The evidence of climate change in Kenya is undeniable, while its effects on economic growth are no more mysterious; rather, they are rapidly unfolding to a startling reality and concern for humankind. Accelerated emissions of green house gases (GHGs) globally, from combustion of fossil fuels and unsustainable land use practices is the key driver of anthropogenic climate change.
Climate change is manifested in; variations in minimum and maximum temperature, frequent and extensive droughts, high intensity rainfalls and floods, seasonal and regional pest and disease prevalence leading to conflicts for pasture and water resources, high food insecurity; encroachment into water catchment towers, human – wildlife conflicts and displacement of people among other adverse effects.(IPPC, 2007). Figure 1 below show temperature trend for Nairobi from 1960 to 2005. Increase in both minimum and maximum temperature is evident.
Although climate change is a challenge that all countries have to contend with, its effects on economic development are not distributed equally among nations and regions. In fact developing countries that have contributed least to the climate change menace will suffer most (Stern, 2006). This is due to their overreliance on climate sensitive sectors and their low capacity to adapt and couple with the changing climate. Moreover given the rising population and projected economic growth in developing countries, they could overcome developed countries in GHGs emissions a key driver of climate change. (Akram and Hamid, 2015)
Figure 1: Temperature trend for Nairobi from 1960 to 2006. (Source KMD)
The sources of GHG emissions are as pervasive as the...
... middle of paper ...
...on level), K is gross capital formation, log is natural logarithm, ε is white noise and β0, β1, β2, β3, β4, β5 are coefficients to be estimated.
Since the variables are time series, a time series model will be fitted to analyze the data.
3.4 Data Analysis Techniques
To achieve the objectives set for this study, annual time series data from 1960 to latest available data will be used. This quantitative data will be analyzed using E-views econometric software. Econometric modeling will be used to establish the relationship and causality between the variables. The following pre estimation tests will be conducted:
3.4.1 Stationarity test
3.4.2 Cointegration test
3.4.3 Causality test
Then a Vector Error correction Model (VECM) will be estimated and the following post estimation tests also carried out:
Impulse response and variance decomposition.
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