Package 'magpie4'

Description

Common output routines for extracting results from the MAgPIE framework (versions 4.x).

Author(s)

Maintainer: Jan Philipp Dietrich <[email protected]>

See Also

Useful links:

• https://github.com/pik-piam/magpie4

• doi:10.5281/zenodo.1158582

• Report bugs at https://github.com/pik-piam/magpie4/issues

Description

Enriches land use data on cluster resolution geometry information as required for conversion by magclass::as.SpatVector

Usage

addGeometry(x, clustermap)

Arguments

Value

A magclass object enriched with geometry information

Author(s)

Jan Philipp Dietrich, Pascal Sauer

See Also

Other Spatial: clusterOutputToTerraVector(), gdxAggregate(), mappingToLongFormat(), superAggregateX()

Examples

## Not run: 
landUse <- magpie4::land("fulldata.gdx", level = "cell")
clustermap <- readRDS(Sys.glob("clustermap_*.rds"))
landUseEnriched <- magpie4::addGeometry(landUse, clustermap)
attr(landUseEnriched, "geometry")
attr(landUseEnriched, "crs")

## End(Not run)

Description

Prepend data from the given report to the changelog.

Usage

addToDataChangelog(
  report,
  changelog,
  versionId,
  years = changelogYears(),
  variables = changelogVariables(),
  ...,
  maxEntries = 15,
  roundDigits = 2
)

Arguments

Value

Invisibly, the written changelog as data.frame

Author(s)

Pascal Sauer

See Also

Other Data Changelog: changelogVariables(), changelogYears()

Description

returns employment in crop+livestock production from MAgPIE results

Usage

agEmployment(gdx, type = "absolute", detail = TRUE, level = "reg", file = NULL)

Arguments

Value

employment in agriculture as absolute value or as percentage of working age population

Author(s)

Debbora Leip, David M Chen

Examples

## Not run: 
x <- agEmployment(gdx)

## End(Not run)

Description

Reads data to calculate the agricultural GDP

Usage

AgGDP(gdx, file = NULL, level = "reg")

Arguments

Value

A MAgPIE object containing values related with overall value of production [million US$17]

Author(s)

Edna Molina Bacca

Examples

## Not run: 
    x <- AgGDP(gdx)
  
## End(Not run)

Description

calculates Agricultural Research Intensity (Investment in AgR&D/Total GDP) from a MAgPIE gdx file

Usage

AgriResearchIntensity(gdx, file = NULL, level = "reg")

Arguments

Author(s)

David M Chen

Examples

## Not run: 
x <- AgriResearchIntensity(gdx)

## End(Not run)

Description

Calculates anthropometic indicators from the food demand model

Usage

anthropometrics(
  gdx,
  indicator = "bodyheight",
  age = "adults",
  sex = FALSE,
  bmi_groups = FALSE,
  level = "iso",
  final = TRUE,
  file = NULL,
  calibrated = TRUE
)

Arguments

Details

Demand definitions are equivalent to FAO Food supply categories

Value

bodyweight (kg), bodyheight (cm), BMI or PAL as magpie objects

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- anthropometrics(gdx)
  
## End(Not run)

Description

calculates the area weighted biodiversity intactness index (BII) out of a MAgPIE gdx file

Usage

BII(
  gdx,
  file = NULL,
  level = "glo",
  mode = "auto",
  landClass = "sum",
  spatialWeight = NULL,
  adjusted = FALSE,
  bii_coeff = NULL,
  side_layers = NULL
)

Arguments

Details

Calculates global, regional and cluster-level biodiversity intactness index (BII)

Value

Biodiversity intactness index (unitless)

Author(s)

Patrick v. Jeetze, Florian Humpenoeder, Felicitas Beier

Examples

## Not run: 
x <- BII(gdx)

## End(Not run)

Description

Calculates biochar-related indicators from a MAgPIE gdx file. This function provides all indicators for biochar-related material flows and conversion, including biochar production, feedstock demand, and long-term stable carbon storage in soils.

Usage

biochar(
  gdx,
  indicator,
  level = "reg",
  feedstockAggr = FALSE,
  systemAggr = FALSE,
  attributes = "c",
  file = NULL
)

Arguments

Value

Selected biochar-related indicator as MAgPIE object:

• bc_production: Biochar production (unit depends on attributes). Available attributes are "dm", "ge", and "c".

• bc_feedstock_dem: Biomass feedstock demand for biochar (unit depends on attributes). Available attributes are "dm", "ge", and "c".

• bc_stable_carbon: Stable C in soil from biochar after 100 years (mio. tC per yr). Available attribute is "c".

Author(s)

Isabelle Weindl

Examples

## Not run: 
  x <- biochar(gdx, indicator = "bc_production", level = "regglo", feedstockAggr = TRUE)

## End(Not run)

Description

returns demand for bioplastic or demand for substrate for bioplastic production

Usage

bioplasticDemand(
  gdx,
  type = "bioplastic",
  detail = FALSE,
  level = "regglo",
  file = NULL
)

Arguments

Author(s)

Debbora Leip

Examples

## Not run: 
x <- bioplasticDemand(gdx)

## End(Not run)

Description

Calculates the prevalence of underweight, normalweight, overweight (excluding obesity) and obesity. For more detailed body mass classifications see functions population or anthropometrics.

Usage

bodyweight(
  gdx,
  level = "reg",
  age = FALSE,
  sex = FALSE,
  share = FALSE,
  population = NULL
)

Arguments

Details

Demand definitions are equivalent to FAO Food supply categories

Value

MAgPIE object with mio people or share of people in each weight category

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- bodyweight(gdx)
  
## End(Not run)

Description

reads carbon stored in harvested timber out of a MAgPIE gdx file

Usage

carbonHWP(
  gdx,
  file = NULL,
  level = "cell",
  unit = "element",
  half_life = 35,
  cumulative = FALSE,
  baseyear = 1995
)

Arguments

Details

Annual (and cumulative) Carbon stored in harvested wood products as well as slow emissions from half life deacy.

Value

carbon stocks in MtC from harvested timber

Author(s)

Abhijeet Mishra, Florian Humpenoeder

Examples

## Not run: 
    x <- carbonHWP(gdx)
  
## End(Not run)

Description

reads carbon stored in harvested timber out of a MAgPIE gdx file

Usage

carbonLTS(
  gdx,
  file = NULL,
  level = "cell",
  unit = "element",
  cumulative = FALSE,
  baseyear = 1995
)

Arguments

Details

Annual (and cumulative) Carbon stored in harvested wood products, as well as, slow emissions from half life deacy.

Value

carbon stocks in MtC from harvested timber

Author(s)

Abhijeet Mishra, Florian Humpenoeder

Examples

## Not run: 
x <- carbonLTS(gdx)

## End(Not run)

Description

reads carbon stocks out of a MAgPIE gdx file

Usage

carbonstock(
  gdx,
  file = NULL,
  level = "cell",
  sum_cpool = TRUE,
  sum_land = TRUE,
  subcategories = NULL,
  stockType = "actual"
)

Arguments

Details

carbon pools consist of vegetation carbon (vegc), litter carbon (litc) and soil carbon (soilc)

Value

carbon stocks in MtC

Author(s)

Florian Humpenoeder

Examples

## Not run: 
x <- carbonstock(gdx)

## End(Not run)

Description

cellular fit/error/bias calculations at regional and global level

Usage

cellularFit(
  gdx,
  file = NULL,
  level = "cell",
  statistic = "MAE",
  variable = "land",
  dataset = "LUH3",
  water_aggr = FALSE
)

Arguments

Value

returns selected statistic at regglo level for the historical part of the time horizon

Author(s)

Edna J. Molina Bacca, Patrick v. Jeetze

Examples

## Not run: 
    x <- cellularFit(gdx)
  
## End(Not run)

Description

Returns a named vector of variables to put into the data changelog, see addToDataChangelog.

Usage

changelogVariables()

Author(s)

Pascal Sauer

See Also

Other Data Changelog: addToDataChangelog(), changelogYears()

Description

Returns the years to put into the data changelog, see addToDataChangelog.

Usage

changelogYears()

Author(s)

Pascal Sauer

See Also

Other Data Changelog: addToDataChangelog(), changelogVariables()

Description

Clear memoise cache for all memoised functions in magpie4

Usage

clearCacheMagpie4()

Value

no return, but cache cleared

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
x <- clearCacheMagpie4()

## End(Not run)

Description

Enriches land use data on cluster resolution with explicit spatial information by creating a terra polygon for each cluster according to the given clustermap.

Usage

clusterOutputToTerraVector(x, clustermap)

Arguments

Value

A SpatVector with the following columns: c("clusterId", "country", "region", "year", "landtype", "value")

Author(s)

Pascal Führlich, Patrick v. Jeetze

See Also

Other Spatial: addGeometry(), gdxAggregate(), mappingToLongFormat(), superAggregateX()

Examples

## Not run: 
landUse <- magpie4::land("fulldata.gdx", level = "cell")
clustermap <- readRDS(Sys.glob("clustermap_*.rds"))
clusterPolygons <- magpie4::clusterOutputToTerraVector(landUse, clustermap)
terra::writeVector(clusterPolygons, "cluster_resolution.shp")

## End(Not run)

Description

calculates consumption value of different types based on a MAgPIE gdx file.

Usage

consumptionValue(
  gdx,
  file = NULL,
  level = "reg",
  products = "kall",
  product_aggr = TRUE,
  type = NULL,
  type_aggr = TRUE
)

Arguments

Value

A MAgPIE object containing consumption value in million $US.

Author(s)

Miodrag Stevanovic

Examples

## Not run: 
    x <- consumptionValue(gdx)
  
## End(Not run)

Description

Reads data to calculate capital stocks

Usage

CostCapital(gdx, type = "stocks", file = NULL, level = "cell")

Arguments

Value

A MAgPIE object containing values related with overall value of production [million US$17]

Author(s)

Edna Molina Bacca

Examples

## Not run: 
    x <- CostCapital(gdx)
  
## End(Not run)

Description

Gross value of productions

Usage

CostOverall(gdx, file = NULL, level = "reg")

Arguments

Value

A MAgPIE object containing values related with overall value of production [million US$17]

Author(s)

Edna Molina Bacca

Examples

## Not run: 
x <- CostOverall(gdx)

## End(Not run)

Description

reads costs entering the objective function from a MAgPIE gdx file

Usage

costs(gdx, file = NULL, level = "reg", type = "annuity", sum = TRUE)

Arguments

Value

A MAgPIE object containing the goal function costs including investments [million US$17]

Author(s)

Jan Philipp Dietrich, Markus Bonsch, Misko Stevanovic, Florian Humpenoeder, Edna J. Molina Bacca, Michael Crawford

Examples

## Not run: 
x <- costs(gdx)

## End(Not run)

Description

reads AEI costs entering the objective function from a MAgPIE gdx file

Usage

CostsAEI(gdx, file = NULL, level = "regglo")

Arguments

Value

MAgPIE object containing costs for AEI [million US$17]

Author(s)

Felicitas Beier

Examples

## Not run: 
    x <- CostsAEI(gdx)
  
## End(Not run)

Description

reads costs entering the objective function from a MAgPIE gdx file

Usage

CostsFertilizer(gdx, file = NULL, level = "regglo", disagg = TRUE)

Arguments

Value

MAgPIE object containing fertilizer costs [million US$17]

Author(s)

Debbora Leip

Examples

## Not run: 
    x <- CostsFertilizer(gdx)
  
## End(Not run)

Description

reads costs entering the objective function from a MAgPIE gdx file

Usage

costsMACCS(gdx, file = NULL, level = "regglo")

Arguments

Value

MAgPIE object containing mitigation costs [million US$17]

Author(s)

Debbora Leip

Examples

## Not run: 
    x <- costsMACCS(gdx)
  
## End(Not run)

Description

reads presovle costs (i.e. without bioenergy demand) entering the objective function from a MAgPIE gdx file

Usage

costsPresolve(gdx, file = NULL, level = "reg")

Arguments

Details

Presolve is without bioenergy demand. Hence costs from a MAgPIE run with bioenergy demand minus costs from presolve reflect costs that can be attributed to bioenergy production

Value

A MAgPIE object containing the goal function costs in presolve mode [million US$17]

Author(s)

Florian Humpenoeder

Examples

## Not run: 
    x <- costsPresolve(gdx)
  
## End(Not run)

Description

Reads data to calculate wholesale costs

Usage

costsWholesale(gdx, file = NULL, level = "regglo")

Arguments

Value

A MAgPIE object containing values related with costs wholesale trade [million US$17/tDM]

Author(s)

David M Chen

Examples

## Not run: 
x <- costsWholesale(gdx)

## End(Not run)

Description

calculates agricultural costs without taxes, incentives and technical penalty costs (i.e. GHG taxes and BII incentives)

Usage

CostsWithoutIncentives(gdx, file = NULL, level = "regglo")

Arguments

Value

A MAgPIE object containing the costs without taxes, incentives and technical penalty costs [million US$17]

Author(s)

David M Chen

Examples

## Not run: 
  x <- CostsWithoutIncentives(gdx)

## End(Not run)

Description

Reads data on TC costs

Usage

CostTC(gdx, file = NULL, level = "reg")

Arguments

Value

A MAgPIE object containing values related with overall value of production [million US$17]

Author(s)

David Chen

Examples

## Not run: 
    x <- CostTC(gdx)
  
## End(Not run)

Description

reads costs entering the objective function from a MAgPIE gdx file

Usage

CostTransport(gdx, file = NULL, level = "cell", sum = FALSE)

Arguments

Value

A MAgPIE object containing the transport costs [million US$17]

Author(s)

David Chen

Examples

## Not run: 
    x <- CostTransport(gdx)
  
## End(Not run)

Description

reads croparea out of a MAgPIE gdx file. Croparea excludes fallow land.

Usage

croparea(
  gdx,
  file = NULL,
  level = "reg",
  products = "kcr",
  product_aggr = TRUE,
  water_aggr = TRUE,
  physical = TRUE
)

Arguments

Value

production as MAgPIE object (unit depends on attributes)

Author(s)

Jan Philipp Dietrich, Florian Humpenoeder

See Also

reportCroparea

Examples

## Not run: 
x <- croparea(gdx)

## End(Not run)

Description

calculates an index that measures the croparea diversity

Usage

CropareaDiversityIndex(
  gdx,
  index = "shannon",
  level = "reg",
  measurelevel = "cell",
  groupdiv = "agg1"
)

Arguments

Value

MAgPIE object (unit depends on attributes)

Author(s)

Benjamin Leon Bodirsky

See Also

CropareaDiversityIndex

Examples

## Not run: 
x <- CropareaDiversityIndex(gdx)

## End(Not run)

Description

reads cropland out of a MAgPIE gdx file. Cropland includes croparea plus fallow plus cropland with tree cover

Usage

cropland(gdx, level = "reg", types = NULL, sum = FALSE)

Arguments

Value

land as MAgPIE object (Mha)

Author(s)

Benjamin Leon Bodirsky

See Also

reportLandUse

Examples

## Not run: 
x <- cropland(gdx)

## End(Not run)

Description

reads cropland area under soil carbon management (SCM) out of a MAgPIE gdx file (Mha)

Usage

croplandSCM(gdx, level = "reg", crop_aggr = TRUE)

Arguments

Value

SCM area as MAgPIE object (mio. ha)

Author(s)

Kristine Karstens

Examples

## Not run: 
x <- croplandSCM(gdx)

## End(Not run)

Description

calculates tree cover on cropland (Mha) from a MAgPIE gdx file

Usage

croplandTreeCover(gdx, level = "reg", sum_ac = TRUE, debugMode = FALSE)

Arguments

Author(s)

Florian Humpenoeder

Examples

## Not run: 
x <- fallow(gdx)

## End(Not run)

Description

Calculates soil carbon share in relation to potential natural vegetation based on a MAgPIE gdx file

Usage

cshare(
  gdx,
  file = NULL,
  level = "reg",
  reference = "actual",
  noncrop_aggr = TRUE
)

Arguments

Value

A MAgPIE object containing som values

Author(s)

Kristine Karstens

Examples

## Not run: 
x <- cshare(gdx)

## End(Not run)

Description

Function that quantifies the influences of the underlying drivers to a dependent output variable. It attributes the changes of the output variable (A) to changes of several drivers (B, B/C, C/A). The output must be the product of the drivers.

Usage

deco(data, names_factor = NULL, plot = FALSE)

Arguments

Details

Use function deco_plot in library luplot to make a plot out of this. It is only usable for the decomposition of 5 or less drivers. For documentation, see paper Huber, Veronika, Ina Neher, Benjamin L. Bodirsky, Kathrin Hoefner, and Hans Joachim Schellnhuber. 2014. "Will the World Run out of Land? A Kaya-Type Decomposition to Study Past Trends of Cropland Expansion." Environmental Research Letters 9 (2): 024011. https://doi.org/10.1088/1748-9326/9/2/024011. Or see master Thesis of Ina Neher (2013)

Value

Decomposes the impact of certain drivers to an output (A) value.

Author(s)

Ina Neher, Benjamin Leon Bodirsky

Examples

Data<-array(c(1,1.1,1.15,1,1.05,1.1,1,1.05,1.15),c(3,3))
 dimnames(Data)<-list(paste("y",2000:2002,sep=""),c("Area","Population","Supply"))
 Data <- as.magpie(Data)
 deco(Data)

Description

Calculates MAgPIE demand out of a gdx file

Usage

demand(
  gdx,
  file = NULL,
  level = "reg",
  products = "kall",
  product_aggr = FALSE,
  attributes = "dm",
  type = NULL,
  type_aggr = FALSE
)

Arguments

Details

Demand definitions are equivalent to FAO CBS categories

Value

demand as MAgPIE object (Unit depends on attributes)

Author(s)

Benjamin Leon Bodirsky, Abhijeet Mishra, Miodrag Stevanovic

Examples

## Not run: 
    x <- demand(level="regglo", products="kcr")
  
## End(Not run)

Description

reads bioenergy demand from a MAgPIE gdx file

Usage

demandBioenergy(gdx, file = NULL, level = "reg", sum = FALSE, round = NULL)

Arguments

Value

A MAgPIE object containing bioenergy demand in EJ/yr

Author(s)

Jan Philipp Dietrich, Florian Humpenoeder

Examples

## Not run: 
    x <- demandBioenergy(gdx)
  
## End(Not run)

Description

Read land conservation data from a fulldata.gdx and disaggregate to high resolution (0.5 deg).

Usage

disaggregateLandConservation(gdx, cfg, mapping, wdpaHr, conservationPrioHr)

Arguments

Value

magclass, high resolution land conservation area

Author(s)

Patrick v. Jeetze, Pascal Sauer

Description

reads discount rates from a MAgPIE gdx file

Usage

discountRates(gdx, file = NULL, level = "reg")

Arguments

Value

A MAgPIE object containing discount rates used in the model

Author(s)

Xiaoxi Wang

Examples

## Not run: 
    x <- discountRates(gdx)
  
## End(Not run)

Description

Calculates embodied land in trade using the domestic technology assumption.

Usage

domesticFootprintTrade(
  gdx,
  file = NULL,
  level = "reg",
  type = "net-trade",
  productsAggr = TRUE,
  maxIntensityRatio = 5
)

Arguments

Details

The domestic technology assumption values all traded goods using each region's own processing shares, feed baskets, and land intensity (yields). For exports this represents the actual land footprint. For imports this is a counterfactual: how much land would the region need if it produced its imports domestically?

Trade flows are decomposed into primary product equivalents via three pathways: (1) direct primary trade, (2) primaries embodied in secondary products, (3) primaries needed as livestock feed. Land intensity (ha/tDM) is then applied per primary product.

Regional land intensities are filled and capped using historical (1995) FAO yields as reference. This ensures comparability across different model runs while accounting for regional differences in agricultural potential.

This is a consistent approach with net trade data (no bilateral flows). With bilateral trade data, different basis for land intensity could be applied.

Value

MAgPIE object with embodied land in trade (Mha)

Author(s)

Kristine Karstens

See Also

tradedPrimaries

Examples

## Not run: 
  x <- domesticFootprintTrade(gdx, type = "net-trade")

## End(Not run)

Description

Calculates production-based and consumption-based (embodied) biodiversity impact accounting using bilateral trade flows. Biodiversity values (BII-weighted area) are allocated to traded products based on cropland and pasture area shares, similar to how CO2 LUC emissions are allocated.

Usage

embodiedBiodiversity(
  gdx,
  file = NULL,
  level = "reg",
  type = "all",
  indicator = "bv",
  bilateral = FALSE
)

Arguments

Details

Biodiversity is measured via the Biodiversity Intactness Index (BII) which ranges from 0 to 1. BII is calculated at the land cover class level (crop_ann, crop_per, manpast, rangeland, etc.) and not directly per product. This function allocates the biodiversity impact from cropland to individual crop products based on their area shares.

The indicator "bv" returns the BII-weighted area (higher = more biodiversity preserved), allocated to each crop by its share of total cropland area.

The indicator "bii_loss" returns the biodiversity loss ((1-BII) * area) for aggregate cropland/pasture, then allocates to individual products by area share. This represents "share of biodiversity loss attributable to this crop based on its area share."

Value

Embodied biodiversity impact as MAgPIE object. When bilateral=FALSE: dimensions are (region, year, accounting.product). When bilateral=TRUE: dimensions are (exporter.importer, year, product).

Author(s)

David M Chen

See Also

BII, land, croparea, trade

Examples

## Not run: 
  x <- embodiedBiodiversity(gdx, type = "all")
  # Bilateral flows
  xBilat <- embodiedBiodiversity(gdx, type = "flows", bilateral = TRUE)

## End(Not run)

Description

Consumption-based (embodied) emissions footprint using the column-normalised Kastner allocation in embodiedResourceKastner. Non-negative and closes globally to total emissions. Pollutants are aggregated to a single CO2-equivalent value per product (via unit); crop emissions are allocated through primary-equivalent trade, livestock emissions through direct trade.

Usage

embodiedEmissions(
  gdx,
  file = NULL,
  level = "reg",
  type = "all",
  unit = "GWP100AR6",
  bilateral = FALSE,
  secdToFeed = TRUE,
  reassignLivestock = TRUE
)

Arguments

Value

MAgPIE object (region, year, accounting.pathway.product) in Mt CO2eq.

Author(s)

David M Chen

See Also

embodiedResourceKastner, embodiedEmissions

Description

Consumption-based (embodied) agricultural-labour footprint using the column-normalised Kastner allocation in embodiedResourceKastner. Non-negative and closes globally to total agricultural employment. Crop/pasture labour is allocated through primary-equivalent trade, livestock labour through direct trade.

Usage

embodiedLabor(
  gdx,
  file = NULL,
  level = "reg",
  type = "all",
  bilateral = FALSE,
  secdToFeed = TRUE,
  reassignLivestock = TRUE
)

Arguments

Value

MAgPIE object (region, year, accounting.pathway.product) in million people.

Author(s)

David M Chen

See Also

embodiedResourceKastner, embodiedLabor

Description

Consumption-based (embodied) land footprint using the column-normalised Kastner allocation in embodiedResourceKastner. Unlike embodiedLand (which uses production + net-trade and can produce local negatives), this distributes each region's actual cropland and pasture to consumers, so the consumption footprint is non-negative and closes globally to total agricultural land.

Usage

embodiedLand(
  gdx,
  file = NULL,
  level = "reg",
  type = "all",
  landType = "all",
  bilateral = FALSE,
  secdToFeed = TRUE,
  reassignLivestock = TRUE
)

Arguments

Value

MAgPIE object in Mha. When bilateral=FALSE: (region, year, accounting.pathway.product). When bilateral=TRUE: (exporter.importer, year, pathway.product).

Author(s)

David M Chen

See Also

embodiedResourceKastner, embodiedLand

Description

Generic consumption-based (embodied) resource footprint using a column-normalised Kastner (2011) allocation. Each producing region's resource total (e.g. cropland, water, emissions, labour) is distributed to consuming regions using the bilateral commodity consumption shares from the Kastner $(I-A)^{-1}$ solution, normalised so that each origin's column sums exactly to its actual resource. This guarantees, by construction:

• non-negativity of the consumption footprint (if the resource is non-negative);

• production[r] == resource[r];

• additivity: production - exports + imports == consumption;

• global closure: sum(consumption) == sum(resource).

Crop/pasture products are allocated through primary-equivalent (feed-traced) trade; livestock products (kli) through direct bilateral trade. The prim/secd/feed pathway split is derived from demand-category shares (processed -> secd, feed -> feed, remainder -> prim), as in embodiedLand/embodiedWater.

Usage

embodiedResourceKastner(
  gdx,
  resource,
  file = NULL,
  level = "reg",
  type = "all",
  bilateral = FALSE,
  secdToFeed = TRUE,
  reassignLivestock = TRUE
)

Arguments

Value

MAgPIE object. When bilateral = FALSE: (region, year, accounting.pathway.product) with accounting in {production, consumption, export, import, net-trade} and pathway in {prim, secd, feed}. When bilateral = TRUE: (exporter.importer, year, pathway.product).

Author(s)

David M Chen

Description

Consumption-based (embodied) water footprint using the column-normalised Kastner allocation in embodiedResourceKastner. Non-negative and closes globally to total water use. Crop water is allocated through primary-equivalent (feed-traced) trade; livestock water through direct trade.

Usage

embodiedWater(
  gdx,
  file = NULL,
  level = "reg",
  type = "all",
  waterType = "consumption",
  bilateral = FALSE,
  secdToFeed = TRUE,
  reassignLivestock = TRUE
)

Arguments

Value

MAgPIE object (region, year, accounting.pathway.product), or bilateral.

Author(s)

David M Chen

See Also

embodiedResourceKastner, embodiedWater

Description

reads detailed CO2 emissions out of a MAgPIE gdx file

Usage

emisCO2(
  gdx,
  file = NULL,
  level = "cell",
  unit = "gas",
  sum_cpool = TRUE,
  sum_land = TRUE
)

Arguments

Value

CO2 emissions as MAgPIE object (unit depends on unit)

Author(s)

Florian Humpenoeder, Michael Crawford

Examples

## Not run: 
x <- emisCO2(gdx)

## End(Not run)

Description

reads GHG emissions out of a MAgPIE gdx file

Usage

Emissions(
  gdx,
  file = NULL,
  level = "reg",
  type = "co2_c",
  unit = "element",
  subcategories = TRUE,
  cumulative = FALSE,
  lowpass = NULL,
  inorg_fert_split = TRUE
)

Arguments

Value

emissions as MAgPIE object (unit depends on unit)

Author(s)

Florian Humpenoeder, Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- Emissions(gdx)
  
## End(Not run)

Description

reads GHG emissions before technical abatement out of a MAgPIE gdx file. Technical abatement includes all abatement done in the MACC curves, but exclude endogenous mitigation. These emissions are NOT the standard reporting emissions, but used for special purposes like remind-magpie coupling.

Usage

EmissionsBeforeTechnicalMitigation(
  gdx,
  file = NULL,
  level = "reg",
  type = "co2_c",
  unit = "element",
  subcategories = FALSE
)

Arguments

Value

emissions as MAgPIE object (unit depends on unit)

Author(s)

Florian Humpenoeder; Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- EmissionsBeforeTechnicalMitigation(gdx)
  
## End(Not run)

Description

Reads detailed CO2 soil emissions out of a MAgPIE gdx file using Shapley-style decomposition with interaction terms

Usage

emisSOC(gdx, file = NULL, sumLand = FALSE)

Arguments

Details

This function uses a structural decomposition approach (Shapley decomposition) to attribute soil carbon emissions to different drivers and their interactions. The framework is based on counterfactual analysis where all possible combinations of driver states are evaluated following the inclusion-exclusion principle.

For four drivers, the total emission is decomposed as:

totEmissions = main_effects + first_order_interactions + second_order_interactions + third_order_interactions + residual

This creates 16 counterfactual scenarios (2^4) to isolate individual and combined effects. Main effects capture changes when one driver varies while others stay constant. Interaction terms capture synergies where driver combinations amplify or dampen effects beyond simple addition.

Drivers included: - Climate (C): Changes in reference soil carbon density - Land-use (LU): Transitions between land types - Management (M): Changes in crop type shares, fallow, and treecover * Can be split into treecover vs other management in attribution - Soil Carbon Management (SCM): Enhanced soil carbon practices

Legacy effects are incorporated using exponential decay (85 over 100 years. Interaction terms are proportionally attributed back to main drivers in the final output.

Value

CO2 emissions as MAgPIE object

Author(s)

Kristine Karstens, Florian Humpenoeder

Examples

## Not run: 
x <- emisSOC(gdx)

## End(Not run)

Description

Checks whether a set of expected variable names is present in a MAgPIE report object. Variable names are normalized in that summation symbols are removed.

Usage

expectVariablesPresent(report, variableNames)

Arguments

Value

NULL invisibly. Issues a warning if expected variables are not present.

Author(s)

Patrick Rein

See Also

Other Infrastructure: metadata_comments(), out(), tryList(), tryReport()

Description

calculates food expenditure index (baseyear = 100) corrected for ghg emission costs based on a MAgPIE gdx file

Usage

expenditureIndexFood(
  gdx,
  file = NULL,
  level = "reg",
  products = "kfo",
  basketyear = "y2010",
  baseyear = "y2010",
  round = TRUE,
  ghgtax = TRUE,
  valueAdded = FALSE
)

Arguments

Value

A MAgPIE object containing food price expenditure index, in 2017Int$PPP

Author(s)

Felicitas Beier, David Chen

Examples

## Not run: 
x <- expenditureIndexFood(gdx)

## End(Not run)

Description

reads factor costs for crops, livestock, residues or pasture entering the objective function from a MAgPIE gdx file. Depending on the product and the MAgPIE version (and factor cost realization), factor costs are either already split into labor and capital, will be split in this function, or are kept as the aggregate

Usage

factorCosts(gdx, products = "kli", file = NULL, level = "regglo")

Arguments

Value

MAgPIE object containing factor costs [million US$17]

Author(s)

Debbora Leip

Examples

## Not run: 
x <- factorCosts(gdx)

## End(Not run)

Description

returns labor and capital cost share out of factor costs (i.e. labor + capital)

Usage

factorCostShares(
  gdx,
  type = "optimization",
  products = "kcr",
  level = "reg",
  file = NULL
)

Arguments

Value

labor and capital cost share out of factor costs

Author(s)

Debbora Leip

Examples

## Not run: 
x <- factorCostShares(gdx)

## End(Not run)

Description

calculates fallow land (Mha) from a MAgPIE gdx file

Usage

fallow(gdx, level = "reg", debugMode = FALSE)

Arguments

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
x <- fallow(gdx)

## End(Not run)

Description

calculates feed demand by animal type out of a gdx file

Usage

feed(
  gdx,
  file = NULL,
  level = "reg",
  detail = TRUE,
  nutrient = "dm",
  balanceflow = TRUE
)

Arguments

Value

feed demand by animal type as MAgPIE object (unit depends on selected nutrient attributes)

Author(s)

Isabelle Weindl

Examples

## Not run: 
    x <- feed(gdx)
  
## End(Not run)

Description

Calculates food prices that enter demand module

Usage

FoodDemandModuleConsumerPrices(gdx, level = "iso", valueAdded = FALSE)

Arguments

Value

magpie object

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- FoodDemandModuleConsumerPrices(gdx)
  
## End(Not run)

Description

Calculates the food expenditure in USD per year

Usage

FoodExpenditure(
  gdx,
  level = "reg",
  after_shock = TRUE,
  products = "kfo",
  product_aggr = TRUE,
  per_capita = TRUE,
  valueAdded = FALSE
)

Arguments

Value

magpie object with per capita consumption

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- FoodExpenditure(gdx)
  
## End(Not run)

Description

Calculates the livestock share from the food demand model

Usage

FoodExpenditureShare(
  gdx,
  level = "reg",
  after_shock = TRUE,
  products = "kfo",
  product_aggr = TRUE,
  valueAdded = FALSE
)

Arguments

Value

magpie object with per capita consumption

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- FoodExpenditureShare(gdx)
  
## End(Not run)

Description

MAgPIE food model statistics with information about convergence and number of iterations

Usage

foodmodelstat(gdx, file = NULL)

Arguments

Value

A MAgPIE object containing number of iterations and convergence information for each time step

Author(s)

Jan Philipp Dietrich

Examples

## Not run: 
x <- foodmodelstat(gdx)

## End(Not run)

Description

Per-tonne denominator for consumption-based (embodied) resource footprints: the tonnes of the FINAL product consumed in each prim/secd/feed pathway. Used by footprints (type = "perTonne") and reportFootprints to turn an absolute footprint into a per-tonne intensity that is comparable across resources.

• prim = primary products eaten directly (all demand categories except the processed and feed categories);

• secd = tonnes of SECONDARY (ksd) products consumed (the processing OUTPUT, not the primary input); with secdToFeed = TRUE the fed-secondary share is excluded so it reads per tonne of secondary eaten as food/non-feed;

• feed = tonnes of LIVESTOCK (kli) products consumed.

Usage

footprintDemand(dem, prods, kli, ksd, secdToFeed = TRUE)

Arguments

Value

MAgPIE object (region, year, pathway) with pathway in {prim, secd, feed}, in tonnes (Mt DM) of the final product consumed.

Author(s)

David M Chen

See Also

footprints, reportFootprints

Examples

## Not run: 
  dem   <- demand(gdx, level = "reg")[, , "dom_balanceflow", invert = TRUE]
  denom <- footprintDemand(dem, getItems(dem, dim = 3.2),
                           kli = readGDX(gdx, "kli"), ksd = readGDX(gdx, "ksd"))

## End(Not run)

Description

Consumption-based (embodied) resource footprints as ready-to-use MAgPIE objects, on top of the column-normalised Kastner allocation in embodiedResourceKastner. A single entry point that (a) chooses the resource (land, water, emissions or labor), (b) derives regional production / consumption / import / export / net-trade by prim/secd/feed pathway and product from the bilateral Kastner allocation, and (c) returns the footprint as an absolute total, per capita, or per tonne of the final product consumed in each pathway.

This is the reusable computation extracted from the bilateral footprint plotting scripts, so any MAgPIE run can obtain the same numbers without the plotting layer. Regional accounting is derived from the bilateral object by aggregation: production = sum over importer; consumption = sum over exporter; domestic = the self-trade diagonal; imports = consumption - domestic; exports = production - domestic. The pathway split of every accounting component is therefore keyed on the CONSUMER's demand mix (consistent bilateral basis).

Usage

footprints(
  gdx,
  resource = "land",
  type = "total",
  level = "reg",
  file = NULL,
  reassignLivestock = TRUE,
  secdToFeed = TRUE,
  bil = NULL,
  dem = NULL,
  ...
)

Arguments

Value

MAgPIE object; layout depends on type (see above).

Author(s)

David M Chen

See Also

embodiedResourceKastner, reportFootprints, footprintDemand, reassignLivestockPathway

Examples

## Not run: 
  land   <- footprints(gdx, "land", type = "total")       # Mha, by product & pathway
  emisPC <- footprints(gdx, "emissions", type = "perCapita")
  waterT <- footprints(gdx, "water", type = "perTonne")   # by pathway

## End(Not run)

Description

Calculates the average growing stock density of harvested forest areas (m3/ha). This is the ratio of timber production volume to harvested area, i.e. the average standing volume removed per hectare harvested. Values are comparable to growing stock (typically 100-300 m3/ha) and should NOT be confused with forestry yield in the MAI sense (5-20 m3/ha/yr).

Usage

ForestYield(gdx, file = NULL, level = "cell")

Arguments

Details

Average growing stock of harvested areas = timber production (m3) / harvested area (ha). Differences to overall growing stock arise from selective harvesting of specific age classes.

Value

Average growing stock density of harvested areas in m3/ha

Author(s)

Abhijeet Mishra, Florian Humpenoeder

Examples

## Not run: 
    x <- ForestYield(gdx)
  
## End(Not run)

Description

aggregates and disaggregates on spatial scales using mappings from the gdx files.

Usage

gdxAggregate(gdx, x, weight = NULL, to, absolute = TRUE, ...)

Arguments

Value

List of magpie objects with results on country level, weight on country level, unit and description.

Author(s)

Benjamin Leon Bodirsky, Edna J. Molina Bacca, Florian Humpenoeder

See Also

Other Spatial: addGeometry(), clusterOutputToTerraVector(), mappingToLongFormat(), superAggregateX()

Examples

## Not run: 
gdp_pc <- income(gdx, level = "reg")
is.function(population)
gdp_pc_iso <- gdxAggregate(gdx = gdx, x = gdp_pc, weight = "population", to = "iso",
                           absolute = FALSE)
gdp_pc_glo <- gdxAggregate(gdx = gdx, x = gdp_pc, weight = "population", to = "glo",
                           absolute = FALSE)
gdp <- income(gdx, level = "reg", per_capita = FALSE)
gdp_iso <- gdxAggregate(gdx = gdx, x = gdp, weight = "population", to = "iso", absolute = TRUE)
gdp_glo <- gdxAggregate(gdx = gdx, x = gdp, weight = "population", to = "glo", absolute = TRUE)

## End(Not run)

Description

Puts together a report based on a MAgPIE gdx file

Usage

getReport(
  gdx,
  file = NULL,
  scenario = NULL,
  filter = c(1, 2, 7),
  detail = TRUE,
  level = "regglo",
  legacyEmis = TRUE,
  ...
)

Arguments

Details

Reports are organize with '|' as level delimiter and summation symbols for grouping subcategories into entities e.g. for stackplots. Notice the following hints for the summation symbol placement:

• Every name should just contain one summation symbol (mostly '+').

• The position of the symbol (counted in '|' from left side) will determine the level.

• Every subitem containing the same summation symbol in the same level with the same supercategory name will be summed.

• Items without any summation symbol will ge ignored.

• Items with different summation symbols will be summed up separately.

• In most of the cases a summation symbol will be just placed before the last level (counted in '|' from left side).

• It is helpful to think about which group of items should be stacked in a stackplot.

An example how a summation symbol placement could look like:

  Toplevel
  Toplevel|+|Item 1
  Toplevel|+|Item 2
  Toplevel|Item 2|+|Subitem 1
  Toplevel|Item 2|+|Subitem 1
  Toplevel|++|Item A
  Toplevel|++|Item B
  Toplevel|Item ?

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
x <- getReport(gdx)

## End(Not run)

Description

Puts together a report for the Agricultural Model Intercom- parison and Improvement Project (AgMIP) based on a MAgPIE gdx file

Usage

getReportAgMIP(
  gdx,
  file = NULL,
  scenario = NULL,
  filter = c(1, 2, 7),
  detail = TRUE,
  ...
)

Arguments

Details

Reports are organize with '|' as level delimiter and summation symbols for grouping subcategories into entities e.g. for stackplots. Notice the following hints for the summation symbol placement:

• Every name should just contain one summation symbol (mostly '+').

• The position of the symbol (counted in '|' from left side) will determine the level.

• Every subitem containing the same summation symbol in the same level with the same supercategory name will be summed.

• Items without any summation symbol will ge ignored.

• Items with different summation symbols will be summed up separately.

• In most of the cases a summation symbol will be just placed before the last level (counted in '|' from left side).

• It is helpful to think about which group of items should be stacked in a stackplot.

An example how a summation symbol placement could look like:

  Toplevel
  Toplevel|+|Item 1
  Toplevel|+|Item 2
  Toplevel|Item 2|+|Subitem 1
  Toplevel|Item 2|+|Subitem 1
  Toplevel|++|Item A
  Toplevel|++|Item B
  Toplevel|Item ?

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Florian Humpenoeder, Isabelle Weindl, Felicitas Beier

Examples

## Not run: 
x <- getReportAgMIP(gdx)

## End(Not run)

Description

Puts together a report based on a MAgPIE gdx file

Usage

getReportDemandStandalone(
  gdx,
  file = NULL,
  scenario = NULL,
  detail = FALSE,
  ...
)

Arguments

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
    x <- getReportDemandStandalone(gdx)
  
## End(Not run)

Description

reports dietary indicators on the country level. These are formatted as data.frames describing: 1. population, anthropometrics, and intake 2. caloric intake by food category (without food waste)

Usage

getReportDietaryIndicators(gdx, scenario)

Arguments

Value

list of data.frames for the dietary indicators

Author(s)

Michael Crawford, Felicitas Beier, Benjamin Bodirsky

Examples

## Not run: 
    getReportDietaryIndicators(gdx, scenario)
  
## End(Not run)

Description

Collects outputs from MAgPIE runs for FABLE Scenathon.

Usage

getReportFableScenathon(gdx, file = NULL, iso = NULL)

Arguments

Author(s)

Miodrag Stevanovic

Examples

## Not run: 
    x <- getReportFableScenathon(gdx, file = "magpie2scenathon.csv", iso = "IND")
  
## End(Not run)

Description

Collects reports for Alessandro Passaro's analysis

Usage

getReportFSECAlessandroPassaro(gdx, reportOutputDir = NULL, scenario = NULL)

Arguments

Value

A list of reports

Author(s)

Michael Crawford

Examples

## Not run: 
    x <- getReportFSECAlessandroPassaro()
  
## End(Not run)

Description

Reports cost indicators for the FSEC project

Usage

getReportFSECCosts(gdx, reportOutputDir = NULL, scenario = NULL)

Arguments

Value

A .csv containing the summed output of reportCostsAccounting on the region level

Author(s)

Michael Crawford

Examples

## Not run: 
    x <- getReportFSECCosts(gdx)
  
## End(Not run)

Description

Reports grid cell level crop diversity for the FSEC project

Usage

getReportFSECCropDiversityGrid(gdx, reportOutputDir = NULL, scenario = NULL)

Arguments

Value

A list of MAgPIE objects containing the reports

Author(s)

Patrick v. Jeetze

Examples

## Not run: 
x <- getReportFSECCropDiversityGrid(gdx)

## End(Not run)

Description

Reports nutrient surplus indicators for the FSEC project

Usage

getReportFSECPollution(gdx, reportOutputDir = NULL, scenario = NULL)

Arguments

Value

A list of MAgPIE objects containing the reports

Author(s)

Michael Crawford

Examples

## Not run: 
    x <- getReportFSECPollution(gdx)
  
## End(Not run)

Description

Collects reports for Simon Dietz' social welfare function analysis

Usage

getReportFSECSimonDietz(gdx, reportOutputDir = NULL, scenario = NULL)

Arguments

Value

A list of reports

Author(s)

Michael Crawford

Examples

## Not run: 
    x <- getReportFSECSimonDietz()
  
## End(Not run)

Description

Collects reports for Steven Lord's cost of action / cost of inaction analysis.

Usage

getReportFSECStevenLord(gdx, reportOutputDir, scenario)

Arguments

Author(s)

Michael Crawford

Examples

## Not run: 
    x <- getReportFSECStevenLord()
  
## End(Not run)

Description

Generates and saves a list of reports relevant to the INMS context

Usage

getReportGridINMS(
  gdx,
  reportOutputDir = NULL,
  scenario = NULL,
  filter = c(2, 7),
  version = "v13"
)

Arguments

Value

A list of reports (MAgPIE objects)

Author(s)

Benjamin Leon Bodirsky, Florian Humpenoeder, Michael Crawford

Examples

## Not run: 
x <- getReportGridINMS(gdx)

## End(Not run)

Description

Reports nutrient surplus indicators as well as exceedance of the critical nitrogen surplus at the grid level

Usage

getReportGridNitrogenPollution(gdx, reportOutputDir = NULL, scenario = NULL)

Arguments

Value

A list of MAgPIE objects containing the reports

Author(s)

Michael Crawford

Examples

## Not run: 
    x <- getReportGridNitrogenPollution()
  
## End(Not run)

Description

Puts together a report for the INMS project based on a MAgPIE gdx file

Usage

getReportINMS(
  gdx,
  file = NULL,
  scenario = NULL,
  filter = c(2, 7),
  detail = TRUE,
  ...
)

Arguments

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Benjamin Bodirsky, Florian Humpenoeder

Examples

## Not run: 
x <- getReport(gdx)

## End(Not run)

Description

Puts together a report based on a MAgPIE gdx file

Usage

getReportIso(
  gdx,
  file = NULL,
  scenario = NULL,
  filter = c(1, 2, 7),
  detail = FALSE,
  ...
)

Arguments

Details

Reports are organize with '|' as level delimiter and summation symbols for grouping subcategories into entities e.g. for stackplots. Notice the following hints for the summation symbol placement:

• Every name should just contain one summation symbol (mostly '+').

• The position of the symbol (counted in '|' from left side) will determine the level.

• Every subitem containing the same summation symbol in the same level with the same supercategory name will be summed.

• Items without any summation symbol will ge ignored.

• Items with different summation symbols will be summed up separately.

• In most of the cases a summation symbol will be just placed before the last level (counted in '|' from left side).

• It is helpful to think about which group of items should be stacked in a stackplot.

An example how a summation symbol placement could look like:

  Toplevel
  Toplevel|+|Item 1
  Toplevel|+|Item 2
  Toplevel|Item 2|+|Subitem 1
  Toplevel|Item 2|+|Subitem 1
  Toplevel|++|Item A
  Toplevel|++|Item B
  Toplevel|Item ?

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
x <- getReport(gdx)

## End(Not run)

Description

Puts together a report for the IIASA GAINS model based on a MAgPIE gdx file

Usage

getReportMAgPIE2GAINS(
  gdx,
  folder = NULL,
  scenario = NULL,
  filter = c(2, 7),
  ...
)

Arguments

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Benjamin Leon Bodirsky, Florian Humpenoeder

Description

Puts together a report for LPJmL or other biophysical models based on a MAgPIE gdx file

Usage

getReportMAgPIE2LPJmL(
  gdx,
  folder = NULL,
  scenario = NULL,
  filter = c(2, 7),
  ...
)

Arguments

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Benjamin Leon Bodirsky, Florian Humpenoeder

Examples

## Not run: 
x <- getReportMAgPIE2LPJmL(gdx)

## End(Not run)

Description

Based on a MAgPIE gdx file, a report is generated containing only the variables relevant for the coupling with REMIND. Basically a copy of getReport, but calling less 'reportXY()' functions. Land-use-Change CO2 uses the legacy-clearing reframe (the reportEmissions default).

Usage

getReportMAgPIE2REMIND(gdx, file = NULL, scenario = NULL)

Arguments

Details

Reports are organized with '|' as level delimiter and summation symbols for grouping subcategories into entities e.g. for stackplots. Notice the following hints for the summation symbol placement:

• Every name should just contain one summation symbol (mostly '+').

• The position of the symbol (counted in '|' from left side) will determine the level.

• Every subitem containing the same summation symbol in the same level with the same supercategory name will be summed.

• Items without any summation symbol will be silently ignored.

• Items with different summation symbols will be summed up separately.

• In most of the cases a summation symbol will be just placed before the last level (counted in '|' from left side).

• It is helpful to think about which group of items should be stacked in a stackplot.

An example how a summation symbol placement could look like:

  Toplevel
  Toplevel|+|Item 1
  Toplevel|+|Item 2
  Toplevel|Item 2|+|Subitem 1
  Toplevel|Item 2|+|Subitem 1
  Toplevel|++|Item A
  Toplevel|++|Item B
  Toplevel|Item ?

Value

A MAgPIE object containing the report.

Author(s)

Florian Humpenoeder, David Klein

Examples

## Not run: 
x <- getReportMAgPIE2REMIND(gdx)

## End(Not run)

Description

Puts together all reporting variables for planetary boundary indicators of MAgPIE

Usage

getReportPBindicators(
  gdx,
  file = NULL,
  scenario = NULL,
  filter = c(1, 2, 7),
  ...
)

Arguments

Details

Reports are organize with '|' as level delimiter and summation symbols for grouping subcategories into entities e.g. for stackplots. Notice the following hints for the summation symbol placement:

• Every name should just contain one summation symbol (mostly '+').

• The position of the symbol (counted in '|' from left side) will determine the level.

• Every subitem containing the same summation symbol in the same level with the same supercategory name will be summed.

• Items without any summation symbol will ge ignored.

• Items with different summation symbols will be summed up separately.

• In most of the cases a summation symbol will be just placed before the last level (counted in '|' from left side).

• It is helpful to think about which group of items should be stacked in a stackplot.

An example how a summation symbol placement could look like:

  Toplevel
  Toplevel|+|Item 1
  Toplevel|+|Item 2
  Toplevel|Item 2|+|Subitem 1
  Toplevel|Item 2|+|Subitem 1
  Toplevel|++|Item A
  Toplevel|++|Item B
  Toplevel|Item ?

Value

A MAgPIE object containing the report in the case that "file" is NULL.

Author(s)

Felicitas Beier

Examples

## Not run: 
x <- getReport(gdx)

## End(Not run)

Description

reads woody growing stock out of a MAgPIE gdx file

Usage

GrowingStock(gdx, file = NULL, level = "regglo", indicator = "relative")

Arguments

Details

Growing stock for producing woody materials consist of growing stock from plantations (forestry), secondary and primary forest as well as other land (natveg)

Value

Growing stock in m3 per ha

Author(s)

Abhijeet Mishra

Examples

## Not run: 
x <- GrowingStock(gdx)

## End(Not run)

Description

Reads wood harvest area separated by source (primforest, secdforest, forestry, other) and age classes from a gdx. The data is on cluster level and the unit is Mha per year.

Usage

harvested_area_timber(
  gdx,
  file = NULL,
  level = "cell",
  aggregateAgeClasses = TRUE,
  annualized = TRUE
)

Arguments

Value

Area harvested for wood in Mha per year (annualized = TRUE) or Mha per time step (annualized = FALSE) as a magpie object

Author(s)

Abhijeet Mishra, Pascal Sauer, Florian Humpenoeder

Description

returns hourly labor costs in agriculture from MAgPIE results

Usage

hourlyLaborCosts(gdx, level = "reg", file = NULL)

Arguments

Value

hourly labor costs in agriculture

Author(s)

Debbora Leip

Examples

## Not run: 
x <- hourlyLaborCosts(gdx)

## End(Not run)

Description

Calculates the share of people living in hunger.

Usage

Hunger(
  gdx,
  level = "reg",
  after_shock = TRUE,
  calibrated = FALSE,
  share = TRUE
)

Arguments

Value

magpie object with hunger (mio people) or hunger share

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- Hunger(gdx)
  
## End(Not run)

Description

Calculates income based on a MAgPIE gdx file

Usage

income(
  gdx,
  file = NULL,
  level = "reg",
  per_capita = TRUE,
  type = "ppp",
  after_shock = FALSE
)

Arguments

Value

annual income as MAgPIE object (unit depends on per_capita: US$2017 MER/cap/yr (TRUE), US$2017 MER/yr (FALSE))

Author(s)

Florian Humpenoeder, Benjamin Bodirsky, Felicitas Beier

Examples

## Not run: 
x <- income(gdx)

## End(Not run)

Description

Calculates the per-capita kcal intake from the food demand model

Usage

Intake(
  gdx,
  file = NULL,
  level = "reg",
  calibrated = TRUE,
  pregnancy = FALSE,
  per_capita = TRUE,
  age = FALSE,
  sex = FALSE,
  bmi_groups = FALSE
)

Arguments

Details

Demand definitions are equivalent to FAO Food supply categories

Value

calories as MAgPIE object (unit depends on per_capita: kcal/cap/day (TRUE), kcal/day (FALSE))

Author(s)

Benjamin Leon Bodirsky

Examples

## Not run: 
    x <- Intake(gdx)
  
## End(Not run)

Description

Calculates detailed or aggregated per-capita kcal intake including exogenous scenarios

Usage

IntakeDetailed(gdx, file = NULL, level = "reg", product_aggr = FALSE)

Arguments

Details

Calculation of kcal food intake is possible for both exogenous diet scenarios and endogenous estimation from food demand model

Value

Calories as MAgPIE object (unit: kcal/cap/day)

Author(s)

Isabelle Weindl

Examples

## Not run: 
    x <- IntakeDetailed(gdx)
  
## End(Not run)

Description

Calculates food-specific per-capita protein intake from magpie results in grams.

Usage

IntakeDetailedProtein(gdx, file = NULL, level = "reg", product_aggr = FALSE)

Arguments

Value

Protein intake as MAgPIE object (unit: grams/cap/day)

Author(s)

Vartika Singh, Isabelle Weindl

Examples

## Not run: 
    x <- IntakeDetailedProtein(gdx)
  
## End(Not run)

Description

Calculates the elements t, d1, d2/d1, ..., dn/dn-1, t/dn in a kaya-like identity of the form t = d1 * d2/d1 * ... * dn/dn-1 * t/dn, based on the variables t, d1, d2, ..., dn.

Usage

kayaFractions(data, driverNames = NULL, fixTimeSteps = FALSE)

Arguments

Value

The function returns a magpie object containing the target variable t and the drivers of the target variable

Author(s)

Debbora Leip

See Also

kayaLaspeyres

Examples

## Not run: 
data <- new.magpie(cells_and_regions = c("EUR", "SSA", "USA", "LAM", "IND", "OAS"),
                   years = c(2000, 2005, 2010),
                   names = as.vector(outer(c("Area", "Population", "Supply"),
                                           c("SSP1", "SSP2"), paste, sep = ".")),
                   sets = c("Region", "Year", "Variable", "Scenario"), fill = runif(108))
kayaFractions(data)

## End(Not run)

Description

Calculates the Laspeyres decomposition of a changes in a target variable t into drivers based on a kaya-like identity. The kaya-like identity needs to be of the form t = d1 * d2/d1 * d3/d2 * ... * dn/dn-1 * t/dn, where d1, d2/d1, ..., dn/dn-1, t/dn are the drivers of change in the target variable t. The Laspeyres decomposition calculates an additive decomposition of the change over time in the target variable based on the changes in the drivers (for formula of the laspeyres decomposition see comment in .laspeyresDriver). The function returns the change in the target variable and the decomposition of the change into the drivers.

Usage

kayaLaspeyres(data, driverNames = NULL, type = "relative", fixTimeSteps = TRUE)

Arguments

Value

The function returns the change in the target variable and the decomposition of the change into the drivers either in percentage points or absolute values (unit of target variable).

Author(s)

Debbora Leip

See Also

kayaFractions

Examples

## Not run: 
data <- new.magpie(cells_and_regions = c("EUR", "SSA", "USA", "LAM", "IND", "OAS"),
                   years = c(2000, 2005, 2010),
                   names = as.vector(outer(c("Area", "Population", "Supply"),
                                           c("SSP1", "SSP2"), paste, sep = ".")),
                   sets = c("Region", "Year", "Variable", "Scenario"), fill = runif(108))
kayaLaspeyres(data)

## End(Not run)

Description

Calculates the per-capita kcal consumption from the food demand model

Usage

Kcal(
  gdx,
  file = NULL,
  level = "reg",
  products = "kfo",
  product_aggr = TRUE,
  after_shock = TRUE,
  calibrated = TRUE,
  magpie_input = FALSE,
  attributes = "kcal",
  per_capita = TRUE
)

Arguments

Details

Demand definitions are equivalent to FAO Food supply categories

Value

calories as MAgPIE object (unit depends on per_capita: kcal/cap/day (TRUE), kcal/day (FALSE))

Author(s)

Benjamin Leon Bodirsky, Isabelle Weindl

Examples

## Not run: 
    x <- Kcal(gdx)
  
## End(Not run)

Description

reads MAgPIE endogenous labor costs for crop and livestock production from gdx file

Usage

laborCostsEndo(gdx, products = "kcr", file = NULL, level = "grid")

Arguments

Value

MAgPIE object containing labor costs [million US$17]

Author(s)

Debbora Leip

Examples

## Not run: 
x <- laborCostsEndo(gdx)

## End(Not run)

Description

calculates labor productivity in crop sector (kg DM per hour) from a MAgPIE gdx file

Usage

laborProductivity(gdx, level = "reg", productAggr = TRUE)

Arguments

Value

labor productivity in crop sector (kg DM per hour)

Author(s)

Xiaoxi Wang, Ruiying Du, Debbora Leip

Examples

## Not run: 
x <- laborProductivity(gdx)

## End(Not run)

Description

reads land out of a MAgPIE gdx file

Usage

land(
  gdx,
  file = NULL,
  level = "reg",
  types = NULL,
  subcategories = NULL,
  sum = FALSE
)

Arguments

Value

land as MAgPIE object (Mha)

Author(s)

Jan Philipp Dietrich, Florian Humpenoeder, Benjamin Leon Bodirsky, Patrick v. Jeetze

See Also

reportLandUse

Examples

## Not run: 
x <- land(gdx)

## End(Not run)

Description

Calculates MAgPIE MAgPIE land shadow prices based on a gdx file

Usage

land_price(
  gdx,
  file = NULL,
  level = "reg",
  ignore_lowbound = FALSE,
  absolute = TRUE,
  digits = 4
)

Arguments

Details

The land price is obtained through marginals of the "oq_cropland" constraint. The majority of these marginals are negative values, and a negligible number of them are positive. This is the consequence of the constraint binding either on upper or lower level. The parameter ignore_lowbound removes all the positive marginals from land price calculation (negligible), and parameter absolute transforms them into negative values (to be all together reported as positive values at the final calculation).

Value

A MAgPIE object containing the land shadow prices (US$2017/ha).

Author(s)

Markus Bonsch, Misko Stevanovic

Examples

## Not run: 
    x <- land_price(level="regglo", products="kcr")
  
## End(Not run)

Description

Indirect human-induced emissions in the land use system

Usage

landCarbonSink(
  gdx,
  file = NULL,
  level = "reg",
  cumulative = FALSE,
  baseyear = 1995,
  source = "Grassi"
)

Arguments

Details

Calculates global and regional Land Carbon Sink Adjustment Factors

Value

Land Carbon Sink Adjustment Factors (Mt CO2 per year or cumulative)

Author(s)

Florian Humpenoeder

Examples

## Not run: 
x <- landCarbonSink(gdx)

## End(Not run)

Description

reads land conservation information out of a MAgPIE gdx file. Land restoration 'restore' is reported in Mha/yr by default but can be also reported both over the time step length and cumulatively.

Usage

landConservation(
  gdx,
  file = NULL,
  level = "cell",
  cumuRestor = FALSE,
  baseyear = 1995,
  annualRestor = FALSE,
  sum = FALSE
)