Emissions - IMAGE

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Model Documentation - IMAGE
Corresponding documentation
Model information
Institution PBL Netherlands Environmental Assessment Agency (PBL), Utrecht University (UU)
Concept The IMAGE framework can best be described as a geographically explicit assessment, integrated assessment simulation model, focusing a detailed representation of relevant processes with respect to human use of energy, land and water in relation to relevant environmental processes.
Solution method Recursive dynamic solution method
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 (NOX, 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 Figure 1.

Figure 1: Flowchart Emissions module.

General approaches

Air pollution and GHG emission sources included in IMAGE are listed in Table 1. 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:

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 1.


Table 1: 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

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 1 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 2. 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)3. 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 (456 78). 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)9, 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)10. The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. (2009)9.

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 (NOX, CO, NMVOC), acidifying compounds (SO2, NH3) and aerosols (SO2, NO3, BC, OC).

For many sources, the emission factor is used (Equation 1). 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 1 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.

References

  1. a b c  Miwa Ngara K T S. Eggleston L. Buendia, K Tanabe (2007). Guidelines for national greenhouse gas inventories. Japan: Prepared by the National Greenhouse Gas Inventories Programme, IGES,.www.ipcc-nggip.iges.or.jp/public/2006gl/index.html. 
  2. ^  Olivia Braspenning Radu, Maarten van den Berg, Zbigniew Klimont, Sebastiaan Deetman, Greet Janssens-Maenhout, Marilena Muntean, Chris Heyes, Frank Dentener, Detlef P van Vuuren (2016). Exploring synergies between climate and air quality policies using long-term global and regional emission scenarios. Atmospheric Environment, 140 (), 577 - 591. http://dx.doi.org/http://dx.doi.org/10.1016/j.atmosenv.2016.05.021
  3. ^  J Cofala, Ch Heyes, Z Klimont, M Amann (2002). Acidification, Eutrophication, and Tropospheric Ozone Impacts for Five Scenarios of Greenhouse Gases Abatement in Europe. International Institute for Applied Systems Analysis, Laxenburg, Austria, ().
  4. ^  David I Stern (2003). International society for ecological economics internet encyclopaedia of ecological economics the environmental Kuznets curve. Department of Economics, Rensselaer Polytechnic Institute, ().
  5. ^  Steven J Smith, Hugh Pitcher, TML Wigley (2005). Future sulfur dioxide emissions. Climatic Change, 73 (3), 267--318.
  6. ^  Bas Van Ruijven, Frauke Urban, Ren{\'e} MJ Benders, Henri C Moll, Jeroen P Van Der Sluijs, Bert De Vries, Detlef P Van Vuuren (2008). Modeling energy and development: an evaluation of models and concepts. World Development, 36 (12), 2801--2821.
  7. ^  Richard T Carson (2010). The environmental Kuznets curve: seeking empirical regularity and theoretical structure. Review of Environmental Economics and Policy, 4 (1), 3--23.
  8. ^  Steven J Smith, J van Aardenne, Zbigniew Klimont, Robert Joseph Andres, A Volke, Sabrina Delgado Arias (2011). Anthropogenic sulfur dioxide emissions: 1850--2005. Atmospheric Chemistry and Physics, 11 (3), 1101--1116.
  9. a b  J Harnisch, S Klaus, S Wartmann, JM Rhiemeier (2009). Development of F-gas module for TIMER model. ECOFYS, Nuremberg, ().
  10. ^  Guus JM Velders, David W Fahey, John S Daniel, Mack McFarland, Stephen O Andersen (2009). The large contribution of projected HFC emissions to future climate forcing. Proceedings of the National Academy of Sciences, 106 (27), 10949--10954.