A Machine Learning Approach for Predicting Emissions Based on GDP: A Case of South Africa in Comparison with the United Kingdom

Researchers believe that economic activity is driving the world’s average temperatures to rise at a rate faster than they have ever before. Because of this, climate change is one of the most serious ecological and socioeconomic concerns that we will confront in this century. Both the temperature of water and its depth are gradually getting higher as time goes on. Droughts that are more severe and endure for longer periods influence agriculture, wildlife habitats, and water resources. People and animals across the world are being put in immediate danger as a direct result of climate change. This includes polar bears in the Arctic and sea turtles off the coast of Africa. As a direct consequence of this crisis, the ecosystems of our planet and the human populations that inhabit them are in jeopardy. This chapter’s goal was to have a better understanding of how to forecast emissions by making use of machine learning (ML) and the Kuznets hypothesis. In this chapter, a comparison is made between the estimations of South Africa’s emissions produced through machine learning and those of the United Kingdom. According to the findings that are presented here, developing countries and emerging economies can learn from developed economies like the United Kingdom about how to better manage emissions. This is because, according to the findings of the machine learning analysis, emissions are already declining in the United Kingdom in line with the Kuznets hysteresis. Consequently, if developing countries and emerging economies consider what is being implemented by developed economies like the United Kingdom about how to better manage emissions, then it is possible to fast-track the attainment of the Sustainable Development Goals.