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|Reference=IPCC, 2006; Cofala et al., 2002; Stern, 2003; Smith et al., 2005; Van Ruijven et al., 2008; Carson, 2010; Smith et al., 2011;  Bouwman et al., 1993; Velders et al., 2009; Kreileman and Bouwman, 1994; Bouwman et al., 1997; Bouwman et al., 2002a;
|Reference=IPCC, 2006; Cofala et al., 2002; Stern, 2003; Smith et al., 2005; Van Ruijven et al., 2008; Carson, 2010; Smith et al., 2011;  Bouwman et al., 1993; Velders et al., 2009; Kreileman and Bouwman, 1994; Bouwman et al., 1997; Bouwman et al., 2002a; Velders et al., 2009; Harnisch et al., 2009; Braspenning Radu et al., 2016;
|Description===General approaches==
|Description====General approaches===
The table on the introduction page (see [[Emissions#Inputs|table]]) lists the different sources of emissions included in the IMAGE model. Emissions that are transported in water (nitrate, phosphorus) are discussed in [[Nutrient balances]]. Regarding the approach and spatial detail for modeling gaseous emissions, IMAGE uses four different ways to represent emissions.
Air pollution emission sources included in IMAGE are listed in [[Emission table]], and emissions transported in water (nitrate, phosphorus) are discussed in Component [[Nutrients]]. In approach and spatial detail, gaseous emissions are represented in IMAGE in four ways:
* ''World number'' (WRLD). The simplest way to estimate emissions in IMAGE is by using a global estimate from the literature. This approach is used for those natural sources that can not be explicitly modelled.
* ''Emission factor method'' (EF). For other sources in IMAGE, past and future developments in anthropogenic emissions are estimated on the basis of projected changes in relevant economic activities and the emissions per unit of activity (emission factor) (see flowchart on the right).


The equation for this emission factor approach is as follows:
1) ''World number (W)''
{{FormulaAndTableTemplate|Formula1_E}}
: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 ([[Emission table]]).


where Emission is the emission of the specific gas; Activity is the Energy input or agricultural activity, r is the index for region, i index for further specification (sector, energy carrier), EF-base is the emission factor in the baseline and AF is the abatement factor, i.e. the reduction of the baseline emission factor as a result of climate policy. The emission factors are time-dependent, representing changes in technology and air pollution control policies.
2) ''Emission factor (EF)''


The emission factor approach is used to calculate energy emissions and several land-use related emissions. Following the equation above, there is a direct relation between the level of economic activity and emission level. Also shifts in economic activity (e.g. use of natural gas instead of coal) may influence the total emissions. Finally, emissions can change as result of changes in the emission factors (EF) or climate policy (AF). Some generic rules are used to describe the changes of emissions over time (see further). The abatement factor (AF) are determined in the climate policy model [[FAIR model|FAIR]] (see [[Climate policy]]). The emission factor approach has some limitations, most importantly that is limited in capturing the consequences of specific emission control technology (or management action) for multiple species (either synergies or trade-offs).  
:Past and future developments in anthropogenic emissions are estimated on the basis of projected changes in activity and emissions per unit of activity (Figure Flowchart).  


* ''Emission factor method with spatial distribution'' (GEF) represents a special case of the EF method where a proxy distribution is used to present gridded emissions. This is done for a number of sources, for example emissions from animals ([[Emissions#Inputs|table]]).
:The equation for this emission factor approach is:
::Emission = Activity<sub>r,i</sub> * EF-base<sub>r,i</sub> * AF <sub>r,i</sub>&nbsp;&nbsp;&nbsp;(Equation 1)
: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;
:* 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.


* ''Process model''. (GPM). 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 (see flowchart on the right). It should be noted that for comparison with other models, IMAGE also includes the N2O methodology as proposed by [[IPCC]] ([[IPCC, 2006]]).
:The emission factor is used to calculate energy and industry emissions, and agriculture, waste and land-use related emissions. Following Equation 1, 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).
:Some generic rules are used in describing changes in emissions over time (see further). The abatement factor (AF) is determined in the climate policy model FAIR (see Component [[Climate policy]]). The emission factor approach has some limitations, the most important of which is capturing the consequences of specific emission control technology (or management action) for multiple gas species, either synergies or trade-offs.  


The approaches used for emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below.
3) ''Gridded emission factor with spatial distribution (GEF)''


==Emissions from energy production and use==
:GEF is a special case of the EF method, where a proxy distribution is used to present gridded emissions. This is done for a number of sources, such as emissions from livestock ([[Emission table]]).
Emission factors (EFs) (see the equation above) are used to estimate emissions from the various energy-related sources (See the table on the additional page: ([[Atmospheric_emissions_table|Table]]). In general, the so-called Tier 1 approach from IPCC guidelines ([[IPCC, 2006]]) is used. In the energy system, emissions are calculated by multiplying energy use fluxes with time-dependent emission factors. Changes in the emission factors represent technological improvements and end of-pipe control techniques, fuel specification standards for transport, clean-coal technologies in industry, etc.  


The emission factors are calibrated for the historical period on the basis of the [[EDGAR database|EDGAR emission model]] as described by [[Braspenning Radu et al., 2012]]. The calibration to the EDGAR database is not always straightforward due to differences in aggregation level. The general rule is to use weighted average emission factors in the case of aggregation. However, in those cases in which this results in incomprehensible emission factors (in particular when large differences exists between the emission factors for the underlying technologies) specific emission factors were chosen.  
4) ''Gridded process model (GPM)''
:Land-use related emissions of NH<sub>3</sub>, N<sub>2</sub>O and NO are calculated with grid-specific models (Figure Flowchart). The models included in IMAGE are simple regression models that generate an emission factor (Figure Flowchart). For comparison with other models, IMAGE also includes the N<sub>2</sub>O methodology generally proposed by {{abbrTemplate|IPCC}} ([[IPCC, 2006]]).


==Future emission factors are based on different rules:==
The approaches used to calculate emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below.
* Emission factors can follow an exogenous scenario such as the Current Legislation Scenario (CLE), a scenario developed by [[Cofala et al., 2002]]. The CLE scenario aims to describe the current policies of different regions in the period 2000-2030.
* An alternative rule is that emissions follow the empirically observed trend of the Environmental Kuznets Curve ([[HasAcronym::EKC]]) ([[Stern, 2003]]; [[Smith et al., 2005]]; [[Van Ruijven et al., 2008]]; [[Carson, 2010]]; [[Smith et al., 2011]]). The EKC (as interpreted here) suggests that, starting from low-income levels, per capita emissions will originally increase with increasing per-capita income but at some point will peak and subsequently decline. The latter is driven by increasingly tight environmental policies, but also by shifts within sectors towards industries with lower emissions and improved technology. While such shifts do not lead to lower absolute emissions, the average emissions per unit of energy use declines (for more discussion on the EKC, see below).
* Combinations of a prescribed period, followed by further rules based on income levels.  


It should be noted that there is a debate whether the EKC is actually observed and whether it can be extrapolated to the future (see earlier references). Here, we use the EKC only as empirically observed trend and realize that there are many other driving forces than income that influence future emissions; still the EKC offers a coherent framework to describe overall trends in emissions in an Integrated Assessment context. Criticism to the EKC is often related to its potential use as (only) explanation for understanding trends, to the application to greenhouse gases (we apply the hypothesis to air pollutants only) and to its universal application. For instance, more dense regions are likely to have more stringent air quality standards. Moreover, technologies developed in high-income regions often tend to diffuse within only a few years to developing regions. The latter can, in fact, be captured in IMAGE by decreasing the threshold values over time
===Emissions from energy production and use===
Emission factors ([[#General approaches|Equation 1]]) are used for estimating emissions from the energy-related sources ([[Emission table]]). In general, the Tier 1 approach from IPCC guidelines ([[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 methodology for EKC scenario development as applied in the energy model is based on 2 types of variables, a) income thresholds (2-3 steps) and b) gas and sector dependent reduction targets for these income levels. The income thresholds are set on historical points 1) average [[OECD]] income at which air pollution control policies were introduced in these countries;
<div class="version changev31">
2) the current income level of OECD countries. We assume that developing countries once they reach the first threshold level will start reducing their emission factors. We also assume that if they reach the second threshold level, the emission factor will converge to the average value of OECD regions once the second threshold level is reached. Beyond this level, we assume that further reductions will take place slowly converging to the minimum emission factor found in OECD regions in 2030 according to projections made by [[IIASA]] under so-called “current legislation” (i.e. current abatement plans).


It should be noted that for CO2 and other greenhouse gases such as halogenated gases, where no evidence of EKC behavior exists, the IMAGE model uses an explicit description of fuel use and deforestation and does not rely on the EKC at all. The IMAGE rules act on the level of regions instead of countries. Interestingly, international agreements cause countries to act as a group so this might not be an important limitation.  
The emission factors for the historical period for the energy system and industrial processes are calibrated with the EDGAR emission model described by Braspenning Radu et al. ([[Braspenning Radu et al., 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.  


</div>


==Emissions from industrial processes==
Future emission factors are based on the following rules:
For the industrial sector, the energy model includes several activity levels that determine emissions. These can divided into three categories:
* Cement and steel production. For these commodities, IMAGE-TIMER actually includes detailed demand models ([[Energy supply and demand]]). Similar to energy, the emissions are calculated by multiplying the activity levels to exogenously set emission factors.
* Other industrial activities. Here the activity levels are formulated as a regional function of industrial value added. Activity levels include for instance copper production, and the production of solvents. Again, emissions are calculated by multiplying the activity levels with emission factors.
* For the halogenated gases, finally we have implemented the approach developed by [[Harnisch et al., 2009]]. They derived relationships with income for the main uses of halogenated gasses (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]]. The marginal abatement cost curve per gas still follows the methodology described by [[Harnisch et al., 2009]].


==Land-use related emissions==
* 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 ({{abbrTemplate|CLE}}) developed by IIASA (for instance, Cofala et al., ([[Cofala et al., 2002|2002]]). The CLE scenario describes the policies in different regions for the 2000–2030 period.  
The CO2 exchange between terrestrial ecosystems and the atmosphere computed by the LPJ model is described in [[Natural vegetation and carbon cycle]]. The land-use emissions model focuses on emissions of other important gases, including greenhouse gases (CH4, N2O), ozone precursors (NOx, CO, VOC), acidifying compounds (SO2, NH3) and aerosols (SO2, NO3, BC, OC).


For many sources, the emission factor approach (see formula 1.) is used ([[Atmospheric_emissions_table|Table]]). For anthropogenic sources, the emission factors are from the EDGAR database, with time-dependent values for historical years. During the scenario period, most emission factors are constant, except for explicit climate abatement policies (see below). However, there are some important exceptions. Atmospheric N emissions are modeled in a detailed way (see below), and in several other cases, the emission factor depends on the assumptions described in other parts of IMAGE. For example, CH4 emissions from nondairy and dairy cattle are calculated on the basis of the energy requirement and feed type (see [[Livestock]]). High-quality feed such as concentrates from feed crops have a lower CH4 emission factor than feeds with lower protein and higher contents of components with lower digestibility. This implies that when the feed conversion ratio changes, the CH4 emission will automatically change as well. Feed conversion ratios are prescribed, or are calculated on the basis of the animal productivity.
* Alternatively, emission factors can be derived from generic rules, one of which in IMAGE is the {{abbrTemplate|EKC}}: Environmental Kuznets Curve ([[Stern, 2003]]; [[Smith et al., 2005]]; [[Van Ruijven et al., 2008]]; [[Carson, 2010]]; [[Smith et al., 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.


Constant emission factors may lead to decreasing emissions per unit of product, for example when the emission factor is specified on a per head basis. An increasing production per head may then lead to a decreasing emission per unit of product. An example is the constant CH4 emission from animal waste per animal, which leads to decreasing emissions per unit of meat or milk when the production per animal increases.
* Combinations of the methods described above for a specific period, followed by additional rules based on income levels.  


A special case is the N2O emission after forest clearing. Deforestation may lead to accelerated decomposition of litter, root material and loss of part of soil organic matter in the first year after the clearing, causing a pulse of N2O emissions. To mimic this effect, emissions in the first year after clearing are assumed to be 5 times the flux in the original ecosystem. They decrease linearly to the level of the new ecosystem in the 10th year, usually lower than the flux in the original forest. More details can be found in [[Kreileman and Bouwman, 1994]].
In IMAGE, {{abbrTemplate|EKC}} is used as an empirically observed trend, as it offers a coherent framework to describe overall trends in emissions in an Integrated Assessment context. However , it is accepted that many driving forces other than income influence future emissions. For instance, more densely populated regions are likely to have more stringent air quality standards. Moreover, technologies developed in high-income regions often tend to spread within a few years to developing regions. The generic equations in IMAGE can capture this by decreasing the threshold values over time. For CO<sub>2</sub> and other greenhouse gases, such as halogenated gases for which there is no evidence of {{abbrTemplate|EKC}} behaviour, IMAGE uses an explicit description of fuel use and deforestation.


Land-use related emissions of NH3, N2O and NO are calculated with a grid-specific model, N2O from soils under natural vegetation is calculated with the model of [[Bouwman et al., 1993]]. This model is a regression model based on temperature, a proxy for soil carbon input, soil water and oxygen status and a proxy for net primary production. Ammonia emission from natural vegetation is based on net primary production, C:Nratio and an emission factor, and the model accounts for in-canopy retention of the emitted NH3 ([[Bouwman et al., 1997]]).
The methodology for EKC scenario development applied in the energy model is based on two types of variables: income thresholds (2–3 steps); and gas- and sector-dependent reduction targets for these income levels. The income thresholds are set to historical points: the average {{abbrTemplate|OECD}} income at which air pollution control policies were introduced in these countries; and current income level in OECD countries. The model assumes that emission factors will start to decline in developing countries, when they reach the first income threshold, reflecting more efficient and cleaner technology. It also assumes that when developing countries reach the second income threshold, the emission factors will be equal to the average level in OECD regions. Beyond this income level, the model assumes further reductions, slowly converging to the minimum emission factor in OECD regions by 2030, according to projections made by {{abbrTemplate|IIASA}} under current legislation (current abatement plans). The IMAGE rules act at the level of regions, this could be seen as a limitation, but as international agreements lead countries to act as a group, this may not be an important limitation.


For agricultural emissions of N2O, the most important determinant factors are N application rate, climate type, soil organic-C content, soil texture, drainage, soil pH, type of crop, and type of fertilizer; the most important controls on NO emission include the N application rate per fertilizer type and soil organic-C content and soil drainage. (for a detailed description, see [[Bouwman et al., 2002a]]. Agricultural emissions of NH3 from fertilized cropland and grassland uses the factors type of crop, fertilizer application rate by type and application mode, temperature, soil pH, and CEC ([[Bouwman et al., 2002a]]).
===Emissions from industrial processes===
For the industry sector, the energy model includes three categories:


It should be noted that for comparison with other models, IMAGE also inludes the N2O methodology for sources as proposed by IPCC ([[IPCC, 2006]]). This methodology represents only the anthropogenic increase. This emission cannot be compared with the above model approach, which calculates the total emission.
# Cement and steel production. IMAGE-TIMER includes detailed demand models for these commodities (Component [[Energy supply and demand]]). Similar to those from energy use, emissions are calculated by multiplying the activity levels to exogenously set emission factors.
# 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.
# For halogenated gases, the approach used was developed by Harnisch et al. ([[Harnisch et al., 2009|2009]]), which derived relationships with income for the main uses of halogenated gases (HFCs, PFCs, SF<sub>6</sub>). In the actual use of the model, slightly updated parameters are used to better represent the projections as presented by Velders et al. ([[Velders et al., 2009|2009]]). The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. ([[Harnisch et al., 2009|2009]]).


==Emission abatement==
===Land-use related emissions===
Future emissions for a number of energy and land-use related sources also vary in future years as a result of climate policy. This is described by using so-called abatement coefficients (see flowchart on the right). The values of these coefficients depend on the scenario assumptions. In scenarios in which climate change or sustainability is an important feature in the storyline, abatement will be more important than in business-as-usual scenarios. Abatement factors are used in particular for CH4 emissions from fossil fuel production and transport, N2O emissions from transport and for CH4 emissions from enteric fermentation and from animal waste, and N2O emissions from animal waste according to the IPCC method. These abatement files are calculated in the climate policy submodel of IMAGE on the basis of comparing the costs of non-CO2 abatement in agriculture against other mitigation options.
CO<sub>2</sub> 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 (CH<sub>4</sub>, N<sub>2</sub>O), ozone precursors (NO<sub>x</sub>, CO, NMVOC), acidifying compounds (SO<sub>2</sub>, NH<sub>3</sub>) and aerosols (SO<sub>2</sub>, NO<sub>3</sub>, BC, OC).


For many sources, the emission factor ([[#General approaches|Equation 1]]) is used ([[Emission table]]). 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 CH<sub>4</sub> emissions from non-dairy and dairy cattle are calculated on the basis of energy requirement and feed type (see Component [[Livestock systems]]). High-quality feed, such as concentrates from feed crops, have a lower CH<sub>4</sub> 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 CH<sub>4</sub> emissions will automatically change. Pigs, and sheep and goats have 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.
Constant emission factors may lead to decreasing emissions per unit of product, for example, when the emission factor is specified on a per-head basis. An increasing production per head may lead to a decrease in emissions per unit of product. For example, the CH<sub>4</sub> emission level for animal waste is a constant per animal, which leads to a decrease in emissions per unit of meat or milk when production per animal increases.
A special case is N<sub>2</sub>O emissions after forest clearing. After deforestation, litter remaining on the soil surface as well as root material and soil organic matter decompose in the first years after clearing, which may lead to pulses of N<sub>2</sub>O emissions. To mimic this effect, emissions in the first year after clearing are assumed to be five times the flux in the original ecosystem. Emissions decrease linearly to the level of the new ecosystem in the tenth year, usually below the flux in the original forest. For more details, see Kreileman and Bouwman ([[Kreileman and Bouwman, 1994|1994]]).
Land-use related emissions of NH<sub>3</sub>, N<sub>2</sub>O and NO are calculated withgrid-specific models.N<sub>2</sub>O from soils under natural vegetation is calculated with the model developed by Bouwman et al. (1993). This regression model is based on temperature, a proxy for soil carbon input, soil water and oxygen status, and for net primary production. Ammonia emissions from natural vegetation are calculated from net primary production, C:N ratio and an emission factor. The model accounts for in-canopy retention of the emitted NH<sub>3</sub> ([[Bouwman et al., 1997]]).
For N<sub>2</sub>O emissions from agriculture, the determining factors in IMAGE are N application rate, climate type, soil organic carbon content, soil texture, drainage, soil pH, crop type, and fertiliser type. The main factors used to calculate NO emissions include N application rate per fertiliser type, and soil organic carbon content and soil drainage (for detailed description, see Bouwman et al. ([[Bouwman et al., 2002a|2002a]]). For NH<sub>3</sub> emissions from fertilised cropland and grassland, the factors used in IMAGE are crop type, fertiliser application rate per type and application mode, temperature, soil pH, and CEC ([[Bouwman et al., 2002a]]).
For comparison with other models, IMAGE also includes the N<sub>2</sub>O methodology proposed by IPCC ([[IPCC, 2006|2006]]). This methodology represents only anthropogenic emissions. For emissions from fertilizer fields this is the emission from a fertilized plot minus that from a control plot with zero fertilizer application. For this reason, soil emissions calculated with this methodology cannot be compared with the above model approaches, which yields total N<sub>2</sub>O emissions.
===Emission abatement===
Emissions from energy, industry, agriculture, waste and land-use sources are also expected to vary in future years, as a result of climate policy. This is described using abatement coefficients, the values of which depend on the scenario assumptions and the stringency of climate policy described in the climate policy component. In scenarios with climate change or sustainability as the key feature in the storyline, abatement is more important than in business-as-usual scenarios. Abatement factors are used for CH<sub>4</sub> emissions from fossil fuel production and transport, N<sub>2</sub>O emissions from transport, CH<sub>4</sub> emissions from enteric fermentation and animal waste, and N<sub>2</sub>O emissions from animal waste according to the IPCC method. These abatement files are calculated in the IMAGE climate policy sub-model FAIR (Component [[Climate policy]]) by comparing the costs of non-CO<sub>2</sub> abatement in agriculture and other mitigation options.
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Revision as of 13:33, 7 July 2017

Link to framework components overview

Parts of Emissions/Description

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references
    6. Additional info
  6. Emission table
Component is implemented in:
Components:and Aggregated component:Components:
Projects/Applications
Models/Databases
Key publications
References
Emission module of IMAGE 3.0
Flowchart Emissions. See also the Input/Output Table on the introduction page. Anthropogenic sources, for natural sources see Table 5.2.2. More detail on inputs and outputs, and how they link to other IMAGE components is presented at the end of this section (Emission table).

Model description of Emissions

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