ghg_inventory
ghg_inventory.RdCalculates a GHG inventory using input data from EF_Library, Asset_Portfolio, and Activity_Data variables in the global environment.
Arguments
- AD
Activity Data organized according to the template.
- AP
Asset Portfolio organized according the template.
- GWP
Select your GWPs. Enter with quotation marks in the function. Choices are "SAR", "TAR", "AR4", or "AR5".
- EFL
Emissions Factor Library organized according to the template. Defaults to
EFLibrary
Examples
AR5_Inventory <- ghg_inventory(ActivityData, AssetPortfolio, "AR5")
head(AR5_Inventory)
#> asset_id asset_name asset_type asset_subtype asset_description address
#> <char> <char> <char> <char> <char> <char>
#> 1: B1456 Building Office
#> 2: B1457 Building Office
#> 3: B1458 Building Office
#> 4: B1458 Building Office
#> 5: B1564 Building Office
#> 6: B1564 Building Office
#> city state zip country region subregion
#> <char> <char> <char> <char> <char> <char>
#> 1: Bellingham Washington 98226 United States West Coast NWPP
#> 2: Bellingham Washington 98225 United States West Coast NWPP
#> 3: Bellingham Washington 98225 United States West Coast NWPP
#> 4: Bellingham Washington 98225 United States West Coast
#> 5: Woodinville Washington 98072 United States West Coast NWPP
#> 6: Woodinville Washington 98072 United States West Coast
#> business_unit year_built sqft service_type unit supplier account_id
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: Operations 1993 342 Electricity kWh
#> 2: Operations 2005 3280 Electricity kWh
#> 3: Operations 2003 20142 Electricity kWh
#> 4: Operations 2003 20142 Natural Gas therms
#> 5: Operations 1990 3636 Electricity kWh
#> 6: Operations 1990 3636 Natural Gas therms
#> meter_number date year cost usage emission_category
#> <char> <char> <int> <char> <num> <char>
#> 1: 2023 31231.907 Indirect Energy
#> 2: 2023 19389.588 Indirect Energy
#> 3: 2023 185862.407 Indirect Energy
#> 4: 2023 2569.690 Stationary
#> 5: 2023 47302.603 Indirect Energy
#> 6: 2023 1382.843 Stationary
#> service_subcategory1 service_subcategory2 emission_scope co2_kgperunit
#> <char> <char> <char> <num>
#> 1: Scope 2 0.2731025
#> 2: Scope 2 0.2731025
#> 3: Scope 2 0.2731025
#> 4: Scope 1 5.3060000
#> 5: Scope 2 0.2731025
#> 6: Scope 1 5.3060000
#> ch4_kgperunit n2o_kgperunit otherghgs_kgco2eperunit gwps_ar co2_gwp ch4_gwp
#> <num> <num> <num> <char> <num> <num>
#> 1: 2.54e-05 3.63e-06 0 AR5 1 28
#> 2: 2.54e-05 3.63e-06 0 AR5 1 28
#> 3: 2.54e-05 3.63e-06 0 AR5 1 28
#> 4: 1.00e-04 1.00e-05 0 AR5 1 28
#> 5: 2.54e-05 3.63e-06 0 AR5 1 28
#> 6: 1.00e-04 1.00e-05 0 AR5 1 28
#> n2o_gwp kgco2e_perunit kg_co2 kg_ch4 kg_n2o kg_co2e mt_co2e
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 265 0.2747756 8529.511 0.7932904 0.11337182 8581.767 8.581767
#> 2: 265 0.2747756 5295.344 0.4924955 0.07038420 5327.786 5.327786
#> 3: 265 0.2747756 50759.484 4.7209051 0.67468054 51070.459 51.070459
#> 4: 265 5.3114500 13634.775 0.2569690 0.02569690 13648.780 13.648780
#> 5: 265 0.2747756 12918.458 1.2014861 0.17170845 12997.602 12.997602
#> 6: 265 5.3114500 7337.365 0.1382843 0.01382843 7344.901 7.344901
#> ef_source ef_publishdate
#> <char> <char>
#> 1: USEPA eGRID2022 01/30/2024
#> 2: USEPA eGRID2022 01/30/2024
#> 3: USEPA eGRID2022 01/30/2024
#> 4: EPA CCCL Emission Factors for GHG Inventories 09/12/2023
#> 5: USEPA eGRID2022 01/30/2024
#> 6: EPA CCCL Emission Factors for GHG Inventories 09/12/2023