Modeling and forecasting of CO2 emissions resulting from air transport with genetic algorithms: the United Kingdom case

Abstract

The increase in the air transportation density affects global warming negatively by increasing the CO2 emitted to the environment. The issue becomes even more important when the agricultural lands and drinking water resources on the flight routes are considered. This situation leads to the development of certain environmental concerns in the society and makes it necessary for the countries to forecast in the correct direction to develop some preventive strategies. To make a contribution to this issue, emission modeling and forecasts regarding emissions originating from air transportation were made in this study through genetic algorithms, a popular artificial intelligence technique. Using the flight information of 32 European countries, the degree of relationship between the number of flights and passengers and CO2 emission from air transportation was calculated. Based on the highly correlating results obtained, time series models were developed for the UK’s domestic and international airline transportation in which the highest number of flights takes place and passengers are carried. Using these models, the forecasts based on the UK’s flight numbers until 2029, the number of passengers to be transported, and air transportation–related emissions were made. Results with high correlation values ranging from 0.99 to 0.87 were obtained in the implementations.