The tool estimates the carbon footprint of a single or multiple hotel room nights and meeting spaces across the world. Companies can use this information to calculate the carbon footprint of hotel stays or meetings during business travels for Scope 3 reporting and offsetting, or to provide clients with information when booking hotels or offsetting carbon on their behalf.
The tool outputs the median carbon footprint in specified geographical locations, and users can filter for hotels by the number of Expedia Stars. It calculates the carbon footprint using the Hotel Carbon Measurement Initiative (HCMI) methodology; ideal for corporate reporting and calculating the amount of carbon to offset.

HCMI is the industry-accepted methodology to measure and compare scope 1 and 2 greenhouse gas emissions of hotels. It includes emissions related to fuels burned on site (e.g. in gas boilers and company vehicles) and electricity used on site. It also accounts for emissions related to any outsourced laundry and refrigerants (e.g. used in air conditioning). For more information, please visit the Sustainable Hospitality Alliance’s webpage.

The data is from the Cornell Hotel Sustainability Benchmarking Tool (CHSB) 2024 data set (based on 2022 data), with over 27,000 hotels in over 1,000 geographies. The number of geographies covered nearly doubles in size every year.
The CHSB data set, along with the accompanying guidance document outlining the methodology, are freely available online through the Cornell University Center for Hospitality Research or here.

The minimum threshold is five properties for market areas and eight properties for all other geographies (i.e. country, state, province). If a particular segment within a market contained at least five properties, or if a particular segment within a region, country, and climate zone contained at least eight properties, the results were included. On the other hand, data for geographies that did not meet the minimum threshold were excluded from the final outputs.

Some countries will not have actual median figures available, due to limitations in the raw data received for CHSB. The carbon footprint per occupied room (kg CO2e) [Measure 1] and the carbon footprint per sqm/hour of meeting space (kg CO2e) [Measure 7] for these countries are extrapolated using the following data points:

• the global average carbon footprint per occupied room (kg CO2e) [Measure 1]
• the global average carbon footprint per sqm/hour of meeting space (kg CO2e) [Measure 7]
• the percentage of energy from electric sources for each country
• the percentage of energy from non-electric sources for each country
• the purchased electricity emission factor for each country
• the global weighted average of purchased electricity emission factors

For more information on the data set and the extrapolation methodology, please contact us at data@greenview.sg
While we understand that global extrapolations are broad and high-level figures, we believe that the HFT produces more accurate and reliable data than other existing hotel footprinting methods because of the extensive CHSB source data. Furthermore, as the CHSB data set grows in reach and depth over time, we will be able to reduce extrapolation, while expanding coverage in the remaining countries that still require it for footprinting exercises (e.g. by increasing the number of properties per country and number of countries with actual data).

Actual Data

• Indicates that figures were available in the source data received from the Cornell Hotel Sustainability Benchmarking Tool (CHSB) 2024 data set (2022 data).

Extrapolated Data

• Proxy country/group of countries were used to estimate the figure using the extrapolation methodology explained above in Section 5 (If there is insufficient data to generate median figures, how is the data extrapolated for these countries?) for countries that lack the minimum number of properties to generate a median figure. The proxy group of countries for each climate grouping and country grouping are listed in the Legend to the Hotel Footprinting Tool.

Extrapolation by Stars, within a specific country
Average of 2 Stars and 4 Stars

• The average of 2 Stars and 4 Stars coefficients was calculated and used as a proxy to fill in the gap in the 3 Stars coefficient for a specific country.

Average of 3 Stars and 5 Stars

• The average of 3 Stars and 5 Stars coefficients was calculated and used as a proxy to fill in the gap in the 4 Stars coefficient for a specific country.

Note: If coefficients for 2 Stars or 5 Stars are lacking, partial data extrapolation or figures from other countries are used as proxy.

Extrapolation based on other countries

Climate Grouping Extrapolation

• Countries from the same climate zone are grouped together and used as a proxy to fill in the gaps among countries based on the coefficient generated from the grouping.

Country Grouping Extrapolation

• Countries from the same location are grouped together and used as a proxy to fill in the gaps among countries based on the coefficient generated from the grouping. The full list of country groupings can be found in the Legend to the Hotel Footprinting Tool.

Global Extrapolation

• For countries that lack the minimum number of properties to generate a median figure or good proxy, its coefficient was estimated based on the coefficient generated from the whole list of countries that have actual data.

Partial Data Extrapolation

• For countries that lack the minimum number of properties to generate a median figure for some of the Expedia Star segments (i.e., data not available for 3 stars and 4 stars), its coefficient was estimated based on the coefficient generated from partially available data. In this case, data would be extrapolated from the 2 stars and 5 stars’ coefficients within the same country.

Extrapolation for more granular geographies within a specific country
Sub-National Province Level Extrapolation

• Provinces that lack the minimum number of properties to generate a median figure are grouped together. The coefficients from other provinces within the same country which meet the minimum number of properties are used as a proxy to fill in the gaps.

Sub-National State Level Extrapolation

• States that lack the minimum number of properties to generate a median figure are grouped together. The coefficients from other states within the same country which meet the minimum number of properties are used as a proxy to fill in the gaps.

Under the GET THE RESULT section, below the text “Rooms Footprint” and “Meeting Footprint”, the following text is displayed to state the data status:

• Available data: Singapore (Actual) all hotels, City Data
• Available data: Albania (Climate Grouping Extrapolation) all hotels, Country Data

The publishing cycle for the Cornell Hotel Sustainability Benchmarking Tool (CHSB) takes up to two years, which includes data collection from major hotel chains (generally available in the third quarter of the following calendar year), data aggregation, rigorous validity testing and analysis, data verification, and finally drafting and publishing of the report.

The benchmarking data for CHSB2024 (2022 data) includes 27,467 hotels from 42 international hotel chains: ACCOR S.A., AINA Hospitality, AMAN Resorts, Casale Panayiotis, Centara Hotels & Resorts, Chatham Lodging Trust, Deutsche Hospitality, DiamondRock Hospitality Company, FIVE Holdings, Four Seasons Hotels and Resorts, Highgate, Hilton Worldwide, Hong Kong & Shanghai Hotels, Horwath HTL Asia Pacific, Hotel Asset Value Enhancement (HotelAVE), Hyatt Hotels Corporation, Intercontinental Hotels Group, Jumeirah Group, KHP Capital Partners, Mandarin Oriental Hotel Group, Marriott International, Millennium Hotels and Resorts, Park Hotel Group, Park Hotels & Resorts, Pebblebrook Hotel Trust, Pineapple Hospitality Company, Playa Hotels & Resorts, Post Ranch Inn, Radisson Hotel Group, RLJ Lodging Trust, Rosewood Hotels & Resorts, Ryman Hospitality Properties, Six Senses Hotels Resorts Spas, Sutton Hotel Collection, The Ascott Limited, The Fullerton Hotels Singapore, The Ranch at Laguna Beach, The RuMa Hotel and Residences, Valamar Riviera, Vista Hospitality Group, Wyndham Hotels & Resorts, Xenia Hotels & Resorts.

Data sets are updated annually. The next update will be available in the first quarter of 2025 with the 2023 calendar year dataset.

If a country does not have granular level data (e.g. by metropolitan statistical areas (MSAs)), a default value for the entire country is provided.

The Hotel Footprinting Tool calculates the carbon footprint of hotel stays based on the number of rooms occupied, which you can derive by multiplying the number of rooms and the number of nights. The tool does not take into account the number of guests occupying each room.
The below examples illustrate the calculation methodology:

• If you are occupying 1 room for 2 nights, the total occupied rooms would be 2 occupied rooms (1 room x 2 nights).
• If you are occupying 2 rooms for 5 nights, the total occupied rooms would be 10 occupied rooms (2 rooms x 5 nights)

The tool is updated annually to represent the latest CHSB dataset. If users wish to make calculations for any prior years, please contact Greenview to request the HFT bulk data set for the prior years’ figures.

Please use the following citation for Footprinting:

• Hotel Footprinting Tool, V[version number]. HCMI values obtained using [indicate hotel type] hotel type and median, www.hotelfootprints.org, retrieved [date].
• Example: Hotel Footprinting Tool, V3.0. HCMI values obtained using all hotel type and median, www.hotelfootprints.org, retrieved 10 March 2024.

Submit data by participating in Cornell Hotel Sustainability Benchmarking (CHSB)! For more information, refer to the CHSB webpage or email us at info@greenview.sg and we will send a template for you to fill in the data.

Please email us at info@greenview.sg and we will be happy to receive your feedback to help us improve the tool.