Forecasting the CO2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling

Pradyot Ranjan Jena,Babita Majhi,Shunsuke Managi

doi:10.3390/en14196336

Abstract

Better accuracy in short-term forecasting is required for intermediate planning for the national target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating countries to make informed decisions. The current study forecasts the CO2 emissions of the 17 key emitting countries. Unlike previous studies where linear statistical modeling is used to forecast the emissions, we develop a multilayer artificial neural network model to forecast the emissions. This model is a dynamic nonlinear model that helps to obtain optimal weights for the predictors with a high level of prediction accuracy. The model uses the gross domestic product (GDP), urban population ratio, and trade openness, as predictors for CO2 emissions. We observe an average of 96% prediction accuracy among the 17 countries which is much higher than the accuracy of the previous models. Using the optimal weights and available input data the forecasting of CO2 emissions is undertaken. The results show that high emitting countries, such as China, India, Iran, Indonesia, and Saudi Arabia are expected to increase their emissions in the near future. Currently, low emitting countries, such as Brazil, South Africa, Turkey, and South Korea will also tread on a high emission growth path. On the other hand, the USA, Japan, UK, France, Italy, Australia, and Canada will continuously reduce their emissions. These findings will help the countries to engage in climate mitigation and adaptation negotiations.

Highlights

There is wide consensus among scientists and policymakers that global warming as defined by the Intergovernmental Panel on Climate Change (IPCC) should be pegged at1.5◦ Celsius above the pre-industrial level of warming in order to maintain environmental sustainability [1]
Considering that there might be a nonlinear relationship between the indicators of economic growth and the CO2 emissions, we develop a multilayer artificial neural network (MLANN) model
The mean absolute percentage error (MAPE) values for the Group-2 countries are given in Table 3 which shows that the values lie between 1.92 and 3.8 except for Brazil and Italy

Summary

Introduction

There is wide consensus among scientists and policymakers that global warming as defined by the Intergovernmental Panel on Climate Change (IPCC) should be pegged at1.5◦ Celsius above the pre-industrial level of warming in order to maintain environmental sustainability [1]. Since the United States (US) is not a signatory to the Paris climate accords, the international cooperation sought between the industrialized and industrializing countries is slow. Given this broad context of looming climate change threats and the slow pace of actions on reducing CO2 emissions by the countries, more scientific research must be undertaken to understand the exact nature of the threats. Knowing the level of CO2 emissions by the high emitting countries in near future will provide actionable insights on climate policy Such information will aid in fostering the cooperation talks in the upcoming United Nations (UN) COP26 climate conference from 31 October–12 November

Methods

Results

Conclusion