This module describes technical mitigation of GHG emissions. It allows to reduce GHG emissions by undertaking mitigation measures in exchange for additional mitigation costs. The technical mitigation measures include, for example, better spreader maintenance, feed additives or investments in animal waste management facilities. Please note that technical mitigation is possible only in the “on” module realization below. For simplicity, we considered only the effects of mitigation measure costs and emissions. Their direct consequences on biophysical values like yields or water requirements is ignored at the moment.
Mitigation costs are estimated using marginal abatement cost curves (MACCs). The curves are applied on the original emissions, and reduce them by a certain percentage in exchange for additional costs. The MACCs used in this module are based on the data from Lucas et al. (2007).
Description | Unit | A | |
---|---|---|---|
im_pollutant_prices (t_all, i, pollutants, emis_source) |
Certificate prices for N2O-N CH4 CO2-C used in the model | \(USD_{05MER}/Mg\) | x |
sm_fix_SSP2 | year until which all parameters are fixed to SSP2 values | \(year\) | x |
vm_emissions_reg (i, emis_source, pollutants) |
Regional emissions by source and gas after technical mitigation N CH4 C | \(Tg/yr\) | x |
Description | Unit | |
---|---|---|
im_maccs_mitigation (t, i, emis_source, pollutants) |
Technical mitigation of GHG emissions | \(percent\) |
vm_maccs_costs (i) |
Costs of technical mitigation of GHG emissions | \(10^6 USD_{95MER}/yr\) |
Unlike the previous realization, this implementation allows for the possibility that non-CO2 emissions can be reduced by technical mitigation at additional costs. The following MACC data sets are available in this module: Lucas et al. (2007) (PBL_2007) and Harmsen et al. (2019) (PBL_2019).
Therefore, the equation below is used to estimate the mitigation costs. MACC costs are calculated based on emissions BEFORE technical mitigation. These emissions are here calculated back from the final emissions, using the mitigated shares. Please note that in some cases (e.g. N2O emissions from soils) there is an intended mismatch between the actual mitigated quantities, and the quantities for which abatement has to be paid. As can be seen in the respective emission module (e.g. 50_nr_soil_budget), the mitigation quantity is reduced if baseline efficiencies are already high, but the mitigation costs still apply fully, as the mitigation “effort” is the same. Mitigation costs are then calculated as the product of GHG emissions before technical mitigation, and the costs per unit of technical mitigation. The mitigation costs will go into the objective function of the model. To avoid double-accounting, the fertilizer savings that reduce the costs of the MACCs have to be added again to the MACCs, as the fertilizer costs are endogenous in our model and fall with rising MACCs. We ex-post replicate these implicit cost savings by assuming that they calculate the MACCs emission savings proportional to fertilizer savings. Fertilization quantity is derived by calculating emissions back to fertilization using the default emission factor by the IPCC that is likely the basis for their estimates (E=F*EF, F= E/EF).
\[\begin{multline*} vm\_maccs\_costs(i2) = \sum_{ct,emis\_source,pollutants\_maccs57}\left( p57\_maccs\_costs\_integral(ct,i2,emis\_source,pollutants\_maccs57) \cdot \frac{ vm\_emissions\_reg(i2,emis\_source,pollutants\_maccs57) }{ \left(1 - im\_maccs\_mitigation(ct,i2,emis\_source,pollutants\_maccs57)\right) }\right) + \sum_{emis\_source\_inorg\_fert\_n2o}\left( \left(\frac{vm\_emissions\_reg(i2,emis\_source\_inorg\_fert\_n2o,"n2o\_n\_direct") }{ s57\_implicit\_emis\_factor}\right) \cdot \sum_{ct}im\_maccs\_mitigation(ct,i2,emis\_source\_inorg\_fert\_n2o,"n2o\_n\_direct") \cdot s57\_implicit\_fert\_cost \right) \end{multline*}\]
Limitations The data set PBL_2007 is outdated and only kept for backward compatibility
Description | Unit | A | |
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f57_maccs_ch4 (t_all, i, maccs_ch4, maccs_steps) |
CH4 MACC from Image model | \(percent\) | x |
f57_maccs_ch4_2022 (t_all, i, maccs_ch4, scen57, maccs_steps) |
N2O MACC from Image model | \(percent\) | x |
f57_maccs_n2o (t_all, i, maccs_n2o, maccs_steps) |
N2O MACC from Image model | \(percent\) | x |
f57_maccs_n2o_2022 (t_all, i, maccs_n2o, scen57, maccs_steps) |
N2O MACC from Image model | \(percent\) | x |
i57_mac_step_ch4 (t, i, emis_source) |
Helper to map CH4 prices and maccs_steps | \(1\) | x |
i57_mac_step_n2o (t, i, emis_source) |
Helper to map N2O prices and maccs_steps | \(1\) | x |
p57_maccs_costs_integral (t, i, emis_source, pollutants) |
Costs of technical mitigation | \(USD_{95MER}/Tg N CH4 C\) | x |
q57_total_costs (i) |
Calculation of total costs of technical mitigation | \(10^6 USD_{95MER}/yr\) | x |
s57_implicit_emis_factor | emission factor for direct soil emissions implicit to MACC curves | \(tN2ON/tN\) | x |
s57_implicit_fert_cost | fertilizer costs implicit to MACC curves | \(USD_{05MER}/ton N\) | x |
s57_maxmac_ch4_awms | activate awms CH4 mitigation independent of pollutant price | \(step of MACC and -1 is inactive\) | x |
s57_maxmac_ch4_entferm | activate enteric fermentation CH4 mitigation independent of pollutant price | \(step of MACC and -1 is inactive\) | x |
s57_maxmac_ch4_rice | activate rice CH4 mitigation independent of pollutant price | \(step of MACC and -1 is inactive\) | x |
s57_maxmac_n_awms | activate awms N mitigation independent of pollutant price | \(step of MACC and -1 is inactive\) | x |
s57_maxmac_n_soil | activate soil N mitigation independent of pollutant price | \(step of MACC and -1 is inactive\) | x |
s57_step_length | Step length in MACC data | \(yr\) | x |
description | |
---|---|
awms | animal waste management systems |
ct(t) | Current time period |
emis_source | Emission sources |
emis_source_awms_ch4(emis_source) | subset awms emissions |
emis_source_awms_n2o(emis_source) | subset awms_manure_n2o |
emis_source_ent_ferm_ch4(emis_source) | subset ent_ferm emissions |
emis_source_inorg_fert_n2o(emis_source) | subset inorg_fert_n2o emissions |
emis_source_rice_ch4(emis_source) | subset rice emissions |
i | all economic regions |
i2(i) | World regions (dynamic set) |
maccs_ch4 | ch4 mitigation categories with MACCS |
maccs_n2o | n2o mitigation categories with MACCS |
maccs_steps | maccs tax level steps |
pollutants_maccs57(pollutants) | pollutants via which MAC costs are calculated |
pollutants(pollutants_all) | subset of pollutants_all that can be taxed |
scen57 | scenarios |
t_all(t_ext) | 5-year time periods |
t(t_all) | Simulated time periods |
type | GAMS variable attribute used for the output |
Benjamin Leon Bodirsky, Florian Humpenoeder
09_drivers, 11_costs, 50_nr_soil_budget, 51_nitrogen, 53_methane, 56_ghg_policy
Harmsen, J. H. M., Detlef P. van Vuuren, Dali R. Nayak, Andries F. Hof, Lena Höglund-Isaksson, Paul L. Lucas, Jens B. Nielsen, Pete Smith, and Elke Stehfest. 2019. “Long-Term Marginal Abatement Cost Curves of Non-CO2 Greenhouse Gases.” Environmental Science & Policy 99 (September): 136–49. https://doi.org/10.1016/j.envsci.2019.05.013.
Lucas, Paul L., Detlef P. van Vuuren, Jos G. J. Olivier, and Michel G. J. den Elzen. 2007. “Long-Term Reduction Potential of Non-Co2 Greenhouse Gases.” Environmental Science & Policy 10 (2): 85–103. https://doi.org/https://doi.org/10.1016/j.envsci.2006.10.007.