Abstract
In this paper, we introduce a novel approach that utilizes aircraft seat capacity and its route flight time to predict carbon emissions for aircraft manufacturers during the aircraft design stage. To address data fluctuations arising from the human-aircraft-environment-management system throughout the production and operation of the future-designed aircraft, we employ the interval-valued data type, replacing the traditional point data type, and construct an interval-valued regression model. Within the parameterized method framework, our programming model incorporates additional constraints to ensure the intersection of each interval-valued sample. This addresses the challenges significant data fluctuations pose, leading to improved fitting performance. Furthermore, the model is demonstrated to satisfy Kuhn-Tucker conditions for solvability, and the obtained regressed parameters exhibit favorable small-sample properties. An empirical study using airline operating data from China validates the prediction effectiveness of the programming model. Finally, based on these predictions, we provide applications and suggestions for decision-makers aimed at reducing CO2 emissions.
Published Version
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