  In the last 150 years, CO2 concentration in the atmosphere has increased from
280 parts per million to 400 parts per million. This has caused an increase in
the average global temperatures by nearly 0.7 degree centigrade due to the
greenhouse effect. However, the most prosperous states are the highest emitters
of greenhouse gases (specially, CO2). This indicates a strong relationship
between gaseous emissions and the gross domestic product (GDP) of the states.
Such a relationship is highly volatile and nonlinear due to its dependence on
the technological advancements and constantly changing domestic and
international regulatory policies and relations. To analyse such vastly
nonlinear relationships, soft computing techniques has been quite effective as
they can predict a compact solution for multi-variable parameters without any
explicit insight into the internal system functionalities. This paper reports a
novel transfer learning based approach for GDP prediction, which we have termed
as Domain Adapted Transfer Learning for GDP Prediction. In the proposed
approach per capita GDP of different nations is predicted using their CO2
emissions via a model trained on the data of any developed or developing
economy. Results are comparatively presented considering three well-known
regression methods such as Generalized Regression Neural Network, Extreme
Learning Machine and Support Vector Regression. Then the proposed approach is
used to reliably estimate the missing per capita GDP of some of the war-torn
and isolated countries.
