Estimating Long-Run Relationship between Renewable Energy Use and CO2 Emissions: A Radial Basis Function Neural Network (RBFNN) Approach

Sustainability

Babita Majhi¹  Ritanjali Majhi²  Pradyot Ranjan Jena² 

[1] Department of CSIT, Guru Ghasidas Vishwavidyalaya (Central University), Bilaspur 495009, India;School of Management, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India;

关键词: EKC estimation;    CO₂ emissions prediction;    neural networks;    radial basis function neural network;    renewable energy consumption;   

DOI  :  10.3390/su14095260

来源: DOAJ

【 摘 要 】

The long-run relationship between economic growth and environmental quality has been estimated within the framework of the environmental Kuznets Curve (EKC). Several studies have estimated this relationship by using statistical models such as panel regression and time series regression. The current study argues that there is a nonlinear relationship between environmental quality indicators and economic and non-economic predictors and hence an appropriate nonlinear model is required to predict it. An adaptive and nonlinear model, namely radial basis function neural network (RBFNN) has been developed in this study. CO₂ emission is used as the target output and renewable energy consumption share, real GDP, trade openness, urban population ratio, and democracy index are used as the predictors to estimate the EKC relationship for nineteen major CO₂ emitting countries that account for 78% of the global emissions. The model developed in this study could predict the CO₂ emissions of all the countries with more than 95% accuracy. This finding underlines the usefulness of the RBFNN model which can be used to predict emission levels of other pollution indicators at the global level. Further, comparing two models, one with all the predictors and the other excluding the renewable energy share, it was found that the model with renewable energy share predicts CO₂ emissions more accurately. This reinforces the already strengthening campaign to encourage industries and governments to increase the share of renewable energy in total energy use.

【 授权许可】

Unknown