A novel GDP prediction technique based on transfer learning using CO2 emission dataset

Abstract

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 °C due to the greenhouse effect. However, the most prosperous states are the highest emitters of greenhouse gases (especially 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.

The Economic Effects of COVID-19 Containment Measures

“…For example, Lin and McElroy (2011) show that variation in NO 2 emissions in China resemble its GDP growth during and after the GFC. Kumar and Muhuri (2019) employ a transfer learning-based approach to predict per capita GDP of a country using CO 2 emissions. Chen et al (2020) use google mobility indicators to capture the economic impact of the COVID-19 pandemic.…”

Do Lockdown Policies Reduce Economic and Social Activities? Evidence from NO2 Emissions

“…One is the application of remote‐sensing data, primarily nighttime light (NTL) observed from space, in economics studies (e.g., Chen and Nordhaus 2011; Henderson, Storeygard, and Weil 2012). Although NTL remains the most widely used indicator, other types of remotely sensed data have recently started being explored in the economics literature, including remotely sensed land coverage data (Keola, Andersson, and Hall 2015), nighttime and daytime satellite imagery (Jean et al 2016), and carbon dioxide (CO2) emissions (Kumar and Muhuri 2019). The advantage of NO2 data collected by the TROPOMI, compared with NTL and land coverage data from various sources, is their very high temporal frequency.…”

Prediction with Multi-layer Perceptrons of Greenhouse Gas Emission Belonging to Different Sectors in Turkey

“…ABD ve Çin, CO2 salınımının büyük miktarını yapan ülkelerin başında gelmektedir. Sera gazı salınımında etkin olan ülkelerdeki CO2 emisyonunun sektörlere göre dağılımı incelendiğinde, %22'lik oran ile birinci sırada sanayi sektörü, ikinci sırada %20'lik oran ile ulaşım sektörü gelmektedir (Işık, 2014;Kumar & Muhuri, 2019). N2O, SO2 gibi kükürt oksitleri ve ayrıca CH4 gibi diğer sera gazları da inorganik gübrelerden oluşmaktadır (Fu vd., 2019;Kolasa-Więcek, 2018;Mahesh, 2018).…”

“…Çünkü sanayileşmede kullanılan altyapı ve hizmetler, araçlar, elektrikli makine ve fabrikalar enerjilerini biyo yakıtlardan (Fan, Zhang, Zhang, & Peng, 2015) sağlamaktadır. Baskın sera gazı olan CO2, bu yakıtlardan türemektedir (Kumar & Muhuri, 2019).…”

“…Endüstriyel dönemin başlamasıyla insan faaliyetine bağlı sera gazı üretiminin, dünya sıcaklığındaki anormal artışın ana nedeni olduğu anlaşılmıştır (Araabi, 2012;Cui, Sham, Lo, & Lum, 2011;Quesada-Rubio, Villar-Rubio, Mondéjar-Jiménez, & Molina-Moreno, 2011). Sıcaklık artışının geçen yüzyıl boyunca yaklaşık 0.4°C ile 0.8°C arasında olduğu ve yüzyılın sonuna kadar bu değerin yaklaşık 2°C olacağı öngörülmektedir (Fu vd., 2019;Kumar & Muhuri, 2019). Sera gazlarının miktarlarındaki anormal artışın canlıların yaşamında birçok olumsuz faktörü beraberinde getirdiği bir gerçektir.…”