https://models.pbl.nl/image/api.php?action=feedcontributions&user=Harmsenm&feedformat=atomIMAGE - User contributions [en]2024-03-28T17:17:36ZUser contributionsMediaWiki 1.31.1https://models.pbl.nl/image/index.php?title=Emissions&diff=35998Emissions2019-04-03T18:10:57Z<p>Harmsenm: </p>
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<div>{{ComponentTemplate2<br />
|Application=Roads from Rio+20 (2012) project; LIMITS (2014); Shared Socioeconomic Pathways - SSP (2014) project; EMF30;<br />
|Model-Database=EDGAR database; GAINS database; CEDS database;<br />
|KeyReference=Van Vuuren et al., 2006; Van Vuuren et al., 2011b; Braspenning Radu et al., 2016; Rao et al., 2016; Rao et al., 2017; Harmsen et al., 2019c; Lucas et al., 2007<br />
|InputVar=Energy and industry activity level; Feed crop requirement; Animal stocks; Land cover, land use - grid; Emission abatement; GDP per capita;<br />
|Parameter=Emission factors; Relationship income and emission factor;<br />
|OutputVar=CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and F-gases); BC, OC and NOx emissions; SO2 emissions; Nitrogen deposition - grid;<br />
|ComponentCode=E<br />
|FrameworkElementType=interaction component<br />
}}<br />
<div class="page_standard"><br />
==Introduction==<br />
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.<br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=CEDS_database&diff=35995CEDS database2019-04-03T18:07:53Z<p>Harmsenm: Created page with "{{ComputerModelTemplate |Subject=Historical emissions database for greenhouse gases and pollutants. |Description=The CEDS project is building a data-driven, open source framew..."</p>
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<div>{{ComputerModelTemplate<br />
|Subject=Historical emissions database for greenhouse gases and pollutants.<br />
|Description=The CEDS project is building a data-driven, open source framework that will produce annually updated emission estimates for research and analysis.<br />
<br />
The data system produces emission estimates by country, sector, and fuel with the following characteristics:<br />
◾Annual estimates of anthropogenic emissions (not including open burning) to latest full calendar year over the entire industrial era. Readily updated every year.<br />
◾Emission species: aerosol (BC, OC) and aerosol precursor and reactive compounds (SO2, NOx, NH3, CH4, CO, NMVOC) and CO2 (as reference)<br />
◾State/province spatial detail for large countries – in progress<br />
◾Seasonal cycle (monthly) and aggregate NMVOCs by sector/sub-sector<br />
◾Gridded emissions (up to 0.1°) w/ sub-national resolution for large countries<br />
◾Uncertainty estimated at the same level (country, fuel, sector) – in progress<br />
|Creator=PNNL; University of Maryland;<br />
|ExternalURL=http://www.globalchange.umd.edu/ceds/<br />
|Reference=Hoesly et al., 2018;<br />
|FrameworkRelation=core model<br />
|Component=Air pollution and energy policies;<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=EMF30&diff=35992EMF302019-04-03T18:00:56Z<p>Harmsenm: Created page with "{{ApplicationTemplate |Website=https://emf.stanford.edu/projects/emf-30-short-lived-climate-forcers-air-quality |ApplicationType=4. Model comparison |IMAGERoleDescription=One..."</p>
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<div>{{ApplicationTemplate<br />
|Website=https://emf.stanford.edu/projects/emf-30-short-lived-climate-forcers-air-quality<br />
|ApplicationType=4. Model comparison<br />
|IMAGERoleDescription=One of the models used for scenario analysis.<br />
|Summary=The 30th energy modeling forum (EMF30) is aimed at understanding the climatic role of short-lived climate forcers (SLCFs), such as methane, black carbon and hydrofluorocarbons, both in mitigation and reference cases.<br />
|Partners=Enerdata; IIASA; PIK; NIES; RITE; PNNL; CMCC; PBL;<br />
|KeyReference=Harmsen et al., 2019a; Harmsen et al., 2019b;<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Description&diff=35989Emissions/Description2019-04-03T15:32:19Z<p>Harmsenm: </p>
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<div>{{ComponentDescriptionTemplate<br />
|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; Velders et al., 2015; Lucas et al., 2007; Harmsen et al., 2019c; EC-JRC/PBL, 2016; Hoesly et al., 2018; Rao et al., 2016; Rao et al., 2017;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===General approaches===<br />
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: <br />
<br />
1) ''World number (W)''<br />
<br />
: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]]).<br />
<br />
2) ''Emission factor (EF)'' <br />
<br />
: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). <br />
<br />
:The equation for this emission factor approach is:<br />
::<math>Emission_{r,i,t} = Activity_{r,i,t} * EFbase_{r,i,t} * AF_{r,i,t}</math> (Equation 1) <br />
:where:<br />
:* <math>Emission</math> is the emission of the specific gas or aerosol;[[Amann et al., 2011]] <br />
:* <math>Activity</math> is the energy input or agricultural activity; r is the index for region; <br />
:* <math>i</math> is the index for further specification (sector, energy carrier); <br />
:* ''t'' is the index for time (years). All factors are time-dependent; <br />
:* <math>EFbase</math> is the emission factor in the baseline; <br />
:* <math>AF</math> is the abatement factor (reduction in the baseline emission factor as a result of climate policy). <br />
:<br />
:<br />
: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. These activities are calculated in various sub-modules of the model (e.g. related to energy supply and demand, food production and industry). Finally, emissions can change as a result of changes in emission factors (EF) and climate policy (AF). Emission factors indicate the emission rate of an air pollutant per activity level and differences in time represent technological changes in air pollution control. This information is generally obtained from specialized research groups and databases, such as GAINS ([[Amann et al., 2011]]) , EDGAR ([[EC-JRC/PBL, 2016]]) and CEDS ([[Hoesly et al., 2018]]). Abatement factors are applied to non-CO<sub>2</sub> greenhouse gases (methane (CH<sub>4</sub>), nitrous oxide (N<sub>2</sub>O) and fluorinated gases (F-gases: HFCs, PFCs and SF<sub>6</sub>)) and are determined in the climate policy model FAIR (see Component [[Climate policy]]). <br />
:The emission factor approach has some limitations, the most important of which is not capturing the consequences of specific emission control technologies (or management action) for multiple species, either synergies or trade-offs. <br />
: <br />
3) ''Gridded emission factor with spatial distribution (GEF)''<br />
<br />
: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]]).<br />
<br />
4) ''Gridded process model (GPM)'' <br />
: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]]).<br />
<br />
The approaches used to calculate emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below.<br />
<br />
===Emissions from energy production and use===<br />
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.<br />
<br />
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. <br />
<br />
Future emission factors are based on the following rules:<br />
<br />
* 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 those developed by GAINS/IIASA ([[Amann et al., 2011]]). These represent the policies in different regions for the 2000–2030 and 2030-2100 periods. Future emission factors for air pollutants have been set in accordance with the SSP storylines ([[Rao et al., 2016]]). <br />
<br />
* 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.<br />
<br />
* Combinations of the methods described above for a specific period, followed by additional rules based on income levels. <br />
<br />
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.<br />
<br />
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.<br />
<br />
===Emissions from industrial processes===<br />
For the industry sector, the energy model includes three categories:<br />
<br />
# 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.<br />
# 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.<br />
# 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]], [[Velders et al., 2015|2015]]). The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. ([[Harnisch et al., 2009|2009]]).<br />
<br />
===Land-use related emissions===<br />
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).<br />
<br />
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). <br />
<br />
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.<br />
<br />
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.<br />
<br />
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]]).<br />
<br />
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]]).<br />
<br />
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]]).<br />
<br />
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.<br />
<br />
===Emission abatement===<br />
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.<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35986Emissions/Policy issues2019-04-03T15:28:18Z<p>Harmsenm: </p>
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<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; Lucas et al., 2007; Harmsen et al., 2019c; Harmsen et al., 2019a; Harmsen et al., 2019b; Rao et al., 2016; Rao et al., 2016; Velders et al., 2015;<br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
<br />
[!CHANGE]<br />
<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al., 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. As such, they are used in the [[FAIR model|FAIR]] model to determine strategies and costs of comprehensive {{abbrTemplate|GHG}} mitigation strategies. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}}, {{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
[!CHANGE]<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35983Emissions/Policy issues2019-04-03T15:25:31Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; Lucas et al., 2007; Harmsen et al., 2019c; Harmsen et al., 2019a; Harmsen et al., 2019b;<br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
<br />
[!CHANGE]<br />
<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al., 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. As such, they are used in the [[FAIR model|FAIR]] model to determine strategies and costs of comprehensive {{abbrTemplate|GHG}} mitigation strategies. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}}, {{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
[!CHANGE]<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions&diff=35980Emissions2019-04-03T15:21:47Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentTemplate2<br />
|Application=Roads from Rio+20 (2012) project; LIMITS (2014); Shared Socioeconomic Pathways - SSP (2014) project;<br />
|Model-Database=EDGAR database; GAINS database;<br />
|KeyReference=Van Vuuren et al., 2006; Van Vuuren et al., 2011b; Braspenning Radu et al., 2016; Rao et al., 2016; Rao et al., 2017; Harmsen et al., 2019c; Lucas et al., 2007<br />
|InputVar=Energy and industry activity level; Feed crop requirement; Animal stocks; Land cover, land use - grid; Emission abatement; GDP per capita;<br />
|Parameter=Emission factors; Relationship income and emission factor;<br />
|OutputVar=CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and F-gases); BC, OC and NOx emissions; SO2 emissions; Nitrogen deposition - grid;<br />
|ComponentCode=E<br />
|FrameworkElementType=interaction component<br />
}}<br />
<div class="page_standard"><br />
==Introduction==<br />
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.<br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions&diff=35977Emissions2019-04-03T15:16:56Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentTemplate2<br />
|Application=Roads from Rio+20 (2012) project; LIMITS (2014); Shared Socioeconomic Pathways - SSP (2014) project;<br />
|Model-Database=EDGAR database; GAINS database;<br />
|KeyReference=Van Vuuren et al., 2006; Van Vuuren et al., 2011b; Braspenning Radu et al., 2016; Rao et al., 2016; Rao et al., 2017; Harmsen et al., 2019c;<br />
|InputVar=Energy and industry activity level; Feed crop requirement; Animal stocks; Land cover, land use - grid; Emission abatement; GDP per capita;<br />
|Parameter=Emission factors; Relationship income and emission factor;<br />
|OutputVar=CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and F-gases); BC, OC and NOx emissions; SO2 emissions; Nitrogen deposition - grid;<br />
|ComponentCode=E<br />
|FrameworkElementType=interaction component<br />
}}<br />
<div class="page_standard"><br />
==Introduction==<br />
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.<br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions&diff=35974Emissions2019-04-03T15:15:59Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentTemplate2<br />
|Application=Roads from Rio+20 (2012) project; LIMITS (2014); Shared Socioeconomic Pathways - SSP (2014) project;<br />
|Model-Database=EDGAR database; GAINS database;<br />
|KeyReference=Van Vuuren et al., 2006; Van Vuuren et al., 2011b; Braspenning Radu et al., 2016; Rao et al., 2016, Rao et al., 2017, Harmsen et al., 2019c<br />
|InputVar=Energy and industry activity level; Feed crop requirement; Animal stocks; Land cover, land use - grid; Emission abatement; GDP per capita;<br />
|Parameter=Emission factors; Relationship income and emission factor;<br />
|OutputVar=CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and F-gases); BC, OC and NOx emissions; SO2 emissions; Nitrogen deposition - grid;<br />
|ComponentCode=E<br />
|FrameworkElementType=interaction component<br />
}}<br />
<div class="page_standard"><br />
==Introduction==<br />
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.<br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions&diff=35971Emissions2019-04-03T15:13:08Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentTemplate2<br />
|Application=Roads from Rio+20 (2012) project; LIMITS (2014); Shared Socioeconomic Pathways - SSP (2014) project;<br />
|Model-Database=EDGAR database; GAINS database;<br />
|KeyReference=Van Vuuren et al., 2006; Van Vuuren et al., 2011b; Braspenning Radu et al., 2016; Rao et al., 2016; Rao et al., 2017;<br />
|InputVar=Energy and industry activity level; Feed crop requirement; Animal stocks; Land cover, land use - grid; Emission abatement; GDP per capita;<br />
|Parameter=Emission factors; Relationship income and emission factor;<br />
|OutputVar=CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and F-gases); BC, OC and NOx emissions; SO2 emissions; Nitrogen deposition - grid;<br />
|ComponentCode=E<br />
|FrameworkElementType=interaction component<br />
}}<br />
<div class="page_standard"><br />
==Introduction==<br />
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.<br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35700Emissions/Policy issues2019-03-06T10:18:43Z<p>Harmsenm: Indicated large changes</p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; Lucas et al., 2007;<br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
<br />
[!CHANGE]<br />
<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. As such, they are used in the [[FAIR model|FAIR]] model to determine strategies and costs of comprehensive {{abbrTemplate|GHG}} mitigation strategies. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}}, {{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
[!CHANGE]<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Climate_policy/Description&diff=35544Climate policy/Description2019-03-05T16:10:32Z<p>Harmsenm: Added non-CO2 MAC references</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Den Elzen et al., 2007; Van Vuuren et al., 2011a; Meinshausen et al., 2011b; Den Elzen et al., 2013; Hof et al., 2013; Roelfsema et al., 2014; Den Elzen et al., 2012a; Hof et al., 2012; Hof et al., 2008; Hof et al., 2010; De Bruin et al., 2009; Admiraal et al., 2016; Van den Berg et al., 2015; UNFCCC (2015b); Den Elzen et al., 2016; UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
FAIR consists of six linked modules as presented in the flowchart and described briefly below. <br />
<br />
===Global pathfinder and climate module===<br />
The pathfinder module FAIR-SiMCaP calculates global emission pathways that are consistent with a long-term climate target ([[Den Elzen et al., 2007]]; [[Van Vliet et al., 2012]]; [[Van den Berg et al., 2015]]). Inputs are climate targets defined in terms of concentration levels, radiative forcing, temperature, and cumulative emissions. In addition, intermediate restrictions on overshoot levels or intermediate emission targets representing climate policy progress can be included. The model combines the FAIR mitigation costs model and a module that minimises cumulative discounted mitigation costs by varying the timing of emission reductions. For climate calculations, FAIR-SiMCaP uses the [[MAGICC model|MAGICC 6 model]], with parameter settings calibrated to reproduce the medium response in terms of time scale and amplitude of 19 IPCC {{abbrTemplate|AR}}4 General Circulation Models ([[Meinshausen et al., 2011b]]).<br />
<br />
===Policy evaluation module===<br />
<br />
The Policy evaluation module calculates emission levels resulting from the reduction [!CHANGE] proposals (pledges and NDCs) and mitigation actions submitted by developed and developing countries as part of the 2015 {{abbrTemplate|UNFCCC}} Paris agreement ([[Den Elzen et al., 2016]]; [[Rogelj et al., 2016]]). This module also collects the emission projections of current and planned policy scenarios as calculated by the IMAGE-TIMER model ([[Roelfsema et al., 2018]]). These scenarios take into account the impact of individual policies in different subsectors that are implemented in 25 major emitting countries ([[Kuramochi et al., 2018]]). [!CHANGE]The module is used in conjunction with a wide range of evaluation tools developed in cooperation with [[IIASA]] and [[JRC]], such as tools for analysing policy options for land-use credits and surplus emissions. The PBL [https://themasites.pbl.nl/climate-ndc-policies-tool/ Climate Pledge NDC tool] gives a summary of the greenhouse gas emission reduction proposals, domestic policies of major countries and the impact on the emissions by 2030.<br />
<br />
===Effort sharing module===<br />
The Effort sharing module calculates emission targets for regions and countries, resulting from different emission allocation or effort-sharing schemes ([[Den Elzen et al., 2012a]]; [[Hof et al., 2012]]; [[Van den Berg et al., 2019]]). Such schemes start either at the global allowed emission level, after which the effort-sharing approach allocates emission allowances across regions, or at the required global reduction level, after which various effort-sharing approaches allocate regional emission reduction targets. Both approaches use information from the Global Pathfinder and Climate module on the required global emission level or emission reductions. As an alternative, emission allowances can be allocated to regions without a predefined global reduction target, based on different effort-sharing approaches. The model includes effort-sharing approaches such as Contraction & Convergence, common-but-differentiated convergence, ability to pay, and a multi-stage approach.<br />
<br />
===Mitigation costs module===<br />
The Mitigation costs module is used for calculating the regional mitigation costs of achieving the targets calculated in the Policy Evaluation and/or the Effort Sharing modules, and to determine the buyers and sellers on the international emissions trading market ([[Den Elzen et al., 2011a]]; [[Hof et al., 2017]]). Inputs to the model are regional gas- and source-specific Marginal Abatement Cost (MAC) curves that reflect the additional costs of abating one extra tonne of CO<sub>2</sub> equivalent emissions. CO<sub>2</sub> MAC curves are derived from the energy and land-use modules of IMAGE. Non-CO<sub>2</sub> MAC curves are based on [[Lucas et al., 2007|Lucas et al. (2007)]] and [[Harmsen et al., 2019c|Harmsen et al. (2019c)]]. The {{abbrTemplate|MAC}} curves describe the potential and costs of the abatement options considered. The model uses aggregated regional permit demand and supply curves derived from the MAC curves to calculate the equilibrium permit price on the international trading market, its buyers and sellers, and the resulting domestic and external abatement per region. The design of the emissions trading market can include: constraints on imports and exports of emission permits; non-competitive behaviour; transaction costs associated with the use of emission trading; a less than fully efficient supply of viable {{abbrTemplate|CDM}} projects with respect to their operational availability; and the banking of surplus emission allowances. <br />
<br />
===Damage and Cost-Benefit Analysis modules===<br />
The Damage and Cost-Benefit Analysis modules calculate the consumption loss resulting from climate change damage, and compare these with the consumption losses of adaptation and mitigation costs ([[Hof et al., 2008]]; [[Hof et al., 2009|2009]]; [[Hof et al., 2010|2010]]; [[Admiraal et al., 2016]]). Estimates of adaptation costs and residual damage (defined as the damage that remains after adaptation) [!CHANGE]are based either on the [[AD RICE model]] ([[De Bruin et al., 2009]]), which are based on total damage projections made by the [[RICE model]], or on output from sectoral impact models[!CHANGE]. Calibration of the regional adaptation cost functions of AD RICE is based on an assessment of each impact category described in the RICE model, using relevant studies and with expert judgement where necessary. The optimal level of adaptation can be calculated by the model, but may also be set to a non-optimal level by the user. <br />
<br />
===Estimation of consumption losses===<br />
Consumption losses due to mitigation, adaptation and climate change damage are estimated based on a simple Cobb–Douglas production function.<ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs and the amount of output that can be produced by those inputs.</div></ref> [!CHANGE]The production factors are labour and capital. Regional changes in labour over time are based on the projected changes in total regional population. Initial regional capital stocks are based on the Investment and Capital Stock Dataset of the IMF. Future capital stocks depend on depreciation (set at 5% per year) and investments. Investments depend on the savings rate and initial savings rates are taken from the same IMF source. Total factor productivity of each region is calibrated to the exogenous GDP path without damage or mitigation costs. In a second step, damages, adaptation and abatement costs are subtracted from investment or consumption to determine both the direct replacement effect on consumption and the indirect effect from replacing productive investments.[!CHANGE]<br />
<references /><br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Climate_policy/Description&diff=35541Climate policy/Description2019-03-05T16:08:20Z<p>Harmsenm: Added references for the non-CO2 MAC curves</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|Reference=Den Elzen et al., 2007; Van Vuuren et al., 2011a; Meinshausen et al., 2011b; Den Elzen et al., 2013; Hof et al., 2013; Roelfsema et al., 2014; Den Elzen et al., 2012a; Hof et al., 2012; Hof et al., 2008; Hof et al., 2010; De Bruin et al., 2009; Admiraal et al., 2016; Van den Berg et al., 2015; UNFCCC (2015b); Den Elzen et al., 2016; UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
FAIR consists of six linked modules as presented in the flowchart and described briefly below. <br />
<br />
===Global pathfinder and climate module===<br />
The pathfinder module FAIR-SiMCaP calculates global emission pathways that are consistent with a long-term climate target ([[Den Elzen et al., 2007]]; [[Van Vliet et al., 2012]]; [[Van den Berg et al., 2015]]). Inputs are climate targets defined in terms of concentration levels, radiative forcing, temperature, and cumulative emissions. In addition, intermediate restrictions on overshoot levels or intermediate emission targets representing climate policy progress can be included. The model combines the FAIR mitigation costs model and a module that minimises cumulative discounted mitigation costs by varying the timing of emission reductions. For climate calculations, FAIR-SiMCaP uses the [[MAGICC model|MAGICC 6 model]], with parameter settings calibrated to reproduce the medium response in terms of time scale and amplitude of 19 IPCC {{abbrTemplate|AR}}4 General Circulation Models ([[Meinshausen et al., 2011b]]).<br />
<br />
===Policy evaluation module===<br />
<br />
The Policy evaluation module calculates emission levels resulting from the reduction [!CHANGE] proposals (pledges and NDCs) and mitigation actions submitted by developed and developing countries as part of the 2015 {{abbrTemplate|UNFCCC}} Paris agreement ([[Den Elzen et al., 2016]]; [[Rogelj et al., 2016]]). This module also collects the emission projections of current and planned policy scenarios as calculated by the IMAGE-TIMER model ([[Roelfsema et al., 2018]]). These scenarios take into account the impact of individual policies in different subsectors that are implemented in 25 major emitting countries ([[Kuramochi et al., 2018]]). [!CHANGE]The module is used in conjunction with a wide range of evaluation tools developed in cooperation with [[IIASA]] and [[JRC]], such as tools for analysing policy options for land-use credits and surplus emissions. The PBL [https://themasites.pbl.nl/climate-ndc-policies-tool/ Climate Pledge NDC tool] gives a summary of the greenhouse gas emission reduction proposals, domestic policies of major countries and the impact on the emissions by 2030.<br />
<br />
===Effort sharing module===<br />
The Effort sharing module calculates emission targets for regions and countries, resulting from different emission allocation or effort-sharing schemes ([[Den Elzen et al., 2012a]]; [[Hof et al., 2012]]; [[Van den Berg et al., 2019]]). Such schemes start either at the global allowed emission level, after which the effort-sharing approach allocates emission allowances across regions, or at the required global reduction level, after which various effort-sharing approaches allocate regional emission reduction targets. Both approaches use information from the Global Pathfinder and Climate module on the required global emission level or emission reductions. As an alternative, emission allowances can be allocated to regions without a predefined global reduction target, based on different effort-sharing approaches. The model includes effort-sharing approaches such as Contraction & Convergence, common-but-differentiated convergence, ability to pay, and a multi-stage approach.<br />
<br />
===Mitigation costs module===<br />
The Mitigation costs module is used for calculating the regional mitigation costs of achieving the targets calculated in the Policy Evaluation and/or the Effort Sharing modules, and to determine the buyers and sellers on the international emissions trading market ([[Den Elzen et al., 2011a]]; [[Hof et al., 2017]]). Inputs to the model are regional gas- and source-specific Marginal Abatement Cost (MAC) curves that reflect the additional costs of abating one extra tonne of CO<sub>2</sub> equivalent emissions. CO<sub>2</sub> MAC curves are derived from the energy and land-use modules of IMAGE. Non-CO<sub>2</sub> MAC curves are based on Lucas et al. (2007) and Harmsen et al. (2019c). The {{abbrTemplate|MAC}} curves describe the potential and costs of the abatement options considered. The model uses aggregated regional permit demand and supply curves derived from the MAC curves to calculate the equilibrium permit price on the international trading market, its buyers and sellers, and the resulting domestic and external abatement per region. The design of the emissions trading market can include: constraints on imports and exports of emission permits; non-competitive behaviour; transaction costs associated with the use of emission trading; a less than fully efficient supply of viable {{abbrTemplate|CDM}} projects with respect to their operational availability; and the banking of surplus emission allowances. <br />
<br />
===Damage and Cost-Benefit Analysis modules===<br />
The Damage and Cost-Benefit Analysis modules calculate the consumption loss resulting from climate change damage, and compare these with the consumption losses of adaptation and mitigation costs ([[Hof et al., 2008]]; [[Hof et al., 2009|2009]]; [[Hof et al., 2010|2010]]; [[Admiraal et al., 2016]]). Estimates of adaptation costs and residual damage (defined as the damage that remains after adaptation) [!CHANGE]are based either on the [[AD RICE model]] ([[De Bruin et al., 2009]]), which are based on total damage projections made by the [[RICE model]], or on output from sectoral impact models[!CHANGE]. Calibration of the regional adaptation cost functions of AD RICE is based on an assessment of each impact category described in the RICE model, using relevant studies and with expert judgement where necessary. The optimal level of adaptation can be calculated by the model, but may also be set to a non-optimal level by the user. <br />
<br />
===Estimation of consumption losses===<br />
Consumption losses due to mitigation, adaptation and climate change damage are estimated based on a simple Cobb–Douglas production function.<ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs and the amount of output that can be produced by those inputs.</div></ref> [!CHANGE]The production factors are labour and capital. Regional changes in labour over time are based on the projected changes in total regional population. Initial regional capital stocks are based on the Investment and Capital Stock Dataset of the IMF. Future capital stocks depend on depreciation (set at 5% per year) and investments. Investments depend on the savings rate and initial savings rates are taken from the same IMF source. Total factor productivity of each region is calibrated to the exogenous GDP path without damage or mitigation costs. In a second step, damages, adaptation and abatement costs are subtracted from investment or consumption to determine both the direct replacement effect on consumption and the indirect effect from replacing productive investments.[!CHANGE]<br />
<references /><br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions&diff=35538Emissions2019-03-05T15:58:30Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentTemplate2<br />
|Application=Roads from Rio+20 (2012) project; LIMITS (2014); Shared Socioeconomic Pathways - SSP (2014) project;<br />
|Model-Database=EDGAR database; GAINS database;<br />
|KeyReference=Van Vuuren et al., 2006; Van Vuuren et al., 2011b; Braspenning Radu et al., 2016;<br />
|InputVar=Energy and industry activity level; Feed crop requirement; Animal stocks; Land cover, land use - grid; Emission abatement; GDP per capita;<br />
|Parameter=Emission factors; Relationship income and emission factor;<br />
|OutputVar=CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and F-gases); BC, OC and NOx emissions; SO2 emissions; Nitrogen deposition - grid;<br />
|ComponentCode=E<br />
|FrameworkElementType=interaction component<br />
}}<br />
<div class="page_standard"><br />
==Introduction==<br />
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.<br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Climate_policy&diff=35535Climate policy2019-03-05T15:52:47Z<p>Harmsenm: Added references for non-CO2 MAC curves</p>
<hr />
<div>{{ComponentTemplate2<br />
|IMAGEComponent=Drivers; Emissions; Energy supply; Energy conversion; Energy supply and demand; Carbon, vegetation, agriculture and water; Atmospheric composition and climate;<br />
|Model-Database=MAGICC model; AD RICE model; TIMER model; IIASA database;<br />
|KeyReference=Den Elzen et al., 2016; Hof et al., 2008; Hof et al., 2017;<br />
|Reference=UNFCCC (2015); UNFCCC (2015b); UNEP (2016); Rogelj et al., 2016; Den Elzen et al., 2015a; Kuramochi et al., 2016; Hof et al., 2016;<br />
|InputVar=Population; GDP per capita; CO2 emission from energy and industry; CO and NMVOC emissions; Non-CO2 GHG emissions (CH4, N2O and Halocarbons); Marginal abatement cost; Climate target; Domestic climate policy; Marginal abatement costs; BC, OC and NOx emissions; SO2 emissions; Land-use CO2 emissions - grid; Equity principles; Adaptation level;<br />
|Parameter=Other energy and land-use models; Investment and Capital Stock;<br />
|OutputVar=Carbon price; Emission abatement; Global emission pathways; Mitigation costs; Emission trading; Consumption loss; Adaptation costs; Residual damage;<br />
|ComponentCode=CP<br />
|FrameworkElementType=response component<br />
|AggregatedComponent=Policy responses<br />
}}<br />
<div class="page_standard"><br />
==Introduction==<br />
With the 2015 Paris Agreement, all Parties to the United Nations Framework Convention on Climate Change (UNFCCC) have agreed to limit global warming to 2 °C compared to pre-industrial levels and to pursue efforts to further limit this increase to a maximum of 1.5 °C [[UNFCCC (2015b)]].<br />
<br />
To achieve this goal, countries have proposed short- and long-term reduction targets in the {{abbrTemplate|UNFCCC}} climate negotiating process and in domestic policies. To support climate policymakers, the IMAGE model is used in conjunction with the climate policy model [[FAIR model|FAIR]]. FAIR is a decision support tool to analyse the costs, benefits, and climate effects of mitigation regimes, emission reduction commitments, and climate policies. <br />
<br />
FAIR can work in stand-alone mode using exogenous data, but in recent applications it interacts with several IMAGE components. For instance, mitigation cost curves for the energy sector are derived from the [[Energy supply and demand|Energy Supply and Demand model TIMER]] and land-use mitigation options from [[Agriculture and land use|Agriculture and Land Use]], while non-CO<sub>2</sub> {{abbrTemplate|MAC}} curves are based on [[Lucas et al., 2007|Lucas et al. (2007)]] and [[Harmsen et al., 2019c|Harmsen et al. (2019c)]]. Data from FAIR on marginal abatement costs and reduction efforts per sector and greenhouse gases are used as input for IMAGE to evaluate the impacts under different assumptions for climate mitigation. <br />
<br />
FAIR in combination with IMAGE can analyse the interaction between long-term climate targets and short-term regional emission targets. Regional targets are based on effort-sharing approaches and/or national emission reduction proposals, taking into account decisions on accounting rules as agreed under the Paris Agreement. The central purposes of the model are the calculation of mitigation costs and trade in emission allowances, and the net mitigation costs of a region to achieve its mitigation target. FAIR enables evaluation of proposed effort-sharing regimes, including differentiated timing and participation of a limited number of parties to the climate convention. Furthermore, FAIR analyses the trade-offs between costs and benefits of mitigation and adaptation policy.<br />
<br />
{{InputOutputParameterTemplate}}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35422Emissions/Policy issues2019-03-05T15:45:17Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; Lucas et al., 2007;<br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. As such, they are used in the [[FAIR model|FAIR]] model to determine strategies and costs of comprehensive {{abbrTemplate|GHG}} mitigation strategies. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}}, {{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=CEDS&diff=35402CEDS2019-03-05T15:41:15Z<p>Harmsenm: Created page with "{{AcronymTemplate |AbbrDescription=Community Emissions Data System for historical emissions }}"</p>
<hr />
<div>{{AcronymTemplate<br />
|AbbrDescription=Community Emissions Data System for historical emissions<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=PFC&diff=35399PFC2019-03-05T15:40:54Z<p>Harmsenm: Created page with "{{AcronymTemplate |AbbrDescription=perfluorocarbon }}"</p>
<hr />
<div>{{AcronymTemplate<br />
|AbbrDescription=perfluorocarbon<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=PFCs&diff=35396PFCs2019-03-05T15:40:36Z<p>Harmsenm: Created page with "{{AcronymTemplate |AbbrDescription=perfluorocarbons }}"</p>
<hr />
<div>{{AcronymTemplate<br />
|AbbrDescription=perfluorocarbons<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=HFC&diff=35393HFC2019-03-05T15:39:51Z<p>Harmsenm: Created page with "{{AcronymTemplate |AbbrDescription=hydrofluorocarbon }}"</p>
<hr />
<div>{{AcronymTemplate<br />
|AbbrDescription=hydrofluorocarbon<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=HFCs&diff=35390HFCs2019-03-05T15:39:26Z<p>Harmsenm: Created page with "{{AcronymTemplate |AbbrDescription=hydrofluorocarbons }}"</p>
<hr />
<div>{{AcronymTemplate<br />
|AbbrDescription=hydrofluorocarbons<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=SF6&diff=35387SF62019-03-05T15:38:34Z<p>Harmsenm: Created page with "{{AcronymTemplate |AbbrDescription=sulphur hexafluoride }}"</p>
<hr />
<div>{{AcronymTemplate<br />
|AbbrDescription=sulphur hexafluoride<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35381Emissions/Policy issues2019-03-05T15:33:58Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; Lucas et al., 2007;<br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. As such, they are used in the [[FAIR model|FAIR]] model to determine strategies and costs of comprehensive {{abbrTemplate|GHG}} mitigation strategies. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}},{{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35378Emissions/Policy issues2019-03-05T15:33:14Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; Lucas et al., 2007;<br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. As such, they are used in the [[FAIR model|FAIR]] to determine strategies and costs of comprehensive {{abbrTemplate|GHG}} mitigation strategies. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}},{{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Data_uncertainties_limitations&diff=35362Emissions/Data uncertainties limitations2019-03-05T15:24:57Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentDataUncertaintyAndLimitationsTemplate<br />
|Reference=IPCC, 2006; Granier, 2011; Lamarque et al., 2010; JRC/PBL, 2012; IPCC, 2007a;<br />
}}<br />
div class="page_standard"><br />
==Data, uncertainty and limitations==<br />
===Data===<br />
Global, historical emission data are provided in a range of inventories. The [[EDGAR database]] ([[JRC/PBL, 2012]]) was preferred for IMAGE because of its high level of detail and the similar sectoral and regional definitions. As part of the {{abbrTemplate|SSP}} project, non-CO<sub>2</sub> {{abbrTemplate|GHG}} and pollutant emissions have been calibrated until 2015 with the {{abbrTemplate|CEDS|Community Emissions Data System for historical emissions=Community Emissions Data System for historical emissions}} database ([[Hoesly et al., 2018|Hoesly et al, 2018]]), which includes the most recent updates in emission factors, is based on a more consistent and reproducible methodology, and leads to estimates that are comparable to, but generally slightly higher than existing global inventories. Earlier, now less used, inventories include the database underlying the RAINS/GAINS system, the RETRO database and the RCP database ([[Lamarque et al., 2010]]). An overview of available inventories by Granier ([[Granier, 2011|2011]]) has shown large differences between the databases for carbon monoxide, nitrogen oxides, sulphur dioxide and black carbon on global and regional scales. Most emission factors for land-use emissions are based on {{abbrTemplate|IPCC}} methodologies and parameters ([[IPCC, 2006]]) <br />
<br />
===Uncertainties===<br />
Data on activities and emission factors need to be aggregated in order to be used in IMAGE. In this process, decisions need to be made (e.g., on the use of weighted averages and representative sectors), which lead to additional uncertainties. In general terms there are three levels of uncertainty. For energy and industry, emission factors for CO<sub>2</sub> are less uncertain than those for non-CO<sub>2</sub> emissions. In turn, the uncertainty in emission factors for land use and natural sources is larger than for energy and industry sources because of the extreme variability of the factors controlling processes in space and time.<br />
<br />
Future emissions and their uncertainty depend on the activity levels determined by other IMAGE components, and on the emission factors. Estimations of future emission factors in the energy and industry systems, described above, rely on historical observations and learning curves. However, future legislation and effective implementation may influence these factors more, and more abruptly. Emission factors for land-use activities may change in the future, also in the absence of climate policy, but are assumed to be constant because of lack of data. As the future development of emission factors is per definition uncertain, the influence is explored by changing the emission factors for different storyline-based scenarios. <br />
<br />
===Limitations ===<br />
IMAGE covers almost all emission sources and gases within a consistent framework, based on a few international data sets and authoritative sources. However, some specific emissions are only included as a group, without the underlying production processes. Even more importantly, IMAGE does not include emissions from peat and peat fires, although they constitute an important source of air pollutants and CO<sub>2</sub> emissions ([[IPCC, 2007a]]).</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Data_uncertainties_limitations&diff=35359Emissions/Data uncertainties limitations2019-03-05T15:23:50Z<p>Harmsenm: Goes with last commit</p>
<hr />
<div>{{ComponentDataUncertaintyAndLimitationsTemplate<br />
|Reference=IPCC, 2006; Granier, 2011; Lamarque et al., 2010; JRC/PBL, 2012; IPCC, 2007a;<br />
}}<br />
div class="page_standard"><br />
==Data, uncertainty and limitations==<br />
===Data===<br />
Global, historical emission data are provided in a range of inventories. The [[EDGAR database]] ([[JRC/PBL, 2012]]) was preferred for IMAGE because of its high level of detail and the similar sectoral and regional definitions. As part of the {{abbrTemplate|SSP}} project, non-CO<sub>2</sub> {{abbrTemplate|GHG}} and pollutant emissions have been calibrated until 2015 with the {{abbrTemplate|CEDS|Community Emissions Data System for historical emissions=}} database ([[Hoesly et al., 2018|Hoesly et al, 2018]]), which includes the most recent updates in emission factors, is based on a more consistent and reproducible methodology, and leads to estimates that are comparable to, but generally slightly higher than existing global inventories. Earlier, now less used, inventories include the database underlying the RAINS/GAINS system, the RETRO database and the RCP database ([[Lamarque et al., 2010]]). An overview of available inventories by Granier ([[Granier, 2011|2011]]) has shown large differences between the databases for carbon monoxide, nitrogen oxides, sulphur dioxide and black carbon on global and regional scales. Most emission factors for land-use emissions are based on {{abbrTemplate|IPCC}} methodologies and parameters ([[IPCC, 2006]]) <br />
<br />
===Uncertainties===<br />
Data on activities and emission factors need to be aggregated in order to be used in IMAGE. In this process, decisions need to be made (e.g., on the use of weighted averages and representative sectors), which lead to additional uncertainties. In general terms there are three levels of uncertainty. For energy and industry, emission factors for CO<sub>2</sub> are less uncertain than those for non-CO<sub>2</sub> emissions. In turn, the uncertainty in emission factors for land use and natural sources is larger than for energy and industry sources because of the extreme variability of the factors controlling processes in space and time.<br />
<br />
Future emissions and their uncertainty depend on the activity levels determined by other IMAGE components, and on the emission factors. Estimations of future emission factors in the energy and industry systems, described above, rely on historical observations and learning curves. However, future legislation and effective implementation may influence these factors more, and more abruptly. Emission factors for land-use activities may change in the future, also in the absence of climate policy, but are assumed to be constant because of lack of data. As the future development of emission factors is per definition uncertain, the influence is explored by changing the emission factors for different storyline-based scenarios. <br />
<br />
===Limitations ===<br />
IMAGE covers almost all emission sources and gases within a consistent framework, based on a few international data sets and authoritative sources. However, some specific emissions are only included as a group, without the underlying production processes. Even more importantly, IMAGE does not include emissions from peat and peat fires, although they constitute an important source of air pollutants and CO<sub>2</sub> emissions ([[IPCC, 2007a]]).</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Data_uncertainties_limitations&diff=35356Emissions/Data uncertainties limitations2019-03-05T15:23:03Z<p>Harmsenm: Goes with last commit</p>
<hr />
<div>{{ComponentDataUncertaintyAndLimitationsTemplate<br />
|Reference=IPCC, 2006; Granier, 2011; Lamarque et al., 2010; JRC/PBL, 2012; IPCC, 2007a;<br />
}}<br />
div class="page_standard"><br />
==Data, uncertainty and limitations==<br />
===Data===<br />
Global, historical emission data are provided in a range of inventories. The [[EDGAR database]] ([[JRC/PBL, 2012]]) was preferred for IMAGE because of its high level of detail and the similar sectoral and regional definitions. As part of the {{abbrTemplate|SSP}} project, non-CO<sub>2</sub> {{abbrTemplate|GHG}} and pollutant emissions have been calibrated until 2015 with the {{abbrTemplate|CEDS|Community Emissions Data System for historical emissions=}} database ([[Hoesly et al, 2018]]), which includes the most recent updates in emission factors, is based on a more consistent and reproducible methodology, and leads to estimates that are comparable to, but generally slightly higher than existing global inventories. Earlier, now less used, inventories include the database underlying the RAINS/GAINS system, the RETRO database and the RCP database ([[Lamarque et al., 2010]]). An overview of available inventories by Granier ([[Granier, 2011|2011]]) has shown large differences between the databases for carbon monoxide, nitrogen oxides, sulphur dioxide and black carbon on global and regional scales. Most emission factors for land-use emissions are based on {{abbrTemplate|IPCC}} methodologies and parameters ([[IPCC, 2006]]) <br />
<br />
===Uncertainties===<br />
Data on activities and emission factors need to be aggregated in order to be used in IMAGE. In this process, decisions need to be made (e.g., on the use of weighted averages and representative sectors), which lead to additional uncertainties. In general terms there are three levels of uncertainty. For energy and industry, emission factors for CO<sub>2</sub> are less uncertain than those for non-CO<sub>2</sub> emissions. In turn, the uncertainty in emission factors for land use and natural sources is larger than for energy and industry sources because of the extreme variability of the factors controlling processes in space and time.<br />
<br />
Future emissions and their uncertainty depend on the activity levels determined by other IMAGE components, and on the emission factors. Estimations of future emission factors in the energy and industry systems, described above, rely on historical observations and learning curves. However, future legislation and effective implementation may influence these factors more, and more abruptly. Emission factors for land-use activities may change in the future, also in the absence of climate policy, but are assumed to be constant because of lack of data. As the future development of emission factors is per definition uncertain, the influence is explored by changing the emission factors for different storyline-based scenarios. <br />
<br />
===Limitations ===<br />
IMAGE covers almost all emission sources and gases within a consistent framework, based on a few international data sets and authoritative sources. However, some specific emissions are only included as a group, without the underlying production processes. Even more importantly, IMAGE does not include emissions from peat and peat fires, although they constitute an important source of air pollutants and CO<sub>2</sub> emissions ([[IPCC, 2007a]]).</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Data_uncertainties_limitations&diff=35351Emissions/Data uncertainties limitations2019-03-05T15:19:34Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentDataUncertaintyAndLimitationsTemplate<br />
|Reference=IPCC, 2006; Granier, 2011; Lamarque et al., 2010; JRC/PBL, 2012; IPCC, 2007a;<br />
}}<br />
div class="page_standard"><br />
==Data, uncertainty and limitations==<br />
===Data===<br />
Global, historical emission data are provided in a range of inventories. The [[EDGAR database]] ([[JRC/PBL, 2012]]) was preferred for IMAGE because of its high level of detail and the similar sectoral and regional definitions. As part of the {{abbrTemplate|SSP}} project, non-CO<sub>2</sub> {{abbrTemplate|GHG}} and pollutant emissions have been calibrated until 2015 with the {{abbrTemplate|CEDS}} database (Hoesly et al, 2018), which includes the most recent updates in emission factors, is based on a more consistent and reproducible methodology, and leads to estimates that are comparable to, but generally slightly higher than existing global inventories. Earlier, now less used, inventories include the database underlying the RAINS/GAINS system, the RETRO database and the RCP database ([[Lamarque et al., 2010]]). An overview of available inventories by Granier ([[Granier, 2011|2011]]) has shown large differences between the databases for carbon monoxide, nitrogen oxides, sulphur dioxide and black carbon on global and regional scales. Most emission factors for land-use emissions are based on {{abbrTemplate|IPCC}} methodologies and parameters ([[IPCC, 2006]]) <br />
<br />
===Uncertainties===<br />
Data on activities and emission factors need to be aggregated in order to be used in IMAGE. In this process, decisions need to be made (e.g., on the use of weighted averages and representative sectors), which lead to additional uncertainties. In general terms there are three levels of uncertainty. For energy and industry, emission factors for CO<sub>2</sub> are less uncertain than those for non-CO<sub>2</sub> emissions. In turn, the uncertainty in emission factors for land use and natural sources is larger than for energy and industry sources because of the extreme variability of the factors controlling processes in space and time.<br />
<br />
Future emissions and their uncertainty depend on the activity levels determined by other IMAGE components, and on the emission factors. Estimations of future emission factors in the energy and industry systems, described above, rely on historical observations and learning curves. However, future legislation and effective implementation may influence these factors more, and more abruptly. Emission factors for land-use activities may change in the future, also in the absence of climate policy, but are assumed to be constant because of lack of data. As the future development of emission factors is per definition uncertain, the influence is explored by changing the emission factors for different storyline-based scenarios. <br />
<br />
===Limitations ===<br />
IMAGE covers almost all emission sources and gases within a consistent framework, based on a few international data sets and authoritative sources. However, some specific emissions are only included as a group, without the underlying production processes. Even more importantly, IMAGE does not include emissions from peat and peat fires, although they constitute an important source of air pollutants and CO<sub>2</sub> emissions ([[IPCC, 2007a]]).</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Harmsen_et_al.,_2019b&diff=35343Harmsen et al., 2019b2019-03-05T14:36:41Z<p>Harmsenm: Created page with "{{ReferenceTemplate |Author=Mathijs Harmsen, Oliver Fricko, Jérôme Hilaire, Detlef P. van Vuuren, Laurent Drouet, Olivier Durand-Lasserve, Shinichiro Fujimori, Kimon Keramid..."</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=Mathijs Harmsen, Oliver Fricko, Jérôme Hilaire, Detlef P. van Vuuren, Laurent Drouet, Olivier Durand-Lasserve, Shinichiro Fujimori, Kimon Keramidas, Zbigniew Klimont, Gunnar Luderer, Lara Aleluia Reis, Keywan Riahi, Fuminori Sano, Steven J. Smith<br />
|Year=2019<br />
|Title=Taking some heat off the NDCs? The limited potential of additional short-lived climate forcers’ mitigation<br />
|PublicationType=Journal article<br />
|Journal=Climatic Change (under review)<br />
}}<br />
Several studies have shown that the greenhouse gas reduction resulting from the current Nationally Determined Contributions (NDCs) will not be enough to meet the overall targets of the Paris Climate Agreement. It has been suggested that more ambitions mitigation of short-lived climate forcer (SLCF) emissions could potentially be a way to reduce the risk of overshooting the 1.5 or 2oC target in a cost-effective way. In this study, we employ eight state-of-the-art Integrated Assessment models (IAMs) to examine the global temperature effects of ambitious reductions of methane, black and organic carbon, and hydrofluorocarbon emissions. The SLCFs measures considered are found to add significantly to the effect of the NDCs on short-term global mean temperature (GMT) (In the year 2040: -0.03oC to -0.15oC) and on reducing the short-term rate-of-change (by -2% to 15%), but only a small effect on reducing the maximum temperature change before 2100. This, because later in the century under assumed ambitious climate policy, SLCF mitigation is maximized, either directly or indirectly due to changes in the energy system. All three SLCF groups can contribute to achieving GMT changes.</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35313Emissions/Policy issues2019-03-05T14:24:36Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; Lucas et al., 2007;<br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}},{{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35307Emissions/Policy issues2019-03-05T14:22:04Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}},{{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}), followed by followed by methane (CH<sub>4</sub>)(68%) and nitrous oxide (N<sub>2</sub>O)(62%)([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35303Emissions/Policy issues2019-03-05T14:18:09Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}},{{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}})([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35297Emissions/Policy issues2019-03-05T14:15:52Z<p>Harmsenm: Goes with last commits. Added non-CO2 GHG mitigation (MAC curves).</p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}},{{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}})([[Harmsen et al., 2019c]]). Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential.<br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35291Emissions/Policy issues2019-03-05T14:13:14Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}},{{abbrTemplate|PFCs}}, {{abbrTemplate|SF6}}.<br />
<br />
Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH<sub>4</sub> from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential. <br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35284Emissions/Policy issues2019-03-05T14:10:23Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}}Hydrofluorocarbons{{abbrTemplate|PFCs}}perfluorocarbons{{abbrTemplate|SF6}}sulphur hexafluoride<br />
<br />
Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH4 from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential. <br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35281Emissions/Policy issues2019-03-05T14:07:48Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}}, {{abbrTemplate|PFCs}} and{{abbrTemplate|SF<sub>6</sub>}}), (96%), followed by methane (CH<sub>4</sub>) (68%) and nitrous oxide (N<sub>2</sub>O) (62%). CH<sub>4</sub> emission reductions from (fossil) energy and waste sources are moderately difficult to realize (with reduction potentials between +/- 50% and 80% in 2100). This is even more the case for emissions from agricultural sources (with reduction potentials between +/- 50% an 70% at very high implementation costs (3000 – 4000 $/tonne of Carbon).<br />
<br />
Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH4 from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential. <br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35278Emissions/Policy issues2019-03-05T14:06:39Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The {{abbrTemplate|MAC}} curves have been developed using existing short-term {{abbrTemplate|MAC}} datasets as well as recent literature on emission source-specific mitigation measures. The new {{abbrTemplate|MAC}} curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases ({{abbrTemplate|HFCs}}, {{abbrTemplate|PFCs}}{{abbrTemplate|SF<sub>6</sub>}}), (96%), followed by methane (CH<sub>4</sub>) (68%) and nitrous oxide (N<sub>2</sub>O) (62%). CH<sub>4</sub> emission reductions from (fossil) energy and waste sources are moderately difficult to realize (with reduction potentials between +/- 50% and 80% in 2100). This is even more the case for emissions from agricultural sources (with reduction potentials between +/- 50% an 70% at very high implementation costs (3000 – 4000 $/tonne of Carbon).<br />
<br />
Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH4 from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential. <br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35270Emissions/Policy issues2019-03-05T13:59:00Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The MAC curves have been developed using existing short-term MAC datasets as well as recent literature on emission source-specific mitigation measures. The new MAC curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases (hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF<sub>6</sub>), (96%), followed by methane (CH<sub>4</sub>) (68%) and nitrous oxide (N<sub>2</sub>O) (62%). CH<sub>4</sub> emission reductions from (fossil) energy and waste sources are moderately difficult to realize (with reduction potentials between +/- 50% and 80% in 2100). This is even more the case for emissions from agricultural sources (with reduction potentials between +/- 50% an 70% at very high implementation costs (3000 – 4000 $/tonne of Carbon).<br />
<br />
Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH4 from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential. <br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35258Emissions/Policy issues2019-03-05T12:59:43Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> {{abbrTemplate|GHG}} emission sources. Lucas et al. (2007) followed by Harmsen et al. (2019c) described the development and application of sets of long-term non-CO<sub>2</sub> marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures. The MAC curves have been developed using existing short-term MAC datasets as well as recent literature on emission source-specific mitigation measures. The new MAC curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
The maximum reduction potential (MRP) of all non-CO<sub>2</sub> {{abbrTemplate|GHG}} measures is estimated at 71% in the year 2100. The MRP is the highest for F-gases (hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF<sub>6</sub>), (96%), followed by methane (CH<sub>4</sub>) (68%) and nitrous oxide (N<sub>2</sub>O) (62%). CH<sub>4</sub> emission reductions from (fossil) energy and waste sources are moderately difficult to realize (with reduction potentials between +/- 50% and 80% in 2100). This is even more the case for emissions from agricultural sources (with reduction potentials between +/- 50% an 70% at very high implementation costs: 3000 – 4000 $/tonne of Carbon).<br />
<br />
Mitigation costs for most non-CO<sub>2</sub> {{abbrTemplate|GHG}} sources are lower than are lower than those of CO<sub>2</sub> mitigation, on average, but costs vary considerably by source. Note, however, that the mitigation potential of non-CO<sub>2</sub> measures is limited and that further mitigation would be far more costly and/or likely not possible. Mitigation of HFCs and CH4 from fossil fuel sources can be considered very attractive, due to a large (in absolute terms), but relatively low-cost reduction potential. <br />
<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35232Emissions/Policy issues2019-03-05T10:57:30Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., [[Harmsen et al. 2019a|2019a]], [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
A large variety of mitigation options exist or could be developed to abate emissions from each of the main non-CO<sub>2</sub> GHG emission sources. Lucas et al. (2007), followed by Harmsen et al. (2019c) described the development and application of a set of long-term non-CO2 marginal abatement cost ({{abbrTemplate|MAC}}) curves of all major non-CO<sub>2</sub> GHG sources. These curves represent the mitigation potentials and costs of region- and source-specific mitigation measures and are used in the climate policy module FAIR. The MAC curves have been developed using existing short-term MAC datasets as well as recent literature on emission source-specific mitigation measures. The MAC curves include estimates of future technology development and removal of implementation barriers to capture long-term dynamics.<br />
<br />
When applying the new MAC curves to create a 2.6 W/m2 scenario (i.e. likely limiting global warming to 2oC in 2100), the total non-CO2 mitigation was projected to be 10.9 Mt CO2equivalents in 2050 (i.e. 58% reduction compared to baseline emissions) and 15.6 Mt CO2equivalents in 2100 (i.e. a 71% reduction).<br />
The maximum reduction potential (MRP) of all non-CO2 greenhouse gas measures is estimated at 71% in the year 2100. The maximum reduction potential is the highest for F-gases(hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF<sub>6</sub>), followed by methane (CH<sub>4</sub>) (68%) and nitrous oxide (N<sub>2</sub>O)(62%). CH<sub>4</sub>) emission reductions from (fossil) energy and waste sources are moderately difficult to realize (with reduction potentials between +/- 50% and 80% in 2100). This more the case for emissions from agricultural sources (with reduction potentials between +/- 50% an 70% at very high implementation costs: 3000 – 4000 $/tonne of Carbon).<br />
<br />
Maximizing non-CO<sub>2</sub> mitigation is generally a cost-effective strategy in reaching long-term climate targets, but costs vary considerably by source. The curves have been used in the IMAGE IAM framework to derive attractive mitigation strategies. The cost-effectiveness of non-CO2 mitigation varies strongly by emission source. This is shown in Figure 8.2 (lower panel) by plotting the cost of mitigation on the x-axis and the amount of mitigation on the y-axis. As a result, more costly measures are depicted more to the right while sources with a larger absolute reduction potential are depicted more to the top. Mitigation costs for most non-CO2 sources are lower than are lower than those of CO2 mitigation, on average. Note, however, that the mitigation potential of non-CO2 measures is limited and that further mitigation would be far more costly and/or likely not possible. The figure shows that mitigation of HFCs and CH4 from fossil fuel sources can be considered very attractive, due to a large, but relatively low-cost reduction potential. Conversely, reduction of CH4 from wastewater is projected to be relatively costly, due to large infrastructure investments. Here, public health considerations pose a much larger investment incentive than climate.<br />
<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Harmsen_et_al.,_2019a&diff=35201Harmsen et al., 2019a2019-03-05T10:23:34Z<p>Harmsenm: Created page with "{{ReferenceTemplate |Author=Mathijs Harmsen, Detlef P. van Vuuren, Benjamin Leon Bodirsky, Jean Chateau, Olivier Durand-Lasserve, Laurent Drouet, Oliver Fricko, Shinichiro Fuj..."</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=Mathijs Harmsen, Detlef P. van Vuuren, Benjamin Leon Bodirsky, Jean Chateau, Olivier Durand-Lasserve, Laurent Drouet, Oliver Fricko, Shinichiro Fujimori, David E.H.J. Gernaat, Tatsuya Hanaoka, Jérôme Hilaire, Kimon Keramidas, Gunnar Luderer, Maria Cecilia P. Moura, Fuminori Sano, Steven J. Smith, Kenichi Wada<br />
|Year=2019<br />
|Title=The role of methane in future climate strategies: Mitigation potentials and climate impacts<br />
|PublicationType=Journal article<br />
|Journal=Climatic Change (in press)<br />
}}<br />
This study examines model specific assumptions and projections of methane (CH4) emissions in deep mitigation scenarios generated by Integrated Assessment Models (IAMs). For this, scenarios of nine models are compared in terms of sectoral and regional CH4 emission reduction strategies, as well as resulting climate impacts. The models’ projected reduction potentials are compared to sector and technology specific reduction potentials found in literature. Significant cost-effective and non-climate policy related reductions are projected in the reference case (10%-36% compared to a “frozen emission factor” scenario in 2100). Still, compared to 2010, CH4 emissions are expected to rise steadily by 9%-72% (up to 412 to 654 Mt CH4/year). Ambitious CO2 reduction measures could by themselves lead to a reduction of CH4 emissions due to a reduction of fossil fuels (22%-48% compared to the reference case in 2100). However, direct CH4 mitigation is crucial and more effective in bringing down CH4 (50%-74% compared to the reference case). Given the limited reduction potential, agriculture CH4 emissions are projected to constitute an increasingly larger share of total anthropogenic CH4 emissions in mitigation scenarios. Enteric fermentation in ruminants is in that respect by far the largest mitigation bottleneck later in the century with a projected 40% to 78% of total remaining CH4 emissions in 2100 in a strong (2oC) climate policy case.</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35189Emissions/Policy issues2019-03-05T10:17:53Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century ([[Lucas et al., 2007]], Harmsen et al., 2019a, [[Harmsen et al., 2019c|2019c]]). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35186Emissions/Policy issues2019-03-05T10:16:10Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). Without climate policy, all non-CO<sub>2</sub> {{abbrTemplate|GHG}}s emissions are expected to increase towards the end of the century (Lucas et al., 2007, Harmsen et al., 2019a, 2019c). For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35171Emissions/Policy issues2019-03-05T10:10:30Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). For non-CO<sub>2</sub> greenhouse gases For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35165Emissions/Policy issues2019-03-05T10:10:05Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}} ) emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). For non-CO<sub>2</sub> greenhouse gases For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Policy_issues&diff=35154Emissions/Policy issues2019-03-05T10:08:05Z<p>Harmsenm: </p>
<hr />
<div>{{ComponentPolicyIssueTemplate<br />
|Reference=PBL, 2012; Braspenning Radu et al., 2016; <br />
}}<br />
<div class="page_standard"><br />
==Baseline developments==<br />
In a baseline scenario, most greenhouse gas ({{abbrTemplate|GHG}}, emissions tend to increase, driven by an increase in underlying activity levels (This is shown in the figure below for a baseline scenario for the [[Roads from Rio+20 (2012) project|Rio+20]] study ([[PBL, 2012]]). For non-CO<sub>2</sub> greenhouse gases For air pollutants, the pattern also depends strongly on the assumptions on air pollution control. In most baseline scenarios, air pollutant emissions tend to decrease, or at least stabilise, in the coming decades as a result of more stringent environmental standards in high and middle income countries.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}<br />
==Policy interventions==<br />
<br />
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):<br />
* Introduction of climate policy, which leads to systemic changes in the energy system (less combustion) and thus, indirectly to reduced emissions of air pollutants ([[Van Vuuren et al., 2006]]). <br />
* Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations ({{abbrTemplate|EKC}}, {{abbrTemplate|CLE}}). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.<br />
* Policies may influence emission levels for several sources, for instance, by reducing consumption of meat products. By improving the efficiency of fertiliser use, emissions of N<sub>2</sub>O, NO and NH<sub>3</sub> can be decreased ([[Van Vuuren et al., 2011b]]). By increasing the amount of feed crops in the cattle rations, CH<sub>4</sub> emissions can be reduced. Production of crop types has a significant influence on emission levels of N<sub>2</sub>O, NO<sub>x</sub> and NH<sub>3</sub> from spreading manure and fertilisers.<br />
* Assumptions related to soil and nutrient management. The major factors are fertiliser type and mode of manure and fertiliser application. Some fertilisers cause higher emissions of N<sub>2</sub>O and NH<sub>3</sub> than others. Incorporating manure into soil lowers emissions compared to broadcasting.<br />
The impacts of more ambitious control policies ({{abbrTemplate|CLE}} versus {{abbrTemplate|EKC}}) on SO<sub>2</sub> and NO<sub>x</sub>, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO<sub>2</sub> emissions, air pollution control policies are effective in reducing NO<sub>x</sub> emissions.<br />
<br />
See also the Policy interventions Table below.<br />
<br />
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}<br />
<br />
{{PIEffectOnComponentTemplate }}<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Harmsen_et_al.,_2019c&diff=35124Harmsen et al., 2019c2019-03-05T09:56:28Z<p>Harmsenm: Created page with "{{ReferenceTemplate |Author=Mathijs J.H.M. Harmsen, Detlef P. van Vuuren, Dali R. Nayak, Andries F. Hof, Lena Höglund-Isaksson, Paul L. Lucas, Jens B. Nielsen, Pete Smith, El..."</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=Mathijs J.H.M. Harmsen, Detlef P. van Vuuren, Dali R. Nayak, Andries F. Hof, Lena Höglund-Isaksson, Paul L. Lucas, Jens B. Nielsen, Pete Smith, Elke Stehfest<br />
|Year=2019<br />
|Title=Long-term marginal abatement cost curves of non-CO2 greenhouse gases<br />
|PublicationType=Journal article<br />
|Journal=Environmental Science and Policy (under review)<br />
}}<br />
This study presents a new comprehensive set of long-term Marginal Abatement Cost (MAC) curves of all major non-CO2 greenhouse gas emission sources. The work builds on existing short-term MAC curve datasets and recent literature on individual mitigation measures. The new MAC curves include current technology and costs information as well as estimates of technology development and removal of implementation barriers to capture long-term dynamics. Compared to earlier work, we find a higher projected maximum reduction potential (MRP) of nitrous oxide (N2O) and a lower MRP of methane (CH4). The combined MRP for all non-CO2 gases is similar but has been extended to also capture mitigation measures that can be realized at higher implementation costs. When applying the new MAC curves in a 2.6 W/m2 scenario, the total non-CO2 mitigation is projected to be 10.9 Mt CO2equivalents in 2050 (i.e. 58% reduction compared to baseline emissions) and 15.6 Mt CO2equivalents in 2100 (i.e. a 71% reduction). In applying the new MAC curves, we account for inertia in the implementation speed of mitigation measures. Although this does not strongly impact results in an optimal strategy, it means that the contribution of non-CO2 mitigation could be more limited if ambitious climate policy is delayed.</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Description&diff=35096Emissions/Description2019-03-04T17:12:23Z<p>Harmsenm: Goes with last commit</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|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;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===General approaches===<br />
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: <br />
<br />
1) ''World number (W)''<br />
<br />
: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]]).<br />
<br />
2) ''Emission factor (EF)'' <br />
<br />
: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). <br />
<br />
:The equation for this emission factor approach is:<br />
::<math>Emission_{r,i,t} = Activity_{r,i,t} * EFbase_{r,i,t} * AF_{r,i,t}</math> (Equation 1) <br />
:where:<br />
:* <math>Emission</math> is the emission of the specific gas or aerosol;[[Amann et al., 2011]] <br />
:* <math>Activity</math> is the energy input or agricultural activity; r is the index for region; <br />
:* <math>i</math> is the index for further specification (sector, energy carrier); <br />
:* ''t'' is the index for time (years). All factors are time-dependent; <br />
:* <math>EFbase</math> is the emission factor in the baseline; <br />
:* <math>AF</math> is the abatement factor (reduction in the baseline emission factor as a result of climate policy). <br />
:<br />
:<br />
: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. These activities are calculated in various sub-modules of the model (e.g. related to energy supply and demand, food production and industry). Finally, emissions can change as a result of changes in emission factors (EF) and climate policy (AF). Emission factors indicate the emission rate of an air pollutant per activity level and differences in time represent technological changes in air pollution control. This information is generally obtained from specialized research groups and databases, such as GAINS ([[Amann et al., 2011]]) , EDGAR ([[EC-JRC/PBL, 2016]]) and CEDS ([[Hoesly et al., 2018]]). Abatement factors are applied to non-CO<sub>2</sub> greenhouse gases (methane (CH<sub>4</sub>), nitrous oxide (N<sub>2</sub>O) and fluorinated gases (F-gases: HFCs, PFCs and SF<sub>6</sub>)) and are determined in the climate policy model FAIR (see Component [[Climate policy]]). <br />
:The emission factor approach has some limitations, the most important of which is not capturing the consequences of specific emission control technologies (or management action) for multiple species, either synergies or trade-offs. <br />
: <br />
3) ''Gridded emission factor with spatial distribution (GEF)''<br />
<br />
: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]]).<br />
<br />
4) ''Gridded process model (GPM)'' <br />
: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]]).<br />
<br />
The approaches used to calculate emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below.<br />
<br />
===Emissions from energy production and use===<br />
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.<br />
<br />
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. <br />
<br />
Future emission factors are based on the following rules:<br />
<br />
* 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 those developed by GAINS/IIASA ([[Amann et al., 2011]]). These represent the policies in different regions for the 2000–2030 and 2030-2100 periods. Future emission factors for air pollutants have been set in accordance with the SSP storylines ([[Rao et al., 2016]]). <br />
<br />
* 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.<br />
<br />
* Combinations of the methods described above for a specific period, followed by additional rules based on income levels. <br />
<br />
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.<br />
<br />
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.<br />
<br />
===Emissions from industrial processes===<br />
For the industry sector, the energy model includes three categories:<br />
<br />
# 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.<br />
# 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.<br />
# 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]], [[Velders et al., 2015|2015]]). The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. ([[Harnisch et al., 2009|2009]]).<br />
<br />
===Land-use related emissions===<br />
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).<br />
<br />
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). <br />
<br />
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.<br />
<br />
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.<br />
<br />
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]]).<br />
<br />
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]]).<br />
<br />
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]]).<br />
<br />
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.<br />
<br />
===Emission abatement===<br />
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.<br />
</div></div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Velders_et_al.,_2015&diff=35093Velders et al., 20152019-03-04T17:10:04Z<p>Harmsenm: Created page with "{{ReferenceTemplate |Author=Guus J. M.Velders, David W. Fahey, John S. Daniel, Stephen O. Andersen, Mack McFarland |Year=2015 |Title=Future atmospheric abundances and climate..."</p>
<hr />
<div>{{ReferenceTemplate<br />
|Author=Guus J. M.Velders, David W. Fahey, John S. Daniel, Stephen O. Andersen, Mack McFarland<br />
|Year=2015<br />
|Title=Future atmospheric abundances and climate forcings from scenarios of global and regional hydrofluorocarbon (HFC) emissions<br />
|DOI=10.1016/j.atmosenv.2015.10.071<br />
|PublicationType=Journal article<br />
|Journal=Atmospheric Environment<br />
|Volume2=123<br />
|Pages2=200-209<br />
}}</div>Harmsenmhttps://models.pbl.nl/image/index.php?title=Emissions/Description&diff=35090Emissions/Description2019-03-04T17:06:11Z<p>Harmsenm: Added several references on the emission page + text edits</p>
<hr />
<div>{{ComponentDescriptionTemplate<br />
|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;<br />
}}<br />
<div class="page_standard"><br />
==Model description of {{ROOTPAGENAME}}==<br />
===General approaches===<br />
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: <br />
<br />
1) ''World number (W)''<br />
<br />
: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]]).<br />
<br />
2) ''Emission factor (EF)'' <br />
<br />
: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). <br />
<br />
:The equation for this emission factor approach is:<br />
::<math>Emission_{r,i,t} = Activity_{r,i,t} * EFbase_{r,i,t} * AF_{r,i,t}</math> (Equation 1) <br />
:where:<br />
:* <math>Emission</math> is the emission of the specific gas or aerosol;[[Amann et al., 2011]] <br />
:* <math>Activity</math> is the energy input or agricultural activity; r is the index for region; <br />
:* <math>i</math> is the index for further specification (sector, energy carrier); <br />
:* ''t'' is the index for time (years). All factors are time-dependent; <br />
:* <math>EFbase</math> is the emission factor in the baseline; <br />
:* <math>AF</math> is the abatement factor (reduction in the baseline emission factor as a result of climate policy). <br />
:<br />
:<br />
: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. These activities are calculated in various sub-modules of the model (e.g. related to energy supply and demand, food production and industry). Finally, emissions can change as a result of changes in emission factors (EF) and climate policy (AF). Emission factors indicate the emission rate of an air pollutant per activity level and differences in time represent technological changes in air pollution control. This information is generally obtained from specialized research groups and databases, such as GAINS ([[Amann et al., 2011]]) , EDGAR ([[EC-JRC/PBL, 2016]]) and CEDS ([[Hoesly et al., 2018]]). Abatement factors are applied to non-CO<sub>2</sub> greenhouse gases (methane (CH<sub>4</sub>), nitrous oxide (N<sub>2</sub>O) and fluorinated gases (F-gases: HFCs, PFCs and SF<sub>6</sub>)) and are determined in the climate policy model FAIR (see Component [[Climate policy]]). <br />
:The emission factor approach has some limitations, the most important of which is not capturing the consequences of specific emission control technologies (or management action) for multiple species, either synergies or trade-offs. <br />
: <br />
3) ''Gridded emission factor with spatial distribution (GEF)''<br />
<br />
: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]]).<br />
<br />
4) ''Gridded process model (GPM)'' <br />
: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]]).<br />
<br />
The approaches used to calculate emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below.<br />
<br />
===Emissions from energy production and use===<br />
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.<br />
<br />
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. <br />
<br />
Future emission factors are based on the following rules:<br />
<br />
* 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 those developed by GAINS/IIASA ([[Amann et al., 2011]]). These represent the policies in different regions for the 2000–2030 and 2030-2100 periods. Future emission factors for air pollutants have been set in accordance with the SSP storylines ([[Rao et al., 2016]]). <br />
<br />
* 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.<br />
<br />
* Combinations of the methods described above for a specific period, followed by additional rules based on income levels. <br />
<br />
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.<br />
<br />
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.<br />
<br />
===Emissions from industrial processes===<br />
For the industry sector, the energy model includes three categories:<br />
<br />
# 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.<br />
# 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.<br />
# 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]], 2012). The marginal abatement cost curve per gas still follows the methodology described by Harnisch et al. ([[Harnisch et al., 2009|2009]]).<br />
<br />
===Land-use related emissions===<br />
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).<br />
<br />
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). <br />
<br />
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.<br />
<br />
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.<br />
<br />
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]]).<br />
<br />
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]]).<br />
<br />
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]]).<br />
<br />
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.<br />
<br />
===Emission abatement===<br />
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.<br />
</div></div>Harmsenm