Predicting the Environmental Change of Carbon Emission Patterns in South Asia: A Deep Learning Approach Using BiLSTM

Abstract

China’s economy has made significant strides in the past three decades. As a direct result of China’s “one belt, one road” (OBOR) initiative, the country’s rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world’s high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China’s CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model’s results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India’s high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions.