Analyzing the Impact of Economic Growth, FDI and Energy Use on CO2 Emission in Kenya: An ARDL Approach

This study estimates the effects of Gross Domestic Product (GDP), population, renewable energy consumption, fossil fuels, and foreign direct investment (FDI) on Kenya's carbon emissions between 1972 and 2021. This investigation makes use of the “Autoregressive Distributed Lag (ARDL)” method, which is grounded in the theoretical framework as the “Stochastic Impacts by Regression on Population, Affluence, and Technology” model known as (STIRPAT) model. The ARDL bound test and structural break test were also used in the study. According to our preliminary results, the data exhibits long-run cointegration; as a result, the uses of ARDL, which is adept at handling both short- and long-term effects, is essential. This study lends credence to earlier research by demonstrating that a rise in Kenya's GDP and population can result in an increase in that country's CO ₂ emissions. Kenya may reduce its damaging carbon dioxide emissions by transitioning to renewable energy sources. All estimates place the impacts of GDP growth and population growth at parity. Achieving Kenya's sustainable development goals will require substantial investment in the country's energy infrastructure, making this analysis potentially useful in planning and establishing strategies for future financial funding in the energy sector. For ARDL, the effects of fossil fuels are negative but insignificant. FDI has an insignificant but positive effect on the environment. Based on these findings, policymakers can make informed decisions to sustainable use of renewable energy.