In seeking to advance sustainability in the aviation industry, robust estimates of emissions are essential because they support informed decision-making. Previous ICCT research found that travelers on U.S. routes can reduce carbon dioxide (CO₂) emissions attributable to their ticket by 22% on average, and up to 63%, by choosing the least-emitting flight available.  

Consumers can access emissions estimates for future flights around the world from Google’s Travel Impact Model (TIM), and since 2023, the ICCT has worked with global experts to refine the model through an Advisory Committee that we lead. This partnership established seven core principles and a roadmap to ensure continuous improvement of the TIM for rigorous, transparent, and consistent emissions reporting.  

As the TIM continues to evolve, it holds promise to become the global standard in low-carbon travel search. Here I’ll highlight some of the work behind its development, including the model validation and model selection approaches that enhance its reliability for travelers, airlines, and policymakers.  

Estimating fuel burn is the first step in assessing emissions, and this is challenging due to the variability in flight operations. Emissions will differ based on aircraft technology, weather, and operational practices, and even for the same aircraft and route, fuel burn can vary significantly. The TIM uses the European Environment Agency (EEA) model, which bases estimates on aircraft type and route distance and creates simplified linear relationships. Using the operations of Brazilian airlines in 2019 and the four most commonly used aircraft types, Figure 1 compares the TIM (version 1.8.0) estimates (dashed lines) with real-world fuel burn at the flight level (dots). The large variability reflects the complexity and uncertainty of flight operations, where fuel burn depends on a range of interdependent and sometimes unpredictable factors such as weather and operational practices.
 

Figure 1. Fuel burn versus distance for each individual flight by the four most common aircraft types in the ANAC data in 2019

Model validation

Validation is a quantitative assessment of how well model prediction represents real-world data. For validating the TIM, we needed data from past flights, including fuel burn, ideally at the flight level or at least aggregated by route and aircraft type. The only public dataset identified that met the requirements was the Brazilian Civil Aviation Agency (ANAC) microdata, which provides historical flight data for Brazil since the year 2000 at the flight level. As it’s limited to Brazilian airlines, Google combined ANAC’s public data with private operational data shared by partner airlines worldwide. The aircraft types covered by this sample represent approximately 76% of global flights in 2019 and the validation sample now contains more than 3 million flights. The Google engineering team is continuously working with airlines to expand it to enhance model representativeness and reliability. There is a three-step process to promote reliability: 

1. Data cleaning: We remove irrelevant or incomplete data. 
2. Data aggregation: We group fuel burn data by route, aircraft type, and airline. This is necessary because some private airline data was shared in aggregated form; it contained fuel burn averages by route and aircraft type rather than at the flight level. By aligning our analysis with the level of granularity available in the shared data, we ensure consistency. 
3. Error analysis: We compare the TIM’s estimates with real-world fuel burn using metrics such as median absolute error and error distribution. “Error” is defined as the difference between actual and estimated fuel burn, expressed as a percentage. The actual fuel burn refers to the values in the validation dataset, and estimated fuel burn refers to the TIM estimates. Positive errors indicate overestimation and negative errors indicate underestimation. 

The TIM validation framework uses four key metrics for evaluation: median absolute error (the central tendency of errors), error threshold analysis (the percentage of estimates within different error bounds), distance-based metrics (error trends by route length), and the distance and aircraft error metric (error trends by route length and aircraft type). Details of the metrics are in this technical brief, and Figure 2 illustrates the error distribution curve for the TIM (version 1.8.0) estimates. As shown, the fuel burn is more often underestimated than overestimated by the model. The TIM underestimates the fuel burn for nearly 75% of the airline-aircraft type-route combinations in the validation sample.

Figure 2. Frequency (left) and cumulative (right) distributions of the error in the TIM’s fuel burn estimates when compared with the real-world fuel burn from the combination of ANAC 2019 and private airlines data

Model selection 

The TIM fuel burn estimation was originally based on the EEA 2019 model, which allows users to define only aircraft type and stage length; other significant factors like flight trajectory and payload are not included. Recognizing these limitations, the TIM Advisory Committee explored alternative fuel burn models. 

Nine models were assessed qualitatively (details in the technical brief), and five were shortlisted for detailed evaluation using the validation methodology: EEA 2023, OpenAP, Poll-Schumann, Piano 5, and ICAO ICEC. Because these models vary in structure and require different operational assumptions such as trajectory and payload, we standardized assumptions where possible to be able to compare them. The tested scenarios, based on real-world operations and described in the technical memo, reflect these simplifications. Figure 3 illustrates how the error distributions of these models compare with EEA 2019.  

Both EEA 2019 and EEA 2023 showed narrow error distributions, reflecting good accuracy. However, EEA 2023 consistently outperformed EEA 2019 across key metrics. In contrast, OpenAP demonstrated a wider error spread, indicating lower predictive accuracy for the data used. Intermediate performers, such as ICAO ICEC, Poll-Schumann, and Piano 5, showed moderate error variability. These evaluations showed EEA 2023 to be the most suitable model, and it was adopted in mid-2024. 

Figure 3. Comparison of the error distribution across alternative models

In January 2024, the Advisory Committee incorporated validation into the TIM workflow to evaluate fuel burn model updates. Then, in June 2024, in addition to adopting EEA 2023, they applied a distance correction factor that enhanced the TIM’s accuracy and alignment with real-world operations. The distance correction factor refines stage length inputs by replacing Great Circle Distance with an average route distance based on real-world flight paths. This adjustment reduced the median absolute error from 7.80% to 6.30%. Future Advisory Committee work on second-order fuel burn effects like payload, engine variants, and aircraft age is expected to further improve the accuracy of the TIM and thus further improve its value for a wide range of stakeholders, including the flying public.

Special thanks to Ana Beatriz Reboucas and Jayant Mukhopadhaya for their significant contributions to the research on the TIM website.

The most high-profile setback came when Airbus pushed back its ambitious goal to introduce hydrogen-powered commercial aircraft in 2035. Citing a mix of infrastructure challenges and slower-than-expected technology development, it made the announcement during the Airbus Summit 2025 and expects a 5-10-year delay. Airbus isn’t alone. Other manufacturers have recently dialed back plans or hit financial difficulties. Why this turbulence? Well, as with Airbus, the primary challenges are technology and infrastructure. 

Technologically, battery-electric aircraft remain limited to serving short routes (less than 500 km) due to severe battery weight limitations. Jet fuel is still roughly 50 times more energy dense than today’s most advanced lithium battery. While jet fuel contains about 43 MJ/kg of energy, the most advanced lithium-ion batteries today offer only around 0.9–1.0 MJ/kg, making them roughly 40 to 50 times less energy dense by weight. The additional battery weight increases the total mass the aircraft must lift and thus the energy to sustain flight—creating a compounding penalty. Hydrogen technologies such as fuel cells and combustion engines can offer greater range but face their own set of engineering hurdles, particularly concerning the need to develop lighter hydrogen storage tanks and more powerful fuel cells to minimize their significant mass and volume penalty. One of the major aircraft manufacturers, Embraer, recently pushed back its Energia project, a family of low-emission hybrid-electric aircraft, because both battery and hydrogen fuel cell advancements have been slower than hoped. This necessitated the delay of the project from its original intended entry-into-service year of 2035. 

Infrastructure is probably an even bigger barrier. Airbus noted that inadequate hydrogen infrastructure—including hydrogen production, distribution, and fueling facilities, airport expansion and modification, and new safety protocols—is one of the major roadblocks to its ZEROe program, the flagship hydrogen-powered aircraft initiative aimed at launching the world’s first commercial ZEP. Indeed, green hydrogen, which is produced using renewable electricity and water electrolysis, comprised less than 1% of global hydrogen production in 2024. And even if green hydrogen were being produced in large volumes, the infrastructure to reliably distribute hydrogen remains sparse and expensive to build. Regulatory frameworks are also lagging, and that’s creating uncertainty and slowing investment. For example, Airbus recently reduced the budget for ZEROe by 25%. 

What about the innovative startups we heard about a few years ago? Universal Hydrogen, a U.S.-based company that aimed to decarbonize regional aviation by retrofitting planes to run on hydrogen fuel cells, achieved a remarkable goal when it flew the largest aircraft ever on hydrogen-electric power. However, Universal shut down a year later after failing to secure enough funding to continue research and development. Eviation, an electric-plane startup based in Washington state, paused its Alice program and laid off most of its employees. That these firms struggled to secure the substantial funding and regulatory support necessary to scale is a lesson for the aviation industry: Zero-emission flight requires sustained and coordinated investment and collaboration beyond the reach of individual companies.  

What does this all mean for aviation’s climate targets? Well, our recent study found that even with optimistic sustainable aviation fuel (SAF) and fuel efficiency assumptions, the net-zero carbon target could be exceeded as early as 2037. Therefore, to meet the 2050 net-zero target, accelerating efforts to develop ZEPs seems critical. But with Airbus and others suggesting that 2035 is infeasible, achieving the net-zero target by 2050 looks more difficult than ever.  

This puts more pressure on SAF, technical efficiency improvements, and operational optimization to fill the gap. Due in part to this reality, hybrid-electric solutions have emerged as intermediate alternatives because they can be integrated in the medium term and offer moderate emission reductions (even though they are short of climate goals, including when combined with SAFs).  

The upcoming update to our Vision 2050 report will reflect the latest industry realities and reassess the role that ZEPs, SAFs, and other mitigation measures must play to realistically meet global targets. It will also feature an updated version of the ICCT’s net-zero aviation roadmap, which for the first time will include the climate impact of non-CO₂ emissions. 

Ultimately, stakeholders have to ask: Are hydrogen tanks getting lighter? Are fuel cells getting more powerful? Is battery chemistry improving? The recent setbacks are a wake-up call and these indicators will tell us if ZEPs can move from prototypes to actionable climate solutions. Even if there is continued progress on the technology side, the business case will need to be addressed—both the capital and regulatory aspects—to make ZEPs into a reality. In the meantime, as important as ZEPs are to the long-term solution, to stay on track for 2050 it’s likely that policymakers and the aviation industry will have to focus on alternative measures that can deliver medium-term emission savings.