Assessment of climate mitigation policies related to indoor environment

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Scope

Purpose

What are the greenhouse gas emission and health impacts of some policies in the buildings sector that might help mitigate climate change?

Boundaries

• Spatial: European Union
  • The case study looks at the European scale (EU 30). The spatial resolution differs for areas as appropriate. For air pollutants, e.g. it is based on 50x50 km Emep grid cells for regional effects and on a smaller grid for local effects (e.g. traffic in cities). For water, modelling might be based on river basins, while for indoor air pollution a country basis with probability distributions will be applied.
• Temporal: years 2030, 2030, 2050
  • The case study looks at the years 2020, 2030 and 2050, i.e. at the state of the system (policies, physical parameters…) and emissions of pollutants in these years. Effects of those emissions might be observed only later (e.g. exposure is delayed due to a slow dispersion of stressors in the environment, or health impacts can occur only years after exposure) but are attributed to the year of emission – in this case they are discounted to the year of emission (or to a common year for comparison) to reflect the time preference people give to effects in the future.
• Population: The whole population living in the European Union
  • Receptor for the exposure is the European population. According to needs it is stratified by age groups (5 years) and gender for each 50x50 km Emep grid cell. Its growth is also projected to the years 2020, 2030, and 2050.
• Scenarios:
  • The assessment is of a prognostic nature, i.e. a policy scenario is compared to a business as usual scenario (BAU) in the future years (for a description of the scenarios see chapter 4). Scenarios are needed to compare a do-nothing situation with a do-something situation, i.e. with a situation in which mitigation measures have been applied to change the system boundaries (in this case the temperature implying also other changes like decreased air conditioning use compared to the BAU).

The scenarios are defined in such a way that feedback loops and adaptive processes (e.g. behaviour) have been dynamically modelled in advance and the scenarios have been selected as possible equilibrium states of the respective modelled part. They feed into the actual assessment as assumptions and input parameters.

Scenarios

   * Scenarios define particular conditions that are of interest irrespective whether they describe 
     reality or not (e.g. what-if scenarios).

Intended users

   * Intended users are those for whom the assessment is made.

Participants

   * Participants are those who may participate in the making of the assessment. 
     The minimum group of people for a successful assessment is always described. 
     If some groups must be excluded, this must be explicitly motivated.

Definition

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Decision variables

   * Decision variables: decisions that are considered.

Indicators

   * Indicators: outcome variables of interest.

Value variables

   * Value variables: value judgements (usually about indicators).

Other variables

   * Other variables: any variables that link to the causal network and are within the boundaries of the assessment.

Analyses

   * Analyses: statistical and other analyses that contain two or more variables, e.g. optimizing.

Indices

   * Indices: lists of particular locations along spatial, temporal, or other dimensions. 

Result

   * Results of indicators and assessment-specific analyses. 

Conclusions

   * Conclusions are based on the results, given the scope.