Agriculture - REMIND-MAgPIE: Difference between revisions
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REMIND derives non-CO2 emissions in the absence of climate policies from various agricultural activities for given assumptions on socio-economic pathways from corresponding MAgPIE scenarios. An important nexus between the energy system and agriculture is bioenergy demand. In standalone mode, REMIND uses bioenergy supply costs derived from MAgPIE, see section “Bioenergy”. To account for the sensitivity of resource potentials to carbon pricing, REMIND uses different supply curve parameterizations in baseline and climate policy scenarios. Bioenergy-induced emissions of N2O (fertilizer use) and CO2 (land-use change) are accounted for using specific per-unit emission coefficients. | REMIND-MAgPIE derives non-CO2 emissions in the absence of climate policies from various agricultural activities for given assumptions on socio-economic pathways from corresponding MAgPIE scenarios. An important nexus between the energy system and agriculture is bioenergy demand. In standalone mode, REMIND-MAgPIE uses bioenergy supply costs derived from MAgPIE, see section “Bioenergy”. To account for the sensitivity of resource potentials to carbon pricing, REMIND-MAgPIE uses different supply curve parameterizations in baseline and climate policy scenarios. Bioenergy-induced emissions of N2O (fertilizer use) and CO2 (land-use change) are accounted for using specific per-unit emission coefficients. | ||
In standalone mode, REMIND derives the economic mitigation potential of agricultural CH4 and N2O emissions is calculated using marginal abatement cost curves (MACCs) from Lucas | In standalone mode, REMIND-MAgPIE derives the economic mitigation potential of agricultural CH4 and N2O emissions is calculated using marginal abatement cost curves (MACCs) from Lucas <ref>Lucas PL, van Vuuren DP, Olivier JGJ, den Elzen MGJ (2007) Long-term reduction potential of non-CO2 greenhouse gases. Environmental Science & Policy 10:85–103. doi: 10.1016/j.envsci.2006.10.007</ref>. For land-use related CO2, similar MACCs derived from MAgPIE are employed. | ||
As described in Figure | As described in [http://themasites.pbl.nl/models/advance/index.php/Land-use_-_REMIND-MAgPIE Figure 1], if run in coupled mode REMIND-MAgPIE adopts consistent GHG emission projections and bioenergy supply curves from MAgPIE. | ||
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Latest revision as of 14:34, 21 November 2021
| Corresponding documentation | |
|---|---|
| Previous versions | |
| Model information | |
| Model link | |
| Institution | Potsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik-potsdam.de. |
| Solution concept | General equilibrium (closed economy)MAgPIE: partial equilibrium model of the agricultural sector; |
| Solution method | OptimizationMAgPIE: cost minimization; |
| Anticipation | |
REMIND-MAgPIE derives non-CO2 emissions in the absence of climate policies from various agricultural activities for given assumptions on socio-economic pathways from corresponding MAgPIE scenarios. An important nexus between the energy system and agriculture is bioenergy demand. In standalone mode, REMIND-MAgPIE uses bioenergy supply costs derived from MAgPIE, see section “Bioenergy”. To account for the sensitivity of resource potentials to carbon pricing, REMIND-MAgPIE uses different supply curve parameterizations in baseline and climate policy scenarios. Bioenergy-induced emissions of N2O (fertilizer use) and CO2 (land-use change) are accounted for using specific per-unit emission coefficients.
In standalone mode, REMIND-MAgPIE derives the economic mitigation potential of agricultural CH4 and N2O emissions is calculated using marginal abatement cost curves (MACCs) from Lucas [1]. For land-use related CO2, similar MACCs derived from MAgPIE are employed.
As described in Figure 1, if run in coupled mode REMIND-MAgPIE adopts consistent GHG emission projections and bioenergy supply curves from MAgPIE.
- ↑ Lucas PL, van Vuuren DP, Olivier JGJ, den Elzen MGJ (2007) Long-term reduction potential of non-CO2 greenhouse gases. Environmental Science & Policy 10:85–103. doi: 10.1016/j.envsci.2006.10.007