Determinant of Climate finance in East Africa countries

The paper analyzed determinant variables for climate finance in East African concerning using 4P framework (Poverty, Population, Policy, and readiness) introduced as a way of understanding a donor’s aid allocation. countries for the year 2013-2020. Different Panel data model used for eight study countries and 10 variables. However, GMM by system dynamic panel data technique solve the endogeneity problem. Principal component analysis is applied on the instrument matrix, and the principal-component analysis scores are used as instruments for the panel generalized method of moments estimation. The Hansen and Sargan test of overidentification have p value of greater than 0.05 which is significant. Hansen test reject the null hypothesis of overidentification. The lag dependent variable of climate finance has significant at 5% and the remaining component has insignificant and positive coefficient for climate finance. Sensitivity analysis performed using panel quantile regressions. Whereas the fixed model F (10,45) statistic 3.93 and the probability value less than 0.5 significant level. Within fixed model only the variable population growth is significant with positive coefficient. The variable population growth, control of corruption and political instability have significant impact on climate finance on the region. the findings from our analysis, we concluded that countries in the east Africa countries region who attract the most climate funding are those characterized by higher education, higher social readiness, lower control of corruption, economic readiness, higher ICT usage, lower rule of law, higher populations and, lower per capita GDP levels