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")
#> EFL conversion successful
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 business_unit
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: Bellingham Washington 98226 USA West Coast NWPP Operations
#> 2: Bellingham Washington 98225 USA West Coast NWPP Operations
#> 3: Bellingham Washington 98225 USA West Coast NWPP Operations
#> 4: Bellingham Washington 98225 USA West Coast Operations
#> 5: Woodinville Washington 98072 USA West Coast NWPP Operations
#> 6: Woodinville Washington 98072 USA West Coast Operations
#> year_built sqft service_type unit supplier account_id meter_number
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: 1993 342 electricity kWh
#> 2: 2005 3280 electricity kWh
#> 3: 2003 20142 electricity kWh
#> 4: 2003 20142 natural gas therm
#> 5: 1990 3636 electricity kWh
#> 6: 1990 3636 natural gas therm
#> date year cost usage emission_category service_subcategory1
#> <char> <num> <char> <num> <char> <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_subcategory2 emission_scope co2_kgperunit ch4_kgperunit
#> <char> <char> <num> <num>
#> 1: scope 2 0.2731025 2.540117e-05
#> 2: scope 2 0.2731025 2.540117e-05
#> 3: scope 2 0.2731025 2.540117e-05
#> 4: scope 1 5.3060000 1.000000e-04
#> 5: scope 2 0.2731025 2.540117e-05
#> 6: scope 1 5.3060000 1.000000e-04
#> n2o_kgperunit otherghgs_kgco2eperunit gwps_ar co2_gwp ch4_gwp n2o_gwp
#> <num> <num> <char> <num> <num> <num>
#> 1: 3.628738e-06 0 ar5 1 28 265
#> 2: 3.628738e-06 0 ar5 1 28 265
#> 3: 3.628738e-06 0 ar5 1 28 265
#> 4: 1.000000e-05 0 ar5 1 28 265
#> 5: 3.628738e-06 0 ar5 1 28 265
#> 6: 1.000000e-05 0 ar5 1 28 265
#> kgco2e_perunit kg_co2 kg_ch4 kg_n2o kg_co2e mt_co2e
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.2747753 8529.511 0.7933269 0.11333242 8581.757 8.581757
#> 2: 0.2747753 5295.344 0.4925182 0.07035974 5327.780 5.327780
#> 3: 0.2747753 50759.484 4.7211223 0.67444604 51070.403 51.070403
#> 4: 5.3114500 13634.775 0.2569690 0.02569690 13648.780 13.648780
#> 5: 0.2747753 12918.458 1.2015414 0.17164877 12997.588 12.997588
#> 6: 5.3114500 7337.365 0.1382843 0.01382843 7344.901 7.344901
#> ef_source ef_publishdate
#> <char> <Date>
#> 1: eGRID2022 2024-01-30
#> 2: eGRID2022 2024-01-30
#> 3: eGRID2022 2024-01-30
#> 4: EPA CCCL Emission Factors for GHG Inventories 2024-06-05
#> 5: eGRID2022 2024-01-30
#> 6: EPA CCCL Emission Factors for GHG Inventories 2024-06-05