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Abstract. The Coupled Climate–Economy–Biosphere (CoCEB) model described herein takes an integrated assessment approach to simulating global change. By using an endogenous economic growth module with physical and human capital accumulation, this paper considers the sustainability of economic growth, as economic activity intensifies greenhouse gas emissions that in turn cause economic damage due to climate change. Different types of fossil fuels and different technologies produce different volumes of carbon dioxide in combustion. The shares of different fuels and their future evolution are not known. We assume that the dynamics of hydrocarbon-based energy share and their replacement with renewable energy sources in the global energy balance can be modeled into the 21st century by use of logistic functions. Various climate change mitigation policy measures are considered. While many integrated assessment models treat abatement costs merely as an unproductive loss of income, we consider abatement activities also as an investment in overall energy efficiency of the economy and decrease of overall carbon intensity of the energy system. The paper shows that these efforts help to reduce the volume of industrial carbon dioxide emissions, lower temperature deviations, and lead to positive effects in economic growth.

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In investigating Kenya rainfall variability and its relationship to other climatic elements it has become imperative to analyze the irregularly distributed rainfall events in time. To meet this requirement, this study used a stepwise regression technique. The study seeks to improve existing rainfall monitoring and prediction in Nairobi. Monthly rainfall data was fitted to several mathematical functions. The best mathematical model which best simulated the March-May (MAM) and October -December (OND) seasonal rainfall over the three stations of analysis was chosen using a stepwise regression technique. The value of R-squared for the best fit was computed to show the percentage of rainfall information that is explained by the variation in the independent (time) variable. From the results obtained, the stepwise regression technique selected the fourth degree polynomial as the best fit for analyzing the March-May (MAM) and October -December (OND) seasonal rainfall data set. Solar cycle period of ten (10) years was employed to get the fourth degree polynomial variables. Hence from the study, it can be deducted that the 4th degree polynomial function can be used to predict the peak and the general pattern of seasonal rainfall over Nairobi, with acceptable error values.  This information can be used in the planning and management of water resources over Nairobi. The same information can be extended to other areas.

In this paper we evaluate the performance of step-wise group screening designs in which group-factors contain an equal number of factors in the initial step.  A usual assumption in group screening designs is that the directions of possible effects are known a-priori. In practice, however, this assumption is unreasonable. We shall examine step-wise group screening designs without errors in observations when this assumption is relaxed. We shall consider cancellations of effects within group-factors. The performance of step-wise group-screening designs shall then be compared with the performance of multistage group screening designs.