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

这项小企业创新研究（SBIR）第一阶段项目的广泛影响和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。本项目提出了一种碳计算器算法，为消费旅游和航空货运市场有效、科学、有效、准确地计算碳排放量。这项研究将为航空货运和航空旅行提供首个可公开访问的航班碳排放数据，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定，就像他们根据价格和时间表做出决定一样。航空公司可以将其用于重要的航班运营决策，以支持其可持续发展目标。这也适用于其他运输方式。这项研究将导致预测每位乘客碳排放算法的发展，作为预订航班前的决策支持工具。这种类型的研究和开发从未针对航空旅行碳排放进行过，目前也没有针对航空碳排放的外部认证和验证体系。计量经济学建模和主成分分析将用于开发和训练预测碳排放算法，之后，软件工程将用于开发应用程序编程接口产品，将其与消费者行为和旅行决策联系起来。该项目的一个固有技术风险是获取和维护对描述飞机和航班的准确、精心策划的数据的一致访问的能力。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主要挑战。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.