ESG Scope 3 CO 2 Emissions for Business Travel: A Comparison of Static and Dynamic Models
The energy industry comprises 76% of the United States total anthropogenic emissions profile, of which oil and gas producers are the largest emitters. Because climate change is a complex and global problem, the Paris Agreement and the concept of ‘net zero emissions’ have driven the recognition that emissions (including scope 3) have to be viewed in the context of their systems or value chains.
All U.S. onshore and offshore operators are required to report emissions under the three scopes. According to the Greenhouse Gas Protocol that has standardized a framework to measure and manage greenhouse gases; Scope 1 emissions are direct emissions from owned or controlled sources, Scope 2 emissions are indirect emissions from the generation of purchased energy and finally Scope 3 emissions are all indirect emissions that occur in the value chain of the reporting company, including both upstream and downstream emissions.
Scope 3 emissions are reported in 15 sub-categories one of which is Business Travel. This category includes all emissions from the use of air, rail, rental cars and lodging by employees on business related travel. The American Petroleum Association has laid out three methods specific to oil and gas companies to calculate Business Travel emissions as summarized in Table 1.
Table 1. Summary of Business Travel emissions calculations methods as described in American Petroleum Institute’s Scope 3 Guidelines
The Fuel Based method is very difficult to implement as fuel used by individual avenues such as air, rail, and car rentals is extremely hard to capture. This data is likely available with the travel vendors, however not with the reporting operators. Whereas, the data required by Spend Based and Distance Based would be easier to capture for the reporting operator.
The theory is that each method would be more accurate than the one preceding it. So Fuel Based would be the most accurate, provided data is available, while Spend Based would be the least.
A client dataset of approximately 2,000 flights was utilized to test this theory for just Spend Based and Distance Based. This dataset contained flights categorized, based on EPA, into short, medium and long haul flights as summarized in Table 2. As well as 2% of the flights were chartered planes, also ranging from short, medium and long haul flights.
Table 2. Categorization of flights and their corresponding Emissions Factors based on EPA
For the commercial flights and chartered flights datasets, the following variables were known:
1. Total Spend
2. Departure and arrival cities, countries
3. Flight duration
Using the total spend, a spend based emissions in kg CO2 was determined for both commercial and chartered flights. To apply the distance-based method, the distance between each departure and arrival cities were required. The first approach was to calculate the CO2 emissions using approximate distances.
The Approximate Distance Based method was simply assigning a short, medium, long haul categorization to each flight based on knowledge about geographical proximity. For example: a flight from Houston to Dallas was considered short haul, while somewhere in United States to Canada was considered medium and United States to Italy was considered long. All short flights were assumed to have 299 miles distance, medium flights had 2299 miles distance and finally all long haul flights was assigned a distance of 5000 miles.
It was determined that the CO2 emissions from Approximate Distance Based method decreased by 43% for commercial flights and by 96% for chartered airlines from the emissions calculated by Spend Based method! This is explained by the variations in cost for commercial flights on a day to day basis as well as Business class versus Economy seats. Chartered flights are extremely expensive as it is an entire chartered plane privately booked, so the ratio of flight cost to distance would be highly pronounced.
To further improve these numbers, exact distances were determined between each departure and arrival cities. The specific distance each flight flew would be difficult to assess as it is dependent on the route a flight would have taken on a particular day. So, a static distance from city to city was captured from Google Maps and flights were categorized into short, medium, and long based on these exact distances. The CO2 emissions from Static Distance Based method improved by 21% from Approximate Distance Based calculated emissions for commercial flights and 28% for chartered flights.
If increasing the accuracy in the distances helped, would applying specific emissions factors to each distance also make a difference? As seen in Table 2, only one emissions factor value is provided for the general range of distances. A mathematical model was developed, illustrated in Figure 1, to map a unique emissions factor to each distance from the range 0 to 10,000 miles.
The CO2 emissions calculated using the Dynamic Distance Based further increased precision by 3% for both commercial flights and chartered flights. Figure 2 illustrates the growth in precision with each model.
As summarized in Table 3, granularity in the dataset highly improves accuracy and precision.
Table 3: Summary of all methods and level of precision of each method
Based on the findings of this experiment, the following recommendations are made:
1. Aim to use Distance Based method where possible; especially for executive chartered flights since the cost of chartered flights greatly exaggerate the emissions rate
2. Reporting companies should plan to capture exact distance for their employees’ business travel flights keeping the year end emissions reporting in mind so these calculations are made easy and accuracy can be ensured
3. It is recommended for EPA to provide emission factors for narrower range of miles or ideally a list of unique factors for specific distance, like the mathematical model used in Dynamic Distance Based for a higher degree of precision
4. Finally, it is recommended for EPA to provide emission factors for various types of planes as well to understand which planes have higher/lower emissions to enable mindful travel decisions
In summary, Spend Based method tends to largely overestimate emissions due to massive variations in the dataset. If distances can be estimated they should be, even an Approximate Distance will improve accuracy in the emissions being reported. Of course, the goal should be to capture exact distances and use the mathematical model to achieve granularity in both the distances as well as the emissions factors used to estimate CO2 emissions from business related air travel.
Chirality Research Inc. is a data science start up that analyzes structured, non-structured and real-time data, to provide actionable solutions to help our clients manage their day to day operations or devise a roadmap for their long-term strategies.
Ama Motiwala, Environmental Engineer, has found a niche in the blend between environmental engineering practices and data science principles introducing digital solutions to an otherwise traditional field of oil and gas.
Dr. Huzeifa Ismail, Founder, has more than 10 years of experience addressing cross-industry challenges in the area of engineering and data science. Ismail holds bachelor’s and master’s degrees from Brandeis University and a PhD degree in chemical physics from the Massachusetts Institute of Technology and has authored numerous technical articles and patents.
