Sustainability Dashboard Methodology
QUICK LINKS
- Purpose
- Inventory Scope and Organizational Boundary
- Operational Boundaries
- Methodology
- Scope 1 Methodology
- Scope 2 Methodology
- Scope 3 Methodology
- Category 1: Purchased Goods and Services, Subgrants to Partners
- Category 3: Fuel and Energy-Related Activities
- Category 5: Waste Generated in Operations
- Category 6: Business Travel
- Category 7: Employee Commuting
- Category 15: Investments
Purpose
This manual outlines the scope, organizational boundary, operational boundaries, and methodology of the World Resources Institute’s greenhouse gas inventory. These disclosures are consistent with the reporting requirements of the GHG Protocol Corporate Accounting and Reporting Standard, Revised Edition(2004) and the GHG Protocol Corporate Value Chain (Scope 3) Accounting and Reporting Standard (2011). WRI will revise this manual at least annually to include developments such as changes in standards, operations, or methodology, and updated emission factors.
Inventory Scope and Organizational Boundary
Table 1 | WRI Greenhouse Gas Inventory Scope
| Descriptive Information | Organization Response |
| The reporting period covered | Fiscal Years 2010 (October 1, 2009 to September 30, 2010) to 2019 (October 1, 2020 to September 30, 2021) |
| Chosen consolidation approacha | Operational control |
| Description of the businesses and organizations included in the organizational boundary |
World Resources Institute (WRI) is a global organization with staff around the world in a variety of working environments. All material offices within WRI’s global network are considered part of the organizational boundary and are asked to report Scopes 1 and 2 emissions and participate in Scope 3 emissions data collection.
Offices are considered material when they are under operational control of WRI, and if any of the additional criteria are met:
|
| The year chosen as base year and rationale for choosing the base year | WRI has updated the base year to 2019 for its GHG inventory. While WRI had a previous base year of 2010, for its 2020 reduction targets, the Sustainability Initiative and WRI’s management established new reduction targets for 2030 and used a more accurate base year of 2019, which includes more of WRI’s international offices under operational control. |
| The chosen base year emissions recalculation policy |
WRI will recalculate its base year emissions if there are significant changes to its GHG inventory following the guidance given in the GHG Protocol Corporate Standard and Scope 3 Standard. Significant is defined as a change or series of changes that impact the base year inventory by more than 5 percent.
Any updates to the WRI organizational boundary that meet the materiality criteria will trigger a base year recalculation.
While there are changes from year-to-year in methodology and data quality, these do necessitate a base year recalculation because the cumulative changes in each year do not meet the requirements for recalculation.
Staff increases also do not necessitate a base year recalculation. Staff count data are provided alongside annual emissions data for reference. Staff count includes any staff employed by WRI at any point within each reporting year and have the following designations:
|
Note: a “Consolidation approach” refers to the method chosen to determine which sources of emissions are included in the GHG inventory, as defined by the Corporate Standard and Scope 3 Standard. More information can be found in Chapter 3 of the Corporate Standard and Chapter 5.2 of the Scope 3 Standard.
Source: WRI.
Operational Boundaries
Table 2 | Status of Emission Sources in WRI’s Greenhouse Gas Inventory
| Emission Source | Status | Exclusionsa |
| Scope 1: Direct emissions from owned/controlled operations | Calculated | Scope 1 emissions from WRI’s international offices are only available for certain years from 2014 onward. As Scope 1 emissions from WRI’s international offices have historically been <0.0001 percent of the total GHG inventory, they are insignificant enough to exclude when international offices are unable to gather data. |
| Scope 2: Indirect emissions from the use of purchased electricity, steam, heating, and cooling | Calculated | Scope 2 emissions are based on electricity bill data. Electricity bill data is prorated for the square footage of occupancy for WRI offices spaces and also include a portion of common spaces as defined by lease agreements. When WRI is the sole occupant of a building, data reflects the entire building and common spaces. Scope 2 emissions from WRI’s US office is available from 2010 onward. WRI’s international offices are only available from office initiation or data reporting initiation onward. For 2019-onwards, any offices that do not have electricity bill or submeter data apply estimated electricity calculations, which are based on using available regional averages of commercial office space electricity use. |
| Scope 3, Category 1: Purchased goods & services | Calculated | The smallest individual vendor payments that cumulatively represent 5 percent of total spend are excluded as they represent an insignificant portion (<0.0001 percent) of the total GHG inventory and would be time-consuming to account for. Emissions associated with operational cost (i.e. staff payroll, payments to WRI’s international offices, rent, credit card purchases, and retirement funds) are excluded to avoid double-counting and to be consistent with the methodology in previous reports. |
| Scope 3, Category 2: Capital goods | Accounted for in Category 1 | Limited capital goods purchases are aggregated with Category 1. |
| Scope 3, Category 3: Fuel- and energy-related activities | Calculated | |
| Scope 3, Category 4: Upstream transportation & distribution | Accounted for in Category 1 | Limited sources of upstream transportation and distribution; difficult to disaggregate from Category 1 data. |
| Scope 3, Category 5: Waste generated in operations | Calculated | For WRI US, annual weight totals (kg) of landfill and recycling collected are reported from the vendor and prorated to WRI’s occupancy ratio of the building. When available, the US office conducts quarterly waste audit on daily samples of landfill and recycling and scales these samples up for annual waste calculations. Monthly weight totals (kg) of compost collected by WRI are reported from the vendor. Estimated emissions from waste generated by all WRI international offices amount to <0.1 percent of the total inventory; therefore, they are excluded because of challenges and costs associated with data collection. |
| Scope 3, Category 6: Business travel | Calculated | Business travel data is collected from WRI’s corporate travel agency on a monthly basis for all of WRI US and China staff air and rail travel, as well as a significant portion of WRI Africa and Europe travel. Other office data are reported on a voluntary basis until consistent corporate travel becomes available. |
| Scope 3, Category 7: Employee Commuting | Calculated | Employee commute and telework impact data are collected using an annual survey. Survey results are analyzed for representation of offices and staff and look to achieve at least 50% response rate as a minimum threshold. Employee commute data from WRI’s international offices are only available for certain years between 2013-2019. |
| Scope 3, Category 8: Upstream leased assets | Accounted for in Scope 2 & Category 1 | WRI leases its office space, copiers, and printers. Electricity use is included in Scope 2, office space lease is excluded, and maintenance of the copiers and printers is included in Category 1. |
| Scope 3, Category 9: Downstream transportation & distribution | Accounted for in Category 1 | Limited sources of downstream transportation; difficult to disaggregate from vendor data used for category 1. |
| Scope 3, Category 10: Processing of sold products | Accounted for in Category 1 | Processing of WRI’s sold products (publications) through activities such as printing are accounted for in Category 1. It is not possible to disaggregate the data between services. |
| Scope 3, Category 11: Use of sold products | N/Ab | Sold products (publications) do not use energy or emit GHGs during use. |
| Scope 3, Category 12: End-of-life treatment of sold products | N/A | Historically, emissions from end-of-life treatment of WRI’s sold products (printed publications) accounted for <0.05 percent of WRI’s total inventory; therefore, Category 12 is excluded because of challenges associated with data collection. |
| Scope 3, Category 13: Downstream leased assets | N/A | WRI does not lease assets to other entities. |
| Scope 3, Category 14: Franchises | N/A | WRI does not own any franchises. |
| Scope 3, Category 15: Investments | Calculated | WRI has a defined sustainable investment approach for its endowment. GHG calculations of WRI’s endowment portfolio use fund- level and securities-level data for its assets and public equity investments for 2019 onward. |
Note: a Exclusions are purposefully omitted from GHG emission sources because of their insignificance and are distinct from data or sources not included for reasons of data quality concerns or availability. Please review our methodology and approach for each emission source to learn about WRI’s inventory data limitations. b N/A = Not applicable.
Source: WRI.
Methodology
General Formula Used to Calculate WRI’s Emissions
WRI’s GHG calculations follow the formula below unless otherwise indicated:
Activity data x emission factor x global warming potential (GWP) = CO2 equivalent (CO2e) emissions
Where:
- Activity data is a quantitative measure of a level of activity (e.g. liters of fuel consumed, kilometers traveled, etc.) that results in GHG emissions
- Emission factor is a factor that converts activity data into GHG emissions data (e.g. kg CO2 emitted per liter of fuel consumed, kg CH4 emitted per kilometer traveled, etc.)
- Global warming potential (GWP) is a factor describing the radiative forcing impact (degree of harm to the atmosphere) of one unit of a given GHG, relative to one unit of CO2 over a 100-year time horizon. Multiplying emissions of a given GHG by its GWP gives us the CO2 equivalent emissions.
The global warming potential factors, detailed methodology, and emission factors used for each emission source are listed in this manual.
Global Warming Potentials Used to Calculate WRI’s Emissions
Table 3 | Global Warming Potentials Used in This Inventory
| Greenhouse Gas | GWP (100-year) |
Source |
| CO2 | 1 | Intergovernmental Panel on Climate Change, Fifth Assessment Report (2014) |
| CH4 | 28 | (*) |
| N2O | 265 | (*) |
| HFC-134a | 1300 | (*) |
| R-410 A | 2088 | (*) |
| R600A | 3 | High-GWP Refrigerants, California Air Resources Board |
Source: WRI.
Scope 1 Methodology
Scope 1 includes direct GHG emissions from sources that are owned or controlled by the company. For example, emissions from combustion in owned or controlled boilers, furnaces, or vehicles; and emissions from chemical production in owned or controlled process equipment.
Table 4 | Approach for Diesel Generators
| Methodology | Description |
| Activity data | WRI US has a diesel generator on the roof that is used during electricity outages and for testing. The building manager provides activity data annually (gallons of diesel fuel used by the generator). |
| Method | The activity data (gallons of diesel fuel used) are allocated to WRI using a ratio of WRI’s square-feet occupation to the total leasable square feet of the office building. Calculation follows the general formula. |
| Methodology Changes | 2019: Emission factors were updated. |
| Limitations | The use of an occupancy ratio based on square footage or other estimation methods are not as accurate as submetering WRI’s own operations. However, submetering does not account for common space energy use in commercial buildings with multiple tenants. |
Source: WRI.
Table 5 | Emission Factors for Diesel Generators
| Greenhouse Gas | Emission Factor (MT CO2e/gal) | Source |
| CO2 | 0.01018 | EPA Greenhouse Gas Inventory Guidance: Direct Emissions from Stationary Combustion Sources (2016) |
| CH4 | 0.00001176 | |
| N2O | 0.0000212 |
Source: WRI.
Table 6 | Approach for Office Refrigerators
| Methodology | Description |
| Activity data | Cooling unit inventories are reported by building management or WRI staff. Total refrigerant volumes (kg) per cooling unit are estimated. |
| Method | Calculation uses estimation equations from the Climate Registry’s General Reporting Protocol (GRP) v 1.1 (2008) - total annual refrigerant emissions (metric tons) = [ (CN * k) + (C * x * T) + (CD * y * (1 – z) ) ] ÷ 1,000, where: CN = Quantity of refrigerant charged into the new equipment C = Total full charge (capacity) of the equipment T = Fraction of the year the equipment was in use CD = Total full charge (capacity) of equipment being disposed of k = Installation emission factor x = Operating emission factor y = Refrigerant remaining at disposal z = Recovery efficiency The GWPa is then applied to the total emissions to determine WRI’s refrigerant emissions. |
| Methodology Changes | WRI has updated its Scope 1 methodology to include the offices in its organizational boundary from 2019 onward.. Both confirmed data and estimations were used. |
| Limitations | Despite including offices based on the organizational boundary, refrigeration data can be limited or unavailable for certain offices. If offices provide only the model name and/or the model information is unavailable on the internet, specifications of similar models in brand and size are used for calculations. The sources for Greenhouse Gases and GWPs in Table 4 may differ and lack consistency. The inclusion of more offices within WRI’s organizational boundaries expands WRI’s fridge inventory, where information may not be readily available for all purchased fridges by currently utilized sources (IPCC, EPA). |
Notes: aGWP = Global warming potential. Source: WRI.
Table 7 | Emission Factors for Office Refrigerators
| Emission Source | Emission Factor (% of Capacity/Year) | Source |
| Domestic refrigeration (operation) | 0.5 | The Climate Registry (2016) |
| Domestic refrigeration (installation) | 1 | (*) |
| Domestic refrigeration (remaining at disposal) | 0.8 | (*) |
| Domestic refrigeration (recovery efficiency) | 0.7 | (*) |
Scope 2 Methodology
Scope 2 includes GHG emissions from the generation of purchased electricity consumed by the company. Purchased electricity is electricity to be consumed that is purchased or otherwise brought into the organizational boundary of the company. Scope 2 guidance requires dual reporting, following emission factor hierarchies.
Location-based Method
The location-based method calculates emissions based on electricity consumption at the location where the energy is used, taking into account the fuel mix used to generate electricity within the locations and time periods in which WRI operates. WRI uses local or country-level grid average emission factors to report location-based emissions for all offices included in the inventory scope.
Table 8 | Location-based Approach
| Methodology | Description |
| Activity data | Monthly totals of electricity consumption compiled from bills provided by building management. In cases where WRI office space is within a building with multiple tenants and organizations, the electricity purchase data for the entire building is multiplied by WRI’s square-foot occupancy ratio to determine the portion of electricity consumed by WRI. In WRI offices where electricity consumption data is not available, average regional datasets are used for the closest available region. |
| Method | Calculation follows the general formula. Activity data for each WRI location (MWh) are multiplied by grid average emission factors and GWP factors to convert electricity consumption into CO2e emissions. |
| Methodology Changes | Emission factors for US operations are updated every year, as US EPA eGRID data and IEA updates are available. WRI has updated its Scope 2 methodology to include the offices in its organizational boundary from 2019 onward.y. Both confirmed data and estimations were used. 2019: For offices that don’t have electricity bill data, total MWh is estimated based on average energy intensity for its closest climate zone, with the following formula: Total MWh = (Square Footage x kWh/Sq ft)/1000a For offices that are unable to confirm leased office size, approximate square footage is calculated by allocating 150 square feet per employee. |
| Limitations | The use of an occupancy ratio based on square footage and other electricity use estimates are not as accurate as submetering WRI’s own electricity consumption. However, submetering does not account for common space electricity use in commercial buildings with multiple tenants. For most offices outside of the US, emissions factors are country average data and are not regionally specific to WRI’s locations. |
Notes: a See Table 9 Source: WRI.
Table 9 | Average Energy Intensity for Climate Zones
| Country | IECC Zone | Climate Region | kWh/Sq ft | Source |
| United States | 5 | Very cold/Cold | 15.40 | David Wheeler . 2012. “Energy+ Country Performance Ratings 2001–2010.” CGD Working Paper 301. Washington, D.C.: Center for Global Development CBECS (2012) Table C20: Electricity Consumption and Conditional Energy Intensity by Climate Region. |
| China | 6 | Very cold/Cold | 15.40 | (*) |
| India | 3B | Mixed-dry/Hot-dry | 14.30 | (*) |
| Indonesia | 2 | Hot-humid | 19.10 | (*) |
| Brazil | 3A | Mixed-humid | 15.80 | (*) |
| Mexico | 3B | Mixed-dry/Hot-dry | 14.30 | (*) |
| Netherlands | 7 | Very cold/Cold | 15.40 | (*) |
| United Kingdom (England) | 7 | Very cold/Cold | 15.40 | (*) |
| Africa (Ethiopia) | 4B | Mixed-dry/Hot-dry | 14.30 | (*) |
| Africa (Kenya) | 4B | Mixed dry/Hot-dry | 14.30 | (*) |
| Africa (DRC) | 4A | Mixed-humid | 15.80 | (*) |
Table 10 | Emission Factors for Location-based Method
| Location | Fiscal Year | Emission Factor for CO2 (mt/MWh) | Emission Factor for CH4 (mt/MWh) | Emission Factor for N2O (mt//MWh) | Emission Factor for CO2e (mt/MWh) | Source |
| United States (RFC East Subregion) | 2010 | 0.5166755 | 1.373E-05 | 8.489E-06 | 0.5166977 | US EPA eGRID2007 Version 1.1 Year 2005 GHG Annual Output Emission Rates |
| 2011 | 0.5166755 | 1.373E-05 | 8.489E-06 | 0.5166977 | (“) | |
| 2012 | 0.4804995 | 1.243E-05 | 7.725E-06 | 0.4805197 | US EPA eGRID2010 Version 1.0 (2007 data: eGrRID subregion data) | |
| 2013 | 0.4543708 | 1.228E-05 | 6.953E-06 | 0.4543900 | US EPA eGRID2010 Version 1.0 (9th edition with 2010 data: eGrRID subregion data) | |
| 2014 | 0.4543708 | 1.228E-05 | 6.953E-06 | 0.4543900 | (“) | |
| 2015 | 0.3762095 | 3.352E-05 | 5.08E-06 | 0.3762481 | US EPA eGRID2014 (2014 data: Subregion Output Emission Rates) | |
| 2016 | 0.3762095 | 3.352E-05 | 5.08E-06 | 0.3762481 | (“) | |
| 2017 | 0.3439137 | 2.268E-05 | 4.082E-06 | 0.3439405 | US EPA eGRID2016 (2016 data: Subregion Output Emission Rates) (Scroll to 'Other Historical Data' to download zip data to view) |
|
| 2018 | 0.3439137 | 2.268E-05 | 4.082E-06 | 0.3439405 | (“) | |
| 2019-2021 | 0.31524993 | 0.00002404 | 0.00000318 | 0.31527715 | US EPA eGRID2019 (2019 data; Subregion Output Emission Rates) | |
| China | 2010 | 0.839 | 1.458E-05 | 1.841E-05 | 0.8390330 | Appendix F. Electricity Emission Factors from Energy Information Administration Form EIA-1605 (2007) Voluntary Reporting of Greenhouse Gases, OMB No. 1905-0194 |
| 2011 | 0.839 | 1.458E-05 | 1.841E-05 | 0.8390330 | (“) | |
| 2013-2018 | 1.12816 | 1.169E-05 | 1.692E-05 | 1.1281886 | Getting Every Ton of Emissions Right; An Analysis of Emission Factors for Purchased Electricity in China (WRI 2013) | |
| 2019-2021 | 0.62300000 | 0.00000661 | 0.00000835 | 0.62301497 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) | |
| India | 2013-2014 | 0.926 | N/A | N/A | N/A | GHGP Purchased Electricity tool (v4.7, May 2015), which uses IEA emission factors from 2012 |
| 2019-2021 | 0.71800000 | 0.00000755 | 0.00000889 | 0.71801643 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) | |
| Indonesia | 2018 | 0.774388897 | N/A | N/A | N/A | Electricity-specific emission factors for grid electricity (Brander et al. 2011) |
| 2019-2021 | 0.76900000 | 0.00000926 | 0.00000388 | 0.76901314 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) (EIA) | |
| Brazil | 2019-2021 | 0.11700000 | 0.00000114 | 0.00000048 | 0.11700162 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Mexico | 2019-2021 | 0.47700000 | 0.00000760 | 0.00000104 | 0.47700865 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Netherlands | 2019-2021 | 0.43700000 | 0.00000453 | 0.00000223 | 0.43700676 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| United Kingdom (London) | 2019-2021 | 0.24500000 | 0.00000360 | 0.00000249 | 0.24673595 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Africa (Ethiopia) | 2019-2021 | 0.00000000 | 0.00000018 | 0.00000004 | 0.00000022 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Africa (Kenya) | 2019-2021 | 0.18400000 | 0.00000609 | 0.00000122 | 0.18400731 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Africa (DRC) | 2019-2021 | 0.00100000 | 0.00000005 | 0.00000001 | 0.00100006 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
Notes: aN/A = Not applicable. Source: WRI.
Market-based Method
The market-based method shows emissions for which WRI is responsible through its purchasing decisions based on contractual emissions.
Table 11 | Market-based Approach
| Methodology | Description |
| Activity data | Total electricity consumed (MWh) (see “location-based activity data,” above); Total MWh of renewable energy certificates (RECs) purchased by WRI US’ building management company. |
| Method |
WRI USRenewable energy emissions
|
| Methodology Changes | The market-based calculation method was added to incorporate renewable energy certificates purchased on behalf of WRI US operations, in accordance with the GHG Protocol Corporate Standard, Scope 2 Guidance addressing contractual purchases of electricity. REC purchased on behalf of WRI US operations previously had been acknowledged but not accounted for in WRI’s annual GHG inventory. Subregional residual mix emission rate is currently unavailable. Given that the regional residual emission rate is 50 percent greater than the subregional emission rate for WRI’s location and could lead to inaccurate estimation, NERCa subregion emissions factor was used. |
| Limitations | WRI US: No qualified residual emission data were available. Emissions factor for NERC subregion RFC Eastb was used as a better estimation. WRI International Offices: Activity data gaps for certain years. WRI will work to fill these data gaps. |
Notes: aNERC = North American Electric Reliability Corporation. bRFC East = Reliability First Corporation/East Source: WRI.
Table 12 | Quality Criteria for Market-based Method
| Location | Energy Resource Type | Facility Location | Facility Age | Cap and Trade | Funding |
| Washington, DC (WRI US) | Green-e energy-certified wind power from Direct Energy Business | All over the US | Within 25 years | N/Aa | Production tax credit (PTC) |
Notes: aN/A = Not applicable. Source: WRI.
Table 13 | Emission Factors for Market-based Method
| Location | Fiscal Year | Emission Factor for CO2 (mt/MWh) | Emission Factor for CH4 (mt/MWh) | Emission Factor for N2O (mt//MWh) | Emission Factor for CO2e (mt/MWh) | Source |
| United States (RFC East Subregion) | 2019-2021 | 0.31524993 | 0.00002404 | 0.00000318 | 0.31527715 | US EPA eGRID2019 (2019 data; Subregion Output Emission Rates) |
| China | 2019-2021 | 0.62300000 | 0.00000661 | 0.00000835 | 0.62301497 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| India | 2019-2021 | 0.71800000 | 0.00000755 | 0.00000889 | 0.71801643 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Indonesia | 2019-2021 | 0.76900000 | 0.00000926 | 0.00000388 | 0.76901314 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Brazil | 2019-2021 | 0.11700000 | 0.00000114 | 0.00000048 | 0.11700162 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Mexico | 2019-2021 | 0.47700000 | 0.00000760 | 0.00000104 | 0.47700865 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Netherlands | 2019-2021 | 0.43700000 | 0.00000453 | 0.00000223 | 0.43700676 | IEA, AIB European Residual Mixes, Electricity Factors by Country 1999-2002 (2018) |
| United Kingdom (London) | 2019-2021 | 0.24500000 | 0.00000360 | 0.00000249 | 0.24500609 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Africa (Ethiopia) | 2019-2021 | 0.00000000 | 0.00000018 | 0.00000004 | 0.00000022 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Africa (Kenya) | 2019-2021 | 0.18400000 | 0.00000609 | 0.00000122 | 0.18400731 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
| Africa (DRC) | 2019-2021 | 0.00100000 | 0.00000005 | 0.00000001 | 0.00100006 | IEA CO2 Emissions from Fuel Combustion, Electricity Factors by Country 1999-2022 (2017) |
Source: WRI
Scope 3 Methodology
Category 1: Purchased Goods and Services, Subgrants to Partners
This category includes all upstream (i.e. cradle-to-gate) emissions from the production of goods (tangible products), as well as services and subgrants (intangible products). WRI uses the spend-based approach to estimate emissions for goods, services, and partners by multiplying the economic value of goods, services, and subgrants (i.e. spend data) by relevant industry average emission factors (i.e. emissions per dollar spent).
Table 14 | Approach for Category 1
| Methodology | Description |
| Activity data | Total amount spent (US$) with each contract vendor or subgrant agreement, provided by WRI’s central accounting system (secondary data). Percentage of supplier-specific data used = 0 |
| Method | WRI’s Category 1 emissions were determined by modeling secondary data (total vendor expenses and subgrants) using the US Environmental Protection Agency’s Environmentally Extended Input-Output (USEEIO) model. Input-output tables traditionally represent the monetary transactions between industry sectors in mathematical form. Environmentally Extended Input-Output (EEIO) models indicate what goods or services (or output of an industry) are consumed by other industries (or used as input). EEIO tables used in life cycle assessment also calculate the average emissions of pollutants associated with spending a certain amount of funds on a particular industry. Operational costs (i.e. staff payroll, funding for WRI’s international offices, rent, credit card purchases, and retirement funds) were excluded from the total vendor expenses for consistency with the methodology in previous reports. The following information was entered into the USEEIO model for each good or service purchased or subgrant awarded by WRI:
|
| Methodology Changes | 2019: The EEIO model used to estimate Category 1 emissions for all years since 2010 was updated to the USEEIO model, developed by US EPA in 2017 based on the 2007 benchmark input-output table from BEA with 2013 dollars used as the basis for demand. Previously, the Carnegie Mellon Environmental Input-Output Lifecycle Assessment (EIO-LCA) model was used. This model was developed by the Green Design Institute at Carnegie Mellon University in 2007, based on the 2002 benchmark input-output table from BEA with 2002 dollars used as the basis for demand. 2022: WRI has not added additional offices from its organizational boundary to Scope 3, Category 1. |
| Limitations and Discussion | The USEEIO model was developed with input-output tables representing exchanges among industries in the US, and with US economic data. Thus, when using USEEIO emission factors to estimate emissions, we assume that vendors and partners originate in the US, even though that is not always the case. The US EPA is currently developing a global EEIO model that may help WRI make better location-specific estimates. Furthermore, there are challenges with assigning specific vendors to the correct industry sectors. The assignment process involves some subjectivity. While the differences in the emission per dollar spent rate may be small, these discrepancies change total emissions when large transactions are converted using different emission factors. This manual recognizes this limitation of the spend based method, especially given that Scope 3 Category 1 accounts for the majority of WRI’s total emissions. To minimize inconsistencies and improve quality control, WRI created an ongoing tracking table of historic industry sector assignments for each vendor. This mechanism ensures that vendors are always assigned to the same industry sector across years. Each new vendor and its assignment is recorded, providing year-to-year continuity. WRI also periodically reviews the accuracy of these assignments. The spend-based approach using industry average emission factors is less granular than using the supplier-specific approach, whereby vendors provide high quality Scope 1 and 2 data that can be used to calculate vendor-specific emissions for each good or service purchased. We do not use this approach because our vendor and partner surveys indicate that the majority of our vendors and partners are not able to provide Scope 1 and 2 data. WRI plans to move toward a hybrid approach, where some vendor-specific data is incorporated with the use of the USEEIO model. Incorporating more vendor-specific data should have the effect of lowering emissions, given WRI’s requirements for its vendors. For example, WRI’s food and events policy requires all purchased food to be plant-based, which produces significantly lower emissions than does meat. However, using the USEEIO model reflects only average emissions associated with prepared food purchases, which include impacts from meat. Calculating the specific impacts of the plant-based food policy would help evaluate the outcomes of the program in the scope of all GHG emissions for the organization, but it would require additional methodology considerations. Another challenge is the proper classification and inclusion of international office transactions. WRI’s international offices have different levels of inclusion in WRI’s central accounting tools. In some cases, offices have spend data that are difficult to extract and that represent other scopes of emissions. For example, there are offices that appear in WRI transactions as vendors, and as such, their purchased goods and services cannot be parsed out from the amounts spent on operational costs. Other offices lack comprehensive spend data altogether. As such, all international office data were excluded from the calculations in this category. |
Source: WRI.
Table 15 | Sample of Emission Factors for Category 1
| USEEIOa Sector Name | Emission Factor (MT CO2e/dollar spend) | Source |
| Grantmaking, giving, and social advocacy organizations | 7.74257E-05 | US EPA Environmentally Extended Input-Output Model (2017) |
| Colleges, universities, junior colleges, and professional schools | 0.000331922 | |
| Environmental and other technical consulting services | 0.000119521 | |
| Civic, social, professional, and similar organizations | 0.000143823 | |
| Architectural, engineering, and related services | 0.000159178 |
Notes: aUSEEIO = United States Environmentally Extended Input-Output Model
Source: WRI.
Category 3: Fuel and Energy-Related Activities
This category includes indirect upstream emissions related to the production of fuels and energy purchased and consumed in the reporting year, which are not included in Scope 1 or Scope 2. For WRI, these emissions include well-to-tank (WTT) emissions of purchased fuels, well-to-tank (WTT) emissions of purchased electricity, and transmission and distribution (T&D) losses for purchased electricity. WTT emissions account for the emissions arising from the extraction, production, and transportation of fuels consumed or used to generate electricity. Since WRI does not produce energy directly, it uses the average-data method to estimate emissions by using secondary regional-average emission factors for upstream emissions per unit of consumption.
Table 16 | Approach for Category 3
| Methodology | Description |
| Activity data | Total annual diesel consumption (gallons) and electricity consumption (MWh) used for Scope 1 and Scope 2 calculations. Percentage of supplier-specific data used = 0 |
| Method | Calculation follows the general formula. To estimate emissions, diesel consumption data are multiplied by regional-average well-to-tank emission factors for diesel fuel. Electricity consumption data are multiplied by regional-average well-to-tank emission factors, and separately by regional-average transmission and distribution losses emission factors. |
| Methodology Changes | 2019: Upstream WTT emissions of purchased fuels were added to the inventory. Previously, only electricity-related emissions were included in Scope 3 Category 3. 2022: WRI has updated its Scope 3, Category 3 methodology to include the offices in its organizational boundary from 2019 onward. |
| Limitations | The emission factors for electricity used are developed by UK DEFRAa and are country average factors. They are not regionally specific to WRI’s locations. In 2018, DEFRA stopped publishing T&D emissions factors for regions outside of the UK. This inventory currently uses T&D factors from 2017 for years beyond 2017. 2022: WTT T&D emission factors are not included in the 2019-2021 GHG Inventory update. Will be considered in the future. |
Notes: aDEFRA = Department for Environment, Food, and Rural Affairs (UK). Source: WRI.
Table 17 | Well-to-tank (WTT) Emission Factors for Diesel Fuel
Notes: aDEFRA did not publish well-to-tank conversion factors for 2010 or 2011, so 2012 conversion factors were used. Source: WRI.
Table 18 | Well-to-tank (WTT) Emission Factors for Purchased Electricity
Source: WRI.
Table 19 | Transmission and Distribution (T&D) Losses Emission Factors for Purchased Electricity
Notes: aDEFRA stopped publishing transmission and distribution conversion factors in 2018, so 2017 conversion factors were used. Source: WRI.
Category 5: Waste Generated in Operations
This category includes emissions from third-party disposal and treatment of solid waste and wastewater that is generated in the reporting company’s owned or controlled operations in the reporting year. For solid waste, WRI uses the waste-type-specific method to estimate emissions. WRI does not report emissions from wastewater treatment, as it is currently unable to obtain this data.
Table 20 | Approach for Category 5
| Methodology | Description |
| Activity data | From 2010 to 2016, the building management for WRI’s US operations provided annual reports for recycling and landfill weights collected for the entire building. This data is prorated for WRI allocation by occupancy ratio. From 2018 onward, WRI negotiated for compost collection under its green lease provision with building management. The vendor provides monthly invoices with compost weights for WRI US office space. From 2017 to 2020, WRI US began conducting its own waste audits. Recycling and landfill activity data from 2017 onward are scaled up to annual emissions using the four representative daily waste audit samples applied to an entire year (250 days, Monday-Friday with 10 public holidays). From 2020 and 2021, due to the global pandemic and office closure, an estimate based on 2020 data and 25% office occupancy was used for US office. The 25% rate is based on the maximum allowed occupancy for the US office during that phase of the pandemic. It is likely occupancy and waste activity data was much lower. Estimated emissions from waste generated by all WRI international offices amount to <0.1 percent of the total inventory; therefore, they are excluded because of challenges and costs associated with data collection. |
| Method | For data provided by building management, weight data was multiplied by WRI US’ square-foot occupancy ratio. To calculate emissions, activity (waste weight) data are multiplied by waste-type (e.g. food waste) and waste-treatment-specific (e.g. landfilled, composted) emission factors that represent average end-of-life processes for the transportation and treatment of waste. |
| Methodology Changes | 2016: US-specific emission factors provided by the EPA WARMa tool were updated. 2022: WRI has not added additional offices from its organizational boundary to Scope 3, Category 5. International office waste data will be collected or estimated when offices have fully re-opened for a full year in FY 2023. |
| Limitations and Discussion | The allocation approach based on occupancy used before 2017 is not as accurate as using primary activity data. The WRI US waste audit process is also limited in accuracy as it applies 4 representative days in a year to 250 business days. The waste generated at any site is removed to a multitude of landfills or waste-to-energy centers. It is not possible to know how much waste ends up at each facility; thus, we assumed the average methane recovery rate for the United States when determining emissions from the disposal of this residual waste. For recycling and composting, WARM often attributes negative or avoided emissions. To avoid creating a misleading offset in emissions totals, all recycling weights were considered to have no impact instead of negative or avoided emissions. This consideration is in line with the GHGPb Technical Guidance for Calculating Scope 3 Emissions (version 1.0) (2013). The inconsistent upward and downward trends of the emissions-from-waste category may be due to activity data sources and vendor collection methods. In 2013-2014, the building management for WRI US switched waste hauling vendors. In 2017, WRI US began conducting its own waste audits to collect primary data, and a new compost vendor provided separate monthly data specifically for WRI offices. WRI is also not currently able to collect wastewater treatment data. |
Notes:
aEPA WARM = Environmental Protection Agency Waste Reduction Model
bGHGP = Greenhouse Gas Protocol
Source: WRI.
Table 21 | Emission Factors for Category 5
| Waste type | Fiscal Year | Emission Factor for CO2 (mt/short ton) | Source |
| Mixed municipal solid waste (MSW) | 2019 | 0.347 | Landfill no recovery (Mixed MSW – EPAa Waste Reduction Model (WARM) Version 14, March 2016 |
| 2020-2021 | 0.31 | Landfill no recovery (Mixed MSW – EPAa Waste Reduction Model (WARM) – Version 15, May 2019 | |
| Recylables | 0 | Modified from EPA WARM so that emissions from recycling are not considered negative or avoided emissions. | |
| Compostables | 0 | Modified from EPA WARM so that emissions from composting are not considered negative or avoided emissions. |
Category 6: Business Travel
This category includes emissions from the transportation of employees for business-related activities in vehicles owned or operated by third parties, such as aircraft, trains, buses, and passenger cars. WRI uses the distance-based approach to estimate emissions from air and rail travel, by multiplying the distance traveled by mode-specific emission factors.
Table 22 | Approach for Category 6
| Methodology | Description |
| Activity data | Air travel: Individual flight distances for each leg of travel paid for by WRI or taken on behalf of WRI business. Activity data come from flight reports generated by WRI’s travel agent company and self-reported trips. This includes any travel booked and paid for by WRI for a partner or non-WRI individual to attend a meeting or conference. Rail travel: Individual rail trips booked through WRI’s travel service (secondary data). Other associated impacts from travel, such as local transit or ground transportation, hotels, and food are included in scope 3, category 1. |
| Method | Air travel: All flights were grouped into short, medium, or long haul flights based on leg distances, because each haul distance has a slightly different emission factor associated with it. The general formula was followed for each haul type, using the appropriate emission factor and distance. Rail travel: The activity data from WRI’s travel agent company include the origin and destination stations but not the distance traveled. To determine the distance traveled, the mile or km marker for the stations were identified and then subtracted from one another to find the distance between. The total distance traveled for the year was applied to the general formula, along with the above emission factors to determine WRI’s emissions. |
| Methodology Changes | Air travel: Emission factors are updated annually when DEFRAa releases updates. 2016: Haul distances for short, medium, and long haul flights were updated to specifications from the Carbon Neutral Calculator (2016). 2019: Historic emission factors were updated to include radiative forcing, a 1.9x multiplier that captures the increased warming impact of air travel emissions in the upper atmosphere. This update was undertaken to ensure that WRI is doing enough to measure and address the impacts of air travel. With this change, WRI’s emissions from air travel roughly doubled in magnitude. 2022: Haul distances for short, medium, and long-haul flights were updated to use the EPA Simplified GHG Emissions Calculator (2022). WRI will update Scope 3 Category 6 data from 2019 onward with updated haul ranges. Rail travel: Emission factors are added periodically when EPAb releases updates. |
| Limitations and Discussion | Air travel: The activity data for air travel come from WRI’s travel agent captures near 100% of WRI US and WRI China travel, aligned with a new travel policy provision requiring all travel bookings through the agency. Data also includes some Europe and Africa based staff travel, however, staff may choose to use other booking systems. 2022: WRI has not added additional offices from its organizational boundary to Scope 3, Category 6. For trips arranged outside the centralized booking system, staff are requested to self-report, which is not 100 percent accurate. The tax charged for 2019-2022 was not updated for the new haul rate method adjustment. WRI performed a comparative analysis of haul rates using FY21 data and found a small decrease of 0.95331 mt CO2e and $47.665 in the annual data. Beginning in FY23, the update in calculation will affect both carbon emissions and carbon tax totals. Rail travel: Very few train trips taken on behalf of WRI outside the United States are captured through WRI’s centralized travel system, resulting in less than complete data for rail impacts. Emissions from rail travel are extremely small compared to the emissions from air travel, and are included whenever the activity data is reported by the travel agency. |
Notes: aDEFRA = Department for Environment, Food, and Rural Affairs (UK). bEPA = Environmental Protection Agency (US). Source: WRI.
Table 23 | Emission Factors for Air Travel
Notes:
aThe DEFRA conversion factor selected reflects emissions per unit distance for an economy class passenger, with radiative forcing impacts.
b2012 conversion factors from DEFRA were used for 2010, 2011, and 2012.
Source: WRI.
Table 24 | Emission Factors for Rail Travel
| Fiscal Year | Emission Factor for CO2 (kg CO2/mile) | Emission Factor for CH4 (g CH4/mile) | Emission Factor for N2O (g N2O/mile) | Source |
| 2010-2012 | 0.185 | 0.002 | 0.001 | EPAa Climate Leaders (2008), Optional Emissions from Commuting, Business Travel and Product Transport |
| 2013 | 0.185 | 0.002 | 0.001 | EPAa Climate Leaders (2009), Rail emissions for AMTRAK planes (Updated May 2013) |
| 2014 | 0.144 | 0.0085 | 0.0032 | US EPAa (2014), Emission Factors for Greenhouse Gas Inventories |
| 2015-2016 | 0.185 | 0.002 | 0.001 | EPAa Climate Leaders (2009), Rail emissions for AMTRAK planes (Updated May 2013) |
| 2017 | 0.140 | 0.0087 | 0.0031 | EPAa GHG Emissions Factors Hub (Updated May 2018) |
| 2018-2021 | N/R | N/R | N/R | N/Rb |
Category 7: Employee Commuting
This category includes emissions from the transportation of employees between their homes and worksites. WRI uses the distance-based method to calculate emissions.
Table 25 | Approach for Category 7
| Methodology | Description |
| Activity data | Total distance commuted annually per mode of travel for each WRI office, including telework impacts. |
| Method | WRI surveys its staff annually to obtain commuting data for up to 3 different multi-mode commute patterns. The survey data includes typical number of weeks worked during the year and the distances and days traveled by each mode of transportation during an average week. The total number of weeks worked is multiplied by the total distance traveled for each mode of transportation for each staff member. The distances for each mode are totaled to give the total distance traveled by WRI staff for each different mode of transportation. A scaling factor (number of staff responses / total number of staff) is applied to the mode of transportation totals to account for staff members who did not respond to the survey. Since 2010, this scaling factor has fallen within the range of 0.61 to 0.92 (also survey response rate). The scaled totals for each mode of transportation are then inputted into the general formula using the appropriate emission factors for each mode and region to determine WRI’s employee commuting emissions for the given year. |
| Methodology Changes | 2016: Commute survey was changed from a single primary mode question to a pattern-based format to collect more granular data. In primary mode-based surveys, employees would report the distance traveled for the primary mode of transportation used. Pattern-based surveys capture the distances traveled for each mode of transportation within a multi-modal commute pattern. Staff report their top three commute patterns, frequency of each patterns, as well as distances and fuel type and efficiencies (if applicable) about each mode within a pattern. In citieswith many modes and even multimodal trip options, simply asking staff for their primary mode of travel and distance could result in the loss of important information to help steer sustainable mobility programs and capture GHG impacts. For example, staff could take transit twice a week and bike in the remaining days, but in a primary mode survey, only the biking or transit would be captured. Other staff had more complicated multimodal trips each day, such as a drive to a transit hub. In primary mode-based surveys, they would likely report the transit use since the drive may be relatively short. However, in terms of GHG emissions, there may be a relatively significant impact from driving alone, even for shorter distances. 2020: Employees were not surveyed on their commute during the global pandemic. 2019 data were used for partial reporting year commutes before the pandemic began (Oct 2019 – Mar 2020). 2021: Employees were surveyed on their work from home (telework) emissions impacts. This data (and associated methods) will be reported starting in 2022 for reporting period 2020 onward. 2022: Employees will be surveyed for both pattern-based commute and work-from-home emissions, under a fully re-opened office status and flexible work policy. Data variations are expected given the unknown nature of these commute and telework patterns. |
| Limitations and Discussion | Employees were not surveyed in 2015. 2015 data were extrapolated by taking the average of 2014 and 2016 commute data. Total distances traveled per mode of transportation are an approximation based on assumptions about the total number of days per year that staff commute, as well as accounting for staff who do not respond to the survey. To approximate the total number of days per year that employees commute, holidays, paid time-off, and employee estimates of telework days, alternative work schedules, and business travel are used to extrapolate weekly commute pattern data collected via the survey to the rest of the year. The survey is sent out and the total staff per WRI office is calculated once a year, which means that staff who worked during the year in question but left WRI before the survey was sent out are excluded. 2022: WRI has not added additional offices from its organizational boundary to Scope 3, Category 7. Employee commute data from WRI’s international offices are only available for certain years from 2013 onward. Employee commute data from WRI’s international offices are only available for certain years from 2013 onward. |
Table 26 | US Emission Factors for Category 7
| Location | Fiscal Year | Mode of Transportation | Emission Factor for CO2 (kg CO2/km) | Emission Factor for CH4 (g CH4/km) | Emission Factor for N2O (g N2O/km) | Source |
| United States | 2010, 2011, 2012 | Bus | 0.06649 | 4E-04 | 0.0003728 | EPAa Climate Leaders (2008), Optional Emissions from Commuting, Business Travel and Product Transport |
| Metro | 0.10128 | 0.002 | 0.0012427 | (“) | ||
| Commuter Rail | 0.10688 | 0.001 | 0.0006214 | (“) | ||
| Car | 0.22618 | 0.019 | 0.0198839 | (“) | ||
| Walk or Bike | 0 | 0 | 0 | (“) | ||
| 2013, 2014 | Bus | 0.03604 | 0.0004 | 2E-04 | US EPAa (2014), Emission Factors for Greenhouse Gas Inventories | |
| Metro | 0.08264 | 0.0016 | 0.001 | (“) | ||
| Commuter Rail | 0.10812 | 0.0052 | 0.002 | (“) | ||
| Car | 0.22866 | 0.0112 | 0.008 | (“) | ||
| Walk or Bike | 0 | 0 | 0 | (“) | ||
| 2015b | N/Ac | N/A | N/A | N/A | N/A | |
| 2016 - 2021 | Bus | 0.0348 | 8E-04 | 6E-04 | EPAa GHG Emissions Factors Hub (Updated May 2018) | |
| Metro | 0.07394 | 0.002 | 0.001 | (“) | ||
| Commuter Rail | 0.10004 | 0.005 | 0.002 | (“) | ||
| Car | 0.21313 | 0.012 | 0.007 | (“) | ||
| Walk or Bike | 0 | 0 | 0 | (“) |
Notes:
aEnvironmental Protection Agency (US).
b2015 emissions were extrapolated by taking the average of 2014 and 2016 emissions from each mode of transportation. No activity data or emission factors were used.
cN/R = Not reported - associated activity data not collected, extrapolated from surrounding year data.
Source: WRI.
Table 27 | Non-US Emission Factors for Category 7
| Location | Fiscal Year | Mode of Transportation | Emission Factor for CO2e (kg CO2e/km) | Source |
| China, India, Brazil | 2013, 2014 | Bus | 0.10155 | DEFRAa Government emission conversion factors for greenhouse gas company reporting, Business travel- land (2014) |
| Metro | 0.06312 | (“) | ||
| Commuter Rail | 0.06168 | (“) | ||
| Car (Diesel) | 0.18546 | (“) | ||
| Car (Gasoline) | 0.19388 | (“) | ||
| Taxi | 0.17755 | (“) | ||
| Walk or Bike | 0 | (“) | ||
| Auto-Rickshaw | 0.0864 | EMBARQb Urban Mobility Forecasts (2010) | ||
| Motorbike | 0.0452 | (“) | ||
| 2018 – 2021 | Bus | 0.10097 | DEFRAa Government emission conversion factors for greenhouse gas company reporting, Business travel- land (2018) | |
| Metro | 0.0376 | (“) | ||
| Commuter Rail | 0.03967 | (“) | ||
| Car (Diesel) | 0.17753 | (“) | ||
| Car (Gasoline) | 0.18368 | (“) | ||
| Taxi | 0.15344 | (“) | ||
| Walk or Bike | 0 | (“) | ||
| Auto-Rickshaw | 0.0864 | EMBARQb Urban Mobility Forecasts (2010) | ||
| Motorbike | 0.0452 | (“) |
Notes:
aDEFRA = Department for Environment, Food, and Rural Affairs (UK).
bEMBARQ was founded in 2002 and is now part of WRI Ross Center for Sustainable Cities. EMBARQ works to advance sustainable urban mobility solutions.
Source: WRI.
Category 15: Investments
This category includes emissions from WRI's US-based endowment portfolio. WRI does not report emissions from any operational financial accounts, staff retirement accounts, or operating reserve funds.
Table 28 | Approach for Category 15
| Methodology | Description |
|---|---|
| Activity data | From 2019 to current year, WRI's endowment investment management company provides an asset allocation breakdown. WRI selected December as the snapshot month and will continue to use this sample month for each annual GHG emissions estimate. |
| Method |
WRI uses both GHG Protocol guidance and specific methodology published by PCAFa. Agendi, Inc. applied the following methods and calculations on behalf of WRI.
|
| Methodology Changes | 2021: 2019 - 2021 activity data added. |
| Limitations and Discussion |
As noted above, a materiality threshold of 80% was used and the calculated emissions scaled up for the remaining 20% of activity data.
Opportunities to improve the data quality score and overall activity data include:
- Private equity data details at the security-level were not available, these were pro-rated as well. - Improve EVIC data, used for market cap and GHG allocation in Method A (above). - Improve public disclosures of company GHG emissions, which may occur through regulatory efforts in the EU and by the US SEC - Consistently include and breakout scope 1+2 from scope 3 emissions for each company, fund, and sector within investment emissions. Currently, scope 3 is included but not separated in WRI calculations. |
Notes:
a PCAF = Partnership for Carbon Accounting Financials
b CDP = formerly Carbon Disclosure Project, now known as CDP, provides company reported sustainability data on climate, water, forests.
c EEIO = Environmentally Extended Input-Output Model
d US EPA = US Environmental Protection Agency
Source: WRI & Agendi, Inc.