Why is the content of this map important?
Electricity consumption is of crucial importance for adapting to climate change in terms of adjusting to heating and cooling needs in the context of temperature changes. It is also important in terms of mitigation as electricity accounts for more greenhouse gas emissions than any other sector in Europe.
Which sectors are affected by this result?
The electricity sector and its power generation is vulnerable to climate change both due to the growing share of renewable energies and due to temperature related changes in seasonal demand patterns.
What is shown on the maps?
The maps show the Weather-Value at Risk (95%) for electricity demand on working days. The values indicate Weather-VaR in GWh/day for the reference period (1971-2000), for the +2°C global warming period (2036-2065, RCP4.5), and for the +3°C global warming period (2051-2080, RCP8.5), respectively.
Weather-VaR (95%) represents the risk of extraordinarily high electricity demand, i.e. the weather-induced additional amount of electricity consumption that has to be expected once in 20 days.
Details and further information:
Measured in absolute terms, France is projected to experience by far the highest decrease in Weather-VaR (95%) for electricity demand. In relative terms (not shown on the map), the highest decrease in Weather-VaR (95%) for electricity demand is projected for Sweden and Norway, followed by France. Global warming by 3°C further decreases the Weather-VaR (95%) for electricity demand up to 2.8% (relative to the 2°C global warming). A further increase by 3% (relative to the 2°C global warming) is determined for Italy.
The non-linear relationship between temperature and electricity demand is estimated by the use of smooth transition models.
The smoothed transition model is used to examine the non-linear relationship between temperature and electricity demand for altogether 26 European countries (EU28 + NO, except CY, MT and EL due to data availability). Therefore, daily electricity load data (ENTSO-E 2014) is corrected for non-temperature related effects such as public holidays, summer holidays, bridging days, weekends, and economic effects. The data from the ensemble of the five mandatory climate simulations is aggregated from grid to national level by taking the population distribution within a country into account. The ensemble consists of 5 simulations in total.
Andrea DammJoanneum Research Forschungsgesellschaft mbH (JR), Austria