SBIR Phase I: Aviation Carbon Emissions Calculator for Commercial Air Travel and Cargo

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力将加速航空业采用更高效的飞机和可持续燃料来源。该项目提出了一种碳计算器算法，可以有效计算消费者旅行和航空货运市场科学有效且准确的碳排放量。这项研究将产生第一个可公开获取的关于航空货运和航空旅行的航班碳排放数据，作为预订决策支持工具。这将使旅客能够根据碳排放和可持续性做出航班决策，就像他们对价格和时间表做出决策一样。 航空公司可以利用它来做出重要的航班运营决策，以支持其可持续发展目标。这可以应用于其他交通方式。这项研究将导致开发预测每位乘客的碳排放算法，以用作预订航班之前的决策支持工具。航空旅行碳排放方面从未进行过此类研究和开发，目前也没有针对航空碳排放的外部认证和核查体系。将使用计量经济学建模和主成分分析来开发和训练预测碳排放算法，之后将使用软件工程来开发应用程序编程接口产品，以将其与消费者行为和旅行决策联系起来。该项目的一项固有技术风险是获取并保持一致访问描述飞机和航班的准确、精选数据的能力。 以所需的速度和准确性实时处理来自不同来源的大量数据的能力是另一个重大挑战。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will accelerate the aviation industry’s adoption of more efficient aircraft and sustainable fuel sources. This project proposes a carbon calculator algorithm to effectively calculate scientifically valid and accurate carbon emissions for the consumer travel and air cargo markets. This research will lead to the first publicly accessible data on flight carbon emissions for air cargo and air travel as a point of booking decision support tool. This will enable travelers to make flight decisions based on carbon emissions and sustainability, as they do price and schedule. Airlines can use this for important flight operations decisions in support of their sustainability goals. This can be applied to other modes of transportation.This research will lead to the development of a predictive per-passenger carbon emissions algorithm to be used as a decision support tool before booking a flight. Research and development of this type have never been done for air travel carbon emissions, and currently, no external certification and verification system exists for aviation carbon emissions. Econometrics modeling and principal component analysis will be used to develop and train a predictive carbon emission algorithm, after which software engineering will be used to develop an Application Programming Interface product to link this with consumer behavior and travel decisions. One inherent technical risk for this project is the ability to obtain and maintain consistent access to accurate, curated data describing aircraft and flights. The ability to process such a vast amount of data from disparate sources in real-time, with the required speed and accuracy, is another major challenge.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.