Energy Use and CO₂ Emissions in Guangdong's Manufacturing Hub: An econometric and Deep learning approach

China's most populous and economically dynamic province (Guangdong), is facing challenges in balancing rapid industrialization with sustainable energy use. As a major contributor to CO₂ emissions, the province's energy consumption patterns reflect its reliance on fossil fuels. The paper investigates the impact of fossil fuel energy consumption, particularly the interconnection between coal, petroleum and CO2 emissions, consequently, the paper forecast CO2 emissions trend in Guangdong province. The paper employs econometric technique (Vector Autoregression_VAR model) and deep learning approach (Long Short-Term Memory_LSTM model) covering a dataset between 1999-2021. The results based on VAR technique suggest that Coal and petroleum significantly impact CO2 emission, subsequently degrades environment. The forecasting results based on LSTM model suggest increasing CO2 emission trend until 2025, however, the trend declines in the following years. The LSTM-based forecasting results indicate that CO₂ emissions in Guangdong will continue rising until 2025, followed by a steady decline in subsequent years. These findings suggest that alternative energy sources such as wind and solar are reliable and friendly energy sources in achieving sustainable energy targets.