Climate change, savanna grassland, autoregressive model, time series data

Abstract

Climate change and climate variability is perhaps one of the major challenge s facing the world today. There is an equivocal agreement that climate change is not only a threat to the economies of developing world, but also to those of the developed economies. One of the key drivers of global warming is the greenhouse gas (GHG) emissions. Even though several studies have in the recent past evaluated various sources of GHG emissions and their associated impacts, little empirical information exists on the role played by burning savanna grasslands as far as global warming is concerned. This study is an attempt to determine the emission pattern over time and consequently forecast the linear trend in GHG emissions from the Kenya’ Savanna. Using Autoregressive (AR) modelling, the study analyzes and forecasts time series data ra nging from the year 1993 to 2012 . The key finding of the study indicate that emissions resulting from continual burning of Savanna grasslands will continue in an upward trend if no serious mitigation measure is put in place to revert the statusquo. Averting the current state of affairs requir es policies aimed at reducing the levels of GHGs in the atmosphere for instance promotion of Climate Smart Agricultural (CSA) Practices.