Agricultural economy/Description and Emissions/Policy issues: Difference between pages

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{{ComponentDescriptionTemplate
{{ComponentPolicyIssueTemplate
|Status=On hold
|Reference=PBL, 2012; Braspenning Radu et al., 2016;  
|Reference=Hertel, 1997; Britz, 2003; Armington, 1969; Huang et al., 2004; Helming et al., 2010; Banse et al., 2008;
}}
|Description=<h2>Model description Agricultural economy and forestry</h2>
<div class="page_standard">
The MAGNET model [[Woltjer et al., 2011]] is based on the standard GTAP model (Hertel, 1997]], which is a multi-regional, static, applied computable general equilibrium (CGE) model based on neoclassical microeconomic theory. Although it covers the entire economy, there is a special focus on agricultural sectors. The MAGNET model is a further development of GTAP regarding land use, household consumption, livestock, food, feed and energy crop production, and emission reduction cost calculations.
==Baseline developments==
 
In a baseline scenario, most greenhouse gas 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 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.
===Demand and supply===: Household demand for agricultural products is calculated as a function of income, income elasticities, price elasticities, and cross-price elasticities. Income elasticities for agricultural commodities are consistent with FAO estimates (Britz 2003), and dynamically depend on purchasing power parity corrected GDP per capita. The supply of all commodities is modelled by an input–output structure that explicitly links the production of goods and services for final consumption via different stages of processing back to primary goods (crops and livestock products) and resources. At each production level, input of labour, capital, and intermediate input or resources (e.g. land) can substitute each other. For example, labour, capital and land are input factors in crop production, and substitution of these production factors is driven by changes in their relative prices. If the price of one input factor increases, it is substituted by other factors, following the price elasticity of substitution.  
 
===Regional aggregation and trade===: MAGNET is flexible in its regional aggregation (129 regions). For the link with IMAGE, it distinguishes single European countries and, in addition to Europe, 22 large world regions, closely matching the regions in IMAGE (Figure zzz IMAGE regions). Similar to most other CGE models, MAGNET assumes that products traded internationally are differentiated according to country of origin, i.e. domestic and foreign products are not fully identical, but are imperfect substitutes (the so-called Armington assumption (Armington 1969)).


Land use: In addition to the standard GTAP model, MAGNET  includes a dynamic land supply function (Van Meijl et al. 2006) that accounts for the availability and suitability of land for agricultural use, based on information from IMAGE (see below, and Figure 4.2.1). A nested land-use structure accounts for the different degrees of substitutability between types of land use (Huang et al. 2004; Van Meijl et al. 2006). In addition, the MAGNET model includes international and EU agricultural policies, such as production quota and export\import tariffs  (Helming et al. 2010).
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
==Policy interventions==


===Biofuel crops===: MAGNET includes ethanol and biodiesel as first-generation biofuels made from wheat, sugar cane, maize, and oilseeds (Banse et al. 2008), and the use of by-products (DDGS, oilcakes) from biofuel production in the livestock sector.
Policy scenarios present several ways to influence emission of air pollutants ([[Braspenning Radu et al., 2016]]):
* 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]]).
* 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.
* 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.
* 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.
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.


===Livestock===: MAGNET distinguishes the livestock commodities of beef and other ruminant meats, dairy cattle (grass- and crop-fed), and a category ‘other animals’ (e.g. chickens and pigs) that is primarily crop-fed. The modelling of the livestock sector includes different feedstuffs, including feed crops, co-products from biofuels (e.g. oil cakes from rapeseed-based biofuel, or distillers grain from wheat-based biofuels), and grass (Woltjer 2011). Grass may be substituted by feed from crops, in the case of ruminants.  
See also the Policy interventions Table below.


===Land supply in MAGNET===: In MAGNET, land supply is calculated using a land supply curve that relates the area in use for agriculture to the price of land (Figure 4.1.1.c). Total land supply contains all the land that is potentially available for agriculture, i.e. where crop production is possible given soil and climatic conditions, and where no other restrictions apply (e.g. urban or protected area designations). In the IMAGE model, for every region, total land supply is obtained from potential crop productivity and land availability on a resolution of 5 by 5 arcminutes. Within this total potential, the land supply curve indicates the price at which additional land may be taken in use. The supply curve depends on total land supply, actual agricultural area, the actual land price, and the estimated actual price elasticity of land supply. Regions differ in the fraction of land already in use, and in the change in land prices related to changes in agricultural land use. In regions where most of the area suitable for agriculture is already in use, the price elasticity of land supply is small, with little expansion occurring at strong price changes. In contrast, in regions with a large reserve of suitable agricultural land, such as sub-Saharan Africa and some regions in South America, the price elasticity of land supply is large, i.e. strong expansion of agricultural land occurs at small price changes.
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Policy intervention figure}}


===Reduced availability of land===: By restricting land supply in IMAGE and MAGNET, the models can assess scenarios with additional protected areas, or reduced emissions from deforestation and forest degradation (REDD).
{{PIEffectOnComponentTemplate }}
 
</div>
===Intensification of crop and pasture production===: Crop and pasture yields in MAGNET may change as a result of the following four processes:
#autonomous technological change (external scenario assumption);
##intensification due to the substitution of production factors (endogenous);
###climate change (from IMAGE);
####change in agricultural area affecting crop yields (e.g. decreasing average yields due to expansion into less suitable regions) (from IMAGE).
 
For the biophysical yield effects due to climate and area changes, yields as calculated by IMAGE’s crop model are communicated to MAGNET. External assumptions on autonomous technological changes are mostly based on FAO projections (Bruinsma 2003). They describe. per region and commodity, the assumed future changes in yields for a wide range of crop types. In MAGNET, the biophysical yield changes are combined with the autonomous technological change, to give the total exogenous yield change. In addition, during the simulation period, MAGNET calculates an endogenous intensification as a result of price-driven substitution between labour, land and capital. In IMAGE, regional yield changes due to both autonomous technological change and endogenous intensification according to MAGNET are used in the spatially explicit allocation of land use (Section 4.2.3). 
 
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Revision as of 16:34, 15 November 2018

Link to framework components overview

Parts of Emissions/Policy issues

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

Baseline developments

In a baseline scenario, most greenhouse gas 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 Rio+20 study (PBL, 2012). 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.


Gloval greenhouse gas emissions and temperature changes under a baseline scenario
Future greenhouse gas emissions are mostly driven by an increase in energy use, while the relative contribution of land-use related emissions is projected to decrease.

Policy interventions

Policy scenarios present several ways to influence emission of air pollutants (Braspenning Radu et al., 2016):

  • 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).
  • Policy interventions can be mimicked by introducing an alternative formulation of emission factors to the standard formulations (EKC, CLE). For instance, emission factors can be used to deliberately include maximum feasible reduction measures.
  • 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 N2O, NO and NH3 can be decreased (Van Vuuren et al., 2011b). By increasing the amount of feed crops in the cattle rations, CH4 emissions can be reduced. Production of crop types has a significant influence on emission levels of N2O, NOx and NH3 from spreading manure and fertilisers.
  • 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 N2O and NH3 than others. Incorporating manure into soil lowers emissions compared to broadcasting.

The impacts of more ambitious control policies (CLE versus EKC) on SO2 and NOx, emissions, and the influence of climate policy are presented in the figure below. Where climate policy is particularly effective in reducing SO2 emissions, air pollution control policies are effective in reducing NOx emissions.

See also the Policy interventions Table below.


Global emission of NOx and SO2 per sector under baseline and policy scenarios
Climate policy has important co-benefits for air pollution.

Effects of policy interventions on this component

Policy interventionDescriptionEffect
Apply emission and energy intensity standards Apply emission intensity standards for e.g. cars (gCO2/km), power plants (gCO2/kWh) or appliances (kWh/hour).
Capacity targets It is possible to prescribe the shares of renewables, CCS technology, nuclear power and other forms of generation capacity. This measure influences the amount of capacity installed of the technology chosen.
Carbon tax A tax on carbon leads to higher prices for carbon intensive fuels (such as fossil fuels), making low-carbon alternatives more attractive.
Change market shares of fuel types Exogenously set the market shares of certain fuel types. This can be done for specific analyses or scenarios to explore the broader implications of increasing the use of, for instance, biofuels, electricity or hydrogen and reflects the impact of fuel targets. (Reference:: Van Ruijven et al., 2007)
Change the use of electricity and hydrogen It is possible to promote the use of electricity and hydrogen at the end-use level.
Excluding certain technologies Certain energy technology options can be excluded in the model for environmental, societal, and/or security reasons. (Reference:: Kruyt et al., 2009)
Implementation of biofuel targets Policies to enhance the use of biofuels, especially in the transport sector. In the Agricultural economy component only 'first generation' crops are taken into account. The policy is implemented as a budget-neutral policy from government perspective, e.g. a subsidy is implemented to achieve a certain share of biofuels in fuel production and an end-user tax is applied to counterfinance the implemented subsidy. (Reference:: Banse et al., 2008)
Implementation of sustainability criteria in bio-energy production Sustainability criteria that could become binding for dedicated bio-energy production, such as the restrictive use of water-scarce or degraded areas.
Improving energy efficiency Exogenously set improvement in efficiency. Such improvements can be introduced for the submodels that focus on particular technologies, for example, in transport, heavy industry and households submodels.
REDD policies The objective of REDD policies it to reduce land-use related emissions by protecting existing forests in the world; The implementation of REDD includes also costs of policies. (Reference:: Overmars et al., 2012) Less emissions due to deforestation and land-use change.
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