36 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. . . . . . . . . . . . . . v 14 17 18 19 21 23 23 24 24 26 26 29 30 32 33 57 61 65 69 77 List of Tables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 20 22 23 25 26 28 31 33 53 54 54 55 55 56 56 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% % )*++%&'(( 701/',%&'()%*'&+',(*-,'.% 6/02/%.-+(..%*/'%2""#0)1% */,'./"#$4% !#0&(*'!/()1'% !"#$%&'(% 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 ● ● 0.00 ● ● ● ● ● ● 0 Jun. 2007−11 Jun. 1991−5 Jul. 2007−11 Jul. 1991−5 Aug. 2007−11 Aug. 1991−5 5 15 20 25 30 0.2 ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● 10 ● ●● ● 0.1 0.05 ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ●●● ● ●● ●● ● ●● ● ● ●● ● ● ● ●● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ●● ●● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● 0.0 Consumption (% diff from Ave.) 0.10 ● ● ● ● ● ● ● ●● ● ● ●● ● ●● −0.1 0.15 ● ● −0.10 Consumption (% diff from Ave.) ● 0 5 10 ● ●● ● ● ● ● ●● ● ●● ● ●●● ● ● ● ● ●● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●●● ●● ●● ● ● ● ● 15 20 Mean Temperature (°C) Mean Temperature (°C) (a) ES (1991-2011) (b) GR (1991-2011) ● ● ● ● ● ● Jun. 2005−9 Jun. 1991−5 Jul. 2005−9 Jul. 1991−5 Aug. 2005−9 Aug. 1991−5 25 30 0.1 0.0 −0.1 ● ●● ● ● ● ● ●●●● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ●● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ●●● ●● ● ● ●● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ●●● ● ●●●●● ● ●● ● ●● ●● ●●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ●● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ●● ●●●●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ●● ●● ● ●● ● ●● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● −0.2 Consumption (% diff from Ave.) ● ● ● ● ● ● 0 Jun. 2006−10 Jun. 1981−5 Jul. 2006−10 Jul. 1981−5 Aug. 2006−10 Aug. 1981−5 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 10 15 20 ● 25 30 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. ● −10 0 10 20 30 ● ● Production Consumption −10 0 Mean Temperature (ºC) 10 20 30 −10 0 10 20 30 Production Consumption 10 20 30 0.5 0.5 ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ●● ● ●● ●● ● ●●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ●●●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ●● ●● ● ● ●●●● 0.0 −0.5 Production Consumption ● GR Electricity (% diff from Ave.) 0.5 0.0 −0.5 Electricity (% diff from Ave.) ● ● Mean Temperature (ºC) IT ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ●●●● ● ● ● ●● ● ●● ●●●● ●● ● ● ● ● ●●●● ●●● ●● ● ●● ●●● ● ● ● ● ●●● ● ● ●● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ●● ● ●● ●● ● ● ●● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ●● ● ●●● ● ●● ● ●●●● ●● ● ● ● ●●● ● ● ● −10 0 Mean Temperature (ºC) ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●●● ● ●●● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ●● ● ● ● ● ●●●● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ●● 0.0 0.5 0.0 ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ●●● ● ● ●●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ●●● ● −0.5 Production Consumption FI Electricity (% diff from Ave.) ● −0.5 0.5 Electricity (% diff from Ave.) DE ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ●●●● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●●●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● 0.0 −0.5 Electricity (% diff from Ave.) NL ● ● Production Consumption −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 ● −10 0 10 20 30 0.0 ●● ●● ●● ● ● ●● ● ● ● ● ●● ●●● ●● ● ● ● ●●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●●●● ● ●● ● ● ● ●● ● ● ●● ● ●●● ● ● ●● ●● ● ●● ●● ● ● ●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●●●● ●● ●● ● ● ●● ● ●●● ●● ● ●● ●●●●● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ●● ●●● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● Production Consumption −10 0 Mean Temperature (ºC) ● ● 10 20 30 ● Production Consumption −10 0 10 20 30 0.5 ● Production Consumption 10 20 30 Mean Temperature (ºC) 0.5 ● ●●●● ●● ●● ● ● ● ●● ● ● ●● ● ●●●● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 −0.5 ● ● ES Electricity (% diff from Ave.) 0.5 0.0 −0.5 Electricity (% diff from Ave.) ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ●●●● ● ● ● ● ●● ●● ● ● ● ● ● ●● ●● ● ●● ●●● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●●● ●● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ●● ●● ● ● −10 0 Mean Temperature (ºC) PT ● ● 0.0 0.5 ● ● −0.5 Production Consumption Electricity (% diff from Ave.) ● SE ● −0.5 0.5 Electricity (% diff from Ave.) SK ● ● ● ●●●●●●● ● ● ●● ●● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ● ●●●●● ● ●●● ●● ● ● ●● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● 0.0 −0.5 Electricity (% diff from Ave.) PL ● ● Production Consumption −10 0 Mean Temperature (ºC) 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 References Adnot, J., Grignon-Masse, L., Legendre, S., Marchio, D., Nermond, G., Rahim, S., Riviere, P., Andre, P., Detroux, L., Lebrun, J., L’Hoest, J., Teodorose, V., Alexandre, J. L., Sa, E., Benke, G., Bogner, T., Conroy, A., Hitchin, R., Pout, C., Thorpe, W., and Karatasou, S. (2008). Preparatory study on the environmental performance of residential room conditioning appliances (airco and ventilation) - Economic and Market analysis. Technical report, The European Commission. Alcamo, J., Moreno, J. M., Nováky, B., Bindi, M., Corobov, R., Devoy, R., Giannakopoulos, C., Martin, E., Olesen, J. r. E., and Shvidenko, A. (2007). Europe. In Parry, M., Canziani, O., Palutikof, J., van der Linden, P., and Hanson, C., editors, Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, pages 541–580. Cambridge Uniceristy Press, Cambridge, UK. Bakhat, M. and Rosselló, J. (2011). A new approach for estimating tourism-induced electricity consumption. Energy Economics, 33(3):1–25. Becken, S. and Simmons, D. G. (2002). Understanding energy consumption patterns of tourist attractions and activities in New Zealand. Tourism Management, 23:343–354. Beenstock, M., Goldin, E., and Nabot, D. (1999). The demand for electricity in Israel. Energy Economics, 21(2):168–183. Bertoldi, P. and Atanasiu, B. (2009). Electricity Consumption and Efficiency Trends in European Union. Technical report, European Commission, Joint Research Centre, Institute for Energy, Luxemburg. Bosco, B. P., Parisio, L. P., and Pelagatti, M. M. (2007). Deregulated Wholesale Electricity Prices in Italy: An Empirical Analysis. International Advances in Economic Research, 13(4):415–432. Breidthardt, A. (2011). German government wants nuclear exit by 2022 at latest. Retrieved from http://uk.reuters.com/article/2011/05/30/us-germany-nuclear-idUKTRE74Q2P120110530. Commission of the European Communities (2006). Green Paper - A European Strategy for Sustainable, Competitive and Secure Energy. Commission of the European Communities (2009). White Paper - Adapting to climate change: Towards a European framework for action. Crook, J. A., Jones, L. A., Forster, P. M., and Crook, R. (2011). Climate change impacts on future photovoltaic and concentrated solar power energy output. Energy & Environmental Science, 4(9):3101. Dougherty, C. (2007). Germany finds solution to its withering coal mines. Retrieved from http://www.nytimes.com/2007/06/14/world/europe/14iht-coal.4.6143627.html? r=1. EIA (2003). Portugal Country Analysis Brief. Technical report, U.S. Energy Information Administration. 48 Engle, R., Mustafa, C., and J, R. (1992). Modelling and forecasting daily series of electricity demand. Journal of Forecasting, 11:241–251. ENTSO-E (2011). Consumption Data. Retrieved from https://www.entsoe.eu/resources/dataportal/consumption/. Eskeland, G. S. and Mideksa, T. K. (2010). Electricity demand in a changing climate. Mitigation and Adaptation Strategies for Global Change, 15(8):877–897. ESRI (2011). ArcGIS Desktop. European Climate Assessment and Dataset (2012). E-OBS Gridded Dataset. Retrieved from http://eca.knmi.nl/download/ensembles/download.php#datafiles. European Commission (2007). Luxembourg - Energy Mix Fact Sheet. Technical report, European Commission. European Commission (2009a). Economic Crisis in Europe : Causes , Consequences and Responses. Technical report, Economic and Financial Affairs, Luxembourg. European Commission (2009b). Portugal: National Renewable Energy Action Plan in accordance with Directive 2009/28/EC on the promotion of the use of energy from renewable sources. Technical report, European Commission. European Commission (2009c). Regions 2020 - The climate change challenge for European regions. Technical Report March, European Commission, Brussels. European Parliament and Council (2001). DIRECTIVE 2000/84/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 19 January 2001 on summer-time arrangements. EUROSTAT (2006). GEOSTAT 1km2 population grid dataset. Retrieved from http://epp.eurostat.ec.europa.eu/portal/page/portal/gisco Geographical information maps /popups/references/population distribution demography. EUROSTAT (2012). Energy Statistics Database. Retrieved from http://epp.eurostat.ec.europa.eu/portal/page/portal/energy/data/database. EWEA (2012). EU met its 2010 Renewable electricity target - ambitious 2030 target needed. Retrieved from http://www.ewea.org/index.php?id=60&no cache=1&tx ttnews%5Btt news%5 D=1928&tx ttnews%5BbackPid%5D=1&cHash=4b7e762152ac15e75a14d10ccd960778. Date Accessed: September 4, 2012. Flörke, M., Bärlund, I., and Kynast, E. (2011). Will climate change affect the electricity production sector? A European study. Journal of Water and Climate Change, 3(1):44. Förster, H. and Lilliestam, J. (2009). Modeling thermoelectric power generation in view of climate change. Regional Environmental Change, 10(4):327–338. Gnansounou, E. (2008). Assessing the energy vulnerability: Case of industrialised countries. Energy Policy, 36(10):3734–3744. 49 Guan, L. (2009). Preparation of future weather data to study the impact of climate change on buildings. Building and Environment, 44(4):793–800. Hekkenberg, M., Benders, R., Moll, H., and a.J.M. Schoot Uiterkamp (2009a). Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands. Energy Policy, 37(4):1542–1551. Hekkenberg, M., Moll, H., and Uiterkamp, a. S. (2009b). Dynamic temperature dependence patterns in future energy demand models in the context of climate change. Energy, 34(11):1797– 1806. Holmgren, A. k. J. (2007). A Framework for Vulnerability Assessment of Electric Power Systems. In Fischer, M., Hewings, G., Nijkamp, P., Snickars, F., Murray, A., and Grubesic, T., editors, Critical Infrastructure, chapter 3, pages 31–55. Springer Berlin Heidelberg. Huber, C. and Gutschi, C. (2010). Pump-Storage Hydro Power Plants in the European Electricity Market. Technical report, Institute for Electricity and Energy Innovations, Graz University of Technology, Graz. IEA (2007). Energy Policies of IEA Countries: Switzerland 2007 Review. Technical report, International Energy Agency, Paris. IEA (2009a). Energy Policies of IEA Countries: Luxembourg 2008 Review. Technical report, International Energy Agency, Paris. IEA (2009b). Energy Policies of IEA Countries: Portugal 2009 Review. Technical report, International Energy Agency, Paris. IEA (2009c). Energy Policies of IEA Countries: Spain 2009 Review. Technical report, International Energy Agency, Paris. IEA (2010a). Energy Policies of IEA Countries: France 2009 Review. Technical report, International Energy Agency, Paris. IEA (2010b). Energy Policies of IEA Countries: The Czech Republic 2010 Review. Technical report, International Energy Agency, Paris. IEA (2012). Monthly Electricity Statistics Archives. Retrieved from http://www.iea.org/stats/surveys/elec archives.asp. IPCC (2000). Emissions Scenarios - Summary for Policymakers. Technical report, Intergovenmental Panel on Climate Change. Kellogg, R. and Wolff, H. (2007). Does Extending Daylight Saving Time Save Energy? Evidence from an Australian Experiment. Technical report, Institute for the Study of Labor (IZA), Bonn. Kotchen, M. J. and Grant, L. E. (2008). Does daylight saving time save energy? Evidence from a natural experiement in Indiana. Lapillonne, B., Sebi, C., and Pollier, K. (2010). Energy efficiency trends for household in the EU. 50 Matzarakis, A. and Thomsen, F. (2007). Heating and cooling degree days as an indicator of climate change in Freiburg. Technical report, University of Freiburg, Freiburg. McGregor, G. R., Ferro, C. A. T., and Stephenson, D. B. (2005). Projected Changes in Extreme Weather and Climate Events in Europe. In Wilhelm, K., Menne, B., and Bertollini, R., editors, Extreme Weather Events and Public Health Responses, chapter 2. Springer. Michaelowa, A., Connor, H., and Williamson, L. E. (2010). Use of indicators to improve communication on energy systems vulnerability, resilience and adaptation to climate change. In Troccoli, A., editor, Management of Weather and Climate Risk in the Energy Insudstry, pages 69–87. Springer. Mimler, S., Müller, U., Greis, S., and Rothstein, B. (2009). Impacts of Climate Change on Electricity Generation and Consumption. In Leal Filho, W., editor, Interdisciplinary Aspects of Climate Change, pages 11–37. Peter Land Scientific Publishers, Frankfurt. Mitchell, T., Hulme, M., and New, M. (2002). Climate data for political areas. Area 34:109– 112. Naish, C., McCubbin, I., Edberg, O., and Harfoot, M. (2008). Outlook of Energy Storage Technologies. Technical Report January 2004, European Parliament, Brussels. Olonscheck, M., Holsten, A., and Kropp, J. P. (2011). Heating and cooling energy demand and related emissions of the German residential building stock under climate change. Energy Policy, 39(9):4795–4806. Paul, F., Kääb, A., and Haeberli, W. (2007). Recent glacier changes in the Alps observed by satellite: Consequences for future monitoring strategies. Global and Planetary Change, 56(1-2):111–122. Prek, M. and Butala, V. (2010). Base temperature and cooling degree days. Technical report, University of Ljubljana. Prettenthaler, F. and Gobiet, A. (2008). Studien zum Klimawandel in Österreich. In Klimabedingte Änderungen des Heiz-und Kühlenergiebedarfs für Österreich, volume 2. Joanneum Research Forschungsgesellschaft mbH. R Development Core Team (2012). R: A Language and Environment for Statistical Computing. Rademaekers, K., van de Laan, J., Boeve, S., Lise, W., and Kirchsteiger, C. (2011). Investment needs for future adaptation measures in EU nuclear power plants and other electricity generation technologies due to effects of climate change. Technical Report March, European Commission, Brussels. Rebetez, M., Dupont, O., and Giroud, M. (2008). An analysis of the July 2006 heatwave extent in Europe compared to the record year of 2003. Theoretical and Applied Climatology, 95(12):1–7. Rothstein, B. and Parey, S. (2011). Impacts of and Adaptation to Climate Change in the Electricity Sector in Germany and France. In Ford, J. D. and Berrang-Ford, L., editors, Climate Change Adaptation in Developed Nations: From Theory to Practice, Advances in Global Research, chapter 16. Springer, Dordrecht. 51 Rübbelke, D. and Vögele, S. (2011a). Distributional Consequences of Climate Change Impacts on the Power Sector : Who gains and who loses ? Technical Report 349, Centre for European Policy Studies, Brussels. Rübbelke, D. and Vögele, S. (2011b). Impacts of climate change on European critical infrastructures: The case of the power sector. Environmental Science & Policy, 14(1):53–63. Sailor, D. J. and Muiqoz, J. R. (1997). Sensitivity of electricity and natural gas consumption to climate in the U.S.A. - Methodology and results for eight states. Energy, 22(10):987–998. Schaeffer, R., Szklo, A. S., Pereira de Lucena, A. F., Moreira Cesar Borba, B. S., Pupo Nogueira, L. P., Fleming, F. P., Troccoli, A., Harrison, M., and Boulahya, M. S. (2012). Energy sector vulnerability to climate change: A review. Energy, 38(1):1–12. Semadeni, M. (2003). Energy storage as an essential part of sustainable energy systems A review on applied energy storage technologies. Technical Report 24, Centre for Energy Policy and Economics, Zürich. SFOE (2008). Electricity Consumption in 2007. Technical report, Swiss Federal Office of Energy. Terna (2012). Domanda di energia elettrica in Italia GWh (1981-2012). Thatcher, M. J. (2007). Modelling changes to electricity demand load duration curves as a consequence of predicted climate change for Australia. Energy, 32(9):1647–1659. The World Bank (2008). Europe and Central Asia Region - How Resilient is the Energy Sector to Climate Change? Technical report, The World Bank. The World Bank (2009). Adapting to Climate Change in Europe and Central Asia. Technical report, The World Bank. Thevenard, D. (2011). Methods for Estimating Heating and Cooling Degree-Days to Any Base Temperature. Technical report, American Society of Heating, Refrigerating and AirConditioning Engineers, Inc. Turner, B. L., Kasperson, R. E., Matson, P. a., McCarthy, J. J., Corell, R. W., Christensen, L., Eckley, N., Kasperson, J. X., Luers, A., Martello, M. L., Polsky, C., Pulsipher, A., and Schiller, A. (2003). A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences of the United States of America, 100(14):8074–9. Valor, E., Meneu, V., and Caselles, V. (2001). Daily Air Temperature and Electricity Load in Spain. Journal of Applied Meteorology, 40:1413–1421. van Vliet, M. T. H., Yearsley, J. R., Ludwig, F., Vögele, S., Lettenmaier, D. P., and Kabat, P. (2012). Vulnerability of US and European electricity supply to climate change. Nature Climate Change, 2(7):1–6. Wilbanks, T. J., Bhatt, V., Bilello, D. E., Bull, S. R., Ekmann, J., Horak, W. C., Huang, Y. J., Levine, M. D., Sale, M. J., Schmalzer, D. K., and Scott, M. J. (2008). Effects of Climate Change on Energy Production and Use in the United States. Technical Report February, U.S. Climate Change Science Program. World Energy Council (2008). Europe’s Vulnerability to Energy Crises: Executive Summary. Technical report, World Energy Council, London. 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 B Additional Results Figures B.1 Electricity Production and Consumption by Mean Temperature −10 0 10 20 30 Production Consumption −10 0 10 0.5 ● ●● ●● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ●●● ●●●●● ●● ● ●●●● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●● ●● ●● ●● ● ● ● ●●● ●● ● ●●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ●●● ●●● ● ● ● ●● ●● ● ● ●● ● ●● ●●● ●● ● ●● ●● ● ●● ●● ● ● ●● ● ●●●● ● ●● ● ● ● ●●● ● ● ● ● ● 0.0 0.5 0.0 ● ● ● ● ● 20 30 Production Consumption −10 0 10 20 30 CZ DE DK ● Production Consumption −10 0 10 ● ● 20 30 Mean Temperature (ºC) Production Consumption −10 0 10 ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ●●●● ● ●● ● ●● ●● ● ● ● ● ●●● ● ● ●● ● ●● ● ●●● ● ● ●●● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ● ● ● ● ●● ●● ● ●●● ●●● ● ● ● ●● ● ● ● ●●● ●●●● ● ● ● ●●●● ●● ● ● ●● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ●● ●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●●● ● ●● ●● ● ●●● ● 0.0 0.5 0.0 ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ●●● ● ● ● ●●●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ●●● ● −0.5 ● −0.5 0.5 0.0 ●● ● ● ●●● ●● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ●● ● ●● ●●● ● ● ●●● ● ●● ● ●● ● ● ●●● ● ●● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ●● ● ●●● ●●● 0.5 Mean Temperature (ºC) Electricity (% diff from Ave.) Mean Temperature (ºC) Electricity (% diff from Ave.) Mean Temperature (ºC) ● −0.5 ●●●● ●●● ● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● −0.5 Production Consumption CH Electricity (% diff from Ave.) ● −0.5 Electricity (% diff from Ave.) 0.5 0.0 ● ●● ● ● ●● ● ● ●●●●● ● ●● ● ● ●●● ● ●●●●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ●●●● ● ●● ●● ●● ● ● ●● ● ●● ● ● ● ● ● ●●●● ●● ● ● ●●●● ●● ● ● ●● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ●● ● ●● ● ●● ● ● ● 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 10 30 Production Consumption −10 0 10 0.5 0.0 −0.5 0.0 0.5 ● ●● ● ● ● ●●● ● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ●● ● ● ● ● ● ●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●● ● ●● ● ● ● 20 30 Production Consumption −10 0 10 20 30 GR HU ● Production Consumption −10 0 10 ● ● 20 30 Mean Temperature (ºC) Production Consumption −10 0 10 ● ●●● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ●●● ● ●● ●● ● ● ●●● ●● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ●● ●● ● ●●● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●●● ●● ●●● ● ● ● ●● ●● ● ● ● ●●● ●● ●● ● ●● ● ● ● 0.0 0.0 ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ●● ● ● ● ●● ●● ● ● ● ●● ●●● ●●●●● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●●●● −0.5 ● ● −0.5 0.5 ● ● ●●● ●● ● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ●● ●● ● ●● ●● ● ●● ●● ●● ●● ● ● ● ●●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ●● 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 ● ● ● ● ● ● ● ●●● ● ●● ● ●● ●●●● ● ● ● ●● ● ●● ●●●● ●● ● ● ● ● ●●●● ● ● ● ● ●● ●●● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●●● ● ● ●● ● ●● ●● ● ● ●● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ● ●●●● ●● ● ●● ● ● ● ● ● ● 20 FR Electricity (% diff from Ave.) Production Consumption −0.5 Electricity (% diff from Ave.) 0.5 ● ● Electricity (% diff from Ave.) FI ● ●●●● ●● ●● ● ● ● ● ● ● ●● ●●●● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ●● ● ●● ● ● ● ● ● ●●● ● ● ●● ●● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ● ●● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ●● ●●● ● 0.0 −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 −10 0 10 0.5 ● ● ● ● 20 30 ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●●● ●● ● ●● ● ● ● ●●● ●● ● ● ●● ●● ● ●●●●● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ●●●●● ●● ● ●● ● ● ●● ● ●●●● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ●●● ● ● ●●●● ●●● ● ●● ● ●●● ●● ●● ● ● ●●● ● ● ●●●●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 0.5 0.0 Production Consumption ● ● −10 0 ● ● ● ● Production Consumption 10 ● ●● 20 30 NO PL ● Production Consumption −10 0 10 ● ● 20 30 Mean Temperature (ºC) Production Consumption −10 0 10 ● ● ●● ● ●●● ● ●●● ● ●● ●● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ●● ● ● ● ●● ● ● ● ●●●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ●●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● 0.0 0.0 ● ●●● ● ● ● ●●● ●● ●● ●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ●●●●●● ●● ● ●● ● ●● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ●● ● ● ● ● ●●● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ●● ●● ● ●● ● ● ●● ● ●● ●● ● ● ● ●● ● ●●● ●● ● ● ●●● ●● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●●●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●● −0.5 ● −0.5 0.5 ● ●● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ●● 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 ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ●●● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ●● ●● ● ● ● ● ●●●● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ● −0.5 ● ● ● LU Electricity (% diff from Ave.) ● −0.5 Electricity (% diff from Ave.) 0.5 0.0 ●● ● ● ●●● ● ●● ●●● ● ●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ●● ● 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) ● ●● ●● ●● ● ● ●● ● ● ● ● ●● ●●● ●● ● ● ● ●●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●●●● ● ●● ● ● ● ●● ● ● ●● ● ●●● ● ● ●● ●● ● ●● ●● ● ● ●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●●●● ●● ●● ● ● ●● ● ●●● ●● ● ●● ●●●●● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ●● ●●● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ● 0.0 0.5 0.0 ●● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ●●●● ● ● ● ● ●● ●● ● ● ● ● ● ●● ●● ● ●● ●●● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●●● ●● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ●● ●● ● ● −0.5 Production Consumption Electricity (% diff from Ave.) ● ● ● −0.5 Electricity (% diff from Ave.) 0.5 ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ●● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●●● ● ● 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 ● ● ●●● ● ●●● ● ● ● ●● ● ● ●●● ● ●●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ●● ●● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●● ●● ●● ● ●●●● ●● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ●● ● ●●● ●● ● ●● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ●● ● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ●● ● ● ●● ●● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ●●● ● ● ●●● ● ●● ● ● ● ● ● ●● ●● −10 0 10 20 30 Combustible Fuels Nuclear ● ● Hydro Other ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●●● ●● ● ●● ●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● 0 ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ● ●● ● ●● ● ●● ● ● ●● ●● ●●●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●●● ● ●●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ●● ● ● ●●● ● ●● ●● ●● ● ● ● ● ● 0 30 5000 ● 3000 Production (GWh) 3000 1000 Combustible Fuels Nuclear ● ●● ●●●● ● ●●● ●● ●● ● ●●● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ●● ● ● ● ●● ● ● ●●● ● ● ●● ●● ● ●● ● ● ●●● ● ● ●●●● ●● ●● ● ●●● ● ●●● ● ● ●●● ●●● ● ● ● ● ● ●● ● ● 10 ● ● ●● ●● ●● ● ● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●●● ● ● ● ● ●● ●● ●●●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ●● ● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●●●● ● ●●●●● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ●●● ●● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ●●● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ●● ●● ● ● ●● ● ● ● 0 ● 1000 Hydro Other ● −10 ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ●●● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ●●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● −10 0 10 20 30 Mean Temperature (ºC) Mean Temperature (ºC) CZ DE DK Hydro Other ●● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●●●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ●●●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ●● ●●● ●● ● ● ● ●● ● ●● ● ● ●● ●● ● ●● ● ● ●● ●● ●● ● ● ● ● ●● ●● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ●● ●●● ●●● ●● 1000 10 20 Combustible Fuels Nuclear ● ● Hydro Other ● ● ●● ● ●● ● ● ● ●●● ●●● ●● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ●●● ● ● ●● ● ● ●● ● ● ● ● ●●●●●● ●●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ● ●● ● ●● ● ●● ●● ● ●●● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ●●● ● ●●● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ●● ● ● ●● ● ● ●● ●● ●● ● ●● ● ●● ● ● ● ●● ●● ●● ● ●● ●●● ● ● ● ●●●● ●● ● ● ● ● ●● ● ●● ●● ● ● ●● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ●● ●● ●●● ●●●●● ●●● ●●● ●●●●● ●●● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ●●●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●●●●●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●●●● ● ●● ● ● ● ●● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ●● ●● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●●● ● ●● ●● ●● ●●● ● ●●●●● 0 0 ● ●●● ●● ●● ●● ● ●● ●●● ● ● ● ●● ●● ● ● ●● ●●●●● ● ●●● ● ● ●● ●●●● ●●● ● ●● ● ● ●●● ●● ●● ●●● ●● ● ● ● ●● ●● ●● ● ● ●●● ● ●●● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ●● ● ● ● ●● ● ●● ●●● ● ● ●●● ●●● ●● ● ●● ● ● ● ● ● ●● ● ●● ● ●●● ●● ● ● ●● ● ● ●● ●● ● ● ● ●● ● ●● ● ●●● ● ●● ●●●● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ●● ● ● ● ●● ●●● ●●● ●● ● ●● ●● 0 ● ● ●● −10 ● 30 Mean Temperature (ºC) −10 0 10 20 ● ● 30 Mean Temperature (ºC) Combustible Fuels Nuclear ● ● Hydro Other ● ●● ●● ● ● ●● ● ● ●● ● ●●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ●●●● ●● ● ● ●● ● ●● ●● ● ●●● ●● ●● ● ● ● ● ●●● ●● ●●●● ●●● ●●● ● ● ●●● ● ● ●● ● ●●● ●● ● ● ●● ● ●●● ● ● ● ●● ●● ●● ● ● ● ● ●●●●● ● ● ●● ●● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● 0 ● 30000 3000 ● ● 10000 Combustible Fuels Nuclear Production (GWh) 5000 ● 1000 2000 3000 4000 5000 Mean Temperature (ºC) ● Production (GWh) ● CH Production (GWh) ● ● ● 3000 Combustible Fuels Nuclear 1000 ● 0 Production (GWh) 5000 ● BE 5000 AT Production (GWh) B.6 ●● ● ● ● ● ● ●● ● ●● ●● ● ● ●● ● ● ●●●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● −10 0 10 20 30 Mean Temperature (ºC) Figure 36: Mean temperature vs. electricity production by source (1/4). Source: adapted from European Climate Assessment and Dataset (2012) and IEA (2012). 77 ● 10 20 ● ● ● ● ● Combustible Fuels Nuclear ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ●● ●● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ●●●●● ● ●● ● ● ●● ● ●● ● ●●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ●●● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ●●●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●●●●● ● ●● ●●●●● ● ● ● ● ● ●● ●●●● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ●● ●●●●● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ●●●●● ● ● ● ● ● ● ● ●●●● ● ●● ●● ● ● ●●● ●● ● ●●●●●●● ●● ● ● ● ●● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●●● ●● ●●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ●● ●●●●● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ●●● ●● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●●● ● ● ● ● ● ●● ●● ● ● ● ●●●●● ● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● −10 0 10 20 30 ● ● Hydro Other ● ●●● ● ● ● ●● ● ●● ● ● ● ●● ● ●● ● ●●● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ●●●●● ●● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ● ● ●●● ● ● ● ●● ●● ●● ● ●●● ●●● ● ● ● ●●● ●●● ● ● ●● ●● ● ● ● ● ●●● ●● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● 30 Hydro Other ● ● ● ● 0 1000 5000 10000 0 ● 30000 Production (GWh) ● ● 10000 ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●●● ●● ●● ● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ●● ● ●● ● ● ●● ●● ● ●● ● ●● ● ●● ● ●●● ●● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ●●● ●● ●● ● ●● ● ●● ● ●● ● ● ●● ● ● ● ●●●●● ●●● ● ● ●● ● ●●● ● ● ●● ●●● ●● ● ● ●● ● ●● ● ●●●● ●● ●●● ●● ●●● ● ●● ● ●● ●● ●● ●● ● ● ●● ●● ● ● ● ● ● ●●● ●●● ●● ●●● ●● ● ● ●● ●● ●●● ● ●● ● ● ● ●●● ● ●● ● ●●● ● ●●● ● ●● ● ● ● ● ● ●● ● ● ● ●●● ●● ●●●●● ● ● ● ●●● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ●● ●● ●● ● ●● ● ● ● ●● ● ●●●●● ● ●●●● ● ●●● ●●●● ●●● ● ● ● ● ●●●● ●● ● ●● ● ● ●●● ● ● ● ● ●● ● ● ● ●● ● ●● ●● ● ●● ● ● ●● ● ●●●● ● ● ● ●● ● ● ● ● ● ●● ● ●●● ●● ● ●● ● ●●●● ● ●● ● ● ●● ●● ● ● ●● ● ●●● ● ●● ●● ● ●● ●● ●●●● ● ● ● ●●● ● ● Combustible Fuels Nuclear ● 50000 Hydro Other ● ●●● ● ● ● ●● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ● ●●● ● ●●●● ● ●●● ●● ● ● ● ● ● ●● ●● ● ● ●●● ●● ●● ● ● ● ●● ●●●●● ●● ● ●● ●●● ● ●● ● ●● ● ●● ● ●●● ●● ●● ●●● ●●● ● ● ● ● ● ●● ● ● ●● ●●● ● ●● ●● ●● ● ● ●● ●● ●● ● ● ● ● ●● ● ● ●● ●● ● ●● ● ● ● ●● ● ●●● ●● ● ●●● ● ●●●● ● ● ● ● ●● ● ●● ● ●●● ● ●● ● ●●● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●●● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●●● ●● ●● ●●● ● ● ● ●● ●●●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ●● ●● ●● ●●● ● ● ●● ●● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ●● 0 ● ● ● ●● ● −10 −10 0 10 20 30 Mean Temperature (ºC) Mean Temperature (ºC) Mean Temperature (ºC) GB GR HU 0 10 20 30 0 Mean Temperature (ºC) Combustible Fuels Nuclear ● ● Hydro Other ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ●● ●● ● ●● ● ● ● ●● ● ● ● ●●● ● ●● ●● ● ● ● ● ● ●● ● ●● ●● ●●●● ●● ●● ● ●●● ● ● ●● ● ●● ●●●●● ● ● ● ● ● ●● ●● ● ● ● ● ●●● ● ● ● ● ● ●● ●● ●●● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●●● ●● ● ●● ● ● ●● ● ● ● ●● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●● ●● ●● ● ●●● ● ●●●●●● ●●●● ● ●●●● ●●● ●●●● 0 10 20 ● Combustible Fuels Nuclear 30 Mean Temperature (ºC) ● ● Hydro Other ● ● ● −10 ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●●● ●●● ●● ● ● ●● ● ● ● ●● ● ●● ●●●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ●● ● ●●●● ●● ● ● ● ●● ●● ● ● ●● ● ● ● ●●●● ●● ● ●● ● ● ● ●● ● ● ● ● ●● 0 1000 ● ●●● ● ●● ● ●● ● ● ● ● ● ●● ● ●●● ●● ● ●●●● ● ● ● ● ●●●●● ● ● ● ● ● ●● ● ●● ● ●● ●● ●● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ●●● ● ● ●●● ● ● ●●● ● ● ● ●● ● ● ●●●● ● ● ●● ● ● ●● ●● ●● ●● ● ●●●● ● ● ●● ● ● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● −10 ● 2000 ● ●● ● ● ●● ●●●● ● ● ● ● ● ●●● ● ● ● ●●● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ●● ● ● ●● ● ●● ●● ●●● ● ●●● ● ● ● ●● ● ●●●●●●●● ●● ● ●●● ● ●● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ●●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ●●●●● ● ● ●●● ● ●● ● ● ● ●● ●● ●●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●●● ●● ●● ●●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ● ●●● ● ●●● ●● ● ●● ● ●● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● 500 1000 Hydro Other ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ●●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● 0 ● Production (GWh) 10000 20000 ● ● ● 5000 Combustible Fuels Nuclear 3000 30000 ● Production (GWh) ● Production (GWh) ● FR Production (GWh) Combustible Fuels Nuclear 5000 ● FI 3000 ● 0 Production (GWh) 20000 ES −10 0 10 20 30 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 ● ●● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●●●●● ● ● ●●● ●● ●● ●● ●● ● ●● ●● ●● ● ● ● ● ●● ●●● ● ●● ● ●●●● ●● ●● ● ● ●●●● ● ●● ● ●● ● ● ●●●● ● ●●● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ●● ●● ● ● ● ●● ● ● −10 0 10 20 30 ● 400 Hydro Other ● ● ● ●● ●● ● ● ● ● ● ●● ● ●● ● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ●●●● ●●●●● ●● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●●● ● ●● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● −10 0 10 20 Combustible Fuels Nuclear ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●●●● ● ● ● ●●● ● ● ● ● ●● ● ●● ● ● ●●●● ●● ● ● ● ●● ●● ● ● ● ●● ● ●● ● ●● ● ●● ● ●● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●●● ● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● ●●●●● ● ● ●●● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ●● ● ● ● ●● ●●● ●● ●● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ●● ● ● ● ●● ● ●● ●● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ●● 0 0 ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ●●● ● ● ● ●●● ●● ● ●● ● ●●● ●●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●●●● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●●● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● 5000 ● Combustible Fuels Nuclear ● ● Hydro Other ● ● 300 Production (GWh) 2000 ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ●● ● ●● ●● ● ●● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● 200 ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ●●● ●●●●● ● ● ● ● ●● ● ●●● ●● ● ●● ● ●● ●●● ● ● ●●● ●●● ●● ● ● ●● ●●●●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ●●● ●●●● ● ● ●● ●● ●● ● ●● ●● ● ●●●● ● ● ●● ● ●●● ● ● ● ●●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● 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 ● ● ● ● ●● ● ●●● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●●● ● ●● ● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ●●●●● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● −10 0 10 20 30 Mean Temperature (ºC) Combustible Fuels Nuclear ● 15000 ● 10000 ● ● Combustible Fuels Nuclear ● ● ● Hydro Other ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ●● ● ● ●● ● ● ●● ● ● ●● ● ●●● ● ● ●●● ●●●● ● ● ● ●● ●●●●● ●● ● ● ● ●● ● ● ●● ● ● ● ●●●● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ● ●●● ●● ●● ● ● ● ●● ● ● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●●●● ● ●● ● ● 5000 ● 0 ● ●●● ● ● ● ● ● ● ●● ●●● ●● ●● ● ●●●● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●●●● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●●●●● ● ●● ●● ●● ●● ●●● ●● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ●● ● ●● ●● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ●● ● −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 ●● ● ●● ● ●● ●● ●●● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ●● ●●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ● ●● ●● ●● ● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ● ●●● ●● ●● ● ● −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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●● ●●●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ●●● ●●● ● ●● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ●● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ●●● ● ● ●● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ● ●●● ● ● ● ●●● ● ●●● ● ● ●●●●●● ● ● ●● ●● ● ●● ● ● ● ● ●● ●● ● ● ●●● ● ● ●●● ● ● ● ●●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ●● ● ● ●● ●●●● ● ● ●● ● ● ● ● ●●●●● ●● ●● ●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ●● ●●● ● ●● ●● ●● ● ●●● ● ● ● ●●● ● ● ●●● ● ●● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ●● ● ● ●● ●● ●● ●● ● ● ●●● ● ● ●● ● ● ●● ●● ● ● ●●● ● ● ● ● ●● ● ● ●● 500 1000 1500 ● 0 0 Production (GWh) 1000 2000 3000 ● ● ● ● ● ● ● ●● ●● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ●●● ●● ●●●●●● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ●● ● ● ● ●● ●●● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ● ●●● ●●● ● ● ● ● ● ● ●● ●●● ● ● ● ●●● ● ●● ● ● ● ● ●●● ● ● ●●● ● ●●●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ●● ●● ● ● ● ●● ● ●● ●● ●●● ● ● ●●● ● ●● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ●●● ● ●● ●● ●● ● ●● ● ●●● ●● ● ● ●● ● ●●● ● ● ● SK Production (GWh) Hydro Other 8000 ● 6000 ● 4000 Combustible Fuels Nuclear 2000 ● 0 Production (GWh) 4000 ● SE 2000 PT 30 Mean Temperature (ºC) ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ●● ●●● ●●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ●● ● ● ●● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ●● ●● ● ● ●● ●● ● ●●● ●● ●● ●● ● ● ● ● ●●● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●●● ●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●●● ●● ● ●● ●● ● ●● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ●● ● ● ●●●● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ●● ● ●● ●●●● ● ●● ● ● ●● ●● ●●● ●● ●●● ● ●● ●● ● ●●● ●● ●● ● ● ●● ●● ● ● ● ● −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