Impact of Remittance on Economic Growth of Least Developed Countries in Sub Saharan Africa

Countries in Sub-Saharan Africa (SSA) that depend on foreign aid once political independence is gained, continue to be affected by changing aid modalities led by aid communities. It has been claimed that previous irrigation programs in SSA have not improved agricultural production as expected, and that the budget for implementation of further irrigation development has then been decreased. As a result, small-scale operations, which are part of participatory integrated rural development (PIRD), have become mainstream in the implementation of irrigation development in SSA. A small-scale irrigation development (SSID) was considered capable of attracting initial investment, required shorter construction periods, was comparatively easy to design, farmers were able to maintain the system themselves, and it had a lesser environmental impact. In general, to achieve a sustainable irrigation scheme, three systems must be established: a “water utilization system”; an “operation and maintenance (OM and a “succession system”. This paper discusses SSID in SSA with regard to a number of important factors, i.e., environmental, economic, and social factors, all of which impact on the sustainability of SSID. The progress of low-input, effective, and sustainable irrigation development (LESID) and the impact of changing aid modalities is followed in three countries, Ghana, Malawi, and Tanzania. In addition, the future effective implementation of SSID by way of LESID in other SSA countries is discussed. A self-supported SSID (as employed in Malawi) is currently considered the most appropriate LESID for least developed countries (LDCs) in SSA.

Fertility decline in most countries of sub-Saharan Africa has thus far started later and proceeded more slowly than in countries in Asia and Latin America and the Caribbean undergoing the transition in fertility since the 1950s from high levels to near-replacement or even below-replacement levels (Bongaarts and Casterline 2013). Yet there is considerable variation among countries in sub-Saharan Africa: in the duration and magnitude of fertility decline, whether stalls in fertility decline have occurred, shifts in the timing of births, and even the economic and population subgroups that have led declines in family size (Caldwell, Orubuloye, and Caldwell 1992; Bongaarts and Casterline 2013; Cleland, Onuoha, and Timæus 1994; Cohen 1998; Ezeh, Mberu, and Emina 2009; Garenne 2008; Kirk and Pillet 1998; Rossier, Corker, and Schoumaker 2015; Timæus and Moultrie 2008). With current fertility estimated at 5.1 births per woman in the region and 19 countries in sub-Saharan Africa at or above that level and another 21 countries with at least four births per woman on average (United Nations 2015a), the pathways that future fertility takes will significantly determine population growth and age structure shifts not only in the region, but increasingly for the world. Sub-Saharan Africa is projected to grow from 840 million people in 2010 to nearly 1.4 billion in 2030 (United Nations 2015a). Above-replacement fertility is projected to account for 61 percent of this population increase from 2010 to 2030 compared to 4 percent from mortality reduction, 37 percent from a young age structure in 2010 (population momentum), and a small negative contribution from migration (United Nations 2015b). These projections draw on the United Nations medium variant and do not take into account the uncertainty around current and future fertility levels, uncertainty that only increases the farther the projection period extends. For high-fertility countries in sub-Saharan Africa, the wide uncertainty around where fertility is headed can result in substantial differences in population projections (Ezeh, Mberu, and Emina 2009; Fuchs and Goujon 2014; Gerland et al. 2014). Beyond population numbers alone, the uncertainty about fertility decline also bears on policy-relevant questions such as the degree to which the region may realize a demographic dividend (e.g., how fast the shift will occur toward a higher ratio of working-age population to non-working-age population and the consequent effects on economic growth) (Bloom et al. 2013) or the extent to which greenhouse gas emissions might be reduced by slowing population growth (O'Neill et al. 2010). Earlier reviews of the fertility transitions in sub-Saharan Africa through the 1980s and 1990s showed fertility declines underway in most countries and particularly rapid declines in several countries in Eastern Africa (Kenya, Rwanda, and Zimbabwe) and Southern Africa (Botswana and South Africa) (Cleland, Onuoha, and Timæus 1994; Cohen 1998; Kirk and Pillet 1998). Subsequent survey data suggested an apparent slowdown in the pace of fertility decline in more than ten sub-Saharan African countries (Bongaarts 2008). Further analyses of the data indicated far fewer stalls in fertility decline had in fact occurred in the region, with evidence strongly supportive of stalls in Kenya and Rwanda, and the stalls that do occur have been of relatively short duration (Garenne 2011; Machiyama 2010; Schoumaker 2009, 2014). Most countries in sub-Saharan Africa still lack complete and accurate vital registration data on births, so these and other analyses of fertility trends will continue to rely heavily on survey data and require reconciling estimates from different sources (Alkema et al. 2012; United Nations 2015c). Our aim in this chapter is to provide an updated and concise description of the diversity of fertility decline patterns among countries1 in sub-Saharan Africa, drawing on the latest series of fertility estimates that take into account many different data sources and that are harmonized with other demographic components (United Nations 2015d). We focus on the level of fertility prior to the start of fertility decline, the time period of the fertility transition, and the estimated pace of decline. We also explore the implications of different fertility decline patterns for future fertility and population projections in the region. We draw on the distinct patterns of fertility decline among countries worldwide that are advanced in (or have completed) their first fertility transition to construct probabilistic fertility and population projections for sub-Saharan African countries. The illustrative comparisons of projections highlight the demographic impact if future fertility decline in sub-Saharan countries were to accelerate and follow the rapid pace of decline already experienced by a diverse group of countries. The UN Population Division publishes estimates and projections of period total fertility rates in World Population Prospects (WPP) every two years. The estimates of total fertility presented in this chapter are from the 2015 Revision and are for five-year time periods for countries or areas with 90,000 persons or more in 2015 (United Nations 2015a). The most recent data underlying the total fertility estimates from the 2015 Revision for 50 sub-Saharan African countries2 (United Nations 2015c) are from the period 2013–2014 for 14 countries, 2010–2012 for 30 countries, and 2005–2009 for six countries. A common challenge in estimating total fertility over time, especially for countries without accurate or complete vital registration data,3 is that estimates vary across data sources and by the methodology used to derive those estimates. Schoumaker (2014) showed that the underlying data from standardized, high-quality surveys such as the Demographic and Health Surveys vary considerably within and across countries, yielding total fertility estimates from recent fertility data of good quality (e.g., Gabon, Lesotho, Namibia, and Zimbabwe) and of poor quality (e.g., Benin, Burkina Faso, Cameroon, Chad, Ethiopia, Guinea, Madagascar, Mali, Mozambique, Niger, Nigeria, and Uganda). Total fertility estimates based on births in the last three years tend to be under-estimated by 10 percent or more in most of the surveys with poor quality fertility data from retrospective birth histories. Figure 1 illustrates the variation in total fertility estimates based on survey data and estimation methods (direct methods (D) and cohort-completed (C) fertility) for Nigeria for the period 1985 to 2015. The thick lines show the total fertility estimates from the 2010, 2012, and 2015 Revisions of WPP. Given new data from the 2008 DHS and other surveys, total fertility in the 2012 Revision was re-estimated at a higher level than the 2010 Revision beginning in the mid-1980s, resulting in almost half a birth per woman difference in the 2005–2010 period. The 2015 Revision used new data from the 2013 Demographic and Health Survey. The 2013 survey estimates highlight a recurring pattern in which fertility estimates based on a recent reference period are consistently lower than fertility estimates from reconstructed birth histories for the same time point. Looking only at fertility estimates from a three-year reference period, the 2013 DHS shows a decline in total fertility to 5.5 births per woman from a stalling pattern of 5.7 births per woman in the 2003 and 2008 DHS. Yet the absolute differences are large between these three-year reference period estimates and those for the same time point from the reconstructed birth histories: about half a birth difference in the mid-2000s (comparing the 2008 and 2013 survey estimates) and about one birth difference in the early 2000s (comparing the 2003 survey estimate to those from the 2008 and 2013 surveys). Estimates of the total fertility rate for Nigeria 1985–2015 based on various data sources and estimation methods, and WPP estimates from the 2010, 2012, and 2015 Revisions NOTES: DHS = Demographic and Health Survey; MICS = Multiple Indicator Cluster Survey; MIS = Malaria Indicator Survey. (C) refers to cohort completed fertility (i.e., average number of children ever born) for women aged 40–44 and 45–49 at the date of the survey and backdated using their mean age of childbearing. (D) refers to direct fertility estimates based on maternity histories or recent births in the 12 or 24 months preceding the survey. SOURCES: Federal Office of Statistics of Nigeria (1992); National Bureau of Statistics (2008, 2012); National Population Commission (2000, 2002, 2004, 2009, 2012, 2014). WPP fertility estimates consider as many types and sources of empirical estimates as possible, including retrospective birth histories and direct and indirect fertility estimates (Gerland 2014). The 2015 Revision updated all total fertility estimates taking into account new data and the inconsistencies among estimates. Moreover, total fertility estimates are derived to ensure as much internal consistency as possible with all other demographic components and intercensal cohorts enumerated in successive censuses (United Nations 2015d). The advantages of this approach are that the estimates are internally consistent within a country over time and with respect to other related demographic information. A disadvantage is that the estimates can depart from a country's official estimates of fertility. Figure 2 shows the estimated trends in period total fertility for sub-regions of sub-Saharan Africa from 1950 to 2015. It was high (above six births per woman) in all sub-regions in 1950–1955. Fertility remained high in Eastern and Western Africa until the 1980s, after which it began a slow decline to 4.9 births per woman in Eastern Africa and 5.5 in Western Africa in 2010–2015. Fertility in Middle Africa began to decline a decade later and more slowly, reaching 5.8 births per woman in 2010–2015. Southern Africa departed from the overall trends with a decline beginning in the 1950s and dropping below three births per woman in the 2000s. The 2010–2015 estimate of 2.5 births per woman in Southern Africa is about half the total fertility level in Eastern, Middle, and Western Africa. Sub-regional trends in total fertility, sub-Saharan Africa, 1950–2015 SOURCE: United Nations 2015a The sub-regional fertility levels mask diverse levels among countries. Figure 3 shows country-specific total fertility levels in 2010–2015. Among the 16 countries in Western Africa, total fertility ranged from 2.4 in Cabo Verde to 7.6 in Niger. One additional country had a fertility level of more than six births per woman (Mali), six countries had fertility between five and six births per woman (Burkina Faso, Côte d'Ivoire, Gambia, Guinea, Nigeria, and Senegal), and seven had fertility between four and five births per woman. Total fertility levels in countries in Africa, 2010–2015 SOURCE: United Nations 2015a. Total fertility levels ranged even more widely among the 20 countries in Eastern Africa, from 1.5 in Mauritius to 6.6 in Somalia. One additional country in Eastern Africa still had a fertility level above six in 2010–2015 (Burundi) and six countries had fertility between five and six births per woman (Malawi, Mozambique, South Sudan, Uganda, Tanzania, and Zambia). Eight countries had fertility levels between four and five births per woman. The lowest levels of fertility were in Djibouti (3.3 births per woman) and in the small island countries of Mauritius, Réunion, and Seychelles (less than three births per woman). The nine countries of Middle Africa all had fertility levels at or above four children per woman. In Angola, Chad, and DR Congo, fertility levels were six or more births per woman, and in the remaining six countries fertility levels were between four and five births per woman. While fertility in Southern Africa is largely dominated by South Africa's pattern, the range in fertility among the five countries in the sub-region is narrow, from 2.4 births per woman in South Africa to 3.6 in Namibia. Both Botswana and South Africa now have fertility levels below three births per woman. The start of the fertility transition also varies widely across sub-Saharan Africa. We analyze when and at what level of fertility a country experienced a maximum total fertility level before the onset of fertility decline. This maximum is defined as the most recent five-year time period where total fertility is within half a child of the overall maximum fertility in the country over the 1950–2015 estimation period, thus excluding random fluctuations in pre-transition fertility (Alkema et al. 2011). Figure 4 shows the diversity across countries and within sub-regions in the level and time period of the maximum fertility level before the onset of fertility decline, as assessed in the 2015 Revision. By the late 1970s, 29 sub-Saharan countries were on the verge of a fertility decline, increasing to 40 countries by the early 1980s. While the maximum fertility before the onset of fertility decline was reached in all countries in Southern Africa by the late 1970s, the range of experiences was much wider among countries in Eastern Africa (from the early 1950s in Réunion to the late 1990s in Somalia), Middle Africa (from the late 1960s in Angola to the late 1990s in Chad and DR Congo), and Western Africa (from the early 1960s in Cabo Verde to the late 1990s in Niger). Maximum total fertility in the time period before the onset of fertility transition, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The maximum fertility before the onset of fertility transition ranged from less than six births per woman in five countries (Central African Republic, Equatorial Guinea, Gabon, Lesotho, and Seychelles) to more than eight births per woman in two countries (Kenya and Rwanda). There was a positive but weak relationship between the maximum fertility level and the timing of when fertility transition commenced (R2 = .04). The transition from the maximum fertility to the current estimated fertility level in 2010–2015 has been slow for most countries in sub-Saharan Africa. We examine both the maximum decline in fertility in a five-year period and the duration of time between the maximum fertility level and when a country achieved a 10 percent decline in total fertility. A 10 percent decline in total fertility is one of several empirical rules that researchers have used to identify when fertility has begun to decline in a sustained manner from a pre-transitional maximum (Bongaarts and Casterline 2013; Coale and Treadway 1986; United Nations 2014), including differences in the time period selected to identify the onset of fertility decline (Casterline 2001) and the magnitude of decline (e.g., 5 percent decline with further conditions for subsequent changes applied, see Bryant 2007). While the different rules all strive to distinguish sustained decline from random fluctuations in fertility levels, each rule will have different repercussions for interpretations about the timing of onset of fertility decline, duration and pace of decline, and correlations with levels of development and other indicators. In seven countries the first steps of fertility decline took place over a long time period. In Angola, Gambia, and Uganda, a 10 percent decline from the maximum fertility level took 40 years; and in Lesotho, Mozambique, Niger, and Tanzania, it took 30 to 35 years (Appendix Table 1).4 In other countries the fertility decline never gained speed. Overall declines from the country-specific maximum fertility level to the level in 2010–2015 were very slow in nine countries (Angola, Congo, Gambia, Mali, Mozambique, Niger, Nigeria, Uganda, and Tanzania), where average fertility declines were 0.2 children per woman or less, a pace at which it would take at least 25 years to realize a decline of one birth per woman (Appendix Table 1). During the fertility transition, some countries experience an acceleration of fertility decline that, based on historical experience of past transitions in Latin America and the Caribbean and Asia, might reach a decline of more than one birth per woman in a five-year period (United Nations 2015a). In most countries in sub-Saharan Africa, however, the maximum fertility decline is much smaller. The largest declines of more than one birth per five-year period were registered in the early-transition countries in Eastern Africa before 2000 (Djibouti, Mauritius, Mayotte, Réunion, Rwanda, Seychelles, and Zimbabwe). For some countries the maximum fertility decline is projected to be reached in the future: one country in Eastern Africa (Mozambique), four in Middle Africa (Angola, DR Congo, Equatorial Guinea, and Sao Tome and Principe), and five in Western Africa (Gambia, Guinea, Mali, Niger, and Nigeria). Figure 5 shows that countries reaching the maximum fertility decline later tend to reach it at lower levels (the correlation across the 50 countries is R2 = .45). Maximum five-year decline in total fertility since the onset of fertility transition, actual and projected, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The pace of fertility decline is illustrated in Figure 6 for Ethiopia and Nigeria, the two most populous countries in sub-Saharan Africa. Estimated levels of total fertility from 1950–1955 to 2010–2015 are shown with a fitted model of the fertility transition. Both countries reached maximum fertility levels before the onset of fertility decline between the late 1970s and early 1980s, and both reached a 10 percent decline from this maximum in the early 2000s. While Ethiopia reached the peak pace of fertility decline in 2005–2010 (a decline of nearly one birth in the five-year period), Nigeria's fertility decline has been consistently slow, with a low peak pace of decline. The maximum fertility decline per five-year period in Nigeria is projected to be reached in the future (a decline of 0.3 births in 2020–2025). The difference in the pace results in current total fertility of 4.6 births per woman in Ethiopia and 5.7 in Nigeria. The 80 percent prediction intervals (dashed lines in Figure 6) around the probabilistic projections of total fertility indicate the magnitude of the different fertility changes that could reasonably happen. For example, by 2045–2050 there is a one in ten chance that total fertility in Nigeria could be as low as 2.6 or as high as 4.4 (Figure 6 and Appendix Table 2). Observed decline in total fertility from 1950–1955 to 2010–2015, predicted decline, and time period of maximum TFR and maximum decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. Given the slow pace of fertility decline in most sub-Saharan African countries thus far and the uncertainty around the pace of future declines, what might be the impact on future fertility levels and the growth in total population if fertility declines in the region followed an accelerated pace that has already been experienced by countries that have completed or are nearing completion of the fertility transition? To answer this question, we generated probabilistic fertility scenarios for 2015–2100 using a modeling approach described in Alkema et al. (2011) and implemented in a publicly accessible software package BayesTFR (Ševčíková, Alkema, and Raftery 2011). The standard United Nations 2015 probabilistic results pool the experience of all countries having similar fertility levels and trends (the baseline scenario). An alternative probabilistic scenario was created using only the fertility transition experiences of 21 countries5 that shared a similar pattern of accelerated decline in fertility (the accelerated scenario): specifically, a slow pace at the start of the transition that sharply rises to a peak pace of decline before steadily tapering off after total fertility has reached about four births per woman. These 21 countries, which include—among countries with more than 10 million inhabitants in 2015—Bangladesh, China, El Salvador, Morocco, Peru, South Africa, Sri Lanka, Thailand, Turkey and Uzbekistan, represent disparate institutional, social, economic, and cultural contexts and yet experienced a similar pattern of a relatively abrupt acceleration of fertility decline. The objective of this exercise is to examine the impact on population size of fertility decline that is more rapid than currently projected, and to illustrate the implications of such a hypothetical scenario. While we do not theorize or analyze the factors that produced these rapid fertility declines, the fact that a similar pattern of fertility decline took place within such a diverse group of countries raises the possibility that a similar decline could occur within a region that also reflects quite distinctive and diverse contexts. Each fertility scenario is used to simulate 10,000 probabilistic population projections for 2015–2100 under identical conditions (i.e., using the same mortality and migration assumptions) that show the population growth trajectories if sub-Saharan African countries were to follow a specific fertility decline pattern.6 The implications of this specific fertility decline pattern for future fertility trends are shown in Figure 7 for Ethiopia and Nigeria. The accelerated fertility decline scenario leads to much more rapid declines for these countries. By 2045–2050, median projected total fertility in Ethiopia would decline from 2.3 (baseline) to 2.0 (accelerated decline) and in Nigeria from 3.6 (baseline) to 2.6 (accelerated decline) (see Appendix Table 2). Probabilistic fertility projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. The accelerated fertility scenario has greater implications for projected fertility in Nigeria, where the recent pace of fertility decline has been much slower than in Ethiopia. The projected medians of the accelerated scenario are similar to the lower bound of the baseline scenario for parts of the projections, suggesting that Ethiopia and Nigeria would need to experience a rapid fertility decline, similar to the rapid declines experienced by the group of 21 countries, in order to realize the lower-bound fertility level of the baseline scenario. The assumption that Ethiopia and Nigeria would follow the fast fertility decline experiences of these 21 countries leads to substantially lower population projections compared with the baseline scenario (Figure 8 and Appendix Table 3). If Ethiopia experienced an accelerated fertility decline, the projected population grows from 99 million in 2015 to 182 million by 2100 (80 percent prediction interval of 93 million to 324 million) compared with the higher median projection of 234 million under the baseline scenario. If Nigeria adopted the accelerated fertility decline pattern, the projected population grows from 182 million in 2015 to a median of 466 million by 2100 (80 percent prediction interval of 282 million to 777 million) rather than the faster projected growth to 737 million under the baseline scenario. Probabilistic population projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia, Nigeria, and sub-Saharan Africa SOURCES: United Nations 2015a and computations by authors. The counterfactual of sub-Saharan African countries following an accelerated fertility decline, as experienced by the group of 21 countries, results in a projected increase in total population in the region from 962 million in 2015 to 3.2 billion by the end of the century (with 80 percent probability of being between 2.8 and 3.7 billion). The total population projection for the baseline UN 2015 scenario would lead to a median projection of about 4 billion, or a difference of 770 million people with an 80 percent prediction interval mostly above the upper bound of the accelerated fertility decline scenario. We presented here an updated description of fertility decline in sub-Saharan Africa and have explored the implications for fertility and population projections if sub-Saharan African countries follow a pattern of accelerated fertility decline. Because estimates of fertility in the region rely almost entirely on survey and census data, there is still uncertainty about fertility change over time. Our analyses of trends over time were based on a new historical time series of total fertility estimates for the period 1950 to 2015 from the 2015 Revision of World Population Prospects. Fertility has remained persistently high in Eastern, Middle, and Western Africa (4.9 births per woman or higher as of 2010–2015) and declined rapidly in Southern Africa, which currently has about half the total fertility level of other sub-regions births per woman). Southern Africa is dominated by South Africa, TFR of 2.4 is the lowest in the Yet there is wide variation in total fertility across countries, even within from below fertility births per woman) in Mauritius to 7.6 births per woman in Niger. The fertility level before the onset of decline was high in all countries in sub-Saharan Africa, from less than six births per woman in five countries to more than eight births per woman in two countries. By the late 1970s, 29 countries were on the verge of fertility decline, increasing to 40 countries by the early 1980s. The transition from these maximum levels of fertility prior to the onset of fertility decline to current levels has been slow for most countries, including seven in which it took 30 years or more to a 10 percent decline in total fertility. While countries in Asia and in Latin America and the Caribbean experienced an acceleration in fertility decline of more than one birth per five-year time period, this level of acceleration has been experienced thus far by only seven countries in Eastern Africa before the The pace of fertility decline in sub-Saharan Africa will a large in the magnitude of future growth in We showed through an illustrative scenario that an acceleration in the pace of fertility decline to that already experienced by 21 countries South Africa) would the growth in future population in the region from a projected billion by to billion and total population size from a projected 4 billion people by the end of the century to 3.2 While these 21 countries represent a wide range of institutional, social, economic, and cultural a similar fast decline from fertility levels of six or more children per woman in the past to less than two or three in recent years. this accelerated pace of fertility decline to countries at of transition illustrates the substantial impact on future population growth of other possible patterns of fertility decline in sub-Saharan Africa. of this are assumption that the fertility estimates from the World Population Prospects are and the fact that uncertainty around the estimates of period total fertility in recent is not into By scenarios on the distinct fertility decline patterns that have been experienced thus far by countries that have completed or are advanced in their fertility we new patterns of fertility decline in the Because most countries in Middle Africa and Western Africa are still in the early of fertility transition, an sub-Saharan African pattern of fertility decline may still and Timæus The is not for the or of by the authors. than be to the for the

Background: The provision of energy services is a vital component of the energy system. This is often considered emission-intensive and at same time, highly vulnerable to climate change conditions. This forms the fundamental objective of this thesis, poised to examine technoeconomic and environmental implications of policy intervention, targeted at cushioning impacts of climate change on the energy system. Aims: Four research queries are central to this work: (1) Review literature on impacts of CVC (2) Estimate influence of seasonal climatic and socioeconomic factors on energy demand in Australia; (3) Model dynamic interactions between energy policies and climate variability and change (CVC and (4) Identify least-cost combination of electricity generation technologies and effective emissions reduction policies under climate change conditions in Australia. Methods: A systematic scoping review method was first applied to identify consistent pattern of CV&C impacts on the energy system, while spotting research gaps in studies that met the inclusion criteria. Databases consisting of Scopus and Web of Science were searched, and snowballing references in published studies was adopted. Data was collated and summarised to identify the characteristic features of the studies, consistent pattern of CV&C impacts, and locate research gaps to be filled by this study. The second study applied an autoregressive distributed lag (ARDL) model to estimate temperature sensitive electricity demand in Australia. Estimates were used with projected temperatures from global climate models (GCMs) to simulate future electricity demand under climate change scenarios. The study further accounted for uncertainties in electricity demand forecasting under climate change conditions, in relation to energy efficiency improvement, renewable energy adoption and electricity price volatility. The estimates from the ARDL model and projections from GCMs were used for energy system simulation using the Long-range Energy Alternative and Planning (LEAP) system. It considered climate induced energy demand in the residential and commercial sector, alongside linking the non-climate sensitive sector with energy supply sector. This model was vital to justifying policy options under investigation. Further, LEAP modelling analysis was extended by identifying effective emission reduction policies considering CV&C impacts. Here, the Open Source Energy Modelling System (OSeMOSYS) was used for optimisation analysis to identify least-cost combination of electricity generation technologies and GHG emission reduction policies. Whereas, in the third and final study, cost-benefit analysis and estimation of long run marginal cost of electricity were conducted, while decomposition analysis of GHGs were analysed in the third study alone. Data used in the ARDL model included socioeconomic data which includes gross state product, as well as population and electricity prices from 1990-2016. The LEAP and OSeMOSYS model as used, was dated to 2014 as the base year, while several technological (power plant characteristics, household technologies), economic (energy prices, economic growth, carbon price) and environmental (emission factors, emission reduction target) variables were used to develop Australia's energy model. Results: The literature search generated 5,062 articles in which 176 studies met the inclusion criteria for the final literature review. Australian studies were scarce compared to other developed countries. Also, just few articles made attempt to examine decarbonisation under climate change. The ARDL model estimates and GCMs simulation of future electricity demand under CV&C show that Australia had an upward sloping climate-response functions, resulting to an increase in electricity demand. However, the researcher identified an annual increase in projected electricity demand for states and territory in Australia, which calls for the need to scale up RET. The LEAP model results showed substantial impacts on energy demand, as well as impacts on power sector efficiency. Under the BAU scenario, CV&C will result in an increase in energy demand by 72 PJ and 150 PJ in the residential and commercial sectors, respectively. Induced temperature enlarges the non-climate BAU demand, which will increase threefold before 2050. Under the non-climate BAU, there is an expansion of installed capacity to 81.8 GW generating 524.6 TWh. Due to CV&C impacts, power output declines by 59 TWh and 157 TWh in Representative Concentration Pathways (RCP) 4.5 and 8.5 climate scenarios. This leads to an increase in generation costs by 10% from the base year, but a decrease in sales revenue by 8% and 21% in RCP 4.5 and RCP 8.5, respectively. The LEAP-OSeMOSYS model suggests renewables and battery storage systems as least-cost option. However, the configuration varied across Australia. Carbon tax policy was observed to be effective in reducing Australia's emission and foster huge economic benefits when compared to the current emission reduction target policy in the country. Also, renewable energy technologies increase electricity sales and decrease fuel cost better than fossil fuel dominated scenarios. Conclusions: Data from this study reveals that seasonal electricity demand in Australia will be influenced by warmer temperatures. Also, the study identified the possibility of winter peaking which is somewhat higher than summer peak demand in some states located in the southern regions of Australia. However, winter peaking is projected to decline by mid-century across the RCPs, while summer peak load is projected to increase, thereby, causing power companies to expand their generation capacity which may become underutilised. Owing to increase in cooling requirements up to 2050, policy uncertainties analysis recommend renewables to match an increasing future electricity demand. The energy model indicates that ignoring the influence of CV&C may result in severe economic implications which range from increased demand, higher fuel cost, loss in revenue from decreased power output, as well as increased environmental externalities. The study concludes that policy options to reduce energy demand and GHG emissions under climate change may be expensive on the short-run, though, may likely secure long-run benefits in cost savings and emission reductions. It is envisaged that this could provide power sector management with initiatives that could be used to overcome cost ineffectiveness of short-term cost. The modelling results makes a case for renewable energy in Australia as lower demand for energy and increased electricity generation from renewable energy source presents a win-win case for Australia.

This study evaluates and compares the forecasting performances of the ARDL (AutoRegressive Distributed Lag), LSTM (Long Short-Term Memory), and XGBOOST (Extreme Gradient Boosting) models on the MASI (Moroccan All Shares Index). The analysis incorporates daily new COVID-19 cases into the ARDL approach to investigate short-term and long-term relationships with MASI. Cointegration and causality tests are conducted on daily time series data. In terms of accuracy, the ARDL model, especially when including trend and seasonality variables, outperforms LSTM and XGBOOST models. The ARDL model with lags, trend, and seasonality variables achieves the lowest Mean Absolute Percentage Error (MAPE) of 26.7%, with a processing time of 1 second. In comparison, the LSTM and XGBOOST models have MAPE values of 30.5% and 32%, respectively, while requiring significantly longer processing times. These findings suggest that the ARDL model is more efficient and accurate in predicting future values of MASI under pandemic conditions.

Reveals that the available literature on TQM implementation emphasizes the experiences of firms in industrialized nations, and studies dealing with the challenges of implementing TQM in less developed countries (LDCs) are limited. Examines the factors that influence the successful implementation of TQM in LDCs with a particular focus on the countries of Sub‐Saharan Africa (SSA). Using force‐field analysis, identifies the primary environmental factors expected to drive or restrain TQM implementation in SSA and proposes some approaches for enhancing its success. Suggests that advance knowledge of the factors that are likely to promote or obstruct TQM implementation would enable managers in SSA countries to develop more effective strategies that will enhance the chances of implementation success. Asserts that adopting the TQM approach can help to improve the quality of goods and services in SSA countries, increase their export capabilities and facilitate the achievement of their development goals. Cautions that in SSA countries it is not enough that top managers in individual firms commit to the TQM process, noting that, in contrast to industrialized nations, SSA governments play a much more prominent role in economic activity, including direct ownership of major enterprises. Hence, argues that the unwavering support of African governments is crucial if TQM is to be successfully introduced and sustained in private and public organizations in SSA.

The consequences of carbon dioxide (CO2) emissions in Sub Saharan Africa (SSA) countries cannot be ignore given it adverse effect on human health and global warming. With rising CO2 emissions and fallen volume of trade openness and FDI inflows in recent time, we seek to examine the effect of trade openness and foreign direct investment (FDI) on environmental degradation using time series data from 1975 to 2020 in SSA. Using the environmental Kuznets curve (EKC) framework, the study employs a quadratic modeling and turning point approach to realize the study objectives. The findings reveals that (1) the trade openness-EKC and FDI-EKC does not hold given the presence of decreasing effects in the short run and increasing effects in the long run; (2) it confirms that a U-shaped trade openness-emissions and FDI-emission nexus holds given the decrease in trade openness and FDI in the short run and an increase in trade openness and FDI in the longrun; (3) The analysis supports the halo effect hypothesis before the turning point but the pollution haven hypothesis sets in after the turning point; (4) it shows evidence that trade openness and FDI contributes to reduce CO2 emissions in the short but increase it in the long run. The study recommends that SSA countries should adopt stringent environmental policies to attain sustainable economic growth without associative harm to the environment.

Digitalization and economic growth: A comparative analysis of Sub-Saharan Africa and OECD economies

PurposeIndividuals' health is considered one of the major determinants of higher levels of productivity and economic development. Over the past century, the widespread occurrence of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) has been a serious threat to economic development around the globe and has caused a dramatic fall in the life expectancy rate in many nations. This is the first study that examines the impact of HIV prevalence on health expenditure at the national level employing two linear and nonlinear autoregressive distributed lag (ARDL) models and simultaneously tests the long-run and short-run relationship for five selected developed countries. The authors employ annual data from 1981 to 2016. They find that HIV prevalence has a significant impact on health expenditure in the short-run and long-run in all five countries using the linear model and four of the countries in the nonlinear model. They find that HIV/AIDS prevalence has a significant short-run and long-run asymmetric impact on health expenditure of almost all selected developed economies.Design/methodology/approachThe authors are employing two linear and nonlinear ARDL models and simultaneously test the long-run and short-run relationship for five selected developed countries.FindingsThe authors find that HIV/AIDS prevalence has a significant short-run and long-run asymmetric impact on health expenditure of almost all selected developed economies.Originality/valueTo the best of the authors’ knowledge, this is the first research work that empirically examines the link between HIV prevalence and health expenditure for this group of countries using linear and nonlinear ARDL approach for short run and long run.

(ProQuest: ... denotes formulae omitted.)1.INTRODUCTIONPoverty is the main social and economic problem in most developing countries. Most economists also agree that economic performance and level of poverty in a country are determined, to a large degree, by the quality of its institutions (Acemoglu, 2007; Acemoglu et al., 2004; Commander and Nikoloski, 2010). A country's chosen development strategy matters in determining the quality of institutions and, hence, the level of poverty (Lin, 2009). Since the end of the Second World War, developing and developed economies around the world have determinedly sought to alleviate, and even eradicate, poverty. With the exception of a few successes in East Asia including Japan, South Korea, Singapore and Taiwan, such efforts have largely failed. Thus, living standards in most developing countries have not improved substantially and particularly for countries in Sub-Saharan Africa, very little have changed (ibid). Now the most important question becomes what was wrong with the development policies in most developing countries and whether it is possible to avoid these mistakes.Nevertheless, the eradication of poverty remains a high priority for world leaders, as reflected in Millennium Development Goal 1. There is a continuous debate about how to achieve poverty reduction in developing countries, but not enough discussion of why some countries are highly poverty prone and others do not have poverty and what we mean by poverty reduction. It is often understood as shorthand for promoting economic growth that will permanently lift as many people as possible over a poverty line. Thus, many political leaders viewed the development of capital intensive and technologically advanced heavy industries that prevailed in the developed countries as the symbols of modernization and an easy way of reducing poverty. We call this a Comparative Advantage Defying (CAD) strategy because the developing countries have mostly been capital-scarce economies and capital-intensive industries were not to their comparative advantages. Even many economic policymakers were not concerned whether this is really the correct policy measure to reduce poverty. Our motivation is to empirically explore the flawed policy statements taken by the most developing countries and suggest corrections in their development strategies. Thus, the hypothesis that will be tested in this paper is that over an extended period a country adopting a CAD development strategy will have a higher level of poverty.The most important channel through which the CAD strategy can affect the level of poverty is the channel of finance. Many governments of least developed countries (LDCs) which carry out a CAD strategy subsidize the firms in priority sectors by distorting funds prices, foreign exchange rates, and other inputs or input prices; and use administrative authorities to allocate price-distorted inputs to the firms. For example, immediately after independence in 1949, the Chinese government adopted CAD strategy and made a big push for the nationalization movement by increasing the share in the industrial output of State Owned Enterprises (SOEs) from around 40% in 1952 to 90% in 1958. The government suppressed the price of agricultural products to support priority industry - price premium of agricultural products at informal markets relative to state procurement price went negative at 10 points in the 1950s (Lin et al., 2006). During this time, Chinese government failed to reduce poverty until stopping such deviant behavior in 1978 (Lin, 2009).The methodology this paper uses is the Ordinary Least Square (OLS) estimation. But because of endogenous problems it uses the instrumental variable (IV) approach as well. We have found IV for both of our interested endogenous variables - CAD and financial development. Nevertheless, our dependent variable, poverty level, contains lots of zeroes due to lack of data on poverty based on our headcount definition of poverty. …

Globally, financial inclusion is regarded as being crucial for balancing an economy’s financial system. However, despite the significance of financial inclusion, it still needs to be clarified to identify what factors are responsible for the diverse trend of financial inclusion in the forty-five Sub-Saharan Africa (SSA) countries from 1999 to 2024. The main rationale of the study empirically investigated these determinants of financial inclusion in forty-five Sub-Saharan Africa (SSA) countries from 1999 to 2024, which covers three distinct periods: which is the pre-COVID, 2020–2022 is the COVID period, and the post-COVID period from 2023 onward, but examined as a whole from 1999 to 2024 for easy policy formulation for SSA countries. The study was anchored on two main research objectives: firstly, to examine the factors influencing financial inclusion in Sub-Saharan Africa (SSA) in these three distinct periods, and lastly, to present the policy implications of the result of these factors in enhancing financial inclusion in the post-COVID era in SSA. The study used the Panel Least Squares (PLS) technique in the data analysis. The result revealed that economic growth (GRO), Islamic banking (ISMAIC), money supply (MSS), internet users (USERS), and credit availability (CREDIT) positively and significantly enhance financial inclusion with coefficients of 0.001298, 4.926809, 1.08 × 10−6, 0.459388, and 0.657431, respectively, with significant p-values of 0.0008, 0.0023, 0.0000, 0.0000, and 0.000, respectively. On the flip side, internet servers (SERVER) have a negative coefficient value of 4.63 × 10−6 with a p-value of 0.000. Though inflation (INFL) and interest rate (INT.) have negative coefficient values of −0.02853 and −0.08317, they have insignificant p-value impacts of 0.2841 and 0.2501, respectively. The result indicates that many of the variables have a significant impact on financial inclusion. This is shown from the probabilities of the t statistics of each of the independent variables in the estimated model, which are significant at the 5% level. The policy implications of these results include the following: firstly, SSA governments should promote economic growth through investment in productive sectors, infrastructure development, and job creation programs to indirectly improve financial inclusion. Secondly, SSA countries’ policymakers should maintain price stability through sound monetary and fiscal policies to ensure inflation does not hinder access to financial services. Thirdly, SSA countries’ governments and central banks should promote lower interest rates and enhance credit accessibility, especially for marginalized groups, through subsidized loans and targeted credit schemes. Fourthly, policymakers should support the expansion of Islamic finance by improving regulatory frameworks and increasing awareness about Sharia-compliant financial products.

This paper examines the impact of remittance inflows on economic growth in Sub-Saharan Africa (SSA) countries and Rwanda in particular for the period between 1980 and 2014. It explores whether the growth impact of remittances is conditional on the institutional and development factors in SSA countries. The analytical framework of this study is embedded in the debate between two dominant theoretical approaches about the growth effect of remittances; the national accounts and endogenous growth models. As baseline analysis, the paper employs a cross-sectional analysis of 45 SSA countries, follo\\wed by a more in-depth analysis of Rwanda as a case study. The findings reveal that the two theories are complementary but not mutually exclusive in explaining the growth effect of remittances. The cross-sectional analysis of SSA countries shows no statistically significant impact of remittances on economic growth in the region. But the remittance-growth impact is positively and statistically significantly conditioned by the country's level of development, financial development, and education, while the quality of institutional variables adversely affects the remittance-growth impact in the region. In contrast, the same findings reveal a positive and significant growth impact of remittances in Rwanda. The results of the country-level analysis reveal plausible evidence of long-run causality, running from remittances to GDP per capita in Rwanda, but not vice versa. The results demonstrate that the conditional marginal effect of remittances on GDP per capita in Rwanda increases with more remittance inflows to the country. These findings suggest that both the overall institutional environment and that of the financial sector specifically are imperative for enhancing the growth and development impact of remittances in the SSA countries and Rwanda in particular.

The conventional time series methods tend to explore the modeling process and statistics tests to find the best model. On the other hand, machine learning methods are concerned with finding it based on the highest performance in the testing data. This research proposes a mixture approach in the development of the ARDL (Autoregressive Distributed Lags) model to predict the Cayenne peppers price. Multiple time series data are formed into a matrix of input-output pairs with various lag numbers of 3, 5, and 7. The dataset is normalized with the Min-max and Z score transformations. The ARDL predictor variables of each lag number and dataset combinations are selected using the forward selection method with a majority vote of four criteria namely the Cp (Cp Mallow), AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and adjusted R2 . Each ARDL model is evaluated in the testing data with performance metrics of the RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R2 . Both AIC and adjusted R2 always form the majority vote in the determining optimal predictor variable of ARDL models in all scenarios. The ARDL predictor variables in each lag number are different but they are the same in the different dataset scenarios. The price of Cayenne pepper yesterday is the predictor variable with the most contribution in all of the 9 ARDL models yielded. The ARDL lag 3 with the original dataset outperforms in the RMSE and MAE metrics while the ARDL lag 3 with the Z score dataset outperforms in the R2 metric.

Thermal error modeling method is an important field of thermal error compensation on NC machine tools, it is also a key for improving the machining accuracy of machine tools. The accuracy of the model directly affects the quality of thermal error compensation. On the basis of multiple linear regression (MLR) model, this paper proposes an autoregressive distributed lag (ADL) model of thermal error and establishes an accurate ADL model by stepwise regression analysis. The ADL model of thermal error is established with measured data, it proved the ADL model is available and has a high accuracy on predicting thermal error by comparing with MLR models.

Intra-cerebral Haemorrhage (ICH) seems more prevalent in sub-Saharan Africa (SSA) than in High-Income Countries (HIC) with poorer clinical outcome. Higher impact of hypertension and/or amyloid angiopathy could account for this disproportion. Here, we sought to (i) retrospectively compare ICH clinical and imaging patterns in Belgium and Guinea and in a subsequent cohort (ii) prospectively compare brain MRI characteristics to seek evidence for a different proportion of amyloid angiopathy patterns. Ninety one consecutive patients admitted for spontaneous ICH at Brussels Erasme-ULB Hospital and at Conakry Ignace Deen-UGANC were retrospectively compared in terms of ICH volume estimated with the ABC/2 method, clinical characteristics and modified ranking (mRS) score at 30days. mRS was dichotomised as good outcomes (≤3) and poor outcomes (>3). A prospective cohort of 30 consecutive patients with ICH admitted at CHU Conakry Ignace Deen-UGANC was prospectively included to undergo brain MRI. Results of the Guinean MRI were compared to 30 patients randomly selected from Brussels' initial cohort. Paired Student's t-test and Mann-Whitney u-test were used for group comparisons. Age of ICH onset was higher in Belgium (68±17years vs. 56±14years, P<0.01) while ICH volume and 30-day mortality rate were higher in Guinea (20ml vs. 11ml, P<0.01 and mortality 33% vs. 10 %, P<0.01). ICH burden in survivors in Conakry and Brussels showed respectively good outcomes in 56.7% and 60.4% (P=0.09) and poor outcomes in 10.3% vs. 29.6% (P<0.001). MRI analysis of the prospective cohort failed to disclose significant differences regarding brain MRI characteristics. Intra-cerebral Haemorrhage affected patients 15years younger in Guinea with larger haematoma volumes and higher mortality than in Belgium. MRI findings did not show more prevalent amyloid angiopathy pathology suggesting that better primary prevention of hypertension could positively impact ICH epidemiology in Guinea.

African nations encounter difficulties enforcing regulations and providing incentives for using renewable energy sources. However, several nations are making efforts to encourage renewable energy through financial and tax advantages. Therefore, a shift to renewable energy is essential for African nations to experience sustainable growth and lessen environmental deterioration. Similarly, the extant literature examining green taxes' influence on renewable energy technology has documented equivocal findings. Hence, there is a need for a more thorough investigation. This study, therefore, explores the influence of green taxation on renewable energy technologies of emerging countries in Sub-Saharan Africa. We employed data from a sample of 28 countries of 54 African countries spanning 21years from 2001 to 2021, providing a panel of 588 country-year observations. The Organisation for Economic Co-operation and Development (OECD) and the World Bank Dataset provided all the study's data. A heterogeneous dynamic panel data modelling using the autoregressive distributed lag (ARDL) has been adopted. The study found that green taxes might be used to mitigate the adverse effects of non-renewable energy activities on the environment in Africa. Considering the findings of the components of green taxes, it was recognised that an increase in energy-related tariffs would lead to a growth in Africa's use of renewable energy. It was further established that an increase in transport taxes increases the adoption of renewable energy technologies in Africa. A comparative analysis between the commonwealth and non-commonwealth countries showed that green taxes of commonwealth countries in Africa significantly contribute to the growth of renewable energy technologies compared to non-commonwealth countries in Africa. Primarily, the results of this study can be a valuable resource for African governments and policymakers as they develop policies and evaluate legislation about the usage of renewable energy sources and other green practices. Finally, the study can shed light on creating and using efficient tax laws that support renewable energy sources.