Social indicators
Reduction in energy poverty
Building on the module to assume the number of affected buildings, the number
of people lifted out of energy poverty is calculated.
To assume the share of measures addressing energy poor households, the national share of energy poverty is used as a default, with the possibility to adjust it in the measure-specific
parameter template. On the EU level, calculations are carried out separately for each EU Member State and then combined.
The MICATool shows results for two energy poverty metrics, M/2 and 2M.
M/2 assumes households are energy poor when spending less than half of the general population’s median expenditure on energy, pointing to insufficient funds to heat adequately.
In turn, 2M addresses households spending at least twice as much on energy compared to the median energy expenditure, assuming below-standard dwellings with inferior insulation.
In terms of calculation, the two metrics have different energy poverty gaps (EPG).
The exact methodology and its background are described in length in the fact sheet.
The equations used to quantify the number of people lifted out of energy poverty are shown here.
Health impacts due to improved indoor climate
For now, health impacts linked to improved indoor climate are assessed by looking at the reduction in asthma cases.
To do so, assumptions regarding the share of renovations occurring in damp and mouldy buildings as well as the share
of renovations constituting medium and deep renovations. As defaults, the projected rates in PRIMES and the current
national prevalence of damp and mould buildings are being used. Finally, a national coefficient describing the number of
disability-adjusted life years lost per damp or mould building has been calculated from past data as impact factor.
The equations can be found here, the fact sheet can be downloaded as PDF.
Reduced excess cold weather mortality linked to indoor cold
By retrofitting residential buildings, cases of excess cold weather mortality can be reduced.
This phenomenon is generally linked to households not being financially able to heat their home to adequate levels.
The calculation assumes that the share of measures implemented among energy poor households is equivalent to the group’s national prevalence, unless altered by the user.
Moreover, it is based on national figures of current excess cold weather mortality cases.
The monetisation is done by multiplying the result with the value of statistical life (VSL) figures provided by the WHO.
The equations for the quantification can be found here (monetisation is similar to the approach used for air pollution-related mortality), the fact sheet can be downloaded as PDF.
Reduced mortality due to reduced air pollution
The data for the reduction in mortality due to air pollution stems from IIASA, modelled on the basis of the EU Reference
Scenario 2020. However, these factors are constant (but calculated for each five-year-step) and merely calculated for
the seven final energy carriers. This entails that a future scenario strongly diverging from the EU Reference Scenario
2020 still uses the same marginal GHG emission factors.
Furthermore, given the fact that one coefficient for electricity is calculated for every five years step based on
the Reference Scenario’s assumed energy mix, changes in energy mix stated within the tool are currently not accounted
for in mortality coefficients.
The monetisation is based on the value of statistical life (VSL), with values being published by the WHO.
The equations can be found here, the fact sheet can be downloaded as PDF.
Avoided lost working days due to air pollution
Another approach to assess health impacts linked to air pollution is an assessment of avoided lost working days. The data
is also provided by IIASA, again based on the EU Reference Scenario 2020, including the caveats mentioned above.
The monetisation uses the value of a work day provided by the WHO.
The equations, quite similar to the above indicator, are shown on this page. The fact sheet can
be downloaded as a PDF from here, it is the same one as for the indicator above.
Social indicators
Reduction in energy poverty
Building on the module to assume the number of affected buildings, the number of people lifted out of energy poverty is calculated. To assume the share of measures addressing energy poor households, the national share of energy poverty is used as a default, with the possibility to adjust it in the measure-specific parameter template. On the EU level, calculations are carried out separately for each EU Member State and then combined.
The MICATool shows results for two energy poverty metrics, M/2 and 2M. M/2 assumes households are energy poor when spending less than half of the general population’s median expenditure on energy, pointing to insufficient funds to heat adequately. In turn, 2M addresses households spending at least twice as much on energy compared to the median energy expenditure, assuming below-standard dwellings with inferior insulation. In terms of calculation, the two metrics have different energy poverty gaps (EPG).
The exact methodology and its background are described in length in the fact sheet. The equations used to quantify the number of people lifted out of energy poverty are shown here.
Health impacts due to improved indoor climate
For now, health impacts linked to improved indoor climate are assessed by looking at the reduction in asthma cases. To do so, assumptions regarding the share of renovations occurring in damp and mouldy buildings as well as the share of renovations constituting medium and deep renovations. As defaults, the projected rates in PRIMES and the current national prevalence of damp and mould buildings are being used. Finally, a national coefficient describing the number of disability-adjusted life years lost per damp or mould building has been calculated from past data as impact factor.
The equations can be found here, the fact sheet can be downloaded as PDF.
Reduced excess cold weather mortality linked to indoor cold
By retrofitting residential buildings, cases of excess cold weather mortality can be reduced. This phenomenon is generally linked to households not being financially able to heat their home to adequate levels.
The calculation assumes that the share of measures implemented among energy poor households is equivalent to the group’s national prevalence, unless altered by the user. Moreover, it is based on national figures of current excess cold weather mortality cases.
The monetisation is done by multiplying the result with the value of statistical life (VSL) figures provided by the WHO.
The equations for the quantification can be found here (monetisation is similar to the approach used for air pollution-related mortality), the fact sheet can be downloaded as PDF.
Reduced mortality due to reduced air pollution
The data for the reduction in mortality due to air pollution stems from IIASA, modelled on the basis of the EU Reference Scenario 2020. However, these factors are constant (but calculated for each five-year-step) and merely calculated for the seven final energy carriers. This entails that a future scenario strongly diverging from the EU Reference Scenario 2020 still uses the same marginal GHG emission factors.
Furthermore, given the fact that one coefficient for electricity is calculated for every five years step based on the Reference Scenario’s assumed energy mix, changes in energy mix stated within the tool are currently not accounted for in mortality coefficients.
The monetisation is based on the value of statistical life (VSL), with values being published by the WHO.
The equations can be found here, the fact sheet can be downloaded as PDF.
Avoided lost working days due to air pollution
Another approach to assess health impacts linked to air pollution is an assessment of avoided lost working days. The data is also provided by IIASA, again based on the EU Reference Scenario 2020, including the caveats mentioned above.
The monetisation uses the value of a work day provided by the WHO.
The equations, quite similar to the above indicator, are shown on this page. The fact sheet can be downloaded as a PDF from here, it is the same one as for the indicator above.