MAgPIE - An Open Source land-use modeling framework

4.8.2

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Marginal Abatement Cost Curves (57_maccs)

Description

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).

Interfaces

Interfaces to other modules

Input

module inputs (A: on_aug22)
  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
pm_cost_share_crops
(t, i, factors)
Capital and labor shares of the regional factor costs for crop production \(1\) x
pm_hourly_costs
(t, i, wage_scen)
Hourly labor costs in agriculture on regional level before and after including wage scenario \(USD_{MER}05/hour\) x
pm_productivity_gain_from_wages
(t, i)
Multiplicative factor describing productivity gain related to higher wages \(1\) 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

Output

module outputs
  Description Unit
im_maccs_mitigation
(t, i, emis_source, pollutants)
Technical mitigation of GHG emissions \(percent\)
vm_maccs_costs
(i, factors)
Costs of technical mitigation of GHG emissions \(10^6 USD_{95MER}/yr\)

Realizations

(A) on_aug22

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). Costs are assumed to be factor costs and split into a labor and a capital part. The labor share is scaled according to the changes in wages and corresponding productivity change.

\[\begin{multline*} vm\_maccs\_costs(i2,"labor") = \left(\sum_{ct,emis\_source,pollutants\_maccs57}\left( p57\_maccs\_costs\_integral(ct,i2,emis\_source,pollutants\_maccs57) \cdot \frac{ vm\_emissions\_reg(i2,emis\_source,pollutants\_maccs57) }{ \left(1 - im\_maccs\_mitigation(ct,i2,emis\_source,pollutants\_maccs57)\right)}\right) + \sum_{emis\_source\_inorg\_fert\_n2o}\left( \left(\frac{vm\_emissions\_reg(i2,emis\_source\_inorg\_fert\_n2o,"n2o\_n\_direct") }{ s57\_implicit\_emis\_factor}\right) \cdot \sum_{ct}im\_maccs\_mitigation(ct,i2,emis\_source\_inorg\_fert\_n2o,"n2o\_n\_direct") \cdot s57\_implicit\_fert\_cost\right)\right) \cdot \sum_{ct}\left( pm\_cost\_share\_crops\left(ct ,i2,"labor"\right) \cdot \left(\frac{1}{pm\_productivity\_gain\_from\_wages(ct,i2)}\right) \cdot \left(\frac{pm\_hourly\_costs(ct,i2,"scenario") }{ pm\_hourly\_costs(ct,i2,"baseline")}\right)\right) \end{multline*}\]

\[\begin{multline*} vm\_maccs\_costs(i2,"capital") = \left(\sum_{ct,emis\_source,pollutants\_maccs57}\left( p57\_maccs\_costs\_integral(ct,i2,emis\_source,pollutants\_maccs57) \cdot \frac{ vm\_emissions\_reg(i2,emis\_source,pollutants\_maccs57) }{ \left(1 - im\_maccs\_mitigation(ct,i2,emis\_source,pollutants\_maccs57)\right)}\right) + \sum_{emis\_source\_inorg\_fert\_n2o}\left( \left(\frac{vm\_emissions\_reg(i2,emis\_source\_inorg\_fert\_n2o,"n2o\_n\_direct") }{ s57\_implicit\_emis\_factor}\right) \cdot \sum_{ct}im\_maccs\_mitigation(ct,i2,emis\_source\_inorg\_fert\_n2o,"n2o\_n\_direct") \cdot s57\_implicit\_fert\_cost\right)\right) \cdot \sum_{ct}\left( pm\_cost\_share\_crops\left(ct ,i2,"capital"\right)\right) \end{multline*}\]

Limitations The data set PBL_2007 is outdated and only kept for backward compatibility

Definitions

Objects

module-internal objects (A: on_aug22)
  Description Unit A
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_capital_costs
(i)
Calculation of capital costs of technical mitigation \(10^6 USD_{95MER}/yr\) x
q57_labor_costs
(i)
Calculation of labor 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

Sets

sets in use
  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
factors factors included in factor requirements
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
wage_scen version of wages

Authors

Benjamin Leon Bodirsky, Florian Humpenoeder

See Also

09_drivers, 11_costs, 36_employment, 38_factor_costs, 50_nr_soil_budget, 51_nitrogen, 53_methane, 56_ghg_policy

References

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.