7.3 Air pollutant emissions

The air quality module relates economic activities to pollution emission levels of the most important air pollutants. It allows the assessment of air pollution emissions in baseline scenarios or under a climate or pollution regulation scenario. The implementation relies on emission factors calculated from the GAINS model and provided as part of the CMIP7 ScenarioMIP exercise. They are provided both for electricity generation technologies and for final energy (end use) sectors, represented in the code as \(aqemif\_el\_ssp\) and \(aqemif\_fen\_ssp\), respectively. In the following we will refer to both of these as \(aqemif\) generically for simplicity.
We use information on both fuel use and the type of electricity generation technologies employed to compute the emissions \(AQ\_EMI\) of pollutant \(aqe\) at time period \(t\) according to \[ AQ\_EMI(aqe,j,t) = q\_activity(j) \cdot aqemif(aqe,j,t), \] where \(aqemif(aqe,j,t)\) is the emission factor related to the activity level \(q\_activity(j)\) of sector \(j\). We consider the following air pollutants \(aqe\): carbon monoxide CO, methane CH4, black carbon BC, organic carbon OC, sulphur dioxide SO2, nitrogen oxides NOx, ammonia NH3 and volatile organic compounds VOC.

In WITCH we do not model explicitly all the activities that generate air pollution, therefore the non-energy-related pollution is accounted for separately.

• Land use sectors are not explicitly described in WITCH through activity levels. However, their GHG emissions are provided through the link with the GLOBIOM model. We compute the related air quality emissions from the CH\(_4\) emissions through coefficients obtained by linear regression.
• The air quality emissions from waste sectors are also estimated based on the corresponding CH\(_4\) values, obtained from marginal abatement curves using linear coefficients from regressions.
• WITCH includes activity levels for domestic transport and bunkers, but their emission factors were not part of the CMIP7 data exchange. Their air quality emissions are computed from CO\(_2\) values using regressions. For shipping only years starting from 2020 have been considered, as there has been significant changes in the emissions due to IMO rules on shipping fuel sulfur content.
• Emissions from solvents are computed through extrapolation. Emission growth rates are computed based on GDP and population.
• Forest burning emissions are carried forward from the latest available historical data point from CEDS.

Regressions are computed using this script in the witch-data repository.

7.3.1 Air pollution Policies

Air pollution emissions depend on two important factors: activity levels of the pollutant sector, and the emission factor of that given activity. Therefore the implementation of policies can be done via structural measures, such as changes on the model endogenous activities, or via air pollution controls. The latter is undertaken by controlling the emission factor \(aqemif(aqe,j,t)\) for activity category \(j\) and for pollutant \(aqe\).

During the CMIP7 data exchange, emission factors for the different ScenarioMIP scenarios were given; these are the reference scenarios that can be used as pathway options at the moment.