Page 175 - Çevre Şehir İklim İngilizce - Sayı 1

Deniz Kaymak - Merih Aydınalp Köksal

of urbanization, growing middle class, basic economic indications, level of
liberalization and the business model of the airports have a positive effect
on the growth of aviation. On the other hand, environmental awareness, the
population at working age, and external impacts seem to affect sector growth
negatively (Adderpalli et al, 2018).
Studies on estimates of fuel demand have detected that the fuel
consumption of planes is affected by various factors such as plane type,
engine type, flight route and distance, flight altitude, plane weight, the weight
that the plane carries, operational methods, weather conditions, productivity
improvements, etc. As including and guessing all these factors in one model
is hard, it was stated that jet fuel demand cannot be modelled as a function
of the factors mentioned above or as a function of the time series. For this
reason, it is stated that to estimate future fuel demand based on air traffic
growth, a bottom-up approach was adopted. In this approach, also called
fuel density/ efficiency approach, first of all, energy efficiency and fuel density
coefficients (expressed in the amount of fuel use per air traffic) were calculated
and later, estimations on annual improvement percentages were made based
on historical trends in fuel density (Cheze et al., 2010; Eyers et al., 2004; IPCC,
1999; Kousoulidou and Lonza, 2016).
As in the studies in literature, the current study uses fuel density in esmitates
of fuel demand in Türkiye till 2030 after estimate analysis of air passenger
traffic.

Methodology and Data Collection

This study initially forms an air passenger traffic estimation model. Using this
estimation model, fuel consumption and the ensuing CO emissions till 2030
2
are estimated. First of all, the already available international RPK data between
2005-2017 was gathered (ICAO, 2017). Later, the economic, demographic
and social parameters affecting air passenger traffic are identified. After
trying numerous parameters through a hierarchical multi-regression model,
the real gross national product (GDP) and flight number per person and per
population were found as the independent variables suitable for regression
estimation models.
Detailed information on the formation of the model is given in Kaymak
(2019). The relevant model’s R2 value turned out to be close to 1. The model
was operated based on three scenarios, namely, low, medium, and high air
passenger traffic, and projections were made accordingly till 2030. Detailed
information on the creation of these scenarios is given in Kaymak (2019). Next,

160 Journal of Environment, Urbanization and Climate,