Three-level classification:
1. Coarse spatial allocation scheme for each off-road and machinery sub-categories
(e.g. gridding based on land use data about aviation, harbour, military, agricultural, industrial areas, population data, etc.). Temporal variation based on
general national default variations.
2. Spatial allocation with more realistic representation of activity for each off-road
and machinery sub-categories (e.g. gridding with estimate about the location of
activity inside respective land-use classes). Temporal variation based on
nationally or locally defined default variations.
3. Spatial allocation for each off-road and machinery sub-categories based on
activity intensities in respective locations (e.g. based on train/aircraft/vessel
movements, GPS data and/or activity model). Temporal variation based on
locally observed data.
Residential combustion activities are often poorly registered, because in many
countries/cities individual household level heating systems do not need licenses.
Therefore spatial allocation has to be based on some more general household level
data, e.g. building registers.
Three-level classification:
1. Coarse spatial allocation scheme for each residential heating fuels and/or main
heating sub-categories (e.g. gridding based GIS data on number of residential
houses or population data). Temporal variation based on general default
variations.
2. Spatial allocation with more realistic representation of activity for each residential heating fuels and/or main heating sub-categories (e.g. gridding based on
GIS data on number or floor area of different types of buildings or other relevant
information that distinguishes residential fuel use intensities in different building
types). Temporal variation based on nationally or locally defined default
variations.
3. Spatial allocation for each relevant fuels and heating sub-categories with gridding based on information that distinguishes residential fuel use intensities on
building-by-building basis (e.g. gridding based on GIS data on heating/cooling
technologies in use and/or energy efficiency of buildings or city level building
heating/cooling model with GIS capabilities). Housing and/or zoning modelling
approaches are desirable to assess spatial changes in future projections.
Temporal variation based on locally observed data.
Centralized energy production and industrial plants can often be dealt with
as point sources, i.e. attain both location and activity and relevant technology data
directly from the individual plant (level 3). However, sometimes such plant data are
not available, and the spatial assessment of activities/technologies must be based on
a surrogate type of approach. This means that the classification of complexity may
again follow the three levels outlined above.
18
N. Blond et al.
1. Coarse spatial allocation scheme for each off-road and machinery sub-categories
(e.g. gridding based on land use data about aviation, harbour, military, agricultural, industrial areas, population data, etc.). Temporal variation based on
general national default variations.
2. Spatial allocation with more realistic representation of activity for each off-road
and machinery sub-categories (e.g. gridding with estimate about the location of
activity inside respective land-use classes). Temporal variation based on
nationally or locally defined default variations.
3. Spatial allocation for each off-road and machinery sub-categories based on
activity intensities in respective locations (e.g. based on train/aircraft/vessel
movements, GPS data and/or activity model). Temporal variation based on
locally observed data.
Residential combustion activities are often poorly registered, because in many
countries/cities individual household level heating systems do not need licenses.
Therefore spatial allocation has to be based on some more general household level
data, e.g. building registers.
Three-level classification:
1. Coarse spatial allocation scheme for each residential heating fuels and/or main
heating sub-categories (e.g. gridding based GIS data on number of residential
houses or population data). Temporal variation based on general default
variations.
2. Spatial allocation with more realistic representation of activity for each residential heating fuels and/or main heating sub-categories (e.g. gridding based on
GIS data on number or floor area of different types of buildings or other relevant
information that distinguishes residential fuel use intensities in different building
types). Temporal variation based on nationally or locally defined default
variations.
3. Spatial allocation for each relevant fuels and heating sub-categories with gridding based on information that distinguishes residential fuel use intensities on
building-by-building basis (e.g. gridding based on GIS data on heating/cooling
technologies in use and/or energy efficiency of buildings or city level building
heating/cooling model with GIS capabilities). Housing and/or zoning modelling
approaches are desirable to assess spatial changes in future projections.
Temporal variation based on locally observed data.
Centralized energy production and industrial plants can often be dealt with
as point sources, i.e. attain both location and activity and relevant technology data
directly from the individual plant (level 3). However, sometimes such plant data are
not available, and the spatial assessment of activities/technologies must be based on
a surrogate type of approach. This means that the classification of complexity may
again follow the three levels outlined above.
18
N. Blond et al.