Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System
Metadatos
Mostrar el registro completo del ítemAutor
Mardani, Abbas; Streimikiene, Dalia; Nilashi, Mehrbakhsh; Arias Aranda, Daniel; Loganathan, Nanthakumar; Jusoh, AhmadEditorial
MDPI
Materia
Energy CO2 Growth Adaptive neuro-fuzzy inference system (ANFIS)
Fecha
2018-10-16Referencia bibliográfica
Mardani, A. [et al.]. Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System. Energies 2018, 11, 2771; doi:10.3390/en11102771.
Patrocinador
This research was funded by Universiti Teknologi Malaysia (UTM), Flagship UTMSHINE grant PY/2017/02187.Resumen
Understanding the relationships among CO2 emissions, energy consumption, and economic
growth helps nations to develop energy sources and formulate energy policies in order to enhance
sustainable development. The present research is aimed at developing a novel efficient model for
analyzing the relationships amongst the three aforementioned indicators in G20 countries using
an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this
regard, the ANFIS model has been used with prediction models using real data to predict CO2
emissions based on two important input indicators, energy consumption and economic growth. This
study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and
output indicators in order to make a prediction of CO2 emissions. The experimental findings on
a real-world dataset of World Development Indicators (WDI) revealed that the proposed model
efficiently predicted the CO2 emissions based on energy consumption and economic growth. The
direction of the interrelationship is highly important from the economic and energy policy-making
perspectives for this international forum, as G20 countries are primarily focused on the governance
of the global economy.