Emissions/Description: Difference between revisions
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|Reference=IPCC, 2006; Cofala et al., 2002; Stern, 2003; Smith et al., 2005; Van Ruijven et al., 2008; Carson, 2010; Smith et al., 2011; Bouwman et al., 2002; Bouwman et al., 1993; Harnisch et al.,2009a; Harnisch et al., 2009b; Velders et al., 2009; Bouwman, 1994; Bouwman et al., 1997; Bouwman et al., 2002a; Braspenning Radu et al., 2012; | |||
|Description===General approaches== | |Description===General approaches== | ||
[[Table 5.1]] lists the different sources of emissions included in the IMAGE model. Emissions that are transported in water (nitrate, phosphorus) are discussed in [[Nutrient balances]]. Regarding the approach and spatial detail for modeling gaseous emissions, IMAGE uses four different ways to represent emissions. | [[Table 5.1]] lists the different sources of emissions included in the IMAGE model. Emissions that are transported in water (nitrate, phosphorus) are discussed in [[Nutrient balances]]. Regarding the approach and spatial detail for modeling gaseous emissions, IMAGE uses four different ways to represent emissions. | ||
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where Emission is the emission of the specific gas; Activity is the Energy input or agricultural activity, r is the index for region, i index for further specification (sector, energy carrier), EF-base is the emission factor in the baseline and AF is the abatement factor, i.e. the reduction of the baseline emission factor as a result of climate policy. The emission factors are time-dependent, representing changes in technology and air pollution control policies. | where Emission is the emission of the specific gas; Activity is the Energy input or agricultural activity, r is the index for region, i index for further specification (sector, energy carrier), EF-base is the emission factor in the baseline and AF is the abatement factor, i.e. the reduction of the baseline emission factor as a result of climate policy. The emission factors are time-dependent, representing changes in technology and air pollution control policies. | ||
The emission factor approach is used to calculate energy emissions and several land-use related emissions. Following equation 5.1, there is a direct relation between the level of economic activity and emission level. Also shifts in economic activity (e.g. use of natural gas instead of coal) may influence the total emissions. Finally, emissions can change as result of changes in the emission factors (EF) or climate policy (AF). Some generic rules are used to describe the changes of emissions over time (see further). The abatement factor (AF) are determined in the climate policy model [[FAIR model|FAIR]] (see [[Climate policy]]). The emission factor approach has some limitations, most importantly that is limited in capturing the consequences of specific emission control technology (or management action) for multiple species (either synergies or trade-offs). | The emission factor approach is used to calculate energy emissions and several land-use related emissions. Following [[equation 5.1]], there is a direct relation between the level of economic activity and emission level. Also shifts in economic activity (e.g. use of natural gas instead of coal) may influence the total emissions. Finally, emissions can change as result of changes in the emission factors (EF) or climate policy (AF). Some generic rules are used to describe the changes of emissions over time (see further). The abatement factor (AF) are determined in the climate policy model [[FAIR model|FAIR]] (see [[Climate policy]]). The emission factor approach has some limitations, most importantly that is limited in capturing the consequences of specific emission control technology (or management action) for multiple species (either synergies or trade-offs). | ||
* ''Emission factor method with spatial distribution'' (GEF) represents a special case of the EF method where a proxy distribution is used to present gridded emissions. This is done for a number of sources, for example emissions from animals ([[Table 5.1]]). | * ''Emission factor method with spatial distribution'' (GEF) represents a special case of the EF method where a proxy distribution is used to present gridded emissions. This is done for a number of sources, for example emissions from animals ([[Table 5.1]]). | ||
• ''Process model''. (GPM). Land-use related emissions of NH3, N2O and NO are calculated with grid-specific models. The models included in IMAGE are simple regression models that generate an emission factor ([[Figure 5.1]]). It should be noted that for comparison with other models, IMAGE also includes the N2O methodology as proposed by IPCC ([[IPCC, | • ''Process model''. (GPM). Land-use related emissions of NH3, N2O and NO are calculated with grid-specific models. The models included in IMAGE are simple regression models that generate an emission factor ([[Figure 5.1]]). It should be noted that for comparison with other models, IMAGE also includes the N2O methodology as proposed by [[IPCC]] ([[IPCC, 2006]]). | ||
The approaches used for emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below. | The approaches used for emissions from energy production and use, industrial processes and land-use related sources are discussed in more detail below. | ||
==Emissions from energy production and use== | |||
Emission factors (EFs) ([[equation 5.1]]) are used to estimate emissions from the various energy-related sources ([[Table 5.2]]). In general, the so-called Tier 1 approach from IPCC guidelines ([[IPCC, 2006]]) is used. In the energy system, emissions are calculated by multiplying energy use fluxes with time-dependent emission factors. Changes in the emission factors represent technological improvements and end of-pipe control techniques, fuel specification standards for transport, clean-coal technologies in industry, etc. | |||
The emission factors are calibrated for the historical period on the basis of the EDGAR emission model as described by [[Braspenning Radu et al., 2012]]. The calibration to the EDGAR database is not always straightforward due to differences in aggregation level. The general rule is to use weighted average emission factors in the case of aggregation. However, in those cases in which this results in incomprehensible emission factors (in particular when large differences exists between the emission factors for the underlying technologies) specific emission factors were chosen. | |||
==Future emission factors are based on different rules:== | |||
* Emission factors can follow an exogenous scenario such as the Current Legislation Scenario (CLE), a scenario developed by [[Cofala et al., 2002]]. The CLE scenario aims to describe the current policies of different regions in the period 2000-2030. | |||
* An alternative rule is that emissions follow the empirically observed trend of the Environmental Kuznets Curve ([[EKC]]) ([[Stern, 2003]]; [[Smith et al., 2005]]; [[Van Ruijven et al., 2008]]; [[Carson, 2010]]; [[Smith et al., 2011]]). The EKC (as interpreted here) suggests that, starting from low-income levels, per capita emissions will originally increase with increasing per-capita income but at some point will peak and subsequently decline. The latter is driven by increasingly tight environmental policies, but also by shifts within sectors towards industries with lower emissions and improved technology. While such shifts do not lead to lower absolute emissions, the average emissions per unit of energy use declines (for more discussion on the EKC, see below). | |||
* Combinations of a prescribed period, followed by further rules based on income levels. | |||
It should be noted that there is a debate whether the EKC is actually observed and whether it can be extrapolated to the future (see earlier references). Here, we use the EKC only as empirically observed trend and realize that there are many other driving forces than income that influence future emissions; still the EKC offers a coherent framework to describe overall trends in emissions in an Integrated Assessment context. Criticism to the EKC is often related to its potential use as (only) explanation for understanding trends, to the application to greenhouse gases (we apply the hypothesis to air pollutants only) and to its universal application. For instance, more dense regions are likely to have more stringent air quality standards. Moreover, technologies developed in high-income regions often tend to diffuse within only a few years to developing regions. The latter can, in fact, be captured in IMAGE by decreasing the threshold values over time | |||
The methodology for EKC scenario development as applied in the energy model is based on 2 types of variables, a) income thresholds (2-3 steps) and b) gas and sector dependent reduction targets for these income levels. The income thresholds are set on historical points 1) average [[OECD]] income at which air pollution control policies were introduced in these countries; | |||
2) the current income level of OECD countries. We assume that developing countries once they reach the first threshold level will start reducing their emission factors. We also assume that if they reach the second threshold level, the emission factor will converge to the average value of OECD regions once the second threshold level is reached. Beyond this level, we assume that further reductions will take place slowly converging to the minimum emission factor found in OECD regions in 2030 according to projections made by [[IIASA]] under so-called “current legislation” (i.e. current abatement plans). | |||
It should be noted that for CO2 and other greenhouse gases such as halogenated gases, where no evidence of EKC behavior exists, the IMAGE model uses an explicit description of fuel use and deforestation and does not rely on the EKC at all. The IMAGE rules act on the level of regions instead of countries. Interestingly, international agreements cause countries to act as a group so this might not be an important limitation. | |||
==Emissions from industrial processes== | |||
For the industrial sector, the energy model includes several activity levels that determine emissions. These can divided into three categories: | |||
• Cement and steel production. For these commodities, IMAGE-TIMER actually includes detailed demand models ([[Energy supply and demand]]). Similar to energy, the emissions are calculated by multiplying the activity levels to exogenously set emission factors. | |||
• Other industrial activities. Here the activity levels are formulated as a regional function of industrial value added. Activity levels include for instance copper production, and the production of solvents. Again, emissions are calculated by multiplying the activity levels with emission factors. | |||
• For the halogenated gases, finally we have implemented the approach developed by [[Harnisch et al.,2009a]]. They derived relationships with income for the main uses of halogenated gasses (HFCs, PFCs, SF6). In the actual use of the model, slightly updated parameters are used to better represent the projections as presented by [[Velders et al., 2009]]. The marginal abatement cost curve per gas still follows the methodology described by [[Harnisch et al., 2009b]]. | |||
==Land-use related emissions== | |||
The CO2 exchange between terrestrial ecosystems and the atmosphere computed by the LPJ model is described in [[Natural vegetation and carbon cycle]]. The land-use emissions model focuses on emissions of other important gases, including greenhouse gases (CH4, N2O), ozone precursors (NOx, CO, VOC), acidifying compounds (SO2, NH3) and aerosols (SO2, NO3, BC, OC). | |||
For many sources, the emission factor approach ([[equation 5.1]]) is used ([[Table 5.2]]). For anthropogenic sources, the emission factors are from the EDGAR database, with time-dependent values for historical years. During the scenario period, most emission factors are constant, except for explicit climate abatement policies (see below). However, there are some important exceptions. Atmospheric N emissions are modeled in a detailed way (see below), and in several other cases, the emission factor depends on the assumptions described in other parts of IMAGE. For example, CH4 emissions from nondairy and dairy cattle are calculated on the basis of the energy requirement and feed type (see [[Livestock systems]]). High-quality feed such as concentrates from feed crops have a lower CH4 emission factor than feeds with lower protein and higher contents of components with lower digestibility. This implies that when the feed conversion ratio changes, the CH4 emission will automatically change as well. Feed conversion ratios are prescribed, or are calculated on the basis of the animal productivity. | |||
Constant emission factors may lead to decreasing emissions per unit of product, for example when the emission factor is specified on a per head basis. An increasing production per head may then lead to a decreasing emission per unit of product. An example is the constant CH4 emission from animal waste per animal, which leads to decreasing emissions per unit of meat or milk when the production per animal increases. | |||
A special case is the N2O emission after forest clearing. Deforestation may lead to accelerated decomposition of litter, root material and loss of part of soil organic matter in the first year after the clearing, causing a pulse of N2O emissions. To mimic this effect, emissions in the first year after clearing are assumed to be 5 times the flux in the original ecosystem. They decrease linearly to the level of the new ecosystem in the 10th year, usually lower than the flux in the original forest. More details can be found in Kreileman and [[Bouwman, 1994]]. | |||
Land-use related emissions of NH3, N2O and NO are calculated with a grid-specific model, N2O from soils under natural vegetation is calculated with the model of [[Bouwman et al., 1993]]. This model is a regression model based on temperature, a proxy for soil carbon input, soil water and oxygen status and a proxy for net primary production. Ammonia emission from natural vegetation is based on net primary production, C:Nratio and an emission factor, and the model accounts for in-canopy retention of the emitted NH3 ([[Bouwman et al., 1997]]). | |||
For agricultural emissions of N2O, the most important determinant factors are N application rate, climate type, soil organic-C content, soil texture, drainage, soil pH, type of crop, and type of fertilizer; the most important controls on NO emission include the N application rate per fertilizer type and soil organic-C content and soil drainage. (for a detailed description, see [[Bouwman et al., 2002]]. Agricultural emissions of NH3 from fertilized cropland and grassland uses the factors type of crop, fertilizer application rate by type and application mode, temperature, soil pH, and CEC ([[Bouwman et al., 2002]]). | |||
It should be noted that for comparison with other models, IMAGE also inludes the N2O methodology for sources as proposed by IPCC ([[IPCC, 2006]]). This methodology represents only the anthropogenic increase. This emission cannot be compared with the above model approach, which calculates the total emission. | |||
==Emission abatement== | |||
Future emissions for a number of energy and land-use related sources also vary in future years as a result of climate policy. This is described by using so-called abatement coefficients ([[Figure 5.1]]). The values of these coefficients depend on the scenario assumptions. In scenarios in which climate change or sustainability is an important feature in the storyline, abatement will be more important than in business-as-usual scenarios. Abatement factors are used in particular for CH4 emissions from fossil fuel production and transport, N2O emissions from transport and for CH4 emissions from enteric fermentation and from animal waste, and N2O emissions from animal waste according to the IPCC method. These abatement files are calculated in the climate policy submodel of IMAGE on the basis of comparing the costs of non-CO2 abatement in agriculture against other mitigation options. | |||
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Revision as of 11:24, 15 January 2014
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