| Title: | MadRat REMIND Input Data Package |
|---|---|
| Description: | The mrremind packages contains data preprocessing for the REMIND model. |
| Authors: | Lavinia Baumstark [aut, cre], Renato Rodrigues [aut], Antoine Levesque [aut], Julian Oeser [aut], Christoph Bertram [aut], Ioanna Mouratiadou [aut], Aman Malik [aut], Felix Schreyer [aut], Bjoern Soergel [aut], Marianna Rottoli [aut], Abhijeet Mishra [aut], Alois Dirnaichner [aut], Michaja Pehl [aut], Anastasis Giannousakis [aut], David Klein [aut], Jessica Strefler [aut], Lukas Feldhaus [aut], Regina Brecha [aut], Sebastian Rauner [aut], Jan Philipp Dietrich [aut], Stephen Bi [aut], Falk Benke [aut], Pascal Weigmann [aut], Oliver Richters [aut], Robin Hasse [aut], Sophie Fuchs [aut], Rahel Mandaroux [aut], Johannes Koch [aut] |
| Maintainer: | Lavinia Baumstark <[email protected]> |
| License: | LGPL-3 | file LICENSE |
| Version: | 0.194.1 |
| Built: | 2024-10-24 15:20:14 UTC |
| Source: | https://github.com/pik-piam/mrremind |
The mrremind packages contains data preprocessing for the REMIND model.
Maintainer: Lavinia Baumstark [email protected]
Authors:
Renato Rodrigues
Antoine Levesque
Julian Oeser
Christoph Bertram
Ioanna Mouratiadou
Aman Malik
Felix Schreyer
Bjoern Soergel
Marianna Rottoli
Abhijeet Mishra
Alois Dirnaichner
Michaja Pehl
Anastasis Giannousakis
David Klein
Jessica Strefler
Lukas Feldhaus
Regina Brecha
Sebastian Rauner
Jan Philipp Dietrich
Stephen Bi
Falk Benke
Pascal Weigmann
Oliver Richters
Robin Hasse
Sophie Fuchs
Rahel Mandaroux
Johannes Koch
Useful links:
Calculate REMIND final energy variables from historical AGEB values
calcAGEB(subtype = "balances")calcAGEB(subtype = "balances")
subtype |
data subtype. Either "balances" ("Auswertungstabellen zur Energiebilanz Deutschland") or "electricity" ("Bruttostromerzeugung in Deutschland nach Energieträgern") |
A magpie object.
Falk Benke
read biomass supply curves from Magpie emulator
calcBiomassPrices()calcBiomassPrices()
Magpie object with two parameters determining linear biomass supply curve
Calculate REMIND variables from historical BP values
calcBP()calcBP()
Falk Benke
provides historical capacity values in TW
calcCapacity(subtype)calcCapacity(subtype)
subtype |
data subtype. Either "capacityByTech" or "capacityByPE" |
magpie object of capacity data
Renato Rodrigues, Stephen Bi
## Not run: calcOutput("Capacity",subtype="capacityByTech") ## End(Not run)## Not run: calcOutput("Capacity",subtype="capacityByTech") ## End(Not run)
provides capacity factor values
calcCapacityFactor()calcCapacityFactor()
magpie object of the capacity factor data
Renato Rodrigues, Stephen Bi
## Not run: calcOutput("CapacityFactor") ## End(Not run)## Not run: calcOutput("CapacityFactor") ## End(Not run)
provides capacity factor values
calcCapacityFactorHist(subtype)calcCapacityFactorHist(subtype)
subtype |
data subtype. Either "wind" or "windoff" |
magpie object of the capacity factor data
Renato Rodrigues, Stephen Bi
## Not run: calcOutput("CapacityFactor") ## End(Not run)## Not run: calcOutput("CapacityFactor") ## End(Not run)
The capacity targets (GW) at regional level are produced from two different databases- UNFCCC_NDC database, an update of the Rogelj 2017 paper (see readme in inputdata), and REN21 Global Renewables report The UNFCCC_NDC capacity targets are further broken down to conditional and unconditional targets.
calcCapTarget(sources)calcCapTarget(sources)
sources |
Database source |
Aman Malik, Oliver Richters
Calculate CCS bound indicator for 2025 and 2030
calcCCSbounds()calcCCSbounds()
Jessica Strefler, Lavinia Baumstark
Calculate CCS capacity from IEA CCUS data
calcCCScapacity(subtype)calcCCScapacity(subtype)
subtype |
either 'historical' for data until 2022 or 'projections' for projections in 2020, 2025 and 2030 (including some redistribution on EU/NEU level) |
Anne Merfort, Falk Benke
Combines cement production data from readvanRuijven2016() and
readUSGS(cement) into a single data set, using USGS data from
2005 on.
calcCement()calcCement()
A list with a magpie object x with
country-level cement production in tonnes, weight, unit, description,
and min fields.
Michaja Pehl
Calculate Clinker-to-Cement Ratio
calcClinker_to_cement_ratio()calcClinker_to_cement_ratio()
A list with a magpie object x, weight,
unit, and description.
Michaja Pehl
calcOutput(), readADVANCE_WP2(), convertADVANCE_WP2()
Provides REMIND data for PE tradecosts (energy losses on import).
calcCostsTrade()calcCostsTrade()
REMIND data forPE tradecosts (energy losses on import) and corresonding weights (1) as a list of two MAgPIE objects
Lavinia Baumstark
## Not run: calcOutput("calcCostsTrade") ## End(Not run)## Not run: calcOutput("calcCostsTrade") ## End(Not run)
Provides REMIND data for PE trade cost (energy losses on import, export and use).
calcCostsTradePeFinancial()calcCostsTradePeFinancial()
Regina Brecha, Lavinia Baumstark
## Not run: calcOutput("CostsTradePeFinancial") ## End(Not run)## Not run: calcOutput("CostsTradePeFinancial") ## End(Not run)
Calculate costs of transport of enhanced weathering
calcCostsWeathering()calcCostsWeathering()
transport costs of spreading rock on the fields
## Not run: calcOutput("CostsWeathering") ## End(Not run)## Not run: calcOutput("CostsWeathering") ## End(Not run)
Disaggregated investment cost data is aggregated and technologies renamed to REMIND names
calcDiffInvestCosts(subtype)calcDiffInvestCosts(subtype)
subtype |
either "Invest_Costs" or "Efficiency" |
REMIND does not have a classification of coal power plants e.g., sub-critical. Therefore, countries are given coal plant costs assuming what type of coal plants are expected to develop there. For other technologies, certain assumptions are taken to change to REMIND convention.
Magpie object with aggregated but differentiated investment costs for some technologies.
Aman Malik
provides the extra retirement rate to account for relatively old fleet technologies retirement
calcEarlyRetirementAdjFactor(subtype = "none")calcEarlyRetirementAdjFactor(subtype = "none")
subtype |
Some scenarios may require certain regions to increase retirement rate, e.g. PPCA coal phase-out |
magpie object of additional adjusment percentage to be added to the fraction of the early retired capital in countries to account for relatively old technologies fleet
Renato Rodrigues
## Not run: calcOutput(type = "EarlyRetirementAdjFactor") ## End(Not run)## Not run: calcOutput(type = "EarlyRetirementAdjFactor") ## End(Not run)
Provides REMIND data for CO2 parameters to calculate baseline emissions of waste from population and investment.
calcEconometricEmiParameter()calcEconometricEmiParameter()
REMIND data for CO2 parameters to calculate baseline emissions of waste from population and investment and corresponding weights (population) as a list of two MAgPIE objects
Lavinia Baumstark
## Not run: calcOutput("calcEconometricEmiParameter") ## End(Not run)## Not run: calcOutput("calcEconometricEmiParameter") ## End(Not run)
calcEDGAR7Fgases
calcEDGAR7Fgases()calcEDGAR7Fgases()
Gabriel Abrahao
Prepare EDGETransport inputs
calcEDGETransport(subtype)calcEDGETransport(subtype)
subtype |
REMIND/iterative EDGE-T input data subtypes |
REMIND/iterative EDGE-T input data for all scenario combinations
Johanna Hoppe
## Not run: a <- calcOutput(type = "EDGETransport", subtype = "CAPEXandNonFuelOPEX", aggregate = F) ## End(Not run)## Not run: a <- calcOutput(type = "EDGETransport", subtype = "CAPEXandNonFuelOPEX", aggregate = F) ## End(Not run)
Calculate EEA emission projections from the two projections sources provided by EEA
calcEEAGHGProjections()calcEEAGHGProjections()
A magpie object.
Falk Benke
provides region specific Effort Sharing Reference Emissions
calcEffortSharingRefEmi(subtype)calcEffortSharingRefEmi(subtype)
subtype |
type of reference emissions used to define emission reduction target fo European Effort Sharing Decision: EEA_GHG, Eurostat_GHG, REMIND_GHG (deprecated) or REMIND_CO2. |
2005 reference emissions to calculate effort sharing decision targets
Renato Rodrigues
## Not run: calcOutput("EffortSharingRefEmi",subtype="Eurostat_GHG") ## End(Not run)## Not run: calcOutput("EffortSharingRefEmi",subtype="Eurostat_GHG") ## End(Not run)
provides region specific Effort Sharing Emission target
calcEffortSharingTarget()calcEffortSharingTarget()
target data magpie object
Renato Rodrigues
## Not run: calcOutput("EffortSharingTarget") ## End(Not run)## Not run: calcOutput("EffortSharingTarget") ## End(Not run)
prepare the yearly Ember electricity data set To use only a part of the Ember data, call calcOutput("Ember", subtype = "...") and convert to TW if you want to use capacities as input data to REMIND.
calcEmber(subtype = "all")calcEmber(subtype = "all")
subtype |
data subtype. Either "capacity", "generation" or "all" |
A ['magpie'][magclass::magclass] object.
Pascal Weigmann
['calcOutput()']
calcEmiAirPoll calculate Air Pollution Emissions
calcEmiAirPollLandUse()calcEmiAirPollLandUse()
magpie object
Julian Oeser
## Not run: a <- calcOutput(type="EmiAirPollLandUse")## Not run: a <- calcOutput(type="EmiAirPollLandUse")
EmiCO2LandUse calculate co2 emissions from land use change
calcEmiCO2LandUse()calcEmiCO2LandUse()
magpie object
Julian Oeser
## Not run: a <- calcOutput(type="EmiCO2LandUse")## Not run: a <- calcOutput(type="EmiCO2LandUse")
hisorical LULUCF emissions following country accounting
calcEmiLULUCFCountryAcc(subtype)calcEmiLULUCFCountryAcc(subtype)
subtype |
Valid subtypes are 'UNFCCC' |
Magpie object with historical LULUCF emissions
Felix Schreyer
Provides REMIND data for baseline emissions for maccs for 1990.
calcEmiMac1990()calcEmiMac1990()
REMIND data for baseline emissions for maccs for 1990 and corresonding weights (NULL) as a list of two MAgPIE objects
Lavinia Baumstark
## Not run: calcOutput("calcEmiMac1990") ## End(Not run)## Not run: calcOutput("calcEmiMac1990") ## End(Not run)
calcEmiPollutantExo calculate EmiPollutantExo based on RCP data
calcEmiPollutantExo(subtype, aviationshippingsource = "RCP")calcEmiPollutantExo(subtype, aviationshippingsource = "RCP")
subtype |
Either 'Waste' or 'AviationShipping' |
aviationshippingsource |
Defines source for aviation and shipping emissions. Either 'RCP' or 'LeeGAINS'. |
magpie object
Julian Oeser
## Not run: a <- calcOutput(type="EmiPollutantExo")## Not run: a <- calcOutput(type="EmiPollutantExo")
provides European 2030 emission targets in relation to 1990 and 2005 emissions
calcEmiReference()calcEmiReference()
2030 emission reductions tragets for 40
Falk Benke and Renato Rodrigues
## Not run: calcOutput("EmiReference") ## End(Not run)## Not run: calcOutput("EmiReference") ## End(Not run)
Calculate emission factors for feedstocks in the chemicals industry using emissions from UNFCCC and energy demands from IEA Energy Balances
calcEmissionFactorsFeedstocks()calcEmissionFactorsFeedstocks()
A list with a magpie object x, weight,
unit, description.
Falk Benke, Renato Rodrigues, Simón Moreno Leiva
calcEmissions
calcEmissions(datasource = "CEDS16")calcEmissions(datasource = "CEDS16")
datasource |
"CEDS16", "CEDS2REMIND", "CEDS2024", "EDGAR", "EDGAR6", "EDGARghg" "LIMITS", "ECLIPSE", "GFED", "CDIAC" |
magpie object with historical emissions
Steve Smith, Pascal Weigmann
Output for 2 policy cases
calcEmiTarget(sources, subtype)calcEmiTarget(sources, subtype)
sources |
currently only UNFCCC_NDC |
subtype |
"Ghgshare2005", "Ghgfactor", "Ghghistshare" |
Aman Malik, Christoph Bertram, Oliver Richters
provides region specific ETS Reference Emissions
calcETSRefEmi(subtype)calcETSRefEmi(subtype)
subtype |
type of reference emissions used to define emission reduction targets for European regulations: EEA_GHG |
2005 reference emissions to calculate ETS targets
Renato Rodrigues
## Not run: calcOutput("ETSRefEmi",subtype="EEA_GHG") ## End(Not run)## Not run: calcOutput("ETSRefEmi",subtype="EEA_GHG") ## End(Not run)
Calculate REMIND variables from European Energy Datasheets
calcEuropeanEnergyDatasheets(subtype)calcEuropeanEnergyDatasheets(subtype)
subtype |
data subtype. Either "EU28" (data from June 20 including GBR) or "EU27" (latest data from August 23 without GBR) |
A magpie object.
Falk Benke
prepare data for exogenuous FE and ES demand pathways that do not come from EDGE models but from other sources and/or scenario literature. REMIND can be fixed to those demand pathways if the switch cm_exogDem_scen is activated.
calcExogDemScen()calcExogDemScen()
A ['magpie'][magclass::magclass] object.
Felix Schreyer
Calculate expert guesses
calcExpertGuess(subtype)calcExpertGuess(subtype)
subtype |
must be 'tradeConstraints' (more to come) |
Falk Benke
Calculates FE historical from IEA energy balances, projections from EDGE, and historical values from IEA WEO 2019
calcFE(source = "IEA", scenario_proj = "SSP2", ieaVersion = "default")calcFE(source = "IEA", scenario_proj = "SSP2", ieaVersion = "default")
source |
"IEA" or "IEA_WEO" |
scenario_proj |
"SSP2" by default unless overwritten |
ieaVersion |
Release version of IEA data, either 'default' (vetted and used in REMIND) or 'latest'. |
Lavinia Baumstark, Aman Malik
Calculates Final Energy Demand for Industry, Buildings and Transport
calcFEdemand()calcFEdemand()
Falk Benke
Returns the EDGE-Buildings data as REMIND variables
calcFeDemandBuildings(subtype)calcFeDemandBuildings(subtype)
subtype |
either "FE", "FE_buildings", or "UE_buildings" |
Robin Hasse
Calculates FE demand in industry as REMIND variables
calcFeDemandIndustry(use_ODYM_RECC = FALSE)calcFeDemandIndustry(use_ODYM_RECC = FALSE)
use_ODYM_RECC |
per-capita pathways for 'SDP_xx' scenarios? (Defaults to 'FALSE'.) |
Michaja Pehl
Calculates FE demand in transport as REMIND variables
calcFeDemandTransport()calcFeDemandTransport()
Alois Dirnaicher, Johanna Hoppe
Reads in the data of the source IIASA_subs_taxes, by country. and calculate taxes at the final energy delivery level to the end-use sectors (industry, buildings and transport). Regional aggregation is done via the respective energy quantities as weights.
calcFETaxes(subtype = "taxes")calcFETaxes(subtype = "taxes")
subtype |
choose between tax rates ("taxes") or subsidies rate ("subsidies") output |
MAgPIE object
Christoph Bertram and Renato Rodrigues
calcOutput, readIIASA_subs_taxes,
convertIIASA_subs_taxes
## Not run: calcOutput("FETaxes") ## End(Not run)## Not run: calcOutput("FETaxes") ## End(Not run)
generate F-Gases based on IMAGE data
calcFGas(subtype = "interpolate2025")calcFGas(subtype = "interpolate2025")
subtype |
"interpolate2025" will intepolate from EDGAR historical data from 2025-2050 to account for the very old IMAGE scenarios. Any other subtype will ignore this step. |
magpie object with F-gases information
Lavinia Baumstark
## Not run: x <- calcOutput("FGas") ## End(Not run)## Not run: x <- calcOutput("FGas") ## End(Not run)
Residential, commercial and total floor space from EDGE-B. Set
calcFloorspace(onlyTotal = FALSE)calcFloorspace(onlyTotal = FALSE)
onlyTotal |
boolean, only give total instead of sub-sectoral floor space |
MAgPIE object with buildings floor space
Antoine Levesque, Robin Hasse
provides coefficients for fossil fuels (oil, gas and coal) and uranium extraction cost equations.
calcFossilExtraction(subtype = "FossilExtraction")calcFossilExtraction(subtype = "FossilExtraction")
subtype |
Either 'FossilExtraction' or 'UraniumExtraction' |
magpie object of the coefficients for fossil fuels and uranium extraction cost equations
Renato Rodrigues, Felix Schreyer
## Not run: calcOutput(type = "FossilExtraction", subtype = "FossilExtraction") ## End(Not run)## Not run: calcOutput(type = "FossilExtraction", subtype = "FossilExtraction") ## End(Not run)
Calculates air pollutant emissions and emission factors (user can choose) based on GAINS emissions and activity data. Result is given on GAINS sector level. User can choose between aggregated and extended sectoral resolution. Results are given for multiple scenarios. Scenario design is partly taken from the GAINS data and partly created in this function (particularly the SSPs).
calcGAINS(subtype = "emission_factors", sectoral_resolution = "extended")calcGAINS(subtype = "emission_factors", sectoral_resolution = "extended")
subtype |
decides whether emissions or emission factors are returned |
sectoral_resolution |
aggreaged or extenden (uses different GAINS input data) |
Provides input data for exoGAINSAirpollutants.R
calcGAINSEmi(subtype = "emissions")calcGAINSEmi(subtype = "emissions")
subtype |
"emission_factors", "emissions","emissions_starting_values" |
Emissions and emission factors
Sebastian Rauner
## Not run: calcOutput("calcGAINSEmi") ## End(Not run)## Not run: calcOutput("calcGAINSEmi") ## End(Not run)
Extracts oil, gas and coal data from the GEA 2012 into a scenario- and time-dependent grade structure
calcGEA2012(subtype, datatype)calcGEA2012(subtype, datatype)
subtype |
oil, coal, gas, or bounds |
datatype |
extraseed, exportbound, or decoffset for bounds subtype |
MAgPIE object containing regionally aggregated GEA 2012 data
Stephen Bi
## Not run: a <- calcOutput("GEA2012") ## End(Not run)## Not run: a <- calcOutput("GEA2012") ## End(Not run)
Calculate near-term expectations of capacities for use in fullVALIDATION.R
calcGlobalEnergyMonitor()calcGlobalEnergyMonitor()
A magpie object.
Falk Benke
Gather reference data from various sources.
calcHistorical()calcHistorical()
Calculate Final Energy for the buildings sector from Heat Roadmap Europe scenarios
calcHRE()calcHRE()
A magpie object.
Pascal Weigmann
Calculate REMIND emission variables from IEA ETP values
calcIEA_ETP()calcIEA_ETP()
A magpie object.
Falk Benke
Calculate REMIND variables from IEA Global EV Outlook data
calcIEA_EVOutlook()calcIEA_EVOutlook()
Falk Benke
Calculate REMIND variables from IEA World Energy Outlook data.
calcIEA_WorldEnergyOutlook()calcIEA_WorldEnergyOutlook()
Falk Benke
Calculate Limits on Industry CCS Capacities
calcIndustry_CCS_limits( a1 = 0.3, a2 = 0.15, installation_minimum = 1, stage_weight = c(Operational = 1, `In construction` = 1, `Advanced development` = 0.5, `Early development` = 0.2), facility_subsector = c(Cement = "cement", Chemical = "chemicals", `Hydrogen / Ammonia / Fertiliser` = "chemicals", Ethan = "chemicals", `Iron and Steel Production` = "steel"), region_mapping = NULL )calcIndustry_CCS_limits( a1 = 0.3, a2 = 0.15, installation_minimum = 1, stage_weight = c(Operational = 1, `In construction` = 1, `Advanced development` = 0.5, `Early development` = 0.2), facility_subsector = c(Cement = "cement", Chemical = "chemicals", `Hydrogen / Ammonia / Fertiliser` = "chemicals", Ethan = "chemicals", `Iron and Steel Production` = "steel"), region_mapping = NULL )
a1, a2
|
Annual growth factors of CCS capacity limits, for the first ten
years and thereafter, default to |
installation_minimum |
Minimum emission capacity (in MtCO~2~/year)
capacities are rounded up to. Defaults to |
stage_weight |
A named vector of weight factors for different lifecycle stages. See Details. |
facility_subsector |
A named vector mapping the "Facility Industry" of CCS projects to REMIND industry subsectors. See Details. |
region_mapping |
A data frame with columns |
The limits on industry CCS capacities are calculated from data of the
Global Status of CCS 2023 report (through
readGlobalCCSinstitute(). CCS projects are
filtered for valid (i.e. not "Under Evaluation") data for "Operation date" and "CO~2~ capture capacity"
assigned to REMIND industry subsectors according to facility_subsector,
which defaults to
| Facility Industry | subsector |
| Cement | cement |
| Chemical | chemicals |
| Hydrogen / Ammonia / Fertiliser | chemicals |
| Ethan | chemicals |
| Iron and Steel Production | steel |
weighted by lifecycle stage according to stage_weight, which defaults to
| Lifecycle stage | weight |
| Operational | 100 % |
| In construction | 100 % |
| Advanced development | 50 % |
| Early development | 20 % |
The resulting project capacities constitute the limits on industry subsector
CCS capacity for 2025. The limit on CCS capacities for regions (or countries
if region_mapping is NULL) is set to a value of total 2025 subsector CCS
capacity, times the regions share in subsector activity (e.g. cement
production) of the SSP2EU scenario
in 2030 if the region as some CCS capacity in 2025 in a different industry subsector, or
in 2035 if the region has no industry CCS capacity in 2030 at all.
CCS capacities are increased by the annual growth factor a1 for the ten
first years, and by the annual growth factor a2 afterwards (defaulting to
70 % and 20 %, respectively).
A list with a magpie object x, weight,
unit, description, and min.
Michaja Pehl
Industry Energy Efficiency Capital
calcIndustry_EEK(kap)calcIndustry_EEK(kap)
kap |
General internal capital stock, as calculated internally by 'calcCapital()'. |
A list with a ['magpie'][magclass::magclass] object 'x', 'weight', 'unit', and 'description' fields.
Return readindustry_subsectors_specific('industry_specific_FE_limits') in a
format usable as a REMIND input.
calcindustry_specific_FE_limits()calcindustry_specific_FE_limits()
A magpie object.
Michaja Pehl
Computes IEA-based model data for different "subtypes" by use of raw IEA "Energy Balances" data and a mapping that corresponds to the structure of "products" and "flows" of IEA.
calcIO( subtype = c("input", "output", "output_biomass", "trade", "input_Industry_subsectors", "output_Industry_subsectors", "IEA_output", "IEA_input"), ieaVersion = "default" )calcIO( subtype = c("input", "output", "output_biomass", "trade", "input_Industry_subsectors", "output_Industry_subsectors", "IEA_output", "IEA_input"), ieaVersion = "default" )
subtype |
Data subtype. See default argument for possible values. |
ieaVersion |
Release version of IEA data, either 'default' (vetted and used in REMIND) or 'latest'. |
Mapping structure example: IEA product ANTCOAL used for IEA flow TPATFUEL, contributes via REMIND technology coaltr for generating sesofos from pecoal (REMIND names)
When using subtype output_Industry_subsectors, additional corrections are
applied to the IEA data in tool_fix_IEA_data_for_Industry_subsectors.
IEA data as MAgPIE object aggregated to country level
Anastasis Giannousakis
## Not run: a <- calcOutput("IO", subtype = "output") ## End(Not run)## Not run: a <- calcOutput("IO", subtype = "output") ## End(Not run)
Calculate selected REMIND energy and emission variables from historical JRC IDEES values
calcJRC_IDEES(subtype)calcJRC_IDEES(subtype)
subtype |
one of
|
A magpie object.
Falk Benke
write KLW damage parameters (from Kotz et al. 2024) into input data they are country-specific and should not be aggregated to the regional level at all
calcKLWdamage(subtype)calcKLWdamage(subtype)
subtype |
"beta1", "beta2", "maxGMT" |
MAgPIE object of damage parameters for KLW damage function on country level and for 1000 boostrapping samples
Franziska Piontek
Rrange of possible abatement between maximum and minimum emission level in a year
calcMACCsCO2()calcMACCsCO2()
MAgPIE object
David Klein
## Not run: calcOutput("MACCsCO2") ## End(Not run)## Not run: calcOutput("MACCsCO2") ## End(Not run)
Final energy demand for feedstocks (non-energy use)
calcnonEnergyIndFE()calcnonEnergyIndFE()
A magpie object.
Renato Rodrigues
This is used in remind2 reporting as input data to calculate additional capacity and secondary energy variables.
calcOtherFossilInElectricity()calcOtherFossilInElectricity()
The projection focuses on a tight mitigation scenario and assumes that all fossil emissions from waste burning / other fossil processes can be reduced to 0 by 2050. Should be replaced in the future by actual modeling of waste / other fossil plants, or at least connected to RCP scenario assumptions.
A list with a magpie object x, weight,
unit, description.
Robert Pietzcker, Falk Benke
Computes Primary Energy variables
calcPE(subtype = "IEA", ieaVersion = "default")calcPE(subtype = "IEA", ieaVersion = "default")
subtype |
source for calculation, either "IEA" or "IEA_WEO" |
ieaVersion |
Release version of IEA data, either 'default' (vetted and used in REMIND) or 'latest'. |
a magclass object
Reads in the data of the source IIASA_subs_taxes, by country. and calculate taxes at primary energy level. Regional aggregation is done via the respective energy quantities as weights.
calcPETaxes(subtype = "subsidies")calcPETaxes(subtype = "subsidies")
subtype |
subsidies rate ("subsidies") output |
MAgPIE object
Christoph Bertram and Renato Rodrigues
calcOutput, readIIASA_subs_taxes,
convertIIASA_subs_taxes
## Not run: calcOutput("PETaxes") ## End(Not run)## Not run: calcOutput("PETaxes") ## End(Not run)
calculates projections for the end of life fate of plastic waste in particular, calculates the share that is incinerated
calcPlasticsEoL()calcPlasticsEoL()
A list with a magpie object x, weight,
unit, description.
Falk Benke, Simón Moreno Leiva
Provides geothermal potential data
calcPotentialGeothermal()calcPotentialGeothermal()
geothermal potential data MAgPIE object
Renato Rodrigues
## Not run: calcOutput("PotentialGeothermal") ## End(Not run)## Not run: calcOutput("PotentialGeothermal") ## End(Not run)
Provides hydro potential data
calcPotentialHydro()calcPotentialHydro()
hydro potential data and corresonding weights as a list of two MAgPIE objects
Lavinia Baumstark
calcOutput, readWGBU,
convertWGBU, readSource
## Not run: calcOutput("PotentialHydro") ## End(Not run)## Not run: calcOutput("PotentialHydro") ## End(Not run)
Provides weathering potential data
calcPotentialWeathering()calcPotentialWeathering()
weathering potential data and corresonding weights as a list of two MAgPIE objects
Lavinia Baumstark
## Not run: calcOutput("PotentialWeathering") ## End(Not run)## Not run: calcOutput("PotentialWeathering") ## End(Not run)
Provides wind offshore potential data
calcPotentialWindOff()calcPotentialWindOff()
wind offshore potential data and corresonding weights as a list of two MAgPIE objects
Chen Chris Gong
calcOutput, readNREL,
convertNREL, readSource
## Not run: calcOutput("PotentialWindOff") ## End(Not run)## Not run: calcOutput("PotentialWindOff") ## End(Not run)
Provides wind onshore potential data
calcPotentialWindOn()calcPotentialWindOn()
wind onshore potential data and corresonding weights as a list of two MAgPIE objects
Lavinia Baumstark
calcOutput, readNREL,
convertNREL, readSource
## Not run: calcOutput("PotentialWindOn") ## End(Not run)## Not run: calcOutput("PotentialWindOn") ## End(Not run)
Calculate the expected near-term deployment of technologies based on projects that are currently either being built or in a planning stage for some technologies multiple sources are available. Output object currently needs to contain years 2020, 2025 and 2030.
calcProjectPipelines(subtype)calcProjectPipelines(subtype)
subtype |
choose technology 'biomass', 'coal', 'geothermal', 'hydro', 'nuclear', 'solar', 'wind' or 'CCS' |
Discussions on sources and assumptions: https://github.com/pik-piam/mrremind/discussions
Pascal Weigmann
provides RLDC coefficients values
calcRLDCCoefficients(subtype = "LoB")calcRLDCCoefficients(subtype = "LoB")
subtype |
Either 'LoB' or 'Peak' |
magpie object of the RLDC coefficients data
Renato Rodrigues
## Not run: calcOutput(type="RLDCCoefficients",subtype='LoB') ## End(Not run)## Not run: calcOutput(type="RLDCCoefficients",subtype='LoB') ## End(Not run)
calcSolar calculate Area, Capacity and Energy for photovoltaics (PV) and contentrated solar power (CSP)
calcSolar()calcSolar()
magpie object
Julian Oeser, modified by Renato Rodrigues
## Not run: a <- calcOutput(type="Solar")## Not run: a <- calcOutput(type="Solar")
Functions for calculating industry activity trajectories.
calcSteel_Projections( subtype = "production", match.steel.historic.values = TRUE, match.steel.estimates = "none", save.plots = NULL, China_Production = NULL ) calcIndustry_Value_Added( subtype = "physical", match.steel.historic.values = TRUE, match.steel.estimates = "none", save.plots = NULL, China_Production = NULL )calcSteel_Projections( subtype = "production", match.steel.historic.values = TRUE, match.steel.estimates = "none", save.plots = NULL, China_Production = NULL ) calcIndustry_Value_Added( subtype = "physical", match.steel.historic.values = TRUE, match.steel.estimates = "none", save.plots = NULL, China_Production = NULL )
subtype |
One of
|
match.steel.historic.values |
Should steel production trajectories match historic values? |
match.steel.estimates |
Should steel production trajectories match
exogenous estimates?
|
save.plots |
|
China_Production |
A data frame with columns |
A list with a magpie object x, weight,
unit, description, min, and max.
Michaja Pehl
Calculate Steel Stock from Mueller steel stock per capita and WDI population
calcSteelStock()calcSteelStock()
A magpie object.
Falk Benke
provides capacity factor values
calcStorageFactor()calcStorageFactor()
magpie object of the capacity factor data
Lavinia Baumstark
## Not run: calcOutput("StorageFactor") ## End(Not run)## Not run: calcOutput("StorageFactor") ## End(Not run)
tax convergence levels for specific regions
calcTaxConvergence()calcTaxConvergence()
magpie object of the tax convergence levels
Renato Rodrigues
## Not run: calcOutput("TaxConvergence") ## End(Not run)## Not run: calcOutput("TaxConvergence") ## End(Not run)
tax and subsidies maximum levels. The tax limits serve as a work around to avoid excess of subsidy levels that could cause problems on the REMIND model solution. These files should be removed or replaced once a better way to handle this issue is introduced to the REMIND model formulation or once better yearly and country subsidy level data is available for the primary and final energies.
calcTaxLimits(subtype)calcTaxLimits(subtype)
subtype |
Name of the subsidy data type limit, e.g. "maxFeSubsidy" for maximum final energy subsidy,"maxPeSubsidy" for maximum primary energy subsidy or "propFeSubsidy" for proportional cap for final energy subsidy |
magpie object of the subtype tax limit
Renato Rodrigues
## Not run: calcOutput("TaxLimits") ## End(Not run)## Not run: calcOutput("TaxLimits") ## End(Not run)
write TC damage parameters into input data they are country-specific and should not be aggregated to the regional level at all
calcTCdamage(subtype)calcTCdamage(subtype)
subtype |
"const", "tasK" |
MAgPIE object of damage parameters for country level tropical cyclone damage function
Franziska Piontek
To calculate the regional Theil-T index (= correction to welfare function for a lognormal income distribution) we do the following: (1) convert country-level Gini coefficients to Theil (2) calculate contribution to Theil-T index that includes both between-countries and within-country inequality (see e.g. https://en.wikipedia.org/wiki/Theil_index). The latter can then be aggregated with calcOutput().
calcTheil()calcTheil()
NB 1: the aggregation depends on the region mapping. It is implemented such that the regionmapping specified in getConfig()$regionmapping is used.
NB 2: the result of calcOutput('Theil', aggregate = FALSE), is NOT the country Theil-T, but the unweighted contribution from a given country to the regional value.
magpie objects of unweighted contribution to Theil, weights (= country shares of regional GDP)
Bjoern Soergel
calcOutput convertGini,readGini
## Not run: calcOutput("Theil") ## End(Not run)## Not run: calcOutput("Theil") ## End(Not run)
Computes Trade variables based on latest IEA data available
calcTrade()calcTrade()
a magclass object
Calculate REMIND final energy variables from historical UBA values
calcUBA()calcUBA()
A magpie object.
Falk Benke
Calculate REMIND emission variables from historical UNFCCC values
calcUNFCCC()calcUNFCCC()
A magpie object.
Falk Benke, Pascal Weigmann
This function calculates REMIND input data on water consumption coefficients per electricity technology, using as initial information the Macknick (2011) data per electricity technology. The source data provide most required information but some assumptions on missing data are also made.
calcWaterConsCoef()calcWaterConsCoef()
MAgPIE object on water consumption coefficients per electricity technology
Ioanna Mouratiadou
calcOutput, readMacknickIntensities,
calcWaterWithCoef
## Not run: calcOutput("WaterConsCoef") ## End(Not run)## Not run: calcOutput("WaterConsCoef") ## End(Not run)
This function calculates REMIND input data on water withdrawal coefficients per electricity technology, using as initial information the Macknick (2011) data per electricity technology. The source data provide most required information but some assumptions on missing data are also made.
calcWaterWithCoef()calcWaterWithCoef()
MAgPIE object on water withdrawal coefficients per elecricity technology
Ioanna Mouratiadou
calcOutput, readMacknickIntensities,
calcWaterConsCoef
## Not run: calcOutput("WaterWithCoeff") ## End(Not run)## Not run: calcOutput("WaterWithCoeff") ## End(Not run)
Convert ADVANCE WP2 Data
convertADVANCE_WP2(x, subtype)convertADVANCE_WP2(x, subtype)
x |
A |
subtype |
One of
|
A magpie object.
Michaja Pehl
readSource(), readADVANCE_WP2()
Convert AGEB data
convertAGEB(x)convertAGEB(x)
x |
A |
A magpie object.
Falk Benke
convert Ariadne database data
convertAriadneDB(x)convertAriadneDB(x)
x |
A ['magpie'][magclass::magclass] object returned from ['readAriadneDB()']. |
A ['magpie'][magclass::magclass] object.
Felix Schreyer
Converts BGR oil, gas, coal and uranium reserves data
convertBGR(x, subtype)convertBGR(x, subtype)
x |
MAgPIE object to be converted |
subtype |
data subtype. Either "oil", "gas", "coal" or "uranium". |
A MAgPIE object containing BGR (Federal Institute for Geosciences and Natural Resources) country reserves disaggregated data of oil, gas, coal and uranium.
Renato Rodrigues
## Not run: a <- convertBGR(x, subtype = "oil") ## End(Not run)## Not run: a <- convertBGR(x, subtype = "oil") ## End(Not run)
Disaggregates and cleans BP data.
convertBP(x, subtype)convertBP(x, subtype)
x |
MAgPIE object to be converted |
subtype |
Either "Emission", "Capacity", "Generation", "Production", "Consumption", "Trade Oil", "Trade Gas", "Trade Coal" or "Price" |
A ['magpie'][magclass::magclass] object.
Aman Malik, Falk Benke
Convert Davies (2013) data on on shares of cooling types using mapping from GCAM regions to ISO country level.
convertDaviesCooling(x)convertDaviesCooling(x)
x |
MAgPIE object containing DaviesCooling data region resolution |
MAgPIE object of the Davies (2013) data disaggregated to country level
Lavinia Baumstark, Ioanna Mouratiadou
## Not run: a <- convertDaviesCooling(x)## Not run: a <- convertDaviesCooling(x)
Converts Dylan's Australian gas cost to magpie
convertDylanAusGasCost(x)convertDylanAusGasCost(x)
x |
MAgPIE object to be converted |
magpie object of the CEMO data
Felix Schreyer
convertEDGAR7Fgases
convertEDGAR7Fgases(x)convertEDGAR7Fgases(x)
x |
magpie object to be converted |
Gabriel Abrahao
Convert EDGE Buildings data to data on ISO country level.
convertEdgeBuildings(x, subtype = "FE")convertEdgeBuildings(x, subtype = "FE")
x |
MAgPIE object containing EDGE values at ISO country resolution |
subtype |
either FE or Floorspace |
EDGE data as MAgPIE object aggregated to country level
Antoine Levesque, Robin Hasse
Convert EDGEtransport
convertEDGETransport(x, subtype)convertEDGETransport(x, subtype)
x |
MAgPIE object containing EDGE-T values in 21 region resolution |
subtype |
REMIND/iterative EDGE-T input data subtypes |
REMIND/iterative EDGE-T input data as MAgPIE object disaggregated to ISO level
Johanna Hoppe
Convert Ember data
convertEmber(x)convertEmber(x)
x |
A magpie object.
Pascal Weigmann
Convert European Energy Datasheets
convertEuropeanEnergyDatasheets(x, subtype)convertEuropeanEnergyDatasheets(x, subtype)
x |
European Energy Datasheets magpie object derived from readEuropeanEnergyDatasheets function |
subtype |
data subtype. Either "EU28" (data from June 20 including GBR) or "EU27" (latest data from August 23 without GBR) |
converted European Energy Datasheets magpie object
Renato Rodrigues and Atreya Shankar
European Energy Datasheets public database https://energy.ec.europa.eu/data-and-analysis/eu-energy-statistical-pocketbook-and-country-datasheets_en
## Not run: test <- readSource("EuropeanEnergyDatasheets", subtype = "EU27", convert = TRUE) ## End(Not run)## Not run: test <- readSource("EuropeanEnergyDatasheets", subtype = "EU27", convert = TRUE) ## End(Not run)
Converts EU Effort Sharing targets and historical emissions
convertEurostat_EffortSharing(x, subtype)convertEurostat_EffortSharing(x, subtype)
x |
MAgPIE object to be converted |
subtype |
data subtype. Either "target" or "emissions" |
A MAgPIE object containing the EU Effort Sharing targets (
Renato Rodrigues
## Not run: a <- convertEurostat_EffortSharing(x,subtype="target")## Not run: a <- convertEurostat_EffortSharing(x,subtype="target")
Converts data from expert guess
convertExpertGuess(x, subtype)convertExpertGuess(x, subtype)
x |
unconverted magpie object from read-script |
subtype |
Type of data that are converted. |
magpie object with a completed dataset.
Converts oil, gas and coal data from the Global Energy Assessment 2012 to country-level aggregation
convertGEA2012(x, subtype)convertGEA2012(x, subtype)
x |
MAgPIE object to be disaggregated |
subtype |
Type of fossil fuel (oil, coal or gas) |
MAgPIE object containing country-level disaggregation of GEA 2012 data
Stephen Bi
## Not run: a <- readSource("GEA2012") ## End(Not run)## Not run: a <- readSource("GEA2012") ## End(Not run)
Convert GGDC 10-Sector Database - https://www.rug.nl/ggdc/structuralchange/previous-sector-database/10-sector-2014
convertGGDC10(x)convertGGDC10(x)
x |
MAgPIE object to be converted |
A MAgPIE object containing GGDC disaggregated data
Renato Rodrigues
## Not run: a <- convertGGDC10(x) ## End(Not run)## Not run: a <- convertGGDC10(x) ## End(Not run)
Converts Gini data from readGini() to ISO country level. Countries missing in the original data set will have their Gini set to zero ( a very small number for numerical reasons to be precise). The original data range is 2011-2100 in one-year steps, here we extend it to 2000-2150 in 5-year steps. Values before (after) the original range are held fixed at 2011 (2100) levels. Gini values for the SDP scenario are taken from the SSP1 scenario
convertGini(x)convertGini(x)
x |
MAgPIE object containing Gini data with World Bank codes, 2011-2100, in percent (range 0-100) |
MAgPIE object of the Gini data in ISO countries, range 0-1
Bjoern Soergel
## Not run: a <- convertGini(x) ## End(Not run)## Not run: a <- convertGini(x) ## End(Not run)
Convert Global CCS Institute Project Database
convertGlobalCCSinstitute(x, subtype = "08-09-2017")convertGlobalCCSinstitute(x, subtype = "08-09-2017")
x |
A |
subtype |
Project Database version to read, one of
- |
A magpie object.
Convert Global Energy Monitor data
convertGlobalEnergyMonitor(x)convertGlobalEnergyMonitor(x)
x |
A magclass object returned from readGlobalEnergyMonitor(). |
Rahel Mandaroux, Falk Benke
Convert HRE data
convertHRE(x)convertHRE(x)
x |
A magpie object.
Pascal Weigmann
Data on currently operating and under-construction nuclear power plants, reactors planned and proposed, electricity generation from nuclear
convertIAEA(x)convertIAEA(x)
x |
MAgPIE object to be converted |
Christoph Bertram
convert IAEA Power Reactor Information System
convertIAEA_PRIS(x)convertIAEA_PRIS(x)
x |
a magclass object returned from 'readIAEA_PRIS()' |
Pascal Weigmann
Convert IEA CCUS data
convertIEA_CCUS(x)convertIEA_CCUS(x)
x |
A magclass object returned from readIEA_CCUS(). |
A magclass object.
Anne Merfort, Falk Benke
Convert IEA ETP projections
convertIEA_ETP(x, subtype)convertIEA_ETP(x, subtype)
x |
IEA ETP projection magpie object derived from readIEA_ETP function |
subtype |
data subtype. Either "industry", "buildings", "summary", or "transport" |
Falk Benke
Convert IEA EV Outlook
convertIEA_EVOutlook(x)convertIEA_EVOutlook(x)
x |
a magclass object returned from 'readIEA_EVOutlook()' |
Falk Benke
convert IEA Hydro Special Market Report
convertIEA_HSMR(x)convertIEA_HSMR(x)
x |
a magclass object returned from 'readIEA_HSMR()' |
Pascal Weigmann
maps to iso countries
convertIEA_PVPS(x)convertIEA_PVPS(x)
x |
MAgPIE object to be converted |
Magpie object with IEA PVPS investment cost per country
Felix Schreyer
Reads the distributed solar pv capacity from IEA Renewables report (2019).
convertIEA_REN(x)convertIEA_REN(x)
x |
input magpie object |
Capacity in GW. Distributed solar, defined in the IEA Renewables (2019), includes rooftop residential (0-10 kW, grid-connected), rooftop and ground-mounted commercial and industrial (10-1000kW, grid-connected), and off-grid (8W - 100 kW)
magpie object with country-wise distributed solar pv capacity
Aman Malik
Converts IEA World Energy Outlook data
convertIEA_WEO(x, subtype)convertIEA_WEO(x, subtype)
x |
MAgPIE object to be converted |
subtype |
data subtype. Either "Capacity", "Generation", "Emissions", "Investment Costs", "O&M Costs" or "Efficiency" |
magpie object of the WEO data on generation (TWh), capacities (GW), emissions (Mt CO2) or disaggregated investment cost as magpie object
Renato Rodrigues and Aman Malik
## Not run: a <- convertWEO(x, subtype = "Capacity") ## End(Not run)## Not run: a <- convertWEO(x, subtype = "Capacity") ## End(Not run)
Convert IEA World Energy Outlook Data from 2023
convertIEA_WorldEnergyOutlook(x)convertIEA_WorldEnergyOutlook(x)
x |
magclass object to be converted |
Falk Benke
Convert IIASA subsidy and taxes data on ISO country level (removes countries not part of 249 oficial ISO countries and fills missing with zeros).
convertIIASA_subs_taxes(x, subtype)convertIIASA_subs_taxes(x, subtype)
x |
MAgPIE object containing IIASA subsidies and taxes data in country resolution |
subtype |
Type of country level data as compiled by IIASA that should be read in. Available types are:
|
IIASA_subs_taxes data as MAgPIE object aggregated to country level
Christoph Bertram
## Not run: a <- convertIIASA_subs_taxes(x) ## End(Not run)## Not run: a <- convertIIASA_subs_taxes(x) ## End(Not run)
Converts IRENA Regional data
convertIRENA(x, subtype)convertIRENA(x, subtype)
x |
MAgPIE object to be converted |
subtype |
data subtype. Either "Capacity" or "Generation" |
A MAgPIE object containing IRENA country disaggregated data with historical electricity renewable capacities (MW) or generation levels (GWh)
Renato Rodrigues
## Not run: a <- convertIRENA(x, subtype = "Capacity") ## End(Not run)## Not run: a <- convertIRENA(x, subtype = "Capacity") ## End(Not run)
convert KLW damage fills in countries for which no damage parameters are provided, setting parameters to zero
convertKLWdamage(x)convertKLWdamage(x)
x |
is MAgPIE object containing the damage parameters from KLW |
MAgPIE object containing values for all 249 ISO countries
Franziska Piontek
Convert Mueller data
convertMueller(x, subtype)convertMueller(x, subtype)
x |
A |
subtype |
One of:
|
A magpie object.
Falk Benke
Converts Final energy demand for feedstocks (non-energy use)
convertnonEnergyDemand(x)convertnonEnergyDemand(x)
x |
MAgPIE object to be converted |
A MAgPIE object containing country disaggregated data
Renato Rodrigues
## Not run: a <- convertnonEnergyDemand(x) ## End(Not run)## Not run: a <- convertnonEnergyDemand(x) ## End(Not run)
Convert NREL data on ISO country level.
convertNREL(x)convertNREL(x)
x |
MAgPIE object containing NREL data country-region resolution |
NRELWirsenius data as MAgPIE object aggregated to country level
Lavinia Baumstark
## Not run: a <- convertNREL(x, subtype = "onshore") ## End(Not run)## Not run: a <- convertNREL(x, subtype = "onshore") ## End(Not run)
Converts Openmod capacities data
convertOpenmod(x)convertOpenmod(x)
x |
MAgPIE object to be converted |
A MAgPIE object containing openmod EU country disaggregated data with 2010 and 2015 electricity capacities (GW)
Renato Rodrigues
## Not run: a <- convertOpenmod(x) ## End(Not run)## Not run: a <- convertOpenmod(x) ## End(Not run)
convertRCP convert RCP data
convertRCP(x, subtype)convertRCP(x, subtype)
x |
Input object obtained by readSource |
subtype |
Either 'Waste' or 'AviationShipping' |
magpie object of the RCP data
Julian Oeser
Converts REMIND regional data
convertREMIND_11Regi(x, subtype)convertREMIND_11Regi(x, subtype)
x |
MAgPIE object to be converted |
subtype |
Name of the regional data, e.g. "p4", "biomass", "ch4waste", "tradecost", "pe2se", "xpres_tax", "deltacapoffset", capacityFactorRules", "taxConvergence", "maxFeSubsidy", "maxPeSubsidy", "propFeSubsidy", "fossilExtractionCoeff", "uraniumExtractionCoeff", "RLDCCoefficientsLoB", "RLDCCoefficientsPeak", "earlyRetirementAdjFactor" |
A MAgPIE object containing country disaggregated data
original: not defined - capacity factor, tax, fossil and RLDC changes: Renato Rodrigues
## Not run: a <- convertREMIND_11Regi(x,subtype="capacityFactorGlobal")## Not run: a <- convertREMIND_11Regi(x,subtype="capacityFactorGlobal")
Converts CES derivatives/prices from former REMIND runs to ISO level
convertRemindCesPrices(x, subtype = "ccd632d33a")convertRemindCesPrices(x, subtype = "ccd632d33a")
x |
MAgPIE object containing REMIND prices at the REMIND region resolution |
subtype |
Regional resolution of REMIND data which should be loaded. ccd632d33a corresponds to the REMIND-11, and 690d3718e1 to REMIND-H12 |
magpie object of REMIND prices
Antoine Levesque
This code aggregates and homogenises different types of renewable energy targets into total installed capacity targets (in GW).
convertREN21(x, subtype)convertREN21(x, subtype)
x |
MAgPIE object to be converted |
subtype |
Only "Capacity" asof now |
Policy database accessible in "inputdata/sources/REN21/README"
Magpie object with Total Installed Capacity targets. The target years differ depending upon the database.
Aman Malik
Convert Stationary data to data on ISO country level.
convertStationary(x)convertStationary(x)
x |
MAgPIE object to be converted |
Antoine Levesque, Robin Hasse
Converts data from Stegmann2022
convertStegmann2022(x)convertStegmann2022(x)
x |
unconverted magpie object from read-script |
magpie object with a completed dataset.
Converts data on enhanced weathering
convertStrefler(x, subtype)convertStrefler(x, subtype)
x |
unconverted magpie object from read-script |
subtype |
data subtype. Either "weathering_graderegi", or "weathering_costs" |
magpie object with a completed dataset
Convert TCdamage fills in countries not affected by tropical cyclones (TC), setting parameters to zero
convertTCdamageKrichene(x)convertTCdamageKrichene(x)
x |
is MAgPIE object containing the damage parameters for the TC-prone countries |
MAgPIE object containing values for all 249 ISO countries
Franziska Piontek
Converts transport subsidies data
convertTransportSubsidies(x)convertTransportSubsidies(x)
x |
MAgPIE object to be converted |
A MAgPIE object containing transport subsidies per technology
Renato Rodrigues
## Not run: a <- convertTransportSubsidies(x) ## End(Not run)## Not run: a <- convertTransportSubsidies(x) ## End(Not run)
Convert UBA data
convertUBA(x)convertUBA(x)
x |
A magpie object.
Falk Benke
Convert UNFCCC data
convertUNFCCC(x)convertUNFCCC(x)
x |
A |
A magpie object.
Falk Benke
Converts conditional and unconditional capacity targets into total capacity (GW) in target year the Generation targets are similar to the capacity targets but include the capacity factors, the Emissions targets are the total (except land CO2) emissions in the target year
convertUNFCCC_NDC(x, subtype)convertUNFCCC_NDC(x, subtype)
x |
MAgPIE object to be converted |
subtype |
Capacity_YYYY_cond or Capacity_YYYY_uncond for Capacity Targets, Emissions_YYYY_cond or Emissions_YYYY_uncond for Emissions targets, with YYYY NDC version year |
Magpie object with Total Installed Capacity (GW) targets, target years differ depending upon the database.
Aman Malik, Christoph Bertram, Oliver Richters
Convert WGBU data on ISO country level.
convertWGBU(x)convertWGBU(x)
x |
MAgPIE object containing WGBU data country-region resolution |
WGBU data as MAgPIE object aggregated to country level
Lavinia Baumstark
## Not run: a <- convertWGBU(x) ## End(Not run)## Not run: a <- convertWGBU(x) ## End(Not run)
Function that produces the complete regional data set required for the DECENT model.
fullDECENT(rev = 0)fullDECENT(rev = 0)
rev |
data revision which should be used as input (positive numeric). |
Lavinia Baumstark, Lukas Feldhaus
readSource,getCalculations,calcOutput
## Not run: fullDECENT() ## End(Not run)## Not run: fullDECENT() ## End(Not run)
Function that produces the complete regional data set required for the REMIND model.
fullREMIND()fullREMIND()
Lavinia Baumstark
readSource,getCalculations,calcOutput
## Not run: fullREMIND() ## End(Not run)## Not run: fullREMIND() ## End(Not run)
assemble near-term thresholds from project pipelines and potentially other data sources and export them to a file
fullTHRESHOLDS(type = "config")fullTHRESHOLDS(type = "config")
type |
choose either "config" to export thresholds as used in the validationConfig or "full" to export all pipeline data |
Pascal Weigmann
Function that generates the historical regional dataset against which the REMIND model results can be compared.
fullVALIDATIONREMIND(rev = 0)fullVALIDATIONREMIND(rev = 0)
rev |
Unused parameter here for the pleasure of |
David Klein, Falk Benke
fullREMIND(), readSource(), getCalculations(),
calcOutput()
## Not run: fullVALIDATIONREMIND() ## End(Not run)## Not run: fullVALIDATIONREMIND() ## End(Not run)
Read ADVANCE WP2 Data
readADVANCE_WP2(subtype)readADVANCE_WP2(subtype)
subtype |
One of
|
A magpie object.
Michaja Pehl
readSource(), convertADVANCE_WP2()
Read AGEB
readAGEB(subtype = "balances")readAGEB(subtype = "balances")
subtype |
data subtype. Either "balances" ("Auswertungstabellen zur Energiebilanz Deutschland") or "electricity" ("Bruttostromerzeugung in Deutschland nach Energieträgern") |
A magpie object.
Falk Benke
Read GWP (or other metrics) from the AR6 WGIII Table SM7 per GHG species
readAR6GWP(subtype = "GWP100")readAR6GWP(subtype = "GWP100")
subtype |
data subtype. Currently just "GWP100", but other metrics are also available in the input data |
A data.frame with two columns, "Gas", with the common name of the GHG species, and "GWP", with the selected GWP
Gabriel Abrahao
Scenario data from the Ariadne modeling intercomparison project for Germany. See README in input file for more details.
readAriadneDB()readAriadneDB()
A ['magpie'][magclass::magclass] object.
Felix Schreyer
Read-in BGR csv files as magclass object
readBGR(subtype)readBGR(subtype)
subtype |
data subtype. Either "oil", "gas", "coal" or "uranium". |
magpie object of the BGR (Federal Institute for Geosciences and Natural Resources) data of reserves of oil, gas, coal and uranium per country.
Renato Rodrigues
## Not run: a <- readSource(type = "BGR", subtype = "oil") ## End(Not run)## Not run: a <- readSource(type = "BGR", subtype = "oil") ## End(Not run)
BP Capacity and Generation Data
readBP(subtype)readBP(subtype)
subtype |
Either "Emission", Capacity", "Generation", "Production", "Consumption", "Trade Oil", "Trade Gas", "Trade Coal" or "Price" |
A ['magpie'][magclass::magclass] object.
Aman Malik, Falk Benke
Read Employment factors and cumulative jobs for RE techs (for India)
readCEEW(subtype)readCEEW(subtype)
subtype |
data subtype. Either "Employment factors" or "Employment" |
Reports published by CEEW et al. See README.txt in the source folder for more information.
Aman Malik
## Not run: a <- readSource("CEEW",convert=F,subtype="Employment") ## End(Not run)## Not run: a <- readSource("CEEW",convert=F,subtype="Employment") ## End(Not run)
Read in Davies (2013) data on shares of cooling types per electricity technology and GCAM region
readDaviesCooling(subtype)readDaviesCooling(subtype)
subtype |
Type of Davies data that should be read. Available types are:
|
MAgPIE object of the Davies (2013) data
Lavinia Baumstark, Ioanna Mouratiadou
## Not run: a <- readSource(type = "DaviesCooling") ## End(Not run)## Not run: a <- readSource(type = "DaviesCooling") ## End(Not run)
read-in power Australian gas extraction cost curve based on Dylan's data Australian contact: Dylan McConnell, dylan.mcconnell(at)unimelb.edu.au
readDylanAusGasCost()readDylanAusGasCost()
magpie object of the cemo database data
Felix Schreyer
Read EDGAR7 emissions data for F-gases per species, in kt of each gas
readEDGAR7Fgases()readEDGAR7Fgases()
A magpie object with F-gases emissions per gas species and per country
Gabriel Abrahao
Load an EDGE Buildings file as magclass object.
readEdgeBuildings(subtype = c("FE", "Floorspace"))readEdgeBuildings(subtype = c("FE", "Floorspace"))
subtype |
One of the possible subtypes, see default argument. |
magclass object
Antoine Levesque, Robin Hasse
Run EDGE-Transport Standalone in all used scenario combinations to supply input data to REMIND and the iterative EDGE-T script
readEDGETransport(subtype)readEDGETransport(subtype)
subtype |
REMIND/iterative EDGE-T input data subtypes |
magpie object of EDGEtransport iterative inputs
Johanna Hoppe
## Not run: a <- readSource(type = "EDGETransport") ## End(Not run)## Not run: a <- readSource(type = "EDGETransport") ## End(Not run)
Read Ember Yearly Electricity Data
readEmber()readEmber()
A ['magpie'][magclass::magclass] object.
Pascal Weigmann
https://ember-climate.org/data-catalogue/yearly-electricity-data/
['readSource()']
Read European Energy Datasheets .xlsx file as magpie object.
readEuropeanEnergyDatasheets(subtype)readEuropeanEnergyDatasheets(subtype)
subtype |
data subtype. Either "EU28" (data from June 20 including GBR) or "EU27" (latest data from August 23 without GBR) |
magpie object of European Energy Datasheets
Renato Rodrigues, Atreya Shankar, Falk Benke
European Energy Datasheets public database https://energy.ec.europa.eu/data-and-analysis/eu-energy-statistical-pocketbook-and-country-datasheets_en
## Not run: test <- readSource("EuropeanEnergyDatasheet", subtype = "EU27", convert = FALSE) ## End(Not run)## Not run: test <- readSource("EuropeanEnergyDatasheet", subtype = "EU27", convert = FALSE) ## End(Not run)
Read-in EU Effort Sharing targets and historical emissions csv files as magclass object
readEurostat_EffortSharing(subtype)readEurostat_EffortSharing(subtype)
subtype |
data subtype. Either "target" or "emissions" |
magpie object of the EU Effort Sharing targets (
Renato Rodrigues
## Not run: a <- readSource(type = "Eurostat_EffortSharing", subtype = "target") ## End(Not run)## Not run: a <- readSource(type = "Eurostat_EffortSharing", subtype = "target") ## End(Not run)
Read-in data that are based on expert guess
readExpertGuess(subtype)readExpertGuess(subtype)
subtype |
Type of data that should be read. One of
|
magpie object of the data
Lavinia Baumstark
## Not run: a <- readSource(type = "ExpertGuess", subtype = "ies") ## End(Not run)## Not run: a <- readSource(type = "ExpertGuess", subtype = "ies") ## End(Not run)
Historical data of operating, under-construction, planned and announced Coal Plants by country (in MW) from the Global Energy Monitor's Global Coal Plant Tracker, and extrapolations for 2025 capacity scenarios
readGCPT(subtype)readGCPT(subtype)
subtype |
Options are status, historical, future, lifespans, comp_rates and emissions |
Stephen Bi
Read in datafiles comprising fossil fuel data from the Global Energy Assessment 2012
readGEA2012(subtype)readGEA2012(subtype)
subtype |
Type of fossil fuel and type of data (oil, coal, or gas + costs, qtys, or dec) |
MAgPIE object of the GEA data
Stephen Bi
## Not run: a <- readSource("GEA2012", "coal") ## End(Not run)## Not run: a <- readSource("GEA2012", "coal") ## End(Not run)
Read GGDC 10-Sector Database - https://www.rug.nl/ggdc/structuralchange/previous-sector-database/10-sector-2014
readGGDC10()readGGDC10()
Renato Rodrigues
## Not run: a <- readSource("GGDC10",convert=F) ## End(Not run)## Not run: a <- readSource("GGDC10",convert=F) ## End(Not run)
Read Gini coefficients for SSP scenarios from Rao et al., Futures, 2018. Data has been provided by the authors, but will be made publicly available as well. This contains data for 184 countries and from 2011 onwards.
readGini()readGini()
Copied from the documentation provided by the authors: This sheet contains the original Gini projections for 43 countries from the underlying empirical model (See reference to RSP 2016 in the main paper) and the extrapolations to all countries using the methodology described in the article. The country codes are the World Bank codes.
magpie object of the Gini data
Bjoern Soergel
## Not run: a <- readSource(type="Gini")## Not run: a <- readSource(type="Gini")
Read Global CCS Institute Project Database
readGlobalCCSinstitute(subtype = "08-09-2017")readGlobalCCSinstitute(subtype = "08-09-2017")
subtype |
Project Database version to read, one of
- |
A magpie object.
read GEM data for all available technologies and relevant statuses
readGlobalEnergyMonitor()readGlobalEnergyMonitor()
Rahel Mandaroux, Falk Benke, Pascal Weigmann
Read Heat Roadmap Europe data
readHRE()readHRE()
A ['magpie'][magclass::magclass] object.
Pascal Weigmann
https://heatroadmap.eu/roadmaps/
['readSource()']
Data on currently operating and under-construction nuclear power plants, reactors planned and proposed, electricity generation from nuclear
readIAEA()readIAEA()
Christoph Bertram
read Nuclear capacities and near-term outlook from data scraped from https://pris.iaea.org/PRIS/CountryStatistics/CountryStatisticsLandingPage.aspx
readIAEA_PRIS()readIAEA_PRIS()
Pascal Weigmann
Reads in capacities from projects in IEA CCUS database
readIEA_CCUS(subtype)readIEA_CCUS(subtype)
subtype |
either 'historical' for data until 2023, 'projections' for "high" and "low" projections up to 2030 used as input-data or 'pipeline' separated by status for use in formulating near-term bounds |
Anne Merfort, Falk Benke
Read IEA ETP projections
readIEA_ETP(subtype)readIEA_ETP(subtype)
subtype |
data subtype. Either "industry", "buildings", "summary", or "transport" |
Falk Benke
Read IEA EV Outlook
readIEA_EVOutlook()readIEA_EVOutlook()
Falk Benke
read Hydro capacities and near-term outlook from data scraped from https://www.iea.org/data-and-statistics/data-tools/hydropower-data-explorer
readIEA_HSMR()readIEA_HSMR()
Pascal Weigmann
reads excel sheet with PV investment cost data
readIEA_PVPS()readIEA_PVPS()
magpie object with PV investment cost data
Felix Schreyer
Reads the distributed solar pv capacity from IEA Renewables report (2019).
readIEA_REN()readIEA_REN()
Capacity in GW. Distributed solar, defined in the IEA Renewables (2019), includes rooftop residential (0-10 kW, grid-connected), tooftop and ground-mounted commercial and industrial (10-1000kW, grid-connected), and off-grid (8W - 100 kW)
Aman Malik
Read projected 2014-20 investments into industry energy efficiency from the [IEA World Energy Investment Outlook (2014)](http://www.iea.org/publications/freepublications/publication/weo-2014-special-report—investment.html)
readIEA_WEIO_2014()readIEA_WEIO_2014()
A [madrat_mule()] with a list containing the [tibble] 'data' with 2014–20 average annual investments into 'Energy intensive' and 'Non-energy intensive' industry, in $bn 2012, and the [tibble] 'country_groups' with 'IEA region's and corresponding 'iso3c' country codes.
Read-in IEA WEO 2016 data for investment costs, O&M costs and Efficiency of different technologies, and WEO 2017 data for historical electricity capacities (GW), generation (TWh) or emissions (Mt CO2). WEO 2019 data for PE and FE (Mtoe).
readIEA_WEO(subtype)readIEA_WEO(subtype)
subtype |
data subtype. Either "Capacity", "Generation", "Emissions", "Investment Costs", "O&M Costs" or "Efficiency" |
magpie object of the WEO data on generation (TWh), capacities (GW), emissions (Mt CO2) or disaggregated investment cost as magpie object
Renato Rodrigues, Aman Malik, and Jerome Hilaire
## Not run: a <- readSource(type = "WEO", subtype = "Capacity") ## End(Not run)## Not run: a <- readSource(type = "WEO", subtype = "Capacity") ## End(Not run)
Read in IEA World Energy Outlook Data from 2023
readIEA_WorldEnergyOutlook()readIEA_WorldEnergyOutlook()
Falk Benke
Read-in country level data on final energy taxes and subsidies as provided from IIASA from .csv file as magclass object
readIIASA_subs_taxes(subtype)readIIASA_subs_taxes(subtype)
subtype |
Type of country level data as compiled by IIASA that should be read in. Available types are:
|
magpie object of the IIASA_subs_taxes data
Christoph Bertram
## Not run: a <- readSource(type = "IIASA_subs_taxes", "tax_rate") ## End(Not run)## Not run: a <- readSource(type = "IIASA_subs_taxes", "tax_rate") ## End(Not run)
Change factors of specific FE and material demand for the
industry/subsector realisation of REMIND.
readindustry_subsectors_specific(subtype = NULL) calcindustry_subsectors_specific( subtype = NULL, scenarios = NULL, regions = NULL, direct = NULL )readindustry_subsectors_specific(subtype = NULL) calcindustry_subsectors_specific( subtype = NULL, scenarios = NULL, regions = NULL, direct = NULL )
subtype |
One of
|
scenarios |
A vector of scenarios for which factors are to be returned. |
regions |
A vector of regions for which factors are to be returned. |
direct |
A data frame as returned by
|
Factors are read from the files specific_FE.csv,
specific_material_alpha.csv, specific_material_relative.csv, and
specific_material_relative_change.csv, respectively. NA is used to mark
defaults for the scenario and region columns, and specified values will
overwrite these defaults.
So
NA,NA,cement,1 will be extended to all scenarios and regions
scen1,NA,cement,2 will overwrite this default for all regions in
scen1
NA,regi1,cement,3 will overwrite this again for all scenarios
(including scen1) for regi1
scen1,regi1,cement,4 will lastly overwrite the value for the scen1,
regi1 combination
Replacements occure in this fixed order (NA/NA, scenario/NA,
NA/region, scenario/region).
Lastly, output is filtered for scenarios and regions.
For debugging and development, instead of modifying the .csv files in
sources/industry_subsectors_specific/ and interfering with production runs,
modify the calling code (e.g. calcFEdemand.R) to use direct data (entered
verbatim or loaded from somewhere else.)
A magpie object.
Michaja Pehl
Read-in an IRENA csv file as magclass object
readIRENA(subtype)readIRENA(subtype)
subtype |
data subtype. Either "Capacity" or "Generation" |
magpie object of the IRENA data with historical electricity renewable capacities (MW) or generation levels (GWh)
Renato Rodrigues
## Not run: a <- readSource(type = "IRENA", subtype = "Capacity") ## End(Not run)## Not run: a <- readSource(type = "IRENA", subtype = "Capacity") ## End(Not run)
Reads country-specific damage coefficients for the damage function presented in Kotz et al. (2024). Data has been provided by the authors. This contains data for all countries and for 1000 boostrapping realizations per country, capturing uncertainty from climate and empirical modeling. Subtypes are the temperature and temperature^2 coefficients and the maximum temperature per country for which the function is defined.
readKLWdamage(subtype)readKLWdamage(subtype)
subtype |
data subtype. Either "beta1", "beta2" or "maxGMT" |
KLW damage coefficients
Franziska Piontek
readLee Read in Aviation emission data from Lee
readLee(subtype)readLee(subtype)
subtype |
Either 'emi' or 'ef' |
magpie object of Aviation emission / emission factors data
Julian Oeser
## Not run: a <- readSource(type = "", subtype = "Waste") ## End(Not run)## Not run: a <- readSource(type = "", subtype = "Waste") ## End(Not run)
Read in Macknick (2011) data on water consumption and withdrawal coefficients per electricity technology
readMacknickIntensities(subtype)readMacknickIntensities(subtype)
subtype |
Type of Macknick data that should be read. Available types are:
|
MAgPIE object of the Macknick (2011) data
Ioanna Mouratiadou
## Not run: a <- readSource(type = "MacknickIntensities", convert = FALSE) ## End(Not run)## Not run: a <- readSource(type = "MacknickIntensities", convert = FALSE) ## End(Not run)
Read data from Müller et al. 2013 (http://dx.doi.org/10.1021/es402618m).
readMueller(subtype)readMueller(subtype)
subtype |
One of:
|
A magpie object.
Michaja Pehl
Read Final energy demand for feedstocks (non-energy use)
readnonEnergyDemand()readnonEnergyDemand()
magpie object of region dependent data
Renato Rodrigues
## Not run: a <- readSource(type = "nonEnergyDemand") ## End(Not run)## Not run: a <- readSource(type = "nonEnergyDemand") ## End(Not run)
Read-in NREL xlsx file as magclass object
readNREL(subtype)readNREL(subtype)
subtype |
type either "onshore" or "offshore" |
magpie object of NREL
Lavinia Baumstark
## Not run: a <- readSource(type = "NREL", subtype = "onshore") ## End(Not run)## Not run: a <- readSource(type = "NREL", subtype = "onshore") ## End(Not run)
Read ODYM_RECC data from the SHAPE Project
readODYM_RECC(subtype, smooth = TRUE) calcODYM_RECC(subtype, smooth = TRUE)readODYM_RECC(subtype, smooth = TRUE) calcODYM_RECC(subtype, smooth = TRUE)
subtype |
One of
|
smooth |
Smooth REMIND_industry_trends (default) or not. |
A magpie object.
Michaja Pehl
Read-in risk premium
readOECD() convertOECD(x)readOECD() convertOECD(x)
x |
MAgPIE object returned from readOECD |
The read-in data, usually a magpie object. If supplementary is TRUE a list including the data and metadata is returned instead. The temporal and data dimensionality should match the source data. The spatial dimension should either match the source data or, if the convert argument is set to TRUE, should be on ISO code country level.
[madrat::readSource()]
## Not run: readSource("OECD") ## End(Not run)## Not run: readSource("OECD") ## End(Not run)
Read-in an modified openmod capacities data file as magclass object
readOpenmod()readOpenmod()
magpie object of the LIMES team updated Openmod data on capacities (GW)
## Not run: a <- readSource(type = "Openmod") ## End(Not run)## Not run: a <- readSource(type = "Openmod") ## End(Not run)
Read data from Pauliuk et al. 2013 (https://dx.doi.org/10.1016/j.resconrec.2012.11.008).
readPauliuk(subtype = "lifetime")readPauliuk(subtype = "lifetime")
subtype |
One of:
|
A magpie object.
Michaja Pehl
Read-in PWT data as magclass object
readPWT() convertPWT(x)readPWT() convertPWT(x)
x |
MAgPIE object returned by readPWT |
The read-in data, usually a magpie object. If supplementary is TRUE a list including the data and metadata is returned instead. The temporal and data dimensionality should match the source data. The spatial dimension should either match the source data or, if the convert argument is set to TRUE, should be on ISO code country level.
[madrat::readSource()]
## Not run: readSource("PWT") ## End(Not run)## Not run: readSource("PWT") ## End(Not run)
Read RCP Read in RCP data
readRCP(subtype)readRCP(subtype)
subtype |
Either 'Waste' or 'AviationShipping' |
magpie object of the RCP data
Julian Oeser
## Not run: a <- readSource(type="RCP", subtype="Waste")## Not run: a <- readSource(type="RCP", subtype="Waste")
Read-in an csv files that contains regional data
readREMIND_11Regi(subtype)readREMIND_11Regi(subtype)
subtype |
Name of the regional data, e.g. "p4", "biomass", "ch4waste", "tradecost", "pe2se", "xpres_tax", "deltacapoffset", "capacityFactorGlobal", "capacityFactorRules", "residuesShare", "taxConvergence", "maxFeSubsidy", "maxPeSubsidy", "propFeSubsidy", "fossilExtractionCoeff", "uraniumExtractionCoeff", "RLDCCoefficientsLoB", "RLDCCoefficientsPeak", "earlyRetirementAdjFactor" |
magpie object of region dependent data
original: not defined, capacity factor, tax, fossil and RLDC changes: Renato Rodrigues
## Not run: a <- readSource(type = "REMIND_11Regi", subtype = "capacityFactorGlobal") ## End(Not run)## Not run: a <- readSource(type = "REMIND_11Regi", subtype = "capacityFactorGlobal") ## End(Not run)
Reads excel sheet with data on proposed policies, on Renewable energy capacity targets (which are broken down into Total Installed Capacity (TIC-Absolute), Additional Installed Capacity (AC-Absolute), and Production Absolute targets) or regional technology costs
readREN21(subtype)readREN21(subtype)
subtype |
Capacity Generation Emissions Share |
Country name is ISO coded. Capacity/Additional Capacity targets are in GW. Generation/Production targets are in GWh.
magpie object with Total Installed Capacity targets in GW for different target years
Aman Malik, Lavinia Baumstark
Load Stationary File as magclass object
readStationary()readStationary()
magclass object
Antoine Levesque, Robin Hasse
Read-in data for the End-of-Life fate of plastics from 1.Stegmann, P., Daioglou, V., Londo, M., van Vuuren, D. P. & Junginger, M. Plastic futures and their CO2 emissions. Nature 612, 272–276 (2022). https://www.nature.com/articles/s41586-022-05422-5 Link to SI: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-022-05422-5/MediaObjects/41586_2022_5422_MOESM1_ESM.xlsx #nolint
readStegmann2022()readStegmann2022()
magpie object of the data
Falk Benke, Simón Moreno
## Not run: a <- readSource(type = "Stegmann2022") ## End(Not run)## Not run: a <- readSource(type = "Stegmann2022") ## End(Not run)
Get data on enhanced weathering
readStrefler(subtype)readStrefler(subtype)
subtype |
type of data, one of "weathering_graderegi", "weathering_costs" |
magpie object of region dependent data
## Not run: a <- readSource(type="Strefler", subtype="weathering_graderegi") ## End(Not run)## Not run: a <- readSource(type="Strefler", subtype="weathering_graderegi") ## End(Not run)
Reads country-specific damage coefficients for tropical cyclones from Krichene et al. (in prep.). Data has been provided by the authors, but will be made publicly available as well. This contains data for 41 countries (those exposed to tropical cyclones), and two coefficients (constant and linear temperature)
readTCdamageKrichene(subtype)readTCdamageKrichene(subtype)
subtype |
data subtype. Either "const" or "tasK" |
TC damage coefficients
Franziska Piontek
Read-in transport subsidies csv files as magclass object
readTransportSubsidies()readTransportSubsidies()
magpie object of the transport subsidies for BEV, FCEV and PHEV (euros/car) for private and legal entities
Renato Rodrigues
## Not run: a <- readSource(type="TransportSubsidies")## Not run: a <- readSource(type="TransportSubsidies")
Read UNFCCC data
readUNFCCC()readUNFCCC()
A ['magpie'][magclass::magclass] object.
Falk Benke
['readSource()']
Reads excel sheet with NDC (Nationally Determined Contributions) data on different policy targets (capacity, emission, and share targets) with different variations
readUNFCCC_NDC(subtype)readUNFCCC_NDC(subtype)
subtype |
Capacity_2023_cond (or 2018/2021/2022 or uncond) for capacity target, Emissions_2023_cond (or 2018/2021/2022 or uncond) for Emissions targets |
Country name is ISO coded. Capacity/Additional Capacity targets are in GW. Generation/Production targets are in GWh.
magpie object
Aman Malik, Christoph Bertram, Oliver Richters, Sophie Fuchs, Rahel Mandaroux
Read and convert data from United Nations Industrial Organisation.
readUNIDO(subtype = "INDSTAT2") convertUNIDO(x, subtype = "INDSTAT2") calcUNIDO(subtype = "INDSTAT2")readUNIDO(subtype = "INDSTAT2") convertUNIDO(x, subtype = "INDSTAT2") calcUNIDO(subtype = "INDSTAT2")
subtype |
one of
- |
x |
result from |
A magpie object.
readUNIDO returns raw INDSTAT2 data. convertUNIDO converts to iso3c
country codes, selects industry subsectors value added data according to this
table
| subsector | ISIC | ctable | utable |
| manufacturing | D | 20 | 17–20 |
| cement | 20 | 20 | 17–20 |
| chemicals | 24 | 20 | 17–20 |
| steel | 27 | 20 | 17–20 |
and filters data that is either unreasonable or would unduly bias regional regressions according to this table
| subsector | iso3c | years |
| manufacturing | BIH | 1990–91 |
| manufacturing | CHN | 1963–97 |
| manufacturing | HKG | 1963–2015 |
| manufacturing | IRQ | 1994–98 |
| manufacturing | MAC | 1963–2015 |
| manufacturing | MDV | 1963–2015 |
| cement | BDI | 1980–2010 |
| cement | CIV | 1990–93 |
| cement | HKG | 1973–79 |
| cement | IRQ | 1992–97 |
| cement | NAM | 2007–10 |
| cement | RUS | 1970–90 |
| chemicals | CIV | 1989 |
| chemicals | HKG | 1973–79, 2008–15 |
| chemicals | MAC | 1978–79 |
| chemicals | NER | 1999–2002 |
| steel | BGD | 2011 |
| steel | CHE | 1995–96 |
| steel | CHL | 2008 |
| steel | HKG | 1973–79 |
| steel | HRV | 2012 |
| steel | IRL | 1980 |
| steel | LKA | 2006 |
| steel | MAR | 1989–2004 |
| steel | MKD | 1996 |
| steel | PAK | 1981–82 |
| steel | TUN | 2003–06 |
calcUNIDO() calculates otherInd subsector values as the difference
between manufacturing and cement, chemicals, and steel values and is
intended to be called through calcOutput(), which will aggregate regions.
Michaja Pehl
Read U.S. Geological Survey data
readUSGS(subtype = "cement") convertUSGS(x, subtype = "cement")readUSGS(subtype = "cement") convertUSGS(x, subtype = "cement")
subtype |
One of
|
x |
Data returned by |
A magpie object.
Michaja Pehl
Read data from van Ruijven et al. 2016, (http://dx.doi.org/10.1016/j.resconrec.2016.04.016, https://www.zotero.org/groups/52011/rd3/items/itemKey/6QMNBEHQ), obtained through personal communication (e-mail to Michaja Pehl). Units are tonnes per year.
readvanRuijven2016()readvanRuijven2016()
A magpie object.
Michaja Pehl
Read-in an WGBU xlsx file as magclass object
readWGBU()readWGBU()
magpie object of WGBU
Lavinia Baumstark
## Not run: a <- readSource(type = "WGBU") ## End(Not run)## Not run: a <- readSource(type = "WGBU") ## End(Not run)
Read combined data of World Steel Association statistical yearbooks (https://www.worldsteel.org/steel-by-topic/statistics/steel-statistical-yearbook.html).
readworldsteel(subtype = "detailed")readworldsteel(subtype = "detailed")
subtype |
One of - 'detailed' returning data for the worksheets - 'Pig Iron Production' - 'DRI Production' - 'Total Production of Crude Steel' - 'Production in Oxygen-Blown Converters' - 'Production in Open Hearth Furnaces' - 'Production in Electric Arc Furnaces' - 'Apparent Steel Use (Crude Steel Equivalent)' from 1991 on or - 'long' returning total production data from 1967 on |
A ['magpie'][magclass::magclass] object.
Michaja Pehl
['readSource()']
The data.frame 'd' is expanded in such a manner that all rows with 'NA' in either the 'scenario' or 'region' columns are extended to repeat for all scenarios and regions listed in 'scenarios' and 'regions'. Rows with specified scenarios and/or regions will overwrite extended ones. Regions are expanded before scenarios.
tool_expand_tibble(d, scenarios, regions, structure.columns = NULL)tool_expand_tibble(d, scenarios, regions, structure.columns = NULL)
d |
A data.frame with columns 'scenario' and 'region'. |
scenarios |
A character vector of scenario names. |
regions |
A character vector of region names. |
structure.columns |
A character vector of column names to be carried along. |
A 'tibble'.
## Not run: tribble( ~scenario, ~region, ~value, NA, NA, 0, NA, 'CHA', 1, 'SSP1', NA, 2, 'SSP2EU', 'DEU', 3) %>% tool_expand_tibble(scenarios = c('SSP1', 'SSP2EU', 'SSP5'), regions = c('CHA', 'DEU', 'USA')) %>% pivot_wider(names_from = 'region') tribble( ~scenario, ~region, ~name, ~value, NA, NA, 'A', 0, NA, 'CHA', 'B', 1, 'SSP1', NA, 'A', 2, 'SSP2EU', 'DEU', 'B', 3) %>% tool_expand_tibble(scenarios = c('SSP1', 'SSP2EU', 'SSP5'), regions = c('CHA', 'DEU', 'USA'), structure.columns = 'name') ## End(Not run)## Not run: tribble( ~scenario, ~region, ~value, NA, NA, 0, NA, 'CHA', 1, 'SSP1', NA, 2, 'SSP2EU', 'DEU', 3) %>% tool_expand_tibble(scenarios = c('SSP1', 'SSP2EU', 'SSP5'), regions = c('CHA', 'DEU', 'USA')) %>% pivot_wider(names_from = 'region') tribble( ~scenario, ~region, ~name, ~value, NA, NA, 'A', 0, NA, 'CHA', 'B', 1, 'SSP1', NA, 'A', 2, 'SSP2EU', 'DEU', 'B', 3) %>% tool_expand_tibble(scenarios = c('SSP1', 'SSP2EU', 'SSP5'), regions = c('CHA', 'DEU', 'USA'), structure.columns = 'name') ## End(Not run)
Apply corrections to IEA data to cope with fragmentary time series and replace outputs from blast furnaces and coke ovens, that are inputs into industry subsectors, by their respective inputs. The corrections done by this function are rather rudimentary and crude. This gets smoothed away in regional aggregation. But do not use the resulting country-level data without additional scrutiny.
tool_fix_IEA_data_for_Industry_subsectors(data, ieamatch, threshold = 0.01)tool_fix_IEA_data_for_Industry_subsectors(data, ieamatch, threshold = 0.01)
data |
MAgPIE object containing the IEA Energy Balances data |
ieamatch |
mapping of IEA product/flow combinations to REMIND
|
threshold |
minimum share each industry subsector uses of each product. Defaults to 1 %. |
Use regional or global averages if IEA industry data lists energy use only as
"non-specified".
Outputs from blast furnaces (BLFURGS, OGASES) and coke ovens (OVENCOKE,
COKEOVGS, COALTAR, NONCRUDE), that are inputs into industry subsectors.
Used internally in calcIO() for subtype output_Industry_subsectors.
a MAgPIE object
Michaja Pehl
Wrapper around magclass::add_dimension supporting more than one value for the new dimension. For each value, the input magclass object is copied, extended by the new dimension and appended to the output.
toolAddDimensions(x, dimVals, dimName, dimCode)toolAddDimensions(x, dimVals, dimName, dimCode)
x |
a magclass object |
dimVals |
list of values for the new dimension to be added |
dimName |
name of the new dimension |
dimCode |
dimension number of the new dimension (e.g. 3.1) |
the extended magclass object
Aggregate values to n-year averages to suppress volatility
toolAggregateTimeSteps(x, nYears = 5)toolAggregateTimeSteps(x, nYears = 5)
x |
a magclass object |
nYears |
time steps to be used for averaging, defaults to 5 |
magclass object with averages
Robin Hasse
toolBiomassSupplyAggregate The function aggregates biomass supply curves to regionmapping different from H12. It only works if all regions are subregions of H12 regions. The offset parameter (a) is taken from the H12 region. The slope parameter (b) is multiplied by a weight. The weight is the inverse of the share of agricultural area of the subregion in the H12 region.
toolBiomassSupplyAggregate( x, rel = NULL, weight = calcOutput("FAOLand", aggregate = F)[, , "6610", pmatch = TRUE][, "y2010", ] )toolBiomassSupplyAggregate( x, rel = NULL, weight = calcOutput("FAOLand", aggregate = F)[, , "6610", pmatch = TRUE][, "y2010", ] )
x |
magclass object that should be aggregated |
rel |
relation matrix containing a region mapping. |
weight |
aggregation weight |
return: returns region aggregated biomass supply curve data
Felix Schreyer
Estimates the function that represents the sum of cubic function inverses (sum in the x-axis)
toolCubicFunctionAggregate( x, rel = NULL, xLowerBound = 0, xUpperBound = 100, returnMagpie = TRUE, returnCoeff = TRUE, returnChart = FALSE, returnSample = FALSE, numberOfSamples = 1000, unirootLowerBound = -10, unirootUpperBound = 1e+100, colourPallete = FALSE, label = list(x = "x", y = "y", legend = "legend"), steepCurve = list() )toolCubicFunctionAggregate( x, rel = NULL, xLowerBound = 0, xUpperBound = 100, returnMagpie = TRUE, returnCoeff = TRUE, returnChart = FALSE, returnSample = FALSE, numberOfSamples = 1000, unirootLowerBound = -10, unirootUpperBound = 1e+100, colourPallete = FALSE, label = list(x = "x", y = "y", legend = "legend"), steepCurve = list() )
x |
magclass object that should be aggregated or data frame with coefficients as columns. |
rel |
relation matrix containing a region mapping. A mapping object should contain 2 columns in which each element of x is mapped to the category it should belong to after (dis-)aggregation |
xLowerBound |
numeric. Lower bound for x sampling (default=0). |
xUpperBound |
numeric. Upper bound for x sampling (default=100). |
returnMagpie |
boolean. if true, the function will return a single data table with all the countries in MagPie format. returnChart and returnSample are set to FALSE automatically if this option is active (default=TRUE). |
returnCoeff |
boolean. Return estimated coefficients (default=TRUE). |
returnChart |
boolean. Return chart (default=FALSE). |
returnSample |
boolean. Return samples used on estimation (default=FALSE). |
numberOfSamples |
numeric. NUmber of y-axis samples used on estimation (default=1e3). |
unirootLowerBound |
numeric. Lower bound to search for inverse solution in the initial bounds (default = -10). |
unirootUpperBound |
numeric. Upper bound to search for inverse solution in the initial bounds (default = 1e100). |
colourPallete |
vector. colour pallete to use on chart (default=FALSE). |
label |
list. List of chart labels (default=list(x = "x", y = "y", legend = "legend")). |
steepCurve |
list. List with coefficients for a very "vertical" function for the case with all countries with upper bound zero in an specific region aggregation (default= empty list, list()). |
Use case: aggregate country cubic cost functions to a single function that represents the entire region.
input: coefficients of the n-th country level cubic cost function.
Description of the problem: the aggregation of functions that represent unit costs, or prices in the y-axis, and quantities in the x-axis require operations with the inverse of the original functions. As complex functions present analytically challenging inverse function derivations, we adopt a sampling method to derive the function that corresponds to the sum of cubic function inverses.
Further extensions: the R function can be extended to support more complex curve estimations (beyonf third degree), whenever the mathematical function have a well defined inverse function in the selected boundaries.
return: returns a list of magpie objects containing the coefficients for the aggregate function. If returnMagpie is FALSE, returns a list containing the coefficients for the aggregate function (returnCoeff=TRUE), charts (returnChart=FALSE) and/or samples used in the estimation (returnSample=FALSE).
Renato Rodrigues
# Example # data EUR <- setNames(data.frame(30, 50, 0.123432, 2), c("c1", "c2", "c3", "c4")) NEU <- setNames(data.frame(30, 50, 1.650330, 2), c("c1", "c2", "c3", "c4")) df <- rbind(EUR, NEU) row.names(df) <- c("EUR", "NEU") # maxExtraction (upper limit for function estimation) maxExtraction <- 23 # output output <- toolCubicFunctionAggregate(df, xUpperBound = maxExtraction, returnMagpie = FALSE, returnChart = TRUE, returnSample = TRUE, label = list(x = "Cumulated Extraction", y = "Cost", legend = "Region Fuel Functions") ) output$coeff output$chart# Example # data EUR <- setNames(data.frame(30, 50, 0.123432, 2), c("c1", "c2", "c3", "c4")) NEU <- setNames(data.frame(30, 50, 1.650330, 2), c("c1", "c2", "c3", "c4")) df <- rbind(EUR, NEU) row.names(df) <- c("EUR", "NEU") # maxExtraction (upper limit for function estimation) maxExtraction <- 23 # output output <- toolCubicFunctionAggregate(df, xUpperBound = maxExtraction, returnMagpie = FALSE, returnChart = TRUE, returnSample = TRUE, label = list(x = "Cumulated Extraction", y = "Cost", legend = "Region Fuel Functions") ) output$coeff output$chart
Estimates cubic function inverses based on a weight factor that sum up to the original cubic function (sum in the x-axis)
toolCubicFunctionDisaggregate( x, weight, rel = NULL, xLowerBound = 0, xUpperBound = 100, returnMagpie = TRUE, returnCoeff = TRUE, returnChart = FALSE, returnSample = FALSE, numberOfSamples = 1000, unirootLowerBound = -10, unirootUpperBound = 1e+100, colourPallete = FALSE, label = list(x = "x", y = "y", legend = "legend") )toolCubicFunctionDisaggregate( x, weight, rel = NULL, xLowerBound = 0, xUpperBound = 100, returnMagpie = TRUE, returnCoeff = TRUE, returnChart = FALSE, returnSample = FALSE, numberOfSamples = 1000, unirootLowerBound = -10, unirootUpperBound = 1e+100, colourPallete = FALSE, label = list(x = "x", y = "y", legend = "legend") )
x |
magclass object that should be aggregated or data frame with coefficients as columns. |
weight |
magclass object containing weights which should be considered for a weighted aggregation. The provided weight should only contain positive values, but does not need to be normalized (any positive number>=0 is allowed). |
rel |
relation matrix containing a region mapping. A mapping object should contain 2 columns in which each element of x is mapped to the category it should belong to after (dis-)aggregation |
xLowerBound |
numeric. Lower bound for x sampling (default=0). |
xUpperBound |
numeric. Upper bound for x sampling (default=100). |
returnMagpie |
boolean. if true, the function will return a single data table with all the countries in MagPie format. returnChart and returnSample are set to FALSE automatically if this option is active (default=TRUE). |
returnCoeff |
boolean. Return estimated coefficients (default=TRUE). |
returnChart |
boolean. Return chart (default=FALSE). |
returnSample |
boolean. Return samples used on estimation (default=FALSE). |
numberOfSamples |
numeric. NUmber of y-axis samples used on estimation (default=1e3). |
unirootLowerBound |
numeric. Lower bound to search for inverse solution in the initial bounds (default = -10). |
unirootUpperBound |
numeric. Upper bound to search for inverse solution in the initial bounds (default = 1e100). |
colourPallete |
vector. colour pallete to use on chart (default=FALSE). |
label |
list. List of chart labels (default=list(x = "x", y = "y", legend = "legend")). |
Use case: disaggregate a single region cubic cost function to multiple country cubic functions weighted by a contribution factor. The sum of the countries function output is equal to the original regional function.
input: coefficients of the n-th country level cubic cost function.
Description of the problem: the disaggregation of functions that represent unit costs (or prices) in the y-axis and quantities in the x-axis require operations with the inverse of the original functions. As complex functions present analytically challenging inverse function derivations, we adopt a sampling method to derive the function that corresponds to the sum of cubic function inverses.
Further extensions: the R function can be extended to support more complex curve estimations (beyond third degree), whenever the mathematical function have a well defined inverse function in the selected boundaries.
return: returns a list of magpie objects containing the coefficients for the aggregate function. If returnMagpie is FALSE, returns a list containing the coefficients for the aggregate function (returnCoeff=TRUE), charts (returnChart=FALSE) and/or samples used in the estimation (returnSample=FALSE).
Renato Rodrigues
# Example # LAM coefficients df <- setNames(data.frame(30, 50, 0.34369, 2), c("c1", "c2", "c3", "c4")) row.names(df) <- "LAM" # weight weight <- setNames(c(21, 0, 579, 3, 228), c("ARG", "BOL", "BRA", "CHL", "COL")) # maxExtraction (upper limit for function estimation) maxExtraction <- 100 # output output <- toolCubicFunctionDisaggregate(df, weight, xUpperBound = maxExtraction, returnMagpie = FALSE, returnChart = TRUE, returnSample = TRUE, label = list(x = "Cumulated Extraction", y = "Cost", legend = "Region Fuel Functions") ) #' output$chart output$coeff output$chart# Example # LAM coefficients df <- setNames(data.frame(30, 50, 0.34369, 2), c("c1", "c2", "c3", "c4")) row.names(df) <- "LAM" # weight weight <- setNames(c(21, 0, 579, 3, 228), c("ARG", "BOL", "BRA", "CHL", "COL")) # maxExtraction (upper limit for function estimation) maxExtraction <- 100 # output output <- toolCubicFunctionDisaggregate(df, weight, xUpperBound = maxExtraction, returnMagpie = FALSE, returnChart = TRUE, returnSample = TRUE, label = list(x = "Cumulated Extraction", y = "Cost", legend = "Region Fuel Functions") ) #' output$chart output$coeff output$chart
Sets values for 6 EU countries not belonging to EU 28 but EU 34 to zero if they are NA. Used to avoid EUR region yielding NA because of these countries.
toolFillEU34Countries(x)toolFillEU34Countries(x)
x |
magpie object with 249 ISO country codes in the spatial dimension |
Falk Benke
Aggregate Solar data into regions
toolSolarFunctionAggregate( x, rel = NULL, weight = calcOutput("FE", aggregate = FALSE)[, "y2015", "FE|Electricity (EJ/yr)"] )toolSolarFunctionAggregate( x, rel = NULL, weight = calcOutput("FE", aggregate = FALSE)[, "y2015", "FE|Electricity (EJ/yr)"] )
x |
magclass object that should be aggregated |
rel |
relation matrix containing a region mapping. A mapping object should contain 2 columns in which each element of x is mapped to the category it should belong to after (dis-)aggregation |
weight |
aggregation weight (should be FE|Electricity (EJ/yr) in 2015) |
return: returns region aggregated solar data
Felix Schreyer, Renato Rodrigues, Julian Oeser