This study examines outward-looking foreign direct investment (FDI) and the determinants of four highly indebted low-income countries in East Africa. To achieve the stated objective, the study ...utilizes the pooled mean group (PMG) approach for panel data encompassing the period from 1990 to 2022. Additionally, bound testing and the autoregressive distributed lag (ARDL) model are applied to analyze time series data from individual countries within the sample. The panel PMG/ARDL estimation suggests that both market size and exchange rate have a significant positive impact on FDI inflows, both in the short run and the long run. Specifically, the time series analysis using ARDL estimation reveals that market size has a positive and significant impact on FDI inflows for both Rwanda and Tanzania, both in the long run and the short run. Furthermore, the association between the labor force and FDI inflows is positive only in the long run for Rwanda, while for Tanzania, it shows a positive association in both the short run and the long run. In terms of the availability of natural resources, the analysis indicates a positive impact in the short run but a negative association with FDI inflows in the long run, with the exception of a positive association with Rwanda in the long run. Additionally, external debt has a positive and significant impact on FDI inflows for Kenya, both in the short run and the long run. Based on the findings, the study recommends that policymakers should focus on policies and strategies that promote market expansion and create a larger consumer base. This can be achieved through initiatives such as market development programs, trade agreements, and regional cooperation.
Projected changes in climatic extremes, compared to the mean climate, exhibit a greater negative impact on the natural environment. Several studies reported that multi-model ensemble approach can ...improve the reliability of hydro-climatic extreme projection by extracting important information from a large number of general circulation models (GCMs). However, most of the available multi-model assembling methods do not consider both the spatial and temporal variabilities. Thus, this study reflects both the spatial and temporal climate characteristics during multi-model averaging through the Taylor diagram skill metrics. The capability of the proposed multi-model assembling approach was evaluated for reproducing the multitude of climate extreme indices. Moreover, the reliability of a multi-model assembling approach was assessed for preserving the maximum variability of the GCMs output. In general, the results showed that multi-model assembling approach outperformed the individual climate models for reproducing the hydro-climatic extremes; however, it artificially corrupted and narrowed the projected climate extremes variability of the GCMs output. Thus, it is worthwhile to consider both the individual climate models and multi-model ensemble projections toward an improved projection of hydro-climatic extremes. In general, the study proved that the impacts of climate change on the hydro-climatic extremes are more amplified compared to the changes in mean climate. Hence, this study suggests that meaningful efforts should be put in the future to proactively manage the risks of climate extremes.
Persistence of spatial analyticity is studied for solutions of the generalized Korteweg‐de Vries (KdV) equation with higher order dispersion
∂tu+(−1)j+1∂x2j+1u=∂xu2k+1,$$\begin{equation*} \partial ..._{t} u+(-1)^{j+1}\partial _{x}^{2j+1} u= \partial _x{\left(u^{2k+1} \right)}, \end{equation*}$$where j≥2$j\ge 2$, k≥1$k\ge 1$ are integers. For a class of analytic initial data with a fixed radius of analyticity σ0$\sigma _0$, we show that the uniform radius of spatial analyticity σ(t)$\sigma (t)$ of solutions at time t$t$ cannot decay faster than 1t$\frac{1}{\sqrt t}$ as t→∞$t\rightarrow \infty$. In particular, this improves a recent result due to Petronilho and Silva Math. Nachr. 292 (2019), no. 9, 2032–2047 for the modified Kawahara equation (j=2$j=2$, k=1$k=1$), where they obtained a decay rate of order t−4+$ t^{-4 +}$. Our proof relies on an approximate conservation law in a modified Gevrey spaces, local smoothing, and maximal function estimates.
•Hydrological model comparison in a data-scarce region.•We demonstrate the sensitivity of the complex model for the number of partitioned subbasins.•The simple conceptual models perform comparably to ...the more complex model.•The combination of the three model outputs with the artificial neural network produce the minimum root mean square error.
The Lake Tana Basin (15,114km2) in Ethiopia, which is a source of the Blue Nile River Basin.
We assessed daily streamflow predictions by applying two simple conceptual models and one complex model for four major gauged watersheds of the study area and compared these model’s capabilities in reproducing observed streamflow in the time and quantile domains.
The multi-criteria based model comparison shows that the simple conceptual models performed best in smaller watersheds for reproducing observed streamflow in the time domain, whereas the complex model performed best for the largest watershed. For reproducing observed streamflow in the quantile domain, the simple conceptual models performed best for simulation of high, moist, mid-range, and dry-flows in the Gilgelabay watershed; of dry and low-flows in the Gummera and Megech watersheds; and of high flows in the Ribb watershed. For the remaining flow ranges of each watershed, the complex model performed better. This study also addressed the sensitivity of the complex model for the number of partitioned subbasins. In the largest watershed, the performance of the complex model improved when the number of partitioned subbasins was increased. This finding indicates that the distributed models are especially applicable for the complex watershed because of its physical heterogeneity. In general, integrating these three models may be suitable for water resources assessment.
This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) ...was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios.
Projected changes in precipitation extremes can greatly impact the natural environment. Hence, the precipitation extremes must be precisely estimated with an appropriate bias correction algorithm to ...provide reliable information for the formulation of climate change impact adaptation and mitigation strategies. However, there is a lack of studies that discuss the effect of bias correction algorithms on the reproduction of precipitation extremes in the Blue Nile River Basin. This study compared three commonly used bias correction algorithms: the quantile mapping (QM), detrended QM (DQM), and quantile delta mapping (QDM). The QDM and DQM algorithms outperformed the standard QM bias correction algorithm in preserving the raw climate models projected relative changes of precipitation extremes. The performance differences between the standard QM and other bias correction algorithms (DQM and QDM) were more pronounced in the projection of extreme daily precipitation. Conversely, the projection of dry and wet spells was less sensitive for the choice of the bias correction algorithm. In general, the climate change impact analysis with the QDM algorithm revealed the increase in the frequency and severity of precipitation extremes. Moreover, the results showed the increase (decrease) in the maximum length of dry (wet) spells; indicating the increase in the severity of the meteorological droughts in the future that could potentially reduce the rain-fed agricultural productivity of the region.
The study examines the financial distress situation and its determinants in insurance sectors in Ethiopia. To achieve study objectives, revised Altman's 2000 is adopted to measure the financial ...distress situation. The study adopted an explanatory research design with an arrangement of secondary data analysis via document analysis, quantitative approach, and deductive method of inquiry. The study used panel data from ten insurance companies over the study period 2010/11-2020/21. Descriptive and regression analyses were performed to analyze the data using STATA 14. Econometric model estimation procedures and multiple regression assumptions were tested accordingly. The random effect regression result revealed that firm-specific factors (liquidity and profitability) have a significant positive association, whereas firm size significantly negatively impacts financial distress. While the random effect regression result also proposed inflation has a positive and significant association with financial distress. However, firm-specific factors (revenue growth and leverage) have positive and negative, respectively, and macroeconomic factors (Gross Domestic Product) have positive but statically insignificant to the financial distress situation of insurance sectors in Ethiopia.
Insurance company managers and shareholders should be conscious of the effect of intellectual capital efficiency and its components on financial performance. The purpose of this study is to examine ...the role of intellectual capital efficiency and its components on the financial performance of insurance companies. To achieve study objectives Modified value-added intellectual coefficient is adopted to measure the effect of intellectual capital efficiency. The study adopted an explanatory research design with an arrangement of secondary data analysis via document analysis, quantitative approach, and deductive method of inquiry. Panel data was used with a sample of 14 insurance companies from 2012-2022. Descriptive and regression analyses were performed to analyze the data using STATA version 15.0. Econometric model estimation procedures and multiple regression assumptions were tested accordingly. The random effect regression result revealed that the value-added intellectual capital and its component human capital and capital employed efficiency have a positive significance association with financial performance. Whereas, relational capital efficiency and structural capital efficiency do not have a significant contribution to the financial performance of insurance sectors in Ethiopia. The findings of this study contribute to the theoretical and practical understanding of the relationship between intellectual capital efficiency and financial performance in the context of insurance companies in Ethiopia.
Extreme hydrological events, like floods and droughts, exert considerable effects on both human and natural systems. The frequency, intensity, and duration of these events are expected to change due ...to climate change, posing challenges for water resource management and adaptation. In this study, the Soil and Water Assessment Tool plus (SWAT +) model was calibrated and validated to simulate flow under future shared socioeconomic pathway (SSP2-4.5 and SSP5-8.5) scenarios in the Baro River Basin with R2 values of 0.88 and 0.83, NSE of 0.83 and 0.74, and PBIAS of 0.39 and 8.87 during calibration and validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected and utilized to investigate the effects of climate change on the magnitude and timing of hydrological extremes. All climate model simulation results suggest a general increase in streamflow magnitude for both emission scenarios (SSP2-4.5 and SSP5-8.5). The multi-model ensemble projections show yearly flow increases of 4.8% and 12.4% during the mid-term (MT) (2041–2070) and long-term (LT) (2071–2100) periods under SSP2-4.5, and 15.7% and 35.6% under SSP5-8.5, respectively. Additionally, the analysis revealed significant shifts in the projected annual 1 day, 3 day, 7 day, and 30 day maximum flows, whereas the annual 3 day and 7 day minimum flow fluctuations do not present a distinct trend in the future scenario compared to the baseline (1985–2014). The study also evaluated the timing of hydrological extremes, focusing on low and peak flow events, utilizing the annual 7 day maximum and minimum flow for this analysis. An earlier occurrence was noted for both peak and low flow in the SSP2-4.5 scenario, while a later occurrence was observed in the SSP5-8.5 scenario compared to the baseline. In conclusion, this study showed the significant effect of climate change on river hydrology and extreme flow events, highlighting their importance for informed water management and sustainable planning.
Evaluating meteorological dynamics is a challenging task due to the variability in hydro-climatic settings. This study is designed to assess the sensitivity of precipitation and temperature dynamics ...to catchment variability. The effects of catchment size, land use/cover change, and elevation differences on precipitation and temperature variability were considered to achieve the study objective. The variability in meteorological parameters to the catchment characteristics was determined using the coefficient of variation on the climate data tool (CDT). A land use/cover change and terrain analysis was performed on Google Earth Engine (GEE) and ArcGIS. In addition, a correlation analysis was performed to identify the relative influence of each catchment characteristic on the meteorological dynamics. The results of this study showed that the precipitation dynamics were found to be dominantly influenced by the land use/cover change with a correlation of 0.65, followed by the elevation difference with a correlation of −0.47. The maximum and minimum temperature variations, on the other hand, were found to be most affected by the elevation difference, with Pearson correlation coefficients of −0.53 and −0.57, respectively. However, no significant relationship between catchment size and precipitation variability was observed. In general, it is of great importance to understand the relative and combined effects of catchment characteristics on local meteorological dynamics for sustainable water resource management.