You are here

Methodology

Quick Links


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, 2018 to September 30, 2019)

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 offices within WRI’s global network are considered under operational control and are asked to report Scopes 1 and 2 emissions and participate in Scope 3 emissions data collection.

The year chosen as base year and rationale for choosing the base year

WRI has selected a single year (2010) as the base year for its GHG inventory. While WRI had reduction targets that predate 2010, the Sustainability Initiative and WRI’s management decided to establish new reduction targets based on the results of the first full value chain inventory completed in 2010.

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.

In the base year (2010), only WRI’s Washington, DC, office and the China office in Beijing met the requirement for significant WRI changes and were included in the inventory. WRI considers new data from WRI offices created after 2010 as natural growth and did not recalculate the base year.

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.

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, which are not directly invoiced to WRI. Scope 2 emissions from WRI’s international offices other than WRI China are only available for certain years from 2013 onward.

As Scope 2 emissions from WRI’s international offices have historically been <1 percent of the total GHG inventory, they are insignificant enough to exclude when international offices are unable to gather data from landlords and building management companies.

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, funding for WRI’s international offices, rent, credit card purchases, and retirement funds) are not included 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

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

 

Scope 3, Category 7: Employee Commuting

Calculated

Employee commute data from WRI’s international offices are only available for certain years from 2013 onward. Related emissions from international offices are likely to be minimal due to their office sizes.

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

N/A

WRI does not have any investments required by the GHG Protocol Corporate Standard (e.g. equity, debt, or project finance). WRI is working to integrate sustainability considerations into its endowment management and will consider how this impacts WRI’s emissions profile as the project proceeds.

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

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 allocation factor based on square footage is not as accurate as submetering WRI’s own operations.

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

Total refrigerant volumes (kg) per cooling unit used in WRI’s office spaces

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

No changes

Limitations

N/Ab

Notes:

aGWP = Global warming potential.

bN/A = Not applicable.

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

Source: WRI.

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 within a building is not metered separately, 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.

Electricity purchase data from WRI’s international offices other than WRI China are only available for certain years from 2013 onward. As Scope 2 emissions from WRI’s international offices have historically been <1 percent of the total GHG inventory, they are insignificant enough to exclude when international offices are unable to gather data from building management.

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 time US EPA releases eGRID data.

Limitations

WRI US: The use of an allocation factor based on square footage is not as accurate as submetering WRI’s own electricity consumption.

WRI International Offices: Activity data gaps for certain years. WRI will work to fill these data gaps.

Source: WRI.

Table 9 | Emission Factors for Location-based Method

Location

Fiscal Year

Emission Factor for CO2 (MT CO2e/MWh)

Emission Factor for CH4 (MT CH4/MWh)

Emission Factor for N2O (MT N2O/MWh)

Source

United States (RFC East Subregion)

2010

0.5166755

1.373E-05

8.489E-06

US EPA eGRID2007 Version 1.1 Year 2005 GHG Annual Output Emission Rates

 

2011

0.5166755

1.373E-05

8.489E-06

(“)
 

2012

0.4804995

1.243E-05

7.725E-06

US EPA eGRID2010 Version 1.0 (2007 data: eGrRID subregion data)  

 

2013

0.4543708

1.228E-05

6.953E-06

US EPA eGRID2010 Version 1.0 (9th edition with 2010 data: eGrRID subregion data)  

 

2014

0.4543708

1.228E-05

6.953E-06

(“)
 

2015

0.3762095

3.352E-05

5.08E-06

US EPA eGRID2014 (2014 data: Subregion Output Emission Rates)

 

2016

0.3762095

3.352E-05

5.08E-06

(“)
 

2017

0.3439137

2.268E-05

4.082E-06

US EPA eGRID2016 (2016 data: Subregion Output Emission Rates)

 

2018

0.3439137

2.268E-05

4.082E-06

(“)
 

2019

0.324772137

2.76691E-05

3.62874E-06

US EPA eGRID2018 (2018 data: Subregion Output Emission Rates)

China

2010

0.839

1.458E-05

1.841E-05

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

(“)
 

2013

1.12816

1.169E-05

1.692E-05

Getting Every Ton of Emissions Right; An Analysis of Emission Factors for Purchased Electricity in China (WRI 2013)

 

2014

1.12816

1.169E-05

1.692E-05

(“)
 

2015

1.12816

1.169E-05

1.692E-05

(“)
 

2016

1.12816

1.169E-05

1.692E-05

(“)
 

2017

1.12816

1.169E-05

1.692E-05

(“)
 

2018

1.12816

1.169E-05

1.692E-05

(“)
 

2019

1.12816

1.169E-05

1.692E-05

(“)

India

2013

0.926

N/Aa

N/A

GHGP Purchased Electricity tool (v4.7, May 2015), which uses IEA emission factors from 2012

 

2014

0.926

N/A

N/A

(“)

Indonesia

2018

0.774388897

N/A

N/A

Electricity-specific emission factors for grid electricity (Brander et al. 2011)

 

2019

0.774388897

N/A

N/A

(“)

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 10 | 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 US

Renewable energy emissions

  • RECs purchased for WRI by its building management were allocated using the ratio of WRI’s total space occupancy to total leasable space in the two Washington, DC buildings operated by WRI’s building management, multiplied by the total MWh of RECs purchased for the buildings.
  • The total MWhs of REC claims account for zero emissions.

Residual energy emissions.

  • The total MWhs of RECs was subtracted from the total MWh of electricity used by WRI US to provide the consumption of electricity not covered by REC claims.
  • Using the general formula, the nonrenewable energy consumption and the residual emissions factor (above) were applied to provide the residual grid emissions.

WRI International Offices: Not located in markets with supplier or contract-specific information, so the location-based method is applied (see above).

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 11 | 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.

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 12 | Approach for Category 1

Methodology

Description

Activity data

Total amount spent (US$) with each 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:

  • The detailed sector that each good, service, or partner belongs to
  • The amount of economic activity in the sector (i.e. the amount of money spent or awarded)
  • The input-output table to be used, in this case the 2007 benchmark input-output table from the Bureau of Economic Analysis (BEA)

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.

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 13 | 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 emissions of purchased fuels, well-to-tank emissions of purchased electricity, and transmission and distribution losses for purchased electricity. Well-to-tank 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 14 | 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 well-to-tank emissions of purchased fuels were added to the inventory. Previously, only electricity-related emissions were included in Scope 3 Category 3.

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 transmission and distribution factors for regions outside of the UK. This inventory currently uses transmission and distribution factors from 2017 for years beyond 2017.

Notes:

aDEFRA = Department for Environment, Food, and Rural Affairs (UK).

Source: WRI.

Table 15 | Well-to-tank Emission Factors for Diesel Fuel

Fiscal Year

Emission Factor for CO2 (MT CO2e/gallon)

Source

2010

0.002136

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2012)a

2011

0.002136

(“)

2012

0.002136

(“)

2013

0.002149

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2013)

2014

0.00219

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2014)

2015

0.002194

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2015)

2016

0.002092

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2016)

2017

0.002368

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2017)

2018

0.002368

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2018)

2019

0.00237

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-fuels (2019)

Notes:

aDEFRA did not publish well-to-tank conversion factors for 2010 or 2011, so 2012 conversion factors were used.

Source: WRI.

Table 16 | Well-to-tank Emission Factors for Purchased Electricity

Location

Fiscal Year

Emission Factor for CO2 (MT CO2e/MWh)

Source

United States

2010

0.07637

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2010)

 

2011

0.07335

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2011)

 

2012

0.07393

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2012)

 

2013

0.07884

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2013)

 

2014

0.07144

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2014)

 

2015

0.0766

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2015)

 

2016

0.07541

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2016)

 

2017

0.07653

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2017)

 

2018

0.07697

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2018)

 

2019

0.07134

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2019)

China

2010

0.11223

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2010)

 

2011

0.10814

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2011)

 

2012

0.11444

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2012)

 

2013

0.11571

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2013)

 

2014

0.10852

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2014)

 

2015

0.11563

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2015)

 

2016

0.11383

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2016)

 

2017

0.1171

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2017)

 

2018

0.11795

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2018)

 

2019

0.1095

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2019)

India

2013

0.13774

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2013)

 

2014

0.12158

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2014)

Indonesia

2018

0.1209

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2018)

 

2019

0.11254

DEFRA Government emission conversion factors for greenhouse gas company reporting, WTT-UK & overseas elec (2019)

Source: WRI.

Table 17 | Transmission and Distribution Losses Emission Factors for Purchased Electricity

Location

Fiscal Year

Emission Factor for CO2 (MT CO2e/MWh)

Source

United States

2010

0.03907

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2010)

 

2011

0.03516

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2011)

 

2012

0.03337

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2012)

 

2013

0.0337

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2013)

 

2014

0.03445

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2014)

 

2015

0.03595

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2015)

 

2016

0.03365

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2016)

 

2017

2018

2019

0.03257

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2017)a

China

2010

0.06038

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2010)

 

2011

0.05422

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2011)

 

2012

0.05402

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2012)

 

2013

0.05173

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2013)

 

2014

0.05342

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2014)

 

2015

0.05279

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2015)

 

2016

0.05252

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2016)

 

2017

2018

2019

0.0486

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2017)a

India

2013

0.29383

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2013)

 

2014

0.24612

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2014)

Indonesia

2018

2019

0.08144

DEFRA Government emission conversion factors for greenhouse gas company reporting, Transmission and distribution (2017)a

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 18 | Approach for Category 5

Methodology

Description

Activity data

From 2010 to 2016, the building management for WRI’s US operations provided monthly reports for recycling and landfill weights collected for the entire building.

From 2017 onward, WRI US began conducting its own quarterly waste audits and negotiated for compost collection under its green lease provision with building management. Recycling and landfill weights from 2017 onward are estimated from waste audit data. WRI US’ compost vendor provides monthly reports for weight of compost collected.

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.

Percentage of supplier-specific data used = 0%

Method

For data provided by building management, weight data was multiplied by WRI US’ square-foot occupancy ratio to determine the portion of waste generated by WRI US.

Calculation follows the general formula. To estimate emissions, 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.

Limitations and Discussion

The allocation approach based on occupancy used before 2017 is not as accurate as using primary data. WRI is also not currently able to collect wastewater treatment data.

The waste generated at WRI US is removed to a multitude of landfills in the mid-Atlantic region of the United States. 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 GHGP’sb Technical Guidance for Calculating Scope 3 Emissions (version 1.0) (2013).

There may be limitations in comparing data across years. Between 2013 and 2014, the building management for WRI US switched to a new waste hauling vendor. The data provided by the new vendor showed a significant increase in landfill weights collected and decline in recycling rates. The reason for this is unclear but may be linked to a switch from dual-stream to single-stream recycling, the WRI US office renovation which used temporary waste stations resulting in a lack of signage during construction, and different data collection methods between waste haulers. In 2017, WRI US began conducting its own waste audits to collect primary data, and also began composting. As such, the inconsistent upward and downward trends of the emissions-from-waste category may be due to factors outside of WRI’s actions on waste, such as changes in data collection methods.

Notes:

aEPA WARM = Environmental Protection Agency Waste Reduction Model

bGHGP = Greenhouse Gas Protocol

Source: WRI.

Table 19 | Emission Factors for Category 5

Waste type

Emission Factor for CO2 (MT CO2e/short ton)

Source

Mixed municipal solid waste (MSW)

0.347

Landfill no recovery (Mixed MSW - EPAa Waste Reduction Model (WARM) - Version 14, March 2016

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.

Notes:

aEPA = Environmental Protection Agency.

Source: WRI.

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 20 | 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).

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 added every year when DEFRAa releases updates.

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.

2016: Haul distances for short, medium, and long haul flights were updated to specifications from the Carbon Neutral Calculator (2016).

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; however, a few trips were arranged outside the centralized booking system. To capture that data, staff were requested to self-report, which was not 100 percent accurate.

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.

Given the availability of distance data from the travel agencies for WRI US and WRI China, WRI selected the distance-based method to calculate emissions from business travel. Emissions from rail travel, although extremely small compared to the emissions from air travel, were included for consistency with previous reports.

Notes:

aDEFRA = Department for Environment, Food, and Rural Affairs (UK).

bEPA = Environmental Protection Agency (US).

Source: WRI.

Table 21 | Emission Factors for Air Travel

Fiscal Year

Haul Type

Leg Distance (km)

Emission Factor for CO2ea (kg CO2e/km)

Source

2010, 2011, 2012

Short

<785

0.34387

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2012)b

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.187049

(“)
 

Long

>3,700

0.163962

(“)

2013

Short

<785

0.326615

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2013)

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.183404

(“)
 

Long

>3,700

0.165362

(“)

2014

Short

<785

0.29316

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2014)

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.15835

(“)
 

Long

>3,700

0.15054

(“)

2015

Short

<785

0.29795

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2015)

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.16634

(“)
 

Long

>3,700

0.15175

(“)

2016

Short

<785

0.27867

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2016)

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.16508

(“)
 

Long

>3,700

0.14678

(“)

2017

Short

<785

0.26744

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2017)

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.15845

(“)
 

Long

>3,700

0.15119

(“)

2018

Short

<785

0.29832

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2018)

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.1597

(“)
 

Long

>3,700

0.16279

(“)

2019

Short

<785

0.25493

DEFRA Government emission conversion factors for greenhouse gas company reporting, Business travel-air (2019)

Haul distances from Carbon Neutral Calculator (2016)

 

Medium

785-3,700

0.15573

(“)
 

Long

>3,700

0.14981

(“)

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 22 | 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

2011

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

2018

2019

0.140

0.0087

0.0031

EPAa GHG Emissions Factors Hub (Updated May 2018)

Notes:

aEPA = Environmental Protection Agency (US).

Source: WRI.

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 23 | Approach for Category 7

Methodology

Description

Activity data

Total distance commuted annually per mode of travel for each WRI office.

Method

WRI surveys its staff annually to obtain commuting data. The survey provides information on the typical number of weeks worked during the year and the distances traveled by 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. 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 primary mode-based format 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. For our survey, employees report their top three commute patterns, frequency of those patterns, as well as details about each mode within them. In a city with 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. For example, staff could take transit twice a week and bike in the remaining days, but in a typical

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 transit hubs. 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.

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.

Employee commute data from WRI’s international offices are only available for certain years from 2013 onward. While related emissions from international offices are likely to be minimal due to their office sizes, we will work to ensure more consistent data collection for future inventories.

Table 24 | US Emission Factors for Category 7

US EPA eGRID2018 (2018 data: Subregion Output Emission Rates)

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, 2017, 2018

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/A = Not applicable.

Source: WRI.

Table 25 | 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, 2019

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.