Emissions - IMAGE

Model Documentation - IMAGE
Model scope and methods - IMAGE Model concept, solver and details - IMAGE Temporal dimension - IMAGE Spatial dimension - IMAGE Policy - IMAGE Socio-economic drivers - IMAGE Population - IMAGE Economic activity - IMAGE Macro-economy - IMAGE Production system and representation of economic sectors - IMAGE Energy - IMAGE Energy resource endowments - IMAGE Fossil energy resources - IMAGE Energy conversion - IMAGE Electricity - IMAGE Heat - IMAGE Gaseous fuels - IMAGE Grid, pipelines and other infrastructure - IMAGE Energy end-use - IMAGE Transport - IMAGE Residential and commercial sectors - IMAGE Industrial sector - IMAGE Other end-use - IMAGE Energy demand - IMAGE Technological change in energy - IMAGE Land-use - IMAGE Agriculture - IMAGE Forestry - IMAGE Bioenergy land-use - IMAGE Other land-use - IMAGE Emissions - IMAGE GHGs - IMAGE Climate - IMAGE Modelling of climate indicators - IMAGE Non-climate sustainability dimension - IMAGE References - IMAGE
Corresponding documentation
AIM-Hub BLUES C3IAM COFFEE-TEA DNE21+ GCAM GEM-E3 GRACE IFs IMACLIM IMAGE MESSAGE-GLOBIOM POLES PROMETHEUS REMIND-MAgPIE TIAM-UCL WITCH
Previous versions
Archive of IMAGE version 3.0
Model information
Model link	https://models.pbl.nl/image
Institution	PBL Netherlands Environmental Assessment Agency (PBL), Netherlands, https://www.pbl.nl/en.
Solution concept	Partial equilibrium (price elastic demand)
Solution method	Simulation
Anticipation	Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations.

Introduction

Emissions of greenhouse gases and air pollutants are major contributors to environmental impacts, such as climate change, acidification, eutrophication, urban air pollution and water pollution. These emissions stem from anthropogenic and natural sources. Anthropogenic sources include energy production and consumption, industrial processes, agriculture and land-use change, while natural sources include wetlands, oceans and unmanaged land. Better understanding the drivers of these emissions and the impact of abatement measures is needed in developing policy interventions to reduce long-term environmental impacts. On this page the general approaches to projecting emissions in the IMAGE framework are described for modelling greenhouse gases (CH₄, N₂O), ozone precursors (NO_X, CO, NMVOC), acidifying compounds (SO₂, NH₃) and aerosols (SO₂, NO₃, BC, OC). The methods used for modelling both GHGs emissions, pollutants, and non-GHG forcing agents are very similar and therefore described together. On the GHGs page the modelling of emission abatement is described.

An overview of the emissions model structure is provided in <xr id="fig:Emissions_IMG"/>.

<figure id="fig:Emissions_IMG">

Flowchart Emissions module.

</figure>

General approaches

Air pollution and GHG emission sources included in IMAGE are listed in <xr id="tab:emission"/>. In approach and spatial detail, gaseous emissions are represented in IMAGE in four ways:

1) World number (W)

The simplest way to estimate emissions in IMAGE is to use global estimates from the literature. This approach is used for natural sources that cannot be modelled explicitly.

2) Emission factor (EF)

Past and future developments in anthropogenic emissions are estimated on the basis of projected changes in activity and emissions per unit of activity. The equation for this emission factor approach is:

<equation id="eqn:IMG_eq1"> $Emission=Activity_{r,i}*EF_{r,i}*AF_{r,i}$ </equation>

where:

• Emission is the emission of the specific gas or aerosol
• Activity is the energy input or agricultural activity
• r is the index for region
• i is the index for further specification (sector, energy carrier)
• EF-base is the emission factor in the baseline
• and AF is the abatement factor (reduction in the baseline emission factor as a result of climate policy).

The emission factors are time-dependent, representing changes in technology and air pollution control and climate mitigation policies. The emission factor is used to calculate energy and industry emissions, and agriculture, waste and land-use related emissions. Following the equation, there is a direct relationship between level of economic activity and emission level. Shifts in economic activity (e.g., use of natural gas instead of coal) may influence total emissions. Finally, emissions can change as a result of changes in emission factors (EF) and climate policy (AF).

3) Gridded emission factor with spatial distribution (GEF)

GEF is a special case of the EF method, where the activity is grid-specific, resulting in grid-specific emissions. This is done for a number of sources, such as emissions from livestock.

4) Gridded model (GM)

Land-use related emissions of NH₃, N₂O and NO are calculated with grid-specific models. The models included in IMAGE are simple regression models that generate an emission factor. For comparison with other models, IMAGE also includes the N₂O methodology generally proposed by IPCC IMG_IPCC_2006.

<figtable id="tab:emission">

Atmospheric emissions calculated in IMAGE, by source and by method applied

Source	Activity	CO2	CH4	N2O	SO2	NOx	CO	NMVOC	F-gases	BC	OC	NH3
a). Energy related
End-use energy use (industry, transport, residential, services and other)	Energy consumption rates	EF	EF	EF	EF	EF	EF	EF		EF	EF
Energy sector (production of power, hydrogen, coal, oil, gas, bioenergy)	Energy prodcution rates	EF	EF	EF	EF	EF	EF	EF		EF	EF
Energy transport	Energy transport rates		EF
Other energy conversion	Energy conversion rates	EF	EF	EF	EF	EF	EF	EF		EF	EF
b). Industry related
Emissions from industrial process	Industry value added (IVA)	EF	EF	EF	EF	EF	EF	EF	EF	EF	EF
Cement and Steel	Regional production	EF
c). Agriculture-, waste-, and land-use related
Enteric fermentation, cattle	Feed type and amount		GMa
Animal water, all animal categories	Number of animals		GEF	GEF		GEF						GEFb
Enteric fermentation, cattle	Feed type and amount		GMa
Landfills	Population		GEF
Enteric fermentation, cattle	Feed type and amount		GMa
Deforestation	Carbon burnt	GM	GEF	GEF	GEF	GEF	GEF	GEF		GEF	GEF	GEF
Agriculture waste burning	Carbon burnt	GM	GEF	GEF	GEF	GEF	GEF	GEF		GEF	GEF	GEF
Traditional biomass burning	Carbon burnt	GM	GEF	GEF	GEF	GEF	GEF	GEF		GEF	GEF	GEF
Savannah burning	Carbon burnt	GM	GEF	GEF	GEF	GEF	GEF	GEF		GEF	GEF	GEF
Domestic sewage treatment	Population, GDP		GEF	GEF
Wetland rice field	Area wetland rice		GEF
Crops	N fertiliser and manure input, croptype			GM		GM						GM
Managed grassland	N fertiliser and manure input			GM		GM						GM
Indirect emissions	N crops, fertiliser and manure input			GM
Land-use change	Clearing forest areas			GM
d). Natural sources
Soils under natural vegetation	Net primary production			GM		GM						GEF
Natural vegetation	N/A						W	W
Wildfires	N/A		W				W
Oceans	N/A		W	W			W					W
Natural wetlands	N/A		W
Termites	N/A		W
Wild animals	N/A		W
Methane hydrates	N/A		W
Volcanoes	N/A		W		W
Lightning	N/A			W		W

</figtable> Activity describes the activity level to which the emission factor is applies, or, if only GM method occurs, the main determinant for the gridded model.

Methods:

• W=Global emission
• EF=Regional emission factor applied to the specified activity level
• GEF=Grid-specific emission calculated from gridded activity level and (regional) emission factor
• GM= Gridded, model-based emission (statistical or process-based model).

Footnotes:

^a GM for dairy and non-dairy cattle, EF for other animal categories.

^b EF for NH₃ emissions from animal houses, manure storage and grazing livestock;GM for NH₃ emissions from manure spreading.

Emissions from energy production and use

Emission factors are used for estimating emissions from the energy-related sources. In general, the Tier 1 approach from IPCC guidelines IMG_IPCC_2006 is used. In the energy system, emissions are calculated by multiplying energy use fluxes by time-dependent emission factors. Changes in emission factors represent, for example, technology improvements and end-of-pipe control techniques, fuel emission standards for transport, and clean-coal technologies in industry.

The emission factors for the historical period for the energy system and industrial processes are calibrated with the EDGAR emission model described by IMG_BraspenningRadu_2016. Calibration to the EDGAR database is not always straightforward because of differences in aggregation level. The general rule is to use weighted average emission factors for aggregation. However, where this results in incomprehensible emission factors (in particular, large differences between the emission factors for the underlying technologies), specific emission factors were chosen.

Future emission factors are based on the following rules:

• Emission factors can follow an exogenous scenario, which can be based on the storyline of the scenario. In some cases, exogenous emission factor scenarios are used, such as the Current Legislation Scenario (CLE) developed by IIASA (for instance, Cofala et al., (2002)IMG_Cofala_2002. The CLE scenario describes the policies in different regions for the 2000–2030 period.

• Alternatively, emission factors can be derived from generic rules, one of which in IMAGE is the EKC: Environmental Kuznets Curve (IMG_Stern_2003IMG_Smith_2005IMG_vanRuijven_2008 IMG_Carson_2010IMG_smith_2011). EKC suggests that starting from low-income levels, per-capita emissions will increase with increasing per-capita income and will peak at some point and then decline. The last is driven by increasingly stringent environmental policies, and by shifts within sectors to industries with lower emissions and improved technology. Although such shifts do not necessarily lead to lower absolute emissions, average emissions per unit of energy use decline. See below, for further discussion of EKC.

• Combinations of the methods described above for a specific period, followed by additional rules based on income levels.

Emissions from industrial processes

For the industry sector, the energy model includes three categories:

1. Cement and steel production. IMAGE-TIMER includes detailed demand models for these commodities (See Industrial sector page). Similar to those from energy use, emissions are calculated by multiplying the activity levels to exogenously set emission factors.
2. Other industrial activities. Activity levels are formulated as a regional function of industry value added, and include copper production and production of solvents. Emissions are also calculated by multiplying the activity levels by the emission factors.
3. For halogenated gases, the approach used was developed by Harnisch et al. (2009)IMG_Harnisch_2009, which derived relationships with income for the main uses of halogenated gases (HFCs, PFCs, SF6). In the actual use of the model, slightly updated parameters are used to better represent the projections as presented by Velders et al. (2009)IMG_Velders_2009. The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. (2009)IMG_Harnisch_2009.

Land-use related emissions

CO₂ exchanges between terrestrial ecosystems and the atmosphere computed by the LPJ model are described in Carbon cycle and natural vegetation. The land-use emissions model focuses on emissions of other compounds, including greenhouse gases (CH₄, N2O), ozone precursors (NO_X, CO, NMVOC), acidifying compounds (SO₂, NH₃) and aerosols (SO₂, NO₃, BC, OC).

For many sources, the emission factor is used (<xr id="eqn:IMG_eq1"/>). Most emission factors for anthropogenic sources are from the EDGAR database, with time-dependent values for historical years. In the scenario period, most emission factors are constant, except for explicit climate abatement policies (see below).

There are some other exceptions: Various land-use related gaseous nitrogen emissions are modelled in grid-specific models (see further), and in several other cases, emission factors depend on the assumptions described in other parts of IMAGE. For example, enteric fermentation CH₄ emissions from non-dairy and dairy cattle are calculated on the basis of energy requirement and feed type. High-quality feed, such as concentrates from feed crops, have a lower CH₄ emission factor than feed with a lower protein level and a higher content of components of lower digestibility. This implies that when feed conversion ratios change, the level of CH₄ emissions will automatically change. Pigs, and sheep and goats have IPCC 2006 IMG_IPCC_2006 emission factors, which depend on the level of development of the countries. In IMAGE, agricultural productivity is used as a proxy for the development. For sheep and goats, the level of development is taken from EDGAR.