Methodology

Methods, limitations, and data sources for all calculations contained in Sustain.Life's Data Bank.

Purchased electricity impact calculator

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, we 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.

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.

Assumptions

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.

Limitations

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.

Sources

U.S. EPA eGrid

The IFI Dataset of Grid Factors

Australian Government, National Greenhouse Accounts Factors: 2021

Entwicklung der spezifischen KohlendioxidEmissionen des deutschen Strommix in den Jahren 1990 – 2020

Canada. 2021 National Inventory Report (NIR)

ADEME, Bilans GES

DEFRA, 2021 Government Greenhouse Gas Conversion Factors for Company Reporting

Sustainable Energy Authority of Ireland – Conversion Factors

U.S. Energy Information Administration, Commercial Buildings Energy Consumption Survey (CBECS)

Greenhouse Gas Protocol, Corporate Standard

Scope 2 Guidance GHG Protocol

Energy Benchmarking Report, IOTA

IPCC AR6

Remote workforce impact calculator

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.

Assumptions

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.

Limitations

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.

Sources

Purchased steam, heat, and cooling impact calculator

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.

Assumptions

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.

Limitations

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.

Sources

Stationary combustion impact calculator

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.

Assumptions

Due to the similarity of most scope 1 factors across regions, proxy factors applied within regions are assumed to be reliable.

Limitations

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.

Sources

Mobile sources impact calculator

We follow the GHGP fuel-based method, multiplying fuel consumption by a CO2 emission factor for each fuel type. Emissions outputs separate biogenic CO2 emissions from reportable scope 1 emissions for methanol, ethanol and biodiesel. Since CH4 and N2O emissions differ by vehicle/equipment type for non-road vehicles, and are represented per distance unit for road vehicles, we offer fuel reporting by vehicle. In order to allow users to report liters/gallons of fuel (or weight for CNG and jet fuel) without detailing distance traveled for each vehicle type, we convert CH4 and N2O factors based on the average fuel efficiency of each vehicle type. For users who want to report total fuel consumption, we developed average factors for road and non-road vehicles/equipment. We compared fuel factors between countries and found only minor differences, usually due to differences in heating values used in calculations. We therefore apply the same factors to international locations.

Assumptions

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. For instance, an emission factor for light-duty vehicles is applied to both cars and light-duty trucks. 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.

Limitations

This calculator does not accommodate the GHGP distance-based method. While distance traveled would return more accurate CO2e values for CH4 and N2O, these values are very small in comparison to CO2, which is more accurately measured per unit of fuel combusted.

Sources

Refrigerants impact calculator

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.

Assumptions

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).

Limitations

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 (*0.0001).

Sources

Fire suppression impact calculator

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.

Assumptions

We assume that user data covers releases from all activities surrounding refrigeration equipment management (installation, use, maintenance, decommissioning).

Limitations

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).

Sources

Non-hazardous waste impact calculator

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 estimating emissions based on estimated waste data, we multiply the reported amounts of recycled and composted material by their respective emission factors. We then use a blended landfill-incineration factor to estimate emissions for the remaining portion.

Assumptions

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.

Limitations  

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.

Sources

Commuting transportation impact calculator

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 isn't directly available, we aggregate Moovit Public Transit data to the national or regional level and take the average.

Assumptions

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.

Limitations

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. 

Sources

Business travel impact calculator

Air Travel

When users provide air travel distance, we apply the GHG Protocol's distance-based method by mode, using DEFRA emissions factors which include radiative forcing and distance uplift. In the absence of distance data, we use flight time averages to convert user provided flight time to distance and apply the same factors.

Rail travel

We apply regional intercity rail emission factors to distance data provided by the user based on the region of travel.

Car 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.

Assumptions

For air travel, we use the following distance categorization to determine which emission factor to apply: 

  • Short Haul: Flights shorter than 300mi/500km
  • Medium Haul: Between 300mi/500km and 2300mi/3700km
  • Long Haul: Flights longer than 2300mi/3700km

Limitations

The time-based air travel and distance-based car travel calculations rely on broader assumptions for flight time and regional variations. These should be viewed as a calculation with a higher degree of uncertainty. Sources

U.S. EPA GHG Emissions Factors Hub

DEFRA 2021 Conversion Factors

GHG Protocol, Corporate Standard

IPCC AR6

IEA 2017 Fuel Economy in Major Car Markets

Fraunhofer-Institute for Systems and Innovation Research IS

Upstream transportation and distribution

We calculate emissions by multiplying user-reported spend data for various transportation modes by emission factors found in the US EPA’s Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities data set.

Assumptions

Emission factors developed from 2016 economic data are still representative in the present year.

Limitations

User-reported spend is not adjusted for inflation in this version.

Sources

US EPA Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities

Downstream transportation and distribution

We calculate emissions by multiplying user-reported ton-miles (i.e., one ton of product being transported over one mile) per transportation mode by factors obtained from the EPA’s Emission Factors for Greenhouse Gas Inventories data set.

Assumptions

2018 data is representative of present year transport emissions.

Limitations

None

Sources

US EPA Emission Factors for Greenhouse Gas Inventories

Investments – 401(k) 

The potential impact of 401(k) investments is based on data from 8,893 funds aggregated by As You Sow, specifically ETFs and mutual funds including and excluding fossil fuel assets.

Assumptions

Average emissions of fossil fuel and fossil fuel-free funds in this database are representative of the organization’s holdings. We encourage users to search for their specific funds at https://fossilfreefunds.org/.

Limitations

Not all the organization’s funds may be included in this database. The database consists of ETFs and mutual funds. Only 66% of the average 401(k) is in mutual funds.

Sources

As you Sow – Fossil free funds

ICI - 401(k) Plan Research: FAQs

Purchased goods and services

We calculate emissions from Purchased Goods & 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 data. 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.  

Assumptions 

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 

Limitations 

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 user does not map 100% of their spending), we assume that emissions of unmapped categories reflect 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 overall 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. 

Source 

U.S. EPA Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities
U.S. Bureau of Labor Statistics - Consumer Price Index
U.S. Bureau of Economic Analysis - Industry Classifications

Personal emissions calculator

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.

Assumptions

Heavy meat eater is defined as eating 30% more meat than the regional average, but only 70% of vegetables. Light meat eater is defined as eating 70% of the regional average consumption of meat, but an additional 30% of vegetables. Vegetarian is defined as eating 50% more vegetables than the regional average, and vegan is defined as eating double the vegetables. No adjustments were made for cereals. 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.

Limitations

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.

Sources

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

Fuel prices

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://www.eia.gov/tools/faqs/faq.php?id=97&t=3

https://www.odyssee-mure.eu/publications/efficiency-by-sector/households/electricity-consumption-dwelling.html

Diet

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    

https://datacatalog.worldbank.org/search/dataset/0039597

https://ec.europa.eu/eurostat/web/products-datasets/-/enps_env_wasgenh

Events impact calculator

Travel

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.

Hotel

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. 

Venue

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. 

Waste

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.

Food

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. 

Merchandise

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. 

Assumptions

Travel

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. 
  • Average car trip distance: 807 km 
  • Average rail trip distance: 1,327 km 
  • Average flight trip distance: 1,859 km 
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% 

Hotel

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. 

Venue

We assume the below average daily energy use per square meter of venue area based on US CBECS data.  
  • Electricity: 0.039 kWh/m2/day 
  • Natural gas: 0.001029 MMBTU/m2/ day 

Waste

We assume that all waste is sent to landfill and that all recyclables are a mix of paper, plastic, metal, and glass. 

Food

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

Limitations

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 U.S. or Canada. 

Sources


Food and Agriculture Organization of the United Nations

Poore, J. and Nemecek, T., 2018. Reducing food’s environmental impacts through producers and consumers. Science

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

Dettore, C. 2009. Comparative Life-Cycle Assessment of Bottled vs. Tap Water Systems. University of Michigan Center for Sustainable Systems
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