Methods, limitations, and data sources for Sustain.Life’s carbon calculators.
Updated: February 2023
If a user purchases electricity without a contract (typically through the local utility at standard rates), we calculate location-based emissions by multiplying reported grid electricity consumption by a local grid factor. We use EPA (Environmental Protection Agency) PowerProfiler data to refine eGrid subregion selection in the U.S. and provincial or country-level factors in most other regions. If the user also purchases electricity from a direct-line source (i.e., not from the grid), we multiply reported consumption by the same local grid factor as a proxy. If a user purchases electricity through contractual instruments, we calculate location-based and market-based emissions. Location-based emissions are calculated by the method described above. Renewable energy purchases and energy supply contractors are excluded from location-based emissions calculations (per GHG Protocol guidance).
To calculate market-based emissions, multiply contractual electricity supply purchases by a custom factor, if provided by the user. Otherwise, we apply the default grid factor. We use an emission factor of 0 for offsite renewable energy in the market-based approach. Emissions from direct-line microgrid electricity use are calculated in the same way as emissions under the location-based approach, multiplying by the custom factor or defaulting to the local grid factor.
This approach is consistent with the Greenhouse Gas Protocol’s emission factor hierarchy for market-based emissions. Since the electricity compensated by contractual instruments is still delivered through the grid, we subtract electricity use from supply contracts and renewable energy instruments from the grid electricity value. We multiply the remainder by the local grid factor (or custom factor) to calculate the emissions from the remaining grid electricity. We then sum the market-based emissions values to return total market-based emissions. While there is a difference between delivery of energy through physical PPAs and financial instruments (e.g., RECs, VPPAs, Equity investments), the emissions calculation functions in the same way, applying a factor of 0 for all renewable energy purchases. Therefore, we selected to combine data entry for all offsite renewable energy instruments. Onsite renewable energy installations have no associated scope 2 emissions, and onsite electricity generation only has associated emissions in Scope 1. We ask for electricity use from these sources to provide users with a complete picture of their electricity use.
Our emission factors account for renewable energy in the local grid mix. Users with advanced renewable energy strategies who would like to break out renewable sources from their local grid mix should contact our customer support team.
When users select the market-based approach but don’t provide a custom emission factor, we default to the emission factor for their local grid. We assume that for any offsite renewables the user purchases that any associated energy attributes are transferred to the user or retired on behalf of the user. For onsite renewable generation, we assume the user does not generate certificates or retains them if they are generated.
In instances where the user purchases offsite renewables, we apply the local grid emission factor to any remaining electricity not covered by renewable energy claims. We do not use a residual grid mix factor because these factors are not published for most parts of the world. Although this approach is aligned with the GHG Protocol, it can lead to an underestimate of market-based emissions, particularly in areas with significant amounts of renewable energy in the grid mix. We recommend that users obtain supplier-specific factors whenever possible.
When users provide equipment wattage, we calculate emissions based on the country-level emission factor matching the user's currently selected location. In the absence of equipment data, we make assumptions about average equipment used and its wattage. We estimate other work-related home energy use (heating, cooling) by using an average annual U.S. household member consumption value to prorate work-from-home energy use and applying a country-specific emission factor. We arrived at MT CO2e per household member by applying an emission factor for each fuel (electricity, natural gas, fuel oil, propane) and adjusting the result based on total national consumption of each fuel.
We assume that, on average, remote employees use one laptop (60W), one monitor (10W), and one task light (20W) to perform work activities for 8 hours per day and 261 days per year. We also assume that one third of a household member's energy use is attributable to work-from-home activities, such as space heating and cooling, overhead lighting, preparing food and beverages during the workday and water use-related energy.
This calculator does not follow the GHGP recommendation of establishing a baseline of employee home emissions to calculate work-related emissions. It does not account for specialized equipment with above or below average wattage and excludes printers and small peripherals, like keyboards and pointing devices. The calculator applies a country factor and doesn’t consider the regional distribution of employees. To determine WFH emissions with accuracy, we recommend per employee calculations that will apply regional emission factors and consider variables like heating fuel type, floor space of the working area, and presence of other household members. This calculator can only be used for home office emissions estimates in one country at a time.
When a user provides a market based CO2e factor, we calculate market-based emissions from consumption values. When market-based factors are not available, we apply the most granular emission factor available from our factor set (grid factor, state/provincial factor, country factor, or approximated factors for global regions) to the consumption value. CO2 emissions from biomass-generated steam are not included in the total emissions for steam or reported separately because there is no direct combustion of biomass. Biogenic/biogas emissions appear as 0 MT CO2e in outputs. U.S. and Canada-based users have the option of specifying chiller type for purchased cooling. We apply a chiller-specific factor if users make a selection.
Assumed thermal efficiency is 80%. We use natural gas as the default energy source for steam generation, hot water as the default source of heating, and electric chillers as the default chiller type in the U.S. and Canada, unless users make a chiller type selection.
The calculator does not account for variations in thermal efficiency. Many countries do not publish steam, heating and cooling factors and recommend calculating emissions from the energy source used to generate steam, heat, or cooling. In these cases, we apply the nearest known country or regional factor. Users will get the most accurate outcomes by providing supplier-specific emission factors for steam, heat, and cooling.
We apply emission factors for reported quantities of fuel based on user location. If country-level factors aren't available in our dataset, we default to available country factors within proximity to the user's location. Biogenic CO2 from landfill gas and wood products is a separate output from total CO2e in alignment with GHG Protocol guidelines. Where countries combine CH4 and N2O into a single factor, we use the ratio of CH4 and N2O to CO2 from a comparable factor to break them out. Where countries report only CO2 for a specific fuel, we add regional CH4 and N2O factors. We use EIA and EPA heating values of fuels to convert user consumption units. This calculator aligns with the GHG Protocol guidelines for calculating scope 1 emissions from fuel combustion.
Due to the similarity of most scope 1 factors across regions, proxy factors applied within regions are assumed to be reliable.
This calculator does not include all scope 1 fuel sources. Mixed coal, petroleum coke, non-wood biomass, and gaseous fuels categorized as "Other" (e.g., blast furnace gas) are not currently available. Transferred CO2, CO2 capture and storage, and non-combustion process emissions are also excluded. We do not apply an oxidation factor.
This calculator uses two different calculation methods based on the activity data the user provides. These methods are the fuel-based method, where the volume/mass of fuel is multiplied by an emission factor, and the distance-based method, where the distance travelled is multiplied by an emission factor. The application of these methods is described below: Calculation methods based on user inputs (from most to least accurate): - Fuel and distance (most accurate)– fuel-based method for CO2, distance-based method for CH4 and N2O - Fuel volume only – fuel-based method - Distance only – distance-based method for CH4 and N2O. For CO2, we use the fuel efficiency of the vehicle (mpg or l/100km) to estimate the fuel consumed and use the fuel-based method. - Spend only for gas and diesel (least accurate) - we retrieve historical fuel prices for your location and calculate fuel volume. Then we implement the fuel-based method. Our emission factors are specific to the vehicle class (car, light truck, etc). If the user chooses to track emissions according to the fuel type then we assume this is a road vehicle. If the user has equipment/non-road vehicles, they should track them as a group. They may be entered as individual vehicles or the user can enter the bulk fuel use and track them as a group. Selecting “road” or “equipment/non-road” as a vehicle type will result in an average emission factor of vehicle classes in that type. Emissions outputs separate biogenic CO2 emissions from reportable scope 1 emissions for methanol, ethanol, biodiesel and renewable diesel. When a user selects renewable diesel and indicates the blend, we label that percentage of CO2 emissions as biogenic. Based on our comparison of fuel factors between countries, geographical differences are minor, usually due to differences in heating values used in calculations. We therefore apply the same factors to all locations.
In the absence of specific factor descriptors (e.g., passenger cars), factors from non-specific sources (e.g., light-duty vehicles) are applied to best meet the definition of these sources. We make assumptions about average fuel economy for calculating CH4 and N2O emissions per unit of fuel. For instance, we assume that the EPA has obtained the fuel economy of CNG cars from its own database, fueleconomy.gov, even though other reports may show significantly different values.
Entering distance- or spend-only activity data are less accurate ways for calculating emissions. Distance-only relies on the average fuel efficiency of the vehicle, which can vary significantly across driving conditions. It may be acceptable to some organizations that do not have significant fleet emissions but is not recommended for organizations with large fleets. Spend-only is recommended only as a last resort when no other data is available, but should not be used as a long term solution for tracking vehicle emissions.
NREL: Using LNG as a Fuel in Heavy-Duty Tractors
We calculate emissions by multiplying the reported amount of refrigerant by its global warming potential (GWP). We divide outputs into Kyoto Protocol greenhouse gases that must be included in scope 1 emissions inventories, and greenhouse gases that fall outside of the Kyoto Protocol, such as HCFCs. Excluded refrigerants may have been banned under the Montreal Protocol, but remain in use, for instance to deplete existing stock or through permitted reclamation and reuse. These emissions are reported separately from scope 1 emissions to provide a complete account of organizational impacts.
Where recent GWP values are not available, we assume that previously published values (e.g., IPCC AR4) are still accurate. We assume that user data covers releases from all activities surrounding refrigeration equipment management (installation, use, maintenance, decommissioning).
While we list the most common and several less common refrigerants, our list isn't exhaustive. If users enter a value for externally calculated emissions that are neither PFCs nor HFCs in the "Other" field, we will surface them as emissions outside of scope 1. To calculate emissions from refrigerants not listed, we recommend that users contact their suppliers to obtain the global warming potential of the refrigerant, multiply this value by kilograms released during the reporting period, and convert the result to metric tons (*0.001).
We calculate emissions by multiplying the reported amount of fire suppression chemicals by their global warming potential (GWP) value. We divide outputs into Kyoto Protocol greenhouse gases that must be included in scope 1 emissions inventories, and greenhouse gases that fall outside of the Kyoto Protocol, such as halons and ketones. Some fire suppressants have been banned under the Montreal Protocol, but are still in use, for instance to deplete existing stock or through permitted reclamation and reuse. These emissions can be reported separately from scope 1 emissions to provide a complete account of organizational impacts.
We assume that user data covers releases from all activities surrounding refrigeration equipment management (installation, use, maintenance, decommissioning).
Wetting agents and other fire suppression formulations protected as trade secrets are excluded because global warming potential is unavailable. We list common fire suppressants, but our list isn't exhaustive. We provide an option to enter externally calculated emissions. To calculate emissions from fire suppressants not listed, we recommend that clients contact their suppliers to obtain a global warming potential value. Multiply this value by the weight of fire suppressant releases in kilograms and then convert it to metric tons (*0.0001).
This calculator uses the GHG Protocol's average data method for aggregated waste streams (e.g., single-stream recycling) and the waste-type-specific method for material-specific waste streams (e.g., paper recycling). We calculate emissions by multiplying reported waste material weights by emission factors for each disposal method. When users report volume of waste, we use U.S. EPA conversion factors to obtain weight estimates. If users are unable to break out landfilled and incinerated waste, we use a blended factor. In order to estimate biogenic CO2 emissions that occur during composting, incineration, and waste-to-energy generation, we use data from the IPCC to estimate the biogenic carbon in each waste stream based on user practices and convert this into CO2. When users do not know how their trash is treated, we use a blended landfill-incineration emission factor that reflects the relative shares of landfilling and incineration for trash treatment in the U.S.
Following IPCC 2006 Guidelines, we assume that dry matter content, total carbon, and share of fossil carbon in waste materials follows Table 2.4 (Volume 5 Chapter 2). For biogenic CO2 emissions from composting, we assume 12% of total dry weight is degradable carbon based on an U.S. EPA estimate. We assume the user’s municipal solid waste (MSW) stream resembles the breakdown in U.S. EPA’s characterization studies to estimate. Although specific to the US, this data is generally representative of global commercial MSW. When a user does not know if their waste goes to landfill or for combustion, we calculate a blended emissions factor using a landfill:combustion ratio of 4.2:1 based on 2018 U.S. EPA data. We assume that materials not listed in the calculator are a part of the waste stream that goes to landfill, incineration, or waste-to-energy.
Volume-to-weight conversions of waste increase the level of uncertainty in outputs. We recommend that users obtain tonnage reports from their waste management providers or weigh their waste for accuracy. This calculator is not designed for waste streams from highly specialized operations or processes, if this applies to you contact us directly.
When users provide distance data, we use the GHG Protocol's distance-based method, applying emissions factors by mode. When users don't provide distance data, we use the GHGP average-data method to apply mode-specific regional average commuting distances and emission factors to the number of employees using each mode. Where average public transit commute distances aren't directly available, we aggregate Moovit Public Transit data to the national or regional level and take the average.
We assume average commute distances for each mode according to the sources listed below. When distances aren’t available, we assume that each employee makes a single round trip using the same mode each day.
The average-data method using national averages does not account for geographic variations (e.g., better public transit systems in some regions or urban versus rural settings) and should be viewed as an estimation with a high degree of uncertainty.
We use the GHG Protocol's distance-based method by mode, using DEFRA emissions factors which include radiative forcing and distance uplift.
We apply regional intercity rail emission factors to distance data provided by the user based on the region of travel.
When users provide vehicle travel fuel, we apply standard vehicle combustion emission factors to the amount of fuel consumed. When users provide distance data, we use regional distance-based emission factors based on regional differences in vehicle fuel economy and the user-specified region of travel.
For air travel, we use the following distance categorization to determine which emission factor to apply. These categories match industry guidance:
The distance-based car travel calculations rely on broader assumptions of regional variations in fuel economy. These should be viewed as a calculation with a higher degree of uncertainty.
When the user has exact travel data for attendees, we pass the distances to our Business Travel calculator to calculate emissions. When the user does not have exact data, we make assumptions about the average travel distances for attendees traveling by each mode. If the user does not have an estimate of the mode share of attendees, we assume a mode share based on US EPA long distance travel data.
We use the number of non-local attendees and duration of the event and apply average hotel energy use to estimate the total hotel energy consumption. We then apply the appropriate emission factors for the location of the event to calculate emissions.
We use average energy consumption data for public assembly buildings and the area of the venue to estimate the energy consumption for the event. We then apply location-specific emission factors to calculate the emissions from the venue’s energy consumption.
When the user has exact waste data, we pass these inputs to our Non-Hazardous Waste calculator to estimate emissions. This calculator uses location-specific emission factors to estimate the emissions in the region of the event.
For each food and beverage category we make an assumption of the daily consumption of each by each attendee. We then calculate the total food and beverage consumed for the event and apply appropriate food and beverage emission factors.
We adjust the user’s spending for inflation and use the US EPA EEIO’s dataset to estimate emissions based on the user’s spending on merchandise.
When the user does not have exact travel data, we assume the following average one-way trip distances for each mode. These assumptions are based on published US EPA data. We use a weighted average emission factor for economy, business, and first class flights and assume that all flights are medium haul.
When the user does not have an estimate for the mode share of attendees, we assume the following mode share based on US EPA data: Car: 63% Rail: 1% Air: 36%
We country-specific hotel stay emission factors from UK DEFRA data to estimate the emissions of non-local attendees for the duration of the event.
We assume the below average daily energy use per square meter of venue area based on US CBECS data.
We assume that all waste is sent to landfill and that all recyclables are a mix of paper, plastic, metal, and glass.
If the event serves them, we make the following assumptions about each attendee’s daily consumption of food and beverages. Non-alcoholic beverages: three per day Alcoholic beverages: one per day Snacks: three per day Meals: One per day
This impact calculator performs the best when provided exact measured data for travel, energy, and waste. Due to a lack of international data, this calculator relies heavily on US-based assumptions and, in the absence of measured data, is best suited for events located in the US or Canada.
Nab, C. and Maslin, M., 2020. Life cycle assessment synthesis of the carbon footprint of Arabica coffee: Case study of Brazil and Vietnam conventional and sustainable coffee production and export to the United Kingdom. The Geographic Journal
For purchases of household goods and services, we used published data for each category from the OECD. We then applied population data to estimate per capita spending patterns. We used environmentally extended input-output data for some purchasing categories and adjusted for inflation.
For waste and recycling rates, we used a combination of data from Eurostat and the World Bank. Where household waste data was available, we directly estimated household waste generation. Where household waste data was not available, we estimated household waste generation based on existing data and total waste production to estimate household waste generation. To estimate recycling rates, we used waste composition data from the World Bank to estimate the amount of waste that is recycled and we used waste characterization data from the EPA to estimate the share that is plastic, glass, metal, or paper.
Energy data was obtained from numerous public sources by country. Regional averages represent the average of all country-level data points within a region. Where annual average fuel prices were unavailable, we used daily snapshots. U.S. propane prices represent an average of three geographic regions within the country. Biogenic carbon dioxide from biomass used in home heating is included in the overall carbon footprint figure.
For emissions based on diet, we obtained GHG emissions per kilogram of each food item from a scientific journal (Poore & Nemecek, 2018), then categorized the items into food groups and calculated average emissions for meat, fish, dairy eggs, rice, grain, fruits, and vegetables. We obtained annual (2017) consumption of each group by country and calculated total emissions per country. Consumption data for agricultural crops under the category of cereals (wheat, oat, maize, barley, rye, etc.) was not readily available, so we used total production by country and deducted the portion used for animal feed and biofuel generation. We then divided the sum of country emissions by global population to obtain average per capita emissions. Available country data covers over 95% of the global population for all food groups.
For calculations using household energy spend and cost per energy unit to derive consumption, we made conservative deductions for fixed fees (5-10% depending on the region), though these fees may be much higher in some countries.
EEIO data availability is limited and often dated. Any emissions derived from spend-based inputs should be considered a rough estimate. Equivalency outputs for number of cell phone charges and vehicle miles are based on U.S. electricity and fuel efficiency. Data is not available for all countries across all indicators. We close gaps by applying a regional average factor. Data from various sources isn't always consistent, but we try to eliminate outliers to the extent possible. Data for food production includes food losses and land use change. These boundaries render our food-related emission factors higher than many published factors based on narrower boundaries.
EEIO and purchasing data
https://melbourne.figshare.com/articles/dataset/2014-15_Australian_input-output_data_and_associated_environmental_satellites_energy_water_and_greenhouse_gas_emissions_/6628277 https://bilans-ges.ademe.fr/documentation/UPLOAD_DOC_EN/index.htm?ratio-monetaires.htm U.S. EPA EEIO data Defra, Table 13, (discontinued after 2011) https://stats.oecd.org/Index.aspx?DataSetCode=SNA_TABLE5 Household size, number of households https://ceoworld.biz/2020/02/19/these-are-the-countries-with-the-largest-household-size/ https://www.pewresearch.org/fact-tank/2021/10/12/u-s-household-growth-over-last-decade-was-the-lowest-ever-recorded/ https://ec.europa.eu/eurostat/databrowser/view/lfst_hhnhwhtc/default/table?lang=en
https://www.eia.gov/dnav/pet/pet_pri_wfr_dcus_nus_w.htm https://www.globalpetrolprices.com/heating_oil_prices/ https://www.homeadvisor.com/cost/heating-and-cooling/wood-pellet-prices/ https://www.greenmatch.co.uk/boilers/wood-pellet/prices https://woodpelletfuel.co.uk/woodlets-wood-pellets-9-c.asp https://www.eia.gov/dnav/ng/hist/n3010us3m.htm https://www.eia.gov/dnav/pet/pet_pri_wfr_dcus_nus_w.htm https://www.eia.gov/outlooks/steo/tables/pdf/wf-table.pdf https://www.globalpetrolprices.com/lpg_prices/ https://www.cable.co.uk/energy/worldwide-pricing/
Household electricity use
https://ourworldindata.org/environmental-impacts-of-food https://science.sciencemag.org/content/360/6392/987 https://ourworldindata.org/grapher/daily-meat-consumption-per-person https://ourworldindata.org/grapher/fish-and-seafood-consumption-per-capita https://ourworldindata.org/grapher/per-capita-meat-type https://ourworldindata.org/grapher/per-capita-milk-consumption https://ourworldindata.org/grapher/cereal-allocation-by-country https://ourworldindata.org/grapher/cereal-production https://ourworldindata.org/grapher/fruit-consumption-per-capita https://ourworldindata.org/grapher/vegetable-consumption-per-capita
Waste and recycling
We calculate emissions from Purchased Goods and Services using the GHG Protocol spend-based approach. First, we convert the user's spending to 2018 USD using U.S. Bureau of Labor Statistics (BLS) data, which we update twice per year. Then we multiply this converted spending by the summary commodity emissions factor from the U.S. EPA's EEIO model, using the commodity the user has indicated via US Bureau of Economic Analysis Industry Classifications. When the user does not map 100% of their spending, we extrapolate emissions from their existing spending onto unmapped categories.
Emission factors from the EPA EEIO dataset are developed with the following assumptions: - All suppliers of a given commodity have similar emissions - Goods and services produced or procured outside of the U.S. have GHG footprints resembling their U.S. alternatives - Generally, fluctuations in inflation have an insignificant impact on final GHG calculations. Changes in inflation that occur between our inflation updates are assumed to be insignificant compared to the margin of error of the EEIO dataset and spend-based methodology.
While the spend-based approach is effective for providing an initial estimate for emissions from purchases, because it is based on general commodity emission factors, it cannot provide supplier-specific emissions estimates. For example, switching to a supplier with lower emissions will not be reflected in a spend-based analysis since the EEIO commodity factor would remain the same.
Furthermore, because this approach estimates emissions based on $ spent, only reductions in spending will show reductions in emissions. In instances where environmentally preferable products and services are more expensive than their traditional counterparts (which is common), a spend-based approach will estimate higher emissions for the environmentally preferable alternative. Lastly, because the most reliable spend-based emission factors are based on an analysis of the U.S. economy, they may provide less accurate results for spending that occurs outside of the U.S.
Despite these drawbacks, spend-based analyses still provide value in instructing organizations where to start when it comes to gathering more information or implementing policies. Results from this calculator should be used as a springboard for supplier-engagement surveys to collect specific supplier emissions data. By extrapolating mapped emissions onto unmapped categories (when the used does not map 100% of their spending), we assume that emissions of unmapped categories reflects emissions of the user’s mapped categories.
For this reason, we require the user to map a minimum of 75% of their spending so that such extrapolation does not have a large impact on overrall emissions. However, in cases where the user does not map particularly GHG-intensive categories, it could reduce the accuracy of our emissions calculation. To avoid this, users can map 100% of their spending.
When the user inputs shipment weight along with origin and destination information, we follow the Global Logistics Emissions Council's (GLEC) Framework for calculating logistics emissions. For transportation emissions, we multiply the emission factor by the weight of the shipment and the distance it was shipped. We calculate distance differently for each mode:
For road transport, we use shortest feasible distance (SFD), which is the shortest road network distance between two points, and add 5% per GLEC guidance to bring the estimate closer to actual distance.
For air transport, we use great circle distance in accordance with the International Air Transport Association's RP 1678.
For sea transport, we use the Sea Distance database from Centre d'Études et de Recherches sur le Développement International (CERDI) which provides sea route distance between common ports in country pairs. We then add 15% per GLEC guidance to bring the estimate closer to the actual distance.
For rail transport, due to the lack of standardized data on global rail networks, we use the SFD approach for road distance as a proxy. For all modes, we use blended emission factors comprised of the major fuel types for that mode. When calculating warehousing emissions, we multiply the emission factor by the weight of the goods and length of storage time. When the user provides volume of goods, we convert that to weight using the "Average Goods" conversion provided by GLEC.
To categorize road and air transport distances into short-, medium-, and long-haul, we use the following bins:
We assume different truck types for each road transport trip distance. For example, for the EU we assume short haul trips are done with commercial van/small box trucks, medium haul trips are done with medium-goods vehicles, and long-haul trips are done with heavy-goods vehicles. When the user inputs spend data, we adjust for inflation and then multiply the spent amount by the EPA EEIO emission factor for the given mode.
We assume that each shipment uses a single mode of transport. Although this is not often the case, by selecting the prominent mode or selecting air (if air transport is involved), the calculator will provide a sufficient estimate. To achieve the most accurate calculation, users should separate each leg of the journey into their respective modes. We assume that the start and end of each trip is at a transshipment site, which transfers the cargo to/from the transport vehicle.
For sea shipments, we assume this occurs at a maritime container terminal. We assume that all sea shipments are containerized and transported via general cargo ships and that all road transport is non-refrigerated. If exact addresses are not given for origin and destination, we assume that origin/destination locations are the geographic centroids for the given cities.
This calculator will provide less accurate results for shipments that use many different modes, refrigerated shipments, or shipments with road transport carriers where a significant portion of their fleet is electrified or uses biofuel. Because our emission factor for warehousing is a global value based on EU logistic sites, it may be less accurate outside of the EU (although it is deemed sufficient by GLEC). In general, results based on spend data should be taken to be high-level estimates due to the inherent inaccuracies of spend-based analysis. Furthermore, the EPA EEIO dataset is specific to the US, therefore the spend-based approach is even less accurate for non-US transportation.
We calculate emissions from fuel- and energy-related activities from three sources:
Upstream emissions of purchased fuels
To calculate the upstream emissions of purchased fuels, we apply well-to-tank fuel-specific emission factors to fuels the user has already entered into the Buildings and Vehicles categories. These factors represent emissions in the supply chain for those fuels and not combustion.
Upstream emissions of purchased energy
We break this category out by purchased energy type. For electricity, we use the specific grid mix for the country or subregion (where available) and adjust for the efficiency of power generation to estimate the per kilowatt-hour fuel input for the grid region. We then apply well-to-tank fuel-specific emission factors to create a blended emission factor for each country or subregion (dataset available here). Finally, we multiply this emission factor by grid electricity the user has input into the Buildings category. For purchased heat and steam, we multiply the amount of heat and steam the user has purchased by a well-to-tank emission factor that adjusts for thermal efficiency. We assume that heat and steam is generated from a combined heat and power plant. For purchased cooling, we divide the user’s cooling consumption by an assumed efficiency (based on cooling type) and multiply by the appropriate well-to-tank emission factor.
Transmission & distribution losses
We calculate all T&D losses by multiplying the user’s consumption by lifecycle emission factors and estimated distribution losses for the region, country, or subregion.
We assume that heat and steam purchased by the user is generated from a natural gas CHP plant. We assume that direct line microgrid electricity purchased by the user is also generated by a CHP plant. We use average efficiency values for natural gas and electric chillers from US DOE to estimate the fuel input for purchased cooling. Based on data from the EIA, we use average power plant generation efficiencies (or heat rates) to estimate upstream emissions from the generation of electricity. We assume a 5% transmission and distribution loss for heat and steam systems based on information from UK DEFRA. We assume a 3% transmission and distribution loss for purchased cooling based on information from the US EPA. For electricity delivered through a direct line, we assume a T&D loss factor that matches the local grid. When the user burns wood onsite, we use an average emission factor from wood chips, wood logs, and wood pellets.
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