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Indicator Assessment
Trend in heating and cooling degree days (1981-2014)
Note: This map shows observed linear trends in heating degree days (left) and cooling degree days (right) over 1981–2014 for all EEA member and cooperating countries. Stippling depicts regions where the trend is statistically significant at the 5% level.
Past trends
The number of population-weighted heating degree days (HDDs) decreased by 8.2 % between the periods 1951–1980 and 1981–2014. The decrease during the period 1981–2014 was on average 9.9 HDDs per year, although with substantial interannual variation (Figure 1, left); this linear trend corresponds to an annual decrease of 0.45% (relative to the 1951–1980 average). The largest absolute decrease occurred in northern and north-western Europe, where the heating demand is highest (Figure 2, left).
The number of population-weighted cooling degree days (CDDs) increased by 49.1 % between the periods 1951–1980 and 1981–2014. The increase during the period 1981–2014 was on average 1.2 CDDs per year, although with substantial interannual variation (Figure 1, right); this linear trend corresponds to an annual increase of 1.9 % (relative to the 1951–1980 average). The largest absolute increase occurred in southern Europe (latitudes below 45 °N), where the energy demand for cooling in summer is highest (Figure 2, right).
The relative increase in CDDs is much higher than the relative decrease in HDDs, because of lower absolute values. In principle, HDD and CDD values can be added together to give a new indicator, energy degree days, which has shown a decrease since the 1950s. However, one must consider that HDDs and CDDs are climatological parameters and that the energy demand linked to their values is not the same, as heating and cooling systems are often based on different technologies.
Figure 1 highlights some important features of the evolution of the pattern of HDDs and CDDs in Europe since the 1950s. In the first three decades, HDDs were roughly constant and CDDs declined slightly. Since the beginning of the 1980s, Europe has started experiencing a markedly declining overall trend in HDDs, and a markedly increasing trend in CDDs, pointing to a general increase in cooling needs and a general decrease in heating needs. Figure 2 shows that the decrease in HDDs has been particularly strong in the Alpine areas and the Baltic and Scandinavian countries, whereas the increase in CDDs is particularly strong in southern Europe, around the Mediterranean and in the Balkan countries. Some overlapping of medium to strong HDD and CDD effects is noticeable in Bulgaria, southern France, Italy, Portugal, Romania and Spain.
Projections
Temperatures in Europe are projected to continue to increase. Hence, the trend of a decreasing number of HDDs and an increasing number of CDDs is very likely to continue, and most likely to accelerate [i]. Model simulations performed in the ClimateCost project (based on HDD and CDD data) have estimated the decrease in residential heating energy demand in the EU as a result of climate change alone (above the SRES A1B baseline without climate change) to be 28 million tonnes of oil equivalent (Mtoe)/year by 2050 and 65 Mtoe/year by 2100; the corresponding projected increase in cooling energy demand is 16 Mtoe/year by 2050 and 53 Mtoe/year by 2100. While the projected physical energy reductions for heating are higher than the increase in cooling demand, in economic terms the projected reduction in total heating demand is about the same as the increase in cooling demand, as cooling is more expensive than heating. In cold countries, such as Norway, the net effect of projected temperature increases reduces total energy demand, whereas in warm countries, such as Spain, it increases energy demand. All projected changes are considerably lower under a mitigation scenario with lower emissions [ii].
[i] Rasmus E. Benestad, ‘Heating Degree Days, Cooling Degree Days and Precipitation in Europe’, met.no report (Oslo: Norwegian Meteorological Institute, 2008), http://met.no/Forskning/Publikasjoner/metno_report/2008/filestore/metno_04-2008.pdf.
[ii] Silvana Mima and Patrick Criqui, ‘The Costs of Climate Change for the European Energy System, an Assessment with the POLES Model’,Environmental Modeling & Assessment 20, no. 4 (2015): 303–19, doi:10.1007/s10666-015-9449-3.
In April 2013, the European Commission (EC) presented the EU Adaptation Strategy Package. This package consists of the EU Strategy on adaptation to climate change (COM/2013/216 final) and a number of supporting documents. The overall aim of the EU Adaptation Strategy is to contribute to a more climate-resilient Europe.
One of the objectives of the EU Adaptation Strategy is Better informed decision-making, which will be achieved by bridging the knowledge gap and further developing the European climate adaptation platform (Climate-ADAPT) as the ‘one-stop shop’ for adaptation information in Europe. Climate-ADAPT has been developed jointly by the EC and the EEA to share knowledge on (1) observed and projected climate change and its impacts on environmental and social systems and on human health, (2) relevant research, (3) EU, transnational, national and subnational adaptation strategies and plans, and (4) adaptation case studies.
Further objectives include Promoting adaptation in key vulnerable sectors through climate-proofing EU sector policies and Promoting action by Member States. Most EU Member States have already adopted national adaptation strategies and many have also prepared action plans on climate change adaptation. The EC also supports adaptation in cities through the Covenant of Mayors for Climate and Energy initiative.
In September 2016, the EC presented an indicative roadmap for the evaluation of the EU Adaptation Strategy by 2018.
In November 2013, the European Parliament and the European Council adopted the 7th EU Environment Action Programme (7th EAP) to 2020, ‘Living well, within the limits of our planet’. The 7th EAP is intended to help guide EU action on environment and climate change up to and beyond 2020. It highlights that ‘Action to mitigate and adapt to climate change will increase the resilience of the Union’s economy and society, while stimulating innovation and protecting the Union’s natural resources.’ Consequently, several priority objectives of the 7th EAP refer to climate change adaptation.
No targets have been specified.
HDDs and CDDs are defined relative to a base temperature — the outside temperature — below which a building is assumed to need heating or cooling. They can be computed in different ways, depending, among other things, on the specific target application and the availability of sub-daily temperature data. The previous version of this indicator applied the methodology of Eurostat, which uses daily mean temperature only and has a jump discontinuity when daily mean temperature falls below the base temperature. This indicator uses an approach developed by the UK Met Office, which uses daily mean, minimum and maximum temperatures and does not exhibit a discontinuity. Note that this approach, being based on both minimum (Tn) and maximum (Tx) temperatures and not solely on the mean temperature (Tm), increases the accuracy of HDDs and CDDs for the purpose of gauging the impacts of climate change on energy demand, because the cooling of the environment depends more on Tx than on Tm, while Tn is more relevant for heating. The baseline temperatures for HDDs and CDDs are 15.5 °C and 22 °C, respectively. As a result of the methodological changes, the magnitudes of the trends between the previous version and this version of the indicator cannot be directly compared.
The aggregation of regional changes in HDDs and CDDs to larger areas can be done using area weighting or population weighting (with a fixed population). Population weighting is preferable for estimating trends in energy demand over large regions with an uneven population distribution, such as Europe.
Not applicable
Not applicable
The climatological input datasets for computing HDDs and CDDs for Europe combine temperature data with daily resolution from three different station datasets — the JRC’s MARS meteorological database, the NOAA National Climatic Data Center (NCDC)’s Global Historical Climatology Network dataset and the European Climate and Assessment Dataset of the Royal Meteorological Institute of the Netherlands and from one gridded dataset (E-OBS versions 10 and 11). These datasets are considered rather robust. However, different definitions exist for computing HDDs and CDDs, which can lead to different magnitudes of calculated trends.
No uncertainty has been specified
For references, please go to https://www.eea.europa.eu/data-and-maps/indicators/heating-degree-days/assessment or scan the QR code.
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