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dc.contributor.authorOLILA, Dennis O
dc.contributor.authorWasonga, Oliver V.
dc.identifier.citationOlila, Dennis Opiyo, and Oliver V. Wasonga. "Time Series Analysis and Forecasting of Carbon Dioxide Emissions: A Case of Kenya’s Savanna Grasslands." 2016 AAAE Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia. No. 246394. African Association of Agricultural Economists (AAAE), 2016.en_US
dc.description.abstractClimate change and 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 countries . 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 trend in GHG emissions from the Kenya’ Savanna. Using Autoregressive (AR) modelling, the study analyzes and forecasts time series data from the year 1993 to 2012 . The key finding s of the study indicate that emissions resulting from continual burning of Savanna grasslands will continue in an upward trend if no mitigation measure is put in place to revert the statusquo. Averting the current state of affairs requires policies aimed at reducing the levels of GHGs in the atmosphere such as promotion of Climate Smart Agricultural (CSA) Practices.en_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectClimate Change, Savanna grassland, Autoregressive model, Time series dataen_US
dc.titleTime series analysis and forecasting of carbon dioxide emissions: a case of Kenyaen_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States