ghgtools
ghgtools.Rmd
ghgtools is designed to make greenhouse gas
(GHG) accounting more accessible and standardized. Tracking, measuring,
and reporting GHG emissions is essential to understand risks and
identify opportunities related to climate change and the transition
towards a renewable energy economy. The goal of this package is to
advance the practice of GHG accounting with tools rooted in
uncompromising transparency, rigorous data quality, and purposeful
versatility.
The Basics
The GHG inventory of any entity can be summarized as follows: Assets, such as buildings, vehicles, and equipment, engage in Activities, such as electricity consumption, travel, or purchasing, which each have a specific Emission Factor that tells us the rate at which that activity generates GHG emissions. These three variables - assets, actives, and emission factors - compose the underlying calculation methodology. ghgtools uses a set of standardized data templates to match each record of activity data to an emission factor using information about your assets. Our methodology is built on the best practices set forth by the GHG Protocol Corporate Standard.
The following data sets are required to use ghgtools and calculate GHG emissions. When you library(ghgtools), these data sets are available.
Emission Factor Library - EFLibrary - The catalog of emission factors for a variety of activities. Our team consolidates emission factors from a collection of reputable sources, such as the EPA’s Emission Factor Hub. Unless you want to add your own custom emission factors (which you certainly can, these tools are open source after all!), you don’t need to worry about the emission factor library. It is pre-populated and ready to go.
Asset Portfolio - AssetPortfolio - The list of all your assets. This likely includes buildings and vehicles. You may also designate an Enterprise asset to calculate scope 3 emissions for purchasing, business travel, and other supply chain related activities.
Activity Data - ActivityData - The record of energy consumption and other GHG-producing activities across each of your assets.
Data and Templates
First, we need to load ghgtools
The emission factor library, EFLibrary, is loaded from
the ghgtools package. There is a row of data for each greenhouse gas
produced by the activity. For example, burning natural gas will generate
CO2, CH4 and N2O. EFLibrary is loaded when you library
ghgtools. An example of the emission factors for Aviation
Gasoline is provided below. Check out the Emission
Factor Library article for more guidance and information.
| ef_source | ef_publishdate | ef_activeyear | supplier | service_type | emission_category | service_subcategory1 | service_subcategory2 | unit | emission_scope | country | subregion | ghg | ghg_unit | source_emission_factor | unit_conversion | conversion_factor | kg_ghg_perunit |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EPA CCCL Emission Factors for GHG Inventories | 2025-01-15 | 2024 | anthracite | stationary | short ton | scope 1 | global | co2 | kg | 2602 | 1 | kg_per_kg | 2602 | ||||
| EPA CCCL Emission Factors for GHG Inventories | 2025-01-15 | 2024 | bituminous | stationary | short ton | scope 1 | global | co2 | kg | 2325 | 1 | kg_per_kg | 2325 | ||||
| EPA CCCL Emission Factors for GHG Inventories | 2025-01-15 | 2024 | sub-bituminous | stationary | short ton | scope 1 | global | co2 | kg | 1676 | 1 | kg_per_kg | 1676 |
ghgtools comes with templates for activity data and the asset
portoflio. You can use create_templates() to write these
templates to excel files in your working directory.
create_templates() also writes the full emission factor
library to an excel file in your working directory.
create_templates()
#> [1] "Success! Check your directory for ActivityData.csv, AssetPortfolio.csv, and EFLibrary.csv"See below for some example data from the templates.
Activity Data Template - See ActivityData
reference documentation for more information about the activity data
template.
| asset_id | supplier | account_id | meter_number | date | year | cost | service_type | emission_category | service_subcategory1 | service_subcategory2 | unit | usage |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B1456 | 2023 | electricity | indirect energy | kWh | 31231.91 | |||||||
| B1457 | 2023 | electricity | indirect energy | kWh | 19389.59 | |||||||
| B1458 | 2023 | electricity | indirect energy | kWh | 185862.41 | |||||||
| B1458 | 2023 | natural gas | stationary | therm | 2569.69 |
Asset Portfolio Template - See AssetPortfolio
reference documentation for more information about the asset
portfolio template.
| asset_id | asset_name | asset_description | address | city | state | zip | country | sqft | region | business_unit | asset_type | asset_subtype | year_built |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B1456 | Bellingham | Washington | 98226 | USA | 342 | West Coast | Operations | Building | Office | 1993 | |||
| B1457 | Bellingham | Washington | 98225 | USA | 3280 | West Coast | Operations | Building | Office | 2005 | |||
| B1458 | Bellingham | Washington | 98225 | USA | 20142 | West Coast | Operations | Building | Office | 2003 |
Running ghg_inventory()
The core function of ghgtools is ghg_inventory(). This
function requires activity data, an asset portoflio, and a GWP
selection.You may choose to provide an emission factor library,
otherwise ghg_inventory() will default to the internal
emission factor library EFLibrary
- Activity Data must be a data frame following the structure of the activity data template.
- Asset Portfolio must be a data frame following the structure of the asset portfolio template.
- GWP choose a global wamring potential to use for the GHG inventory calcualtions. Choices are “SAR”, “AR4”, “AR5”, or “AR6”. If you have no preference for GWPs, we recommend following UNFCCC guidelines, which require the use of GWP values from the IPCC’s Fifth Assessment Report (AR5).
-
Emission Factor Library must be a data frame
following the structure of
EFLibrary. You may have custom or unique emission factors that you wish to use in ghgtools, in which case you can load your own emission factor library into r studio. You may also append additional emission factors onto the defaultEFLibrary.
You may prefer to use excel to overwrite the activity data and asset
portfolio data templates with your own data. This is why
create_templates() writes an excel file in your working
directory. Once you add your data to the templates, you can use the
fread() function to create global environment variables for
use in ghgtools. If you decide to make any changes to the emission
factor library, again use fread() to create a global
environment variable.
library(data.table)
My_ActivityData <- fread("ActivityData.csv")
My_AssetPortfolio <- fread("AssetPortfolio.csv")
My_EFLibrary <- fread("EFLibrary.csv")With the variables created above, you can run
ghg_inventory().
GHG_rawdata <- ghg_inventory(My_ActivityData, My_AssetPortfolio, "ar5", My_EFLibrary)Let’s take a look at the ghg inventory that was calculated:
| asset_id | asset_name | asset_type | asset_subtype | asset_description | address | city | state | zip | country | region | subregion | business_unit | year_built | sqft | service_type | unit | supplier | account_id | meter_number | date | year | cost | usage | emission_category | service_subcategory1 | service_subcategory2 | emission_scope | co2_kgperunit | ch4_kgperunit | n2o_kgperunit | otherghgs_kgco2eperunit | gwps_ar | co2_gwp | ch4_gwp | n2o_gwp | kgco2e_perunit | kg_co2 | kg_ch4 | kg_n2o | kg_co2e | mt_co2e | ef_source | ef_publishdate |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B1456 | Building | Office | Bellingham | Washington | 98226 | USA | West Coast | NWPP | Operations | 1993 | 342 | electricity | kWh | 2023 | 31231.907 | indirect energy | scope 2 | 0.2731025 | 2.54e-05 | 3.60e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2747753 | 8529.5111 | 0.7933269 | 0.1133324 | 8581.7574 | 8.5817574 | eGRID2022 | 2024-01-30 | ||||||||||
| B1457 | Building | Office | Bellingham | Washington | 98225 | USA | West Coast | NWPP | Operations | 2005 | 3280 | electricity | kWh | 2023 | 19389.588 | indirect energy | scope 2 | 0.2731025 | 2.54e-05 | 3.60e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2747753 | 5295.3445 | 0.4925182 | 0.0703597 | 5327.7803 | 5.3277803 | eGRID2022 | 2024-01-30 | ||||||||||
| B1458 | Building | Office | Bellingham | Washington | 98225 | USA | West Coast | NWPP | Operations | 2003 | 20142 | electricity | kWh | 2023 | 185862.407 | indirect energy | scope 2 | 0.2731025 | 2.54e-05 | 3.60e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2747753 | 50759.4836 | 4.7211223 | 0.6744460 | 51070.4032 | 51.0704032 | eGRID2022 | 2024-01-30 | ||||||||||
| B1458 | Building | Office | Bellingham | Washington | 98225 | USA | West Coast | Operations | 2003 | 20142 | natural gas | therm | 2023 | 2569.690 | stationary | scope 1 | 5.3060000 | 1.00e-04 | 1.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 5.3114500 | 13634.7751 | 0.2569690 | 0.0256969 | 13648.7800 | 13.6487800 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||
| B1564 | Building | Office | Woodinville | Washington | 98072 | USA | West Coast | NWPP | Operations | 1990 | 3636 | electricity | kWh | 2023 | 47302.603 | indirect energy | scope 2 | 0.2731025 | 2.54e-05 | 3.60e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2747753 | 12918.4580 | 1.2015414 | 0.1716488 | 12997.5881 | 12.9975881 | eGRID2022 | 2024-01-30 | ||||||||||
| B1564 | Building | Office | Woodinville | Washington | 98072 | USA | West Coast | Operations | 1990 | 3636 | natural gas | therm | 2023 | 1382.843 | stationary | scope 1 | 5.3060000 | 1.00e-04 | 1.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 5.3114500 | 7337.3650 | 0.1382843 | 0.0138284 | 7344.9015 | 7.3449015 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||
| B1662 | Building | Office | Portland | Oregon | 97202 | USA | West Coast | NWPP | Operations | 2007 | 4821 | electricity | kWh | 2023 | 106760.001 | indirect energy | scope 2 | 0.2731025 | 2.54e-05 | 3.60e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2747753 | 29156.4206 | 2.7118288 | 0.3874041 | 29335.0139 | 29.3350139 | eGRID2022 | 2024-01-30 | ||||||||||
| B1662 | Building | Office | Portland | Oregon | 97202 | USA | West Coast | Operations | 2007 | 4821 | natural gas | therm | 2023 | 3541.052 | stationary | scope 1 | 5.3060000 | 1.00e-04 | 1.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 5.3114500 | 18788.8219 | 0.3541052 | 0.0354105 | 18808.1206 | 18.8081206 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||
| B1663 | Building | Office | Portland | Oregon | 97229 | USA | West Coast | NWPP | Operations | 2015 | 4316 | electricity | kWh | 2023 | 74746.451 | indirect energy | scope 2 | 0.2731025 | 2.54e-05 | 3.60e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2747753 | 20413.4408 | 1.8986472 | 0.2712353 | 20538.4803 | 20.5384803 | eGRID2022 | 2024-01-30 | ||||||||||
| B1663 | Building | Office | Portland | Oregon | 97229 | USA | West Coast | Operations | 2015 | 4316 | natural gas | therm | 2023 | 1793.131 | stationary | scope 1 | 5.3060000 | 1.00e-04 | 1.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 5.3114500 | 9514.3531 | 0.1793131 | 0.0179313 | 9524.1256 | 9.5241256 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||
| B1866 | Building | Office | Sacramento | California | 95816 | USA | West Coast | CAMX | Operations | 1999 | 6074 | electricity | kWh | 2023 | 85928.162 | indirect energy | scope 2 | 0.2256363 | 1.36e-05 | 1.80e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2264981 | 19388.5135 | 1.1692906 | 0.1559054 | 19462.5685 | 19.4625685 | eGRID2022 | 2024-01-30 | ||||||||||
| B1867 | Building | Office | Sacramento | California | 95825 | USA | West Coast | CAMX | Operations | 2004 | 3959 | electricity | kWh | 2023 | 44956.454 | indirect energy | scope 2 | 0.2256363 | 1.36e-05 | 1.80e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2264981 | 10143.8084 | 0.6117570 | 0.0815676 | 10182.5530 | 10.1825530 | eGRID2022 | 2024-01-30 | ||||||||||
| B1868 | Building | Office | Sacramento | California | 95825 | USA | West Coast | CAMX | Operations | 2004 | 8070 | electricity | kWh | 2023 | 106728.394 | indirect energy | scope 2 | 0.2256363 | 1.36e-05 | 1.80e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.2264981 | 24081.8011 | 1.4523353 | 0.1936447 | 24173.7823 | 24.1737823 | eGRID2022 | 2024-01-30 | ||||||||||
| B2074 | Building | Office | Indio | California | 92203 | USA | West Coast | AZNM | Operations | 2010 | 3543 | electricity | kWh | 2023 | 49348.000 | indirect energy | scope 2 | 0.3520039 | 2.31e-05 | 3.20e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.3534931 | 17370.6908 | 1.1415775 | 0.1566871 | 17444.1771 | 17.4441771 | eGRID2022 | 2024-01-30 | ||||||||||
| B2074 | Building | Office | Indio | California | 92203 | USA | West Coast | Operations | 2010 | 3543 | natural gas | therm | 2023 | 25.981 | stationary | scope 1 | 5.3060000 | 1.00e-04 | 1.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 5.3114500 | 137.8552 | 0.0025981 | 0.0002598 | 137.9968 | 0.1379968 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||
| LS101 | Equipment | Mobile | Landscaping equipment | Bellingham | Washington | 98226 | USA | West Coast | Operations | motor gasoline | gallon | 2023 | 708.000 | mobile | scope 1 | 8.7800000 | 3.80e-04 | 8.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 8.8118400 | 6216.2400 | 0.2690400 | 0.0566400 | 6238.7827 | 6.2387827 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | ||||||||||||
| Enterprise | Enterprise | USA | West Coast | capital goods | usd | 2023 | 22219.240 | supply chain | electronic computer manufacturing | scope 3 | 0.0490000 | 2.03e-04 | 7.80e-06 | 0.0016187 | ar5 | 1 | 28 | 265 | 0.0583617 | 1088.7428 | 4.5105057 | 0.1726435 | 1296.7537 | 1.2967537 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | capital goods | usd | 2023 | 424731.110 | supply chain | all other miscellaneous general purpose machinery manufacturing | scope 3 | 0.2260000 | 4.90e-04 | 1.95e-05 | 0.0044154 | ar5 | 1 | 28 | 265 | 0.2493029 | 95989.2309 | 208.1182439 | 8.2822566 | 105886.7026 | 105.8867026 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 533840.640 | supply chain | all other legal services | scope 3 | 0.0342000 | 1.66e-04 | 5.10e-06 | 0.0012316 | ar5 | 1 | 28 | 265 | 0.0414443 | 18257.3499 | 88.6175462 | 2.7492793 | 22124.6573 | 22.1246573 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 130615.220 | supply chain | human resources consulting services | scope 3 | 0.0648000 | 2.60e-04 | 9.80e-06 | 0.0029779 | ar5 | 1 | 28 | 265 | 0.0776549 | 8463.8663 | 33.9599572 | 1.2800292 | 10142.9058 | 10.1429058 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 988346.400 | supply chain | landscape architectural services | scope 3 | 0.0875000 | 2.97e-04 | 1.74e-05 | 0.0029476 | ar5 | 1 | 28 | 265 | 0.1033746 | 86480.3100 | 293.5388808 | 17.1972274 | 102169.8782 | 102.1698782 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 154157.660 | supply chain | facilities support services | scope 3 | 0.1740000 | 5.47e-04 | 2.32e-05 | 0.0036002 | ar5 | 1 | 28 | 265 | 0.1990642 | 26823.4328 | 84.3242400 | 3.5764577 | 30687.2785 | 30.6872785 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 42621.180 | supply chain | janitorial services | scope 3 | 0.1480000 | 5.80e-04 | 4.36e-05 | 0.0372984 | ar5 | 1 | 28 | 265 | 0.2130924 | 6307.9346 | 24.7202844 | 1.8582834 | 9082.2487 | 9.0822487 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 160131.340 | supply chain | office administrative services | scope 3 | 0.0835000 | 3.62e-04 | 1.70e-05 | 0.0020639 | ar5 | 1 | 28 | 265 | 0.1002049 | 13370.9669 | 57.9675451 | 2.7222328 | 16045.9447 | 16.0459447 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 15929.275 | supply chain | security systems services (except locksmiths) | scope 3 | 0.0631000 | 2.50e-04 | 8.70e-06 | 0.0016453 | ar5 | 1 | 28 | 265 | 0.0740482 | 1005.1373 | 3.9823188 | 0.1384254 | 1179.5341 | 1.1795341 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 321181.500 | supply chain | professional and management development training | scope 3 | 0.0892000 | 4.78e-04 | 1.22e-05 | 0.0022896 | ar5 | 1 | 28 | 265 | 0.1081066 | 28649.3898 | 153.5247570 | 3.9184143 | 34721.8390 | 34.7218390 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | purchased goods and services | usd | 2023 | 1795761.130 | supply chain | wireless telecommunications carriers (except satellite) | scope 3 | 0.0837000 | 2.42e-04 | 9.60e-06 | 0.0033181 | ar5 | 1 | 28 | 265 | 0.0963487 | 150305.2066 | 434.5741935 | 17.3111373 | 173019.3367 | 173.0193367 | US EPA supply chain greenhouse gas emission factors v1.3 by naics-6 | 2024-07-10 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | business travel | vehicle mile | 2023 | 15000.000 | mobile | passenger car | scope 1 | 0.3061310 | 9.00e-06 | 6.00e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.3079762 | 4591.9651 | 0.1347935 | 0.0902014 | 4619.6427 | 4.6196427 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | business travel | vehicle mile | 2023 | 500.000 | mobile | light duty truck | scope 1 | 0.4054557 | 1.11e-05 | 9.80e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.4083695 | 202.7278 | 0.0055354 | 0.0049129 | 204.1847 | 0.2041847 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | employee commuting | vehicle mile | 2023 | 7000.000 | mobile | medium and heavy duty truck | scope 1 | 1.3597866 | 1.19e-05 | 3.77e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 1.3701234 | 9518.5062 | 0.0833611 | 0.2642390 | 9590.8636 | 9.5908636 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | employee commuting | vehicle mile | 2023 | 8400.000 | mobile | passenger car | scope 1 | 0.3061310 | 9.00e-06 | 6.00e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.3079762 | 2571.5005 | 0.0754844 | 0.0505128 | 2586.9999 | 2.5869999 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||||
| Enterprise | Enterprise | USA | West Coast | employee commuting | vehicle mile | 2023 | 3000.000 | mobile | light duty truck | scope 1 | 0.4054557 | 1.11e-05 | 9.80e-06 | 0.0000000 | ar5 | 1 | 28 | 265 | 0.4083695 | 1216.3670 | 0.0332123 | 0.0294775 | 1225.1085 | 1.2251085 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||||
| V0001 | Vehicle | Light-Duty Truck | 1988 GMC Van | West Coast | Operations | 1988 | motor gasoline | gallon | 2023 | 500.000 | mobile | scope 1 | 8.7800000 | 3.80e-04 | 8.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 8.8118400 | 4390.0000 | 0.1900000 | 0.0400000 | 4405.9200 | 4.4059200 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||
| V0002 | Vehicle | Passenger Car | 2018 Hyundai Tucson | West Coast | Operations | 2018 | motor gasoline | gallon | 2023 | 600.000 | mobile | scope 1 | 8.7800000 | 3.80e-04 | 8.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 8.8118400 | 5268.0000 | 0.2280000 | 0.0480000 | 5287.1040 | 5.2871040 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||
| V0002 | Vehicle | Passenger Car | 2018 Hyundai Tucson | West Coast | Operations | 2018 | motor gasoline | gallon | 2023 | 15000.000 | mobile | scope 1 | 8.7800000 | 3.80e-04 | 8.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 8.8118400 | 131700.0000 | 5.7000000 | 1.2000000 | 132177.6000 | 132.1776000 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 | |||||||||||||||
| V0003 | Vehicle | Light-Duty Truck | 2022 Dodge Ram | West Coast | Operations | 2022 | diesel fuel | gallon | 2023 | 20000.000 | mobile | scope 1 | 10.2100000 | 4.10e-04 | 8.00e-05 | 0.0000000 | ar5 | 1 | 28 | 265 | 10.2426800 | 204200.0000 | 8.2000000 | 1.6000000 | 204853.6000 | 204.8536000 | EPA CCCL Emission Factors for GHG Inventories | 2024-06-05 |
ghg_inventory() has merged our AssetPortoflio,
ActivityData, and EFL to generate a GHG emissions inventory report. This
data can now be used for a variety of climate disclosure needs.
Visualize your GHG Inventory
Using ggplot2() we can begin to visualize the inventory
created by ghgtools.
Visit ghgtools.io for updates and more information!